air pollution research paper topics

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• Volume 22, issue 7
• ACP, 22, 4615–4703, 2022
• Peer review
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Advances in air quality research – current and emerging challenges

Ranjeet s. sokhi, nicolas moussiopoulos, alexander baklanov, john bartzis, isabelle coll, sandro finardi, rainer friedrich, camilla geels, tiia grönholm, tomas halenka, matthias ketzel, androniki maragkidou, volker matthias, jana moldanova, leonidas ntziachristos, klaus schäfer, peter suppan, george tsegas, greg carmichael, vicente franco, steve hanna, jukka-pekka jalkanen, guus j. m. velders, jaakko kukkonen.

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., Halenka, T., Ketzel, M., Maragkidou, A., Matthias, V., Moldanova, J., Ntziachristos, L., Schäfer, K., Suppan, P., Tsegas, G., Carmichael, G., Franco, V., Hanna, S., Jalkanen, J.-P., Velders, G. J. M., and Kukkonen, J.: Advances in air quality research – current and emerging challenges, Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, 2022.

We wish to dedicate this article to the following eminent scientists who made immense contributions to the science of air quality and its impacts: Paul J. Crutzen (1933–2021), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Mario Molina (1943–2020), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Samohineeveesu Trivikrama Rao (1944–2021), air pollution meteorology and atmospheric modelling; Kirk Smith (1947–2020), global environmental health; Martin Williams (1947–2020), air quality science and policy; Sergej Zilitinkevich (1936–2021), atmospheric turbulence, awarded the IMO Prize 2019.

Air pollution remains one of the greatest environmental risks facing humanity. WHO (2016) estimated that over 90 % of the global population is exposed to air quality that does not meet WHO guidelines, and Shaddick et al. (2020) report that 55 % of the world's population were exposed to PM 2.5 concentrations that were increasing between 2010 and 2016. Shaddick et al. (2020) also highlighted marked inequalities between global regions, with decreasing trends in annual average population-weighted concentrations in North America and Europe but increasing trends in central and southern Asia. WHO (2016) has evaluated that approximately 7 million people died prematurely in 2012 throughout the world as a result of air pollution exposure originating from emissions from outdoor and indoor anthropogenic sources. The recent update from the World Health Organization (WHO) of air quality guidelines (WHO, 2021) has emphasized the need to further curtail air pollution emissions and improve air quality globally.

Over the past decade there have been significant developments in the field of air quality research spanning improvements in characterizing sources and emissions of air pollution, new measurement technologies offering the possibility of low-cost sensors, advances in air quality prediction and forecasting, understanding interactions with meteorology and climate, and exposure assessment and management. However, there has not been a broader and comprehensive review of recent developments that push the boundaries of air quality research forward. This was recognized as a major gap in the literature at the last International Conference on Air Quality – Science and Application held online due to the COVID 19 restrictions during 18–26 May 2020. While the concept of this review originated at the International Conference on Air Quality and was stimulated by the presentations and discussions at the conference, this article has been extended to incorporate a wider landscape of research literature in the field of air quality, spanning in particular the developments occurring over the last decade. It is hoped that such a review will help to pave the path for further research in key areas where significant gaps of knowledge still exist and also to make recommendations to guide the direction for future research within the wider community. Although this paper has been written to be accessible to readers from a wide scientific and policy background, it does not seek to provide an introduction to the topic of air quality science. For readers less familiar with the research area, an introductory lecture with a focus on air quality in megacities has been published by Molina (2021). There are also other recent specific reviews, e.g. Manisalidis et al. (2020) on health impacts and Fowler et al. (2020) on air quality developments. This section begins with a short historical perspective on air quality research, before providing the underlying rationale for the key areas considered in this paper.

1.1  A brief historical perspective

In order to provide context to the topics considered in this review, this section briefly touches upon developments of air quality research since the last century. For a more thorough historical survey of air quality issues, the reader is referred to Fowler et al. (2020). Over the previous century there have been a number of landmark events of elevated air pollution that have brought air quality increasingly to prominence, especially in relation to the adverse health impacts. It has been well-known since the early 1900s that cold weather in winter can lead to increased mortality (e.g. Russell, 1926).

The perception that air pollution can have severe health impacts significantly changed when a high-air-pollution episode occurred from 1–5 December 1930 over an industrial town in the Meuse Valley in Belgium (Firket, 1936). The atmospheric conditions were foggy and stagnant. A large proportion of the population experienced acute respiratory symptoms; in addition, health conditions of people with pre-existing cardiorespiratory problems worsened (e.g. Nemery et al., 2001; Anderson, 2009). A similar event was recorded in Donora, Pennsylvania, USA, during October 1948, reported by Schrenk (1949). Although air pollution was generally treated as a nuisance, this “unusual episode” along with that over the Meuse Valley raised awareness and acceptance of the seriousness of air pollution for human health. Both air pollution events, Meuse Valley and Donora, were associated with air pollution from industrial emissions, which accumulated during cold winter periods exhibiting atmospheric stagnation caused by thermal inversions.

The so-called “Great London Smog” occurred from 5–9 December 1952, when similar stagnant atmospheric conditions were prevalent. However, in this case the cause of the severe air pollution was mainly the burning of low-grade, sulfur-rich coal for home heating (e.g. Anderson, 2009). Estimates of deaths resulting from this smog episode range from 4000 to 12 000 (e.g. Stone, 2002).

Since these historical events, the prominence of air pollution sources has changed from industrial and heating to road traffic and become a global threat to health. Trends of air pollution emissions over the past decades have been markedly different for different regions of the world, which has led to similar disparities in air quality concentrations (e.g. Sokhi, 2012). These disparities still exist, as shown in Fig. 1. Spatial distributions in this figure are based on recent analysis showing the large variations in population-weighted annual mean PM 2.5 concentrations across the globe. Commonly, now some of the highest concentrations occur in parts of Asia, Africa, and Latin America as reported by Health Effects Institute (HEI, 2020).

Figure 1 Global distribution of population-weighted annual PM 2.5 concentrations for 2019 (HEI, 2020). Figure produced from https://www.stateofglobalair.org/data/#/air/map (last access: 10 December 2021).

As the recognition of poor air quality has increased, so has the need for the capability to assess levels of key air pollutants not only through monitoring but also through modelling. Historically, although air pollution was obviously poor prior to the first World War (WWI), the primary impetus for development of transport and dispersion (T&D) models during and after WWI was the widespread use of chemical weapons. Fundamental theoretical advances were made by Lewis Fry Richardson, George Keith Batchelor, and many other famous fluid dynamicists. The earliest models were analytical (e.g. Gaussian and K-theory) models used for surface boundary layer releases. With the advent of nuclear weapons in WWII, new emphasis was placed on plume rise and dispersion of large thermal radiological explosions. Thus, the full troposphere and stratosphere had to be modelled.

Later in the 1980s the first investigations came up about the atmospheric consequences of a hypothetical nuclear war initiated by Paul Crutzen (Crutzen and Birks, 1982) and others (Aleksandrov and Stenchikov, 1983; Turco et al., 1983). The concept of a nuclear winter was created. It is one of the first examples that enormous emissions of dust into the atmosphere cause global effects and catastrophic long-term climate change. Also, the nuclear winter scenario was examined in recent years with current model tools for certain nuclear war scenarios (Robock et al., 2007; Toon et al., 2019).

Deposition (wet and dry) was a main concern for many radiological substances, especially for accidental plume dispersion monitoring and modelling of nuclear power plants. In the US, a major change was the introduction of the Clean Air Act in the 1970s. A similar legislation was also issued in other countries. This effort initially focused on T&D models for industrial sources, such as the stacks of fossil power plants. The first applied models were analytical plume rise and Gaussian T&D models. Soon computer codes were written to solve these equations and produce outputs at many spatial locations and for every hour of the year.

1.2  Sources and emissions of air pollutants

From a human health perspective, the key emission sources are those affecting concentration of particulate matter and its size fractions (PM 2.5 and PM 10 ), but also sources affecting other air pollutants, such as ozone and nitrogen dioxide (NO 2 ), especially in highly populated urban areas. Sources in the direct vicinity of urban areas could also be considered especially important, including vehicular traffic and shipping, local industrial sources, various abrasive processes, and residential and commercial heating.

An important component of PM is secondary; regional sources of the precursors of secondary PM are therefore of major importance. These include volatile organic compounds (VOCs), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ammonia (NH 3 ), the first two also being precursors of ozone (O 3 ). Important regional precursor sources are biogenic and industrial emissions of VOCs, agriculture (NH 3 ), road traffic (nitrogen oxides, NO x = NO + NO 2 ), shipping ( NO x and SO 2 ) , and industrial and power generation sources, along with biomass burning and forest fires (VOC, NO x , also primary PM). An important source of PM is the resuspension of dust, especially in arid regions and seasonally also in areas with intensive agriculture.

While Europe and many other parts of the world have experienced decreasing anthropogenic emissions since 1990, climate change and its associated impacts can lead to an increase in dust and wildfire emissions, as a result of increased drought and desertification. Climate change is also expected to lead to significantly higher biogenic VOC emissions in different regions, e.g. Arctic and China (Kramshøj et al., 2016; Liu et al., 2019), also from urban vegetation (Churkina et al., 2017).

The emission inventory work in Europe is harmonized through the official reporting of EU member states of their emissions to the European Commission through an e-reporting scheme (Implementing Provisions for Reporting, IPR of EU Air Quality Directive, 2008/50/EC). The methodologies applied by the individual member states can, however, differ, which can sometimes bring inconsistencies into the reported national emissions. Within the last decade the EU-funded MACC project and the on-going Copernicus service have been developing consistent European-wide and global gridded emission inventories, which are suitable for air quality modelling. The access to the different inventories and analysis of differences have been facilitated by centralized databases like Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD, https://eccad.aeris-data.fr/ , last access: 7 July 2021).

Developing innovative methods to refine the emission inventories feeding the models and conducting studies to discriminate the role of different sources in local air quality have become essential to reduce uncertainties in predictions of urban air quality and help target effective abatement measures (Borge et al., 2014). The emission compilation that needs to be carried out also requires (i) the involvement of all stakeholders (e.g. citizens, decision-makers, service providers, and industrialists) and (ii) the implementation of dedicated and specific tools for assessing quality of the urban environment. This type of research can be used for quantifying the impacts of different emission control scenarios and supporting incentive policies (Fulton et al., 2015).

One area that has been receiving increased attention recently is ship emissions, which are an important source of air pollution, especially in coastal areas and harbour cities. Detailed bottom-up emission inventories based on ship position data have been established for SO 2 , NO x , PM, carbon monoxide (CO), and VOCs for various marine regions and also globally (Jalkanen et al., 2009, 2012, 2016; Aulinger et al., 2016; Johansson et al., 2017). Despite these advances, the evaluation of the shipping emissions for products of incomplete combustion, such as black carbons (BC), CO, and VOCs, is uncertain, as these may depend on characteristics which are not known accurately, such as the service history of ships. Regional model applications have quantified the contribution of shipping to air pollution to be of the order of up to 30 %, depending on pollutant and region (e.g. Matthias et al., 2010; Jonson et al., 2015; Aulinger et al., 2016; Karl et al., 2019a; Kukkonen et al., 2018, 2020a). More recent studies focus on the harbour and city scale, where relative contributions from ships to NO 2 concentrations may be even higher (Ramacher et al., 2019, 2020). Effects of in-plume chemistry, e.g. regarding the NO x removal and secondary aerosol formation, are not sufficiently well considered in larger-scale dispersion models (e.g. Prank et al., 2016).

1.3  Air quality in cities

Extensive and growing urban sprawl in different cities of the world is leading to environmental degradation and the depletion of natural resources, including the availability of arable land, thereby resulting in per capita increases of resource use and greenhouse gas emissions as well as air pollution, with significant impacts on health (WHO, 2016). Urban features have a profound influence on air quality in cities due to diurnal changes in urban air temperature; the urban heat island, which develops in particular during heat waves (Halenka et al., 2019); stable stratification and air stagnations; and wind flow and turbulence near and around streets and buildings affecting air pollution hotspots. Climate change will modify urban meteorology patterns which will affect air quality in cities and may even affect atmospheric chemistry reaction rates. The relative role of urban meteorology and climate compared to local emissions and chemistry is complex, non-linear, and subject to continued research, especially with boundary layer feedback (Baklanov et al., 2016).

With air quality standards being regularly exceeded in many urban areas across the globe, air quality issues are today strongly centred on the phenomena of proximity to emitters such as traffic – or certain industrial activities present in urban areas – but they also call for better understanding of contributions from long-range regional, diffuse, or specific local sources (e.g. residential wood combustion and maritime traffic) to the daily exposure of city dwellers (e.g. EEA, 2020b). In particular, the prevalent issue of individual exposure calls for a better understanding of the variability of concentrations at street level and the dispersion of emissions in the built environment. However, the approach implemented should not only be local, since urban air quality management involves a set of scales going beyond the city limits, in terms of the economic, societal, or logistical levers involved, but also include the interplay of pollutant sources and transport extending to regional and even global scales.

Beyond the scales of governance and urban functioning, it becomes essential to take into account the fact that scale interactions also exist in a geophysical context. The urban dweller has become especially exposed and vulnerable to the impacts of natural disasters, weather, and climate extreme events and their environmental consequences. These events often result in domino effects in the densely populated, complex urban environment in which system and services have become interdependent. There has never been a bigger need for user-focused urban weather, climate, water, and related environmental services in support of safe, healthy, and resilient cities (Baklanov et al., 2018b; Grimmond et al., 2020). The 18th World Meteorological Congress (2019) noted the current rapid urbanization and recognized the need for an integrated approach providing weather, climate, water, and related environmental services tailored to the urban needs (WMO, 2019).

1.4  Measuring air pollution

Measurements in the atmosphere are necessary not only for air quality monitoring but also for different purposes in weather forecast and climate change study, energy production, agriculture, traffic, industry, health protection, or tourism (e.g. Foken, 2021). Additional areas of application include the detection of emissions into the atmosphere, disaster monitoring, and the initialization and evaluation of modelling. Depending on the different objectives, in situ measuring, and ground-based, aircraft-based, and space-based remote sensing techniques and integrated measuring techniques are available. Satellite observations are a growing field of development due to increasingly small and thus cost-effective platforms (down to nanosatellites). Another area of growth is the use of unmanned aerial vehicles (UAVs) for air pollution measurements (Gu et al., 2018).

Networks of ground-level measurements with continuous monitoring stations remain a major effort, but the coverage is starkly regionally dependent and with scarce measurements in the continent of Africa (Rees et al., 2019; Bauer et al., 2019).

Over the past decade, there has been increasing recognition that measuring air pollution at outdoor locations may not necessarily reflect the health impact on individuals or populations. The research should therefore be directed to the evaluation of both personal exposure and dynamic population exposure (Kousa et al., 2002; Soares et al., 2014). Temporal concentration and location information is needed on air pollution concentrations at all the relevant outdoor and indoor microenvironments. The actual exposure of individuals and populations cannot realistically be represented by selected concentrations at fixed outdoor locations, due to the fine-resolution spatial variability of concentrations in urban areas and the mobility of people (Kukkonen et al., 2016b; Singh et al., 2020b).

Further development of the installation of a larger number of cheap measurement devices, especially for PM 2.5 , that are operated by people interested in air quality in so-called citizen science projects is ongoing ( https://www.eea.europa.eu/publications/assessing-air-quality-through-citizen-science , last access: 21 February 2022). Examples of such projects are the Open Knowledge Foundation Germany; OK Labs ( https://luftdaten.info/ , last access: 21 February 2022), Opensense (open air quality, meteorological, and noise data platform), connected with OK Labs ( https://opensensemap.org/ , last access: 21 February 2022); or AirSensEUR, an open framework for air quality monitoring ( https://airsenseur.org/website/airsenseur-air-quality-monitoring-open-framework/ , last access: 21 February 2022). However, the accuracy of these measurements is still debated (Duvall et al., 2021; Concas et al., 2021), although the development of more accurate but still low-cost devices is ongoing for denser measurement networks, 3D measurements, and new modelling. Measurements are not only required for compliance and for monitoring long-term trends. Observations are used more and more for evaluating models and where measurements might also be used to nudge the model results, for example through data assimilation (see for example Campbell et al., 2015; K. Wang et al., 2015).

1.5  Air quality modelling from local to regional scales

Air pollution models have played and continue to play a pivotal role in furthering scientific understanding and supporting policy. Additionally, for air quality assessments by regulatory methods, it is also important to predict or even forecast peak pollutant concentrations to prevent or reduce health impacts from acute episodes. Both complex and simple models have also been developed for dispersion on urban and local scales. A review has been provided by Thunis et al. (2016) that examines local- and regional-scale models, especially from an air quality policy perspective. Briefly, the spectrum of finer- and urban-scale air quality models applied for urban areas is very broad and includes urbanized chemistry–transport models (CTMs) coupled with high-resolution meso-scale numerical weather prediction (NWP) models, computational fluid dynamics (CFD) obstacle-resolved models in Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) formulations (the latest mostly only for research studies), and statistical and land use regression (LUR) models. Developments in local-scale air quality models continue. For example, the dispersion on local or urban scales that also considers obstacle effects has recently been investigated using wind tunnels and CFD models (e.g. Badeke et al., 2021).

During the last decades many countries have established real-time air quality forecasting (AQF) programmes to forecast concentrations of pollutants of special health concerns. The history of AQF can be traced back to the 1960s, when the US Weather Bureau provided the first forecasts of air stagnation or pollution potential using numerical weather prediction (NWP) models to forecast conditions conducive to poor air quality (e.g. Niemeyer, 1960). Accurate AQF can offer tremendous societal and economic benefits by enabling advanced planning for individuals, organizations, and communities in order to avoid exposure and reduce adverse health impacts resulting from air pollution. Forecasts can also assist urban authorities, for example, in changing and managing traffic and hence reduce road emissions in a particular area. Air quality modelling, however, can provide a more holistic assessment of air pollution for policy makers and decision makers to develop strategies that do not compromise benefits in one area while worsening air pollution in another.

Two main approaches can be generally distinguished in AQF: empirical/statistical methods and chemical transport modelling. Until the mid-1990s, AQF was mainly performed using empirical approaches and statistical models trained with or fitted to historical air quality and meteorological data (e.g. Aron, 1980). The empirical/statistical approaches have several common drawbacks for AQF which are reviewed and discussed by Zhang et al. (2012a) and Baklanov and Zhang (2020).

The chemical transport models (CTMs) are more commonly used today for air quality assessment and forecasting. Over the last decade AQF systems based on CTMs have been developed rapidly and are currently in operation in many countries. Progress in CTM development and computing technologies has allowed daily AQFs using simplified or more comprehensive 3D CTMs, such as offline-coupled and online-coupled meteorology–chemistry models. There are several comprehensive review papers, e.g. Kukkonen et al. (2012), Zhang et al. (2012a, b), Baklanov et al. (2014), Bai et al. (2018), and Baklanov and Zhang (2020), which have more thoroughly examined the development and principles of 3D global and regional AQF models and identified areas of improvement in meteorological forecasts, chemical inputs, and model treatments of atmospheric physical, dynamic, and chemical processes.

Interest in regional pollution arose in the 1980s, initially spurred by the acid rain problem (Sokhi, 2012; Fowler et al., 2020). In the past few years, these regional air pollution models have become routinely linked with outputs of NWP models such as WRF and ECMWF. Models such as WRF coupled with CTMs are often run in a nested mode down to an inner domain with a grid size of 1 km. As computer speed and storage continually improve with developments in parameterization, in the future, these nested models may potentially take over most applied T&D analyses on local scales. Another development over the last decade is the increasing use of ensemble techniques which have also progressed and make it possible to cover an increasing range of pollutants and physical parameters, using a multiplicity of observations (e.g. ground, airborne, satellite) that enable the different dimensions of models to be investigated. At the same time that the use of regional Eulerian models has grown (e.g. Rao et al., 2020), the puff, particle, and plume T&D models for small scales and mesoscales have been improved. Several agencies and countries now have Lagrangian particle or puff models that are linked with an NWP model and are applied at all scales (Ngan et al., 2019).

1.6  Interactions of air quality, meteorology, and climate

Meteorological processes are the main driver for atmospheric pollutant dispersion, transformation, and removal. However, as studies have shown (e.g. Baklanov et al., 2016; Pfister et al., 2020), the chemistry dynamics feedbacks exist among the Earth system components, including the atmosphere. Potential impacts of aerosol feedbacks can be broadly explained in terms of four types of effects: direct, semidirect, first indirect, and second indirect (e.g. Kong et al., 2015; Fan et al., 2016). Such feedbacks, forcing mechanisms, and two-way interactions of atmospheric composition and meteorology can be important not only for air pollution modelling but also for NWP and climate change prediction (WMO, 2016).

There is a strong scientific need to increase interfacing or even coupling of prediction capabilities for weather, air quality, and climate. The first driver for improvement is the fact that information from predictions is needed at higher spatial resolutions (and longer lead times) to address societal needs. Secondly, there is the need to estimate the changes in air quality in the future driven by climate change. Thirdly, continued improvements in prediction skill require advances in observing systems, models, and assimilation systems. In addition, there is also growing awareness of the benefits of more closely integrating atmospheric composition, weather, and climate predictions, because of the important feedbacks resulting from the role that aerosols (and atmospheric composition in general) play in these systems. Recently, this trend for further integration has led to greater coupling of atmospheric dynamics and composition models to deliver seamless Earth system modelling (ESM) systems.

1.7  Air quality and health perspectives

Air pollution has serious impacts on our health by reducing our life span and exacerbating numerous illnesses. The Global Burden of Disease Study 2019 (GBDS, 2020) summarizes a comprehensive assessment of the impact of a large number of stressors including air pollution. One of the most hazardous air pollutants is particulate matter. Primary particles are directly released into the atmosphere and originate from natural and anthropogenic sources. Secondary particles are formed in the atmosphere by chemical reactions involving, in particular, gas-to-particle conversion. Primary particles tend to be larger than secondary particles. Ultra-fine and fine particles, on the other hand, deposit into the respiratory system; these may reach human lungs and blood circulation and may therefore cause severe adverse health effects (e.g. Maragkidou, 2018; Stone et al., 2017).

When considering numbers of particles, most of these in the atmosphere are smaller than 0.1  µm in diameter (e.g. Jesus et al., 2019). On the other hand, the majority of the particle volume and mass is found in particles larger than 0.1  µm (e.g. Filella, 2012). The particle number concentrations are therefore in most cases dominated by the ultra-fine aerosols, whereas the mass or volume concentrations are dominated by the coarse and accumulation mode aerosols (e.g. Seinfeld and Pandis, 2016). Other characteristics of PM have also been shown to be important in relation to health impact. The characteristics of atmospheric particles in addition to the size include mass, surface area, chemical composition, and shape and morphology (Gwaze, 2007).

It has been convincingly shown in previous literature that the exposure to particulate matter (PM) in ambient air can be associated with negative health impacts (e.g. Hime et al., 2018; Thurston et al., 2017). It is also known that PM can cause health effects combined with other environmental stressors, such as heat waves and cold spells, allergenic pollen, or airborne microorganisms. For understanding such associations, reliable methods are needed to evaluate the exposure of human populations to air pollution.

The strong association between the exposure to mass-based concentrations of ambient PM air pollution and severe health effects has been found by numerous epidemiological studies (e.g. Pope et al., 2020). In particular, there is extensive scientific evidence to suggest that exposure to PM air pollution can have acute effects on human health, resulting in respiratory, cardiovascular and lung problems, chronic obstructive pulmonary diseases (COPDs), asthma, oxidative stress, immune response, and even lung cancer (e.g. Chen et al., 2017; Hime et al., 2018; Falcon-Rodriguez et al., 2016; Thurston et al., 2017). For instance, a cohort study conducted across Montreal and Toronto (Canada) on 1.9 million adults during four cycles (1991, 1996, 2001, and 2006) resulted in a possible connection between ambient ultra-fine particles and incident brain tumours in adults (Weichenthal et al., 2020). Recent work has also investigated assessment of the health impacts of particulate matter in terms of its oxidative potential (e.g. Gao et al., 2020; He et al., 2021).

1.8  Air quality management and legislative and policy responses

Air quality management and policy is an important but also complex task for political decision makers. It started in the middle of the last century when concerns about smoke and London smog arose. The national authorities at that time reacted by stipulating efficient dust filters and high stacks for large firings. In the 1980s, forest dieback led to a shift in focus to other important air pollutants, especially SO 2 , NO x , and later ozone, and so also on the ozone precursors including VOCs. In the 1990s studies showed a relation between PM 10 and “chronic” mortality, thus drawing particular attention to the health effects of fine particles (WHO, 2013b). Also, in the 1990s, the European Commission (EC) increasingly took over the responsibility for air pollution control from the authorities of the member states, on the basis that there is free trade of goods in the European Union and also transboundary air pollutants.

The EC launched the first Air Quality Framework Directive 96/62/EC and its daughter directives, which regulated the concentrations for a range of pollutants including ozone, PM 10 , NO 2 , and SO 2 . The first standard for vehicles (Euro 1) was established in 1991. The sulfur content in many oil products was reduced starting in the late 1990s. Some of the problems with air pollution in the EU, e.g. the acidification of lakes, were caused by the transport of air pollutants from eastern Europe to the EU. This problem was discussed in the United Nations Economic Commission for Europe (UNECE), as all countries involved were members of this commission. The Convention on Long-range Transboundary Air Pollution within the UNECE agreed on eight protocols, which set aims for reducing emissions, starting in 1985 with reducing national SO 2 emissions, with the latest protocol being the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone (Gothenburg Protocol), which limits national SO 2 , NO x , VOC, NH 3 , and PM 2.5 emissions.

Over time, regulation of air pollution has become more stringent and thus more complex and more costly. To achieve acceptance, it had to be demonstrated that the measures would achieve the environmental and climate protection goals safely and efficiently, i.e. with the lowest possible costs and other disadvantages, and that the advantages of environmental protection outweigh the disadvantages (Friedrich, 2016). It is a scientific task to support this demonstration, mainly by developing and applying integrated assessments of air pollution control strategies, e.g. by carrying out cost–effectiveness and cost–benefit analyses. With a cost–effectiveness analysis (CEA) the net costs (costs minus monetizable benefits) for improving an indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the effectiveness of a policy or measure. The CEA is mostly used for assessing the effects associated with climatically active species, as the effects are global. The situation is different for air pollution, where the avoided damage of emitting 1 t of a pollutant varies widely depending on time and place of the emission.

The more general methodology is cost–benefit analysis (CBA). In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs will result in the net benefit per euro spent, which can be used for ranking policies and measures.

Of course, for performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, i.e. euros, is usually chosen.

The term “integrated” in the context of integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence on the result of the assessment. Given the high complexity of answering questions related to managing the impacts of air quality, a scientific approach is required to conduct an integrated assessment, which is defined here as “a multidisciplinary process of synthesizing knowledge across scientific disciplines with the purpose of providing all relevant information to decision makers to help to make decisions” (Friedrich, 2016).

The focus of this review is on research developments that have emerged over approximately the past decade. Where needed, older references are given, but these either provide a historical perspective or support emerging work or where no recent references were available. The following areas of air quality research have been examined in this review:

air pollution sources and emissions;

air quality observations and instrumentation;

air quality modelling from local to regional scales;

interactions between air quality, meteorology, and climate;

air quality exposure and health;

air quality management and policy development.

Each section begins with a brief overview and then examines the current status and challenges before proceeding to highlight emerging challenges and priorities in air quality research. In terms of climate research, the focus is more on the interactions between air quality and meteorology with climate and not on climate change per se.

The section on air quality observations focuses on new technological developments that have led to remote sensing, low-cost sensors, crowdsourcing, and modern methods of data mining rather than attempting to cover the more traditional instrumentations and measurements which are dealt with, e.g. in Foken (2021). After considering these themes of research, the Discussion section pulls together common strands on science and implications for policy makers.

3.1  Brief overview

A fundamental prerequisite of successful abatement strategies for reduction of air pollution is understanding the role of emission sources in ambient concentration levels of different air pollutants. This requires a good knowledge of air pollution sources regarding their strength, chemical characterization, spatial distribution, and temporal variation along with knowledge on their atmospheric transport and processing. In observations of ambient air pollution, typically a complex mixture of contributions from different pollution sources is observed. These source contributions have to be disentangled before efficient reduction strategies targeting specific sources can be set up. Consequently, our discussion below is divided into two main topics: (i) emission inventories and emission pre-processing for model applications and (ii) source apportionment methods and studies.

This paper cannot give a full overview of the status of and the emerging challenges in all emissions sectors. For example, we do not deal with aviation as the impact on air quality in cities is generally rather small or concentrated around the major airports, or with construction machinery or industrial sources which make significant contributions to air pollution in some areas. Instead, we put emphasis on two emission sectors that have experienced important methodology developments in recent years in terms of emission inventories and that are of major concern for health effects: exhaust emissions from road traffic and shipping. We also touch other anthropogenic emissions, e.g. from agriculture and wood burning, As later in this paper we will explain, since individual exposure including the exposure to indoor pollution should gain importance in assessing air pollution, emissions from indoor sources will be addressed in a subchapter. Natural and biogenic emissions encompass VOC emissions from vegetation, NO emissions from soil, primary biological aerosol particles, windblown dust, methane from wetlands and geological seepages, and various pollutants from forest fires and volcanoes; these are described in a series of papers edited by Friedrich (2009). As natural and biogenic emissions depend on meteorological data, which are input data for the atmospheric model, they are usually estimated in a submodule of the atmospheric model. They are not further discussed here.

3.2  Current status and challenges

3.2.1  emissions inventories.

In the European Union, emissions of the most important gaseous air pollutants have decreased during the last 30 years (see Fig. 2). SO 2 and CO show reductions of at least 60 % (CO) or almost 90 % (SO 2 ). Also, NO x and non-methane volatile organic compound (NMVOC) emissions decreased by approx. 50 % while NH 3 shows much lower reductions of 20 % only. Similar to NH 3 , PM emissions also stay at similar levels compared to 2000 (Fig. 2b). Only black carbon shows considerably larger reductions, because of larger efforts to reduce BC, in particular from traffic. While traffic is the most important sector for NO x emissions and an important source for BC, PM emissions stem mainly from numerous small emission units like households and commercial applications (Fig. 2c).

Figure 2 EU-28 emission trends in absolute and relative numbers for (a)  the main gaseous air pollutants and (b)  particulate matter. Panel  (c) shows the share of EU emissions of the main pollutants by sector in 2018 (EEA, 2020b).

In parallel, research came on the path of accompanying and evaluating local emission control measures in a more comprehensive and systemic approach to urban space. The main technical advances of this research field have consisted in producing a more reliable assessment of the predominant emissions on the scale of an agglomeration/region. This has been done in order to feed the models with activity-based emission data such as population energy-consuming practices or local characteristics of road traffic, with the concern to better include their temporal variability or weather condition dependency. The originality of these approaches has been to develop the emissions inventories and modelling efforts in collaboration with stakeholders, for better data reliability and greater realism in policy support.

Improved and innovative representation of emissions, such as real configuration of residential combustion emission sources (location of domestic households using biomass combustion and surveys regarding the characteristics and use of wood stoves, boilers, and other relevant appliances) allows more realistic diagnoses (e.g. Ots et al., 2018; Grythe et al., 2019; Savolahti et al., 2019; Plejdrup et al., 2016; Kukkonen et al., 2020b). Also, increased use of traffic flow models for the representation of mobile emissions have provided refined traffic and emission estimates in cities and on national levels, as a path for improved scenarios (e.g. Matthias et al., 2020a). Kukkonen et al. (2016a) presented an emission inventory for particulate matter numbers (PNs) in the whole of Europe, and in more detail in five target cities. The accuracy of the modelled PN concentrations (PNCs) was evaluated against experimental data on regional and urban scales. They concluded that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remained in the evaluation of both the emissions and atmospheric transformation of PNCs.

For shipping, and in most recent development also aviation, inventories based on position data from transponders on individual vessels are becoming more widely used and provide refined emission inventories with high spatial resolution for use in harbour-city and airport studies (e.g. Johansson et al., 2017; Ramacher et al., 2019, 2020). Refined emission inventory and emission modelling are in many cases integrated into a complete regional-to-local modelling chain, which allows these refined data to be taken into account and ensures the consistency of the final results. This links to the subsequent chapters on air quality and exposure modelling.

3.2.2  Preprocessing emission data for use in atmospheric models

Emission inventories usually contain annual data for administrative units apart from data for large point sources and line sources. Atmospheric models, however, need hourly emission data for the grid cells of the model domain. Furthermore the height of the emissions (above ground), and for NMVOC, PM, and NO x a breakdown into species or classes of species according to the chemical scheme of the atmospheric model, is necessary. For PM, information is also required on the size distribution. Thus, a transformation of the available data into structure and resolution as needed by the models has to be made (Matthias et al., 2018).

For the spatial resolution, standard procedures for several emission sectors are described in Chap. 7 of the EMEP/EEA air pollutant emission inventory guidebook 2019 (EMEP/EEA, 2019). In principle, proxy data that are available in high spatial resolution and that are correlated to the activity data of the emission sources are used. For point sources (larger sources like power plants) these are coordinates of the stack. For road transport, shape files with coordinates at least for the main road network are used together with traffic counts (for past times) or traffic flow modelling for scenarios for future years. Figure 3 shows as an example the result of a distribution of road transport emissions to grid elements for the EU countries Norway and Switzerland. The major roads as well as the urban areas can be identified as sites for the NO x emitters. For households, land use data (e.g. residential area with a certain density) combined with statistical data (number of inhabitants, use of heating technologies) are used. Especially for heating with wood-specific algorithms using data on forest density and specific residential wood combustion, emission inventories and models have been developed (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014; Paunu et al., 2020; Kukkonen et al., 2020b). Thiruchittampalam (2014) contains a comprehensive description of the methodology for the spatial resolution of emissions for Europe for all emission source categories.

The algorithms for disaggregating annual emission data into hourly data follow a similar scheme. All kinds of available data containing information about the temporal course of activities leading to emissions are used for temporal disaggregation. For road transport, data from continuously monitoring the traffic volume are available, and statistical data provide the electricity production from power plants. The activity of firings for heating depends on the outside temperature or more precisely on the degree days, an indicator for the daily heating demand, together with an empirical daily course of the use of the heating (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014).

Figure 3 Spatial distribution of national PM 10 emissions from road transport in the EU28 on a 5 km×5 km grid (Schmid, 2018).

A detailed description of the methodology for the temporal resolution of emission data for all source sectors in Europe is contained in Thiruchittampalam (2014). A compilation of temporal profiles for disaggregating annual into hourly data is published by Denier van der Gon (2011) and in Matthias et al. (2018). New sets of global time profiles for numerous emission sectors have recently been provided by Crippa et al. (2020) and Guevara et al. (2021). Crippa et al. (2020) provide high-resolution temporal profiles for all parts of the world including Europe. Guevara et al. (2021) developed temporal profiles as part of the Copernicus Atmosphere Monitoring Service and also include higher-resolution European profiles designed for regional air pollution forecasting. The temporal profiles include time-dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, country-specific weekly and daily profiles, and a flexible system to compute hourly emissions. Thus, a harmonized temporal distribution of emissions is given, which can be applied to any emission database as input for atmospheric models up to the global scale.

For the temporal and spatial distribution of agricultural emissions a number of approaches have been established; these are based on information on farmer practice, available proxy data, and meteorological data, e.g. farmland and animal densities and the consideration of temperature and wind speed for agricultural emissions (e.g. Skjøth et al., 2011; Backes et al., 2016; Hendriks et al., 2016; see Fig. 4).

Figure 4 Break-down of agricultural emissions into sub-sectors in order to improve the spatial and temporal distribution (from Backes et al., 2016).

Comprehensive VOC split vectors are provided by Theloke and Friedrich (2007) and more recently by Huang et al. (2017). Region- and source-specific speciation profiles of NMVOC species or species groups are compiled and provided, with corresponding quality codes specifying the quality of the mapping. They can then be allocated to the reduced number of VOC species used in the chemical reaction schemes implemented in atmospheric chemistry–transport models. Typical heights for the release of emissions, e.g. typical stack heights, are given by Pregger and Friedrich (2009) and Bieser et al. (2011b).

Model systems have been developed that perform the entire temporal and spatial emission distribution and the NMVOC and PM speciation in order to provide hourly gridded emission data for use in different chemistry–transport models. Recent examples are the HERMES model (Baldasano et al., 2008; Guevara et al., 2013, 2019, 2020), FUME (Benešová et al., 2018), and the Community Emissions Data System (CEDS) model system (Hoesly et al., 2018). Because natural emissions, e.g. biogenic emissions, sea spray, and dust, depend strongly on the meteorological conditions, these emissions are frequently calculated within the chemistry–transport models (CTMs). Other established CTMs like the EMEP model (Simpson et al., 2012) or LOTOS-EUROS (Manders et al., 2017) do not use emissions preprocessors but distribute gridded emissions in time based on standard temporal and speciation profiles alongside the chemistry–transport calculations in order to avoid storing and reading large emission data sets.

3.2.3  Road transport emissions

Exhaust emissions from road transport have been a significant source of primarily NO x and ultra-fine particles (UFPs) in urban areas around the world. In the EU, road transport is the single most important source of NO x , producing 28.1 % of total NO x emissions (EEA, 2019b). In terms of PM 10 , its contribution is 7.7 % when both exhaust and non-exhaust sources are counted and 2.9 % when only exhaust emissions are considered (EEA, 2019b). Road transport contributes 32 %–97 % of total UFP in urban areas (Kumar et al., 2014). The difference between PM 10 and UFP contributions from road transport is a direct outcome of the small size of exhaust particles that mostly reside in the UFP range (Vouitsis et al., 2017).

The proximity of people to the emission source (vehicles) significantly increases exposure to traffic-induced pollution (Żak et al., 2017). Consequently, traffic exhaust emissions have been extensively studied, and comprehensive sets of emission factors have been available for a long time. The two most widespread methods to estimate emissions in Europe include COPERT ( https://www.emisia.com/utilities/copert/ , last access: 22 February 2022) and HBEFA ( https://www.hbefa.net , last access: 22 February 2022). These methods share the same experimental database of vehicular emissions – the so-called ERMES database ( https://www.ermes-group.eu/ , last access: 22 February 2022) – but express emission factors in different modelling terms. COPERT is also a part of the EMEP/CORINAIR Emission Inventory Guidebook (EMEP/EEA, 2019).

These models define the emissions for several pollutant species, for a wide range of vehicles and operating conditions. Emission factors are regularly being updated in an effort to reflect the best knowledge of on-road vehicle emission levels. Despite this, there are still some uncertainties in estimating emissions from road transport, in particular when these are to be used as input to air quality models. More attention is therefore needed in the following directions.

Emission factors for the latest vehicle technologies always come with some delay. This is the result of the time lag between placement of a new vehicle technology on the road and the organization of measurement campaigns to collect the experimental information required to develop the emission factors. The latest regulation (Reg. (EU) 2018/858) – mandating a minimum number of market surveillance tests in the different member states – may help to reduce this lag and to extend the availability of vehicle tests on which to base emission factors.

The availability of measurements of pollutants which are currently not included in emissions regulations (NH 3 , N 2 O, CH 4 , PAHs, etc.) is limited compared to regulated pollutants. Moreover, any available measurements have been mostly collected in the laboratory, due to instrumentation limitations for on-road measurements. Therefore, emission models may miss on-road operation conditions that potentially lead to high emissions rates of non-regulated pollutants.

The increase in emissions with vehicle age is still subject to high uncertainty. Emission increases with age may be due to normal system degradation, the presence of high emitters on the road (Murena and Prati, 2020) or vehicle tampering to improve performance or decrease operational costs. Current models use degradation functions based on remote sensing data (e.g. Borken-Kleefeld and Chen, 2015). This is a useful source of information, but remote sensing data need to be collected in additional locations in the EU, covering a range of climatic and operation conditions.

Emission models may be conservative in their approach of estimating emissions in extreme conditions of temperature (Lozhkina et al., 2020), altitude, road gradient, or creeping speeds. Although such conditions may not be substantial for estimating the total emissions of most countries, they can potentially lead to a significant underestimation of emissions that have to be locally calculated for high-resolution air quality modelling.

Despite uncertainties in modelling emissions, there is a high level of confidence that exhaust gas emissions of mobile sources will continue to decrease in the years to come. For example, Matthias et al. (2020b) projected that the contribution of road traffic to ambient NO 2 concentrations will decrease from 40 %–60 % in 2010 to 10 %–30 % in 2040. This is the result of relevant technological development driven by demanding CO 2 reduction targets and air pollutant emission standards applicable to new vehicles. An example of such technological development is the increase in the availability of plug-in hybrid vehicles, which have exhibited great potential in reducing both pollutant emissions and CO 2 emissions from traffic (Doulgeris et al., 2020).

Technological improvement in decreasing emissions from internal combustion engines will be accelerated in the EU market due to the current Euro 6d emission standard and the upcoming Euro 7 regulation but also the proliferation of electric power trains to meet CO 2 targets. The only road transport pollutant not significantly affected by the introduction of electric vehicles is non-exhaust PM coming from tyre, brake, and road wear, with estimates suggesting both increases due to heavier vehicles and reductions due to wider exploitation of regenerating braking systems (Beddows and Harrison, 2021).

New techniques are also being developed with the capacity to monitor emissions of vehicles in operation. This can verify that emissions remain below limits in actual use and not just in type approval testing conditions. A current example of such on-board monitoring systems is the on-board fuel consumption measurement (OBFCM) device which is already mandatory for new light-duty vehicles and is being extended for heavy-duty vehicles (Zacharof et al., 2020). Information from such systems, together with new computation methods (big data), can provide very useful information for improving the reliability and temporal and spatial resolution of current emissions inventories.

3.2.4  Shipping emissions

Ships consume high amounts of fossil fuels. On the global scale they emit amounts of CO 2 comparable to big industrialized countries like Germany and Japan. Because ships use high-sulfur fuels, regardless of the global introduction of the 0.5 % sulfur cap in 2020, and typically are not equipped with advanced exhaust gas cleaning systems, their share from global CO 2 is 2.9 %, but corresponding shares of NO x and SO x are considerably higher, 13 % and 12 %, respectively (IPCC, 2014; Smith et al., 2015; Faber et al., 2020). Ship routes are frequently located in the vicinity of the coast, which may go along with significant contributions to air pollution in coastal areas. Effects on ozone formation and secondary aerosol formation also need to be considered.

The environmental regulation concerning the sulfur emissions from ships has been in place in the Baltic Sea since 2006, with the North Sea following in 2007. Currently, also North America and some Chinese coastal areas have stringent sulfur limits for ship fuels. Everywhere else the use of high-sulfur fuel in ships was allowed until the start of 2020, when sulfur reductions of a maximum of 0.5 % S were extended to all ships (IMO, 2019). This has been estimated to reduce the premature deaths by 137 000 each year (Sofiev et al., 2018). Nitrogen oxide emissions from ships are regulated by NO x Emission Control Areas (ECAs), which currently exist only in the coastlines of Canada and the US. The Baltic Sea and the North Sea areas will quickly follow, because in 2021 all new ships sailing these areas must comply with 80 % NO x reduction.

The introduction of the automatic identification system (AIS), long-range identification and tracking (LRIT), and vessel monitoring systems (VMSs) have enabled tracking of individual ships in unprecedented detail. These navigational aids offer an excellent description of vessel activities on both local and global scales.

Currently, ship emission models using AIS data as an activity source are most popular. They can have accurate information about quantity, location, and time of the emissions. Most of the model systems applied today use a bottom-up approach to calculate shipping emissions (e.g. Jalkanen et al., 2009, 2012, 2016; Johansson et al., 2017; Aulinger et al., 2016). The combination of vessel activity, technical description, and an emission model allows for prediction of emissions for individual ships. This also facilitates comparisons to fuel reports, like those of the EU Monitoring, Reporting, and Verification (MRV) scheme or IMO Data Collection System (DCS). Emission models may also include external contributions, like wind, waves, ice, or sea currents in vessel performance prediction, which brings them closer to realistic conditions experienced by ships than the assumptions applied for ideal conditions (Jalkanen et al., 2009; Yang et al., 2020). A vessel-level modelling approach allows for very high spatio-temporal resolution and flexible 4D grids (lat, long, height, time) on which the data can be given. New information about modified or new emission factors for certain chemical species can easily be adopted in the models. Ship emission data are available on a global grid at 0.1 ∘ × 0.1 ∘ and in higher resolution for regional domains in Europe (see Fig. 5), North America, and East Asia (e.g. Johansson et al., 2017). The emission model systems also allow for the construction of future scenarios; see e.g. Matthias et al. (2016) for the North Sea, Karl et al. (2019c) for the Baltic Sea or Geels et al. (2021) for a possible opening of new routes in the Arctic.

Figure 5 The predicted SO x emissions from ships in Europe in 2018, computed using the STEAM model (e.g. Johansson et al., 2017). Use of low-sulfur fuels and SO x scrubbers is concentrated to the North Sea and Baltic Sea ECAs. Background map © US Geological Survey, Landsat8 imagery.

Emissions from ships in ports can be quantified for arrival and departure following the same AIS-based approach as for regional and global shipping emissions. Emissions for ships at berth are estimated based on ship type and size, but with large uncertainties.

Introduction of emission limits gives shipowners a choice to comply with at least three options. The first of these is the use of low-sulfur fuels, and the second option involves the use of aftertreatment devices ( SO x scrubbers), which remove air pollutants by spraying the exhaust with seawater. The third option probably applies only to new ships, because it involves the use of liquid natural gas (LNG) as a marine fuel.

Exhaust aftertreatment systems, which are commonly used to remove NO x , SO x , or PM often involve chemical additives (urea, caustic soda) or large amounts of seawater. Use of so-called open-loop SO x scrubbers, which use seawater spray to wash the ship exhaust, releases the effluent back to the sea. This may lead to a creation of a new water quality problem, especially in areas where water volumes are small (estuaries, ports) or water exchange is slow (e.g. the Baltic Sea) (Teuchies et al., 2020).

The use of low-sulfur or LNG fuels is a fossil-based solution, unless the fuel was made using renewable or fully synthetic sources. However, emissions of NO x , SO x , and PM from LNG engines can be very low, but this depends very much on the engine type selected.

Methane, methanol, and ammonia are three fuels which can be produced by fossil, bio, and synthetic pathways. These three fuels are also suitable for use in internal combustion engines as well as fuel cells. All three are hydrogen carriers and processes, which lead to synthesis of these three fuels and have hydrogen production as an intermediary step. This could offer a viable pathway towards hydrogen-based shipping but also allows the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019).

3.2.5  Emissions of indoor sources

The shift in focus from regulating the outdoor concentration of pollutants to putting more emphasis on reducing the individual exposure to pollutants, which is described later in Sects. 7.4 and 8 of this paper, makes it necessary to analyse not only possibilities for reducing emissions from outdoor sources, but also those from indoor sources. Thus, detailed knowledge about emission factors from indoor sources is needed.

Smoking, combustion appliances, and cooking are important sources of PM 2.5 , NO, NO 2 , and PAHs (Hu, 2012; Li, 2020; Weschler and Carslaw, 2018). Particularly important indoor sources of NO and NO 2 are gas appliances such as stoves and boilers (Farmer et al., 2019). For PM 2.5 , apart from diffuse abrasion processes, passive smoking is still the most important source, although the awareness that passive smoking is unhealthy has been increasing with the EU ban of smoking in public buildings. Schripp et al. (2013) report that not only smoking but also consuming e-cigarettes leads to a high emission of VOCs and fine and ultra-fine particles. Frying and baking lead to the evaporation and later condensation of fat and are a large source for PM 2.5 , especially if no kitchen hood is used; a larger number of studies on frying are available and listed in Li (2020) and Hu et al. (2012). Hu et al. (2012) reviewed emissions of PM 2.5 from the use of candles and incense sticks and found that incense sticks have much higher emission rates than candles. Zhao et al. (2020a) simultaneously measured indoor and outdoor concentrations of PM in homes in Germany and report abrasion and resuspension processes as major contributors of coarser particles (PM 2.5−10 ) and toasting, frying, baking, and burning of candles and incense sticks as important sources for ultra-fine particles. Also, the use of open chimneys and older wood stoves in the living area is an important source. For wood stoves, mostly measured indoor concentrations of PM 2.5 are used to characterize the pressure coming from indoor emissions, or emissions are estimated as a fraction of the overall emissions of a stove. As only a few studies measuring emissions from wood stoves into the interior exist (Li et al., 2019b; Salthammer et al., 2014), more measurements are necessary. Schripp et al. (2014) report very high emission factors of ethanol-burning fireplaces, as these have no chimney.

Laser printers emit ultra-fine particles, especially longer-chained alkanes (C21–C45) and siloxanes (Morawska et al., 2009). Also the new 3D printers are a source of nanoparticles, as Gu et al. (2019) found out. Schripp et al. (2011) analysed the emissions from electric household appliances and reported high emission rates in particular from toasters, raclette grills, flat irons, and hair blowers.

New furniture is often a source of formaldehyde. The use of chemicals such as cleaning agents and personal care products leads to VOC and semi-volatile organic compounds (SVOC) emissions, which are partly oxidized and condensate and thus transform into fine particles. McDonald et al. (2018) point out that with rapidly decreasing emissions of VOC from transportation, emissions from the use of volatile chemical products indoors are becoming the dominant sources in the urban VOC emission inventory, so that VOC concentrations often are higher indoors than outdoors (Kristensen et al., 2019).

Excreta of house dust mites use of fan heaters; vacuum cleaning; especially without HEPA filters; and pets are further indoor emission sources. Furthermore, all kinds of human activities produce abrasion. As there are numerous different processes causing these emissions, instead of estimating emissions, measured concentrations, which typically stem from abrasion processes, are used.

Apart from reducing emissions, the concentration of pollutants indoors can also be reduced by ventilation, i.e. by opening windows or using mechanical ventilation, or by filtering the air, e.g. with HEPA filters for the removal of fine particles.

3.2.6  Source apportionment methods and studies

The question of how much the different sources are contributing to the ambient levels of different air pollutants is critical for the design of effective strategies for urban air quality planning. Different methods are used for source apportionment of ambient concentrations, each including certain limitations given by the intrinsic assumptions underpinning the individual methods and by availability and robustness of data underpinning the source apportionment. In many cases these methods are complementary to each other, and implementation of a combination of different methods decreases the uncertainties (Thunis et al., 2019). There are two principally different source apportionment models: the receptor models apportioning the measured mass of an atmospheric pollutant at a given site to its emission sources and the source-oriented models based on sensitivity analyses performed with different types of air quality models (Gaussian, Lagrangian, or Eulerian chemistry–transport models) (Viana et al., 2008; Hopke, 2016; Mircea et al., 2020). Another method addressing the source–receptor relation of air pollution is inverse modelling used for improvement of emission inventories from global scale to individual industrial sources (e.g. Stohl et al., 2010; Henne et al., 2016; Bergamaschi et al., 2018).

The main receptor models are the incremental (Lenschow) method, the chemical mass balance (CMB) method, and the positive matrix factorization (PMF) (Mircea et al., 2020). The Lenschow method is based on the assumption that source contributions can be derived from the differences in measured concentrations at specific locations not affected and affected by the emission sources. This approach is based on the assumptions that the regional contribution is constant at both locations and that the sources do not contribute to the regional background. The CMB is based on known source composition profiles and measured receptor species concentrations. The result depends strongly on the availability of source profiles, which ideally are from the region where the receptor is located and that should be contemporary with the underpinning ambient air measurements. PMF is the most commonly used analytical technique operating linear transformation of the original variables to create a new set of variables, which better explain cause–effect patterns. Hopke (2016) provides a complete review of receptor models.

The source-oriented apportionment methods utilizing source-specific gridded emission inventories and air pollution models include two in principal different methods, the widely used sensitivity analysis, also called brute-force method, or emission reduction potential (Mircea et al., 2020) or emission reduction impact (ERI) method (Thunis et al., 2019), and the tagged species methodology which involves computational algorithms solving reactive tracer concentrations within the chemistry–transport models. ERI and tagged species methods are conceptually different and address different questions. Generally, the ERI method analyses how the concentrations predicted by an air quality model respond to variations in input emissions and their uncertainties. An important aspect to consider when using this method is that the relationship between precursor emissions and concentrations of secondary air pollutants may include non-linear effects. In non-linear situations, the sum of the concentrations of each source is different from the total concentration obtained in the base case. The magnitude of the emission variations considered in ERI may vary from small perturbations, studying the model response in the same chemical and physical regime as the base case, to removing 100 % of the studied emissions (the zero-out method), which may include non-linear effects present in the model response (Mircea et al., 2020). The tagged species method is based on CTM simulations with the tagging/labelling technique, which keeps track of the origin of air pollutants through the model simulation. This accountability makes it possible to quantify the mass contributed by every source or area to the pollutant concentration (Thunis et al., 2019; Im et al., 2019).

The principal differences between the different source-apportionment methods and implications of these differences on apportionment of sources to the observed or modelled ambient concentration levels are in detail explained and discussed in Clappier et al. (2017) and Thunis et al. (2019). Belis et al. (2020) evaluated 49 independent source apportionment results produced by 40 different research groups deploying both receptor and source-oriented models in the framework of the FAIRMODE intercomparison study of PM 10 source apportionment. The results have shown good performance and intercomparability of the receptor models for the overall data set while results for the time series were more diverse. The source contributions of the source-oriented models to PM 10 were less than the measured concentrations.

In this section we further focus on new developments in source characterization with the help of receptor-oriented models and in construction of emission inventories while the air quality models and emission sensitivity studies are the subject of Sect. 5 of this paper. Several new studies reported on characterization of local composition of particulate matter as well as of NMVOCs and PAHs, tracking the contribution of main emission sources (Christodoulou et al., 2020; Diémoz et al., 2020; Saraga et al., 2021; Liakakou et al., 2020; Kermenidou et al., 2020). The particulate matter has been characterized in terms of carbonaceous matter – elemental or black and organic carbon, organic matter, metals, ionic species, and elemental composition. An Aethalometer model to identify BC related to fossil fuel combustion and biomass burning has been applied in several studies (Grange et al., 2020; Christodoulou et al., 2020; Diémoz et al., 2020). Combination of the different analytical methods and analysis of temporal and spatial variation in the data allowed for identification of chemical fingerprints of different emission sources. Belis et al. (2019) present a multistep PMF approach where a high-time-resolution data set from Italy of aerosol organic and inorganic species measured with several online and offline techniques gave internally consistent results and could identify additional emission sources compared to earlier studies.

The local studies characterizing the local composition of PM, as well as NMVOCs and PAHs, revealed the important roles of road traffic and residential combustion for concentration levels of air pollutants in both urban and rural areas. Wood burning has an important share in many residential areas, especially those outside the city centres and in the countryside (Saraga et al., 2021; Fameli et al., 2020). Fuel oil is another important fuel in residential combustion; in some cities such as Athens it is the dominating one (Fameli et al., 2020). The studies show important differences in the diurnal and seasonal patterns of these two emission sources. While road traffic emissions have maxima in the morning and afternoon hours, contributions from residential combustion dominate at night-time and in the cold season. Important contributions of traffic are found in all studies. Saraga et al. (2021) show, as results from the ICARUS study performed in six European cities, that the main contribution to road-traffic-related PM 2.5 is the tyre and brake wear and resuspension of the particles. The fuel oil combustion source is, apart from residential heating, also associated with industrial emissions and shipping emissions. Contributions from these sources become important at specific locations, like in cities with certain industrial plants or in harbour cities.

Analyses of data from longer time series show a decreasing trend for exhaust gas emissions in road traffic. Its contributions to BC in the last decade decreased while the residential combustion, especially the wood burning contribution, does not show any clear trend (Grange et al., 2020). Efficient abatement measures for improvement of the local air quality need to address the important sources. In most cases these are the local traffic and residential combustion, but in many cases these also include industrial sources and in some cases shipping. Targeting these different sources requires a different approach for each.

Inverse modelling is mainly used for improvement of emission inventories with the help of measurements. Different inversion methods applied in Lagrangian dispersion models (e.g. Stohl et al., 2010; Manning et al., 2011; Henne et al., 2016) and global and regional Eulerian models have been widely used for improvements of emission inventories of greenhouse gases on a wide range of geographical scales from global to national, urban, and local. An overview of different inverse modelling approaches applied to a European CH 4 emission inventory is presented by Bergamaschi et al. (2018). Inverse modelling has the potential to reduce uncertainties of emission inventories comparable to other approaches, e.g. an incremental method combining aircraft measurements and a high-resolution emission inventory (Gurney et al., 2017).

3.3  Emerging challenges

3.3.1  emission inventories and preprocessors.

Emission inventories still have large uncertainties. In particular, PM emissions stemming from all kinds of diffuse processes, especially from abrasion processes in industry, households, agriculture, and traffic, show a large variability and uncertainty. For example, abrasion processes of trains may cause very large PM concentrations in underground train stations, but emission factors and total emissions are not well-known. With the ongoing reduction of exhaust gas emissions and the continuing introduction of electric vehicles, abrasion will become the most important process for traffic emissions.

For residential wood combustion many uncertainties relate to the quality and refinement of information about the use of wood and the heating device technologies, tree species, wood storage conditions, or combustion procedures implemented. Their impact on emission inventories is not well evaluated, but new research underlines how national characteristics need to be taken into account and also shows what type of data can be used in order to improve the spatial representation of these emissions.

Despite the activities to improve temporal profiles of agricultural emissions, more detailed information about the amount of NH 3 and PM emissions is still needed for many regions of the world. Also, natural emissions like dust, marine VOCs, and marine organic aerosols remain a challenge, in particular when climate change might lead to the formation of new source regions in high latitudes.

Chemical composition of NMVOC emissions from combustion processes remains highly uncertain, especially when new fuels enter the market like low-sulfur residual fuels in shipping or when new exhaust gas cleaning technologies are introduced that modify the chemical composition of the exhaust gas. Advanced instrumentation for the characterization of new emission profiles are needed here. Measurement techniques employed in the characterization of emissions impact the results; for example, the dilution methods used have a large impact on the measured gas-to-particle partitioning. Better understanding of these impacts and a robust assessment of the uncertainties and variabilities remain a challenge. Emission inventories should include air pollutants and greenhouse gases at the same time. Integrated assessments analyse measures and policies targeting air pollution control as well as climate protection at the same time and potential, and their co-benefits need to be investigated.

Emissions preprocessors aim at increasing the level of detail they take into account for calculating the spatial and temporal resolution of emissions. However, the availability of input data sets (e.g. traffic data from mobile phone positions, AIS ship position data), the huge size of these data sets, and also data protection rules currently hinder their use. Still, there is big potential in extending the data sources used for emissions preprocessing towards big data, e.g. from mobile phone positions, traffic counts, or online emission reporting, in order to reach real-time emission data and improved dynamic emission inventories to be used in air quality forecast systems. Monitoring data from numerous air quality sensors at multiple locations might help in advancing these inventories.

3.3.2  Road emissions

The accuracy and relevance of our current emission estimation and modelling approaches may in the future be challenged by relevant developments, the most important ones being the following.

The exhaust emissions from road transport are continuously decreasing, as exhaust filters become increasingly efficient and are used in a wider range of vehicle technologies, including gasoline vehicles, while the market share of electric cars is also increasing. However, PM 2.5 , PM 10 , and heavy metal emissions from wear and abrasion processes increase with increasing traffic volume as they are not regulated, and electric cars also produce emissions from tyre wear and road abrasion. For instance, the emissions of PM 2.5 reported by Germany to the EEA for 2018 show 9.9 kt a −1 for exhaust gases of cars, trucks, and motorcycles; 7.6 kt for tyre and brake wear; and 4.3 kt from road abrasion. A scenario reported by Germany for 2030 shows only 2.0 kt PM 2.5 for exhaust emissions, but 7.9 kt from tyre and brake wear and 4.4 kt from road abrasion (EIONET, 2019). Emissions from wear of tyres and brakes and abrasion of road surfaces are less studied than exhaust emissions. Wear emissions depend on a range of parameters including driving behaviour (acceleration and braking pattern), vehicle weight and loading, structure and material of brakes and tyres, road surface material, and weather conditions (e.g. road water coverage) (e.g. Denby et al., 2013; Stojiljkovic et al., 2019; Beddows and Harrison, 2021). Capturing the effect of technological developments in this area would be therefore important for relevant air quality estimates.

The profile of non-methane organic gases (NMOGs) is important to estimate the contribution of exhaust to secondary organic aerosol formation. NMOGs depend on fuel and lube oil use, combustion, aftertreatment, and operation conditions. The profile of emission species may be differentiated as new fuels, including renewable, oxygenated, and other organic components are being increasingly used to decarbonize fuels. Hence, although total hydrocarbon emissions are still controlled by emission standards, the speciation of these emissions may vary in the future. Monitoring those changes is cumbersome as the study of the chemistry and/or volatility of organic species is a tedious and expensive procedure. Hence any changes may escape relevant experimental campaigns.

Questions remain about the suitability of widespread emission factors and models to capture the effects of lane layouts, vehicle interactions, and driving behaviour, while lane-wide average traffic parameters are a structural limitation to emission modelling. As urban policies are advancing in an effort to decrease the usage of private vehicles in cities, the impact of traffic calming and banning measures may not be satisfactorily captured by today's available emission models. In order to take driving behaviours into account, it is necessary to improve so-called microscopic models such as the “Passenger car and Heavy duty Emission Model“ (PHEM) (Hausberger et al., 2003) that calculate emissions from high-temporal-frequency information on network configuration as well as traffic and driving conditions (see review by Franco et al., 2013). Their use calls for the development of new methodologies to provide the simulation with individual speed profiles, taking into account the actual road usage and the specificities of the emissions of the most recent vehicles.

3.3.3  Shipping emissions

The efforts of decarbonizing shipping have thus far concentrated on minimizing the energy need of ships, but a shift to carbon-neutral or non-carbon fuels is necessary. Methane, methanol, and ammonia are three fuels that could offer a viable pathway towards hydrogen-based shipping but also allow for the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019). Regardless of the fuel or aftertreatment technique used, detailed emission factor measurements for various combinations of fuels and engines are needed (Anderson et al., 2015; Winnes et al., 2020) to reliably model the emissions.

Little is known about emissions of VOCs from ships and how much they contribute to particle formation and ozone formation. VOC emissions from ships are not included in most ship emission models, because emission factors are not available or stem from comparably old observations. In addition, VOC emissions are expected to vary considerably with the type of fuel burned and the lubricants used on board, both of which have changed considerably with the introduction of low-sulfur fuels in 2015 (in ECAs) and in 2020 (on a global level). The most recent greenhouse gas emission report from IMO (2021) states that evaporation might be the most important source for VOCs from shipping, which is not considered in any emission inventory, yet.

Current exhaust gas cleaning technologies, in particular scrubbers applied for removing SO 2 from the ship exhaust, dump large parts of the scrubbed pollutants into the sea. More comprehensive research is therefore urgently needed on the combined effects of shipping, which will treat both the impacts via the atmosphere and those on the marine environments. The impacts via the atmosphere include the health effects on humans, the deposition of pollutants to the sea, and climatic forcing. The impacts on the marine environment include acidification, eutrophication, accumulation of pollution in the seas, and marine biota. Recently, there have been attempts to combine the expertise of oceanic and atmospheric researchers for resolving these issues (Kukkonen et al., 2020a).

Ships have high emissions when they arrive in ports and also when they depart a short time later. In addition, they need electricity and heat when they stay at berth, leading to additional emissions in ports stemming from their auxiliary engines and boilers. The impact of these emissions on urban air quality in port areas is of high interest because of their large impact on human exposure.

3.3.4  Indoor sources

Even though people in industrialized countries spend more than 80 % of their time indoors, systematic knowledge on indoor air quality, source strength of the indoor air pollution sources, and physico-chemical transformation of indoor air pollutants is still limited. Therefore, systematic quantification of different indoor air pollution sources, such as building material, consumer products, and human activities, is needed, including exploitation of the already existing test chamber, and other relevant laboratory data are needed. Special attention is also needed to the outdoor source component. Besides obtaining new data on indoor-to-outdoor (I  /  O) ratios, the existing data need to be systematically analysed. One of the key challenges here is how to translate such data from the outdoor contribution into a real indoor environment with considerable heterogeneity in terms of ventilation, volume, microclimatic characteristics, and multiple indoor sources (Bartzis et al., 2015).

Development of indoor air quality models with accurate description of the key chemical and physical processes involved in outdoor–indoor air interaction as well as processing and transport of indoor air pollution inside the buildings is needed to properly address connection between the outdoor air quality and indoor air pollution sources. Additional advanced modelling is needed for air–surface interactions targeting emissions and sinks on different surfaces including those in the ventilation set-up (Liu et al., 2013) along with verification of the indoor air models with measurements in a variety of indoor air environments.

3.3.5  Source apportionment

Continuous improvement of emission inventories with help of verification with source- and receptor-oriented source apportionment methods is needed, especially as large changes in emissions, in terms of both the emission totals and profiles of emission species from individual sources, are expected as a result of upcoming new technologies, fuels, and changes in lifestyle emerging mainly from the Paris Agreement climate change targets.

Currently, apportionments of the overall measurement data sets usually give consistent results while source apportionment of data with high temporal resolution still remains challenging. With rapid development of both advanced online measurement instruments and low-cost measurement sensors, development of source apportionment methods towards high-temporal-resolution data and increasing number of parameters is necessary. This also requires improvements in characterization of sources in terms of both speciation and temporal profiles. This in particular concerns emission profiles for NMVOCs, PAHs, and particulate organic matter (e.g. most existing profiles for PAH emission from vehicles are quite old and do not follow vehicle technology evolution; Cecinato et al., 2014; Finardi et al., 2017). Inverse modelling methods are very powerful and promising tools for source estimation and improvement of emission inventories, but the current models provide large spread in results and need to be further improved and intercompared.

Here we concentrate on another growing field of development: low-cost sensor (LCS) networks, crowdsourcing, and citizen science together with small-scale air quality model simulations to provide personal air pollution exposure. Modern satellite and remote sensing techniques are not in focus here.

4.1  Brief overview

Europe's air quality has been improved over the past decade. This has led to a significant reduction in premature deaths over the same period in Europe, but all Europeans still suffer from air pollution (EEA, 2020a). The most serious air pollutants, in terms of harm to human health, are particulate matter (PM), NO 2 , and ground-level ozone (O 3 ). The analysis of concentrations in relation to the defined EU and World Health Organization (WHO) standards is based on measurements at fixed monitoring points, officially reported by the member states. Supplementary assessment by modelling is also considered, particularly when it results in exceeding the legislated EU standards. But in parallel new monitoring techniques and strategies for observation of ambient air quality are available and applied, which are discussed below.

The motivations for new developments in observation and instrumentation are, on the one hand, obtaining necessary information about air pollutant concentrations and exposure as a basis for compliance and health protection measures and on the other hand supporting improvements in weather, climate, and air quality forecasts. Remote sensing techniques are developed further to get 3D coverage of observations globally by establishment of networks with mini-lidar for example (so-called ceilometers), for evaluation of satellite measurements, to contribute to atmospheric super sites (extension of in situ measurements), or for chemistry–transport model (CTM) evaluations. These techniques can provide nearly continuous monitoring data, only interrupted by certain weather conditions. Satellite measurements are becoming more important for air quality management because their spatial resolution can reach down to 1 km, while their information content is suitable for the assessment of modelling results and combination with modelling tasks (Hirtl et al., 2020). All these techniques enable unattended detection at different altitudes and thus of the composition, clouds, structure, and radiation fluxes of the atmosphere as well as Earth surface characteristics, relevant for atmosphere–surface feedback processes.

Some examples of modern remote sensing techniques as described in Foken (2021) are the sun photometer networks (determination of aerosol optical depth), MAX-DOAS (e.g. NO 2 and HCHO column densities), lidar (e.g. water vapour, temperature, wind, and air pollutants), and more recently ceilometers (e.g. cloud altitude and mixing layer height). Machine learning algorithms, such as neural networks, are now deployed for remote sensing applications (Feng et al., 2020). Satellite observations have become available for column densities of aerosol, NO 2 , CO, HCHO, O 3 , PM 10 , CH 4 , and CO 2 as well as aerosol optical depth and various image analyses (Foken, 2021). Together with improved spatial coverage and high resolution, these data become increasingly important for assessment in urban areas (Letheren, 2016).

The distribution of ambient air composition exhibits large spatial variations; therefore high-resolution measurement networks are required. This has become possible with LCS networks, which are used in both research and operational applications of air pollution measurement and in global networks of observations such as the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme (Lewis et al., 2017). WMO/GAW (Global Atmosphere Watch Programme of the World Meteorological Organization; https://public.wmo.int/en , last access: 21 February 2022) addresses atmospheric composition on all scales from global and regional to local and urban (see GAW Station Information System, https://gawsis.meteoswiss.ch/GAWSIS/#/ , last access: 21 February 2022) and thus provides information and services on atmospheric composition to the public and to decision-makers, which requires quality assurance elements and procedures as described by the WMO/GAW Implementation Plan: 2016–2023 (WMO, 2017). This topic is further discussed with respect to the related sensor, network, and data analysis requirements.

4.2  Current status and challenges

To describe the current trends of air quality monitoring, certain lines of research and technical development are formulated in the following section. This section concentrates on high-resolution measurement networks by the installation of a larger number of small and low-cost measurement sensors. The measurements by traditional in situ measuring as well as ground-based, aircraft-based, and space-based remote sensing techniques or integrated measuring techniques are no longer considered. Also, satellite observations, which are a growing field of development towards even smaller and thus cost-effective platforms, are not the focus here.

The configurations of ambient air measurements can be described as a space, time, and precision-dimensional feature space shown as large arrows in Fig. 6 where crowds with LCS (green) are distributed irregularly in space and time at low precision and high number. Stationary measurements (yellow) are performed at high precision and thus of the highest quality as well as continuously over time but only at a few points in space requiring high effort and cost. Between the two layers, mobile measurements are available on a medium level of precision: in one case regularly on certain routes (red) and in another case with high spatial density at a few points during intensive measurement campaigns (blue). The crowd measurements by LCS can be geo-statistically projected onto a higher quality level together with the high-precision measurements (thin black arrows). Following this, an overall higher information density at an elevated quality level than the sum of the individual measurements alone is possible, so that continuous data by LCS can be applied (Budde et al., 2017).

There is an increasing interest in air quality forecast and assessment systems by decision makers to improve air quality and public health; mitigate the occurrence of acute air pollution episodes, particularly in urban areas; and reduce the associated impacts on agriculture, ecosystems, and climate. Current trends in the development of modern atmospheric composition modelling and air quality forecast systems are described in review by Baklanov and Zhang (2020), which includes for instance the multi-scale prediction approach, multi-platform observations, and data assimilation as well as data fusion, machine learning methods, and bias correction techniques. This shows the general development towards spatial and temporal high resolution as well as better knowledge of personal air pollution exposure.

Figure 6 Configuration of ambient air measurements modelled as a space, time, and precision-dimensional feature space (large arrows): crowds with low-cost sensors (green) scatter irregularly in space and time at low precision but high number (source: Budde et al., 2017).

4.2.1  Low-cost sensors and citizen science for atmospheric research

Many manufacturers (more than 50 worldwide, with their numbers growing fast) are working in the market for air quality monitoring with different business models (Alfano et al., 2020). There are companies which produce and/or sell medium-cost sensors (MCSs) with a cost per compound on the order of EUR 100 and EUR 1000 and LCS on the order of EUR 10 and EUR 100 for all key air pollutants (Concas et al., 2021). Furthermore, manufacturers and integrators often provide installation of LCS and MCS for networks and on mobile monitoring platforms. The operation of such networked and mobile platform measurements is also often supported by the companies which install the sensors. However, the monitoring of air pollutant limit value exceedances is still a task of governmental agencies which are responsible for air quality.

These developments point to a new era in detecting the quality of air which we breathe (Munir et al., 2019; Schade et al., 2019; Schäfer et al., 2021) where virtually everybody can measure air pollutants. Following this potential high number of sensors, fine-granular assessment of air quality in urban areas is possible at lower costs. The data platforms of these LCS and MCS networks collect enormous amounts of data, and new data products like personal exposure of air pollutants, spatial distribution of air pollutants down to 1 m resolution, information about least polluted areas, and forecast of air quality are supplied for users. Figure 7 shows these possibilities on the Internet of Everything with things, sensor data, open data platforms, and citizen actions.

Figure 7 Exploitation of Internet of Everything technology with things, sensor data, open data platforms, and actions of people.

Algorithms from machine learning and big data, together with data from reference instruments as well as monitoring data owned by governmental agencies, are often working on a central data server. Thus, an overall higher information density at an elevated quality level than the sum of the individual measurement components is possible. Also, a dynamic evaluation technique can be applied, which is built upon mobile sensors on board vehicles, for example, trams, buses, and taxis combined with the existing monitoring infrastructure by intercomparison between any two devices which requires a corresponding high dynamic of their sensitivity. Pre-/post-calibrations are possible by using high-end instruments or adjustment in a reference atmosphere under prescribed laboratory and/or field conditions. Based on these achievements in the monitoring networks it is possible to identify emission hot spots and thus to assess spatially resolved, high-resolution emission inventories. Such emission inventories are a prerequisite for supporting high-resolution numerical simulations of air pollutant concentrations and eventually the forecast of air quality.

Furthermore, because of the small size and low weight, sensors can be installed on board unmanned aerial vehicles (UAVs) so that these platforms become complex air quality (Burgués and Marco, 2020) and meteorological instruments. This means vertical profiling is possible with aerial atmospheric monitoring to understand the influence of air pollutant emissions upon air quality.

4.2.2  Quality of sensor-measured and numerical simulation data

An increasing number of evaluations of MCS and LCS as well as of networks based on such sensors are being performed, and conclusions are available from these studies such as Thompson (2016), Morawska et al. (2018), and Karagulian et al. (2019). It is well-known that these sensors suffer from drift and ageing (Brattich et al., 2020). The drift can vary even among the same model sensors that come from the same factory. Furthermore, sensor data evaluation is necessary due to cross-sensitivities of sensors with other air pollutants in ambient air and the influences of different temperatures and humidity in ambient air upon the sensor response.

Activities for the standardization of a protocol for evaluation of MCS and LCS at an international level and for inter-comparison exercises are ongoing, where MCS and LCS are tested at the same sites and at the same time (e.g. Williams et al., 2019). The European Committee for Standardization/Technical Committee (CEN/TC) 264/Working Group (WG) 42 “Ambient air – Air quality sensors” works for a Technical Specification of LCS (CEN/TS 17660-1; https://standards.cencenelec.eu/dyn/www/f?p=CEN:105::RESET:::: last access: 21 February 2022). Such guidelines and sensor certifications are required for data products such as personal air pollution exposure, emission source identification, and nowcasting of air quality as well as for applications as traffic management (Lewis et al., 2018; Morawska et al., 2018).

In the area of high-resolution modelling, the creation of a model data standard for obstacle-resolving models ( https://www.atmodat.de/ , last access: 21 February 2022) has started (Voss et al., 2020) as already done for coupled models (CESM – CMIP6 (ucar.edu); https://www.cesm.ucar.edu/projects/CMIP6/ , last access: 21 February 2022).

4.2.3  Importance of crowdsourcing, big data analysis, and data assimilation

Data from high-resolution measurement networks can provide the base for application of small-scale 3D process-based CTMs by means of assessment of emission inventory and model results. Additionally, it can support the operation of statistical, artificial intelligence, neural network, machine learning, and hybrid modelling methods (Bai et al., 2018; WMO, 2020; Baklanov and Zhang, 2020). Statistical methods are simple but require a large amount of historical data and are extremely sensitive to them. Artificial intelligence, neural network, and machine learning methods can have better performance but can be unstable and depend on data quality. Hybrid or combined methods often provide better performance. Such methods can also improve the CTM forecast by utilizing added observation data. For example, Mallet et al. (2009) have applied machine learning methods for the ozone ensemble forecast, performing sequential aggregation based on ensemble simulations and past observations. The latest results of the integration of air quality sensor network data with numerical simulation and neural network modelling results by data assimilation methods are for the Balkan region (Barmpas et al., 2020); Grenoble, France (Zanini et al., 2020); Leipzig, Germany (Heinold et al., 2020); and the inner city of Paris, France (Otalora et al., 2020), and they show how modelling can be used to support and consolidate information from observation data products.

The trend to improve air quality forecasting systems leads to the development of new methods of utilizing modern observational data in models, including data assimilation and data fusion algorithms, machine learning methods, and bias correction techniques (Baklanov and Zhang, 2020). Typically, as a first step data verification and validation of different data sources are performed, including data from LCS and MCS networks, permanent monitoring networks, and UAV-based, aircraft-based, and satellite-based measurements (in situ and remote sensing). Subsequently, emission information data assimilation methods are applied for integration with urban-scale CTM or neural network modelling or fluid dynamics modelling or combining these to provide a flexible framework for air quality modelling (Barmpas et al., 2020). Such approaches that combine the use of observations with models can lead to improved new tools to deliver high-quality information about air quality, spatial high-resolution forecasts of air quality for hours up to days, and health protection to the public.

Further, literature already provides QA–QC methods for MCS and LCS based on big data analyses and machine learning as well as data analyses in the cloud (Foken, 2021). Evaluation methods for measurement and modelling results are selected and combined to show the application potential of data sets of the new sensors, networks, and air quality model simulations. The further development and application of assimilation and quality evaluation methods is ongoing with the aim that distributed data sources will form the basis for new data products, making possible new applications for citizens, local authorities, and stakeholders.

4.2.4  Applicability of sensor observations

Crowdsourcing of sensor observations is applied to get information for personal air pollution exposure and for supporting decisions on personal health protection measures such as information about the least polluted areas for outdoor activities. Using this data-based information, citizens can recognize heavily polluted areas, which could be especially important for sensitive groups.

The platforms for the combination of ground-based stationary and mobile sensors, the complementation with 3D measurement data by in situ and remote sensing observations, and model evaluation and assessment can support such applications. This trend of cost-effective air quality monitoring includes user-oriented data services and education about air pollution and climate change to best exploit the knowledge and information content of measured data. Local authorities already use such data (e.g. English et al., 2020) for identifying emission hot spots, management of city infrastructure, and road traffic management towards improving air quality.

MCS and LCS and their advantages in operation and data availability via citizen sciences can also support the understanding of indoor air quality. The investigations of indoor air pollution in conjunction with outdoor air pollution monitoring provide more realistic data of personal air pollution exposure and for assessing measures of health protection.

4.2.5  Modelling for urban air quality to support observation data products

Numerical modelling results are traditionally evaluated against data from air quality monitoring networks (see also Sect. 5). At high resolution, this process requires the use of a sensor network specifically configured to meet the needs of the exercise. Conversely, modelling can also be used to support air quality mapping based on observational data. Indeed, while the use of LCS for high-density observations can provide information on the variability of pollutant concentration on a fine spatial scale, the spatial (and temporal) global coverage of the areas being monitored nevertheless can prove to be irregular and incomplete.

Data-driven modelling over combined stationary- and mobile-generated pollution data requires the deployment of dedicated statistical methodologies. Although little research effort has been devoted to such developments so far, recent advances in machine learning and artificial intelligence have highlighted the exciting potential of several statistical analysis tools (data envelopment analysis, unsupervised neural learning algorithms, decision trees, etc.) to predict air quality at the city scale from data generated by mobile sensors, which are supported by citizen involvement (Mihăiţă et al., 2019).

Another approach that appears very promising to meet the operational challenges associated with fine spatial mapping is to combine sensor data with mapped data from models. The technique used is geostatistical data fusion, an approach similar to data assimilation and based on kriging interpolation. It produces a new map whose added value lies in obtaining the most probable field of concentration, at the time when the sensor observations were made but also the combination of information provided by the two data sources (Ahangar et al., 2019; Schneider et al., 2017). A study carried out on a medium-density urban area in France showed that the bias found between the outputs of an urban model and the data from the local air quality network was reduced from 8 % to 2.5 % following fusion with the sensor data. However, the results of the fusion technique are characterized by a lower dispersion than the input data sets, which leads to a smoothing of the peaks and thus an underestimation of the maximum values. Finally, the performance of fusion is logically degraded by the uncertainty in the sensor measurements and the low correlation between the two data sources due to biases in the LCS measurements (Gressent et al., 2020). This underlines the importance of accurate calibration of portable devices to achieve reliable air quality mapping on a fine scale.

4.3  Emerging challenges

4.3.1  use of low-cost sensors.

Providing citizens and stakeholders with innovative information from large networks of sensors can yield added value and is fast becoming one of the main emerging challenges in air quality management. Nevertheless, with the greater range of observational techniques available now, there is a need for the application of instrumentation consistency, involving operation of mobile sensors by citizen for routine inter-calibrations and approaches for sensor intercomparison in networks, using correction algorithms for sensors which should be described in a common way. When sensors are installed on board vehicles or UAV, detailed information about the sensor response time should be provided taking account of the compatibility with its movement speed and data gathering frequency.

There is also the need to strengthen the linkages between existing measurement data sets. For example, air pollution monitoring networks of governmental agencies operating at local and national levels incorporating reference data with certified QA–QC methods need to be explored to exploit numerical algorithms, especially from artificial intelligence or dynamic data assimilation, for example as part of sensor and network certifications and standardization, so that these measurement methodologies and the available enormous amount of data can be useful for air quality research and assessment, including legislative reporting.

In the case of low-cost sensors, guidelines and sensor certifications for LCS and MCS are prerequisites for their application. Because such documentation has not been consistently available up to now, LCS and MCS data cannot be used for official assessment of WHO or EU limit value exceedances. Furthermore, the level of acceptable data quality of LCS and MCS is difficult to ascertain, and presently the LCS and MCS networks are difficult to integrate into or extend the air pollution monitoring networks of responsible authorities.

4.3.2  Multi-pollutant instruments

Depending on the monitoring task of air quality or personal exposure, sensors for detection of all air pollutants including ultra-fine particles (UFPs) and particle size distribution (PSD) but also greenhouse gases (GHGs) are necessary. In the application case of sensors embedded at the surface of clothes or carried by individuals, extended miniaturization of LCS and MCS must measure the personal air pollution exposure. Relevant developments could also include personal measurements of bioaerosols (e.g. pollen and fungi). Such data are required to study the combined health effects of air pollutants, bioaerosols, and meteorological parameters. In this sense the speciation or chemical composition and physical characteristics of particles of all sizes are needed too.

4.3.3  Modelling for urban air quality to support observation data products

The small-scale forecast of air quality for different applicants and personal health protection must be improved by adaptation of corresponding numerical simulations of air pollution, based on online input data, which requires readily accessible sources like traffic counting and household heating activities. Alternatively, inverse modelling approaches can help quantify the strengths of diffusive emission sources and identify hot spots. Running spatial and temporal highly resolved numerical simulations requires online evaluation data from the combination of different platforms and the application of data algorithms from the area of machine learning or artificial intelligence.

The assimilation of small-scale data from measurements and numerical simulation of air pollution should be used for reduction of the space-time gaps of measurement networks. This is needed because measurement networks cannot be as dense as the spatial grids of numerical simulations. This implies further development of integration of observations by different platforms and methods as well as the assessment of numerical simulation results together with the application of crowdsourcing. Big data analyses and data assimilation methods can provide new areas of modelling applications in the field of improvement of air quality, determination of air pollution emissions and emission inventories, and development of personal health protection measures. Finally, it is necessary that these data eventually become suitable for monitoring and assessment of air quality in agreement with national and international guidelines.

Measurements and numerical simulation of coupled outdoor and indoor air quality must be supported for obtaining more realistic personal air pollution exposure information, given that most people are mainly exposed to indoor air, which, in turn, is strongly influenced by the quality of the outdoor air.

5.1  Brief overview

Over the last years, it became obvious that our understanding of pollution and exposure processes at the urban scale could be improved by combining multi-scale models and creating new dedicated numerical approaches and that the representation of scale interactions for dynamic phenomena, pollutant emission sources, and pollutant ageing would be a critical element in the realism of the simulation outputs. New developments have therefore aimed at restoring the spatial variability and heterogeneity of air pollution due to the turbulent transport of pollutants, whether in urbanized valleys, city centres, or confined urban spaces such as canyon streets.

The motivation of these works is to address societal issues with a focus on street-level representations of pollutant concentration fields to support the assessment of individual exposure to pollution. In this context, it is now acknowledged that statistical and other data analysis techniques such as machine learning have an important role to play in identifying underlying patterns and trends as well as relationships between different parameters. At the same time, air quality monitoring has been progressing by improving ensemble techniques that allow for more in-depth model evaluation and provide a solid basis for consistent operational work on air quality. The following section reviews current challenges and highlights emerging areas of research covering the development, application, and evaluation of air quality models.

5.2  Current status and challenges

5.2.1  innovative combinations of models.

To meet the need to represent concentration gradients of primary pollutants in large agglomerations, the use of urban-scale dispersion models has increased since the 2010s (Singh et al., 2014; Soulhac et al., 2012). These models indeed allowed the resolution of dispersion effects in a complex emitting and built environment, whereas chemistry–transport models (CTMs) cannot provide an explicit representation of near-source characteristics and meet computational time issues as the resolution increases. However, both the lack of connection between local emission effects and the regional transport of pollutants and the absence of a relevant representation of atmospheric reactivity limit the scope of this type of model. Therefore, interest is progressively turned to the nesting of CTMs and urban models, which allows the exploitation of the advantages of both approaches. Over the last decade, approaches either coupling or nesting Eulerian models with Gaussian source dispersion models (Hood et al., 2018; Hamer et al., 2020), microscale CFD models (Tsegas et al., 2015), obstacle-resolving Lagrangian particle models (Veratti et al., 2020), and/or street models (Jensen et al., 2017; Kim et al., 2018; Khan et al., 2021) have thus been developed with the aim of producing comprehensive cross-scale simulations of air quality in the city. An organization chart for such combined models is illustrated in Fig. 8.

Figure 8 Schematic diagram of the EPISODE model with the CityChem extension (EPISODE–CityChem model), from Karl et al. (2019b).

The interest of the “CTM-Urban dispersion model” approaches called plume-in-grid or street-in-grid lies in the fact that they allow in a single time step the simulation of urban background and to solve at low cost the dispersion of near-field emissions, for more resolved and realistic pollutant concentration fields. Compared to an urban model alone, those systems improve NO 2 scores in areas upwind of urban sources, as well as the average concentration levels of compounds that have a strong long-range transport component such as PM 2.5 , PM 10 , and ozone (Hood et al., 2018). Implemented at the scale of an agglomeration or a region, this approach demonstrated its ability to represent the diversity of urban microenvironments (e.g. proximity to road traffic versus urban background, effect of building density, and street configuration) that were until now poorly considered by the Eulerian approach alone. The representation of road traffic and its influence on urban air quality have been the main focus of these studies. Reaching a resolution from a few metres to a few tens of metres, the simulation outputs indeed accurately reproduce the gradients observed along road axes (see Fig. 9) and show greater comparability with urban-scale measurement data than CTMs alone (especially for NO 2 ). Particularly improved performances have been observed under stable winter conditions, and for some studies, the deviation from measurements is within the 15 % maximum uncertainty allowed by the EU directive for continuous measurements (Hamer et al., 2020). Mostly, the results show a better representation of the amplitude of the local signal than an improvement of the correlation with the observed concentrations, and it is concluded that these multi-scale approaches are a significant advance to predict local peaks and episodes. These skills set them apart as essential tools for providing high-resolution air quality data for street-level exposure purposes (Singh et al., 2020b). Statistical evaluations of the model outputs based on the EU DELTA Tool have been carried out as part of several studies: they show that the models comply well with the quality objectives of the FAIRMODE approach ( https://fairmode.jrc.ec.europa.eu/document/fairmode/WG1/MQO_GuidanceV3.2_online.pdf , last access: 23 Febraury 2022). In the end, although the performances of the models remain dependent on the relative importance of local emissions, as well as transport and chemical processes at each computation grid point, most of the residual biases could be attributed to a lack of realism in the emissions. This includes the presence of poorly characterized local sources (works on the street, road particulate resuspension processes) but also insufficient temporal refinement of road traffic profiles. In this respect, it should be emphasized that the improvement of particulate representation in the model and the restitution of near-field chemical equilibria are also expected as major evolution pathways for the models.

Figure 9 NO 2 annual average concentrations from the coupled ADMS-Urban–EMEP4UK model for (a)  the whole of Greater London and (b)  an area of central London. Monitoring data are overlaid as coloured symbols (Hood et al., 2018).

The study of the impact of shipping activities on urban air quality has also benefited from these multi-scale modelling approaches. Indeed, while conventional CTM approaches simulating the effect of shipping emissions in coastal areas of the North and Baltic seas agreed on the average contribution of shipping to air pollution (around 15 %–30 % of elevated concentrations of SO 2 , NO 2 , ozone, and PM 2.5 ; see Aulinger et al., 2016; Jonson et al., 2015; Karl et al., 2019a; Geels et al., 2021; Moussiopoulos et al., 2019, 2020), the use of urban and plume dispersion models made it possible to refine this diagnosis and assess near-field effects. As for road traffic, the influence of ship emissions on air quality induces pollution gradients in the city. Karl et al. (2020) thus found out that, in residential areas up to 3600 m from a major harbour, the ultra-fine particle concentrations were increased by a factor of 2 or more compared with the urban background.

5.2.2  Improved turbulence and dynamics for higher-resolution assessment of urban air quality

In parallel, the need for higher-resolution assessment of urban air quality poses new demands on flow and dispersion modelling. As an additional difficulty besides complex-geometry-induced phenomena, we are reaching a spatial resolution of metres and a temporal resolution of seconds, thus entering the space scales and timescales of atmospheric turbulence. Therefore, the exposure-related parameters cannot be described only deterministically without considering their stochastic component. A recent step forward in this direction is the increased use of large-eddy simulation (LES) methodology dealing directly with the stochastic behaviour of flow and concentration parameters (Wolf et al., 2020).

Advanced computational fluid dynamics (CFD), including Reynolds-averaged Navier–Stokes (RANS) equations models that provide concentration standard deviation, have also appeared in literature for some time (Andronopoulos et al., 2019). More precisely, the implementation of LES class models solving the most energetic part of turbulence explicitly as well as 3D primitive hydro-thermodynamical equations and the structural details of the complex urban surface has been carried out at the scale of agglomerations, in meteorological conditions corresponding to typical stratified winter pollution situations, and fed with emission data from the city authorities (residential combustion as well as maritime and road traffic in particular). More specifically, advanced CFD models such as LESs, have shown to better characterize the very fine-scale variability of primary urban pollution, for example regarding the irregular spatial distribution of concentrations in proximity to road traffic at complex built-up intersections, which makes it possible to open a reflection on the representativeness of the levels measured and their regulatory use and to define criteria for the optimization of measurement networks. LES local-scale modelling has been used to refine urban air quality predictions either alone (Esau et al., 2020) or embedded in an urban-scale model (San José et al., 2020). Also, wider use of CFD has taken place to improve understanding of pollution distribution inside a built environment, especially for critical infrastructure protection (Karakitsios et al., 2020).

Microscale models are particularly powerful to resolve the turbulent flow and pollutant dispersion around urban obstacles to reconstruct pollutant concentration variability within the urban canopy. Recent microscale model simulations also showed the importance of barrier effects for emissions from large ships. It was thus shown that turbulence at the stern of the ship may cause a significant decrease in exhaust pollutants, leading to higher concentrations near the ground and, most likely, higher exposure of the nearby urban population (Badeke et al., 2021). The application of LES (Esau et al., 2020; Wolf-Grosse et al., 2017; Resler et al., 2020; Werhahn et al., 2020; Hellsten et al., 2020; Khan et al., 2021) and CFD (San José et al., 2020; Gao et al., 2018; Flageul et al., 2020; Koutsourakis et al., 2020; Nuterman et al., 2011; Buccolieri et al., 2021; Kurppa et al., 2018, 2019; Karttunen et al., 2020; Kurppa et al., 2020) models for air quality assessment in urban environments is becoming a frequent approach. Many papers implementing the PALM LES model (Maronga et al., 2015) have been presented at the 12th International Conference on Air Quality – Science and Application. Yet, their application is still limited by difficulties dealing with urban-scale atmospheric chemistry and by the relevant computational resources required – as the use of advanced models such as LESs requires increased computational capabilities. On the other hand, the heavy computational burden of urban LES computations can be reduced by approximately 80 % or even more by employing the two-way coupled LES–LES nesting technique, recently developed within the LES model PALM (Hellsten et al., 2021). Precomputation of LES in operational modelling can be an acceptable solution, especially combined with big data compression methodologies (Sakai et al., 2013). Another possibility is to focus on limited urban areas with special interest (e.g. street canyons and “hot spots”); however, one should in this case take into account the effect on turbulent transport from the surrounding larger-scale turbulent phenomena. In the problem of urban air quality, an assisted approach in the selection/classification process is the use of clustering (Chatzimichailidis et al., 2020) and artificial intelligence/machine learning technologies (Gariazzo et al., 2020).

5.2.3  Use of advanced numerical approaches and statistical models

At the same time, the complementary role of prognostic and diagnostic approaches has been explored. New methodologies based on artificial neural network models, machine learning, or autoregressive models have been developed in order to achieve a more realistic representation of air quality in inhabited areas than achieved by CTMs (Kukkonen et al., 2003; Niska et al., 2005; Carbajal-Hernández et al., 2012; P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). Likewise, Pelliccioni and Tirabassi (2006) employed neural networks to improve the outputs of Gaussian and puff atmospheric dispersion models. Also, Mallet et al. (2009) applied machine learning methods for ozone ensemble forecast and performed sequential aggregation based on ensemble simulations and past observations.

Kukkonen et al. (2003), through an extensive evaluation of the predictions of various types of neural network and other statistical models, concluded that such approaches can be accurate and easily usable tools of air quality assessment but that they have inherent limitations related to the need to train the model using appropriate site- and time-specific data. This dependence has prevented their use in the evaluation of air pollution abatement scenarios or for the evaluation of multidecadal time series of pollutant concentrations. The works of X. Li et al. (2017) confirmed that methods based on machine learning, and more specifically neural networks, can accurately predict the temporal variability of PM 2.5 concentrations in urban areas but that the model performance may be improved using explanatory training variables. Prospective neural network modelling works were also conducted in a canyon street by Goulier et al. (2020). They proposed a comparison of model outputs with measurements (based mainly on Pearson correlation, rank correlation by Spearman, modelling quality indicator's index from FAIRMODE), for a set of gaseous and particulate pollutants. They confirmed that the modelled data were able to reproduce with a very good accuracy the variability of the concentrations of some gaseous pollutants (O 3 , NO 2 ) but that there was still a significant margin for improvement of the models, notably for particles. Again, an important part of the expected progress lies in the choice of model predictors.

As for multi-scale modelling, the main research efforts associated with these numerical approaches are directed towards the downscaling of simulated pollutant concentration fields in urban areas, the improvement of CTM forecast using additional observation data, and a refined representation of individual exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020). Gariazzo et al. (2020) used a random forest model to enhance CTM results and produce improved population exposure estimates at 200 m resolution, in a multi-pollutant, multi-city, and multi-year study conducted over Italy. In addition to reduced bias, the outputs presented much greater physical consistency in their temporal evolution, when compared to measurements.

Other applications, such as advancing knowledge about exposure in urban microenvironments, have also been made possible by these approaches Thus, the use of Bayesian statistics has shown an ability to predict the concentration gradients of primary pollutants in the immediate vicinity of an air quality monitoring station, by iterating between observations and the outputs of a microscale simulation approach – including both a CFD and a Lagrangian dispersion model (Rodriguez et al., 2019).

5.2.4  Implementation of activity-based data

To take full advantage of the high-resolution simulation capability of these new modelling tools, and to achieve a more comprehensive approach to the determinants of air quality in urban areas, modellers have relied on a new generation of activity-based emissions data.

As for traffic, new methodologies relying on individual data collected through surveys, geocoded activities, improved emission factors, and measured traffic flows (Gioli et al., 2015; Sun et al., 2017) or involving traffic models simulating origin–destination matrices for city dwellers on the road network (Fallah-Shorshani et al., 2017) have been developed to serve as input to the urban dispersion models. Their implementation in a case study in Italy, with a horizontal resolution of 4 m, showed that detailed traffic emission estimates were very effective in reproducing observed NO x variability and trends (Veratti et al., 2020).

Residential wood combustion has also proven to act as a major source of harmful air pollutants in many cities in Europe, and especially in northern-central and northern European countries which have a strong tradition of wood combustion. Yet, until the early 2010s, residential wood combustion (RWC) inventories were still heavily burdened with uncertainties related to actual wood consumption, the location of emitters, emission factors depending on heating equipment, and practices driving the temporality of emissions. To represent RWC emissions more accurately in urban air quality models, new emission estimation methods based on environmental and activity variables that drive pollutant emissions have been developed. They include for example outdoor temperature, housing characteristics and equipment, available heating technologies and associated emission factors, or temporal activity profiles from official wood consumption statistics (Grythe et al., 2019; Kukkonen et al., 2020b). Kukkonen et al. (2020b) notably showed with this approach that the annual average contribution of RWC to PM 2.5 levels could be as high as 15 % to 22 % in Helsinki, Copenhagen, and Umeå and up to 60 % in Oslo. Overall, although the results show a better horizontal and vertical spatial distribution of emissions compared to non-specific inventories, improvements are expected, especially on the use of meteorological parameters and regarding emission factors for specific devices.

Finally, for emissions associated with maritime activity in port areas, the inventories developed specifically for high-resolution modelling approaches include information on the fleet, the ship rotations in the harbour, and the emission heights. The implementation of the EPISODE-CityChem model within a CTM showed that in Baltic Sea harbour cities such as Rostock (Germany), Riga (Latvia), and Gdańsk–Gdynia (Poland), shipping activity could have contributed to 50 % to 80 % of NO 2 concentrations within the port area (Ramacher et al., 2019). As for the other sources, improvements are expected. They concern for instance the energy consumption of the different ships and the propulsion power of the auxiliary systems of the ships during their stay in port.

Because they allow detailed mapping of air quality in urban areas, and realistically represent emitting activities, those approaches allow tackling issues such as chronic exposure and source–concentration relationships, but they also provide elements for increased policy and technical measures, as discussed below: regulation, information campaigns, and economic steering.

5.2.5  Contribution of modelling to policy making and urban management strategies

Applying air quality and emission models allows for projections of future developments in air quality that can shed light on the different effects of alternative policy options, e.g. new regulations or effects of changes in the emissions from certain emission sectors. As an example (Fig. 10), the OSCAR model was run over London to quantify the contribution of sources – such as traffic – to the urban PM 2.5 concentration gradients.

Figure 10 (a)  Predicted spatial distributions of the annual mean PM 2.5 concentrations in µg m −3 , and (b)  urban traffic contributions to the total PM 2.5 concentrations, in %, for London for the year 2008 (Singh et al., 2014).

Air quality modelling is expected to gain relevance following the review of air quality legislation announced as part of the European Green Deal (EC, 2019), whereby the European Commission will also propose strengthening provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air. The construction of these future air quality modelling scenarios can be demanding, in particular when the goal is to be realistic and consistent with technological potentials as well as economic and societal developments (in particular reductions in the use of fossil fuels driven by climate policies).

Another field of action recently explored is that of technology-based and management-based traffic control strategies, and in particular the implementation of low-emission zones (LEZs) in urban areas (e.g. in Portugal, Dias et al., 2016; France, Host et al., 2020; and India, Sonawane et al., 2012). The quantification of the expected gains in terms of pollutant concentrations in ambient air, but also of economic benefits and reduction in the occurrence of chronic respiratory diseases or vascular accidents, provides concrete and robust elements for political and citizen debate and helps to move towards greater acceptability of the measures. In this framework, the degree of realism of the simulated scenarios, the spatial refinement of the approaches used, and also the capacity to evaluate them at the sub-urban scale (street, individual) can become determining elements of their scientific relevance and their legitimacy in the policy debate. Therefore, an increasing number of studies favour the use of multi-scale models with the introduction of puff or Gaussian dispersion models, as well as canyon-street models, with CTMs. When modelled scenarios serve as a basis for political decisions, it is highly valuable to include relevant authorities and decision makers from the beginning in the scenario design. This can be done in common workshops with relevant stakeholders where questions about technological trends and possibilities for emission reduction are discussed.

The analysis of simulation data for the estimation of health impacts can be ensured by integrated approaches – such as the EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) – or more simply by algorithms derived from epidemiology such as population-attributable fractions, which are standard methodology used to assess the contribution of a risk factor to disease. In terms of emissions, depending on the focus of the study, survey data on residential practices or activity-based road traffic models (as well as marine traffic models where appropriate) are increasingly used. Supplementary traffic algorithms can sometimes more accurately represent the effects of congestion on roadway emissions. Finally, for more realism, the scenarios considered can be derived from either the relevant air quality plans implemented at the scale of agglomerations or projections on vehicle fleet evolution (Andre et al., 2020). Some of the models also include the feedback effects of changes in practice, such as the estimate of emission increase due to the energy demand for electric vehicle charging (Soret et al., 2014).

Very small-scale modelling has also been used in other fields such as support in evaluating the effect of roadside structures on near-road air quality. Several studies, mainly based on CFD models, including LES approaches have thus focused on the performance of air pollution dispersion by green infrastructures in open areas and street canyons, even characterizing the capacity of parked vehicles to reduce pedestrian exposure to pollutants (see review article in Abhijith et al., 2017). Also, the link between the morphology of urban buildings, the dispersion of emissions, and air quality is often apprehended through CFD models (Hassan et al., 2020). At an even more operational level, LUR models (based on the spatial analysis of air quality data) have been coupled to high-resolution CTM runs to allow a precise identification of land use classes more exposed to PM 10 , SO 2 , and NO 2 . The results provided a methodological framework that could be used by authorities to assess the impact of specific plans on the exposed population and to include air quality in urban development policies (Ajtai et al., 2020).

Examples also exist in the area of shipping emissions, where several EU-funded projects either involved stakeholders such as IMO and HELCOM from the beginning (e.g. Clean North Sea Shipping, ENVISUM, CSHIPP, EMERGE) or made use of their knowledge in dedicated expert elicitation workshops (e.g. SHEBA). Future scenarios for shipping, some of them developed in these projects, were presented for the North and Baltic seas (Johansson et al., 2013; Matthias et al., 2016; Karl et al., 2019a; Jonson et al., 2015), for Chinese waters (Zhao et al., 2020b), and globally (Sofiev et al., 2018; Geels et al., 2020). However, the process of scenario generation in cooperation with authorities and other stakeholders is rarely described in scientific literature or fully detailed in publications that address various policy options.

5.2.6  Ensemble modelling for air quality research applications

In parallel, statistical developments also serve the evolution of ensemble models. During the last decade, ensemble-building methodologies have been questioned and improved in several international collaborations, and the inclusion of new observational data has allowed a better assessment of the relevance of these approaches. Ensemble forecasting can be implemented using multiple models or one model but with different inputs (e.g. varying meteorological input forcings, emission scenarios, chemical initial conditions), different process parameters (e.g. varying chemical reaction rates), different model configurations (e.g. varying grid spacings), or different models (Hu et al., 2017; Galmarini et al., 2012). A comprehensive study on ensemble modelling of surface O 3 was done as part of the Air Quality Model Evaluation International Initiative (AQMEII), including 11 CTMs operated by European and North American modelling groups (Solazzo et al., 2012). One of the main conclusions was that even if the multi-model ensemble based on all models performed better than the individual models, a selection of both top- and low-ranking models can lead to an even better ensemble (Kioutsioukis et al., 2016). It was also shown that outliers are needed in order to enhance the performance of the ensemble.

Within the CAMS regional forecasting system for Europe, multi-model ensemble modelling is a part of daily operational production ( https://www.regional.atmosphere.copernicus.eu/ , last access: 28 February 2022) for several air quality components. Statistical analyses have shown that an ensemble based on the median of the individual model gives a robust and efficient setup, also in the case of outliers and missing data (Marécal et al., 2015). By combining global- and regional-scale models, Galmarini et al. (2018) have taken this kind of ensemble modelling a step further, by setting up a hybrid ensemble to explore the full potential benefit of the diversity between models covering different scales. The analysis indeed showed that the multi-scale ensemble leads to a higher performance than the single-scale (e.g. regional-scale) ensemble, highlighting the complementary contribution of the two types of models.

5.3  Emerging challenges

5.3.1  on multiscale interaction and subgrid modelling.

The advances in computational capacity, the progress on big data management, and the recent developments on low-cost sensor technology, together with the significant developments in closing the gaps of knowledge when dealing with finer spatial and temporal scales (up to the order of metres and seconds, respectively) give the opportunity for further achievements in terms of innovation and outcome reliability in urban- to local-scale flow and air quality assessment. In such applications, very high spatial resolution modelling outputs are required together with dynamic and geocoded demographic data to conduct health monitoring on the impacts of air pollutants. However, new sub-grid/local approaches such as LESs, advanced CFD-RANS, machine learning statistical tools, and interfaces among different modelling scales (regional, urban, local/sub-grid) require further R&D work, especially when interfacing models using different parameterizations or computational approaches.

Of specific interest here is the case of model nesting in regimes where it has not been extensively applied in the past, as is the case of implementation and validation of multiply nested LESs (see e.g. Hellsten et al., 2021), as well as coupling of urban-scale deterministic models with local probabilistic models. In both areas, complications arise due to the nature of different parameterizations and the way boundary conditions are traditionally treated in LES models, highlighting the need for further validation and tools for the numerical evaluation of coupling implementations. Further areas of development include the better articulation between CTMs and subgrid models, towards solving overlay problems like emission double counting and mass conservation across interpolated interfaces, both critical points for their successful application as assessment tools.

5.3.2  On chemistry and aerosol modelling

One important aspect is the fact that local-scale models often include simple approaches to tropospheric chemistry. Although such an approach can be justified from the fact that computation domain timescales are usually well below lifetime scales of priority pollutants, it also poses limitations that need to be addressed. For example, the lack of full representation of NO x –VOC chemistry, or not considering a delay in establishing the photostationary NO–NO 2 –O 3 equilibrium, can introduce a significant bias in the restitution of concentration gradients at very fine scales. Particle-size-resolved schemes, including for example the discrimination of particle removal phenomena, are also expected to be important developments for these local models. How do simplified chemistry and physics impact on treating traffic emissions in cities? What is their role in the restitution of particle growth, secondary organic aerosol (SOA) formation, and ozone chemistry? These issues require special attention. They are also relevant to the treatment of other urban sources generating strong concentration gradients, such as shipping. Thus, the impact of the representation of VOC behaviour on particle formation and ageing, or the effect of NO 2 removal, both in the early phases of ship plume dispersion, should also be investigated.

More globally, there remain issues in the representation of reactivity in multi-scale modelling approaches and air quality forecasting. On the one hand, although some studies have shown that high-resolution models are good at predicting the occurrence (or non-occurrence) of local pollution events, it has been observed that they do not always capture the full range of pollutant concentrations and, especially, the amplitude of the strongest concentration peaks. On the other hand, there remains a very strong interaction between locally emitted pollutants and those resulting from long-range transport (LRT) to the city. This may be determinant for the operational forecasting of air quality at the urban scale. Thus, the representation, on a fine scale, of the fundamental processes of reactivity is one next challenging issue of multi-scale modelling. For local-scale modelling it is indeed important to make sure that at least we include chemical transformation with timescales significantly smaller than the time ranges imposed by the considered computational domain.

5.3.3  On fine-scale model input and emission data

As we move to finer scales and more advanced modelling, the input data – whether meteorological, descriptive of the urban environment, or related to the sources of pollutant – also require additional knowledge of their time and space variation, even including sufficiently detailed statistical behaviour. The refinement of meteorological and chemical input fields for statistical approaches is an important challenge. Indeed, the application of LES or statistical models in a fine domain embedded into a larger domain where ensemble-average modelling data are available and needed raises the question of how to generate fine-scale or statistical input data that are both mathematically consistent and physically correct. It was highlighted that the role of statistical models based on machine learning is increasing, especially for urban AQ applications. This is due to growing computer and IT networking possibilities, but also to new types of numerous observations, e.g. crowdsourcing, low-cost sensors, or citizen science approaches. The ability of machine learning to capture these new data sources and identify new applications in fine-scale air quality and personal exposure is therefore a great challenge for the coming years.

As far as emissions are concerned, the gain in realism has become a prerequisite to produce decision-support scenarios and requires a strong grounding in reality – i.e. emissions must be based on a census of the activities and on the specificities of the emitters (e.g. car engines, heating equipment, and rotation of boats in the port), which requires increasingly complex phases of model implementation over a territory and the intervention of a multiplicity of actors for data supply. In this context, tabulated emission inventories – even those based on actual activity data – have limited scope for use in future air quality and exposure scenarios. To be realistic, the scenarios must be able to reproduce the variation in emitting activity in relation to changes in transport supply, urban planning, energy costs, and individual or collective energy consumption practices. Therefore, a significant part of the work is now focused on developing air quality modelling platforms integrating emission models centred on the individual (see Fig. 11 in this paper; Elessa Etuman and Coll, 2018).

Figure 11 Schematic representation of OLYMPUS emission operating system (Elessa Etuman and Coll, 2018).

There, the main challenges are related to the representation of individual mobility for both commuting and private activities as well as domestic heating and more broadly energy consumption practices on one side and the consideration of traffic parameters such as urban freight, the distribution of traffic and its speciation, driving patterns, or the effects of road congestion on the other side (Lejri et al., 2018; Coulombel et al., 2019).

Another emerging issue is also how to cope with short-time hazardous emissions in urban areas. Such emissions can be related to accidents or deliberate releases that are of increased concern today. An important characteristic of associated exposures is their inherent stochastic behaviour (Bartzis et al., 2020). Novel modelling approaches are needed to properly assess the impact and support relevant mitigation measures.

5.3.4  On model evaluation

To act on these numerous and expected developments, and use their results for operational decision support, multi-scale models need validation. An often-overseen basic prerequisite here is the availability and representativeness of validation data, particularly at smaller scales. The model's performance indeed needs to be explored in more spatial detail and in all covered spatial scales, preferably as part of multi-scale urban-to-rural intercomparison projects, in order to be able to provide finer assessment on air quality and exposure. Such efforts can be supported by networks of inexpensive sensors as well as smart tags (Sevilla et al., 2018) and other sources of distributed information acting complementary to traditional local monitoring and flow-profiling technologies. To obtain methodology and data refinement as well as outcome reliability, more experience through additional case studies is also needed. Finally, consideration should be given to specific model performance evaluation criteria for various regulatory purposes, including prospective mode operation, i.e. the ability of a model to accurately predict the air quality response to changes in emissions. To this end, evaluations can draw on the very large methodological work that has been carried out since 2007 by the Forum for AIR quality MODelling in Europe (FAIRMODE) for the assessment of CTMs (Monteiro et al., 2018). The objective was to develop and support the harmonized use of models for regulatory applications, based on PM 10 , NO 2 , and O 3 assessments. The main strength of this approach was to produce an in-depth analysis of the performance of different model applications, combining innovative and traditional indicators (Modelling Quality Index and Modelling Quality Objectives) and considering measurement uncertainty. Although FAIRMODE was successful in promoting a harmonized reporting process, there remain major ways of improvement that can be critical for its regulatory acknowledgement – in particular regarding inconsistencies between indicators of different time horizons – and a methodology dedicated to data assimilation assessments.

6.1  Brief overview

There is a need to increase prediction capabilities for weather, air quality, and climate. The new trend in developing integrated atmospheric dynamics and composition models is based on the seamless Earth system modelling (ESM) approach (WWRP, 2015) to evolve from separate model components to seamless meteorology–composition–environment modelling systems, where the different components of the Earth system are taken into account in a coupled way (WMO, 2016). The Coupled Model Intercomparison Project (CMIP) is the main reference for the development ESM models that serve as input to the IPCC assessment reports (Eyring et al., 2016; IPCC, 2022). One driver for improvement is the fact that information from predictions is needed at higher spatial resolutions and longer lead times. In addition, we have to consider two-way feedbacks between meteorological and chemical processes on the one hand and aerosol–meteorology feedback on the other hand, where both are needed to meet societal needs. Continued improvements in prediction will require advances in observing systems, models, and assimilation systems. There is also growing awareness of the benefits of closely integrating atmospheric composition, weather, and climate predictions, because of the important role that aerosols (and atmospheric composition in general) play in these systems. Because the proposed review is focused on air quality and its atmospheric forcings, the present section discusses the atmospheric component of ESMs focusing on coupled chemistry–meteorology models.

While this section also considers challenges related to air quality modelling, it differs in emphasis to Sect. 5, by examining interactions that operate on multiple scales and including multiple processes that affect air quality, especially for cities.

6.2  Current status and challenges

6.2.1  interactions and coupled chemistry–meteorology modelling (ccmm).

Meteorology is one of the main uncertainties of air quality modelling and prediction. Many studies have investigated the role of meteorology in air quality in the past (e.g. Fisher et al., 2001, 2005, 2006; Kukkonen et al., 2005a, b) and even more recently (e.g. McNider and Pour-Biazar, 2020; Rao et al., 2020; Gilliam et al., 2015; Parra, 2020). The relationship between meteorology and air pollution cannot be interpreted as a one-way input process due to the complex two-way interaction between the atmospheric circulation and physical and chemical processes involving trace substances in both gas and aerosol form. The improvement of atmospheric phenomena prediction capability is, therefore, tied to progress in both fields and to their coupling.

The advances made by mesoscale planetary boundary layer meteorology during the last decades have been recently reviewed by Kristovich et al. (2019). During the last decade significant advances have been made even in the capabilities to predict air quality and to model the many feedbacks between air quality, meteorology, and climate, including radiative and microphysical responses (WMO, 2016, 2020; Pfister et al., 2020). Due to advances in air quality models themselves and the availability of more computing resources, air quality models can be run at high spatial resolution and can be tightly (online) or weakly linked to meteorological models (through couplers). This is a pre-requisite to improve prediction skills further, while air quality models themselves will be improved as our knowledge of key processes continues to advance.

Online-coupled meteorology and atmospheric chemistry models have greatly evolved during the last decade (Flemming et al., 2009; Zhang et al., 2012a, b; Pleim et al., 2014; WWRP, 2015; Baklanov et al., 2014; Mathur et al., 2017; Bai et al., 2018; Im et al., 2015a, b), a comprehensive evaluation of coupled model results has been provided by the outcome of AQMEII project (Galmarini and Hogrefe, 2015). Although mainly developed by the air quality modelling community, these integrated models are also of interest for numerical weather prediction and climate modelling as they can consider both the effects of meteorology on air quality and the potentially important effects of atmospheric composition on weather (WMO, 2016). Migration from offline to online integrated modelling and seamless environmental prediction systems are recommended for consistent treatment of processes and allowance of two-way interactions of physical and chemical components, particularly for AQ and numerical weather prediction (NWP) communities (WWRP, 2015; Baklanov et al., 2018a).

It has been demonstrated that prediction skills can be improved through running an ensemble of models. Intercomparison studies such as MICS and AQMEII (Tan et al., 2020; Galmarini et al., 2017; Zhang et al., 2016) serve as important functions of demonstrating the effectiveness of ensemble predictions and helping to improve the individual models. Predictions can also be improved through the assimilation of atmospheric composition data. Weather prediction has relied on data assimilation for many decades. In comparison, assimilation in air quality prediction is much more recent, but important advances have been made in data assimilation methods for atmospheric composition (Carmichael et al., 2008; Bocquet et al., 2015; Benedetti et al., 2018). Community available assimilation systems for ensemble and variational methods make it easier to utilize assimilation (Delle Monache et al., 2008; Mallet, 2010). Furthermore, the amount of atmospheric composition data available for assimilation is increasing, with expanding monitoring networks and the growing capabilities to observe aerosol and atmospheric composition from geostationary satellites (e.g. Kim et al., 2020). Operational systems such as CAMS (Copernicus Atmospheric Monitoring Service) have advanced current capabilities for air quality prediction (Marécal et al., 2015; Barré et al., 2021).

Currently, NWP centres around the world are moving towards explicitly incorporating aerosols into their operational forecast models. Demonstration projects are also showing a positive impact on seasonal to sub-seasonal forecast by including aerosols in their models (Benedetti and Vitart, 2018). Even the usual subdivision between global-scale NWP models and limited-area models employed to resolve regional to local scales is going to be revised. Many groups are building new Earth system models and taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single model run, as in the case of the Model for Prediction Across Scales (MPAS) (Skamarock et al., 2018; Michaelis et al., 2019) and MUSICA (Pfister et al., 2020) approaches.

6.2.2  Aerosol–meteorology feedbacks for predicting and forecasting air quality for city scales

Multiscale CTMs are increasingly used for research and air quality assessment but less for urban air quality. Recently, there have been examples of coupled urban and regional models which allow the prediction and assessment of local, urban, and regional air quality affecting cities (Baklanov et al., 2009; Kukkonen et al., 2012; Sokhi et al., 2018; Kukkonen et al., 2018; Khan et al., 2019b). In particular, a downscaling modelling chain for prediction of weather and atmospheric composition on the regional, urban, and street scales is described and evaluated against observations by Nuterman et al. (2021). Kukkonen et al. (2018) described a modelling chain from global to regional (European and northern European domains) and urban scales and a multidecadal hindcast application of this modelling chain.

There are still uncertainties in prediction of PM components such as secondary organic aerosols (SOAs), especially during stable atmospheric conditions in urban areas which can cause severe air pollution conditions (Beekmann et al., 2015). Moreover, aerosol feedback and interaction with urban heat island (UHI) circulation is a source of uncertainty in CTM predictions. Several studies (Folberth et al., 2015; Baklanov et al., 2016; Huszar et al., 2016) demonstrated that urban emissions of pollutants, especially aerosols, are leading to climate forcing, mostly at local and regional scales through complex interactions with air quality (Fig. 12). These, in addition to almost 70 % of global CO 2 emissions, arise from urban areas, and hence urban areas pose a considerable source of climate forcing species.

Figure 12 The main linkages between urban emissions, air quality, and climate. (Baklanov et al., 2010).

It is necessary to highlight that the effects of aerosols and other chemical species on meteorological parameters have many different pathways (e.g. direct, indirect, semidirect effects) and must be prioritized in integrated modelling systems. Chemical species influencing weather and atmospheric processes over urban areas include greenhouse gases (GHGs), which warm near-surface air, and aerosols, such as sea salt, dust, and primary and secondary particles of anthropogenic and natural origin. Some aerosol particle components (black carbon, iron, aluminium, polycyclic and nitrated aromatic compounds) warm the air by absorbing solar and thermal-infrared radiation, while others (water, sulfate, nitrate, and most organic compounds) cool the air by backscattering incident short-wave radiation to space. It has been demonstrated (Sokhi et al., 2018; Baklanov et al., 2011; Huszar et al., 2016) that the indirect effects of urban aerosols modulate dispersion by affecting atmospheric stability (the difference in deposition fields is up to 7 %). In addition its effects on the urban boundary layer (UBL) thickness could be of the same order of magnitude as the effects of the UHI (a few hundred metres for the nocturnal boundary layer).

6.2.3  Urban-scale interactions

Meteorology is one of the main uncertainties in air quality assessment and forecast in urban areas where meteorological characteristics are very inhomogeneous (Hidalgo et al., 2008; Ching, 2013; Huszar et al., 2018, 2020). For these reasons, models used at the urban level must achieve greater accuracy in the meteorological fields (wind speed, temperature, turbulence, humidity, cloud water, precipitation).

Due to different characteristics of the surface properties (e.g. heat storage, reflection properties), a heat island effect occurs in cities. Urban areas can therefore be up to several degrees Celsius warmer than the surrounding rural areas and experience lighter winds due to the increased drag of urban canopy. This heating impacts the local environment directly, as well as affecting the regional air circulation with complex interactions that can induce pollutant recirculation, worsen stagnation episodes, and influence ozone and secondary aerosol formation and transport.

Studies over the past decade (e.g. McCarthy et al., 2010; Cui and Shi, 2012; González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021) have shown that the effects of the built environment, such as the change in roughness and albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation, can have significant impacts on the urban air quality levels. A reliable urban-scale forecast of air flows and meteorological fields is of primary importance for urban air quality and emergency management systems in the case of accidental toxic releases, fires, or even chemical, radioactive, or biological substance releases by terrorists.

Improvements (so-called “urbanization”) are required for meteorological and NWP models that are used as drivers for urban air quality (UAQ) models. The requirements for the urbanization of UAQ models must include a better resolution in the vertical structure of the urban boundary layer and specific urban feature description. One of the key important characteristics for UAQ modelling is the mixing height, which has a strong specificity and inhomogeneity over urban areas because of the internal boundary layers and blending heights from different urban roughness neighbourhoods (Sokhi et al., 2018; Scherer et al., 2019).

Modern urban meteorology and UAQ models (e.g. WRF, COSMO, ENVIRO-HIRLAM) successfully implemented (a hierarchy of) urban parameterizations with different complexities and reached suitable spatial resolutions (Baklanov et al., 2008; Salamanca et al., 2011, 2018; Sharma et al., 2017; Huang et al., 2019; Mussetti et al., 2020; Trusilova et al., 2016; Wouters et al., 2016; Schubert and Grossman-Clarke, 2014) for an effective description of atmospheric flow in urban areas. The application of urban parameterizations implemented inside limited-area meteorological models is becoming a common approach to drive urban air quality analysis, allowing the improved urban meteorology description in different climatic and environmental conditions (Ribeiro et al., 2021; Salamanca et al., 2018; Gariazzo et al., 2020; Pavlovic et al., 2020; Badia et al., 2020). However, activities to improve the parameterizations (Gohil and Jin, 2019) and provide reliable estimation of the input urban features (Brousse et al., 2016) are continuing.

6.2.4  Integrated weather, air quality, and climate modelling

Since cities are still growing, intensification of urban effects is expected, contributing to regional or global climate changes, including intensification of floods, heat waves, and other extreme weather events; air quality issues caused by pollutant production; and transport. This requires a more integrated assessment of environmental hazards affecting towns and cities.

The numerical models most suitable to address the description of mentioned phenomena within integrated operational urban weather, air quality, and climate forecasting systems are the new-generation limited-area models with coupled dynamic and chemistry modules (so-called coupled chemistry–meteorology models, CCMMs). These models have benefited from rapid advances in computing resources, along with extensive basic science research (Martilli et al., 2015; WMO, 2016; Baklanov et al., 2011, 2018a). Current state-of-the-art CCMMs encompass interactive chemical and physical processes, such as aerosols–clouds–radiation, coupled to a non-hydrostatic and fully compressible dynamic core that includes monotonic transport for scalars, allowing feedbacks between the chemical composition and physical properties of the atmosphere. These models incorporate the physical characteristics of the urban built environment. However, simulations using fine resolutions, large domains, and detailed chemistry over long time durations for the aerosol and gas/aqueous phase are computationally demanding given the models' high degree of complexity. Therefore, CCMM weather and climate applications still make compromises between the spatial resolution, domain size, simulation length, and degree of complexity for the chemical and aerosol mechanisms.

Over the past decade integrated approaches have benefited from coupled modelling of air quality and weather, enabling a range of hazards to be assessed. Research applications have demonstrated the advantages of such integration and the capability to assimilate aerosol information in forecast cycles to improve emission estimates (e.g. for biomass burning) impacting both weather and air quality predictions (Grell and Baklanov, 2011; Kukkonen et al., 2012; Klein et al., 2012; Benedetti et al., 2018).

6.3  Emerging challenges

6.3.1  earth systems modelling for air quality research.

Full integration of aerosols across the various applications requires advances in Earth system modelling, with explicit coupling between the biosphere, oceans, and atmosphere, taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single ESM run. The Earth system models offer many advantages but also create new challenges. Data assimilation in these tightly coupled systems is a future research area, and we can anticipate advances in assimilation of soil moisture and surface fluxes of pollutants and greenhouse gases.

The expected advance of the Earth system approach requires an increased research effort for the different communities to work more closely together to expand and to evolve the Earth observing system capacity. For what concerns the atmospheric models, the improvement of aerosol–cloud interaction description, related sulfate production, and oxidation processes in the aqueous phase are important to provide a better estimate of aerosol and cloud condensation nuclei (CCN) production impacting weather and climate. Their impact on surface PM concentrations, especially in areas with very low SO x emissions like Europe, still needs to be investigated (Schrödner et al., 2020; Genz et al., 2020; Suter and Brunner, 2020).

6.3.2  Constraining models with observations

The use of coupled regional-scale meteorology–chemistry models for AQF represents a desirable advancement in routine operations that would greatly improve the understanding of the underlying complex interplay of meteorology, emission, and chemistry. Chemical species data assimilation along with increased capabilities to measure plume heights will help to better constrain emissions in forecast applications.

While important advances have been made, present challenges require advances in observing systems and assimilation systems to support and improve air quality models. From the perspective of air quality modelling, there are still uncertainties in the emission estimates (especially those driven by meteorology and other conditions such as biomass burning and dust storms).

The impacts of data assimilation of atmospheric composition are limited by the remaining major gaps in spatial coverage in our observing systems. Major parts of the world have limited or no observations (Africa is an obvious case). This is changing thanks to the forthcoming new constellation of geostationary satellites (Sentinel-4, TEMPO, and GEMS; Kim et al., 2020) measuring atmospheric composition and with the advances in low-cost sensor technologies. Machine learning applications will play important roles in improving predictions through better parameterizations, better ways to deal with bias, and new approaches to utilize heterogeneous observations, for example new models for relating aerosol optical depth (AOD) to surface PM 2.5 mass and composition.

Reanalysis products of aerosols and other atmospheric constituents are now being produced (Inness et al., 2019). These can support many applications, and continued development is strongly encouraged and will benefit from the observations and data assimilation advances discussed above.

6.3.3  Multiscale interactions affecting urban areas

For urban applications the main science challenges related to multiscale interactions involved the non-linear interactions of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation. In order to improve air quality modelling for cities, advances are needed in data assimilation of urban observations (including meteorological, chemical, and aerosol species), development of model dynamic cores with efficient multi-tracer transport capability, and the general effects of aerosols on the evolution of weather and climate on different scales. All these research areas are concerned with optimized use of models on massively parallel computer systems, as well as modern techniques for assimilation or fusion of meteorological and chemical observation data (Nguyen and Soulhac, 2021).

In terms of atmospheric chemistry, the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments is still an active research area, and there is an important need to improve the understanding and treatment within two-way coupled chemistry–meteorology models.

Urban areas interact at many scales with the atmosphere through their physical form, geographical distribution, and metabolism from human activities and functions. Urban areas are the drivers with the greatest impact on climate change. The exchange processes between the urban surface and the free troposphere need to be more precisely determined in order to define and implement improved climate adaptation strategies for cities and urban conglomerations. The knowledge of the 3D structure of the urban airshed is an important feature to define temperature, humidity, wind flow, and pollutant concentrations inside urban areas. Although computational resources had great improvement, time and spatial resolution are still imposing some limitations to the correct representation of urban features, especially for the street scale. Urban areas are responsible for the urban heat island circulation, which interacts with other mesoscale circulations, such as the sea breeze and mountain valley circulations, determining the pathways of primary pollutants emitted in the atmosphere but even the production and transport of ozone (see e.g. Finardi et al., 2018) and secondary aerosols (Fig. 13).

Figure 13 Near-surface ozone concentrations ( µg m −3 ) predicted for 15 July 2015 at (a)  08:00, (b)  12:00, and (c)  17:00 LST over Naples. Wind field at 10 m height is represented by grey arrows. (Finardi et al., 2018; © American Meteorological Society. Used with permission.)

Challenges remain on how to include scale-dependent processes and interactions for urban- and sub-urban-scale modelling. These include spatial and temporal distribution of heat, chemical, and aerosol emission source activities down to building-size resolution, flow modification at the micro-scale level by the urban canopy structure and by the urban surface heat balance, enhancement/damping of turbulent fluxes in the urban boundary layer due to surface and emission heterogeneity, and chemical transformation of pollutants during their lifetime within the urban canopy sublayer. Obviously, the scale interaction issues facing air quality–meteorology–climate models are quite in line with those described in Sect. 5 for multi-scale air quality modelling. Thus, on coupling regional to urban and building scales, CTMs coupled with urbanized meteorological models are needed to describe the city-scale atmospheric circulation and chemistry in the urban airshed and the building and evolution of the urban heat island, especially strong during heat waves (Halenka et al., 2019), including the combined effects of urban, sub-urban, and rural pollutant emissions. High spatial resolution is also needed to capture pollutant concentration spatial variability at the pedestrian level in an urban environment, answering epidemiological research questions or emergency preparedness issues. In the near future, microscale CFD, including LES modelling, will probably become an appropriate tool for urban air quality assessment and forecasting purposes due to the expected continuous increase in computational resources enabling the inclusion of chemical reactions (Fig. 14). Nevertheless, today computational resources still limit their application to short-term episodes and often to stationary conditions, while climatological studies require for instance a multi-year approach. Parameterized street-scale models (Singh et al., 2020a; Hamer et al., 2020; Kim et al., 2018) or a database created with CFD simulations of several scenarios (Hellsten et al., 2020) can be alternative ways for the downscaling from the mesoscale to the city and street scale, together with obstacle-resolving Lagrangian particle models driven by Rokle-type diagnostic flow models (Veratti et al., 2020; Tinarelli and Trini Castelli et al., 2019) that can be coupled with CTMs for long-term air quality assessment (Barbero et al., 2021).

Figure 14 Modelled distribution of ground-level nitrogen dioxide  (a) and ozone  (b) at 20:00 CEST for a 6.7 km×6.7 km subarea of Berlin around Ernst-Reuter-Platz. The simulation was performed with the chemistry mechanism CBM4 and a horizontal grid size of 10 m (Khan et al., 2021).

6.3.4  Nature-based solutions for improving air quality

The growing interest for nature-based solutions requires the improvement of models' capability to describe biogenic emissions (Cremona et al., 2020) and deposition processes (Petroff et al., 2008; Petroff and Zhang, 2010), resolving the different species leaf features, biomass density, and physiology. The balance between vegetation drag, pollutant absorption, and biogenic volatile organic compound (BVOC) emissions determines the net positive or negative air quality impact at local and city scales (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017; Jeanjean et al., 2017; Jones et al., 2019; Anderson and Gough, 2020). In most cases this feature cannot be explicitly considered, with some parameterized approach, such as the canyon one being necessary, to deal with it. Nevertheless, the present capabilities of UAQ models to describe biogenic emissions together with gas and particle deposition over vegetation covered surfaces (including green roofs and vertical green surfaces) need to be improved to include nature-based solutions' impact in air quality plan evaluation.

7.1  Brief overview

A substantial amount of research has been conducted regarding the health effects of air pollution, especially those attributed to particulate matter (PM). Nevertheless, it is not conclusively known which properties of PM are the most important ones in terms of the health impacts (e.g. Brook et al., 2010; Beelen et al., 2014; Pope et al., 2019; Schraufnagel et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 .

In this section, we have therefore addressed three topical research areas, associated with air quality and health: (i) the health impacts of particulate matter in ambient air; (ii) the combined effects on human health of various air pollutants, heat waves, and pandemics; and (iii) the assessment of the exposure of populations to air pollution. Research that has been reviewed is based on selected international research projects and publications, but generally these are expected to reflect the general consensus, as both the projects and resulting publications involved a significant section of the air quality and health research community. Regarding pandemics, we will focus on the most recent one that has been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The research and interdependencies of these topics have been illustrated in Fig. 15.

Figure 15 A schematic diagram that illustrates some of the main factors in the evaluation of the exposure and health impacts of particulate matter.

As illustrated in the figure, particulate matter pollution originates from a wide range of anthropogenic and natural sources, and its characteristics can vary in terms of size distributions, chemical composition, and other properties. The resulting health outcomes also vary substantially, depending on the target physiological system or organ of an individual. In addition, the assessments of the interrelations of PM pollution and health outcomes are challenged by various combined and in some cases synergetic effects caused by, for example heat waves and cold spells, allergenic pollen, and airborne microorganisms.

7.2  Current status and challenges

7.2.1  health impacts of particulate matter, (i) overview of the health impacts of particulate matter pollution.

In addition to cardiovascular and respiratory diseases, exposure to ambient air PM may result in acute and severe health problems, such as cardiovascular mortality, cardiac arrhythmia, myocardial infarction (MI), myocardial ischemia, and heart failure (Dockery et al., 1993; Schwartz et al., 1996; Peters et al., 2001; Pope et al., 2002). The Organization for Economic Co-operation and Development (OECD) concluded in its outlook (OECD, 2012) that PM pollution will be the primary cause of deaths of the African population by 2050, in comparison to hazardous water and poor hygiene. Pražnikar and Pražnikar (2012) comprehensively addressed in their review several epidemiological studies throughout the world; they reported a strong association between the PM concentrations and respiratory morbidity, cardiovascular morbidity, and total mortality.

Global assessments of air quality and health require comprehensive estimates of the exposure to air pollution. However, in many developing countries (e.g. Africa; see Rees at al., 2019; Bauer et al., 2019) ground-based monitoring is sparse or non-existent; quality control and the evaluation of the representativeness of stations may also be insufficient. An inter-disciplinary approach to exposure assessment for burden of disease analyses on a global scale has been recently suggested jointly by WHO, WMO, and CAMS (Shaddick et al., 2021). Such an approach would combine information from available ground measurements with atmospheric chemical transport modelling and estimates from remote sensing satellites. The aim is to produce information that is required for health burden assessment and the calculation of air-pollution-related Sustainable Development Goal (SDG) indicators.

(ii) Health effects associated with the long-term exposure to particulate matter

Long-term exposure may potentially affect every organ in the body and hence worsen existing health conditions, and it may even result in premature mortality (see for example a recent review by Schraufnagel et al., 2019; Brook et al., 2010; Brunekreef and Holgate, 2002; Beelen et al., 2015; Im et al., 2018; Liang et al., 2018; Vodonos et al., 2018; Pope et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 . Beelen et al. (2015) analysed an extensive set of data from 19 European cohort studies; they found that long-term exposure to PM 2.5 sulfur was associated with natural-case mortality. Similar results regarding long-term exposure to PM 2.5 and mortality were also presented in other recent studies conducted by Vodonos et al. (2018) and Pope et al. (2019).

Studies conducted in the framework of the European Study of Cohorts for Air Pollution Effects (ESCAPE) project showed that long-term exposure to PM air pollution was linked to incidences of acute coronary (Cesaroni et al., 2014), cerebrovascular events (Stafoggia et al., 2014), and lung cancer in adults (Adam et al., 2015). Moreover, findings from the same project revealed that other health effects related to PM air pollution were reduced lung function in children (Gehring et al., 2013), pneumonia in early childhood and possibly otitis media (MacIntyre et al., 2014), low birthweight (Pedersen et al., 2013), and the incidence of lung cancer (Raaschou-Nielsen et al., 2013). In addition, another finding of the ESCAPE project was the connection between traffic-related PM 2.5 absorbance and malignant brain tumours (Andersen et al., 2018).

The Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU) project, which included three European cohort studies, presented the association between long-term exposure to ambient PM 10 and asthma prevalence (Cai et al., 2017). In the framework of three major cohorts (HUNT, EPIC-Oxford, and UK Biobank) it was shown that, after adjustments for road traffic noise, incidences of cardiovascular disease (CVD) diseases were attributed to long-term PM exposure (Cai et al., 2018). Hoffmann et al. (2015) suggested that long-term exposure to both PM 10 and PM 2.5 is linked to an increased risk for stroke, and it might be responsible for incidences of coronary events.

(iii) Health effects associated with the short-term exposure to particulate matter

Collaborative studies such as the APHENA (Air Pollution and Health: A European and North American Approach) and the MED-PARTICLES project in Mediterranean Europe have evidenced that short-term exposure to PM has been associated with all-cause cardiovascular and respiratory mortality (Katsouyanni et al., 2009; Zanobetti and Schwartz, 2009; Samoli et al., 2013; Dai et al., 2014), hospital admissions (Stafoggia et al., 2013), and occurrence of asthma symptom episodes in children (Weinmayr et al., 2010).

(iv) Health effects associated with the chemical constituents of PM

The chemical composition of PM is associated with the health effects related to PM concentrations, in addition to the mass concentrations of particulate matter (e.g. Maricq, 2007). Chemical composition of particles is complex; generally, it depends on the source origin of particles and their chemical and physical transformations in the atmosphere (e.g. Prank et al., 2016). Some prominent examples of the components of PM are sulfate (SO 4 ), nitrate (NO 3 ), metals, elemental and organic carbon (Yang et al., 2018), ammonium (NH 3 ) (Pražnikar and Pražnikar, 2012), sea salt, and dust (Prank et al., 2016).

The PM components also include biological organisms (e.g. bacteria, fungi, and viruses) and organic compounds (e.g. polycyclic aromatic hydrocarbons, PAHs, and their nitro-derivatives, NPAHs) (Morakinyo et al., 2016; Kalisa et al., 2019). Their content can vary significantly with regard to time and for various climatic regions (Maki et al., 2015; Gou et al., 2016).

Hime et al. (2018) have reviewed studies which investigated which PM components could be mostly responsible for severe health effects. Such studies included the National Particle Component Toxicity (NPACT) initiative, which combined epidemiologic and toxicologic studies. That study concluded that the concentrations of SO 4 , EC, OC, and PM mainly originated from traffic and combustion and had a significant impact on human health (Adams et al., 2015). The European Study of Cohorts for Air Pollution Effects (ESCAPE) project aimed at examining the association of elemental components of PM (copper, Cu; iron, Fe; potassium, K; nickel, Ni; sulfur, S; silicon, Si; vanadium, V; and zinc, Zn) with inflammatory blood markers in European cohorts (Hampel et al., 2015). They focused, together with the TRANSPHORM project (Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter), on investigating the relationship of these components with cardiovascular (CVD) mortality (Wang et al., 2014).

Moreover, other studies conducted within the framework of ESCAPE and TRANSPHORM projects provided evidence that mortality was linked to long-term exposure to PM 2.5 sulfur (Beelen et al., 2015), as well as to the particle mass and nitrogen oxides (NO 2 and NO x ) (Beelen et al., 2014). As part of the NordicWelfAir project, Hvidtfeldt et al. (2019b) connected the risks of being exposed long-term to PM 2.5 , PM 10 , BC, and NO 2 with all-cause and CVD mortality. In another paper, Hvidtfeldt et al. (2019a) demonstrated the association between long-term exposure to PM 2.5 , elemental and primary organic carbonaceous particles ( BC / OC ), secondary organic aerosols (SOA), and all-cause mortality. They also demonstrated the connection between PM 2.5 , BC / OC , and secondary inorganic aerosols (SIAs) and CVD mortality. Recently, a continuation of this study included all Danes born between 1921 and 1985, showing higher mortality related to exposure to NO 2 , O 3 , PM 2.5 , and BC (Raaschou-Nielsen et al., 2020).

In the framework of the Particle Component Toxicity (NPACT) project, Lippmann et al. (2013) showed that PM 2.5 mass and EC were linked to all-cause mortality; EC was also connected with ischemic heart disease mortality. The latter result was quite similar to the findings of Ostro et al. (2010, 2011, 2015), including OC, SO 4 , NO 3 , and SO in addition to EC. Concerning cardiopulmonary disease mortality, a strong association was observed for the exposure to NO 3 and SO 4 (Ostro et al., 2010, 2011). Luben et al. (2017) and Hoek et al. (2013) in their reviews observed the association of BC with cardiovascular disease hospital admissions and mortality.

In a meta-analysis work conducted by Achilleos et al. (2017), elemental carbon (EC), black carbon (BC), black smoke (BS), organic carbon (OC), sodium (Na), silicon (Si), and sulfate (SO 4 ) were associated with all-cause mortality, and BS, EC, nitrate (NO 3 ), ammonium (NH 4 ), chlorine (Cl), and calcium (Ca) were linked to CVD mortality. In addition, some American cohort studies pointed out that long-term exposure to SO 4 was positively connected with all-cause, cardiopulmonary disease, and lung cancer mortality (Dockery et al., 1993; HEI, 2000; Pope et al., 2002; Ostro et al., 2010).

In addition, other kinds of severe health effects related to PM components have been reported. For example, Wolf et al. (2015) showed that long-term exposure to PM constituents, especially of K, Si, and Fe, which are indicators of road dust, provoked coronary events. The findings of a systematic review, where 59 studies were included, indicated that chronic obstructive pulmonary disease (COPD) emergency risk was attributed to short-term exposure to O 3 and NO 2 , whereas short-term exposure to SO 2 and NO 2 was responsible for acute COPD risk in developing countries (Li et al., 2016). The review of Li et al. (2016) also reported that short-term exposure to O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5 was linked to respiratory risks.

Poulsen et al. (2020), using detailed modelling and Danish registers from 1989–2014, showed stronger relationships between primarily emitted black carbon (BC), organic carbon (OC), and combined carbon ( OC / BC ) and malignant brain tumours. Furthermore, the risk for lung cancer was linked to several different compounds and sources of aerosol particles; they found that particles containing S and Ni might be two of the most important components associated with lung cancer (Raaschou-Nielsen, 2016). Park et al. (2018) found that PM 2.5 particles emitted from diesel and gasoline engines were more toxic for humans than, for example, particles from biomass burning or coal combustion. In a recent study, it was concluded that traffic-specific PM components, and in particular NH 4 and SO 4 , lead to higher risks of stroke than PM components linked to industrial sources (Rodins et al., 2020).

(vi) The uncertainties associated with concentration–response functions

Based on previous research, WHO and Europe recommended in 2015 a set of linear concentration–response functions for the main air pollutants and related health outcomes (Héroux et al., 2015). These functions are currently widely used for health assessments, e.g. on a European scale by EEA. EEA (2019) estimated that more than 340 000 premature deaths per year in Europe could be related to the exposure to PM 2.5 . However, it is currently widely debated what the optimal shape of the concentration–response functions is and whether there should be a threshold or lower limit.

A prominent example is the highly cited study by Burnett et al. (2018) on the developments of the Global Exposure Mortality Model (GEMM). By combining data from 41 cohorts from 16 different countries, Burnett et al. (2018) have constructed new hazard ratio functions that to a wider degree than previous studies include the full range of the global exposure to outdoor PM 2.5 . The GEMM functions for PM 2.5 and nonaccidental mortality generally follow a supralinear association at lower concentrations and near-linear association at higher concentrations (Burnett et al., 2018).

The GEMM functions would indicate that health impacts related to PM 2.5 exposure have been underestimated, at both the global and regional scales. In a recent European study on cardiovascular mortality, the GEMM functions were combined with concentration fields from a global atmospheric chemistry–climate model. The results pointed towards a total of 790 000 premature deaths attributed to air pollution in Europe per year, which is significantly higher than the value previously estimated by EEA for example (Lelieveld et al., 2019). Several reviews or meta-analyses have focused on low exposure levels; the conclusion has been that significant associations can be found between PM 2.5 and health effects also at levels below the concentrations of 10–12  µg m −3 . These values are equal to or below the WHO guidelines (10  µg m −3 ) and the US EPA standards (12  µg m −3 ) (Vodonos et al., 2018; Papadogeorgou et al., 2019).

(vii) The use of high-resolution multi-decadal data sets for extensive regions

Developments of air pollution modelling and more efficient computing resources have made it possible to compute high-resolution air pollution data sets that cover larger regions, as well as longer, even multi-decadal, time periods (Fig. 16). The combination of such data with national or international health registers, or cohorts from several countries, improves the representativeness of statistical analyses. The use of more extensive data sets will also reduce the selection biases related to the sizes of the cohorts.

This has resulted in, for example, a better detection of the links between air pollution exposure and new health endpoints, such as psychiatric disorders (e.g. Khan et al., 2019a; Antonsen et al., 2020) and cognitive abilities (e.g. Zhang et al., 2018). Based on high-resolution ( 1 km×1 km ) air pollution data covering the period 1979–2015 and population-based data from the Danish national registers, Thygesen et al. (2020) found that exposure to air pollution (specifically NO 2 ) during early childhood was associated with the development of attention-deficit/hyperactivity disorder (ADHD).

Figure 16 An illustration of how concentration predictions at a high spatial and temporal resolution (panels on the left-hand side) could be used for high-resolution health impact assessments (panel on the right-hand side). The concentration distributions were predicted with the chemical transport model SILAM. The health impact assessment was made with the EVA model in a high-resolution setup for the Nordic region, giving an estimate of the number of premature deaths due to exposure to air pollution (Lehtomäki et al., 2020). The concentrations used in EVA were from the chemical transport system DEHH-UBM, providing 1 km×1 km concentration across the Nordic region.

Kukkonen et al. (2018) presented a multi-decadal global- and European-scale modelling of a wide range of pollutants and the finer-resolution urban-scale modelling of PM 2.5 in the Helsinki metropolitan area. All of these computations were conducted for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the chemical transport model SILAM. These results have been used for health impact assessments by Siddika et al. (2019, 2020). The predicted air quality and meteorological data are also available to be used in any other region globally in health impact assessments.

7.2.2  Combined effects of air pollution, heat waves, and pandemics on human health

It is widely known that poor air quality has severe impacts on the human immune system (Genc et al., 2012). In particular, some of the acute health effects include chronic respiratory and cardiovascular diseases (Ghorani-Azam et al., 2016), respiratory infection (e.g. Conticini et al., 2020), and even cancer and death (IOM, 2011; Villeneuve et al., 2013). Polluted air can cause, for example, damage in epithelial cilia (Cao et al., 2020), which leads to a chronic inflammatory stimulus (Conticini et al., 2020). It has also been shown that the SARS-CoV-2 can stay viable and infectious on aerosol particles that are smaller than 5  µm in diameter for more than 3 h (van Doremalen et al., 2020). Therefore, atmospheric pollutants might play an important role in spreading the virus.

(i) The role of air pollution in pandemics

Previously, Cui et al. (2003) found that the long-term exposure to moderate or high air pollution levels was positively correlated with mortality caused by SARS-CoV-1 in the Chinese population. Therefore, it is possible that poor air quality would enhance the risk of mortality during epidemics or pandemics, such as the COVID-19 disease, caused by SARS-CoV-2. Moreover, poor air quality can enhance the human health effects of heat waves, cold spells, and allergenic pollen. This is because exposure to ambient air pollutants together with microorganisms tend to make the health impacts of pathogens more severe; at the same time, they weaken human immunity, resulting in an increased risk of respiratory infection (e.g. Xu et al., 2016; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

Conticini et al. (2020) concluded that weakened lung defence mechanisms due to continuous exposure to air pollution could partly explain the higher morbidity and mortality caused by SARS-CoV-2 in areas of poor air quality in Italy. Zhu et al. (2020) used the data of daily confirmed COVID-19 cases, air pollution, and meteorology from 120 cities in China to study the association between the concentrations of ambient air pollutants (PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 ), and COVID-19 cases. By applying a generalized additive model, they found a significant correlation between PM 2.5 , PM 10 , CO, NO 2 , and O 3 and daily counts of confirmed COVID-19 patients, while SO 2 was negatively associated with the daily number of new COVID-19 cases.

Ogen (2020) studied 66 regions in Italy, Spain, France, and Germany; he also found a spatial correlation between high NO 2 concentration and fatality from COVID-19. According to this study, 83 % of all fatalities occurred in the regions having a maximum NO 2 concentration above 100  µ molec . m - 2 , and only 1.5 % of all fatalities took place in areas in which the maximum NO 2 concentration was below 50  µ molec . m - 2 . However, Pisoni and Van Dingenen (2020) did not find a similar phenomenon in the UK, where the number of deaths was higher than in Italy, despite a significantly lower NO 2 concentration.

Xie and Zhu (2020) used temperature data from 122 cities mainly in the eastern part of China and observed a linear relationship between ambient temperature and daily number of confirmed COVID-19 counts in cases when the temperature was below 3  ∘ C. At higher temperatures, no correlation was found. This indicates that daily counts of COVID-19 did not decline at warmer atmospheric conditions, although such a dependency was expected based on the previous studies related to coronaviruses SARS-CoV and MERS-CoV (e.g. van Doremalen et al., 2013; Bi et al., 2007; Tan et al., 2005). However, the study of Xie and Zhu (2020) was conducted in winter; the highest temperatures were around 27  ∘ C.

Chen et al. (2017) statistically investigated the correlation between influenza incidences and the concentrations of PM 2.5 in 47 Chinese cities for 14 months during 2013–2014. Based on the results, they concluded that about 10 % of the influenza cases were induced by the exposure to ambient PM 2.5 . They also classified the days as cold, moderately cold, moderately hot, and hot separately for each city and found that the risk for influenza transmission associated with ambient air PM 2.5 was enhanced during cold days.

(ii) Combined effects of air pollutants and heat waves

Siddika et al. (2019) found that prenatal exposure to both PM 2.5 and O 3 increased the risk of preterm birth in Finland in the 1980s. The risk was more pronounced if the mother was exposed to both higher PM 2.5 and higher O 3 concentrations. They explained that O 3 might deplete antioxidants in the lung, and therefore the defence mechanism needed against reactive oxygen species formation was reduced due to the exposure to PM 2.5 . Also, the O 3 concentrations can cause changes in lung epithelium so that it is more permeable for particles to absorb into the circulatory system. The population selected for the study were living in southern Finland in the 1980s, in relatively good air quality. However, the concentrations of many pollutants, e.g. those of PM 2.5 , have been shown to have been twice as high in the 1980s, compared with the corresponding pollutant levels in the same region during the second decade of the 21st century (Kukkonen et al., 2018).

Wang et al. (2020) presented that PM 2.5 exposure strengthened the effect of moderate heat waves (short or only moderate temperature rise) associated with preterm births during January 2015–July 2017 in Guangdong Province, China. However, during the intensive heat waves, the effects were not additive.

Analitis et al. (2018) studied synergetic effects of temperature, PM 10 , O 3 , and NO 2 on cardiovascular and respiratory deaths. They found some correlation between the effects of high ambient temperatures and those caused by O 3 and PM 10 concentrations. However, during the heat waves, no clear synergetic effect was found. In a review article, Son et al. (2019) concluded that there is some evidence between the mortality related to high temperatures and air pollution.

J. Li et al. (2017) wrote a comprehensive literature review about the role of temperature and air pollution in mortality. They determined individual spatial temperature ranges and grouped them in “low”, “medium”, and “high” based on the information given in each study about typical local weather conditions. After a careful selection based on the quality of the data sets, they performed a meta-analysis by using data of 21 studies; they found that high temperature significantly increased the risk of non-accidental and cardiovascular mortality, caused by the exposure to PM 10 or O 3 . The risk of cardiovascular mortality due to PM 10 decreased during low-temperature days in the prevailing climate. However, the exposure to both low temperature and the concentrations of O 3 increased the risk of non-accidental mortality. Similar effects were not found for the concentrations of SO 2 or NO 2 and temperature. Lepeule et al. (2018) found that short-term rise in outdoor air temperature and relative humidity was linked to deteriorated lung function of elderly people. A simultaneous exposure to black carbon amplified the health effects.

7.2.3  Estimation of exposures

(i) modelling of individual exposure.

The currently available epidemiological studies use measured or modelled outdoor concentrations in residential areas or at home addresses, to correlate the concentrations with health effects. However, several studies have pointed out that it is critical to use the exposure of people as indicators for the health effects (Kousa et al., 2002; Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a; Li and Friedrich, 2019; Li, 2020). It is obvious that the effects of air pollutants on human health are caused by the inhaled pollutants, instead of the pollutants at a certain point or area outdoors. Thus, exposure is a much better indicator for estimating health risks than outdoor concentrations. The individual exposure of a person to air pollutants is defined here as the concentration of pollutants at the sites where the person is staying weighted by the length of stay at each of the places of stay and averaged over a certain time span, e.g. a year. The places of stay are in this context called microenvironments. Exposure of a group of people with certain features (e.g. sex, age, place of living) is the average exposure of the individuals in the subgroup. In general, the exposure of a person is calculated by first estimating the concentration of air pollutants in the microenvironments where the person or population subgroup is staying and then by weighting this concentration with the length of time the person has been at the respective microenvironment (Li and Friedrich, 2019; Li, 2020). The result of modelling exposure can be verified by measuring the exposure with personal sensors (e.g. Dessimond et al., 2021).

Exposures to ambient concentrations of PM 2.5 can be substantially different in different microenvironments. The concentrations in microenvironments can be either measured or modelled. Computational results of activity-based dynamic exposures by Singh et al. (2020a) demonstrate that the total population exposure was over one-quarter ( −28  %) lower on a city-wide average level, compared with simply using outdoor concentrations at residential locations, in the case of London in the 2010s. Smith et al. (2016) have shown by modelling that exposure estimates based on space-time activity were 37 % lower than the outdoor exposure evaluated at residential addresses in London. However, this proportion will be different for other urban regions and time periods, or when addressing specific population sub-groups.

The exposure to particulate matter is substantially influenced by indoor environments, as people spend 80 %–95 % of their time indoors (e.g. Hänninen et al., 2005). Indoor air quality mainly depends on the penetration of pollutants in outdoor air, on ventilation, and on indoor pollution sources. For estimating the indoor concentration, commonly a mass-balance model is applied (Hänninen et al., 2004; Li, 2020). With a mass-balance model, the indoor concentration is calculated based on the outdoor concentration, a penetration factor, the air exchange rate, the decay rate, the emission rates of the indoor sources and the room volume, and, if available, by parameters of the mechanical ventilation system.

A complex stochastic model has been developed for estimating the annual individual exposure of people or groups of people in the European Union to PM 2.5 and NO 2 , using characteristics of the analysed subgroup, such as age, gender, place of residence, and socioeconomic status (Li et al., 2019a, c; Li and Friedrich, 2019; Li, 2020). The probabilistic model incorporates an atmospheric model for estimating the ambient pollutant concentrations in outdoor microenvironments and a mass-balance model for estimating indoor concentrations stemming from outdoor concentrations and from emissions from indoor sources. Time-activity patterns (which specify how long a person stays in each microenvironment) were derived from an advancement of the Multinational Time Use Study (MTUS) (Fisher and Gershuny, 2016). The exposures can also be estimated for past years. It is therefore possible to analyse the exposure for the whole lifetime of a person, by using a lifetime trajectory model that retrospectively predicts the possible transitions in the past life of a person.

An exemplary result from Li and Friedrich (2019) is shown in Fig. 17. It displays that the PM 2.5 annual average exposure averaged over all adult persons living in the EU increased since the 1950s from 19.0 (95 % confidence interval, CI: 3.3–55.7)  µg m −3 to a maximum of 37.2 (95 % CI: 9.2–113.8)  µg m −3 in the 1980s. The exposure then declined gradually afterwards until 2015 to 20.1 (95 % CI: 5.8–51.2)  µg m −3 . Indoor air pollution contributes considerably to exposure. In recent years more than 45 % of the PM 2.5 exposure of an average EU citizen has been caused by indoor sources.

Figure 17 Temporal evolution of the annual average exposure of EU adult persons to PM 2.5 from 1950 to 2015 (Li and Friedrich, 2019). All sources, except “outdoor”, refer to indoor sources. ETS denotes environmental tobacco smoke (passive smoking).

The most important indoor sources are environmental tobacco smoke (passive smoking), frying, wood burning in open fireplaces and stoves in the living area, and the use of incense sticks and candles. In addition, nearly all indoor activities include abrasion processes that produce fine dust. For NO 2 , indoor sources cause around 24 % of the exposure, with the main contributions from cooking with gas and from biomass burning in stoves and open fireplaces (Li and Friedrich, 2019). The solid black line in Fig. 7.3 shows the background outdoor concentration at the places where EU citizens spend their lives on average. Urban background concentrations refer to urban concentrations that are not in the immediate vicinity of the emission sources, especially of streets.

The average exposure is higher than the average outdoor background concentration. Epidemiological studies correlate outdoor concentrations with health risks and thus neglect the exposure caused by indoor sources. Such studies therefore implicitly assume that the contribution of indoor sources is the same at all places and for all people. Thus, calculating the burden of disease using exposures to PM 2.5 will yield years of lives lost and other chronic diseases that are about 40 % higher than those calculated with outdoor background concentrations (Li, 2020). Using exposure data, a 70-year-old male EU citizen will have experienced a reduction of life expectancy of about 13 (CI 2–43) d yr −1 of exposure to PM 2.5 , since the age of 30 (Li, 2020). For a person who is now 40 years old or younger, the life expectancy loss per year will be less than half as much as that of a 70-year-old person.

A similar approach for estimating the “integrated population-weighted exposure” of the Chinese population to PM 2.5 has been used by Aunan et al. (2018) and Zhao et al. (2018). Aunan et al. (2018) estimated a mean annual averaged PM 2.5 exposure of 103 [86–120]  µg m −3 in urban areas and 200 [161–238]  µg m −3 in rural areas, with 50 % in urban areas and 78 % in rural areas originating from domestic biomass and coal burning.

(ii) Measurements of indoor concentrations and individual exposure

Zhao et al. (2020a) took measurements of PM concentrations of different size classes in 40 homes in the German cities of Leipzig and Berlin. Measurements were taken in different seasons simultaneously inside and directly outside the homes. Only homes without smokers were analysed. Mean annual indoor PM 10 concentrations were 30 % larger than the outdoor concentrations near the houses. However, the mean indoor concentration of PM 2.5 was 6 % smaller than the outdoor concentration. The infiltration factor was evaluated to be 0.5. They therefore concluded that the indoor concentration of PM 2.5 was considerably influenced by both indoor and outdoor sources; the former included cooking and burning of candles.

Vardoulakis et al. (2020) made a comprehensive literature review on indoor concentration of selected air pollutants associated with negative health effects and listed the main results (concentrations) and other features (e.g. main sources) for the analysed studies. They express the need for “standardized IAQ (indoor air quality) measurement and analytical methods and longer monitoring periods over multiple sites”.

Some studies have focused on the measurements of personal exposure to ambient air concentrations using portable instruments in different microenvironments. For instance, Dessimond et al. (2021) describe the development and use of a personal sensor for measuring PM 1 , PM 2.5 PM 10 , BC, NO 2 , and VOC together with climate parameters, location, and sleep quality. Clearly, such measurements can provide valuable and accurate information on the spatial and temporal variations in exposure, and they can be used to validate exposure models.

7.3  Emerging challenges

7.3.1  emerging challenges for health impacts of particulate matter, (i) classification of particulate matter measures and characteristics and potential health outcomes.

Various studies have described PM in terms of the overall aerosol properties, such as the mass fractions (most commonly PM 2.5 and PM 10 ), the size distributions (mass, area, volume), the chemical composition, primary versus secondary PM, the morphology of particles, and source-attributed PM. Some studies have adopted more specific properties of PM derived based on the above-mentioned overall properties. Such properties include, to mention a few of the most common ones, particle number concentrations (PNCs), PNCs evaluated separately for each particulate mode, ultra-fine PM, nanoparticles, secondary organic PM, primary PM, other combinations of chemical composition, suspended dust (specific class of source-attributed PM), the content of metals, and toxic or hazardous pollutants.

An important emerging area is therefore to understand better which PM properties or measures would optimally describe the resulting health impacts. As mentioned above, one potentially crucial candidate for such a property is particulate number concentration (PNC). Kukkonen et al. (2016a) presented the modelling of the emissions and concentrations of particle numbers on a European scale and in five European cities. Frohn et al. (2021) and Ketzel et al. (2021) performed modelling of particle number concentrations for all Danish residential addresses for a 40-year time period. For all studies, the comparison of the predicted PNCs to measurements on regional and urban scales showed a reasonable agreement. However, there are still substantial uncertainties, especially in the modelling of the emissions of particulate numbers.

Health outcomes can also be classified as overall outcomes and physiologically more specific outcomes. Prominent examples of overall outcomes are mortality and morbidity. Relatively more specific impacts include respiratory and cardiovascular impacts, bronchitis, asthma, neurological impacts, and impacts on specific population groups (such as infants, children, the elderly, prenatal impacts, and persons suffering various diseases).

(ii) Uncertainties and challenges on evaluating the health impacts of particulate matter

Additional uncertainty is included in the concentration versus health response functions, which may be linear or logarithmic or a combination of both of these, and including or excluding a threshold value. When applied in health assessments, the shape of the response functions translates into large differences in the estimated number of premature deaths (Lehtomäki et al., 2020). EEA has made a sensitivity analysis showing that the application of a 2.5  µg m −3 threshold (equivalent to a natural background) will reduce the estimated number of premature deaths related to PM 2.5 by about 20 % in Europe (EEA, 2019a). Clearly, in the evaluation of the health impacts of PM, there are also numerous confounding factors. For population-based studies, these include active and passive smoking, sources and sinks that influence indoor pollution, gaseous pollutants, allergenic pollen, socio-economic effects, age, health status, and gender.

In addition, the health impacts of PM are related to the impacts of other environmental stressors, such as heat waves and cold spells, allergenic pollen, and airborne microorganisms. Commonly, it is challenging to decipher such effects in terms of each other. The factors may also have either synergistic or antagonistic effects. For instance, the health impacts of PM may be enhanced in the presence of a heat wave. The impacts of various PM properties are also known to be physiologically specific; i.e. such a property may contribute to a certain health outcome but not to some other outcomes.

In summary, there are many associations of various PM properties and measures to various health outcomes. Some of these inter-dependencies are known relatively better, either qualitatively or quantitatively, while there are also numerous associations, which are currently known poorly.

(iii) Research recommendations for deciphering the impacts of various particulate matter properties

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions which include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

Pražnikar and Pražnikar (2012) and Rodins et al. (2020) stressed the importance of the identification of the specific sources and the evaluation of the chemical composition of PM responsible for acute health effects. For instance, Hime et al. (2018) reported that there is a severe lack of epidemiological studies investigating the health impacts originating from exposure to ambient diesel exhaust PM. In addition, they pointed out that there is no clear distinction between PM originating from diesel emissions and from other sources; thus, there is a limited number of studies assessing the respective health impacts.

Despite the substantial amount of research on the impacts of various PM properties and measures, the results on the importance of the more advanced measures (in addition to PM mass fractions) are to some extent inconclusive. One reason for this uncertainty is that there are so many associations of various PM properties and measures to various health outcomes. An emerging area related to assessing the health impact of PM is the associated oxidative stress when the particles are inhaled (e.g. see Gao et al., 2020; He et al., 2021). A possible explanation for the health effects from PM is based on PM-bound reactive oxygen species (ROS) being introduced to the surface of the lung, which leads to the depletion of the lung-lining fluid antioxidants as well as other damage (Gao et al., 2020).

One prominent emerging area is the evaluation of long-term, multi-decadal concentrations and meteorology on a sufficient spatial resolution. Long-term and lifetime exposures are known to be more important in terms of human health, compared with short-term exposures. Comprehensive data sets are therefore needed, which will include multi-decadal evaluation of air quality, meteorology, exposure, and a range of health impacts. Some first examples of such data sets have already been reported (Kukkonen et al., 2018; Siddika et al., 2019; Raaschou-Nielsen et al., 2020; Thygesen et al., 2020; Siddika et al., 2020). Although it is clear that chronic diseases and chronic mortality are caused by exposure to fine PM over many years, information is scarce regarding the critical length of the exposure period in terms of premature death for example.

Elderly people are generally regarded as more sensitive to air pollution. It is well-known that the overall trend towards an ageing population can counteract improvements in air pollution levels in the future (e.g. Geels et al., 2015). However, detailed knowledge is scarce regarding whether exposure during specific periods in life can increase the risk of chronic morbidity or mortality. Inequalities in both the exposure to PM and the related risks across different population groups (like gender, ethnicity, socioeconomic position, etc) due to underlying differences in health status will also need further investigations, to ascertain that future mitigation strategies will benefit all population groups (Fairburn et at., 2019; Raaschou-Nielsen et al., 2020). With regard to chronic diseases caused by NO 2 , it is still uncertain whether NO 2 is the cause of the diseases or whether other pressures or a combination of pressures that are correlated with the NO 2 concentration are responsible.

The introduction of green spaces in urban areas can contribute either negatively or positively to air quality. Green spaces can also potentially act as sources of allergenic pollen. The health impacts of introducing green spaces would therefore need to be clarified (Hvidtfeldt et al., 2019b; Engemann et al., 2020).

7.3.2  Emerging challenges for the combined effects of air pollution and viruses

Studying the combined effects of air pollution, heat waves or cold spells, and viruses is challenging, due to numerous confounding factors and incidental correlations. For instance, air pollution is commonly a serious problem in areas where the population density is also high. The high population density tends to allow viruses to spread more easily, compared with the situation in more sparsely populated areas.

Morbidity or mortality due to pandemics is also dependent on the age distribution of the population, cultural and social differences, the level of health care, living conditions, common hygiene, and other factors. Clearly, such demographic differences should be taken into account, when comparing the frequencies of virus infections in different areas.

Due to limited data and the still evolving COVID-19 pandemic, it is difficult to draw definite conclusions related to the role of air pollution or meteorological drivers (like temperature or relative humidity) in transmission rates or in the severity of the disease. Global interdisciplinary studies, open data sharing, and scientific collaboration are the key words towards better understanding of the interaction of COVID-19 and meteorological and environmental variables. Moreover, it is important to know what the role of, for example, PM is in spreading SARS-CoV-2. Indoor or laboratory dispersion experiments are needed to find out if the virus is spreading not only in droplets but also in smaller aerosol particles. Together with a fully validated computational fluid dynamics model, it is possible to get facts about dispersion distances in different conditions and to study for example the effect of ventilation systems, furniture placements, and air cleaners to give information-based recommendations to make the environment as safe as possible without complete lockdowns.

Allergenic pollen can periodically cause substantial health impacts for numerous people. As PM is transported in the atmosphere, microbial pathogens such as bacteria, fungi, and viruses can be attached on the surfaces of particles (Morakinyo et al., 2016); clearly, these may provide an additional risk (Kalisa et al., 2019). The combination of both biological and chemical components of PM can further enhance some health effects, such as asthma and COPD (Kalisa et al., 2019).

Adverse health impacts can also be associated with short-term exposure to atmospheric particles. The short-term impacts may be important, especially during air pollution episodes. Such episodes may be caused, for example, by the emissions originating from wildfires, dust storms, or severe accidents. Episodes can also be caused by extreme meteorological conditions; two prominent examples are heat waves and extremely stable atmospheric conditions.

7.3.3  Other emerging challenges

First attempts have been made to quantify exposures by estimating concentrations in microenvironments, combined with space-time activity data. However, improvements will be necessary for virtually all the components of exposure modelling. Regarding the emissions used for concentration modelling, in particular the evaluation of the emission rates from indoor sources should be improved on a broader empirical basis.

Emission rates depend on human behaviour, for which more detailed information is needed. For example, how many people smoke indoors, and how many family members are exposed to passive smoking? Are kitchen hoods used when cooking and frying? How often are chimneys open, and how often are wood stoves used? For estimating indoor concentrations, one would need further information of ventilation habits in different seasons. Better information would be needed regarding the use of mechanical ventilation with heat recovery in new homes and office buildings.

To validate the results of the indoor air pollution models, one would need more measurements of indoor air concentrations in rooms with different emission sources and ventilation systems. Furthermore, measurements of concentrations are needed in various microenvironments, such as in cars, buses, and the underground.

With growing knowledge of the relation between exposure and health impacts, more detailed exposure indicators might be necessary. For instance, a further differentiation according to size and species of PM 2.5 and PM 10 might be needed, as well as the specification of the temperature and of the breathing rate, in other words the intake of pollutants with respiration. The use of more dynamic exposure data in epidemiological studies in the future could substantially improve the accuracy of health impact assessments.

8.1  Brief overview

While decisions about air quality management and policy development are based on political considerations, it is a scientific task to provide evidence and decision support for designing efficient air pollution control strategies that lead to an optimization of welfare of the population. To do this, integrated assessments of the available policies and measures to reduce air pollution and their impacts are made. In such assessments, two questions are addressed.

Is a policy or measure or a bundle of policies or measures beneficial for society? Does it increase the welfare of society; i.e. do the benefits outweigh the costs (including disadvantages, risks, utility losses)?

If several alternative policy measures or bundles of policy measures are proposed, how can we prioritize them according to their efficiency; i.e. which should be used first to fulfil the environmental aims?

To analyse these questions, two methodologies have been developed: cost–effectiveness analyses and cost–benefit analyses. The concept of “costs” is used here in a broad sense, referring to all negative impacts including – in addition to financial costs – also non-monetary risks and disadvantages, such as time losses, increased health risks, risks caused by climate change, biodiversity losses, comfort losses, and so on, which are monetized to be able to add them to the monetary costs. Benefits encompass all positive impacts including avoided monetary costs, avoided health risks, avoided biodiversity losses, avoided material damage, reduced risks caused by climate change, and time and comfort gains.

With a cost–effectiveness analysis (CEA) the net costs (costs plus monetized disadvantages minus monetized benefits) for improving a non-monetary indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the cost–effectiveness or efficiency of a policy or measure. The CEA is mostly used for assessing the costs associated with climatically active species, as the effects are global. The situation is different for air pollution, where the damage caused by emitting 1 t of a pollutant varies widely depending on time and place of the emission.

Cost–benefit analysis (CBA) is a more general methodology. In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs of a specific measure will result in the net benefit per euro spent, which can be used for ranking policies and measures. For performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, e.g. euros, is usually chosen. Integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence.

When setting up air pollution control plans, it is essential to also consider the effect of these plans on greenhouse gas emissions. Air pollution control measures usually lead to a decrease but sometimes also to an increase in GHG emissions. And vice versa, most measures for GHG reduction influence in fact reduce the emissions of air pollutants in most cases. Thus, an optimized combined air pollution control and climate protection plan is necessary to avoid contradictions and inconsistencies.

Looking at the current praxis in the EU countries, still separate plans are made for air pollution control and climate protection. Air pollution control plans currently estimate the reduction (or sometimes increase) of GHG emissions more and more, but they do not assess these reductions by monetizing them, and thus they cannot be accounted for in a cost–benefit analysis. In the assessment of the National Energy and Climate Plans the EC states:

Despite some efforts made, there continues to be insufficient reporting of the projected impacts of the planned policies and measures on the emissions of air pollutants by Member States in their final plans. Only 13 Member States provided a sufficient level of detail and/or improved analysis of the air impacts compared to the draft plans. The final plans provide insufficient analysis of potential trade-offs between air and climate/energy objectives (mostly related to increasing amounts of bioenergy). (EC, 2020)

So, in an integrated assessment, the assessment of air pollution control measures should always take into account the impact of changes in greenhouse gas emissions. Correspondingly, climate protection plans should take changes in air pollution into account (Friedrich, 2016). In the following, advancements in the quantification and monetizing of avoided impacts from reducing emissions from air pollutants and greenhouse gases are described.

8.2  Current status and challenges

8.2.1  estimation and monetization of impacts from air pollution.

Integrated assessments, which include as a relevant element the assessment of air pollution, encompass many areas, especially the assessment of energy and transport technologies and of policies for air pollution control and climate protection. The development of such integrated assessments started in the early 1990s with a series of EU research projects, which have been called “ExternE-external costs of energy”. A summarized description of the developed methodology can be found in Bickel and Friedrich (2005); further descriptions and project results are addressed in ExternE (2012), Friedrich and Kuhn (2011), Friedrich (2016) and Roos (2017). The framework for integrated environmental assessments has been further consolidated and developed within the EU research projects INTARESE and HEIMTSA. The advanced methodology and its application are described in Friedrich and Kuhn (2011). The processes of an integrated assessment are shown in Fig. 18 (Briggs, 2008; IEHIAS, 2014), where important elements like issue framing, scenario construction, provision of data and models, uncertainty estimation, and stakeholder consultation are addressed. In the beginning of an assessment, the relevant air pollutants have to be identified, which are those that cause substantial damage. In many cases, primary and secondary particulate matter of different size classes and NO 2 will cause the worst damage, followed by O 3 .

The element in the framework that is representing the assessment of air pollution, i.e. the “impact pathway approach”, is shown in detail in Fig. 19. This figure already includes one of the emerging developments described in Sect. 8.3, namely the estimation of individual exposure instead of outdoor concentration. First, scenarios of activities are collected, for instance the distance driven with a Euro 5 diesel car or the amount of wood used in wood stoves. Multiplying the activity data with the appropriate emission factors will result in emissions. The emission data are input for chemical–transport models that are used to calculate concentrations on regional, continental, or global scales; for Europe the EMEP model (Simpson et al., 2012) and worldwide the TM5-FASST model (van Dingenen et al., 2018) are often used – see Sect. 5 of this paper.

In the next phase, concentration–response functions derived from epidemiological studies are used to estimate health impacts. For the most relevant pollutants PM 10 , PM 2.5 , NO 2 , and O 3 , the WHO (2013a) made a meta-analysis of the epidemiological studies available until 2012 and recommended exposure–response relationships for use in integrated assessments, which are still widely used. Newer epidemiological studies in particular investigating the relation between fine particulate air pollution and human mortality have been analysed by Pope et al. (2020), who present a nonlinear exposure–response function with a decreasing slope for cardiopulmonary disease mortality caused by PM 2.5 . The most important concentration–response functions for impacts of air pollution on human health are described in Sarigiannis and Karakitsios (2018) and Friedrich and Kuhn (2011).

Beneath health damage, which is the most important damage category, impacts on ecosystems, especially biodiversity losses, and on materials and crop losses should also be considered. Impacts on ecosystems are usually quantified as pdf, “potentially disappeared fraction of species” per square metre land (Dorber et al., 2020) and thus as biodiversity losses. A first methodology was developed by Ott et al. (2006), which is still used in some studies. Further approaches, partly adopted from methods developed for LCIA (life cycle impact assessment), were developed later (e.g. Souza et al., 2015; Förster et al., 2019), but because of the simplifications and uncertain assumptions made, none of these approaches reached the same full acceptance as the approaches for the other damage categories. For material damage and crop loss, deposition–response relationships have been developed in the ExternE – External Costs of Energy (ExternE, 2012) project series and are described in Bickel and Friedrich (2005); they are still used.

Finally, the health effects and the other impacts are monetized, which means that they are converted into financial costs; for the non-monetary part of the impacts results of contingent valuation (willingness to pay) studies are used (as described in OECD, 2018). As numerous contingent valuation studies have been made in the past, it is not necessary to carry out a further willingness-to-pay study; instead results of existing studies which found monetary values for the damage endpoints to be analysed can be used. Of course, as the contingent valuation studies are usually made at another time, in another area, and with other cultural situations than the planned assessment, the monetary values must be transformed with a methodology called “benefit transfer” from the original time, place, and cultural features to the ones of the assessment (see Navrud and Ready, 2007). The most important monetary value in the context of air pollution is the value for a statistical life year lost (VLYL) caused by a premature death at the end of life after lifelong exposure to air pollutants. It is often based on a study of Desaigues et al. (2011). The result for average EU citizens – transformed to 2020 – is EUR 2020 75 200 (47 000–269 450) per VLYL. A list of monetary values for health endpoints, which are used in most studies, can be found in Friedrich and Kuhn (2011).

Figure 18 Integrated assessment process involving air pollution (Briggs, 2008).

Figure 19 Schematic presentation of the use of models and the flow of data in the enhanced impact pathway approach (Friedrich, 2016).

Based on this principal approach, a growing number of tools have been developed and applied for supporting air quality control for urban, national, and regional to global scales. The tool used for the assessments for DG Environment and for the Convention on Long-range Transboundary Air Pollution of the UN ECE is GAINS (Greenhouse gas – Air pollution Interactions and Synergies) developed by IIASA (Amann et al., 2017; Klimont, 2021).

A specific development in GAINS is the use of source–receptor matrices as a proxy for using an atmospheric model. A limitation of chemical transport models has been the substantial computational requirements for running the models for estimating hourly concentration values caused by an emission scenario for an entire year. To be able to simulate many scenarios within a short time, results of certain runs with the complex atmospheric model EMEP (Simpson et al., 2012) were transformed into source–receptor matrices, which provided information of the relationship between a change of emissions in a country and the change of the concentration in grid cells of a European grid. However, because of the relatively large size of the grid cells for European-wide models, concentrations in cities were underestimated; thus an “urban increment” was introduced for cities (Vautard et al., 2007; Torras and Friedrich, 2013; Torras, 2012). Thunis (2018), however, points out that this approach has certain weaknesses. Thus, newer approaches use nested modelling with regional atmospheric models using varying grid sizes (e.g. Brandt, 2012) or modelling of typical days instead of a whole year with a finer grid (Bartzis et al., 2020; Sakellaris et al., 2022). The ECOSENSE model uses a similar method as GAINS, however, distinguishing between parts of larger countries and emission heights. Furthermore a monetary assessment of greenhouse gas emissions is made (ExternE, 2012; Friedrich, 2016; Roos, 2017).

As a major application of the GAINS tool, the European Commission, DG Environment regularly assesses its directives for air pollution control. A well-known example is the impact assessment carried out for assessing the Thematic Strategy on Air Pollution and the Directive on “Ambient Air Quality and Cleaner Air for Europe” (EC, 2005). It was shown that the monetized benefits of implementing the thematic strategy for air pollution control are much higher than the costs. In the most recent assessment, DG Environment assessed the costs and benefits of the so-called NAPCPs, the national air pollution control programmes, which the member states had to provide by 2019 to show how they plan to comply with the emission reduction commitments of the National Emission Reduction Commitments Directive (NEC Directive). The benefits considered were the monetized reduced health and environmental impacts caused by the requested air pollution control measures. The results show that the health benefits alone with EUR 8 billion per year to EUR 42 billion per year are much larger than the costs of the considered measures with EUR 1.4 billion per year (EC, 2021) and that further emission reductions might also be efficient. The UN ECE (UN Economic Commission for Europe) has launched eight so-called protocols guided by the Convention on Long-range Transboundary Air Pollution, which require the member states to provide information on air pollution in their countries and to take actions to improve it (UNECE, 2020). The latest protocol entering into force was the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone, as amended on 4 May 2012. To prepare for these protocols, the effects of air pollution on ecosystems, health, crops, and materials have been assessed with the same methods as used by the EC, i.e. using the GAINS model.

The OECD recommends carrying out cost–benefit analyses with the impact pathway methodology (OECD, 2018). Similarly, national authorities, e.g. the German Federal Environmental Agency, have proposed using the methodology for the assessment of environmental policies and infrastructure projects (Matthey and Bünger, 2019). In Denmark, the method has been used in the EVA system (Economic Valuation of Air pollution, Brandt et al., 2013) to estimate the external costs related to air pollution, as part of the national air quality monitoring programme (Ellermann et al., 2018). The same system has been used to assess the impact from different emission sectors and countries within the Nordic area, by using a CTM model with a tagging method (Im et al., 2019). Kukkonen et al. (2020c) developed an integrated assessment tool based on the impact pathway principle that can be used for evaluating the public health costs. The model was applied for evaluating the concentrations of fine particulate matter (PM 2.5 ) in ambient air and the associated public health costs of domestic PM 2.5 emissions in Finland. Several further integrated assessment models have been described in Thunis et al. (2016). Not only in Europe but also in the USA, integrated assessment of air pollution is an issue. Keiser and Muller (2017) provide an overview of integrated assessment models for air and water in the US and hint at the intersections between air and water pollution.

Several studies are using the impact pathway approach from Fig. 8.2 for estimating health impacts and aggregate them to DALYS (disability adjusted life years), but without monetizing the impacts; i.e. they calculate the burden of disease or the overall health impacts stemming from air pollution. The WHO has estimated the burden of disease from different causes, including air pollution, in the Global Burden of Disease Study (GBDS, 2020). The European Environmental Agency regularly estimates the health impacts from air pollution in Europe and found 4 381 000 life years lost attributable to the emissions of PM 2.5 in 2018 in the EU28 (EEA, 2020a). Hänninen et al. (2014) analysed the burden of disease of nine environmental stressors, including particulate matter, for Europe. Lehtomäki et al. (2020) quantified the health impacts of particles, ozone, and nitrogen dioxide in Finland and found a burden of 34 800 DALYs per year, with fine particles being the main contributor (74 %). Recent studies also include future projections of emissions and climate. Huang (2018) assessed and monetized the health impacts of air pollution in China for 2010 and for several scenarios until 2030. Likewise, Tarín-Carrasco et al. (2021) projected the number of premature deaths in Europe towards 2050 and found that a shift to renewable energy sources (to a share of 80 %) is effective in reducing negative health impacts.

In the following, we address recent improvements in the methodology of integrated assessment with a focus on air pollution control. A milestone was the publication of concentration–response functions for NO 2 by the WHO (2013a). Following this, more and more studies calculated health impacts from exposure not only to PM 10 , PM 2.5 , and ozone but also to NO 2 (e.g. Balogun et al., 2020; Siddika et al., 2019, 2020),

Ideally, human health risks should be evaluated based on exposures instead of ambient concentrations (see Sect. 7). Until now, measured or modelled ambient (outdoor) air concentrations are input to the concentration–response functions used to estimate health risks. However, it is obvious that people are affected by the pollutants that they inhale, and that is decisive for the health impact. Therefore, a better indicator for estimating health impacts than the outside background concentration is exposure, which is the concentration of pollutants in the inhaled air averaged over a certain time interval. Only recently, in the EU projects HEALS and ICARUS, have methodologies been developed to estimate personal exposure, i.e. the concentration in the inhaled air averaged over a year or a number of years as the basis for estimating health impacts from air pollutants. Furthermore, the time span used in the exposure–response relationships commonly ranges from hourly to annual mean concentration values. By far the most important health effects are chronic effects. Although the indicator used to estimate chronic impacts is the annual mean concentrations, chronic diseases develop over several years, or even during the whole lifetime. This is the reason why the EC regulates a 3-year “average exposure index” of PM 2.5 in the air quality directive. But the relevant time period for the exposure might be larger than 3 years. Thus, exposure over a lifetime is important for estimating risks to develop chronic diseases and premature deaths, which are the most important health impacts. The methods for evaluating lifetime exposure have been addressed in Sect. 7.2.3 (see Li and Friedrich, 2019; Li et al., 2019a, c).

Thus, as a major improvement of the impact pathway approach, the exposure to pollutants should be used as an indicator for health impacts, instead of the exposure estimated from outdoor air concentrations at permanent locations. However, epidemiological studies that directly relate health impacts to exposures to air pollutants are not yet available. Instead, the existing concentration–response functions are transformed into exposure–response functions by calculating the increase in the exposure (e.g. x   µg m −3 ) caused by the increase of 1  µg m −3 in the outdoor concentration. Dividing the concentration–response relationship by x will then convert it into an exposure–response relationship (Li, 2020). Of course, it would be better to use results of epidemiological studies that directly relate exposure data with health effects. Thus, such studies should be urgently conducted.

Clearly, indoor pollution sources also influence exposure. It is therefore important to assess possibilities to reduce the contributions of indoor sources to exposure. These might include raising awareness of the dangers of smoking at home indoors, the development of more effective kitchen hoods and promoting their use, ban of incense sticks, and mandatory use of inserts in open fireplaces.

Secondly, a reduction of exposure is also possible by increasing the air exchange rate with ventilation or by filtering the indoor air. For example, if old windows are replaced by new ones, the use of mechanical ventilation with heat recovery might be recommended or even made mandatory. Also, the enhancement of HEPA filters and their use in vacuum cleaners will help as well as using air purifiers/filters. These systems will also be helpful to reduce the indoor transmission of SARS-CoV-2.

Furthermore, there is growing evidence that PM 10 and PM 2.5 concentrations in underground trains and in metro stations can be much higher than the concentration in street canyons with dense traffic (e.g. Nieuwenhuijsen et al., 2007; Loxham and Nieuwenhuijsen, 2019; Mao et al., 2019; Smith et al., 2020). Using ventilation systems with filters might improve this situation.

8.2.2  Monetization of impacts of greenhouse gas emissions

As explained above, air pollution control strategies usually influence and, in most cases, reduce emissions of greenhouse gases (GHGs). Thus, in an integrated assessment, both the reduction of air pollution and of greenhouse gas emissions should be quantitatively assessed. In practice, however, many national air pollution control strategies do not take changes in GHGs into account in the assessment; instead the national authorities develop separate climate protection plans. Similarly, although DG Environment estimates the changes in GHG emissions in their assessment of air pollution control strategies, the changes are not assessed or monetized.

An exception is the UK, where estimations of the “social costs of carbon” are used in assessments (Watkiss and Downing, 2008; DBEIS, 2019). The UK government currently recommends using a carbon price of GBP 69 per tonne of CO 2,eq in 2020 rising to GBP 355 per tonne of CO 2,eq in 2075–2078 at 2018 prices.

How can the benefits of a reduction of greenhouse gas emissions be monetized? A possibility is to use the same approach as with air pollution; i.e. estimate the marginal damage costs (i.e. the monetized damages and disadvantages) of emitting 1 additional tonne of CO 2 . These marginal costs would then be internalized for example as a tax per tonne of CO 2 emitted to allow the market to create optimal solutions (Baumol, 1972). Thus, first scenarios of greenhouse gas (GHG) emissions would be set up, then concentrations of GHGs in the atmosphere would be calculated, and finally changes of the climate followed by the estimation of changes of risks and damages would be calculated. However, this does not lead to useful results. Uncertainties are too high and assumptions of economic parameters like the discount rate or the use of equity weighting influence the result considerably, so that the range of results encompass several orders of magnitude. Furthermore, the precautionary principle tells us that we should avoid possible impacts, even if they cannot (yet) be quantified and thus not be included in the quantitative estimation of impacts.

An alternative approach to estimating marginal damage costs is to use marginal abatement costs. A basic law of environmental economics is that for pollution control a pareto-optimal state should be achieved, where marginal damage costs (MDCs) are equal to marginal abatement costs (MACs). Thus, if MAC at the pareto-optimal state are known, they could be used instead of the MDCs. However, the pareto-optimal state is not known if MDCs are not known. But one could use an environmental aim that is universally agreed upon by society and assume that they represent the optimal solution in the view of society and then estimate the MACs to reach this aim, which is then used for the assessment. This approach was first proposed by Baumol and Oates (1971).

For assessing GHG emissions, especially the aim of the so-called Paris Agreement, which was agreed on at the 2015 United Nations Climate Change Conference, COP 21 in Paris by a large number of countries, the most important aim was to keep a global temperature rise this century well below 2  ∘ C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5  ∘ C. This objective could be used as the basis for generating MACs.

Bachmann (2020) has carried out a literature research of MDCs and MACs for GHG emissions. Based on this review, MACs calculated by a meta-analysis of Kuik et al. (2009) are used here as the basis for the calculation of marginal abatement costs for reaching the above aim, resulting in EUR 2019  286 (162–503) per tonne of CO 2,eq in 2050. However, we propose starting with the most efficient measures now and gradually increasing the specific costs, until they reach the costs mentioned above in 2050. If future innovations lead to a reduction of the avoidance costs, the costs of carbon can be adjusted accordingly. With a real discount rate of 3 % a −1 , social costs of CO 2,eq to be used in 2020 would be EUR 2020  118 (67–207) per tonne of CO 2,eq .

8.2.3  Effect of integrating air pollution control and climate protection

In most cases, especially if a substitution of fossil fuels with carbon-free energy carriers or a reduction of energy demand is foreseen, a reduction of emissions of greenhouse gases and air pollutants is foreseen; thus, taking both air pollution control and climate protection into account will considerably improve the efficiency of such measures.

An example, showing the choice and ranking of measures for combined air pollution control and climate protection are different from the ranking in separate plans is shown in Fig. 20. In the frame of the EU TRANSPHORM project, 24 measures to reduce air pollution and climate change caused by transport in the EU have been assessed with an integrated assessment. Figure 20 shows the 8 most effective measures for both avoided health impacts and reduced climate change, where both benefits are converted into monetary units and combined (Friedrich, 2016). As can be seen, measures with benefits in both air pollution control and climate protection improve their rank compared to the separate rankings for these damage categories. The most effective measure is travel with trains instead of aeroplanes for routes of less than 500 km.

Figure 20 Ranking of measures in transport according to their effectiveness in mitigating damage from air pollutant and greenhouse gas emissions in 2019. Recalculation of results of Friedrich (2016) with abatement costs of EUR 2020  118 per avoided tonne of CO 2,eq as recommended in Sect. 8.2.2.

With another example, Markandya et al. (2018) demonstrate that especially for developing and emerging countries the costs for meeting the aims of the Paris Agreement will be outweighed by the benefits that are achieved by avoiding health impacts from air pollution, so that the climate protection comes without net costs. This is due to the fact that in developing countries the use of fossil fuels is less accompanied by the use of emission reduction technologies (filters), so replacing fossil fuels by electricity from wind or solar energy or saving energy will result in a much higher reduction in air pollution than doing the same in OECD countries. For Europe, the effects of integrating the damage costs of air pollution into the optimization of energy scenarios have been analysed by Korkmaz et al. (2020) and Schmid et al. (2019). Two effects are important: firstly, biomass burning in particular in smaller boilers is significantly reduced, as firing biomass is climate friendly but leads to air pollution. Secondly, the marginal avoidance costs per tonne of avoided carbon are reduced, especially for the period 2020–2035. The reason is that in this period more efficient measures like the replacement of oil and coal with electricity from carbon-free energy carriers (except biomass) and measures for energy savings will also reduce emissions of air pollution significantly, while later more expensive measures like producing and using fuels that are produced from renewable electricity (power to X) will have a lower effect on air pollution reduction.

In most cases, integrated assessment improves the efficiency of measures for environmental and climate protection. In the following an example is shown where an efficient climate protection measure gets inefficient if air pollution is included in the assessment. This example is the use of small wood firings in cities. Wood firings are climate friendly but emit lots of fine particles and NO x . Huang et al. (2016) show that for wood firings that are operated in cities, the damage of more health impacts outweighs the benefit of less greenhouse gas emissions.

Figure 21 shows the social costs per year; this is the annuity of the monetary costs, the monetized impacts of climate change, and the monetized health impacts caused by air pollution for different heating techniques that are used in an older single-family house in the centre of the city of Stuttgart. The social costs are calculated for newly built state-of-the-art technologies fulfilling the currently valid strict regulations for small firings in Germany (BImSchV, 2021). Older stoves have emissions and thus impacts that are much larger than those shown. The social costs are highest for wood and pellet combustion caused by their high air pollution costs, although the climate change costs of wood combustion are very low. This means that the benefit of less greenhouse gas emissions of wood firings is much smaller than the additional burden caused by air pollution. Furthermore, even if we further enhance the emission reduction by equipping the wood and pellet combustion with an efficient particulate filter – these are represented by the columns marked with “ + part. filter” – the ranking is not changed. The reason is the high NO x emissions of wood combustion. The results suggest that wood combustion in rural areas should be equipped with a particulate filter, while in cities a ban on small wood combustion might be considered, unless wood and pellet firings are equipped not only with particulate filters but also with selective catalytic reduction (SCR) filters.

Figure 21 Social costs per year (annuity) of different heating boilers for an older single-family house in Stuttgart. Boilers are state-of-the-art technologies, and + part. filter means that wood or pellet heating is additionally equipped with efficient particulate filters (Huang et al., 2016).

8.3  Emerging challenges

8.3.1  challenges in improving the methodology for integrated assessments.

Estimations of damage costs caused by air pollution and climate change still show large uncertainties. Li (2020) reports a 95 % confidence interval of EUR  3.5×10 11 to EUR  2.4×10 12 for the damage costs caused by the exposure to PM 2.5 and NO 2 for 1 year (2015) for the adult EU28 + 2 population. Kuik et al. (2009) report an uncertainty range for the marginal avoidance costs to reach the “2 ∘ aim” of EUR 2020  162 to EUR 2020  503 per tonne of CO 2,eq in 2050. In addition, systematic errors might occur, for instance still unknown exposure–response relationships. Thus, methodological improvements are necessary.

In principle, all model steps and related input data shown in Fig. 19 would need improvement. Most of the improvements necessary for models and the data shown in Fig. 19 have already been addressed in the previous sections. Challenges for improving the estimation of emissions of indoor and outdoor sources are described in Sect. 3.3. Improvements in atmospheric modelling are addressed in Sect. 5.3. Exposure modelling is a relatively new field, so a lot of gaps have to be filled (see Sect. 7.3.3). Further epidemiological studies, especially for analysing the health impacts of specific PM species and PM size classes, are urgently needed, and contingent valuation studies are needed to improve the methodology. The challenges for these topics are addressed in the relevant sections above and will thus not be repeated here. However, two further methodological improvements have not been mentioned and are thus described in the following.

When assessing a policy measure for the reduction of air pollution, the first step is to estimate the reduction of emissions caused by the policy measure. Measures can be roughly classified in technical measures that improve emission factors (e.g. by demanding filters) and non-technical measures that change the behaviour or choices of emission source operators (e.g. by increasing prices of polluting goods). Especially if non-technical measures are chosen, e.g. the increase in the price for a good that is less environmentally friendly, the identification of the reaction of the operators of the emission sources is not straightforward. Do they keep using the good although it is more expensive? Do they substitute the good or do they renounce the utility of the good by using neither the good nor substitutes anymore? For energy-saving measures, it is well-known that after implementing such a measure, the users do not save the full expected energy amount but instead increase their comfort, for instance by increasing the room temperature. This is known as the rebound effect. The traditional way to deal with behavioural changes is using empirically found elasticity factors. For the transport sector, where most of the applications are made, Schieberle (2019) compiled a literature search for elasticities in the transport sector and demonstrated their use in integrated assessments. However, as a further development, recently first attempts to use agent-based modelling have been made to estimate the behavioural changes of people confronted with policy measures (Chapizanis et al., 2021).

With regard to the marginal costs of CO 2 reduction used in the assessments, further investigations taking into account emerging innovations are necessary. Furthermore, the stated estimates are quite high, so that the question arises of whether the values and thus the Paris aim gain worldwide social acceptance. More emphasis might be laid on research to develop measures for more efficient climate protection as well as measures to remove GHGs from the atmosphere and also to develop adaptation measures.

8.3.2  Challenges for the reduction of ambient air pollution

In recent years, regulations have been implemented that will decrease emissions in two important sectors considerably.

For ships, the IMO (International Maritime Organization) adopted a revised annex VI to the international Convention for the Prevention of Pollution from Ships, now known universally as MARPOL, which reduces the global sulfur limit from 3.50 % to 0.50 %, effective from 1 January 2020 (IMO, 2019). This will drastically reduce the SO 2 emissions around Europe outside of the sulfur emission control areas of the Baltic Sea, North Sea, and English Channel. Furthermore, the IMO has adopted a strategy to reduce greenhouse gas emissions by at least 40 % by 2030, pursuing efforts towards 70 % by 2050, compared to 2008. A revised, more ambitious plan is currently being discussed. Geels et al. (2021) assess the effects of these new regulations by generating several future emission scenarios and assessing their impact on air pollution and health in northern Europe.

Diesel cars now must comply with the Euro 6d norm, which will drastically reduce the real driving emissions of NO x on streets and roads. In addition, electric vehicles are now promoted and subsidized in many EU countries.

In the context of a revision of the EU rules on air quality announced in the European Green Deal, the European Commission is expected to strengthen provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air, notably proposing to revise air quality standards to align them more closely with the World Health Organization recommendations (which was updated in 2021).

The European Commission is also expected to introduce a new Euro 7 norm for passenger cars in 2025. The Industrial Emissions Directive demands permanent reviews of the EU Best Available Techniques reference documents (BREFs), resulting in decreasing emissions from large industrial emitters. The EU has decided to reduce greenhouse gas emissions by at least 55 % compared to 1990. Furthermore, national reduction plans for GHG lead to a further reduction of the combustion of fossil fuels. Thus, emissions of air pollutants from combustion processes will significantly decrease with one exception: wood and pellet firings <500  kW th . Hence, regarding combustion, the main challenge is the development of further PM 2.5 and NO x reduction measures for small wood firings. Similar trends will be observed in a number of other countries. For example the USA wants to reduce their greenhouse gas emissions from 2005 to 2030 by 50 % and China wants to reach carbon neutrality by 2060.

As emissions of particulates from combustion decrease, diffuse emissions, e.g. from abrasion processes, bulk handling, or demolition of buildings, and those from evaporation of volatile organic compounds get more and more dominant. So more emphasis should be put on the determination and reduction of these emissions. In particular, the processes leading to diffuse emissions are not well-known. In transport, emissions from tyre and brake wear and road abrasion heavily depend on driving habits, speed, weather conditions, and especially the traffic situation and layout of the road network. However, emission factors for diffuse emissions are still largely expressed in grammes per vehicle kilometre, not taking situations where braking is necessary, e.g. because of traffic jams or crossroads, into account. Furthermore, reduction measures like the development of tyres and brakes with longer durability should be considered and assessed.

A key challenge for reducing secondary particulates, especially ammonium nitrates, is a further reduction of NH 3 emissions from agriculture. Certain national reduction commitments for EU countries from 2005 until 2030 are regulated by the National Emission Reduction Commitments Directive (NEC Directive) of the EU, but further reductions might be necessary.

8.3.3  Challenges for the reduction of indoor air pollution

A more precise understanding of personal exposure to air pollution and the use of exposure–response relationships (instead of relationships linking outdoor concentration with responses) will potentially change the focus of air pollution control. As people are indoors most of the time, now the reduction of indoor pollution is becoming important. Of course, reducing ambient concentration will also reduce indoor pollution, as pollutants penetrate from outside into the houses. However, around 46 % of the total exposure with PM 2.5 for an average EU citizen stems from indoor sources; for NO 2 about 25 % is caused by indoor sources (Li and Friedrich, 2019). Thus, indoor sources cannot be neglected. The reduction of exposure to emissions from passive smoking, frying, and baking in the kitchen; using open fireplaces and older wood stoves; and incense sticks and candles is especially important. Indoor concentrations can be reduced by reducing the emission factor of the source, changing behaviour when using the source; by banning the use of a source; by increasing the air exchange rate with ventilation; and by using air filters.

This review has covered a larger number of research areas and identified not only the current status but also the emerging research needs. There are of course cross-cutting needs that are a prerequisite to further air quality research and develop more robust strategies for reducing the impact of air pollution on health. The following section discusses some of the key areas and synthesizes these in the form of recommendations for further research.

9.1  Connecting emissions and exposure to air pollution

There is a progressively important need to move from static annual inventories to those that are dynamic in terms of activity patterns and of higher temporal resolution. This is driven partly by the need for activity-dependent exposure modelling and because there is an increasing availability of online observations from sensors to arrive at a better spatial and temporal resolution of emission rates and factors. Clearly community efforts are necessary for identifying and reducing uncertainties in emissions that have a large impact on the resulting air quality and exposure predictions including benefiting from source apportionment methods.

One gap is the evaluation of agricultural emissions, which are still poorly understood, and improvements will support both air quality and climate change assessment, leading to co-benefits. While considerable effort has been devoted to estimating NO x emissions, there are still uncertainties in the estimation of VOC emissions. These uncertainties have direct implications when quantifying changes in ozone levels and contributions from secondary organic aerosols to regional and global scales. One prominent example of such uncertainties is the estimation of VOC profiles in terms of the chemical species and their evaporation rates, including in particular those from shipping activities in the vicinity of ports, as a shift has occurred to both low-sulfur and carbon-neutral or non-carbon fuels.

Similarly, as exhaust emissions decrease with the increase in electric vehicles, the assessment of the consequences of airborne non-exhaust emissions is becoming more and more important. However, this needs to be examined in the context of tighter policy-driven controls on petrol and diesel vehicles. Emission factors for ultra-fine particles are also uncertain; these are also spatially and temporally highly variable, which reduces the reliability of particle number predictions necessary for estimating exposure of people (Kukkonen et al., 2016a).

Exposure connects emissions to concentrations and their impact on health. As exposure to a particular air pollutant is determined by all sources of that air pollutant, both indoor and outdoor sources are important. Indoor sources are considerable in number and variety from tobacco smoking to cooking and heating fuels, indoor furniture, body care and cleaning products, and perfumes. Not only are emission factor data for these sources needed, stricter regulations are necessary for indoor sources (e.g. indoor cleaning products and wood burning for residential heating).

9.2  Extending observations for air quality research

Our review has highlighted the urgent need to strengthen the integration of observations from different platforms, including from reference instruments, mobile and networked low-cost sensors, and other data sources, such as satellite instruments and other forms of remote sensing. In addition to providing greater spatial extent and fine-scale resolution of observations in urban and other areas, these integrated data sets can form the basis of inputs for dynamic data assimilation. Data assimilation can also be performed using machine learning and/or artificial intelligence approaches. These developments can improve the accuracy of chemistry–transport models, including air quality forecasts.

Additional requirements for low-cost sensors are (i) improving their reliability for both the gaseous and particulate matter measurements (including in particular VOCs), (ii) extending the measured size range of particulate matter up to ultra-fine particles, and (iii) including their scope to also measure bioaerosols, such as allergenic pollen species and fungi. Integrating these sensors into existing infrastructures, such as permanent air quality measurement networks, traffic counting sites, and indoor monitoring, would provide a richer data set for air quality and exposure research. Further effort is required to determine the health-relevant PM information, including in particular the chemical composition of PM.

As citizen science and crowdsourcing increase, their use in air quality research needs to be more clearly defined. It could potentially provide near-real-time air pollution information as well as information to be used for personal health protection and lifestyle decisions. Most challenging for this objective is data quality characterization and acceptance of these new data provisioning tools, which do not easily allow an analytical quality assurance and control.

9.3  Bridging scales and processes with integrated air pollution modelling

Continuing developments in fine, urban, and regional modelling have elevated scale interactions as a key area of interest. As highlighted above, research is needed to develop new approaches to connect processes operating on different scales. Baklanov et al. (2014) reviewed online approaches that include coupling of meteorology and chemistry within an Eulerian nested framework, but on the whole air pollution applications are limited to different modelling systems including Eulerian for regional, Gaussian for urban and street, and LES and advanced CFD-RANS for even finer scales. Challenges remain on how to best integrate the fast-emerging machine learning statistical tools and how parameterizations and computational approaches have to be adapted. These scale interactions are of critical importance when examining the impact of air pollution in cities which are subject to heterogeneous distribution of emissions and rapidly changing dispersion gradients of concentrations. New modelling approaches will enable multiple air quality hazards that affect cities to be examined within a consistent multi-scale framework for air quality prediction and forecasting and local air quality management, quantifying the impact of episodic high-air-pollution events involving LRT and even meteorological and climate hazards as cities prepare for the future.

One major development in this vain is that of Earth system model (ESM) approaches, which in the past have been focussed on global scales but have the potential of higher-resolution applications (e.g. WWRP, 2015). Within Earth system models, there is potential for integration of observations (e.g. through data assimilation of soil moisture and surface fluxes of short-lived pollutants and greenhouse gases). These developments are to some degree being aided by the rapidly evolving area of parallel computer systems. While the representation of urban features and processes within ESMs still require further effort, these models have the potential to include dynamical and chemical interactions on a much wider scale than is possible with traditional approaches (e.g. mesoscale circulations, urban heat island circulation, sea-breeze and mountain-valley circulations, floods, heat waves, wildfires, air quality issues, and other extreme weather events).

As primary air pollution emissions are decreasing, the role of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments requires more research. Here coupled systems and potential ESMs in the future will have a key role based on two-way interaction chemistry–meteorology models combining the effects of urban, sub-urban, and rural pollutant emissions with dynamics. This is especially true in a changing climate scenario.

Cities are routinely facing multiple hazards in addition to high levels of air pollution including storm surges, flooding, heat waves, and a changing climate. Moving towards integrated urban systems and services poses research challenges but is viewed as essential to meet sustainable and environmentally smart city development goals, e.g. SDG11: Sustainable Cities and Communities (Baklanov et al., 2018b; Grimmond et al., 2020). More integrated assessment of risk to urban areas necessitates observation and modelling that brings together data from hydrometeorological, soil, hydrology, vegetation, and air quality communities including sophisticated and responsive early warning and forecast capabilities for city and regional administrations.

9.4  Improving air quality for better health

As air quality science continues to develop, the need to improve our understanding of PM properties and resulting health impacts remains a priority. In particular the areas that stand out are the need to better quantify particle number concentrations (PNCs), particle size distributions (PSDs), and the chemical composition of PM, especially in urban areas where population density is higher. An ongoing challenge for the science community is to investigate which of the PM properties or measures optimally describe the resulting health impacts. To aid research, a denser measurement network on advanced PM properties is needed for quantifying chemical and physical characteristics of PM in cities and regionally. Another important requirement is the availability of improved higher-resolution emission inventories of PM components and for different sizes (see Sect. 9.1). To support epidemiological studies, comprehensive long-term data sets are needed including both (i) multi-decadal evaluations of air quality, meteorology, and exposure and (ii) information on a range of health impacts.

9.5  Challenges of global pandemics

In addition to the multiple hazards facing cities mentioned in Sect. 9.3, the COVID-19 pandemic has starkly demonstrated how society can be dramatically affected across the world. Studies are indicating a dramatic impact on air quality due to the lockdown as well as possible connections between air pollutants such as aerosols in spreading the SARS-CoV-2 virus (e.g. Baldasano, 2020; Gkatzelis et al., 2021; Sokhi et al., 2021). To fully assess the interactions of viruses and air pollutants, studies need to consider both indoor and outdoor transmission as well as meteorological and climatological influences. A recent preliminary review (WMO, 2021) has concluded that there are mixed indications of links between meteorology and air quality with COVID-19, and more thorough studies are needed to ascertain the direct and indirect effects. Given the complexity of the topic, cross- and interdisciplinary studies would be needed, including a collaboration of microbiologists, epidemiologists, health professionals, and atmospheric and indoor pollution air scientists.

9.6  Integrating policy responses for air quality, climate, and health

Most control policies and measures targeted at air pollution will also change GHG emissions, which implies that taking them both into account in integrated assessments will in most cases provide considerable co-benefits. There are cases, for example in the case of biomass burning, which will increase air pollution emissions, and hence additional abatement measures (e.g. cleaning systems) will be required. On the whole, however, integrating climate change and air pollution policies where possible has the potential of making the integrated policy more efficient than separate policies for improving air quality and limiting the impact of climate change. Thus, integrated environmental policies based on assessing reductions of impacts on health, the environment, and materials caused by air pollution control and reductions of impacts of climate change caused by measures for climate protection simultaneously should be implemented. The assessment should be made following the impact pathway approach described in Sect. 8.2. The impact pathway approach uses the methods and data from all the sections of this paper, i.e. emission modelling, atmospheric modelling, exposure modelling, and health impact modelling. Thus, addressing the challenges described in this paper would help to reduce uncertainties and improve efficiency in the scientific recommendations for setting up integrated environmental policy plans. Within an integrated air pollution control and climate protection assessment, a particularly important new development would be to use the individual exposure (the concentration of a pollutant where it is inhaled by an individual averaged over a year) instead of some outdoor concentration as an indicator for health impacts, i.e. as input for the exposure–response relationship. In this case, the indoor concentration of air pollutants and thus indoor source emission rates and ventilation air exchange rates would be important elements in the assessment along with contributions from outdoor sources when planning air pollution control strategies.

9.7  Key recommendations

Below in Table 1 we present a synthesis of key recommendations for scientific research and the importance for air quality policy that have emerged from this review. The table also provides an indication of the confidence in the scientific knowledge in each of the areas, the urgency to complete the science gaps in our knowledge, and the importance of each of the listed areas for supporting policy. It should be noted that our approach provides more of an overview and does not consider the needs of specific areas or of national needs which may differ from the regional status of knowledge. For example, in the case of emission inventories for Europe and North America, there is generally high confidence but that may not be the case in other regions of the world or for specific countries or sub-regions.

Table 1 A synthesis of key recommendations for scientific research and the importance for air quality policy. A three-level scale is used to indicate the current confidence in the scientific knowledge and understanding and a measure of the urgency to fill the science gaps where they exist. Similarly, a three-level scale is used to indicate the importance of the specific issues for policy support. Scientific confidence – h: high (progress is useful but may not require significant specific research effort); m: medium (some further research is required); l: low (concerted research effort is required). Scientific urgency to meet gaps in knowledge – v: very urgent need to fill science gap; u: urgent need to fill scientific gap; w: widely accepted with less urgency to fill the science gap. Importance for policy support – H: high (is highly important for developments of new policies); M: medium (can lead to refinements of current policies); L: low (progress is useful but may not require significant effort in the short or medium term).

1  PM properties refer here to particulate matter size distributions, particle number, and chemical composition for example. 2  Dynamic exposure assessment refers to exposure studies, which treat the pollutant concentrations in different microenvironments as well as the infiltration of outdoor air to indoors. In dynamic exposure assessment, one can also treat pollution sources and sinks in indoor air.

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This review has mainly examined research developments that have emerged over the last decade. As part of the review, we have provided a short historical survey, before assessing the current status of the research field and then highlighting emerging challenges. We have had to be selective in the key areas of air quality research that have been examined. While the concept of this review emerged from the 12th International Conference on Air Quality (held virtually during 18–26 May 2020), each of the sections not only provided an air quality research community perspective but also included a wider literature examination of the areas.

10.1  Emissions of air pollution

The emphasis has been on air pollution emissions of major concern for health effects, namely exhaust and non-exhaust emissions from road traffic and shipping, and other anthropogenic emissions, e.g. those from agriculture and wood burning. Developments are continuing to improve global and regional emission inventories and integrating local emissions data into the larger-scale inventories. With increasing demand for cleaner vehicles, there is still the need to assess whether electric and hybrid vehicles actually reduce total PM 2.5 and PM 10 emissions, as emissions from non-exhaust PM from tyre, brake, and road wear are still present. Developments in on board monitoring to help improve estimation of real-world emission estimation is another growing area. Understanding the effects of non-exhaust emission will be important to design robust air quality management strategies in the context of other emissions, including windblown dust.

Uncertainties still exist in estimating emissions from diffuse processes, such as abrasion processes in industry, households, agriculture, and traffic, where large variabilities are still present. Other sources, which are not well characterized, include residential wood combustion as well as the spatial representation of these emissions across regions. While progress in source apportionment models has continued, inverse modelling used for improvement of emission inventories has the potential to reduce their uncertainties.

In terms of chemical speciation, while some improvements have taken place in estimating temporal profiles of agricultural emissions, the amount of NH 3 and PM emissions originating from agriculture are still uncertain for many regions. The impact of new fuels on the chemical composition of NMVOC emissions from combustion processes remains highly uncertain (e.g. low-sulfur residual fuels in shipping and new exhaust gas cleaning technologies).

Bringing together air pollution emission inventories with those of greenhouse gases will facilitate integrated assessment measures and policies benefitting from co-benefits. On the urban and street scales, emission models need to be able to simulate the spatial and temporal variations in emissions at a higher resolution from road traffic, taking account of traffic and driving conditions.

The importance of shipping emissions is growing, as there is a shift to carbon-neutral or zero-carbon fuels. Emission factors for VOC from shipping are generally less certain, and hence little is known about their contribution to particle and ozone formation. To estimate the total environmental impact of shipping, integrated approaches are needed that bring together (i) impacts from atmospheric emissions on air quality and health, deposition of pollutants to the sea; (ii) impacts of discharges to the sea on the marine environments and biota; and (iii) climatic forcing.

The greater emphasis on reducing exposure to air pollution requires consideration of both emissions from outdoor and indoor sources, as well as their exchange between indoor and outdoor environments. Emissions of VOC for example, from transportation and the use of volatile chemical products, such as pesticides, coatings, inks, personal care products, and cleaning agents, are becoming more important, as are combustion gas appliances such as stoves and boilers, smoking, heating, and cooking, which are important sources of PM 2.5 , NO, NO 2 , and PAHs. The complexity of integrated exposure models is expected to increase, as they have to include both indoor and outdoor emissions of air pollution, accurate description of the key chemical and physical processes, and treatment of dispersion of air pollution inside and outside and exchange between buildings and the ambient environment (Liu et al., 2013; Bartzis et al., 2015).

10.2  Observations to support air quality research

Regarding observation of air quality, this review has focused on low-cost sensor (LCS) networks, crowdsourcing, and citizen science and on the development of modern satellite and remote sensing technics. Connecting observational data with small-scale air quality model simulations to provide personal air pollution exposure has also been discussed.

Remote sensing measurements including satellite observations have a significant role in air quality management because of their spatial coverage, improving spatial resolution and their use in combination with modelling tasks (Hirtl et al., 2020), even for urban areas (Letheren, 2016). Machine learning algorithms are increasingly being used with remote sensing applications (e.g. Foken, 2021), and recent advances have highlighted the potential of statistical analysis tools (e.g. neural learning algorithms) for predicting air quality at the city scale based on data generated by stationary and mobile sensors (Mihăiţă et al., 2019). Geostatistical data fusion is allowing fine spatial mapping by combining sensor data with modelled spatial distribution of air pollutant concentrations (Johansson et al., 2015; Ahangar et al., 2019; Schneider et al., 2017).

Applications of LCS as well as networks based on such sensors have increased over the past decade (e.g. Thompson, 2016; Karagulian et al., 2019; Barmpas et al., 2020; Schäfer et al., 2021). These applications have also highlighted the need for proper evaluation, quality control, and calibration of these sensors. The analysis of LCS data should take account of cross-sensitivities with other air pollutants, effects of ageing, and the dependence of the sensor responses on temperatures and humidity in ambient air (e.g. Brattich et al., 2020).

10.3  Air quality modelling

Air quality research, including approaches to manage air pollution, has relied heavily on the continuing developments, applications, and evaluation of air quality models. Air quality models span a wide range of modelling approaches including CFD and RANS models used for very high resolution dispersion applications (e.g. Nuterman et al., 2011; Andronopoulos et al., 2019), and Lagrangian plume models to Eulerian grid CTMs used for urban to regional scales. An interesting development is that of the implementation of multiply nested LESs and coupling of urban-scale deterministic models with local probabilistic models (e.g. Hellsten et al., 2021), although complexities arise because of the different parameterizations and the treatment of boundary conditions. A limitation that needs addressing with CFD, including LES models, is that they are currently suited mainly for dispersion of tracer contaminants or where only simple tropospheric chemistry is relevant. Lack of more sophisticated or realistic description of NO x –VOC chemistry can cause significant bias in the concentration gradients at very fine scales.

Over the last decade new developments have focused on improving scale interactions and model resolution to resolve the spatial variability and heterogeneity of air pollution (e.g. Jensen et al., 2017; Singh et al., 2014, 2020a) at street scales in a city area. New approaches of artificial neural network models and machine learning have shown a more detailed representation of air quality in complex built-up areas (e.g. P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecasting air quality, and simulation of exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020; Jensen et al., 2017). Ensemble simulations have proven to be successful to provide more reliable air quality prediction and forecasting (e.g. Galmarini et al., 2012; Hu et al., 2017), and complementary hybrid approaches have been explored for multi-scale applications (Galmarini et al., 2018).

The strong interaction between local and regional contributions, especially to secondary air pollutants (PM 2.5 and O 3 ), has motivated the coupling of urban- and regional-scale models (e.g. Singh et al., 2014; Kukkonen et al., 2018). With the importance of exposure assessment increasing, the incorporation of finer spatial scales within a larger spatial domain is required, which introduces the challenging issue of representing multiscale dynamical and chemical processes, while maintaining realistic computational constraints (e.g. Tsegas et al., 2015). Similarly, machine learning approaches offer possibilities to use observational data to improve fine-scale air quality and personal exposure predictions (Shaddick et al., 2021).

10.4  Interactions between air quality, meteorology, and climate

Our review has highlighted the need to integrate predictions of weather, air quality, and climate where Earth system modelling (ESM) approaches play an increasing role (WWRP, 2015; WMO, 2016). There are also continued improvements from higher-spatial-resolution modelling and interconnected multiscale processes, while maintaining realistic computational times. Many advances have taken place in the development and use of coupled regional-scale meteorology–chemistry models for air quality prediction and forecasting applications (e.g. Kong et al., 2015; Baklanov et al., 2014, 2018a). These advances contribute to assess complex interactions between meteorology, emission, and chemistry, for example, relating to dust intrusion and wildfires (e.g. Kong et al., 2015). Data assimilation of chemical species data into CTM systems is still an evolving field of research; it has the potential to better constrain emissions in forecast applications. An example would be data assimilation of urban observations (including meteorological, chemical, and aerosol species) to investigate multiscale effects of the impacts of aerosols on weather and climate (Nguyen and Soulhac, 2021).

Urban- and finer-scale (e.g. built environment) studies are showing that improvements in the treatment of albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation can influence urban air quality significantly (e.g. González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021). These considerations can be very important for urban air quality forecasting, as temporal variations in air pollutant concentrations in the short term are largely due to variabilities in meteorology. Understanding and parameterizing multiscale and non-linear interactions, for example evolution and dynamics of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation, remains an ongoing atmospheric science challenge. Another remaining research challenge that involves multiscale interactions includes the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols), especially to describe air quality over urban, sub-urban, and rural environments.

Development and evaluation of nature-based solutions to improve air quality demand an improved understanding of the role of biogenic emissions (Cremona et al., 2020) as a function of vegetation species and characteristics. Interactions are influenced by several factors, such as vegetation drag, pollutant absorption, and biogenic emissions. These factors will determine the impact on air quality, be it positive or negative (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017). Advanced approaches are needed to describe biogenic emissions together with gas and particle deposition over vegetation surfaces to further assess the effectiveness of nature-based solutions to improve air quality in cities.

10.5  Air quality exposure and health

Air-quality-related observations to support air quality health impact studies are heterogeneous; for many developing regions, such as Africa, ground-based monitoring is sparse or non-existent (Rees at al., 2019). The motivation is growing for an inter-disciplinary approach to assess exposure and the burden of disease from air pollution (Shaddick et al., 2021); this could benefit from the combined use of ground and remote sensing measurements, including satellite data, with atmospheric chemical transport and urban-scale dispersion modelling.

Air quality impact on health can occur on short and long timescales. PM, which is one of the most health-relevant air pollutants, is associated with many health effects, such as all-cause, cardiovascular, and respiratory mortality and childhood asthma (e.g. Dai et al., 2014; Samoli et al., 2013; Stafoggia et al., 2013; Weinmayr et al., 2010). There have been significant advances that reveal new evidence of the health impact of PM components, such as SO 4 , EC, OC, and metals (Wang et al., 2014; Adams et al., 2015; Hampel et al., 2015; Hime et al., 2018). Challenges remain to elucidate the relative role of PM components and measures in determining the total health impact. These include particle number concentrations (PNCs), secondary organic PM, primary PM, various chemical components, suspended dust, the content of metals, and toxic or hazardous pollutants.

Improved knowledge on the health impacts of PM components has also stimulated further debate on the optimal concentration–response functions and on the necessity of threshold or lower limit values, below which health impacts might not manifest (Burnett et al., 2018). These challenges will feed into health impact studies, such as EEA (2020a), which estimated that more than 3 848 000 years of life lost (about 374 000 premature deaths) were linked to exposure to PM 2.5 in 2018 in the EU-28. However, another study by Lelieveld et al. (2019) indicated that health impacts from PM 2.5 exposure may have been considerably underestimated.

The worldwide impact from the COVID-19 pandemic caused by the SARS-CoV-2 virus has raised global interest in the links between air quality and the spread of viruses (van Doremalen et al., 2020). However, the exact role and mechanisms are not yet clear and require concerted effort (e.g. Pisoni and Van Dingenen, 2020). There is also evidence that poor air quality can exacerbate health effects from other environmental stressors, including heat waves, cold spells, and allergenic pollen (e.g. Klein et al., 2012; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

The link between population activity and actual exposure is also becoming clearer, where dynamic diurnal activity patterns provide more accurate representation of exposures to air pollution (Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a). Recent work by Ramacher et al. (2019), for example, has also demonstrated the importance of the movements of people to assess exposure.

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions that include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

10.6  Air quality management and policy

Integrated assessment of air pollution control policies has progressively developed over the last 2 decades and has been widely used as a tool for air quality management (e.g. EC, 2021). Relatively recently, integrated assessment for air pollution control in research projects has started to take account of climate change. Correspondingly, integrated assessment activities for climate protection have started to include impacts of air pollution in the assessment (Friedrich, 2016). Some national authorities, such as the German Federal Environmental Agency or the UK Department for Business, Energy and Industrial Strategy, have also recommended an integrated assessment, combining the assessment of climate and air pollution impacts (Matthey and Bünger, 2019; DBEIS, 2019). Impact pathway approaches are also currently increasingly incorporating exposure to air pollutants as an indicator of health impacts, instead of the previously applied concentration of air pollutants at fixed outdoor locations (Li and Friedrich, 2019). This has an implication for epidemiological studies, which usually are based on correlation between modelled or measured concentrations at outdoor locations and health risks (e.g. Singh et al., 2020b).

Interdependence of air pollution and climatically active species allows co-benefits to be optimized. This approach also shows that costs of meeting policy obligations for climate protection (e.g. for the Paris Agreement) can be reduced or offset by the benefits of reduced health impacts from improved air quality (Markandya et al., 2018). On the other hand, for some climate protection measures, the benefits of reduced climate change are much smaller than the impacts caused by increased air pollution. This has been demonstrated for wood combustion, which while being more climate friendly than fossil fuels, will give rise to PM 2.5 and NO x emissions (Huang et al., 2016; Kukkonen et al., 2020b). Some recent studies (e.g. Schmid et al., 2019) have provided evidence on the advantages of using costs and benefits for both climate and air pollution abatement measures in integrated assessments.

Air quality management must adapt to the tightening of policy-driven regulations. Recently, the sulfur content of the fuel for ships has been reduced to 0.5 % worldwide (IMO, 2019). The EURO 6d norm has led to a significant reduction of NO x in the exhaust gas of diesel cars, whereas the EURO 7 norm planned to be implemented in 2025 will further reduce PM and NO x emissions from vehicle engines. The European Council has recently (in September 2020) agreed to reduce the EU's greenhouse gas emissions in 2030 by at least 55 % compared to the corresponding emissions in 1990. Together with national reduction plans for GHG, this will significantly reduce emissions of air pollutants from the combustion of fossil fuels. However, there is one exception: small wood and pellet firings ( <500  kW), where still further measures should be developed for reducing the PM and NO x emissions (e.g. Kukkonen et al., 2020b).

While direct combustion emissions are expected to decrease, a particular challenge will be to control diffuse emissions, e.g. from abrasion processes, bulk handling, demolition of buildings, and use of paints and cleaning agents. Despite cleaner vehicles, emissions from tyre and brake wear and road abrasion remain an important challenge. Other areas that pose challenges for air quality management are the need to reduce agricultural emissions, especially of ammonia, which can lead to the production of secondary aerosols (especially ammonium nitrates).

Using personal exposure instead of outdoor concentration as an indicator in health impact assessments offers the opportunity to assess the impacts of indoor air pollution control. Possibilities to reduce emissions from indoor sources, such as smoking, frying and cooking, candles and incense sticks, open chimneys and wood stoves, and cleaning agents, should be assessed. Furthermore, using HEPA filters in vacuum cleaners, air filters, and cooker bonnets and using mechanical ventilation with heat recovery should be analysed. In addition, possibilities for reducing PM concentrations in underground rail stations should be explored.

Finally, we consider cross-cutting needs as a synthesis of our findings and suggest recommendations for further research.

Specifically, we indicate the confidence in the scientific knowledge, the urgency to complete the science gaps and the importance of each area for supporting policy.

No data sets were used in this article.

All co-authors contributed to conceptualization and design of study, coordination, methodology development of the review, validation and checking, formal analysis, investigation and examination of the literature, writing of original draft, and review and editing of paper.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The support of the following institutions and enterprises is gratefully acknowledged: University of Hertfordshire; Aristotle University Thessaloniki; TITAN Cement S.A., TSI GmbH; APHH UK-India Programme on Air Pollution and Human Health (funded by NERC, MOES, DBT, MRC, Newton Fund); and American Meteorological Society (AMS) Air & Waste Management Association (A&WMA).

We especially acknowledge the tireless effort of Ioannis Pipilis, Afedo Koukounaris, and Eva Angelidou.

World Meteorological Organization (WMO) GAW Urban Research Meteorology and Environment (GURME) programme for supporting and contributing to this review.

Klaus Schäfer is grateful for funding within the frame of the project Smart Air Quality Network by the German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI).

Tomas Halenka is grateful for funding within the activity PROGRES Q16 by the Charles University, Prague.

Vikas Singh is thanked for providing Fig. 10.

This work reflects only the authors' view, and the Innovation and Networks Executive Agency is not responsible for any use that may be made of the information it contains.

We are also thankful for the funding of NordForsk.

We wish to thank Antti Hellsten (FMI) for his useful comments on CFD modelling.

This research has been supported by the European Union's Horizon 2020 Research and Innovation programme (HEALS (grant agreement no. 603946), ICARUS (grant agreement no. 690105), SCIPPER (grant agreement no. 814893), EXHAUSTION (grant agreement no. 820655), and EMERGE (grant agreement no. 874990)), the EU LIFE financial programme through the project VEG-GAP “Vegetation for Urban Green Air Quality Plans” (LIFE18 PRE IT003), German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI; grant no. 19F2003A-F), and the funding of NordForsk under the Nordic Programme on Health and Welfare (project no. 75,007: NordicWelfAir – Understanding the link between Air pollution and Distribution of related Health Impacts and Welfare in the Nordic countries).

This paper was edited by Pedro Jimenez-Guerrero and reviewed by two anonymous referees.

Abhijith, K. V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., Broderick, B., Di Sabatino, S., and Pulvirenti, B.: Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – A review, Atmos. Environ., 162, 71–86, 2017. 

Achilleos, S., Kioumourtzoglou, M.-A., Wu, C.-D., Schwartz, J. D., Koutrakis, P., and Papatheodorou, S. I.: Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis, Environ. Int., 109, 89–100, https://doi.org/10.1016/j.envint.2017.09.010 , 2017. 

Adam, M., Schikowski, T., Carsin, A. E., Cai, Y., Jacquemin, B., Sanchez, M., Vierkötter, A., Marcon, A., Keidel, D., Sugiri, D., Al Kanani, Z., Nadif, R., Siroux, V., Hardy, R., Kuh, D., Rochat, T., Bridevaux, P.-O., Eeftens, M., Tsai, M.-Y., Villani, S., Phuleria, H. C., Birk, M., Cyrys, J., Cirach, M., Nazelle, A. d., Nieuwenhuijsen, M. J., Forsberg, B., Hoogh, K. d., Declerq, C., Bono, R., Piccioni, P., Quass, U., Heinrich, J., Jarvis, D., Pin, I., Beelen, R., Hoek, G., Brunekreef, B., Schindler, C., Sunyer, J., Krämer, U., Kauffmann, F., Hansell, A. L., Künzli, N., and Probst-Hensch, N.: Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis, Eur. Respir. J., 45, 38–50, https://doi.org/10.1183/09031936.00130014 , 2015. 

Adams, K., Greenbaum, D. S., Shaikh, R., van Erp, A. M., and Russell, A. G.: Particulate matter components, sources, and health: Systematic approaches to testing effects, J. Air Waste Manage., 65, 544–558, https://doi.org/10.1080/10962247.2014.1001884 , 2015. 

Ahangar, F., Freedman, F., and Venkatram, A.: Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps, Int. J. Env. Res. Pub. He., 16, 1252, https://doi.org/10.3390/ijerph16071252 , 2019. 

Ajtai, N., Stefanie, H., Botezan, C., Ozunu, A., Radovici, A., Dumitrache, R., Iriza-Burcă, A., Diamandi, A., and Hirtl, M.: Support tools for land use policies based on high resolution regional air quality modelling, Land Use Policy, 95, 103909, https://doi.org/10.1016/j.landusepol.2019.03.022 , 2020. 

Aleksandrov, V. V. and Stenchikov, G. I.: On the modeling of the climatic consequences of the nuclear war, The Proceeding of Appl. Mathematics, The Computing Center of the AS USSR, Moscow, 21 pp., http://climate.envsci.rutgers.edu/pdf/AleksandrovStenchikov.pdf (last access: 21 February 2022), 1983. 

Alfano, B., Barretta, L., Del Giudice, A., De Vito, S., Di Francia, G., Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., and Polichetti, T.: A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives, Sensors, 20, 6819, https://doi.org/10.3390/s20236819 , 2020. 

Alimissis, A., Philippopoulos, K., Tzanis, C. G., and Deligiorgi, D.: Spatial estimation of urban air pollution with the use of artificial neural network models, Atmos. Environ., 191, 205–213, 2018. 

Amann, M., Holland, M., Maas, R., Vandyck, T., and Saveyn, B.: Costs, benefits and economic impacts of the EU Clean Air Strategy and their implications on innovation and competitiveness, IIASA, https://ec.europa.eu/environment/air/pdf/clean_air_outlook_economic_impact_report.pdf (last access 25 February 2022), 2017. 

Analitis, A., de' Donato, F., Scortichini, M., Lanki, T., Basagana, X., Ballester, F., Astrom, C., Paldy, A., Pascal, M., Gasparrini, A., Michelozzi, P., and Katsouyanni, K.: Synergistic Effects of Ambient Temperature and Air Pollution on Health in Europe: Results from the PHASE Project, Int. J. Env. Res. Pub. He., 15, 1856, https://doi.org/10.3390/ijerph15091856 , 2018. 

Andersen, Z. J., Pedersen, M., Weinmayr, G., Stafoggia, M., Galassi, C., Jørgensen, J. T., Sommar, J. N., Forsberg, B., Olsson, D., Oftedal, B., Aasvang, G. M., Schwarze, P., Pyko, A., Pershagen, G., Korek, M., Faire, U. d., Östenson, C.-G., Fratiglioni, L., Eriksen, K. T., Poulsen, A. H., Tjønneland, A., Bräuner, E. V., Peeters, P. H., Bueno-de-Mesquita, B., Jaensch, A., Nagel, G., Lang, A., Wang, M., Tsai, M.-Y., Grioni, S., Marcon, A., Krogh, V., Ricceri, F., Sacerdote, C., Migliore, E., Vermeulen, R., Sokhi, R., Keuken, M., Hoogh, K. d., Beelen, R., Vineis, P., Cesaroni, G., Brunekreef, B., Hoek, G., and Raaschou-Nielsen, O.: Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE), Neuro-Oncology, 20, 420–432, https://doi.org/10.1093/neuonc/nox163 , 2018. 

Anderson, H. A.: Air pollution and mortality: A history, Atmos. Environ., 43, 142–152, 2009. 

Anderson, M., Salo, K., and Fridell, E.: Particle- and Gaseous Emissions from an LNG Powered Ship, Environ. Sci. Technol., 49, 12568–12575, 2015. 

Anderson, V. and Gough, W. A.: Evaluating the potential of nature-based solutions to reduce ozone, nitrogen dioxide, and carbon dioxide through a multi-type green infrastructure study in Ontario, Canada, City and Environment Interactions, 6, 100043, https://doi.org/10.1016/j.cacint.2020.100043 , 2020. 

Andre, M., Sartelet, K., Moukhtar, S., Andre, J. M., and Redaelli, M.: Diesel, petrol or electric vehicles: What choices to improve urban air quality in the Ile-de-France region? A simulation platform and case study, Atmos. Environ., 241, 117752, https://doi.org/10.1016/j.atmosenv.2020.117752 , 2020. 

Andronopoulos, S., Bartzis, J. G., Efthimiou, G. C., and Venetsanos, A. G.: Puff-dispersion variability assessment through Lagrangian and Eulerian modelling based on the JU2003 campaign, Bound.-Lay. Meteorol., 171, 395–422, 2019. 

Antonsen, S., Mok, P. L. H., Webb, R. T., Mortensen, P. B., McGrath, J. J., Agerbo, E., Brandt, J., Geels, C., Christensen, J. H., and Pedersen, C. B.: Exposure to air pollution during childhood and risk of developing schizophrenia: a national cohort study, The Lancet, 4, E64–E73, https://doi.org/10.1016/s2542-5196(20)30004-8 , 2020. 

Aron, R. H.: Forecasting high level oxidant concentrations in the Los Angeles basin, J. Air Pollut. Control Assoc., 20, 1227–1228, 1980. 

Aulinger, A., Matthias, V., and Quante, M.: An Approach to Temporally Disaggregate Benzo(a)pyrene Emissions and Their Application to a 3D Eulerian Atmospheric Chemistry Transport Model, Water Air Soil Poll., 216, 643–655, 2011. 

Aulinger, A., Matthias, V., Zeretzke, M., Bieser, J., Quante, M., and Backes, A.: The impact of shipping emissions on air pollution in the greater North Sea region – Part 1: Current emissions and concentrations, Atmos. Chem. Phys., 16, 739–758, https://doi.org/10.5194/acp-16-739-2016 , 2016. 

Aunan, K., Ma, Q., Lund, M. T., and Wang, S.: Population-weighted exposure to PM 2.5 pollution in China: An integrated approach, Environ. Int., 120, 111–120, https://doi.org/10.1016/j.envint.2018.07.042 , 2018. 

Bachmann, T. M.: Considering environmental costs of greenhouse gas emissions for setting a CO 2 tax: A review, Sci. Total Environ., 720, 137524, https://doi.org/10.1016/j.scitotenv.2020.137524 , 2020. 

Backes, A., Aulinger, A., Bieser, J., Matthias, V., and Quante, M.: Ammonia emissions in Europe, part I: Development of a dynamical ammonia emission inventory, Atmos. Environ., 131, 55–66, 2016. 

Badeke, R., Matthias, V., and Grawe, D.: Parameterizing the vertical downward dispersion of ship exhaust gas in the near field, Atmos. Chem. Phys., 21, 5935–5951, https://doi.org/10.5194/acp-21-5935-2021 , 2021. 

Badia, A., Segura, R., Gilabert, J., Ventura, S., Vidal, V., and Villalba, G.: Air quality modeling study using wrf-chem over Barcelona, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Bai, L., Wang, J., Ma, X., and Lu, H.: Air Pollution Forecasts: An Overview, Int. J. Env. Res. Pub. He., 15, 780, https://doi.org/10.3390/ijerph15040780 , 2018. 

Baklanov, A. and Zhang, Y.: Advances in air quality modeling and forecasting, Global Transitions, 2, 261–270, https://doi.org/10.1016/j.glt.2020.11.001 , 2020. 

Baklanov, A., Lawrence, M., Pandis, S., Mahura, A., Finardi, S., Moussiopoulos, N., Beekmann, M., Laj, P., Gomes, L., Jaffrezo, J.-L., Borbon, A., Coll, I., Gros, V., Sciare, J., Kukkonen, J., Galmarini, S., Giorgi, F., Grimmond, S., Esau, I., Stohl, A., Denby, B., Wagner, T., Butler, T., Baltensperger, U., Builtjes, P., van den Hout, D., van der Gon, H. D., Collins, B., Schluenzen, H., Kulmala, M., Zilitinkevich, S., Sokhi, R., Friedrich, R., Theloke, J., Kummer, U., Jalkinen, L., Halenka, T., Wiedensholer, A., Pyle, J., and Rossow, W. B.: MEGAPOLI: concept of multi-scale modelling of megacity impact on air quality and climate, Adv. Sci. Res., 4, 115–120, https://doi.org/10.5194/asr-4-115-2010 , 2010. 

Baklanov, A., Mestayer, P. G., Clappier, A., Zilitinkevich, S., Joffre, S., Mahura, A., and Nielsen, N. W.: Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description, Atmos. Chem. Phys., 8, 523–543, https://doi.org/10.5194/acp-8-523-2008 , 2008. 

Baklanov, A., Mahura, A., Grimmond, S., and Athanassiadou, M.: Meteorological and Air Quality Models for Urban Areas, Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, 184 pp., https://doi.org/10.1007/978-3-642-00298-4 , 2009. 

Baklanov, A., Mahura, A., and Sokhi, R.: Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-13980-2 , 2011. 

Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014 , 2014. 

Baklanov, A., Molina, L. T., and Gauss, M.: Megacities, air quality and climate, Atmos. Environ., 126, 235–249, https://doi.org/10.1016/j.atmosenv.2015.11.059 , 2016. 

Baklanov, A., Brunner, D., Carmichael, G., Flemming, J., Freitas, S., Gauss, M., Hov, Ø., Mathur, R., Schlünzen, K., Seigneur, C., and Vogel, B.: Key Issues for Seamless Integrated Chemistry-Meteorology Modeling, B. Am. Meteorol. Soc., 98, 2285–2292, https://doi.org/10.1175/bams-d-15-00166.1 , 2018a. 

Baklanov, A., Grimmond, C. S. B., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R. K., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Climate, 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004 , 2018b. 

Baldasano, J. M.: COVID-19 lockdown effects on air quality by NO 2 in the cities of Barcelona and Madrid (Spain), Sci. Total Environ., 741, 140353, https://doi.org/10.1016/j.scitotenv.2020.140353 , 2020. 

Baldasano, J. M., Güereca, L. P., López, E., Gassó, S., and Jiménez-Guerrero, P.: Development of a high resolution (1 km × 1 km, 1 h) emission model for Spain: the High-Elective Resolution Modeling Emission System (HERMES), Atmos. Environ., 42, 7215–7233, https://doi.org/10.1016/j.atmosenv.2008.07.026 , 2008. 

Balogun, H. A., Rantala, A. K., Antikainen, H., Siddika, N., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Effects of Air Pollution on the Risk of Low Birth Weight in a Cold Climate, Appl. Sci., 10, 6399, https://doi.org/10.3390/app10186399 , 2020. 

Barbero, D., Tinarelli, G., Silibello, C., et al.: A microscale hybrid modelling system to assess the air quality over a large portion of a large European city, Atmos. Environ., 264, 118656, https://doi.org/10.1016/j.atmosenv.2021.118656 , 2021. 

Barmpas, F., Tsegas, G., Moussiopoulos, N., and Chourdakis, E.: Interpreting measurements from air quality sensor networks: data assimilation and physical modelling, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 112, https://doi.org/10.18745/pb.22217 , 2020. 

Barré, J., Petetin, H., Colette, A., Guevara, M., Peuch, V.-H., Rouil, L., Engelen, R., Inness, A., Flemming, J., Pérez García-Pando, C., Bowdalo, D., Meleux, F., Geels, C., Christensen, J. H., Gauss, M., Benedictow, A., Tsyro, S., Friese, E., Struzewska, J., Kaminski, J. W., Douros, J., Timmermans, R., Robertson, L., Adani, M., Jorba, O., Joly, M., and Kouznetsov, R.: Estimating lockdown-induced European NO 2 changes using satellite and surface observations and air quality models, Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021 , 2021. 

Bartzis, J., Wolkoff, P., Stranger, M., Efthimiou, G., Tolis, E. I., Maes, F., Nørgaard, A. W., Ventura, G., Kalimeri, K. K., Goelen, E., and Fernandes, O.: On organic emissions testing from indoor consumer products' use, J. Hazard. Mater., 285, 37–45, https://doi.org/10.1016/j.jhazmat.2014.11.024 , 2015. 

Bartzis, J. G., Andronopoulos, S., and Efthimiou, G. C.: Simplified approaches in quantifying exposure statistical behaviour due to airborne hazardous releases of short time duration, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, https://doi.org/10.18745/pb.22217 , 2020. 

Bauer, S. E., Im, U., Mezuman, K., and Gao, C. Y.: Desert dust, industrialization and agricultural fires: Health impacts of outdoor air pollution in Africa, J. Geophys. Res.-Atmos., 124, 4104–4120, https://doi.org/10.1029/2018JD029336 , 2019. 

Baumol, W. J.: On Taxation and the Control of Externalities, Am. Econ. Rev., 62, 307–322, 1972. 

Baumol, W. J. and Oates, W. E.: The Use of Standards and Prices for Protection of the Environment, Swed. J. Econ., 73, 42–54, https://doi.org/10.2307/3439132 , 1971. 

Beddows, D. C. S. and Harrison, R. M.: PM 10 and PM 2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles, Atmos. Environ., 244, 117886, https://doi.org/10.1016/j.atmosenv.2020.117886 , 2021. 

Beekmann, M., Prévôt, A. S. H., Drewnick, F., Sciare, J., Pandis, S. N., Denier van der Gon, H. A. C., Crippa, M., Freutel, F., Poulain, L., Ghersi, V., Rodriguez, E., Beirle, S., Zotter, P., von der Weiden-Reinmüller, S.-L., Bressi, M., Fountoukis, C., Petetin, H., Szidat, S., Schneider, J., Rosso, A., El Haddad, I., Megaritis, A., Zhang, Q. J., Michoud, V., Slowik, J. G., Moukhtar, S., Kolmonen, P., Stohl, A., Eckhardt, S., Borbon, A., Gros, V., Marchand, N., Jaffrezo, J. L., Schwarzenboeck, A., Colomb, A., Wiedensohler, A., Borrmann, S., Lawrence, M., Baklanov, A., and Baltensperger, U.: In situ, satellite measurement and model evidence on the dominant regional contribution to fine particulate matter levels in the Paris megacity, Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015 , 2015. 

Beelen, R., Hoek, G., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P. H., Nieuwenhuijsen, M. J., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Marcon, A., Vartiainen, E., Lanki, T., Yli-Tuomi, T., Oftedal, B., Schwarze, P. E., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Overvad, K., Sørensen, M., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, H. B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K. d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Jaensch, A., Ineichen, A., Tsai, M.-Y., Schaffner, E., Probst-Hensch, N. M., Schindler, C., Ragettli, M. S., Vilier, A., Clavel-Chapelon, F., Declercq, C., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Katsoulis, M., Trichopoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Sokhi, R. S., Vineis, P., and Brunekreef, B.: Natural-Cause Mortality and Long-Term Exposure to Particle Components: An Analysis of 19 European Cohorts within the Multi-Center ESCAPE Project, Environ. Health Persp., 123, 525–533, https://doi.org/10.1289/ehp.1408095 , 2015. 

Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P., Nieuwenhuijsen, M., Vineis, P., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Oudin, A., Forsberg, B., Modig, L., Havulinna, A. S., Lanki, T., Turunen, A., Oftedal, B., Nystad, W., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Overvad, K., Ellermann, T., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K. d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Ineichen, A., Schaffner, E., Probst-Hensch, N., Künzli, N., Schindler, C., Schikowski, T., Adam, M., Phuleria, H., Vilier, A., Clavel-Chapelon, F., Declercq, C., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Katsoulis, M., Trichopoulou, A., Brunekreef, B., and Hoek, G.: Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project, Lancet, 383, 785–795, https://doi.org/10.1016/s0140-6736(13)62158-3 , 2014. 

Belis, C. A., Pikridas, M., Lucarelli, F., Petralia, E., Cavalli, F., Calzolai, G., Berico, M., and Sciare, J.: Source apportionment of fine PM by combining high time resolution organic and inorganic chemical composition datasets, Atmos. Environ. X, 3, 100046, https://doi.org/10.1016/j.aeaoa.2019.100046 , 2019. 

Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J.L., Kuenen, J., Denier van Der Gon, H., Reizer, M., Riffault, V., Alleman, L.Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I., Besombes, J.-L., Bove, M.C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I., Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M.G., Gilardoni, S., Golly, B., Hellebust, S., Hopke, P.K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A.,, and Yubero, E.: Evaluation of receptor and chemical transport models for PM 10 source apportionment, Atmos. Environ. X, 5, 100053, https://doi.org/10.1016/j.aeaoa.2019.100053 , 2020. 

Benedetti, A. and Vitart, F.: Can the Direct Effect of Aerosols Improve Subseasonal Predictability?, Mon. Weather Rev., 146, 3481–3498, https://doi.org/10.1175/MWR-D-17-0282.1 , 2018. 

Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018 , 2018. 

Benešová, N., Belda, M., Eben, K., Geletič, J., Huszár, P., Juruš, P., Krč, P., Resler, J., and Vlček, O.: New open source emission processor for air quality models, in: Proceedings of Abstracts 11th International Conference on Air Quality Science and Application, edited by: Sokhi, R., Tiwari, P. R., Gállego, M. J., Craviotto Arnau, J. M., Castells Guiu, C., and Singh, V., University of Hertfordshire, UK, p. 27, https://doi.org/10.18745/pb.19829 , 2018. 

Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH 4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys., 18, 901–920, https://doi.org/10.5194/acp-18-901-2018 , 2018. 

Berrocal, V. J., Guan, Y., Muyskens, A., Wang, H., Reich, B. J., Mulholland, J. A., and Chang, H. H.: A comparison of statistical and machine learning methods for creating national daily maps of ambient PM 2.5 concentration, Atmos. Environ., 222, 117130, https://doi.org/10.1016/j.atmosenv.2019.117130 , 2020. 

Bi, P., Wang, J., and Hiller, J. E.: Weather: driving force behind the transmission of severe acute respiratory syndrome in China?, Intern. Med. J., 37, 550–554, https://doi.org/10.1111/j.1445-5994.2007.01358.x , 2007. 

Bickel, P. and Friedrich, R. (Eds.): ExternE – Externalities of Energy, Methodology 2005 Update, EUR 21951, https://doi.org/10.18419/opus-11923 , 2005. 

Bieser, J., Aulinger, A., Matthias, V., Quante, M., and Builtjes, P.: SMOKE for Europe – adaptation, modification and evaluation of a comprehensive emission model for Europe, Geosci. Model Dev., 4, 47–68, https://doi.org/10.5194/gmd-4-47-2011 , 2011a. 

Bieser, J., Aulinger, A., Matthias, V., Quante, M., and Denier van der Gon, H. A. C.: Vertical emission profiles for Europe based on plume rise calculations, Environ. Pollut., 159, 2935–2946, https://doi.org/10.1016/j.envpol.2011.04.030 , 2011b. 

BImSchV: Erste Verordnung zur Durchführung des Bundes-Immissionsschutzgesetzes (Verordnung über kleine und mittlere Feuerungsanlagen) – 1. BImSchV, https://www.gesetze-im-internet.de/bimschv_1_2010/BJNR003800010.html (last access: 25 February 2022), 2021. 

Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015 , 2015. 

Borge, R., Lumbreras, J., Pérez, J., de la Paz, D., Vedrenne, M., de Andrés, J. M., and Rodríguez, M. E.: Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain), Sci. Total Environ., 466-467, 809–819, https://doi.org/10.1016/j.scitotenv.2013.07.093 , 2014. 

Borken-Kleefeld, J. and Chen, Y.: New emission deterioration rates for gasoline cars – Results from long-term measurements, Atmos. Environ., 101, 58–64, https://doi.org/10.1016/j.atmosenv.2014.11.013 , 2015. 

Brandt, J., Silver, J. D., Frohn, L. M., Geels, C., Gross, A., Hansen, A. B., Hansen, K. M., Hedegaard, G. B., Skjøth, C. A., Villadsen, H., Zare, A., and Christensen, J. H.: An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport, Atmos. Environ., 53, 156–176, https://doi.org/10.1016/j.atmosenv.2012.01.011 , 2012. 

Brandt, J., Silver, J. D., Christensen, J. H., Andersen, M. S., Bønløkke, J. H., Sigsgaard, T., Geels, C., Gross, A., Hansen, A. B., Hansen, K. M., Hedegaard, G. B., Kaas, E., and Frohn, L. M.: Contribution from the ten major emission sectors in Europe and Denmark to the health-cost externalities of air pollution using the EVA model system – an integrated modelling approach, Atmos. Chem. Phys., 13, 7725–7746, https://doi.org/10.5194/acp-13-7725-2013 , 2013. 

Brattich, E., Bracci, A., Zappi, A., Morozzi, P., Di Sabatino, S., Porcù, F., Di Nicola, F., and Tositti, L.: How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations, Sensors, 20, 3073, https://doi.org/10.3390/s20113073 , 2020. 

Briggs, D. J.: A framework for integrated environmental health impact assessment of systemic risks, Environ. Health, 7, 61, https://doi.org/10.1186/1476-069x-7-61 , 2008. 

Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A., Peters, A., Siscovick, D., Smith, S. C., Whitsel, L., and Kaufman, J. D.: Particulate Matter Air Pollution and Cardiovascular Disease, Circulation, 121, 2331–2378, https://doi.org/10.1161/CIR.0b013e3181dbece1 , 2010. 

Brousse, O., Martilli, A., Foley, M., Mills, G., and Bechtel, B.: WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid, Urban Climate, 17, 116–134, https://doi.org/10.1016/j.uclim.2016.04.001 , 2016. 

Brunekreef, B. and Holgate, S. T.: Air pollution and health, Lancet, 360, 1233–1242, https://doi.org/10.1016/s0140-6736(02)11274-8 , 2002. 

Buccolieri, R., Santiago, J. L., and Martilli, A.: CFD modelling: The most useful tool for developing mesoscale urban canopy parameterizations, Build. Simul., 14, 407–419, https://doi.org/10.1007/s12273-020-0689-z , 2021. 

Budde, M., Riedel, T., Beigl, M., Schäfer, K., Emeis, S., Cyrys, J., Schnelle-Kreis, J., Philipp, A., Ziegler, V., Grimm, H., and Gratza, T.: SmartAQnet – Remote and In-Situ Sensing of Urban Air Quality, in: Remote Sensing of Clouds and the Atmosphere XXII, Bellingham, WA, USA, 6 October 2017, edited by: Comerón, A., Kassianov, E. I., Schäfer, K., Picard, R. H., Weber, K., SPIE, https://doi.org/10.1117/12.2282698 , 2017. 

Burgués, J. and Marco, S.: Environmental chemical sensing using small drones: A review, Sci. Total Environ., 748, 141172, https://doi.org/10.1016/j.scitotenv.2020.141172 , 2020. 

Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A., Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S., Coggins, J., Di, Q., Brunekreef, B., Frostad, J., Lim, S. S., Kan, H., Walker, K. D., Thurston, G. D., Hayes, R. B., Lim, C. C., Turner, M. C., Jerrett, M., Krewski, D., Gapstur, S. M., Diver, W. R., Ostro, B., Goldberg, D., Crouse, D. L., Martin, R. V., Peters, P., Pinault, L., Tjepkema, M., van Donkelaar, A., Villeneuve, P. J., Miller, A. B., Yin, P., Zhou, M., Wang, L., Janssen, N. A. H., Marra, M., Atkinson, R. W., Tsang, H., Quoc Thach, T., Cannon, J. B., Allen, R. T., Hart, J. E., Laden, F., Cesaroni, G., Forastiere, F., Weinmayr, G., Jaensch, A., Nagel, G., Concin, H., and Spadaro, J. V.: Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter, P. Natl. Acad. Sci. USA, 115, 9592–9597, https://doi.org/10.1073/pnas.1803222115 , 2018. 

Cai, Y., Zijlema, W. L., Doiron, D., Blangiardo, M., Burton, P. R., Fortier, I., Gaye, A., Gulliver, J., Hoogh, K. d., Hveem, K., Mbatchou, S., Morley, D. W., Stolk, R. P., Elliott, P., Hansell, A. L., and Hodgson, S.: Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach, Eur. Respir. J., 49, 1502127, https://doi.org/10.1183/13993003.02127-2015 , 2017. 

Cai, Y., Hodgson, S., Blangiardo, M., Gulliver, J., Morley, D., Fecht, D., Vienneau, D., de Hoogh, K., Key, T., Hveem, K., Elliott, P., and Hansell, A. L.: Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts, Environ. Int., 114, 191–201, https://doi.org/10.1016/j.envint.2018.02.048 , 2018. 

Campbell, P., Zhang, Y., Yahya, K., Wang, K., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J. L., Jimenez Guerrero, P., Baro, R., and Makar, P.: A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) phase 2 over North America: Part I. Indicators of the sensitivity of O 3 and PM 2.5 formation regimes, Atmos. Environ., 115, 569–586, https://doi.org/10.1016/j.atmosenv.2014.12.026 , 2015. 

Cao, Y., Chen, M., Dong, D., Xie, S., and Liu, M.: Environmental pollutants damage airway epithelial cell cilia: Implications for the prevention of obstructive lung diseases, Thorac. Cancer, 11, 505–510, https://doi.org/10.1111/1759-7714.13323 , 2020. 

Carbajal-Hernández, J. J., Luis P. Sánchez-Fernández, J. A. C.-O., and Martínez-Trinidad, J. F.: Assessment and prediction of air quality using fuzzy logic and autoregressive Models, Atmos. Environ., 60, 37–50, 2012. 

Carmichael, G. R., Sandu, A., Chai, T., Daescu, D., Constantinescu, E., and Tang, Y.: Predicting air quality: Improvements through advanced methods to integrate models and measurements, J. Comput. Phys., 227, 3540–3571, 2008. 

Cecinato, A., Guerriero, E., Balducci, C., and Muto, V.: Use of the PAH fingerprints for identifying pollution sources, Urban Climate, 10, 630–643, https://doi.org/10.1016/j.uclim.2014.04.004 , 2014. 

Cesaroni, G., Forastiere, F., Stafoggia, M., Andersen, Z. J., Badaloni, C., Beelen, R., Caracciolo, B., Faire, U. d., Erbel, R., Eriksen, K. T., Fratiglioni, L., Galassi, C., Hampel, R., Heier, M., Hennig, F., Hilding, A., Hoffmann, B., Houthuijs, D., Jockel, K.-H., Korek, M., Lanki, T., Leander, K., Magnusson, P. K. E., Migliore, E., Ostenson, C.-G., Overvad, K., Pedersen, N. L., J, J. P., Penell, J., Pershagen, G., Pyko, A., Raaschou-Nielsen, O., Ranzi, A., Ricceri, F., Sacerdote, C., Salomaa, V., Swart, W., Turunen, A. W., Vineis, P., Weinmayr, G., Wolf, K., de Hoogh, K., Hoek, G., Brunekreef, B., and Peters, A.: Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project, BMJ, 348, f7412, https://doi.org/10.1136/bmj.f7412 , 2014. 

Chapizanis, D., Karakitsios, S., Gotti, A., and Sarigiannis, D. A.: Assessing personal exposure using Agent Based Modelling informed by sensors technology, Environ. Res., 192, 110141, https://doi.org/10.1016/j.envres.2020.110141 , 2021. 

Chatzimichailidis, A. C., Argyropoulos, C. D., Assael, M. J., and Kakosimos, K. E.: Using the K-means clustering method to identify flow patterns in a street canyon, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 75, https://doi.org/10.18745/pb.22217 , 2020. 

Chen, G., Zhang, W., Li, S., Zhang, Y., Williams, G., Huxley, R., Ren, H., Cao, W., and Guo, Y.: The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: A multi-city study, Environ. Int., 98, 82–88, https://doi.org/10.1016/j.envint.2016.10.004 , 2017. 

Ching, J. K. S.: A perspective on urban canopy layer modeling for weather, climate, and air quality applications, Urban Climate, 3, 13–39, https://doi.org/10.1016/j.uclim.2013.02.001 , 2013. 

Christodoulou, A., Sauvage, S., Afif, C., Sarda-Estève, R., Stavroulas, I., Pikridas, M., Unga, F., Oikonomou, K., Iakovides, M., and Sciare, J.: Source apportionment of organic carbon at an urban site of the eastern Mediterranean during wintertime, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 25, https://doi.org/10.18745/pb.22217 , 2020. 

Churkina, G., Kuik, F., Bonn, B., Lauer, A., Grote, R., Tomiak, K., and Butler, T. M.: Effect of VOC Emissions from Vegetation on Air Quality in Berlin during a Heatwave, Environ. Sci. Technol., 51, 6120–6130, https://doi.org/10.1021/acs.est.6b06514 , 2017. 

Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017 , 2017. 

Concas, F., Mineraud, J., Lagerspetz, E., Varjonen, S., Liu, X., Puolamäki, K., Nurmi, P., and Tarkoma, S.: Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis, ACM T. Sensor Network., 17, 20, https://doi.org/10.1145/3446005 , 2021. 

Conticini, E., Frediani, B., and Caro, D.: Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?, Environ. Pollut., 261, 114465, https://doi.org/10.1016/j.envpol.2020.114465 , 2020. 

Coulombel, N., Dablanc, L., Gardrat, M., and Koning, M.: The environmental social cost of urban road freight: Evidence from the Paris region, Transport. Res. D-Tr. E., 63, 514–532, 2019. 

Cremona, G., Finardi, S., Mircea, M., Pepe, N., and Silibello, C.: Biogenic Emissions from Urban Vegetation: Impact of Detailed Inventories in Different European Cities, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 162, https://doi.org/10.18745/pb.22217 , 2020. 

Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2 , 2020. 

Crutzen, P. J. and Birks, J. W.: The Atmosphere after a Nuclear War: Twilight at Noon, Ambio, 11, 114–125, https://www.jstor.org/stable/4312777 (last access: 3 June 2021), 1982. 

Cui, L. and Shi, J.: Urbanization and its environmental effects in Shanghai, China, Urban Climate, 2, 1–15, https://doi.org/10.1016/j.uclim.2012.10.008 , 2012. 

Cui, Y., Zhang, Z.-F., Froines, J., Zhao, J., Wang, H., Yu, S.-Z., and Detels, R.: Air pollution and case fatality of SARS in the People's Republic of China: an ecologic study, Environ. Health, 2, 15, https://doi.org/10.1186/1476-069x-2-15 , 2003. 

Dai, L., Zanobetti, A., Koutrakis, P., and Schwartz, J. D.: Associations of fine particulate matter species with mortality in the United States: A multicity time-series analysis, Environ. Health Persp., 122, 837–842, https://doi.org/10.1289/ehp.1307568 , 2014. 

DBEIS: Valuation of energy use and greenhouse gas, Supplementary guidance to the HM Treasury Green Book on Appraisal and Evaluation in Central Government, Data tables 1 to 19: supporting the toolkit and the guidance, Department of Business, Energy and Industrial Strategy, London, 2019. 

Delle Monache, L., Wilczak, J., Mckeen, S., Grell, G., Pagowski, M., Peckham, S., Stull, R., Mchenry, J., and Mcqueen, J.: A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone, Tellus B, 60, 238–249, https://doi.org/10.1111/j.1600-0889.2007.00332.x , 2008. 

Denby, B. R., Sundvor, I., Johansson, C., Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Kauhaniemi, M., and Omstedt, G.: A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions (NORTRIP). Part 2: Surface moisture and salt impact modelling, Atmos. Environ., 81, 485–503, 2013. 

Denier van der Gon, H., Hendriks, C., Kuenen, J., Segers, A., and Visschedijk, A.: Description of current temporal emission patterns and sensitivity of predicted AQ for temporal emission patterns, EU, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf , (last access 25 February 2022), 2011. 

Desaigues, B., Ami, D., Bartczak, A., Braun Kohlová, M., Chilton, S., Mikołaj Czajkowski, M., Farreras, V., Hunt, A., Hutchinson, M., Jeanrenaud, C., Kaderjack, P., Máca, V., Markiewicz, O., Markowska, A., Metcalf, H., Navrud, S., Seested Nielsen, J., Ortiz, R., Pellegrini, S., Rabl, A., Riera, P., Scasny, M., Stoeckl, M., Szánto, R., and Urban, J.: Economic Valuation of Air Pollution Mortality: A 9-Country Contingent Valuation Survey of a Value of a Life Year (VOLY), Ecol. Indic., 11, 902–910, 2011. 

Dessimond, B., Annesi-Maesano, I., Pepin, J.-L., Srairi, S., and Pau, G.: Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project, Sensors, 21, 1876, https://doi.org/10.3390/s21051876 , 2021. 

Dias, D., Tchepel, O., and Antunes, A. P.: Integrated modelling approach for the evaluation of low emission zones, J. Environ. Manage., 177, 253–263, https://doi.org/10.1016/j.jenvman.2016.04.031 , 2016. 

Diémoz, H., Tombolato, I., Zublena, M., Magri, T., and Ferrero, L.: The impact of biomass burning emissions on PM concentration in the Greater Alpine region, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 26, https://doi.org/10.18745/pb.22217 , 2020. 

DNV-GL: Maritime Forecast To 2050, Energy Transition Outlook, DNV GL – Maritime, 118 pp., 2019. 

Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., and Speizer, F. E.: An Association between Air Pollution and Mortality in Six U.S. Cities, New Engl. J. Med., 329, 1753–1759, https://doi.org/10.1056/nejm199312093292401 , 1993. 

Dorber, M., Kuipers, K., and Verones, F.: Global characterization factors for terrestrial biodiversity impacts of future land inundation in Life Cycle Assessment, Sci. Total Environ., 712, 134582, https://doi.org/10.1016/j.scitotenv.2019.134582 , 2020. 

Doulgeris, S., Toumasatos, Z., Raptopoulos, A., Kontses, A., Dimaratos, A., Kolokotronis, D., and Samaras, Z.: Experimental assessment of the power management and pollutant emissions of plug-in hybrid vehicles, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Duvall, R. M., Hagler, G. S. W., Clements, A. L., Benedict, K., Barkjohn, K., Kilaru, V., Hanley, T., Watkins, N., Kaufman, A., Kamal, A., Reece, S., Fransioli, P., Gerboles, M., Gillerman, G., Habre, R., Hannigan, M., Ning, Z., Papapostolou, V., Pope, R., Quintana, P. J. E., and Lam Snyder, J.: Deliberating Performance Targets: Follow-on workshop discussing PM 10 , NO 2 , CO, and SO 2 air sensor targets, Atmos. Environ., 246, 118099, https://doi.org/10.1016/j.atmosenv.2020.118099 , 2021. 

EC: Impact Assessment – Annex to the Communication on Thematic Strategy on Air Pollution and the Directive on Ambient Air Quality and Cleaner Air for Europe, Commission of the European Communities, SEC (2005) 1133, https://ec.europa.eu/environment/archives/cafe/pdf/ia_report_en050921_final.pdf (last access: 22 February 2022), 2005. 

EC: Communication from the Commission COM(2019) 640 final: The European Green Deal, https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en (last access: 25 February 2022), 2019. 

EC: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: An EU-wide assessment of National Energy and Climate Plans, Driving forward the green transition and promoting economic recovery through integrated energy and climate planning, https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2020)564&lang=en (last access: 22 February 2022), 2020. 

EC: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: The Second Clean Air Outlook, https://op.europa.eu/en/publication-detail/-/publication/453fbba1-519a-11eb-b59f-01aa75ed71a1/language-en/format-PDF/source-225358842 (last access: 22 February 2022), 2021. 

EEA: Air quality in Europe – 2019 report, European Environment Agency, https://doi.org/10.2800/02825 , 2019a. 

EEA: Contribution of the transport sector to total emissions of the main air pollutants, EEA, https://www.eea.europa.eu/data-and-maps/daviz/contribution-of-the-transport-sector-6#tab-chart_4 (last access: 22 February 2022), 2019b. 

EEA: Air quality in Europe: 2020 report, European Environmental Agency, Publications Office, https://doi.org/10.2800/602793 , 2020a. 

EEA: European Union emission inventory report 1990–2018 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP), European Environment Agency, Copenhagen, Denmark, 1990–2018, https://doi.org/10.2800/233574 , 2020b. 

EIONET: EIONET Central Data Repository – Data for Germany, European Environment Agency, https://cdr.eionet.europa.eu/de (last access: 22 February 2022), 2019. 

Elessa Etuman, A. and Coll, I.: OLYMPUS v1.0: development of an integrated air pollutant and GHG urban emissions model – methodology and calibration over greater Paris, Geosci. Model Dev., 11, 5085–5111, https://doi.org/10.5194/gmd-11-5085-2018 , 2018. 

Elessa Etuman, A., Coll, I., Makni, I., and Benoussaid, T.: Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris, Atmos. Environ., 239, 117661, https://doi.org/10.1016/j.atmosenv.2020.117661 , 2020. 

Ellermann, T., Nygaard, J., Nøjgaard, J. K., Nordstrøm, C., Brandt, J., Christensen, J., Ketzel, M., Massling, A., Bossi, R., Frohn, L. M., Geels, C., and Jensen, S. S.: The Danish Air Quality Monitoring Programme. Annual Summary for 2018, Aarhus University, DCE – Danish Centre for Environment and Energy, 83 pp., ISBN: 978-87-7156-293-4, 2018. 

EMEP/EEA: Chapter 7: Spatial mapping of emissions, in: EMEP/EEA air pollutant emission inventory guidebook 2019: Technical guidance to prepare national emission inventories, EEA report No 13/2019, https://doi.org/10.2800/293657 , 2019. 

Engemann, K., Svenning, J.-C., Arge, L., Brandt, J., Geels, C., Mortensen, P. B., Plana-Ripoll, O., Tsirogiannis, C., and Pedersen, C. B.: Natural surroundings in childhood are associated with lower schizophrenia rates, Schizophr. Res., 216, 488–495, https://doi.org/10.1016/j.schres.2019.10.012 , 2020. 

English, P., Amato, H., Bejarano, E., Carvlin, G., Lugo, H., Jerrett, M., King, G., Madrigal, D., Meltzer, D., Northcross, A., Olmedo, L., Seto, E., Torres, C., Wilkie, A., and Wong, M.: Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA, Sensors, 20, 3031, https://doi.org/10.3390/s20113031 , 2020. 

Esau, I., Wolf, T., and Pettersson, L.: High-resolution assessment of urban air quality with a 3D turbulence resolving model (PALM), in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R.S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, 9–13, https://doi.org/10.18745/pb.22217 , 2020. 

ExternE: ExternE – External Costs of Energy, Universitaet Stuttgart, https://www.ExternE.info (last access: 22 February 2022), 2012. 

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016 , 2016. 

Faber, J., Hanayama, S., Zhang, S., Pereda, P., Comer, B., Hauerhof, E., Schim van der Loeff, W., Smith, T., Zhang, Y., Kosaka, H., Adachi, M., Bonello, J., Galbraith, C., Gong, Z., Hirata, K., Hummels, D., Kleijn, A., Lee, D., Liu, Y., Lucchesi, A., Mao, X., Muraoka, E., Osipova, L., Qian, H., Rutherford, D., Suárez de la Fuente, S., Yuan, H., Velandia Perico, C., Wu, L., Sun, D., Yoo, D., and Xing, H.: The Fourth IMO GHG Study, London, UK, 2020. 

Fairburn, J., Schüle, S. A., Dreger, S., Karla Hilz, L., and Bolte, G.: Social Inequalities in Exposure to Ambient Air Pollution: A Systematic Review in the WHO European Region, Int. J. Env. Res. Pub. He., 16, 3127, https://doi.org/10.3390/ijerph16173127 , 2019. 

Falcon-Rodriguez, C. I., Osornio-Vargas, A. R., Sada-Ovalle, I., and Segura-Medina, P.: Aeroparticles, Composition, and Lung Diseases, Front. Immunol., 7, 3, https://doi.org/10.3389/fimmu.2016.00003 , 2016. 

Fallah-Shorshani, M., Shekarrizfard, M., and Hatzopoulou, M.: Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near-road air pollution, Atmos. Environ., 153, 21–31, https://doi.org/10.1016/j.atmosenv.2017.01.006 , 2017. 

Fallmann, J., Forkel, R., and Emeis, S.: Secondary effects of urban heat island mitigation measures on air quality, Atmos. Environ., 125, 199–211, 2016. 

Fameli, K.-M. and Assimakopoulos, V. D.: Residential heating in Athens, Greece: emissions and important parameters, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 27, https://doi.org/10.18745/pb.22217 , 2020. 

Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges, J. Atmos. Sci., 73, 4221–4252, 2016. 

Farmer, D. K., Vance, M. E., Abbatt, J. P. D., Abeleira, A., Alves, M. R., Arata, C., Boedicker, E., Bourne, S., Cardoso-Saldaña, F., Corsi, R., DeCarlo, P. F., Goldstein, A. H., Grassian, V. H., Hildebrandt Ruiz, L., Jimenez, J. L., Kahan, T. F., Katz, E. F., Mattila, J. M., Nazaroff, W. W., Novoselac, A., O'Brien, R. E., Or, V. W., Patel, S., Sankhyan, S., Stevens, P. S., Tian, Y., Wade, M., Wang, C., Zhou, S., and Zhou, Y.: Overview of HOMEChem: House Observations of Microbial and Environmental Chemistry, Environ. Sci.-Proc. Imp., 21, 1280–1300, https://doi.org/10.1039/c9em00228f , 2019. 

Feng, L., Yang, T., Wang, D., Wang, Z., Pan, Y., Matsui, I., Chen, Y., Xin, J., and Huang, H.: Identify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods, Environmental Research Communications, 2, 021005, https://doi.org/10.1088/2515-7620/ab7634 , 2020. 

Filella, M.: Nanomaterials, in: Comprehensive Sampling and Sample Preparation, Elsevier, 109–124, https://doi.org/10.1016/b978-0-12-381373-2.00032-6 , 2012. 

Finardi, S., Radice, P., Cecinato, A., Gariazzo, C., Gherardi, M., and Romagnoli, P.: Seasonal variation of PAHs concentration and source attribution through diagnostic ratios analysis, Urban Climate, 22, 19–34, https://doi.org/10.1016/j.uclim.2015.12.001 , 2017. 

Finardi, S., Agrillo, G., Baraldi, R., Calori, G., Carlucci, P., Ciccioli, P., D'Allura, A., Gasbarra, D., Gioli, B., Magliulo, V., Radice, P., Toscano, P., and Zaldei, A.: Atmospheric Dynamics and Ozone Cycle during Sea Breeze in a Mediterranean Complex Urbanized Coastal Site, J. Appl. Meteorol. Clim., 57, 1083–1099, https://doi.org/10.1175/jamc-d-17-0117.1 , 2018. 

Firket, J.: Fog along the Meuse Valley, T. Faraday Soc., 32, 1192–1197, 1936. 

Fisher, K. and Gershuny, J.: Multinational Time Use Study, User's Guide and Documentation – Version 9, Centre for Time Use Research, https://www.timeuse.org/sites/default/files/9727/mtus-user-guide-r9-february-2016.pdf (last access: 25 February 2022), 2016. 

Fisher, B. E. A., Kukkonen, J., and Schatzmann, M.: Meteorology applied to urban air pollution problems COST 715, Int. J. Environ. Pollut., 16, 560–570, https://doi.org/10.1504/IJEP.2001.000650 , 2001. 

Fisher, B., Joffre, S., Kukkonen, J., Piringer, M., Rotach, M., and Schatzmann, M.: Meteorology applied to urban air pollution problems: Final report COST-715 Action, Demetra Ltd. Publ, Bulgaria, 276 pp., ISBN 954-9526-30-5, 2005. 

Fisher, B., Kukkonen, J., Piringer, M., Rotach, M. W., and Schatzmann, M.: Meteorology applied to urban air pollution problems: concepts from COST 715, Atmos. Chem. Phys., 6, 555–564, https://doi.org/10.5194/acp-6-555-2006 , 2006. 

Flageul, C., Kim, Y., Ferrand, M., Bresson, R., and Carissimo, B.: Neighborhood scale air quality simulations with street network model and CFD, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 172, https://doi.org/10.18745/pb.22217 , 2020. 

Flemming, J., Inness, A., Flentje, H., Huijnen, V., Moinat, P., Schultz, M. G., and Stein, O.: Coupling global chemistry transport models to ECMWF's integrated forecast system, Geosci. Model Dev., 2, 253–265, https://doi.org/10.5194/gmd-2-253-2009 , 2009. 

Foken, T. (Ed.): Springer Handbook of Atmospheric Measurements, Springer Nature, Springer International Publishing, Cham, Germany, https://doi.org/10.1007/978-3-030-52171-4 , 2021. 

Folberth, G. A., Butler, T. M., Collins, W. J., and Rumbold, S. T.: Megacities and climate change – A brief overview, Environ. Pollut., 203, 235–242, https://doi.org/10.1016/j.envpol.2014.09.004 , 2015. 

Förster, J., Schmidt, S., Bartkowski, B., Lienhoop, N., Albert, C., and Wittmer, H.: Incorporating environmental costs of ecosystem service loss in political decision making: A synthesis of monetary values for Germany, PLOS ONE, 14, e0211419, https://doi.org/10.1371/journal.pone.0211419 , 2019. 

Fowler, D., Brimblecombe, P., Burrows, J., Heal, M. R., Grennfelt, P., Stevenson, D. S., Jowett, A., Nemitz, E., Coyle, M., Lui, X., Chang, Y., Fuller, G. W., Sutton, M. A., Klimont, Z., Unsworth, M. H., and Vieno, M.: A chronology of global air quality, Philos. T. Roy. Soc. A, 378, 20190314, https://doi.org/10.1098/rsta.2019.0314 , 2020. 

Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., and Dilara, P.: Road vehicle emission factors development: A review, Atmos. Environ., 70, 84–97, 2013. 

Friedrich, R. (Ed.): Natural and Biogenic Emissions of Environmentally Relevant Atmospheric Trace Constituents in Europe, Atmos. Environ., 43, 1377–1486, 2009. 

Friedrich, R.: Integrated Assessment of Policies for Reducing Health Impacts Caused by Air Pollution, in: Environmental Determinants of Human Health, edited by: Pacyna, J. and Pacyna, M., Springer International Publishing, Switzerland, 117–132, https://doi.org/10.1007/978-3-319-43142-0 , 2016. 

Friedrich, R. and Kuhn, A. (Eds.): Integrated Environmental Health Impact Assessment for Europe – Methods and Results of the HEIMTSA/INTARESE Common Case Study, Universitaet Stuttgart, https://doi.org/10.18419/opus-11913 , 2011. 

Friedrich, R. and Li, N.: Life-long Exposure to PM 2.5 and NO 2 and Resulting Health Effects for Population Subgroups in Europe, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 72, https://doi.org/10.18745/pb.22217 , 2020. 

Frohn, L. M., Ketzel, M., Christensen, J. H., Brandt, J., Im, U., Massling, A., Andersen, C., Plejdrup, M.S., Nielsen, O.-K., Manders, A., and Raaschou-Nielsen, O.: Modelling ultrafine particle number concentrations at address resolution in Denmark from 1979 to 2018 – Part 1: regional and urban scale modelling and evaluation, Atmos. Environ., 264, 118631, https://doi.org/10.1016/j.atmosenv.2021.118631 , 2021. 

Fulton, E. A., Boschetti, F., Sporcic, M., Jones, T., Little, L. R., Dambacher, J. M., Gray, R., Scott, R., and Gorton, R.: A multi-model approach to engaging stakeholder and modellers in complex environmental problems, Environ. Sci. Policy, 48, 44–56, https://doi.org/10.1016/j.envsci.2014.12.006 , 2015. 

Galmarini, S. and Hogrefe, C. (Eds.): Special Issue Section: Evaluating Coupled Models (AQMEII P2), Atmos. Environ., 115, 340–755, https://www.sciencedirect.com/journal/atmospheric-environment/vol/115/suppl/C#article-37 (last access: 25 February 2022), 2015. 

Galmarini, S., Bianconi, R., Appel, W., Solazzo, E., Mosca, S., Grossi, P., Moran, M., Schere, K., and Rao, S. T.: ENSEMBLE and AMET: Two systems and approaches to a harmonized, simplified and efficient facility for air quality models development and evaluation, Atmos. Environ., 53, 51–59, https://doi.org/10.1016/j.atmosenv.2011.08.076 , 2012. 

Galmarini, S., Koffi, B., Solazzo, E., Keating, T., Hogrefe, C., Schulz, M., Benedictow, A., Griesfeller, J. J., Janssens-Maenhout, G., Carmichael, G., Fu, J., and Dentener, F.: Technical note: Coordination and harmonization of the multi-scale, multi-model activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories, boundary conditions, and model output formats, Atmos. Chem. Phys., 17, 1543–1555, https://doi.org/10.5194/acp-17-1543-2017 , 2017. 

Galmarini, S., Kioutsioukis, I., Solazzo, E., Alyuz, U., Balzarini, A., Bellasio, R., Benedictow, A. M. K., Bianconi, R., Bieser, J., Brandt, J., Christensen, J. H., Colette, A., Curci, G., Davila, Y., Dong, X., Flemming, J., Francis, X., Fraser, A., Fu, J., Henze, D. K., Hogrefe, C., Im, U., Garcia Vivanco, M., Jiménez-Guerrero, P., Jonson, J. E., Kitwiroon, N., Manders, A., Mathur, R., Palacios-Peña, L., Pirovano, G., Pozzoli, L., Prank, M., Schultz, M., Sokhi, R. S., Sudo, K., Tuccella, P., Takemura, T., Sekiya, T., and Unal, A.: Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more?, Atmos. Chem. Phys., 18, 8727–8744, https://doi.org/10.5194/acp-18-8727-2018 , 2018. 

Gao, D., Godri Pollitt, K. J., Mulholland, J. A., Russell, A. G., and Weber, R. J.: Characterization and comparison of PM 2.5 oxidative potential assessed by two acellular assays, Atmos. Chem. Phys., 20, 5197–5210, https://doi.org/10.5194/acp-20-5197-2020 , 2020. 

Gao, Z., Bresson, R., Qu, Y., Milliez, M., Demunck, C., and Carissimo, B.: High resolution unsteady RANS simulation of wind, thermal effects and pollution dispersion for studying urban renewal scenarios in a neighborhood of Toulouse, Urban Climate, 23, 114–130, 2018. 

Gariazzo, C., Carlino, G., Silibello, C., Renzi, M., Finardi, S., Pepe, N., Radice, P., Forastiere, F., Michelozzi, P., Viegi, G., and Stafoggia, M.: A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data, Sci. Total Environ., 724, 138102, https://doi.org/10.1016/j.scitotenv.2020.138102 , 2020. 

GBDS: The Global Burden of Disease Study 2019, The Lancet, 396, 1129–1306, https://www.thelancet.com/journals/lancet/issue/vol396no10258/PIIS0140-6736(20)X0042-0#closeFullCover , (last access: 28 February 2022), 2020. 

Geels, C., Andersson, C., Hänninen, O., Lansø, A., Schwarze, P., Skjøth, C., and Brandt, J.: Future Premature Mortality Due to O3, Secondary Inorganic Aerosols and Primary PM in Europe — Sensitivity to Changes in Climate, Anthropogenic Emissions, Population and Building Stock, Int. J. Env. Res. Pub. He., 12, 2837–2869, https://doi.org/10.3390/ijerph120302837 , 2015. 

Geels, C., Winther, M., Andersson, C., Jalkanen, J.-P., Brandt, J., Frohn, L. M., Im, U., Leung, W., and Christensen, J. H.: EPITOME ship emissions: Projections of shipping emissions towards 2050, Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.4322247 , 2020. 

Geels, C., Winther, M., Andersson, C., Jalkanen, J.-P., Brandt, J., Frohn, L. M., Im, U., Leung, W., and Christensen, J. H.: Projections of shipping emissions and the related impact on air pollution and human health in the Nordic region, Atmos. Chem. Phys., 21, 12495–12519, https://doi.org/10.5194/acp-21-12495-2021 , 2021. 

Gehring, U., Gruzieva, O., Agius, R. M., Beelen, R., Custovic, A., Cyrys, J., Eeftens, M., Flexeder, C., Fuertes, E., Heinrich, J., Hoffmann, B., Jongste, J. C. d., Kerkhof, M., Klümper, C., Korek, M., Mölter, A., Schultz, E. S., Simpson, A., Sugiri, D., Svartengren, M., Berg, A. v., Wijga, A. H., Pershagen, G., and Brunekreef, B.: Air Pollution Exposure and Lung Function in Children: The ESCAPE Project, Environ. Health Persp., 121, 1357–1364, https://doi.org/10.1289/ehp.1306770 , 2013. 

Genc, S., Zadeoglulari, Z., Fuss, S. H., and Genc, K.: The Adverse Effects of Air Pollution on the Nervous System, J. Toxicol., 2012, 782462 , https://doi.org/10.1155/2012/782462 , 2012. 

Genz, C., Schrödner, R., Heinold, B., Henning, S., Baars, H., Spindler, G., and Tegen, I.: Estimation of cloud condensation nuclei number concentrations and comparison to in situ and lidar observations during the HOPE experiments, Atmos. Chem. Phys., 20, 8787–8806, https://doi.org/10.5194/acp-20-8787-2020 , 2020. 

Ghorani-Azam, A., Riahi-Zanjani, B., and Balali-Mood M.: Effects of air pollution on human health and practical measures for prevention in Iran, J. Res. Med. Sci., 21, 65, https://doi.org/10.4103/1735-1995.189646 , 2016. 

Gilliam, R. C., Hogrefe, C., Godowitch, J. M., Napelenok, S., Mathur, R., and Rao, S. T.: Impact of inherent meteorology uncertainty on air quality model predictions, J. Geophys. Res.-Atmos., 120, 12259–12280, https://doi.org/10.1002/2015jd023674 , 2015. 

Gioli, B., Gualtieri, G., Busillo, C., Calastrini, F., Zaldei, A., and Toscano, P.: Improving high resolution emission inventories with local proxies and urban eddy covariance flux measurements, Atmos. Environ., 115, 246–256, https://doi.org/10.1016/j.atmosenv.2015.05.068 , 2015. 

Gkatzelis, G. I., Gilman, J. B., Brown, S. S., Eskes, H., Gomes, A. R., Lange, A. C., McDonald, B. C., Peischl, J., Petzold, A., Thompson, C. R., and Kiendler-Scharr, A.: The global impacts of COVID-19 lockdowns on urban air pollution: A critical review and recommendations, Elementa: Science of the Anthropocene, 9, 00176, https://doi.org/10.1525/elementa.2021.00176 , 2021. 

Gohil, K. and Jin, M. S.: Validation and Improvement of the WRF Building Environment Parametrization (BEP) Urban Scheme, Climate, 7, 109, https://doi.org/10.3390/cli7090109 , 2019. 

González-Aparicio, I., Baklanov, A., Hidalgo, J., Korsholm, U., and Nuterman, R.: Impact of city expansion and increased heat fluxes scenarios on the urban boundary layer of Bilbao using Enviro-HIRLAM, Urban Climate, 10, 831–845, 2014. 

Gou, H., Lu, J., Li, S., Tong, Y., Xie, C., and Zheng, X.: Assessment of microbial communities in PM 1 and PM 10 of Urumqi during winter, Environ. Pollut., 214, 202–210, https://doi.org/10.1016/j.envpol.2016.03.073 , 2016. 

Goulier, L., Paas, B., Ehrnsperger, L., and Klemm, O.: Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables, Int. J. Env. Res. Pub. He., 17, 2025, https://doi.org/10.3390/ijerph17062025 , 2020. 

Grange, S. K., Lötscher, H., Fischer, A., Emmenegger, L., and Hueglin, C.: Exploring equivalent black carbon (EBC) concentrations in Switzerland with the aethalometer model, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 17, https://doi.org/10.18745/pb.22217 , 2020. 

Grell, G. A. and Baklanov, A.: Integrated modelling for forecasting weather and air quality: A call for fully coupled approaches, Atmos. Environ., 45, 6845–6851, https://doi.org/10.1016/j.atmosenv.2011.01.017 , 2011. 

Gressent, A., Malherbe, L., Colette, A., Rollin, H., and Scimia, R.: Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value, Environ. Int., 143, 105965, https://doi.org/10.1016/j.envint.2020.105965 , 2020. 

Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J., Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt, J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish, P., and Joe, P.: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Climate, 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623 , 2020. 

Grythe, H., Lopez-Aparicio, S., Vogt, M., Vo Thanh, D., Hak, C., Halse, A. K., Hamer, P., and Sousa Santos, G.: The MetVed model: development and evaluation of emissions from residential wood combustion at high spatio-temporal resolution in Norway, Atmos. Chem. Phys., 19, 10217–10237, https://doi.org/10.5194/acp-19-10217-2019 , 2019. 

Gu, J., Wensing, M., Uhde, E., and Salthammer, T.: Characterization of particulate and gaseous pollutants emitted during operation of a desktop 3D printer, Environ. Int., 123, 476–485, https://doi.org/10.1016/j.envint.2018.12.014 , 2019. 

Gu, Q., Michanowicz, D. R., and Jia, C.: Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling, Sensors, 18, 4363, https://doi.org/10.3390/s18124363 , 2018. 

Guevara, M., Martínez, F., Arévalo, G., Gassó, S., and Baldasano, J. M.: Improved system for modeling Spanish emissions: HERMESv2.0, Atmos. Environ., 81, 209–221 https://doi.org/10.1016/j.atmosenv.2013.08.053 , 2013. 

Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module, Geosci. Model Dev., 12, 1885–1907, https://doi.org/10.5194/gmd-12-1885-2019 , 2019. 

Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module, Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020 , 2020. 

Guevara, M., Jorba, O., Tena, C., Denier van der Gon, H., Kuenen, J., Elguindi, N., Darras, S., Granier, C., and Pérez García-Pando, C.: Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling, Earth Syst. Sci. Data, 13, 367–404, https://doi.org/10.5194/essd-13-367-2021 , 2021. 

Gurney, K. R., Liang, J., Patarasuk, R., O'Keeffe, D., Huang, J., Hutchins, M., Lauvaux, T., Turnbull, J. C., and Shepson, P. B.: Reconciling the differences between a bottom-up and inverse-estimated FFCO 2 emissions estimate in a large US urban area, Elementa: Science of the Anthropocene, 5, 44, https://doi.org/10.1525/elementa.137 , 2017. 

Gwaze, P.: Physical and chemical properties of aerosol particles in the troposphere: An approach from microscopy methods, Sierke, Göttingen, 187 pp., http://hdl.handle.net/11858/00-001M-0000-0014-89C8-0 (last access: 11 August 2020), 2007. 

Halenka, T., Belda, M., Huszar, P., Karlicky, J., Novakova, T., and Zak, M.: On the comparison of urban canopy effects parameterisation, Int. J. Environ. Pollut., 65, 177–194, https://doi.org/10.1504/IJEP.2019.101840 , 2019. 

Hamer, P. D., Walker, S.-E., Sousa-Santos, G., Vogt, M., Vo-Thanh, D., Lopez-Aparicio, S., Schneider, P., Ramacher, M. O. P., and Karl, M.: The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions, Geosci. Model Dev., 13, 4323–4353, https://doi.org/10.5194/gmd-13-4323-2020 , 2020. 

Hampel, R., Peters, A., Beelen, R., Brunekreef, B., Cyrys, J., Faire, U. d., Hoogh, K. d., Fuks, K., Hoffmann, B., Hüls, A., Imboden, M., Jedynska, A., Kooter, I., Koenig, W., Künzli, N., Leander, K., Magnusson, P., Männistö, S., Penell, J., Pershagen, G., Phuleria, H., Probst-Hensch, N., Pundt, N., Schaffner, E., Schikowski, T., Sugiri, D., Tiittanen, P., Tsai, M.-Y., Wang, M., Wolf, K., and Lanki, T.: Long-term effects of elemental composition of particulate matter on inflammatory blood markers in European cohorts, Environ. Int., 82, 76–84, https://doi.org/10.1016/j.envint.2015.05.008 , 2015. 

Hänninen, O., Lebret, E., Ilacqua, V., Katsouyanni, K., Künzli, N., Sram, R., and Jantunen, M.: Infiltration of ambient PM 2.5 and levels of indoor generated non-ETS PM 2.5 in residences of four European cities, Atmos. Environ., 38, 6411–6423, https://doi.org/10.1016/j.atmosenv.2004.07.015 , 2004. 

Hänninen, O., Palonen, J., Tuomisto, J., Yli-Tuomi, T., Seppänen, O., and Jantunen, M. J.: Reduction potential of urban PM 2.5 mortality risk using modern ventilation systems in buildings, Indoor Air, 15, 246–256, https://doi.org/10.1111/j.1600-0668.2005.00365.x , 2005. 

Hänninen, O., Knol, A., Jantunen, M., Lim, T., Conrad, A., Rappolder, M., Carrer P., Fanetti, A., Kim, R., Buekers, J., Torfs, R., Iavarone, I., Classen, T., Hornberg, C., and Mekel, O.: Environmental burden of disease in Europe: assessing nine risk factors in six countries, Environ. Health Perspect., 122, 439–446, https://doi.org/10.1289/ehp.1206154 , 2014. 

Hassan, A. M., ELMokadem, A. A., Megahed, N. A., and Abo Eleinen, O. M.: Urban morphology as a passive strategy in promoting outdoor air quality, Journal of Building Engineering, 29, 101204, https://doi.org/10.1016/j.jobe.2020.101204 , 2020. 

Hausberger, S., Rodler, J., Sturm, P., and Rexeis, M.: Emission factors for heavy-duty vehicles and validation by tunnel measurements, Atmos. Environ., 37, 5237–5245, 2003. 

He, L., Norris, C., Cui, X., Li, Z., Barkjohn, K. K., Brehmer, C., Teng, Y., Fang, L., Lin, L., Wang, Q., Zhou, X., Hong, J., Li, F., Zhang, Y., Schauer, J. J., Black, M., Bergin, M. H., and Zhang, J. J.: Personal Exposure to PM 2.5 Oxidative Potential in Association with Pulmonary Pathophysiologic Outcomes in Children with Asthma, Environ Sci Technol., 55, 3101–3111, https://doi.org/10.1021/acs.est.0c06114 , 2021. 

Health Effects Institute (HEI): Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality: A Special Report of the Institute’s Particle Epidemiology Reanalysis Project, Health Effects Institute, Cambridge, MA, https://www.healtheffects.org/system/files/HEI-Reanalysis-2000.pdf (last access: 30 September 2020), 2000. 

Health Effects Institute (HEI): State of Global Air 2020. Special Report on Global Exposure to Air Pollution and its Health Effects, Health Effects Institute, Boston, MA, 2578–6873, 2020. 

Heinold, B., Assmann, D., Käthner, R., Knoth, O., Macke, A., Müller, T., Tõnisson, L., Voigtländer, J., Weger, M., and Wolke, R.: Assessing the spatio-temporal distribution of urban air pollutants – an integrated system based on crowdsourcing with mobile sensors and multi-scale modelling, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 116, https://doi.org/10.18745/pb.22217 , 2020. 

Hellsten, A., Aarnio, M. A., and Hannuniemi, H.: Fast pre-computed large-eddy simulation based dispersion modelling method for hazardous material releases in urban environments – Part 1: the concept, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 73, https://doi.org/10.18745/pb.22217 , 2020. 

Hellsten, A., Ketelsen, K., Sühring, M., Auvinen, M., Maronga, B., Knigge, C., Barmpas, F., Tsegas, G., Moussiopoulos, N., and Raasch, S.: A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0, Geosci. Model Dev., 14, 3185–3214, https://doi.org/10.5194/gmd-14-3185-2021 , 2021. 

Hendriks, C.: Ammonia emission time profiles based on manure transport data improve ammonia modelling across north western Europe, Elsevier Ltd., 2016. 

Henne, S., Brunner, D., Oney, B., Leuenberger, M., Eugster, W., Bamberger, I., Meinhardt, F., Steinbacher, M., and Emmenegger, L.: Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, 3683–3710, https://doi.org/10.5194/acp-16-3683-2016 , 2016. 

Héroux, M. E., Anderson, H. R., Atkinson, R., Brunekreef, B., Cohen, A., Forastiere, F., Hurley, F., Katsouyanni, K., Krewski, D., Krzyzanowski, M., Kunzli, N., Mills, I., Querol, X., Ostro, B., and Walton, H.: Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project, Int. J. Public Health, 60, 619–627, https://doi.org/10.1007/s00038-015-0690-y , 2015. 

Hidalgo, J., Masson, V., Baklanov, A., Pigeon, G., and Gimeno, L.: Advances in Urban Climate Modeling, Ann. N.Y. Acad. Sci., 1146, 354–374, https://doi.org/10.1196/annals.1446.015 , 2008. 

Hime, N., Marks, G., and Cowie, C.: A Comparison of the Health Effects of Ambient Particulate Matter Air Pollution from Five Emission Sources, Int. J. Env. Res. Pub. He., 15, 1206, https://doi.org/10.3390/ijerph15061206 , 2018. 

Hirtl, M., Arnold, D., Briese, C., Figuera, R. M., Flandorfer, C., Haselsteiner, M., Humer, H., Maurer, C., Natali, S., Ng, T., Placho, T., Santillan, D., Scherllin-Pirscher, B., Skarbal, B., Triebnig, G., and Uhrner, U.: Innovative applications for the augmented use of satellite observations to support air quality management, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 9, https://doi.org/10.18745/pb , 2020. 

Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., and Kaufman, J. D.: Long-term air pollution exposure and cardio-respiratory mortality: a review, Environ. Health, 12, 43, https://doi.org/10.1186/1476-069x-12-43 , 2013. 

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018 , 2018. 

Hoffmann, B., Weinmayr, G., Hennig, F., Fuks, K., Moebus, S., Weimar, C., Dragano, N., Hermann, D. M., Kälsch, H., Mahabadi, A. A., Erbel, R., and Jöckel, K.-H.: Air Quality, Stroke, and Coronary Events, Dtsch. Arztebl. Online, 112, 195–201, https://doi.org/10.3238/arztebl.2015.0195 , 2015. 

Hood, C., MacKenzie, I., Stocker, J., Johnson, K., Carruthers, D., Vieno, M., and Doherty, R.: Air quality simulations for London using a coupled regional-to-local modelling system, Atmos. Chem. Phys., 18, 11221–11245, https://doi.org/10.5194/acp-18-11221-2018 , 2018. 

Hopke, P. K.: Review of receptor modeling methods for source apportionment, J. Air Waste Manage., 66, 237–259, https://doi.org/10.1080/10962247.2016.1140693 , 2016. 

Horne, B. D., Joy, E. A., Hofmann, M. G., Gesteland, P. H., Cannon, J. B., Lefler, J. S., Blagev, D. P., Korgenski, E. K., Torosyan, N., Hansen, G. I., Kartchner, D., and Pope, C. A.: Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection, Am. J. Resp. Crit. Care, 198, 759–766, https://doi.org/10.1164/rccm.201709-1883OC , 2018. 

Host, S., Honoré, C., Joly, F., Saunal, A., Le Tertre, A., and Medina, S.: Implementation of various hypothetical low emission zone scenarios in Greater Paris: Assessment of fine-scale reduction in exposure and expected health benefits, Environ. Res., 185, 109405, https://doi.org/10.1016/j.envres.2020.109405 , 2020. 

Hu, J., Li, X., Huang, L., Ying, Q., Zhang, Q., Zhao, B., Wang, S., and Zhang, H.: Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China, Atmos. Chem. Phys., 17, 13103–13118, https://doi.org/10.5194/acp-17-13103-2017 , 2017. 

Hu, T., Singer, B. C., and Logue, J. M.: Compilation of Published PM 2.5 Emission Rates for Cooking, Candles and Incense for Use in Modeling of Exposures in Residences, Tech. report, United States. Dept. of Energy. Office of Science, Washington, D.C., 29 pp., https://doi.org/10.2172/1172959 , 2012. 

Huang, G.: Integrated assessment of atmospheric environmental management in China, dissertation, University of Stuttgart, https://doi.org/10.18419/opus-9852 , 2018. 

Huang, G., Schmid, D., Friedrich, R., Vogt, U., Mahami, G., Struschka, M., and Juschka, W.: Ganzheitliche Bewertung von Holzheizungen, https://doi.org/10.18419/opus-11108 , 2016. 

Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C., Dore, C., Guizzardi, D., Muntean, M., Schaaf, E., and Friedrich, R.: Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970–2012, Atmos. Chem. Phys., 17, 7683–7701, https://doi.org/10.5194/acp-17-7683-2017 , 2017. 

Huang, M., Gao, Z., Miao, S., and Chen, F.: Sensitivity of urban boundary layer simulation to urban canopy models and PBL schemes in Beijing, Meteorol. Atmos. Phys., 131, 1235–1248, https://doi.org/10.1007/s00703-018-0634-1 , 2019. 

Huszár, P., Belda, M., Karlický, J., Pišoft, P., and Halenka, T.: The regional impact of urban emissions on climate over central Europe: present and future emission perspectives, Atmos. Chem. Phys., 16, 12993–13013, https://doi.org/10.5194/acp-16-12993-2016 , 2016. 

Huszar, P., Belda, M., Karlický, J., Bardachova, T., Halenka, T., and Pisoft, P.: Impact of urban canopy meteorological forcing on aerosol concentrations, Atmos. Chem. Phys., 18, 14059–14078, https://doi.org/10.5194/acp-18-14059-2018 , 2018. 

Huszar, P., Karlický, J., Ďoubalová, J., Nováková, T., Šindelářová, K., Švábik, F., Belda, M., Halenka, T., and Žák, M.: The impact of urban land-surface on extreme air pollution over central Europe, Atmos. Chem. Phys., 20, 11655–11681, https://doi.org/10.5194/acp-20-11655-2020 , 2020. 

Hvidtfeldt, U. A., Geels, C., Sørensen, M., Ketzel, M., Khan, J., Tjønneland, A., Christensen, J. H., Brandt, J., and Raaschou-Nielsen, O.: Long-term residential exposure to PM 2.5 constituents and mortality in a Danish cohort, Environ. Int., 133, 105268, https://doi.org/10.1016/j.envint.2019.105268 , 2019a. 

Hvidtfeldt, U. A., Sørensen, M., Geels, C., Ketzel, M., Khan, J., Tjønneland, A., Overvad, K., Brandt, J., and Raaschou-Nielsen, O.: Long-term residential exposure to PM 2.5 , PM 10 , black carbon, NO 2 , and ozone and mortality in a Danish cohort, Environ. Int., 123, 265–272, https://doi.org/10.1016/j.envint.2018.12.010 , 2019b. 

Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Denier van der Gon, H., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Makar, P. A, Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R S., Syrakov, D., Torian, A., Tuccella, P., Wang, K., Werhahn, J., Wolke, R., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational online coupled regional air quality models over Europe and North America in the context of AQMEII phase 2, Part II: particulate matter, Atmos. Environ., 115, 421–441, 2015a. 

Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Kuenen, J. J. P., Makar, P. A., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2, Part I: Ozone, Atmos. Environ., 115, 404–420, 2015b. 

Im, U., Brandt, J., Geels, C., Hansen, K. M., Christensen, J. H., Andersen, M. S., Solazzo, E., Kioutsioukis, I., Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bianconi, R., Bieser, J., Colette, A., Curci, G., Farrow, A., Flemming, J., Fraser, A., Jimenez-Guerrero, P., Kitwiroon, N., Liang, C.-K., Nopmongcol, U., Pirovano, G., Pozzoli, L., Prank, M., Rose, R., Sokhi, R., Tuccella, P., Unal, A., Vivanco, M. G., West, J., Yarwood, G., Hogrefe, C., and Galmarini, S.: Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3, Atmos. Chem. Phys., 18, 5967–5989, https://doi.org/10.5194/acp-18-5967-2018 , 2018. 

Im, U., Christensen, J. H., Nielsen, O.-K., Sand, M., Makkonen, R., Geels, C., Anderson, C., Kukkonen, J., Lopez-Aparicio, S., and Brandt, J.: Contributions of Nordic anthropogenic emissions on air pollution and premature mortality over the Nordic region and the Arctic, Atmos. Chem. Phys., 19, 12975–12992, https://doi.org/10.5194/acp-19-12975-2019 , 2019. 

IMO: Prevention of Air Pollution from Ships, International Maritime Organization, https://www.imo.org/en/OurWork/Environment/Pages/Air-Pollution.aspx (last access: 22 February 2022), 2019. 

IMO: IMO's work to cut GHG emissions from ships, International Maritime Organisation, https://www.imo.org/en/MediaCentre/HotTopics/Pages/Cutting-GHG-emissions.aspx (last access: 25 February 2022), 2021. 

Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019 , 2019. 

IOM (Institute of Medicine): Long-Term Health Consequences of Exposure to Burn Pits in Iraq and Afghanistan, The National Academies Press, Washington, DC, https://doi.org/10.17226/13209 , 2011. 

IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, 151 pp., 2014. 

IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited be: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/#FullReport (last access: 28 February 2022. 

Jalkanen, J.-P., Brink, A., Kalli, J., Pettersson, H., Kukkonen, J., and Stipa, T.: A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area, Atmos. Chem. Phys., 9, 9209–9223, https://doi.org/10.5194/acp-9-9209-2009 , 2009. 

Jalkanen, J.-P., Johansson, L., Kukkonen, J., Brink, A., Kalli, J., and Stipa, T.: Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide, Atmos. Chem. Phys., 12, 2641–2659, https://doi.org/10.5194/acp-12-2641-2012 , 2012. 

Jalkanen, J.-P., Johansson, L., and Kukkonen, J.: A comprehensive inventory of ship traffic exhaust emissions in the European sea areas in 2011, Atmos. Chem. Phys., 16, 71–84, 2016. 

Jeanjean, A. P. R., Buccolieri, R., Eddy, J., Monks, P. S., and Leigh, R. J.: Air quality affected by trees in real street canyons: The case of Marylebone neighbourhood in central London, Urban For. Urban Gree., 22, 41–53, https://doi.org/10.1016/j.ufug.2017.01.009 , 2017. 

Jensen, S. S., Ketzel, M., Becker, T., Christensen, J., Brandt, J., Plejdrup, M., Winther, M., Nielsen, O. K., Hertel, O., and Ellermann, T.: High resolution multi-scale air quality modelling for all streets in Denmark, Transport. Res. D-Tr. E., 52, 322–339, 2017. 

Jesus, A. L. d., Rahman, M. M., Mazaheri, M., Thompson, H., Knibbs, L. D., Jeong, C., Evans, G., Nei, W., Ding, A., Qiao, L., Li, L., Portin, H., Niemi, J. V., Timonen, H., Luoma, K., Petäjä, T., Kulmala, M., Kowalski, M., Peters, A., Cyrys, J., Ferrero, L., Manigrasso, M., Avino, P., Buonano, G., Reche, C., Querol, X., Beddows, D., Harrison, R. M., Sowlat, M. H., Sioutas, C., and Morawska, L.: Ultrafine particles and PM 2.5 in the air of cities around the world: Are they representative of each other?, Environ. Int., 129, 118–135, https://doi.org/10.1016/j.envint.2019.05.021 , 2019. 

Johansson, L., Jalkanen, J.-P., Kalli, J., and Kukkonen, J.: The evolution of shipping emissions and the costs of regulation changes in the northern EU area, Atmos. Chem. Phys., 13, 11375-11389, 2013. 

Johansson, L., Epitropou, V., Karatzas, K., Karppinen, A., Wanner, L., Vrochidis, S., Bassoukos, A., Kukkonen, J., and Kompatsiaris, I.: Fusion of meteorological and air quality data extracted from the web for personalized environmental information services, Environmental Modelling & Software 64, 143-155, 2015. 

Johansson, L., Jalkanen, J.-P., and Kukkonen, J.: Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution., Atmos. Environ., 167, 403–415, 2017. 

Jones, L., Vieno, M., Fitch, A., Carnell, E., Steadman, C., Cryle, P., Holland, M., Nemitz, E., Morton, D., Hall, J., Mills, G., Dickie, I., and Reis, S.: Urban natural capital accounts: developing a novel approach to quantify air pollution removal by vegetation, Journal of Environmental Economics and Policy, 8, 413–428, https://doi.org/10.1080/21606544.2019.1597772 , 2019. 

Jonson, J. E., Jalkanen, J. P., Johansson, L., Gauss, M., and Denier van der Gon, H. A. C.: Model calculations of the effects of present and future emissions of air pollutants from shipping in the Baltic Sea and the North Sea, Atmos. Chem. Phys., 15, 783–798, https://doi.org/10.5194/acp-15-783-2015 , 2015. 

Just, A. C., Arfer, K. B., Rush, J., Dorman, M., Shtein, A., Lyapustin, A., and Kloog, I.: Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM 2.5 ) using satellite data over large regions, Atmos. Environ., 239, 117649, https://doi.org/10.1016/j.atmosenv.2020.117649 , 2020. 

Kalisa, E., Archer, S., Nagato, E., Bizuru, E., Lee, K., Tang, N., Pointing, S., Hayakawa, K., and Lacap-Bugler, D.: Chemical and Biological Components of Urban Aerosols in Africa: Current Status and Knowledge Gaps, Int. J. Env. Res. Pub. He., 16, 941, https://doi.org/10.3390/ijerph16060941 , 2019. 

Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506 , 2019. 

Karakitsios, S., Busker, R., Tjärnhage, T., Armand, P., Dybwad, M., Nielsen, M. F., Burman, J., Burke, J., Brinek, J., Bartzis, J., Maggos, T., Theocharidou, M., Gattinesi, P., Giannopoulos, G., and Sarigiannis, D.: Challenges on detection, identification and monitoring of indoor airborne chemical-biological agents, Safety Sci., 129, 104789, https://doi.org/10.1016/j.ssci.2020.104789 , 2020. 

Karl, M., Jonson, J. E., Uppstu, A., Aulinger, A., Prank, M., Sofiev, M., Jalkanen, J.-P., Johansson, L., Quante, M., and Matthias, V.: Effects of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry transport models, Atmos. Chem. Phys., 19, 7019–7053, https://doi.org/10.5194/acp-19-7019-2019 , 2019a. 

Karl, M., Walker, S.-E., Solberg, S., and Ramacher, M. O. P.: The Eulerian urban dispersion model EPISODE – Part 2: Extensions to the source dispersion and photochemistry for EPISODE–CityChem v1.2 and its application to the city of Hamburg, Geosci. Model Dev., 12, 3357–3399, https://doi.org/10.5194/gmd-12-3357-2019 , 2019b. 

Karl, M., Bieser, J., Geyer, B., Matthias, V., Jalkanen, J.-P., Johansson, L., and Fridell, E.: Impact of a nitrogen emission control area (NECA) on the future air quality and nitrogen deposition to seawater in the Baltic Sea region, Atmos. Chem. Phys., 19, 1721–1752, https://doi.org/10.5194/acp-19-1721-2019 , 2019c 

Karl, M., Pirjola, L., Karppinen, A., Jalkanen, J.-P., Ramacher, M. O. P., and Kukkonen, J.: Modeling of the Concentrations of Ultrafine Particles in the Plumes of Ships in the Vicinity of Major Harbors, Int. J. Env. Res. Pub. He., 17, 777, https://doi.org/10.3390/ijerph17030777 , 2020. 

Karttunen, S., Kurppa, M., Auvinen, M., Hellsten, A., and Järvi, L.: Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard, Atmos. Environ. X, 6, 100073, https://doi.org/10.1016/j.aeaoa.2020.100073 , 2020. 

Katsouyanni, K., Samet, J. M., Anderson, H. R., Atkinson, R., Le Tertre, A., Medina, S., Samoli, E., Touloumi, G., Burnett, R. T., Krewski, D., Ramsay, T., Dominici, F., Peng, R. D., Schwartz, J., and Zanobetti, A.: Air pollution and health: a European and North American approach (APHENA), Research report (Health Effects Institute), 5–90, 2009. 

Keiser, D. and Muller, A.: Air and Water: Integrated Assessment Models for Multiple Media, Annu. Rev. Resour. Econ., 9, 165–184, https://doi.org/10.1146/annurev-resource-100516-053501 , 2017. 

Kermenidou, M., Hondrogiorgis, C., Karakitsios, S., and Sarigiannis, D.: Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in aerosols and study of their effect in human health: a comparison between the warm and the cold season of the year, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 19, https://doi.org/10.18745/pb.22217 , 2020. 

Ketzel, M., Frohn, L. M., Christensen, J. H., Brandt, J., Massling, A., Andersen, C., Im, U., Jensen, S. S., Khan, J., Nielsen, O.-K., Plejdrup, M. S., Manders, A., van der Gon, H. D., Kumar, P., and Raaschou-Nielsen, O.: Modelling ultrafine particle number concentrations at address resolution in Denmark from 1979 to 2018 – Part 2: Local and street scale modelling and evaluation, Atmos. Environ., 264, 118633, https://doi.org/10.1016/j.atmosenv.2021.118633 , 2021. 

Khan, A., Plana-Ripoll, O., Antonsen, S., Brandt, J., Geels, C., Landecker, H., Sullivan, P. F., Pedersen, C. B., and Rzhetsky, A.: Environmental pollution is associated with increased risk of psychiatric disorders in the US and Denmark, PLOS Biol., 17, e3000353, https://doi.org/10.1371/journal.pbio.3000353 , 2019a. 

Khan, J., Kakosimos, K., Raaschou-Nielsen, O., Brandt, J., Jensen, S. S., Ellermann, T., and Ketzel, M.: Development and performance evaluation of new AirGIS – A GIS based air pollution and human exposure modelling system, Atmos. Environ., 198, 102–121, https://doi.org/10.1016/j.atmosenv.2018.10.036 , 2019b. 

Khan, B., Banzhaf, S., Chan, E. C., Forkel, R., Kanani-Sühring, F., Ketelsen, K., Kurppa, M., Maronga, B., Mauder, M., Raasch, S., Russo, E., Schaap, M., and Sühring, M.: Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications, Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021 , 2021. 

Kim, J., Jeong, U., Ahn, M.-H., Kim, J. H., Park, R. J., Lee, H., Song, C. H., Choi, Y.-S., Lee, K.-H., Yoo, J.-M., Jeong, M.-J., Park, S. K., Lee, K.-M., Song, C.-K., Kim, S.-W., Kim, Y. J., Kim, S.-W., Kim, M., Go, S., Liu, X., Chance, K., Chan Miller, C., Al-Saadi, J., Veihelmann, B., Bhartia, P. K., Torres, O., Abad, G. G., Haffner, D. P., Ko, D. H., Lee, S. H., Woo, J.-H., Chong, H., Park, S. S., Nicks, D., Choi, W. J., Moon, K.-J., Cho, A., Yoon, J., Kim, S.-k., Hong, H., Lee, K., Lee, H., Lee, S., Choi, M., Veefkind, P., Levelt, P. F., Edwards, D. P., Kang, M., Eo, M., Bak, J., Baek, K., Kwon, H.-A., Yang, J., Park, J., Han, K. M., Kim, B.-R., Shin, H.-W., Choi, H., Lee, E., Chong, J., Cha, Y., Koo, J.-H., Irie, H., Hayashida, S., Kasai, Y., Kanaya, Y., Liu, C., Lin, J., Crawford, J. H., Carmichael, G. R., Newchurch, M. J., Lefer, B. L., Herman, J. R., Swap, R. J., Lau, A. K. H., Kurosu, T. P., Jaross, G., Ahlers, B., Dobber, M., McElroy, C. T., and Choi, Y.: New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS), B. Am. Meteorol. Soc., 101, E1–E22, https://doi.org/10.1175/bams-d-18-0013.1 , 2020. 

Kim, Y., Wu, Y., Seigneur, C., and Roustan, Y.: Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1), Geosci. Model Dev., 11, 611–629, 2018. 

Kioutsioukis, I., Im, U., Solazzo, E., Bianconi, R., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., van der Gon, H. D., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Jorba, O., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Tuccella, P., Werhahn, J., Wolke, R., Hogrefe, C., and Galmarini, S.: Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data, Atmos. Chem. Phys., 16, 15629–15652, https://doi.org/10.5194/acp-16-15629-2016 , 2016. 

Klein, T., Kukkonen, J., Dahl, A., Bossioli, E., Baklanov, A., Vik, A. F., Agnew, P., Karatzas, K. D., and Sofiev, M.: Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality, Ambio, 41, 851–864, https://doi.org/10.1007/s13280-012-0288-z , 2012. 

Klimont, Z.: Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS), IIASA, https://iiasa.ac.at/models-and-data/greenhouse-gas-and-air-pollution-interactions-and-synergies (last access 25 February 2022), 2021. 

Kong, X., Forkel, R., Sokhi, R. S., Suppan, P., Baklanov, A., Gauss, M., Brunner, D., Baro, R., Balzarini, A., Chemel, C., Curci, G., Jimenez-Guerrero, P., Hirtl, M., Honzak, L., Im, U., Perez, J. L., Pirovano, G., San Jose, R., Schlünzen, K. H., Tsegas, G., Tuccella, P., Werhahn, J., Zabkar, R., and Galmarini, S.: Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII phase-2, Atmos. Environ., 115, 527-540, 2015. 

Korkmaz, P., Cunha Montenegro, R., Schmid, D., Blesl, M., and Fahl, U.: On the Way to a Sustainable European Energy System: Setting Up an Integrated Assessment Toolbox with TIMES PanEU as the Key Component, Energies, 13, 707, https://doi.org/10.3390/en13030707 , 2020. 

Kousa, A., Kukkonen, J., Karppinen, A., Aarnio, P., and Koskentalo, T.: A model for evaluating the population exposure to ambient air pollution in an urban area, Atmos. Environ., 36, 2109–2119, https://doi.org/10.1016/s1352-2310(02)00228-5 , 2002. 

Koutsourakis, N., Bartzis, J. G., and Venetsanos, A.: Determination of optimum positioning of atmospheric pollutant measuring instruments using computational fluid dynamics, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 72, https://doi.org/10.18745/pb.2221729 645–655, 2020. 

Kramshøj, M., Vedel-Petersen, I., Schollert, M., Rinnan, Å., Nymand, J., Ro-Poulsen, H., and Rinnan, R.: Large increases in Arctic biogenic volatile emissions are a direct effect of warming, Nat. Geosci., 9, 349–352, https://doi.org/10.1038/ngeo2692 , 2016. 

Kristensen, K., Lunderberg, D. M., Liu, Y., Misztal, P. K., Tian, Y., Arata, C., Nazaroff, W. W., and Goldstein, A. H.: Sources and dynamics of semivolatile organic compounds in a single-family residence in northern California, Indoor Air, 29, 645–655, https://doi.org/10.1111/ina.12561 , 2019. 

Kristovich, D. A. R., Takle, E., Young, G. S., and Sharma, A.: 100 Years of Progress in Mesoscale Planetary Boundary Layer Meteorological Research, Meteor. Mon., 59, 19.1–19.41, https://doi.org/10.1175/amsmonographs-d-18-0023.1 , 2019. 

Kuik, O. J., Brander, L., and Tol, R. S. J.: Marginal abatement costs of greenhouse gas emissions: A meta-analysis, Energ. Policy, 37, 1395–1403, 2009. 

Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., and Cawley, G.: Extensive evaluation of neural network models for the prediction of NO 2 and PM 10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki, Atmos. Environ., 37, 4539–4550, 2003. 

Kukkonen, J., Pohjola, M., Sokhi, R. S., Luhana, L., Kitwiroon, N., Rantamäki, M., Berge, E., Odegaard, V., Slørdal, L. H., Denby, B., and Finardi, S.: Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo, Atmos. Environ., 39, 2759–2773, 2005a. 

Kukkonen, J., Sokhi, R., Slordal, L. H., Finardi, S., Fay, B., Millan, M., Salvador, R., Palau, J. L., Rasmussen, A., Schayes, G., and Berge, E.: Analysis and evaluation of European air pollution episodes, in: Meteorology applied to urban air pollution problems, Final report COST Action 715, edited by: Fisher, B., Joffre, S., Kukkonen, J., Piringer, M., Rotach, M., and Schatzmann, M., Demetra Ltd Publishers, Bulgaria, 99–114, 2005b. 

Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1–87, https://doi.org/10.5194/acp-12-1-2012 , 2012. 

Kukkonen, J., Karl, M., Keuken, M. P., Denier van der Gon, H. A. C., Denby, B. R., Singh, V., Douros, J., Manders, A., Samaras, Z., Moussiopoulos, N., Jonkers, S., Aarnio, M., Karppinen, A., Kangas, L., Lützenkirchen, S., Petäjä, T., Vouitsis, I., and Sokhi, R. S.: Modelling the dispersion of particle numbers in five European cities, Geosci. Model Dev., 9, 451–478, https://doi.org/10.5194/gmd-9-451-2016 , 2016a. 

Kukkonen, J., Singh, V., Sokhi, R. S., Soares, J., Kousa, A., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., and Karppinen, A.: Assessment of Population Exposure to Particulate Matter for London and Helsinki, in: Air Pollution Modeling and its Application XXIV, edited by: Steyn, D. G. and Chaumerliac, N., Springer Proceedings in Complexity, Springer International Publishing, Cham, 99–105, https://doi.org/10.1007/978-3-319-24478-5_16 , 2016b. 

Kukkonen, J., Kangas, L., Kauhaniemi, M., Sofiev, M., Aarnio, M., Jaakkola, J. J. K., Kousa, A., and Karppinen, A.: Modelling of the urban concentrations of PM 2.5 on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects, Atmos. Chem. Phys., 18, 8041–8064, https://doi.org/10.5194/acp-18-8041-2018 , 2018. 

Kukkonen, J., Fridell, E., Moldanova, J., Jalkanen, J.-P., Maragkidou, A., Sofiev, M., Ntziachristos, L., Borken-Kleefeld, J., Sokhi, R. S., Zervakis, V., Hassellöv, I.-M., Ytreberg, E., Williams, I., Hole, L. R., Petrovic, M., Maragkidou, S., Ktoris, A., and Monteiro, A.: Environmental impacts of shipping: from global to local scales, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/PB.22217 , 2020a. 

Kukkonen, J., López-Aparicio, S., Segersson, D., Geels, C., Kangas, L., Kauhaniemi, M., Maragkidou, A., Jensen, A., Assmuth, T., Karppinen, A., Sofiev, M., Hellén, H., Riikonen, K., Nikmo, J., Kousa, A., Niemi, J. V., Karvosenoja, N., Santos, G. S., Sundvor, I., Im, U., Christensen, J. H., Nielsen, O.-K., Plejdrup, M. S., Nøjgaard, J. K., Omstedt, G., Andersson, C., Forsberg, B., and Brandt, J.: The influence of residential wood combustion on the concentrations of PM 2.5 in four Nordic cities, Atmos. Chem. Phys., 20, 4333–4365, https://doi.org/10.5194/acp-20-4333-2020 , 2020b. 

Kukkonen, J., Savolahti, M., Palamarchuk, Y., Lanki, T., Nurmi, V., Paunu, V.-V., Kangas, L., Sofiev, M., Karppinen, A., Maragkidou, A., Tiittanen, P., and Karvosenoja, N.: Modelling of the public health costs of fine particulate matter and results for Finland in 2015, Atmos. Chem. Phys., 20, 9371–9391, https://doi.org/10.5194/acp-20-9371-2020 , 2020c. 

Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M., Harrison, R. M., Norford, L., and Britter, R.: Ultrafine particles in cities, Environ. Int., 66, 1–10, https://doi.org/10.1016/j.envint.2014.01.013 , 2014. 

Kurppa, M., Hellsten, A., Auvinen, M., Raasch, S., Vesala, T., and Järvi, L.: Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation–Urban Planning Perspective, Atmosphere 9, 65, https://doi.org/10.3390/atmos9020065 , 2018. 

Kurppa, M., Hellsten, A., Roldin, P., Kokkola, H., Tonttila, J., Auvinen, M., Kent, C., Kumar, P., Maronga, B., and Järvi, L.: Implementation of the sectional aerosol module SALSA2.0 into the PALM model system 6.0: model development and first evaluation, Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019 , 2019. 

Kurppa, M., Roldin, P., Strömberg, J., Balling, A., Karttunen, S., Kuuluvainen, H., Niemi, J. V., Pirjola, L., Rönkkö, T., Timonen, H., Hellsten, A., and Järvi, L.: Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0, Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020 , 2020. 

Lehtomäki, H., Geels, C., Brandt, J., Rao, S., Yaramenka, K., Åström, S., Andersen, M. S., Frohn, L. M., Im, U., and Hänninen, O.: Deaths Attributable to Air Pollution in Nordic Countries: Disparities in the Estimates, Atmosphere, 11, 467, https://doi.org/10.3390/atmos11050467 , 2020. 

Lejri, D., Can, A., Schiper, N., and Leclercq, L.: Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale, Transport. Res. D-Tr. E., 63, 588–603, https://doi.org/10.1016/j.trd.2018.06.023 , 2018. 

Lelieveld, J., Klingmüller, K., Pozzer, A., Pöschl, U., Fnais, M., Daiber, A., and Münzel, T.: Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions, Eur. Heart J., 40, 1590–1596, https://doi.org/10.1093/eurheartj/ehz135 , 2019. 

Lepeule, J., Litonjua, A. A., Gasparrini, A., Koutrakis, P., Sparrow, D., Vokonas, P. S., and Schwartz, J.: Lung function association with outdoor temperature and relative humidity and its interaction with air pollution in the elderly, Environ. Res., 165, 110–117, https://doi.org/10.1016/j.envres.2018.03.039 , 2018. 

Letheren, B.: Air Quality Monitoring using Aircraft, Satellite or UAVs, Queensland University of Technology, Australia, https://doi.org/10.13140/rg.2.1.2455.2720 , 2016. 

Lewis, A., Zellweger, C., Schultz, M. G., and Tarasova, O. A.: Technical advice note on lower cost air pollution sensors,, World Meteorological Organization, Global Atmospheric Watch, WMOGeneva, Switzerland, 4 pp., 2017. 

Lewis, A., von Schneidemesser, E., and Peltier, R. E.: Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications, WMO-No. 1215, World Meteorological Organization, Geneva, Switzerland, 46 pp., 2018. 

Li, J., Sun, S., Tang, R., Qiu, H., Huang, Q., Mason, T., and Tian, L.: Major air pollutants and risk of COPD exacerbations: a systematic review and meta-analysis, Int. J. Chronic Obstr., 11, 3079–3091, https://doi.org/10.2147/copd.S122282 , 2016. 

Li, J., Woodward, A., Hou, X.-Y., Zhu, T., Zhang, J., Brown, H., Yang, J., Qin, R., Gao, J., Gu, S., Xu, L., Liu, X., and Liu, Q.: Modification of the effects of air pollutants on mortality by temperature: A systematic review and meta-analysis, Sci. Total Environ., 575, 1556–1570, https://doi.org/10.1016/j.scitotenv.2016.10.070 , 2017. 

Li, N.: Long-term exposure of European population subgroups to PM 2.5 and NO 2 , dissertation, Universität Stuttgart, Stuttgart, https://doi.org/10.18419/opus-11104 , 2020. 

Li, N. and Friedrich, R.: Methodology for Estimating the Lifelong Exposure to PM 2.5 and NO 2 – The Application to European Population Subgroups, Atmosphere, 10, 507, https://doi.org/10.3390/atmos10090507 , 2019. 

Li, N., Friedrich, R., Maesano, C. N., Medda, E., Brescianini, S., Stazi, M. A., Sabel, C. E., Sarigiannis, D., and Annesi-Maesano, I.: Lifelong exposure to multiple stressors through different environmental pathways for European populations, Environ. Res., 179, 108744, https://doi.org/10.1016/j.envres.2019.108744 , 2019a. 

Li, N., Huang, G., Friedrich, R., Vogt, U., Schürmann, S., and Straub, D.: Messung und Bewertung der Schadstoffemissionen von Holzfeuerungen in Innenräumen, Universität Stuttgart, Stuttgart, Forschungsbericht Band 144, https://doi.org/10.18419/opus-11139 , 2019b. 

Li, N., Maesano, C. N., Friedrich, R., Medda, E., Brandstetter, S., Kabesch, M., Apfelbacher, C., Melter, M., Seelbach-Göbel, B., Annesi-Maesano, I., and Sarigiannis, D.: A model for estimating the lifelong exposure to PM 2.5 and NO 2 and the application to population studies, Environ. Res., 178, 108629, https://doi.org/10.1016/j.envres.2019.108629 , 2019c. 

Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., and Chi, T.: Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation, Environ. Pollut., 231, 997–1004, https://doi.org/10.1016/j.envpol.2017.08.114 , 2017. 

Liakakou, E., Stavroulas, I., Kaskaoutis, D. G., Grivas, G., Paraskevopoulou, D., Dumka, U. C., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J., Gerasopoulos, E., and Mihalopoulos, N.: Levels and sources of black carbon long-term measurements in Athens, Greece, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, 18, https://doi.org/10.18745/pb.22217 , 2020. 

Liang, C.-K., West, J. J., Silva, R. A., Bian, H., Chin, M., Davila, Y., Dentener, F. J., Emmons, L., Flemming, J., Folberth, G., Henze, D., Im, U., Jonson, J. E., Keating, T. J., Kucsera, T., Lenzen, A., Lin, M., Lund, M. T., Pan, X., Park, R. J., Pierce, R. B., Sekiya, T., Sudo, K., and Takemura, T.: HTAP2 multi-model estimates of premature human mortality due to intercontinental transport of air pollution and emission sectors, Atmos. Chem. Phys., 18, 10497–10520, https://doi.org/10.5194/acp-18-10497-2018 , 2018. 

Lippmann, M., Chen, L.-C., Gordon, T., Ito, K., and Thurston, G. D.: National Particle Component Toxicity (NPACT) Initiative: integrated epidemiologic and toxicologic studies of the health effects of particulate matter components, Research report (Health Effects Institute), 5–13, 2013. 

Liu, S., Xing, J., Zhang, H., Ding, D., Zhang, F., Zhao, B., Sahu, S. K., and Wang, S.: Climate-driven trends of biogenic volatile organic compound emissions and their impacts on summertime ozone and secondary organic aerosol in China in the 2050s, Atmos. Environ., 218, 117020, https://doi.org/10.1016/j.atmosenv.2019.117020 , 2019. 

Liu, Z., Ye, W., and Little, J. C.: Predicting emissions of volatile and semivolatile organic compounds from building materials: A review, Build. Environ., 64, 7–25, https://doi.org/10.1016/j.buildenv.2013.02.012 , 2013. 

Loxham, M. and Nieuwenhuijsen, M. J.: Health effects of particulate matter air pollution in underground railway systems – a critical review of the evidence, Part. Fibre Toxicol., 16, 12, https://doi.org/10.1186/s12989-019-0296-2 , 2019. 

Lozhkina, O. V., Timofeev, V. D., and Lozhkin, V. N.: Modelling of air pollution by peat fire smoke and forecast of its impact on road visibility and drivers' health, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Luben, T. J., Nichols, J. L., Dutton, S. J., Kirrane, E., Owens, E. O., Datko-Williams, L., Madden, M., and Sacks, J. D.: A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon, Environ. Int., 107, 154–162, https://doi.org/10.1016/j.envint.2017.07.005 , 2017. 

MacIntyre, E. A., Gehring, U., Mölter, A., Fuertes, E., Klümper, C., Krämer, U., Quass, U., Hoffmann, B., Gascon, M., Brunekreef, B., Koppelman, G. H., Beelen, R., Hoek, G., Birk, M., Jongste, J. C. d., Smit, H. A., Cyrys, J., Gruzieva, O., Korek, M., Bergström, A., Agius, R. M., Vocht, F. d., Simpson, A., Porta, D., Forastiere, F., Badaloni, C., Cesaroni, G., Esplugues, A., Fernández-Somoano, A., Lerxundi, A., Sunyer, J., Cirach, M., Nieuwenhuijsen, M. J., Pershagen, G., and Heinrich, J.: Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project, Environ. Health Persp., 122, 107–113, https://doi.org/10.1289/ehp.1306755 , 2014. 

Maki, T., Hara, K., Kobayashi, F., Kurosaki, Y., Kakikawa, M., Matsuki, A., Chen, B., Shi, G., Hasegawa, H., and Iwasaka, Y.: Vertical distribution of airborne bacterial communities in an Asian-dust downwind area, Noto Peninsula, Atmos. Environ., 119, 282–293, https://doi.org/10.1016/j.atmosenv.2015.08.052 , 2015. 

Mallet, V.: Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, J. Geophys. Res., 115, D24303, https://doi.org/10.1029/2010JD014259 , 2010. 

Mallet, V., Stoltz, G., and Mauricette, B.: Ozone ensemble forecast with machine learning algorithms, J. Geophys. Res., 114, D05307, https://doi.org/10.1029/2008jd009978 , 2009. 

Manders, A. M. M., Builtjes, P. J. H., Curier, L., Denier van der Gon, H. A. C., Hendriks, C., Jonkers, S., Kranenburg, R., Kuenen, J. J. P., Segers, A. J., Timmermans, R. M. A., Visschedijk, A. J. H., Wichink Kruit, R. J., van Pul, W. A. J., Sauter, F. J., van der Swaluw, E., Swart, D. P. J., Douros, J., Eskes, H., van Meijgaard, E., van Ulft, B., van Velthoven, P., Banzhaf, S., Mues, A. C., Stern, R., Fu, G., Lu, S., Heemink, A., van Velzen, N., and Schaap, M.: Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model, Geosci. Model Dev., 10, 4145–4173, https://doi.org/10.5194/gmd-10-4145-2017 , 2017. 

Manisalidis, I., Stavropoulou, E., Stavropoulos, A., and Bezirtzoglou, E.: Environmental and Health Impacts of Air Pollution: A Review, Frontiers in Public Health, 8, 14, https://doi.org/10.3389/fpubh.2020.00014 , 2020. 

Manning, A. J., O'Doherty, S., Jones, A. R., Simmonds, P. G., and Derwent, R. G.: Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using an inversion modeling approach, J. Geophys. Res., 116, D02305, https://doi.org/10.1029/2010JD014763 , 2011. 

Mao, P., Li, J., Xiong, L., Wang, L., Wang,X., Tan, Y., and Li, H.: Characterization of Urban Subway Microenvironment Exposure – A Case of Nanjing in China, Int. J. Environ. Res. Public Health, 16, 625, https://doi.org/10.3390/ijerph16040625 , 2019. 

Maragkidou, A.: Exposure to coarse particles and floor dust biological and chemical contamination inside Jordanian indoor environments, University of Helsinki, Helsinki, PhD thesis, http://hdl.handle.net/10138/241380 (last access: 21 February 2022), 2018. 

Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015 , 2015. 

Maricq, M. M.: Chemical characterization of particulate emissions from diesel engines: A review, J. Aerosol Sci., 38, 1079–1118, https://doi.org/10.1016/j.jaerosci.2007.08.001 , 2007. 

Markandya, A., Sampedro, J., Smith, S., van Dingenen, R., Pizarro-Irizar, C., Arto, I., and González-Eguino, M.: Health Co-benefits from Air Pollution and Mitigation Costs of the Paris Agreement: Modelling Study, Lancet, 2, E126–E113, 2018. 

Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015 , 2015. 

Martilli, A., Santiago, J., and Salamanca, F.: On the representation of urban heterogeneities in mesoscale models, Environ. Fluid Mech., 15, 305–328, 2015. 

Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and Young, J.: Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449–12474, https://doi.org/10.5194/acp-17-12449-2017 , 2017. 

Matthey, A. and Bünger, B. C.: Methodenkonvention 3.0 zur Ermittlung von Umweltkosten: Kostensätze: Stand 02/2019, Stand 02/2019, Broschüren/Umweltbundesamt, Umweltbundesamt, Dessau-Roßlau, 48 pp., https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-02-11_methodenkonvention-3-0_kostensaetze_korr.pdf (last access: 25 February 2022), 2019. 

Matthias, V., Bewersdorff, I., Aulinger, A., and Quante, M.: The contribution of ship emissions to air pollution in the North Sea regions, Environ. Pollut., 158, 2241–2250, https://doi.org/10.1016/j.envpol.2010.02.013 , 2010. 

Matthias, V., Aulinger, A., Backes, A., Bieser, J., Geyer, B., Quante, M., and Zeretzke, M.: The impact of shipping emissions on air pollution in the greater North Sea region – Part 2: Scenarios for 2030, Atmos. Chem. Phys., 16, 759–776, https://doi.org/10.5194/acp-16-759-2016 , 2016 

Matthias, V., Arndt, J. A., Aulinger, A., Bieser, J., van der Denier Gon, H., Kranenburg, R., Kuenen, J., Neumann, D., Pouliot, G., and Quante, M.: Modeling emissions for three-dimensional atmospheric chemistry transport models, J. Air Waste Manage., 68, 763–800, https://doi.org/10.1080/10962247.2018.1424057 , 2018. 

Matthias, V., Bieser, J., Mocanu, T., Pregger, T., Quante, M., Ramacher, M. O. P., Seum, S., and Winkler, C.: Modelling road transport emissions in Germany – Current day situation and scenarios for 2040, Transport. Res. D-Tr. E., 87, 102536, https://doi.org/10.1016/j.trd.2020.102536 , 2020a. 

Matthias, V., Bieser, J., Quante, M., Seum, S., and Winkler, C.: Impact of traffic emissions in 2040 on air quality in Germany, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 31, https://doi.org/10.18745/pb.22217 , 2020b. 

McCarthy, M. P., Best, M. J., and Betts, R. A.: Climate change in cities due to global warming and urban effects, Geophys. Res. Lett., 37, L09705, https://doi.org/10.1029/2010GL042845 , 2010. 

McDonald, B. C., Gouw, J. A. d., Gilman, J. B., Jathar, S. H., Akherati, A., Cappa, C. D., Jimenez, J. L., Lee-Taylor, J., Hayes, P. L., McKeen, S. A., Cui, Y. Y., Kim, S.-W., Gentner, D. R., Isaacman-VanWertz, G., Goldstein, A. H., Harley, R. A., Frost, G. J., Roberts, J. M., Ryerson, T. B., and Trainer, M.: Volatile chemical products emerging as largest petrochemical source of urban organic emissions, Science, 359, 760–764, https://doi.org/10.1126/science.aaq0524 , 2018. 

McNider, R. T. and Pour-Biazar, A.: Meteorological modeling relevant to mesoscale and regional air quality applications: a review, J. Air Waste Manage., 70, 2–43, https://doi.org/10.1080/10962247.2019.1694602 , 2020. 

Michaelis, A. C., Lackmann, G. M., and Robinson, W. A.: Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1, Geosci. Model Dev., 12, 3725–3743, https://doi.org/10.5194/gmd-12-3725-2019 , 2019. 

Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., and Cai, C.: Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling, J. Clean. Prod., 221, 398–418, https://doi.org/10.1016/j.jclepro.2019.02.179 , 2019. 

Mircea, M., Calori, G., Pirovano, G., and Belis, C. A.: European guide on air pollution source apportionment for particulate matter with source-oriented models and their combined use with receptor models, JRC, 66 pp., https://doi.org/10.2760/470628 , 2020. 

Molina, L. T.: Introductory lecture: air quality in megacities, Faraday Discuss., 226, 9–52, https://doi.org/10.1039/d0fd00123f , 2021. 

Monteiro, A., Durka, P., Flandorfer, C., Georgieva, E., Guerreiro, C., Kushta, J., Malherbe, L., Maiheu, B., Miranda, A. I., Santos, G., Stocker, J., Trimpeneers, E., Tognet, F., Stortini, M., Wesseling, J., Janssen, S., and Thunis, P.: Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models, Air Qual. Atmos. Hlth., 11, 373–383, https://doi.org/10.1007/s11869-018-0554-8 , 2018. 

Morakinyo, O., Mokgobu, M., Mukhola, M., and Hunter, R.: Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter, Int. J. Env. Res. Pub. He., 13, 592, https://doi.org/10.3390/ijerph13060592 , 2016. 

Morawska, L., He, C., Johnson, G., Jayaratne, R., Salthammer, T., Wang, H., Uhde, E., Bostrom, T., Modini, R., Ayoko, G., McGarry, P., and Wensing, M.: An investigation into the characteristics and formation mechanisms of particles originating from the operation of laser printers, Environ. Sci. Technol., 43, 1015–1022, https://doi.org/10.1021/es802193n , 2009. 

Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M. M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., and Williams, R.: Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?, Environ. Int., 116, 286–299, https://doi.org/10.1016/j.envint.2018.04.018 , 2018. 

Moussiopoulos, N., Tsegas, G., and Chourdakis, E.: The impact of port operations on air quality in Piraeus and the surrounding urban areas, in: Air Pollution Modelling and its Application, edited by: Mensink, C., Gong, W., and Hakami, A., 159–164, https://doi.org/10.1007/978-3-030-22055-6_25 , 2019. 

Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I. (Eds.): Proceedings of 12th International Conference on Air Quality, Science and Application, Hatfield, UK, 162 pp., https://doi.org/10.18745/pb.22217 , 2020. 

Mues, A., Kuenen, J., Hendriks, C., Manders, A., Segers, A., Scholz, Y., Hueglin, C., Builtjes, P., and Schaap, M.: Sensitivity of air pollution simulations with LOTOS-EUROS to the temporal distribution of anthropogenic emissions, Atmos. Chem. Phys., 14, 939–955, https://doi.org/10.5194/acp-14-939-2014 , 2014. 

Munir, S., Mayfield, M., Coca, D., and Jubb, S. A.: Structuring an integrated air quality monitoring network in large urban areas – Discussing the purpose, criteria and deployment strategy, Atmos. Environ. X, 2, 100027, https://doi.org/10.1016/j.aeaoa.2019.100027 , 2019. 

Murena, F. and Prati, M. V.: The contribution of high emitters vehicles to FPS number concentration in the historical centre of Naples, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Mussetti, G., Brunner, D., Henne, S., Allegrini, J., Krayenhoff, E. S., Schubert, S., Feigenwinter, C., Vogt, R., Wicki, A., and Carmeliet, J.: COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees, Geosci. Model Dev., 13, 1685–1710, https://doi.org/10.5194/gmd-13-1685-2020 , 2020. 

Navrud, S. and Ready, R. C.: Environmental value transfer: issues and methods, The economics of non-market goods and resources, Springer, Dordrecht, 9, 290 pp., 2007. 

Nemery, B., Hoet, P. H. M., and Nemmar, A.: The Meuse Valley fog of 1930: an air pollution disaster, Lancet, 357, 704–708, https://doi.org/10.1016/s0140-6736(00)04135-0 , 2001. 

Ngan, F., Loughner, C. P., and Stein, A.: The evaluation of mixing methods in HYSPLIT using measurements from controlled tracer experiments, Atmos. Environ., 219, 117043, https://doi.org/10.1016/j.atmosenv.2019.117043 , 2019. 

Nguyen, C. V. and Soulhac, L.: Data assimilation methods for urban air quality at the local scale, Atmos. Environ., 253, 118366, https://doi.org/10.1016/j.atmosenv.2021.118366 , 2021. 

Nieuwenhuijsen, M. J., Gomez-Perales, J. E., and Colvile, R. N.: Levels of particulate air pollution, its elemental composition, determinants and health effects in metro systems, Atmos. Environ., 41, 7995–8006, 2007. 

Niemeyer, L. E.: Forecasting air pollution potential, Mon. Weather Rev., 88, 88–96, 1960. 

Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J., Kolehmainen, M., Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations, Atmos. Environ., 39, 6524–6536, 2005. 

Nuterman, R., Starchenko, A., and Baklanov, A.: Numerical Model of Urban Aerodynamics and Pollution Dispersion, Int. J. Environ. Pollut., 44, 385–393, 2011. 

Nuterman, R., Mahura, A., Baklanov, A., Amstrup, B., and Zakey, A.: Downscaling system for modeling of atmospheric composition on regional, urban and street scales, Atmos. Chem. Phys., 21, 11099–11112, https://doi.org/10.5194/acp-21-11099-2021 , 2021. 

OECD: OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris, https://doi.org/10.1787/9789264122246-en , 2012. 

OECD: Cost-Benefit Analysis and the Environment – Further Developments and Policy Use, OECD Publishing, Paris, https://doi.org/10.1787/9789264085169-en , 2018. 

Ogen, Y.: Assessing nitrogen dioxide (NO 2 ) levels as a contributing factor to coronavirus (COVID-19) fatality, Sci. Total Environ., 726, 138605, https://doi.org/10.1016/j.scitotenv.2020.138605 , 2020. 

Ostro, B., Lipsett, M., Reynolds, P., Goldberg, D., Hertz, A., Garcia, C., Henderson, K. D., and Bernstein, L.: Long-term exposure to constituents of fine particulate air pollution and mortality: results from the California Teachers Study, Environ. Health Persp., 118, 363–369, https://doi.org/10.1289/ehp.0901181 , 2010. 

Ostro, B., Reynolds, P., Goldberg, D., Hertz, A., Burnett, R. T., Shin, H., Hughes, E., Garcia, C., Henderson, K. D., Bernstein, L., and Lipsett, M.: Assessing Long-Term Exposure in the California Teachers Study, Environ. Health Persp., 119, A242–A243, https://doi.org/10.1289/ehp.119-3114832 , 2011. 

Ostro, B., Hu, J., Goldberg, D., Reynolds, P., Hertz, A., Bernstein, L., and Kleeman, M. J.: Associations of Mortality with Long-Term Exposures to Fine and Ultrafine Particles, Species and Sources: Results from the California Teachers Study Cohort, Environ. Health Persp., 123, 549–556, https://doi.org/10.1289/ehp.1408565 , 2015. 

Otalora, M., Soulhac, L., Nguyen, C. V., and Derognat, C.: Challenges in the assimilation of mobile sensors data for urban air quality – analysis of a Paris study, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 117, https://doi.org/10.18745/pb.22217 , 2020. 

Ots, R., Heal, M. R., Young, D. E., Williams, L. R., Allan, J. D., Nemitz, E., Di Marco, C., Detournay, A., Xu, L., Ng, N. L., Coe, H., Herndon, S. C., Mackenzie, I. A., Green, D. C., Kuenen, J. J. P., Reis, S., and Vieno, M.: Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions, Atmos. Chem. Phys., 18, 4497–4518, https://doi.org/10.5194/acp-18-4497-2018 , 2018. 

Ott, W., Baur, M., Kaufmann, Y., Frischknecht, R., and Steiner, R.: Assessment of Biodiversity Losses – Monetary Valuation of Biodiversity Losses due to Land Use Changes and Airborne Emissions, Deliverable D.4.2.-RS 1b/WP4 of the EU FP6 project No. 02687 NEEDS, https://www.econcept.ch/en/focus/needs-new-energy-externalities-developments-sustainability/ (last access: 25 February 2022), 2006. 

Papadogeorgou, G., Kioumourtzoglou, M.-A., Braun, D., and Zanobetti, A.: Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions, Current Environmental Health Reports, 6, 105–115, https://doi.org/10.1007/s40572-019-00235-7 , 2019. 

Park, M., Joo, H. S., Lee, K., Jang, M., Kim, S. D., Kim, I., Borlaza, L. J. S., Lim, H., Shin, H., Chung, K. H., Choi, Y.-H., Park, S. G., Bae, M.-S., Lee, J., Song, H., and Park, K.: Differential toxicities of fine particulate matters from various sources, Sci. Rep., 8, 17007, https://doi.org/10.1038/s41598-018-35398-0 , 2018. 

Parra, R.: Effects of global meteorological datasets in modeling meteorology and air quality in the andean region of southern Ecuador, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 54, https://doi.org/10.18745/pb.22217 , 2020. 

Paunu, V.-V., Karvosenoja, N., D., S., Lopez-Aparicio, S., Nielsen, O.-K., Plejdrup, M. S., Vo, D. T., Thorsteinsson, T., Denier van der Gon, H., Brandt, J., and Geels, C.: New Nordic emission inventory – spatial distribution of machinery and residential combustion emissions, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 17, https://doi.org/10.18745/pb.22217 , 2020. 

Pavlovic, R., Belair, S., Leroyer, S., Nikiema, O., Popadic, I., Munoz-Alpizar, R., and Stroud, C.: Urban meteorology and air quality as a function of different urban features, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 151, https://doi.org/10.18745/pb.22217 , 2020. 

Pedersen, M., Giorgis-Allemand, L., Bernard, C., Aguilera, I., Andersen, A.-M. N., Ballester, F., Beelen, R. M. J., Chatzi, L., Cirach, M., Danileviciute, A., Dedele, A., van Eijsden, M., Estarlich, M., Fernández-Somoano, A., Fernández, M. F., Forastiere, F., Gehring, U., Grazuleviciene, R., Gruzieva, O., Heude, B., Hoek, G., Hoogh, K. d., van den Hooven, E. H., Håberg, S. E., Jaddoe, V. W. V., Klümper, C., Korek, M., Krämer, U., Lerchundi, A., Lepeule, J., Nafstad, P., Nystad, W., Patelarou, E., Porta, D., Postma, D., Raaschou-Nielsen, O., Rudnai, P., Sunyer, J., Stephanou, E., Sørensen, M., Thiering, E., Tuffnell, D., Varró, M. J., Vrijkotte, T. G. M., Wijga, A., Wilhelm, M., Wright, J., Nieuwenhuijsen, M. J., Pershagen, G., Brunekreef, B., Kogevinas, M., and Slama, R.: Ambient air pollution and low birthweight: a European cohort study (ESCAPE), Lancet Resp. Med., 1, 695–704, https://doi.org/10.1016/s2213-2600(13)70192-9 , 2013. 

Pelliccioni, A. and Tirabassi, T.: Air dispersion model and neural network: a new perspective for integrated models in the simulation of complex situations, Environ. Modell. Softw., 21, 539–546, 2006. 

Peters, A., Dockery, D. W., Muller, J. E., and Mittleman, M. A.: Increased Particulate Air Pollution and the Triggering of Myocardial Infarction, Circulation, 103, 2810–2815, https://doi.org/10.1161/01.Cir.103.23.2810 , 2001. 

Petroff, A. and Zhang, L.: Development and validation of a size-resolved particle dry deposition scheme for application in aerosol transport models, Geosci. Model Dev., 3, 753–769, https://doi.org/10.5194/gmd-3-753-2010 , 2010. 

Petroff, A., Mailliat, A., Amielh, M., and Anselmet, F.: Aerosol dry deposition on vegetative canopies. Part II: A new modeling approach and applications, Atmos. Environ., 42, 3654–3683, 2008. 

Pfister, G., Eastham, S., Arellano, A. F., Aumont, B., Barsanti, K., Barth, M., Conley, A., Davis, N., Emmons, L., Fast, J., Fiore, A., Gaubert, B., Goldhaber, S., Granier, C., Grell, G., Guevara, M., Henze, D., Hodzic, A., Liu, X., Marsh, D., Orlando, J., Plane, J., Polvani, L., Rosenlof, K., Steiner, A., Jacob, D., and Brasseur, G.: The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), B. Am. Meteorol. Soc., 101, E1743–E1760, https://doi.org/10.1175/bams-d-19-0331.1 , 2020. 

Phosri, A., Ueda, K., Phung, V. L. H., Tawatsupa, B., Honda, A., and Takano, H.: Effects of ambient air pollution on daily hospital admissions for respiratory and cardiovascular diseases in Bangkok, Thailand, Sci. Total Environ., 651, 1144–1153, https://doi.org/10.1016/j.scitotenv.2018.09.183 , 2019. 

Pisoni, E. and Van Dingenen, R.: Comment to the paper “Assessing nitrogen dioxide (NO 2 ) levels as a contributing factor to coronavirus (COVID-19) fatality”, by Ogen, 2020, Sci. Total Environ., 738, 139853, https://doi.org/10.1016/j.scitotenv.2020.139853 , 2020. 

Pleim, J., Mathur, R., Rao, S. T., Fast, J., and Baklanov, A.: Integrated Meteorology and Chemistry Modeling: Evaluation and Research Needs, B. Am. Meteorol. Soc., 95, ES81– ES84, https://doi.org/10.1175/BAMS-D-13-00107.1 , 2014. 

Plejdrup, M. S., Nielsen, O.-K., and Brandt, J.: Spatial emission modelling for residential wood combustion in Denmark, Atmos. Environ., 144, 389–396, https://doi.org/10.1016/j.atmosenv.2016.09.013 , 2016. 

Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., and Thurston, G. D.: Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution, JAMA-J. Am. Med. Assoc., 287, 1132–1141, https://doi.org/10.1001/jama.287.9.1132 , 2002. 

Pope III, C. A., Lefler, J. S., Ezzati, M., Higbee, J. D., Marshall, J. D., Kim, S.-Y., Bechle, M., Gilliat, K. S., Vernon, S. E., Robinson, A. L., and Burnett, R. T.: Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of U.S. Adults, Environ. Health Persp., 127, 077007, https://doi.org/10.1289/ehp4438 , 2019. 

Pope III, C. A., Coleman, N., Pond, Z. A., and Burnett, R. T.: Fine particulate air pollution and human mortality: 25 + years of cohort studies, Environ. Res., 183, 108924, https://doi.org/10.1016/j.envres.2019.108924 , 2020. 

Poulsen, A. H., Hvidtfeldt U. A., Sørensen, M., Puett, R., Ketzel, M., Brandt, J., Christensen, J. H., Geels, C., and Raaschou-Nielsen, O.: Components of particulate matter air-pollution and brain tumors, Environ. Int., 144, 106046, https://doi.org/10.1016/j.envint.2020.106046 , 2020. 

Prank, M., Sofiev, M., Tsyro, S., Hendriks, C., Semeena, V., Vazhappilly Francis, X., Butler, T., Denier van der Gon, H., Friedrich, R., Hendricks, J., Kong, X., Lawrence, M., Righi, M., Samaras, Z., Sausen, R., Kukkonen, J., and Sokhi, R.: Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005, Atmos. Chem. Phys., 16, 6041–6070, https://doi.org/10.5194/acp-16-6041-2016 , 2016. 

Pražnikar, Z. and Pražnikar, J.: The effects of particulate matter air pollution on respiratory health and on the cardiovascular system, Slovenian Journal of Public Health, 51, 190–199, https://doi.org/10.2478/v10152-012-0022-z , 2012. 

Pregger, T. and Friedrich, R.: Effective pollutant emission heights for atmospheric transport modelling based on real-world information, Environ. Pollut., 157, 552–560, https://doi.org/10.1016/j.envpol.2008.09.027 , 2009. 

Raaschou-Nielsen, O., Andersen, Z. J., Beelen, R., Samoli, E., Stafoggia, M., Weinmayr, G., Hoffmann, B., Fischer, P., Nieuwenhuijsen, M. J., Brunekreef, B., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Sommar, J., Forsberg, B., Modig, L., Oudin, A., Oftedal, B., Schwarze, P. E., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Sørensen, M., Tjønneland, A., Ellermann, T., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, B., Key, T. J., Hoogh, K. d., Concin, H., Nagel, G., Vilier, A., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Trichopoulou, A., Bamia, C., Vineis, P., and Hoek, G.: Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE), Lancet Oncol., 14, 813–822, https://doi.org/10.1016/s1470-2045(13)70279-1 , 2013. 

Raaschou-Nielsen, O., Beelen, R., Wang, M., Hoek, G., Andersen, Z. J., Hoffmann, B., Stafoggia, M., Samoli, E., Weinmayr, G., Dimakopoulou, K., Nieuwenhuijsen, M., Xun, W. W., Fischer, P., Eriksen, K. T., Sørensen, M., Tjønneland, A., Ricceri, F., Hoogh, K. d., Key, T., Eeftens, M., Peeters, P. H., Bueno-de-Mesquita, H. B., Meliefste, K., Oftedal, B., Schwarze, P. E., Nafstad, P., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Penell, J., Faire, U. d., Korek, M., Pedersen, N., Östenson, C.-G., Pershagen, G., Fratiglioni, L., Concin, H., Nagel, G., Jaensch, A., Ineichen, A., Naccarati, A., Katsoulis, M., Trichpoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Brunekreef, B., Sokhi, R. S., Katsouyanni, K., and Vineis, P.: Particulate matter air pollution components and risk for lung cancer, Environ. Int., 87, 66–73, https://doi.org/10.1016/j.envint.2015.11.007 , 2016. 

Raaschou-Nielsen, O., Thorsteinson, E., Antonsen, S., Holst, G. J., Sigsgaard, T., Geels, C., Frohn, L. M., Christensen, J. H., Brandt, J., Pedersen, C. B., and Hvidtfeldt, U. A.: Long-term exposure to air pollution and mortality in the Danish population a nationwide study, eClinicalMedicine, 28, 100605, https://doi.org/10.1016/j.eclinm.2020.100605 , 2020. 

Ramacher, M. O. P., Karl, M., Bieser, J., Jalkanen, J.-P., and Johansson, L.: Urban population exposure to NO x emissions from local shipping in three Baltic Sea harbour cities – a generic approach, Atmos. Chem. Phys., 19, 9153–9179, https://doi.org/10.5194/acp-19-9153-2019 , 2019. 

Ramacher, M. O. P., Tang, L., Moldanová, J., Matthias, V., Karl, M., Fridell, E., and Johansson, L.: The impact of ship emissions on air quality and human health in the Gothenburg area – Part II: Scenarios for 2040, Atmos. Chem. Phys., 20, 10667–10686, https://doi.org/10.5194/acp-20-10667-2020 , 2020. 

Rao, S. T., Luo, H., Astitha, M., Hogrefe, C., Garcia, V., and Mathur, R.: On the limit to the accuracy of regional-scale air quality models, Atmos. Chem. Phys., 20, 1627–1639, https://doi.org/10.5194/acp-20-1627-2020 , 2020. 

Rees, N., Wickham, A., and Choi, Y.: Silent Suffocation in Africa – Air Pollution is a Growing Menace, Affecting the Poorest Children the Most, United Nations Children's Fund (UNICEF), New York, https://www.unicef.org/media/55081/file/Silentsuffocationinafricaairpollution201920.pdf (last access: 21 February 2022), 2019. 

Resler, J., Geletič, J., Krč, P., Eben, K., Belda, M., Fuka, L., Huszár, P., Karlický, J., Vlček, O., Benešová, N., Keder, J., Bauerová, P., Škáchová, H., Ďoubalová, J., Žák, M., Sühring, M., and Schwenkel, J.: Validation of the air quality and meteorological values modelled by PALM-4U model against observation campaign in Prague-Dejvice, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., Pipilis, I., Hatfield, UK, p. 160, https://doi.org/10.18745/pb.22217 , 2020. 

Ribeiro, I., Martilli, A., Falls, M., Zonato, A., and Villalba, G.: Highly resolved WRF-BEP/BEM simulations over Barcelona urban area with LCZ, Atmos. Res., 248, 105220, https://doi.org/10.1016/j.atmosres.2020.105220 , 2021. 

Robock, A., Oman, L., and Stenchikov, G. L.: Nuclear winter revisited with a modern climate model and current nuclear arsenals: Still catastrophic consequences, J. Geophys. Res., 112, D13107, https://doi.org/10.1029/2006JD008235 , 2007. 

Rodins, V., Lucht, S., Ohlwein, S., Hennig, F., Soppa, V., Erbel, R., Jöckel, K.-H., Weimar, C., Hermann, D. M., Schramm, S., Moebus, S., Slomiany, U., and Hoffmann, B.: Long-term exposure to ambient source-specific particulate matter and its components and incidence of cardiovascular events – The Heinz Nixdorf Recall study, Environ. Int., 142, 105854, https://doi.org/10.1016/j.envint.2020.105854 , 2020. 

Rodriguez, D., Parent, E., Eymard, L., Valari, M., and Payan, S.: NO X and PM 10 Bayesian concentration estimates using high-resolution numerical simulations and ground measurements over Paris, France, Atmos. Environ. X, 3, 100038, https://doi.org/10.1016/j.aeaoa.2019.100038 , 2019. 

Roos, J.: Ermittlung und Bewertung von vermiedenen Gesundheitsschäden im Rahmen der Technikbewertung, Universität Stuttgart, https://doi.org/10.18419/opus-9177 , 2017. 

Russell, W. T.: The relative influence of fog and low temperature on the mortality from respiratory disease, Lancet, 2, 1128, https://doi.org/10.1016/S0140-6736(01)02367-4 , 1926. 

Sakai, R., Sasaki, D., Obayashi, S., and Nakahashi, K.: Wavelet-based data compression for flow simulation on block-structured Cartesian mesh, Int. J. Numer. Meth. Fl., 73, 462–476, https://doi.org/10.1002/fld.3808 , 2013. 

Sakellaris, I., Bartzis, J., Neuhäuser, J., Friedrich, R., Gotti, A., and Sarigiannis, D.: A novel approach for air quality trend studies and its application to European urban environments: The ICARUS project, Atmos. Environ., 273, 118973, https://doi.org/10.1016/j.atmosenv.2022.118973 , 2022. 

Salamanca, F., Martilli, A., Tewari, M., and Chen, F.: A Study of the Urban Boundary Layer Using Different Urban Parameterizations and High-Resolution Urban Canopy Parameters with WRF, J. Appl. Meteorol. Clim., 50, 1107–1128, https://doi.org/10.1175/2010jamc2538.1 , 2011. 

Salamanca, F., Zhang, Y., Barlage, M., Chen, F., Mahalov, A., and Miao, S.: Evaluation of the WRF-Urban Modeling System Coupled to Noah and Noah-MP Land Surface Models Over a Semiarid Urban Environment, J. Geophys. Res.-Atmos., 123, 2387–2408, https://doi.org/10.1002/2018jd028377 , 2018. 

Salthammer, T., Schripp, T., Wientzek, S., and Wensing, M.: Impact of operating wood-burning fireplace ovens on indoor air quality, Chemosphere, 103, 205–211, https://doi.org/10.1016/j.chemosphere.2013.11.067 , 2014. 

Samoli, E., Stafoggia, M., Rodopoulou, S., Ostro, B., Declercq, C., Alessandrini, E., Díaz, J., Karanasiou, A., Kelessis, A. G., Le Tertre, A., Pandolfi, P., Randi, G., Scarinzi, C., Zauli-Sajani, S., Katsouyanni, K., and Forastiere, F.: Associations between Fine and Coarse Particles and Mortality in Mediterranean Cities: Results from the MED-PARTICLES Project, Environ. Health Persp., 121, 932–938, https://doi.org/10.1289/ehp.1206124 , 2013. 

San José, R., Pérez, J. L., Pérez, L., and Gonzalez, R. M.: A multiscale simulation tool to assess the effects of nature-based solutions (NBS) in urban air quality, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 10, https://doi.org/10.18745/pb.22217 , 2020. 

Santiago, J. L., Borge, R., Martin, F., La Paz, D. d., Martilli, A., Lumbreras, J., and Sanchez, B.: Evaluation of a CFD-based approach to estimate pollutant distribution within a real urban canopy by means of passive samplers, Sci. Total Environ., 576, 46–58, https://doi.org/10.1016/j.scitotenv.2016.09.234 , 2017. 

Saraga, D., Maggos, T., Degrendele, C., Klanova, J., Horvat, M., Kocman, D., Kanduc, T., Dos Santos, S. G., Franco, R., Gomez, P. M., Manousakas, M., Bairachtari, K., Eleftheriadis, K., Kermenidou, M., Karakitsios, S., Gotti, A., and Sarigiannis, D.: Multi-city comparative PM 2.5 source apportionment for fifteen sites in Europe: The ICARUS project, Sci. Total Environ., 751, 141855, https://doi.org/10.1016/j.scitotenv.2020.141855 , 2021. 

Sarigiannis, D. and Karakitsios, S.: Report on the methodology for estimating health effects of individuals or population groups and health impact results in the ICARUS participating cities, Deliverable D4.3 of the EU Horizon 2020 project ICARUS, 45 pp., https://icarus2020.eu/wp-content/uploads/2018/06/ICARUS_D4.3.pdf (last access: 25 February 2022), 2018. 

Savolahti, M., Karvosenoja, N., Soimakallio, S., Kupiainen, K., Tissari, J., and Paunu, V.-V.: Near-term climate impacts of Finnish residential wood combustion, Energ. Policy, 133, 110837, https://doi.org/10.1016/j.enpol.2019.06.045 , 2019. 

Schade, S., Herding, W., Fellermann, A., and Kotsev, A.: Joint Statement on new opportunities for air quality sensing – lower-cost sensors for public authorities and citizen science initiatives, Research Ideas and Outcomes, 5, e34059, https://doi.org/10.3897/rio.5.e34059 , 2019. 

Schäfer, K., Lande, K., Grimm, H., Jenniskens, G., Gijsbers, R., Ziegler, V., Hank, M., and Budde, M.: High-resolution Assessment of Air Quality in Urban Areas – A Business Model Perspective, Atmosphere, 12, 595, https://doi.org/10.3390/atmos12050595 , 2021. 

Scherer, D., Ament, F., Emeis, S., Fehrenbach, U., Leitl, B., Scherber, K., Schneider, C., and Vogt, U.: Three-Dimensional Observation of Atmospheric Processes in Cities, Meteorol. Z., 28, 121–138, 2019. 

Schieberle, C.: Development of a stochastic optimization approach to determine cost-efficient environmental protection strategies: case study of policies for the future European passenger transport sector with a focus on rail-bound and on-road activities, Institut fuer Energiewirtschaft und Rationelle Energieanwendung, Universitaet Stuttgart, Stuttgart, https://doi.org/10.18419/opus-10473 , 2019. 

Schmid, D.: D2.1 Report and data on emission inventory at EU-wide level for the considered pollutants and GHGs for the years 2015, 2020 and 2030, EU Horizon 2020 Project: 690105 – ICARUS, https://icarus2020.eu/wp-content/uploads/2018/03/ICARUS-Deliverable-D2.1_FINAL_REVISED.pdf (last access: 22 February 2022), 2018. 

Schmid, D., Korkmaz, P., Blesl, M., Fahl, U., and Friedrich, R.: Analyzing transformation pathways to a sustainable European energy system – Internalization of health damage costs caused by air pollution, Energy Strateg. Rev., 26, 100417, https://doi.org/10.1016/j.esr.2019.100417 , 2019. 

Schneider, P., Castell, N., Vogt, M., Dauge, F. R., Lahoz, W. A., and Bartonova, A.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234–247, https://doi.org/10.1016/j.envint.2017.05.005 , 2017. 

Schraufnagel, D. E., Balmes, J. R., Cowl, C. T., Matteis, S. d., Jung, S.-H., Mortimer, K., Perez-Padilla, R., Rice, M. B., Riojas-Rodriguez, H., Sood, A., Thurston, G. D., To, T., Vanker, A., and Wuebbles, D. J.: Air Pollution and Noncommunicable Diseases, Chest, 155, 409–416, https://doi.org/10.1016/j.chest.2018.10.042 , 2019. 

Schrenk, H. H.: Air pollution in Donora, Pa, Federal Security Agency Public Health Service Bureau of State Services Division of Industrial Hygiene, Washington, Public health bulletin no. 306, 173 pp., 1949. 

Schripp, T., Kirsch, I., and Salthammer, T.: Characterization of particle emission from household electrical appliances, Sci. Total Environ., 409, 2534–2540, https://doi.org/10.1016/j.scitotenv.2011.03.033 , 2011. 

Schripp, T., Markewitz, D., Uhde, E., and Salthammer, T.: Does e-cigarette consumption cause passive vaping?, Indoor Air, 23, 25–31, https://doi.org/10.1111/j.1600-0668.2012.00792.x , 2013. 

Schripp, T., Salthammer, T., Wientzek, S., and Wensing, M.: Chamber studies on nonvented decorative fireplaces using liquid or gelled ethanol fuel, Environ. Sci. Technol., 48, 3583–3590, 2014. 

Schrödner, R., Genz, C., Heinold, B., Baars, H., Henning, S., Madenach, N., Carbajal Henken, C., Costa Surós, M., Sourdeval, O., Hesemann, J., Brueck, M., Cioni, G., Hoose, C., Tegen, I., and Quaas, J.: Aerosol-cloud interaction in 1985 and today, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 61, https://doi.org/10.18745/pb.22217 , 2020. 

Schubert, S. and Grossman-Clarke, S.: Evaluation of the coupled COSMO-CLM/DCEP model with observations from BUBBLE, Q. J. Roy. Meteor. Soc., 140, 2465–2483, https://doi.org/10.1002/qj.2311 , 2014. 

Schwartz, J., Dockery, D. W., and Neas, L. M.: Is Daily Mortality Associated Specifically with Fine Particles?, J. Air Waste Manage., 46, 927–939, https://doi.org/10.1080/10473289.1996.10467528 , 1996. 

Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd edn., John Wiley & Sons, 1152 pp., ISBN: 978-1-118-94740-1, 2016. 

Sevilla, I., Chrobocinski, P., Barmpas, F., Schmidt, F., Kerle, N., Kostaridis, A., Doulamis, A., Russotto, R., and Huang, R.: Improving Resilience of Transport Instrastructure to Climate Change and other natural and Manmande events based on the combined use of Terrestrial and Airbone Sensors and Advanced Modelling Tools, 14 ∘ Congreso Nacional del Medio Ambiente (CONAMA 2018), November 2018, MADRID, Spain, 11 pp., hal-02280917v2, 2018. 

Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., and Gumy, S.: Half the world's population are exposed to increasing air pollution, npj Climate and Atmospheric Science, 3, 23, https://doi.org/10.1038/s41612-020-0124-2 , 2020. 

Shaddick, G., Salter, J. M., Peuch, V.-H., Ruggeri, G., Thomas, M. L., Mudu, P., Tarasova, O., Baklanov, A., and Gumy, S.: Global Air Quality: An Inter-Disciplinary Approach to Exposure Assessment for Burden of Disease Analyses, Atmosphere, 12, 48, https://doi.org/10.3390/atmos12010048 , 2021. 

Sharma, A., Fernando, H. J. S., Hamlet, A. F., Hellmann, J. J., Barlage, M., and Chen, F.: Urban meteorological modeling using WRF: A sensitivity study, Int. J. Climatol., 37, 1885–1900, https://doi.org/10.1002/joc.4819 , 2017. 

Siddika, N., Rantala, A. K., Antikainen, H., Balogun, H., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Synergistic effects of prenatal exposure to fine particulate matter (PM 2.5 ) and ozone (O 3 ) on the risk of preterm birth: A population-based cohort study, Environ. Res., 176, 108549, https://doi.org/10.1016/j.envres.2019.108549 , 2019. 

Siddika, N., Rantala, A. K., Antikainen, H., Balogun, H., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Short-term prenatal exposure to ambient air pollution and risk of preterm birth – A population-based cohort study in Finland, Environ. Res., 184, 109290, https://doi.org/10.1016/j.envres.2020.109290 , 2020. 

Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, https://doi.org/10.5194/acp-12-7825-2012 , 2012. 

Singh, V., Sokhi, R. S., and Kukkonen, J.: PM 2.5 concentrations in London for 2008 – A modeling analysis of contributions from road traffic, J. Air Waste Manage., 64, 509–518, https://doi.org/10.1080/10962247.2013.848244 , 2014. 

Singh, V., Sokhi, R. S., Beig, G., Biswal, A., Sahu, S. K., Sandeepan, S., Stanley, W., Momoh, K., and Fritz, S. C.: Analysis of air quality in the megacity of Delhi with observations and a multiscale coupled modelling system, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 150, https://doi.org/10.18745/pb.22217 , 2020a. 

Singh, V., Sokhi, R. S., and Kukkonen, J.: An approach to predict population exposure to ambient air PM 2.5 concentrations and its dependence on population activity for the megacity London, Environ. Pollut., 257, 113623, https://doi.org/10.1016/j.envpol.2019.113623 , 2020b. 

Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146, 3445–3460, https://doi.org/10.1175/mwr-d-18-0155.1 , 2018. 

Skjøth, C. A., Geels, C., Berge, H., Gyldenkærne, S., Fagerli, H., Ellermann, T., Frohn, L. M., Christensen, J., Hansen, K. M., Hansen, K., and Hertel, O.: Spatial and temporal variations in ammonia emissions – a freely accessible model code for Europe, Atmos. Chem. Phys., 11, 5221–5236, https://doi.org/10.5194/acp-11-5221-2011 , 2011. 

Smith, T. W. P., Jalkanen, J. P., Anderson, B. A., Corbett, J. J., Faber, J., Hanayama, S., O'Keeffe, E., Parker, S., Johansson, L., Aldous, L., Raucci, C., Traut, M., Ettinger, S., Nelissen, D., Lee, D. S., Ng, S., Agrawal, A., Winebrake, J. J., Hoen, M., Chesworth, S., and Pandey, A.: Third IMO GHG Study 2014, International Maritime Organization (IMO) London, UK, April 2015. 

Smith, J. D., Mitsakou, C., Kitwiroon, N., Barratt, B. M., Walton, H. A., Taylor, J. G., Anderson, H. R., Kelly, F. J., and Beevers, S. D.: London Hybrid Exposure Model: Improving Human Exposure Estimates to NO 2 and PM 2.5 in an Urban Setting, Environ. Sci. Technol., 50, 11760–11768, https://doi.org/10.1021/acs.est.6b01817 , 2016. 

Smith, J. D., Barratt, B. M., Fuller, G. W., Kelly, F. J., Loxham, M., Nicolosi, E., Priestman, M., Tremper, A. H., and Green, D. C.: PM 2.5 on the London Underground, Environ. Int., 134, 105188, https://doi.org/10.1016/j.envint.2019.105188 , 2020. 

Soares, J., Kousa, A., Kukkonen, J., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., and Karppinen, A.: Refinement of a model for evaluating the population exposure in an urban area, Geosci. Model Dev., 7, 1855–1872, https://doi.org/10.5194/gmd-7-1855-2014 , 2014. 

Sofiev, M., Winebrake, J. J., Johansson, L., Carr, E. W., Prank, M., Soares, J., Vira, J., Kouznetsov, R., Jalkanen, J.-P., and Corbett, J. J.: Cleaner fuels for ships provide public health benefits with climate tradeoffs, Nat. Commun., 9, 406, https://doi.org/10.1038/s41467-017-02774-9 , 2018. 

Sokhi, R. (Ed.): World Atlas of Atmospheric Pollution, Anthem Press, London, https://doi.org/10.7135/upo9780857288448 , 2012. 

Sokhi, R. S., Baklanov, A., and Schlünzen, K. H. (Eds.): Mesoscale modelling for meteorological and air pollution applications, Anthem Press, an imprint of Wimbledon Publishing Company, London, 342 pp., 2018. 

Sokhi, R. S., Singh, V., Querol, X., Finardi. S., Targino, A. C., Andrade, M. F., Pavlovic, R., Garland, R. M., Massagué, J., Kong, S., Baklanov, A., Ren, L., Tarasova, O., Carmichael, G., Peuch, V. H., Anand, V., Arbilla, G., Badali, K., Beig, G., Belalcazar, L. C., Bolignano, A., Brimblecombe, P., Camacho, P., Casallas, A., Charland, J. P., Choi, J., Chourdakis, E., Coll, I., Collins, M., Cyrys, J., da Silva, C. M., Di Giosa, A. D., Di Leo, A., Ferro, C., Gavidia-Calderon, M., Gayen, A., Ginzburg, A., Godefroy, F., Gonzalez, Y. A., Guevara-Luna, M., Haque, S. M., Havenga, H., Herod, D., Hõrrak, U., Hussein, T., Ibarra, S., Jaimes, M., Kaasik, M., Khaiwal, R., Kim, J., Kousa, A., Kukkonen, J., Kulmala, M., Kuula, J., La Violette, N., Lanzani, G., Liu, X., MacDougall, S., Manseau, P. M., Marchegiani, G., McDonald, B., Mishra, S. V., Molina, L. T., Mooibroek, D., Mor, S., Moussiopoulos, N., Murena, F., Niemi, J. V., Noe, S., Nogueira, T., Norman, M., Pérez-Camaño, J. L., Petäjä, T., Piketh, S., Rathod, A., Reid, K., Retama, A., Rivera, O., Rojas, N. Y., Rojas-Quincho, J. P., San José, R., Sánchez, O., Seguel, R. J., Sillanpää, S., Su, Y., Tapper, N., Terrazas, A., Timonen, H., Toscano, D., Tsegas, G., Velders, G. J. M., Vlachokostas, C., von Schneidemesser, E., VPM, R., Yadav, R., Zalakeviciute, R., and Zavala, M.: A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions, Environ Int., 157, 106818, https://doi.org/10.1016/j.envint.2021.106818 , 2021. 

Solazzo, E., Bianconi, R., Vautard, R., Appel, K. W., Moran, M. D., Hogrefe, C., Bessagnet, B., Brandt, J., Christensen, J. H., Chemel, C., Coll, I., Denier van der Gon, H., Ferreira, J., Forkel, R., Francis, X. V., Grell, G., Grossi, P., Hansen, A. B., Jeričević, A., Kraljević, L., Miranda, A. I., Nopmongcol, U., Pirovano, G., Prank, M., Riccio, A., Sartelet, K. N., Schaap, M., Silver, J. D., Sokhi, R. S., Vira, J., Werhahn, J., Wolke, R., Yarwood, G., Zhang, J., Rao, S. T., and Galmarini, S.: Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII, Atmos. Environ., 53, 60–74, https://doi.org/10.1016/j.atmosenv.2012.01.003 , 2012. 

Son, J.-Y., Liu, J. C., and Bell, M. L.: Temperature-related mortality: a systematic review and investigation of effect modifiers, Environ. Res. Lett., 14, 073004, https://doi.org/10.1088/1748-9326/ab1cdb , 2019. 

Sonawane, N. V., Patil, R. S., and Sethi, V.: Health benefit modelling and optimization of vehicular pollution control strategies, Atmos. Environ., 60, 193–201, 2012. 

Soret, A., Guevara, M., and Baldasano, J. M.: The potential impacts of electric vehicles on air quality in the urban areas of Barcelona and Madrid (Spain), Atmos. Environ., 99, 51–63, 2014. 

Soulhac, L., Salizzoni, P., Mejean, P., Didier, D., and Rios, I.: The model SIRANE for atmospheric urban pollutant dispersion; PART II, validation of the model on a real case study, Atmos. Environ., 49, 320–337, https://doi.org/10.1016/j.atmosenv.2011.11.031 , 2012. 

Souza, D. M., Teixeira, R. F. M., and Ostermann, O. P.: Assessing biodiversity loss due to land use with Life Cycle Assessment: are we there yet?, Glob. Change Biol., 21, 32–47, https://doi.org/10.1111/gcb.12709 , 2015. 

Stafoggia, M., Samoli, E., Alessandrini, E., Cadum, E., Ostro, B., Berti, G., Faustini, A., Jacquemin, B., Linares, C., Pascal, M., Randi, G., Ranzi, A., Stivanello, E., and Forastiere, F.: Short-term Associations between Fine and Coarse Particulate Matter and Hospitalizations in Southern Europe: Results from the MED-PARTICLES Project, Environ. Health Persp., 121, 1026–1033, https://doi.org/10.1289/ehp.1206151 , 2013. 

Stafoggia, M., Cesaroni, G., Peters, A., Andersen, Z. J., Badaloni, C., Beelen, R., Caracciolo, B., Cyrys, J., Faire, U. d., Hoogh, K. d., Eriksen, K. T., Fratiglioni, L., Galassi, C., Gigante, B., Havulinna, A. S., Hennig, F., Hilding, A., Hoek, G., Hoffmann, B., Houthuijs, D., Korek, M., Lanki, T., Leander, K., Magnusson, P. K., Meisinger, C., Migliore, E., Overvad, K., Östenson, C.-G., Pedersen, N. L., Pekkanen, J., Penell, J., Pershagen, G., Pundt, N., Pyko, A., Raaschou-Nielsen, O., Ranzi, A., Ricceri, F., Sacerdote, C., Swart, W. J. R., Turunen, A. W., Vineis, P., Weimar, C., Weinmayr, G., Wolf, K., Brunekreef, B., and Forastiere, F.: Long-Term Exposure to Ambient Air Pollution and Incidence of Cerebrovascular Events: Results from 11 European Cohorts within the ESCAPE Project, Environ. Health Persp., 122, 919–925, https://doi.org/10.1289/ehp.1307301 , 2014. 

Stohl, A., Kim, J., Li, S., O'Doherty, S., Mühle, J., Salameh, P. K., Saito, T., Vollmer, M. K., Wan, D., Weiss, R. F., Yao, B., Yokouchi, Y., and Zhou, L. X.: Hydrochlorofluorocarbon and hydrofluorocarbon emissions in East Asia determined by inverse modeling, Atmos. Chem. Phys., 10, 3545–3560, https://doi.org/10.5194/acp-10-3545-2010 , 2010. 

Stojiljkovic, A., Kauhaniemi, M., Kukkonen, J., Kupiainen, K., Karppinen, A., Denby, B. R., Kousa, A., Niemi, J. V., and Ketzel, M.: The impact of measures to reduce ambient air PM 10 concentrations originating from road dust, evaluated for a street canyon in Helsinki, Atmos. Chem. Phys., 19, 11199–11212, https://doi.org/10.5194/acp-19-11199-2019 , 2019. 

Stone, R.: Counting the Cost of London's Killer Smog, Science, 298, 2106–2107, https://doi.org/10.1126/science.298.5601.2106b , 2002. 

Stone, V., Miller, M. R., Clift, M. J. D., Elder, A., Mills, N. L., Møller, P., Schins, R. P. F., Vogel, U., Kreyling, W. G., Alstrup Jensen, K., Kuhlbusch, T. A. J., Schwarze, P. E., Hoet, P., Pietroiusti, A., Vizcaya-Ruiz, A. d., Baeza-Squiban, A., Teixeira, J. P., Tran, C. L., and Cassee, F. R.: Nanomaterials Versus Ambient Ultrafine Particles: An Opportunity to Exchange Toxicology Knowledge, Environ. Health Persp., 125, 106002, https://doi.org/10.1289/ehp424 , 2017. 

Sun, D., Zhang, Y., Xue, R., and Zhang, Y.: Modeling carbon emissions from urban traffic system using mobile monitoring, Sci. Total Environ., 599–600, 944–951, https://doi.org/10.1016/j.scitotenv.2017.04.186 , 2017. 

Suter, I. and Brunner, D.: Influence of boundary conditions and cloud chemistry on sulfate concentrations in a nested model setup, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 55, https://doi.org/10.18745/pb.22217 , 2020. 

Tan, J., Mu, L., Huang, J., Yu, S., Chen, B., and Yin, J.: An initial investigation of the association between the SARS outbreak and weather: with the view of the environmental temperature and its variation, J. Epidemiol. Commun. H., 59, 186–192, https://doi.org/10.1136/jech.2004.020180 , 2005. 

Tan, J., Fu, J. S., Carmichael, G. R., Itahashi, S., Tao, Z., Huang, K., Dong, X., Yamaji, K., Nagashima, T., Wang, X., Liu, Y., Lee, H.-J., Lin, C.-Y., Ge, B., Kajino, M., Zhu, J., Zhang, M., Liao, H., and Wang, Z.: Why do models perform differently on particulate matter over East Asia? A multi-model intercomparison study for MICS-Asia III, Atmos. Chem. Phys., 20, 7393–7410, https://doi.org/10.5194/acp-20-7393-2020 , 2020. 

Tarín-Carrasco, P., Im, U., Geels, C., Palacios-Peña, L., and Jiménez-Guerrero, P.: Reducing future air pollution-related premature mortality over Europe by mitigating emissions: assessing an 80 % renewable energies scenario, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-86 , in review, 2021. 

Teuchies, J., Cox, T. J. S., Van Itterbeeck, K., Meysman, F. J. R., and Blust, R.: The impact of scrubber discharge on the water quality in estuaries and ports, Environmental Sciences Europe, 32, 103, https://doi.org/10.1186/s12302-020-00380-z , 2020. 

Theloke, J. and Friedrich, R.: Compilation of a database on the composition of anthropogenic VOC emissions for atmospheric modeling in Europe, Atmos. Environ., 41, 4148–4160, https://doi.org/10.1016/j.atmosenv.2006.12.026 , 2007. 

Thiruchittampalam, B.: Entwicklung und Anwendung von Methoden und Modellen zur Berechnung von räumlich und zeitlich hochaufgelösten Emissionen in Europa, Institut für Energiewirtschaft und Rationelle Energieanwendung, https://doi.org/10.18419/opus-2325 , 2014. 

Thompson, J. E.: Crowd-sourced air quality studies: A review of the literature & portable sensors, Trends in Environmental Analytical Chemistry, 11, 23–34, https://doi.org/10.1016/j.teac.2016.06.001 , 2016. 

Thunis, P.: On the validity of the incremental approach to estimate the impact of cities on air quality, Atmos. Environ., 173, 210–222, https://doi.org/10.1016/j.atmosenv.2017.11.012 , 2018. 

Thunis, P., Miranda, A., Baldasano, J. M., Blond, N., Douros, J., Graff, A., Janssen, S., Juda-Rezler, K., Karvosenoja, N., Maffeis, G., Martilli, A., Rasoloharimahefa, M., Real, E., Viaene, P., Volta, M., and White, L.: Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU, Environ. Sci. Policy, 65, 13–21, https://doi.org/10.1016/j.envsci.2016.03.013 , 2016. 

Thunis, P., Clappier, A., Tarrason, L., Cuvelier, C., Monteiro, A., Pisoni, E., Wesseling, J., Belis, C. A., Pirovano, G., Janssen, S., Guerreiro, C., and Peduzzi, E.: Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches, Environ. Int., 130, 104825, https://doi.org/10.1016/j.envint.2019.05.019 , 2019. 

Thurston, G. D., Kipen, H., Annesi-Maesano, I., Balmes, J., Brook, R. D., Cromar, K., De Matteis, S., Forastiere, F., Forsberg, B., Frampton, M. W., Grigg, J., Heederik, D., Kelly, F. J., N., K., Laumbach, R., Peters, A., Rajagopalan, S. T., Rich, D., Ritz, B., Samet, J. M., Sandstrom, T., Sigsgaard, T., Sunyer, J., and Brunekreef, B.: A joint ERA/ATS policy statement: What constitutes an adverse health effect of air pollution? An analytical framework, Eur. Respir. J., 49, 1600419, https://doi.org/10.1183/13993003.00419-2016 , 2017. 

Thygesen, M., Holst, G. J., Hansen, B., Geels, C., Kalkbrenner, A., Schendel, D., Brandt, J., Pedersen, C. B., and Dalsgaard, S.: Exposure to air pollution in early childhood and the association with Attention-Deficit Hyperactivity Disorder, Environ. Res., 183, 108930, https://doi.org/10.1016/j.envres.2019.108930 , 2020. 

Tinarelli, G. L. and Trini Castelli, S.: Assessment of the Sensitivity to the Input Conditions with a Lagrangian Particle Dispersion Model in the UDINEE Project, Bound.-Lay. Meteorol., 171, 491–512, https://doi.org/10.1007/s10546-018-0413-z , 2019. 

Toon, O. B., Bardeen, C. G., Robock, A., Xia, L., and Kristensen, H.: Rapidly expanding nuclear arsenals in Pakistan and India portend regional and global catastrophe, Science Advances, 5, eaay5478, https://doi.org/10.1126/sciadv.aay5478 , 2019. 

Torras Ortiz, S.: A hybrid dispersion modelling approach for quantifying and assessing air quality in Germany with focus on urban background and kerbside concentrations, dissertation, Universtät Stuttgart, Stuttgart, https://doi.org/10.18419/opus-1990 , 2012. 

Torras Ortiz, S. and Friedrich, R.: A modelling approach for estimating background pollutant concentrations in urban areas, Atmos. Pollut. Res., 4, 147–156, https://doi.org/10.5094/apr.2013.015 , 2013. 

Trusilova, K., Schubert, S., Wouters, H., Früh, B., Grossman-Clarke, S., Demuzere, M., and Becker, P.: The urban land use in the COSMO-CLM model: a comparison of three parameterizations for Berlin, Meteorol. Z., 25, 231–244, https://doi.org/10.1127/metz/2015/0587 , 2016. 

Tsegas, G., Moussiopoulos, N., Barmpas, F., Akylas, V., and Douros, I.: An integrated numerical methodology for describing multiscale interactions on atmospheric flow and pollutant dispersion in the urban atmospheric boundary layer, J. Wind Eng. Ind. Aerod., 144, 191–201, 2015. 

Turco, R. P., Toon, O. B., Ackerman, T. P., Pollack, J. B., and Sagan, C.: Nuclear Winter: Global Consequences of Multiple Nuclear Explosions, Science, 222, 1283–1992, https://doi.org/10.1126/science.222.4630.1283 , 1983. 

UNECE: Protocols, United Nations Economic Commission for Europe, https://unece.org/protocols (last access: 21 February 2022), 2020. 

Van Dingenen, R., Dentener, F., Crippa, M., Leitao, J., Marmer, E., Rao, S., Solazzo, E., and Valentini, L.: TM5-FASST: a global atmospheric source–receptor model for rapid impact analysis of emission changes on air quality and short-lived climate pollutants, Atmos. Chem. Phys., 18, 16173–16211, https://doi.org/10.5194/acp-18-16173-2018 , 2018. 

van Doremalen, N., Bushmaker, T., and Munster, V. J.: Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions, Euro Surveill., 18, 20590, https://doi.org/10.2807/1560-7917.es2013.18.38.20590 , 2013. 

van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., Lloyd-Smith, J. O., de Wit, E., and Munster, V. J.: Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1, New Engl. J. Med., 382, 1564–1567, 2020. 

Vardoulakis, S., Giagloglou, E., Steinle, S., Davis, A., Sleeuwenhoek, A., Galea, K. S., Dixon, K., and Crawford, J. O.: Indoor Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review, Int. J. Env. Res. Pub. He., 17, 8972, https://doi.org/10.3390/ijerph17238972 , 2020. 

Vautard, R., Buitjes, P., Thunis, P., Cuvelier, C., Bedonj, M., Bessagnet, B., Honore, C., Moussiopoulos, N., Pirovano, M., Schaap, M., Stern, R., Tarrason, L., and Wind, P.: Evaluation and intercomparison of Ozone and PM 10 simulations by several chemistry transport models over four European cities within the CityDelta project, Atmos. Environ., 41, 173–188, https://doi.org/10.1016/j.atmosenv.2006.07.039 , 2007. 

Veratti, G., Fabbi, S., Bigi, A., Lupascu, A., Tinarelli, G., Teggi, S., Brusasca, G., Butler, T. M., and Ghermandi, G.: Towards the coupling of a chemical transport model with a micro-scale Lagrangian modelling system for evaluation of urban NO x levels in a European hotspot, Atmos. Environ., 223, 117285, https://doi.org/10.1016/j.atmosenv.2020.117285 , 2020. 

Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, M., Szidat, S., Prévôt, A. S. H., Hueglin, C., Bloemen, H., Wåhlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.: Source apportionment of particulate matter in Europe: A review of methods and results, J. Aerosol Sci., 39, 827–849, 2008. 

Villeneuve, P. J., Jerrett, M., Su, J., Burnett, R. T., Chen, H., Brook, J., Wheeler, A. J., Cakmak, S., and Goldberg, M. S.: A cohort study of intra-urban variations in volatile organic compounds and mortality, Toronto, Canada, Environ. Pollut., 183, 30–39, https://doi.org/10.1016/j.envpol.2012.12.022 , 2013. 

Vodonos, A., Awad, Y. A., and Schwartz, J.: The concentration-response between long-term PM 2.5 exposure and mortality; A meta-regression approach, Environ. Res., 166, 677–689, https://doi.org/10.1016/j.envres.2018.06.021 , 2018. 

Voss, V., Schlünzen, K.H., and Grawe, D.: Atmospheric model data (ATMODAT) - creation of a model data standard for obstacle resolving models, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 128, https://doi.org/10.18745/PB.22217 , 2020. 

Vouitsis, I., Ntziachristos, L., Samaras, C., and Samaras, Z.: Particulate mass and number emission factors for road vehicles based on literature data and relevant gap filling methods, Atmos. Environ., 168, 75–89, https://doi.org/10.1016/j.atmosenv.2017.09.010 , 2017. 

Wang, M., Beelen, R., Stafoggia, M., Raaschou-Nielsen, O., Andersen, Z. J., Hoffmann, B., Fischer, P., Houthuijs, D., Nieuwenhuijsen, M., Weinmayr, G., Vineis, P., Xun, W. W., Dimakopoulou, K., Samoli, E., Laatikainen, T., Lanki, T., Turunen, A. W., Oftedal, B., Schwarze, P., Aamodt, G., Penell, J., Faire, U. d., Korek, M., Leander, K., Pershagen, G., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Eriksen, K. T., Sørensen, M., Tjønneland, A., Bueno-de-Mesquita, B., Eeftens, M., Bots, M. L., Meliefste, K., Krämer, U., Heinrich, J., Sugiri, D., Key, T., Hoogh, K. d., Wolf, K., Peters, A., Cyrys, J., Jaensch, A., Concin, H., Nagel, G., Tsai, M.-Y., Phuleria, H., Ineichen, A., Künzli, N., Probst-Hensch, N., Schaffner, E., Vilier, A., Clavel-Chapelon, F., Declerq, C., Ricceri, F., Sacerdote, C., Marcon, A., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Katsoulis, M., Trichopoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Sokhi, R. S., Brunekreef, B., Katsouyanni, K., and Hoek, G.: Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects, Environ. Int., 66, 97–106, https://doi.org/10.1016/j.envint.2014.01.026 , 2014. 

Wang, K., Yahya, K., Zhang, Y., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J. L., Jiménez-Guerrero, P., Baro, R., Makar, P., and Bennartz, R.: A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of column variable predictions using satellite data, Atmos. Environ., 115, 587–603, https://doi.org/10.1016/j.atmosenv.2014.07.044 , 2015. 

Wang, P., Liu, Y., Qin, Z., and Zhang, G.: A novel hybrid forecasting model for PM10 and SO2 daily concentrations, Sci. Total Environ., 505, 1202–1212, https://doi.org/10.1016/j.scitotenv.2014.10.078 , 2015. 

Wang, Q., Li, B., Benmarhnia, T., Hajat, S., Ren, M., Liu, T., Knibbs, L. D., Zhang, H., Bao, J., Zhang, Y., Zhao, Q., and Huang, C.: Independent and Combined Effects of Heatwaves and PM 2.5 on Preterm Birth in Guangzhou, China: A Survival Analysis, Environ. Health Persp., 128, 017006, https://doi.org/10.1289/ehp5117 , 2020. 

Watkiss, P. and Downing, T.: The social cost of carbon: valuation estimates and their use in UK policy, Integr. Assess., 8, 85–105, 2008. 

Weichenthal, S., Olaniyan, T., Christidis, T., Lavigne, E., Hatzopoulou, M., van Ryswyk, K., Tjepkema, M., and Burnett, R.: Within-city Spatial Variations in Ambient Ultrafine Particle Concentrations and Incident Brain Tumors in Adults, Epidemiology (Cambridge, Mass.), 31, 177–183, https://doi.org/10.1097/ede.0000000000001137 , 2020. 

Weinmayr, G., Romeo, E., De Sario, M., Weiland, S. K., and Forastiere, F.: Short-Term Effects of PM 10 and NO 2 on Respiratory Health among Children with Asthma or Asthma-like Symptoms: A Systematic Review and Meta-Analysis, Environ. Health Persp., 118, 449–457, https://doi.org/10.1289/ehp.0900844 , 2010. 

Werhahn, J., Forkel, R., Emeis, S., Reifeltshammer, R., and Uhrner, U.: Air quality simulations in an urban area within a smart air quality network by the large eddy simulation model PALM-4U, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 126, https://doi.org/10.18745/pb.22217 , 2020. 

Weschler, C. J. and Carslaw, N.: Indoor Chemistry, Environ. Sci. Technol., 52, 2419–2428, https://doi.org/10.1021/acs.est.7b06387 , 2018. 

WHO: Health risks of air pollution in Europe – HRAPIE project; Recommendations for concentration–response functions for cost–benefit analysis of particulate matter, ozone and nitrogen dioxide, WHO Regional Office for Europe, https://www.euro.who.int/en/health-topics/environment-and- health/air-quality/publications/2013/health-risks-of-air- pollution-in-europe-hrapie-project.-recommendations-for-concentrationresponse-pollution-in-europe-hrapie-project.-recommendations-for-concentrationresponse- functions-for-costbenefit-analysis-of-particulate-matter,-ozone-and-nitrogen-dioxide (last access: 22 February 2022), 2013a. 

WHO: Review of evidence on health aspects of air pollution – REVIHAAP Project: Technical Report, Regional Office Europe, Copenhagen, https://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf (last access: 25 February 2022), 2013b. 

WHO: Ambient air pollution: A global assessment of exposure and burden of disease, World Health Organization, Geneva, Switzerland, 131 pp., https://apps.who.int/iris/handle/10665/250141 (last access: 25 February 2022), 2016. 

WHO: WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, ISBN 978-92-4-003422-8 (electronic version), https://apps.who.int/iris/handle/10665/345329 (last access: 25 February 2022), 2021. 

Williams, R., Duvall, R., Kilaru, V., Hagler, G., Hassinger, L., Benedict, K., Rice, J., Kaufman, A., Judge, R., Pierce, G., Allen, G., Bergin, M., Cohen, R. C., Fransioli, P., Gerboles, M., Habre, R., Hannigan, M., Jack, D., Louie, P., Martin, N. A., Penza, M., Polidori, A., Subramanian, R., Ray, K., Schauer, J., Seto, E., Thurston, G., Turner, J., Wexler, A. S., and Ning, Z.: Deliberating performance targets workshop: Potential paths for emerging PM 2.5 and O3 air sensor progress, Atmos. Environ. X, 2, 100031, https://doi.org/10.1016/j.aeaoa.2019.100031 , 2019. 

Winnes, H., Fridell, E., and Moldanova, J.: Effects of Marine Exhaust Gas Scrubbers on Gas and Particle Emissions, J. Mar. Sci. Eng., 8, 299, https://doi.org/10.3390/jmse8040299 , 2020. 

WMO: Coupled Chemistry-Meteorology/Climate Modelling (CCMM): Status and Relevance for Numerical Weather Prediction, Atmospheric Pollution and Climate Research, Geneva, Switzerland, 23–25 February 2015, World Meteorological Organization, Geneva, ISBN 978-92-63-11172-2, 165 pp., 2016. 

WMO: WMO Global Atmosphere Watch (GAW) Implementation Plan: 2016–2023, Geneva, Switzerland, 84 pp., ISBN 978-92-63-11156-2, https://library.wmo.int/doc_num.php?explnum_id=10439 (last access: 11 March 2022), 2017. 

WMO: Best Practices and Training Materials for Chemical Weather/Air Quality Forecasting (CW-AQF), WMO Geneva, Chair, Publications Board, World Meteorological Organization 7 bis, avenue de la Paix – P.O. Box 2300 – CH 1211 Geneva 2 – Switzerland, ISBN: 978-92-63-11262-0, 2020. 

WMO: Review on Meteorological and Air Quality Factors Affecting the COVID-19 Pandemic, World Meteorological Organization, WMO-No. 1262, ISBN: 978-92-63-11262-0, 2021. 

Wolf, K., Stafoggia, M., Cesaroni, G., Andersen, Z. J., Beelen, R., Galassi, C., Hennig, F., Migliore, E., Penell, J., Ricceri, F., Sørensen, M., Turunen, A. W., Hampel, R., Hoffmann, B., Kälsch, H., Laatikainen, T., Pershagen, G., Raaschou-Nielsen, O., Sacerdote, C., Vineis, P., Badaloni, C., Cyrys, J., Hoogh, K. d., Eriksen, K. T., Jedynska, A., Keuken, M., Kooter, I., Lanki, T., Ranzi, A., Sugiri, D., Tsai, M.-Y., Wang, M., Hoek, G., Brunekreef, B., Peters, A., and Forastiere, F.: Long-term Exposure to Particulate Matter Constituents and the Incidence of Coronary Events in 11 European Cohorts, Epidemiology, 26, 565–574, https://doi.org/10.1097/ede.0000000000000300 , 2015. 

Wolf, T., Pettersson, L. H., and Esau, I.: A very high-resolution assessment and modelling of urban air quality, Atmos. Chem. Phys., 20, 625–647, https://doi.org/10.5194/acp-20-625-2020 , 2020. 

Wolf-Grosse, T., Esau, I., and Reuder, J.: Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study, Atmos. Chem. Phys., 17, 7261–7276, https://doi.org/10.5194/acp-17-7261-2017 , 2017. 

World Meteorological Congress: Abridged Final Report of the Eighteenth Session, World Meteorological Organization (WMO) – WMO, WMO-No. 1236, ISBN: 978-92-63-11236-1, 2019. 

Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., Tielemans, D., and van Lipzig, N. P. M.: The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer, Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016 , 2016. 

WWRP: Seamless prediction of the earth system: From minutes to months, WMO, Geneva, No. 1156, 471 pp., ISBN: 9789263111562, 2015. 

Xie, J. and Zhu, Y.: Association between ambient temperature and COVID-19 infection in 122 cities from China, Sci. Total Environ., 724, 138201, https://doi.org/10.1016/j.scitotenv.2020.138201 , 2020. 

Xie, Y., Dai, H. C., Zhang, Y. X., Wu, Y. Z., Hanaoka, T., and Masui, T.: Comparison of health and economic impacts of PM 2.5 and ozone pollution in China, Environ. Int., 130, 104881, https://doi.org/10.1016/j.envint.2019.05.075 , 2019. 

Xu, C., Kan, H.-D., Fan, Y.-N., Chen, R.-J., Liu, J.-H., Li, Y.-F., Zhang, Y., Ji, A.-L., and Cai, T.-J. Acute effects of air pollution on enteritis admissions in Xi'an, China, J. Toxicol. Env. Heal. A, 79, 1183–1189, 2016. 

Yang, L. Q., Chen, G., Zhao, J. L., and Rytter, N. G. M.: Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping, Sustainability, 12, 3649, https://doi.org/10.3390/su12093649 , 2020. 

Yang, Y., Pun, V. C., Sun, S., Lin, H., Mason, T. G., and Qiu, H.: Particulate matter components and health: a literature review on exposure assessment, Journal of Public Health and Emergency, 2, 14, https://doi.org/10.21037/jphe.2018.03.03 , 2018. 

Zacharof, N., Doulgeris, S., Myrsinias, I., Toumasatos, Z., Kolokotronis, D., Dimaratos, A., Mellios, G., and Samaras, Z.: MILE 21: Raising user awareness on on-road fuel consumption, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 36, https://doi.org/10.18745/pb.22217 , 2020. 

Żak, M., Melaniuk-Wolny, E., and Widziewicz, K.: The exposure of pedestrians, drivers and road transport passengers to nitrogen dioxide, Atmos. Pollut. Res., 8, 781–790, https://doi.org/10.1016/j.apr.2016.10.011 , 2017. 

Zanini, P., Chevalier, J., Lebegue, B., Allard, J., and Lascaux, F.: High-resolution mapping of urban air quality based on low-cost sensors and neural network model: application to Grenoble City, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 112, https://doi.org/10.18745/pb.22217 , 2020. 

Zanobetti, A. and Schwartz, J.: The effect of fine and coarse particulate air pollution on mortality: A national analysis, Environ. Health Persp., 117, 898–903, https://doi.org/10.1289/ehp.0800108 , 2009.  

Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., and Zhang, M.: Spatiotemporal prediction of continuous daily PM 2.5 concentrations across China using a spatially explicit machine learning algorithm, Atmos. Environ., 155, 129–139, 2017. 

Zhang, X., Chen, X., and Zhang, X.: The impact of exposure to air pollution on cognitive performance, P. Natl. Acad. Sci. USA, 115, 9193–9197, https://doi.org/10.1073/pnas.1809474115 , 2018. 

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part I: History, techniques, and current status, Atmos. Environ., 60, 632–655, https://doi.org/10.1016/j.atmosenv.2012.06.031 , 2012a. 

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects, Atmos. Environ., 60, 656–676, https://doi.org/10.1016/j.atmosenv.2012.02.041 , 2012b. 

Zhang, Y., Hong, C., Yahya, K., Li, Q., Zhang, Q., and He, K.: Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States, Atmos. Environ., 138, 162–182, https://doi.org/10.1016/j.atmosenv.2016.05.006 , 2016. 

Zhao, B., Zheng, H., Wang, S., Smith, K. R., Lu, X., Aunan, K., Gu, Y., Wang, Y., Ding, D., Xing, J., Fu, X., Yang, X., Liou, K.-N., and Hao, J.: Change in household fuels dominates the decrease in PM 2.5 exposure and premature mortality in China in 2005–2015, P. Natl. Acad. Sci. USA, 115, 12401–12406, https://doi.org/10.1073/pnas.1812955115 , 2018. 

Zhao, J., Birmili, W., Wehner, B., Daniels, A., Weinhold, K., Wang, L., Merkel, M., Kecorius, S., Tuch, T., Franck, U., Hussein, T., and Wiedensohler, A.: Particle Mass Concentrations and Number Size Distributions in 40 Homes in Germany: Indoor-to-Outdoor Relationships, Diurnal and Seasonal Variation, Aerosol Air Qual. Res., 20, 576–589, https://doi.org/10.4209/aaqr.2019.09.0444 , 2020a. 

Zhao, J. R., Zhang, Y., Patton, A. P., Ma, W. C., Kan, H. D., Wu, L. B., Fung, F., Wang, S. X., Ding, D., and Walker, K.: Projection of ship emissions and their impact on air quality in 2030 in Yangtze River delta, China, Environ. Pollut., 263, 114643, https://doi.org/10.1016/j.envpol.2020.114643 , 2020b. 

Zhu, Y., Xie, J., Huang, F., and Cao, L.: Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China, Sci. Total Environ., 727, 138704, https://doi.org/10.1016/j.scitotenv.2020.138704 , 2020. 

• Introduction
• Scope and structure of the review
• Air pollution sources and emissions
• Air quality observations and instrumentation
• Air quality modelling from local to regional scales
• Interactions between air quality, meteorology, and climate
• Air quality exposure and health
• Air quality management and policy development
• Discussion, synthesis, and recommendations
• Conclusions and future direction
• Data availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

Air Pollution Research Paper Topics

This comprehensive guide to air pollution research paper topics is designed to assist students studying environmental science in selecting a suitable topic for their research paper. The guide provides a broad range of topics divided into ten categories, each containing ten unique research topics. Additionally, the guide offers expert advice on how to choose a topic from the multitude of air pollution research paper topics and how to write a compelling research paper on air pollution. The guide also introduces iResearchNet’s writing services, which offer students the opportunity to order a custom air pollution research paper on any topic. The services include a range of features designed to ensure the delivery of high-quality, custom-written papers.

100 Air Pollution Research Paper Topics

Air pollution is a critical environmental issue that affects every living being on the planet. It is a topic that requires in-depth understanding and research. To aid students in their quest for knowledge and to help them in their academic pursuits, we have compiled a comprehensive list of air pollution research paper topics. These topics are categorized into ten different sections, each containing ten unique topics.

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Causes of Air Pollution

• The role of industrialization in air pollution.
• The impact of transportation on air pollution.
• The effect of agriculture on air pollution.
• The influence of waste disposal on air pollution.
• The role of deforestation in air pollution.
• The impact of urbanization on air pollution.
• The effect of household activities on air pollution.
• The influence of natural disasters on air pollution.
• The role of power generation in air pollution.
• The impact of mining activities on air pollution.

Effects of Air Pollution

• The impact of air pollution on human health.
• The effect of air pollution on the environment.
• The influence of air pollution on climate change.
• The role of air pollution in biodiversity loss.
• The impact of air pollution on agriculture.
• The effect of air pollution on water bodies.
• The influence of air pollution on the ozone layer.
• The role of air pollution in acid rain.
• The impact of air pollution on urban heat islands.
• The effect of air pollution on mental health.

Air Pollution and Climate Change

• The role of air pollution in global warming.
• The impact of air pollution on weather patterns.
• The influence of air pollution on greenhouse gas emissions.
• The role of air pollution in climate change mitigation.
• The impact of air pollution on climate change adaptation.
• The effect of air pollution on carbon sequestration.
• The influence of air pollution on climate change policies.
• The role of air pollution in climate change communication.
• The impact of air pollution on climate change denial.
• The effect of air pollution on climate change education.

Air Pollution Policies

• The effectiveness of the Clean Air Act in addressing air pollution.
• The impact of the Paris Agreement on air pollution.
• The role of national policies in mitigating air pollution.
• The influence of international cooperation on air pollution.
• The effectiveness of emission standards in addressing air pollution.
• The role of renewable energy policies in mitigating air pollution.
• The impact of transportation policies on air pollution.
• The influence of waste management policies on air pollution.
• The effectiveness of urban planning policies in addressing air pollution.
• The role of education policies in mitigating air pollution.

Air Pollution Solutions

• The role of renewable energy in mitigating air pollution.
• The impact of energy efficiency on air pollution.
• The influence of green building on air pollution.
• The effectiveness of public transportation in addressing air pollution.
• The role of waste management in mitigating air pollution.
• The impact of urban green spaces on air pollution.
• The influence of sustainable agriculture on air pollution.
• The effectiveness of carbon capture and storage in addressing air pollution.
• The role of education in mitigating air pollution.
• The impact of individual actions on air pollution.

Air Pollution and Society

• The social impacts of air pollution.
• The role of media in shaping perceptions of air pollution.
• The influence of air pollution on social inequality.
• The impact of air pollution on social movements.
• The role of community engagement in addressing air pollution.
• The influence of air pollution on public health policies.
• The impact of air pollution on economic development.
• The role of air pollution in urban planning.
• The influence of air pollution on migration patterns.
• The impact of air pollution on cultural practices.

Air Pollution and Health

• The impact of air pollution on respiratory diseases.
• The role of air pollution in cardiovascular diseases.
• The influence of air pollution on allergies.
• The impact of air pollution on mental health.
• The role of air pollution in premature deaths.
• The influence of air pollution on children’s health.
• The impact of air pollution on elderly health.
• The role of air pollution in health inequalities.
• The influence of air pollution on public health interventions.
• The impact of air pollution on health care costs.

Air Pollution and Technology

• The role of technology in monitoring air pollution.
• The impact of technology on reducing air pollution.
• The influence of technology on air pollution modeling.
• The role of technology in air pollution forecasting.
• The impact of technology on air pollution communication.
• The influence of technology on air pollution policies.
• The role of technology in air pollution education.
• The impact of technology on air pollution mitigation.
• The influence of technology on air pollution adaptation.
• The role of technology in air pollution research.

Air Pollution and Economy

• The economic impacts of air pollution.
• The role of air pollution in economic inequality.
• The influence of air pollution on economic development.
• The impact of air pollution on economic policies.
• The role of air pollution in economic planning.
• The influence of air pollution on economic growth.
• The impact of air pollution on economic sustainability.
• The role of air pollution in economic transitions.
• The influence of air pollution on economic resilience.
• The impact of air pollution on economic sectors.

Air Pollution and Ethics

• The ethical implications of air pollution.
• The role of ethics in air pollution policies.
• The influence of ethics on air pollution communication.
• The impact of ethics on air pollution mitigation.
• The role of ethics in air pollution adaptation.
• The influence of ethics on air pollution research.
• The impact of ethics on air pollution education.
• The role of ethics in air pollution decision-making.
• The influence of ethics on air pollution justice.
• The impact of ethics on air pollution futures.

This comprehensive list of topics is designed to inspire and guide students in their quest for knowledge about air pollution. Each topic is a doorway to a vast field of research and understanding. As you embark on your academic journey, remember that the goal is not just to write a research paper but to contribute to the global understanding of air pollution and its impacts. Your research could be the key to solving one of the most pressing environmental issues of our time.

Air Pollution Research Guide

Air pollution is a pressing global issue that poses significant threats to human health and the environment. As students studying environmental science, it is essential to delve into the complexities of air pollution and understand its causes, impacts, and potential solutions. Writing research papers on air pollution topics not only enhances our knowledge but also contributes to the collective effort in combating this environmental challenge. In this comprehensive guide, we will explore a wide range of air pollution research paper topics to inspire and assist you in your academic journey.

The field of environmental science has increasingly focused on air pollution due to its detrimental effects on air quality, climate change, and public health. As the world grapples with the consequences of human activities and industrialization, it becomes crucial to investigate the different dimensions of air pollution and develop innovative approaches to mitigate its impact. Research papers serve as a valuable tool for investigating and disseminating knowledge about air pollution, making them an integral part of environmental science education.

The primary aim of this page is to provide students like you with an extensive array of air pollution research paper topics. By exploring diverse and engaging topics, you can gain a deeper understanding of the various aspects related to air pollution, ranging from its sources and consequences to policy interventions and sustainable solutions. Whether you are just starting your research journey or seeking inspiration for a specific area of interest, this comprehensive list will serve as a valuable resource to guide your exploration and empower you to contribute meaningfully to the field.

Moreover, this page offers expert advice on how to choose the most suitable air pollution research paper topics. With the abundance of available topics, it is important to select a research question that aligns with your interests, academic goals, and the current needs of the field. Our expert tips will help you navigate through the vast landscape of air pollution research and enable you to select a topic that is both relevant and impactful.

In addition to topic selection, we will also provide guidance on how to write an effective air pollution research paper. Writing a research paper requires a systematic approach, from conducting a literature review and collecting data to analyzing findings and presenting a coherent argument. By following our step-by-step instructions and incorporating our writing tips, you can enhance the quality and rigor of your research paper, ensuring that your work makes a valuable contribution to the field of environmental science.

Furthermore, to support your academic journey, we introduce our writing services, offering you the opportunity to order a custom air pollution research paper tailored to your specific requirements. Our team of expert degree-holding writers specializes in environmental science and is equipped with the knowledge and skills to deliver top-quality research papers. With a commitment to in-depth research, customized solutions, and timely delivery, our writing services provide a convenient and reliable option for students seeking assistance in their academic endeavors.

Choosing an Air Topic for Research

Choosing the right air pollution research paper topic is a crucial step in the research process. It sets the foundation for your study and determines the direction of your research. With the vast scope of air pollution issues, it can be challenging to narrow down your focus and select a topic that is both relevant and compelling. In this section, we provide expert advice and 10 valuable tips to help you navigate the process of choosing air pollution research paper topics effectively.

• Identify your research interests : Start by reflecting on your personal interests within the field of air pollution. What aspects of air pollution intrigue you the most? Are you interested in studying the health effects, the impact on ecosystems, policy interventions, or technological solutions? Identifying your research interests will guide you towards topics that resonate with your passion and motivation.
• Consider current issues and debates : Stay informed about the latest developments and ongoing debates in the field of air pollution. Read scientific journals, news articles, and policy reports to understand the pressing issues and emerging trends. By choosing a topic that addresses current concerns, you contribute to the existing knowledge and engage in the relevant conversations.
• Conduct preliminary research : Before finalizing a topic, conduct preliminary research to familiarize yourself with the existing literature and identify research gaps. This will help you refine your research question and ensure that your topic contributes to the existing knowledge base. Look for recent studies, key theories, and seminal works that can provide a solid foundation for your research.
• Define the scope of your study : Determine the scope and boundaries of your research. Are you focusing on a specific geographic region, a particular pollutant, or a certain population group? Clarifying the scope of your study will help you narrow down your topic and ensure that it is manageable within the given time and resources.
• Consider interdisciplinary approaches : Air pollution is a complex issue that requires interdisciplinary perspectives. Consider integrating concepts and methods from various fields such as environmental science, public health, engineering, sociology, and policy studies. This interdisciplinary approach can lead to innovative research and contribute to a holistic understanding of air pollution.
• Engage with stakeholders : Air pollution affects various stakeholders, including communities, policymakers, industry professionals, and advocacy groups. Engaging with these stakeholders can provide valuable insights and enhance the relevance of your research. Consider topics that address the concerns and needs of different stakeholders, ensuring that your research has practical implications and can make a meaningful impact.
• Seek guidance from your professors and mentors : Consult with your professors and mentors who have expertise in the field of air pollution. They can provide valuable guidance, suggest potential research topics, and help you refine your research question. Utilize their knowledge and experience to ensure that your topic aligns with current research trends and academic standards.
• Consider the availability of data : Before finalizing your research topic, consider the availability of data and resources. Ensure that you have access to reliable and relevant data sources that will support your research objectives. Assess the feasibility of data collection and analysis, considering factors such as time constraints, cost, and ethical considerations.
• Aim for a balance between novelty and significance : While it is important to choose a topic that is unique and novel, also consider its significance within the broader field of air pollution research. Balance your desire to explore new avenues with the need for topics that contribute to the existing body of knowledge and have real-world implications.
• Think critically and creatively : Finally, approach the topic selection process with a critical and creative mindset. Think beyond the conventional boundaries and explore unconventional ideas. Consider innovative research methodologies, alternative perspectives, and emerging trends in air pollution research. By thinking critically and creatively, you can identify research topics that are both intellectually stimulating and have the potential for significant contributions.

By following these expert tips, you can navigate the process of choosing air pollution research paper topics with confidence and clarity. Remember that the topic you choose will shape the entire research process, so take the time to select a topic that aligns with your interests, expertise, and aspirations. Now, let’s move on to the next section, where we will provide you with valuable insights on how to write an impactful air pollution research paper.

How to Write an Air Pollution Research Paper

Writing an air pollution research paper requires careful planning, systematic research, and effective organization. In this section, we will guide you through the essential steps and provide you with 10 tips to help you write a well-structured and compelling research paper on air pollution.

• Understand the research question : Start by clearly understanding the research question or objective of your study. Define the specific aspect of air pollution that you intend to investigate and the key research aims. This will provide you with a focused direction and ensure that your paper addresses the core issues related to air pollution.
• Conduct a comprehensive literature review : Before diving into your research, conduct a thorough literature review to familiarize yourself with the existing body of knowledge on air pollution. Identify key theories, concepts, and empirical studies relevant to your topic. The literature review will help you identify research gaps and build a strong theoretical foundation for your study.
• Develop a clear research methodology : Determine the research methodology and data collection techniques that align with your research objectives. Will you conduct experiments, surveys, interviews, or analyze existing datasets? Clearly define your research design, sampling strategy, and data analysis methods to ensure the rigor and validity of your findings.
• Collect and analyze data : If your research involves primary data collection, carefully collect and organize your data using appropriate methods. If you are analyzing secondary data, ensure that the datasets are reliable and relevant to your research objectives. Apply appropriate statistical or qualitative analysis techniques to derive meaningful insights from your data.
• Structure your paper effectively : Organize your research paper using a clear and logical structure. Typically, a research paper includes an introduction, literature review, methodology, results, discussion, and conclusion. Ensure smooth transitions between sections and maintain a coherent flow of ideas throughout your paper.
• Write a compelling introduction : Begin your paper with an engaging introduction that provides context and background information on air pollution. Clearly state the research question, explain the significance of your study, and highlight the objectives and expected outcomes. Grab the reader’s attention and set the tone for the rest of the paper.
• Present your findings accurately : In the results section, present your findings in a clear and concise manner. Use appropriate tables, graphs, and figures to present data effectively. Provide relevant statistical measures and interpret the results objectively. Ensure that your findings directly address the research question and support your hypotheses or research objectives.
• Analyze and discuss your results : In the discussion section, analyze and interpret your findings in light of the existing literature. Compare your results with previous studies, identify similarities and differences, and explain any discrepancies. Discuss the implications of your findings and their significance for understanding air pollution and its effects.
• Address limitations and future research : Acknowledge the limitations of your study, such as sample size constraints, data limitations, or potential biases. Suggest avenues for future research to address these limitations and further advance knowledge in the field of air pollution. This demonstrates your critical thinking and opens up opportunities for future research contributions.
• Craft a strong conclusion : Conclude your research paper by summarizing the key findings, emphasizing their significance, and restating the research question and objectives. Discuss the implications of your study for theory, practice, and policy-making in the context of air pollution. Avoid introducing new information in the conclusion and leave the reader with a lasting impression of your research.

By following these tips, you can effectively structure and write an air pollution research paper that contributes to the existing knowledge, addresses key research questions, and provides valuable insights into this critical environmental issue. In the next section, we will introduce you to the writing services offered by iResearchNet, where you can order a custom research paper on any air pollution topic.

Custom Research Paper Writing Services

At iResearchNet, we understand the challenges faced by students in conducting research and writing a high-quality research paper on air pollution. That’s why we offer custom writing services tailored to meet your specific needs. Our team of expert writers, who hold advanced degrees in environmental science, are dedicated to delivering top-notch research papers that showcase your knowledge and understanding of air pollution. When you choose our writing services, you can expect the following:

• Expert degree-holding writers : Our team consists of skilled writers with expertise in environmental science and air pollution research. They have the knowledge and experience to tackle complex topics and deliver well-researched and insightful papers.
• Custom written works : We understand the importance of originality and uniqueness in academic writing. Our writers craft each research paper from scratch, ensuring that it is tailored to your specific requirements and adheres to the highest standards of quality.
• In-depth research : Our writers conduct thorough research using credible sources to gather the most relevant and up-to-date information on air pollution. They critically analyze the literature and integrate it seamlessly into your research paper to support your arguments and strengthen your findings.
• Custom formatting : We are well-versed in various formatting styles, including APA, MLA, Chicago/Turabian, and Harvard. Our writers will format your research paper according to the specified guidelines, ensuring consistency and professionalism throughout.
• Top quality : Quality is our utmost priority. We strive to deliver research papers that meet the highest academic standards. Our writers pay attention to detail, ensure accurate referencing, and use clear and concise language to convey your ideas effectively.
• Customized solutions : We understand that every research paper is unique. Our writers take a personalized approach, tailoring their writing to your specific research objectives, methodology, and findings. They adapt their writing style and tone to match your requirements and ensure a seamless integration of your ideas.
• Flexible pricing : We offer competitive and flexible pricing options to accommodate your budget. Our pricing is transparent, and there are no hidden fees or additional charges. You can select the pricing plan that suits your needs, whether it’s for a comprehensive research paper or a specific section.
• Short deadlines : We understand that time is of the essence when it comes to academic assignments. Our writers are capable of working under tight deadlines and can deliver your custom research paper within short timeframes, even as little as 3 hours.
• Timely delivery : We are committed to delivering your research paper on time. We understand the importance of meeting deadlines, and our writers work diligently to ensure that your paper is delivered within the agreed-upon timeframe.
• 24/7 support : Our dedicated support team is available 24/7 to assist you with any questions or concerns you may have. Whether you need updates on your order or have inquiries about our services, our friendly support staff is here to provide prompt and helpful assistance.
• Absolute Privacy : We prioritize the privacy and confidentiality of our clients. Your personal information and order details are kept secure and protected. We adhere to strict privacy policies to ensure that your information remains confidential.
• Easy order tracking : Our user-friendly platform allows you to easily track the progress of your order. You can stay updated on the status of your research paper, communicate with your writer, and receive notifications throughout the writing process.
• Money back guarantee : We are confident in the quality of our services and the expertise of our writers. If, for any reason, you are not satisfied with the final product, we offer a money-back guarantee. Your satisfaction is our priority, and we strive to ensure that you are fully content with the research paper you receive.

When you choose iResearchNet, you can be confident in receiving a well-written and thoroughly researched custom air pollution research paper that meets your academic requirements. We value your privacy and guarantee absolute confidentiality throughout the entire process. Our easy order tracking system allows you to stay updated on the progress of your paper, ensuring a seamless experience from start to finish. If, for any reason, you are not satisfied with the final product, we offer a money-back guarantee.

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• Open access
• Published: 17 June 2020

Half the world’s population are exposed to increasing air pollution

• G. Shaddick   ORCID: orcid.org/0000-0002-4117-4264 1 ,
• M. L. Thomas 2 ,
• P. Mudu 3 ,
• G. Ruggeri 3 &
• S. Gumy 3  

npj Climate and Atmospheric Science volume  3 , Article number:  23 ( 2020 ) Cite this article

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• Environmental impact

Air pollution is high on the global agenda and is widely recognised as a threat to both public health and economic progress. The World Health Organization (WHO) estimates that 4.2 million deaths annually can be attributed to outdoor air pollution. Recently, there have been major advances in methods that allow the quantification of air pollution-related indicators to track progress towards the Sustainable Development Goals and that expand the evidence base of the impacts of air pollution on health. Despite efforts to reduce air pollution in many countries there are regions, notably Central and Southern Asia and Sub-Saharan Africa, in which populations continue to be exposed to increasing levels of air pollution. The majority of the world’s population continue to be exposed to levels of air pollution substantially above WHO Air Quality Guidelines and, as such, air pollution constitutes a major, and in many areas, increasing threat to public health.

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Introduction

In 2016, the WHO estimated that 4.2 million deaths annually could be attributed to ambient (outdoor) fine particulate matter air pollution, or PM 2.5 (particles smaller than 2.5 μm in diameter) 1 . PM 2.5 comes from a wide range of sources, including energy production, households, industry, transport, waste, agriculture, desert dust and forest fires and particles can travel in the atmosphere for hundreds of kilometres and their chemical and physical characteristics may vary greatly over time and space. The WHO developed Air Quality Guidelines (AQG) to offer guidance for reducing the health impacts of air pollution. The first edition, the WHO AQG for Europe, was published in 1987 with a global update (in 2005) reflecting the increased scientific evidence of the health risks of air pollution worldwide and the growing appreciation of the global scale of the problem 2 . The current WHO AQG states that annual mean concentration should not exceed 10 μg/m 3  2 .

The adoption and implementation of policy interventions have proved to be effective in improving air quality 3 , 4 , 5 , 6 , 7 . There are at least three examples of enforcement of long-term policies that have reduced concentration of air pollutants in Europe and North America: (i) the Clean Air Act in 1963 and its subsequent amendments in the USA; (ii) the Convention on Long-range Transboundary Air Pollution (LRTAP) with protocols enforced since the beginning of the 1980s in Europe and North America 8 ; and (iii) the European emission standards passed in the European Union in the early 1990s 9 . However, between 1960 and 2009 concentrations of PM 2.5 globally increased by 38%, due in large part to increases in China and India, with deaths attributable to air pollution increasing by 124% between 1960 and 2009 10 .

The momentum behind the air pollution and climate change agendas, and the synergies between them, together with the Sustainable Development Goals (SDGs) provide an opportunity to address air pollution and the related burden of disease. Here, trends in global air quality between 2010 and 2016 are examined in the context of attempts to reduce air pollution, both through long-term policies and more recent attempts to reduce levels of air pollution. Particular focus is given to providing comprehensive coverage of estimated concentrations and obtaining (national-level) distributions of population exposures for health impact assessment. Traditionally, the primary source of information has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is sparse, or even non-existent (see Supplementary Information) 11 . The Data Integration Model for Air Quality (DIMAQ) was developed by the WHO Data Integration Task Force (see Acknowledgements for details) to respond to the need for improved estimates of exposures to PM 2.5 at high spatial resolution (0.1° × 0.1°) globally 11 . DIMAQ calibrates ground monitoring data with information from satellite retrievals of aerosol optical depth, chemical transport models and other sources to provide yearly air quality profiles for individual countries, regions and globally 11 . Estimates of PM 2.5 concentrations have been compared with previous studies and a good quantitative agreement in the direction and magnitude of trends has been found. This is especially valid in data rich settings (North America, Western Europe and China) where trends results are consistent with what has been found from the analysis of ground level PM 2.5 measurements.

Figure 1a shows average annual concentrations of PM 2.5 for 2016, estimated using DIMAQ,; and Fig. 1b the differences in concentrations between 2010 and 2016. Although air pollution affects high and low-income countries alike, low- and middle-income countries experience the highest burden, with the highest concentrations being seen in Central, Eastern Southern and South-Eastern Asia 12 .

a Concentrations in 2016. b Changes in concentrations between 2010 and 2016.

The high concentrations observed across parts of the Middle East, parts of Asia and Sub-Saharan regions of Africa are associated with sand and desert dust. Desert dust has received increasing attention due to the magnitude of its concentration and the capacity to be transported over very long distances in particular areas of the world 13 , 14 . The Sahara is one of the biggest global source of desert dust 15 and the increase of PM 2.5 in this region is consistent with the prediction of an increase of desert dust due to climate change 16 , 17 .

Globally, 55.3% of the world’s population were exposed to increased levels of PM 2.5 , between 2010 and 2016, however there are marked differences in the direction and magnitude of trends across the world. For example, in North America and Europe annual average population-weighted concentrations decreased from 12.4 to 9.8 μg/m 3 while in Central and Southern Asia they rose from 54.8 to 61.5 μg/m 3 . Reductions in concentrations observed in North America and Europe align with those reported by the US Environmental Protection Agency and European Environmental Agency (EEA) 18 , 19 . The lower values observed in these regions reflect substantial regulatory processes that were implemented thirty years ago that have led to substantial decreases in air pollution over previous decades 18 , 20 , 21 . In high-income countries, the extent of air pollution from widespread coal and other solid-fuel burning, together with other toxic emissions from largely unregulated industrial processes, declined markedly with Clean Air Acts and similar ‘smoke control’ legislation introduced from the mid-20th century. However, these remain important sources of air pollution in other parts of the world 22 . In North America and Europe, the rates of improvements are small reflecting the difficulties in reducing concentrations at lower levels.

Assessing the health impacts of air pollution requires detailed information of the levels to which specific populations are exposed. Specifically, it is important to identify whether areas where there are high concentrations are co-located with high populations within a country or region. Population-weighted concentrations, often referred to as population-weighted exposures, are calculated by spatially aligning concentrations of PM 2.5 with population estimates (see Supplementary Information).

Figure 2 shows global trends in estimated concentrations and population-weighted concentrations of PM 2.5 for 2010–2016, together with trends for SDG regions (see Supplementary Fig. 1.1 ). Where population-weighted exposures are higher than concentrations, as seen in Central Asia and Southern Asia, this indicates that higher levels of air pollution coincide with highly populated areas. Globally, whilst concentrations have reduced slightly (from 12.8 μg/m 3 in 2010 to 11.7 in 2016), population-weighted concentrations have increased slightly (33.5 μg/m 3 in 2010, 34.6 μg/m 3 in 2016). In North America and Europe both concentrations and population-weighted concentrations have decreased (6.1–4.9 and 12.4–9.8 μg/m 3 , respectively). The association between concentrations and population can be clearly seen for Central Asia and Southern Asia where concentrations increased from 29.6 to 31.7 μg/m 3 (a 7% increase) while population-weighted concentrations were higher both in magnitude and in percentage of increase, increasing from 54.8 to 61.5 μg/m 3 (a 12% increase).

a Concentrations. b Population-weighted concentrations.

For the Eastern Asia and South Eastern Asia concentrations increase from 2010 to 2013 and then decrease from 2013 to 2016, a result of the implementation of the ‘Air Pollution Prevention and Control Action Plan’ 21 and the transition to cleaner energy mix due to increased urbanization in China 23 , 24 , 25 . Population-weighted concentrations for urban areas in this region are strongly influenced by China, which comprises 62.6% of the population in the region. Population-weighted concentrations are higher than the concentrations and the decrease is more marked (in the population-weighted concentrations), indicating that the implementation of policies has been successful in terms of the number of people affected. The opposite effect of population-weighting is observed in areas within Western Asia and Northern Africa where an increasing trend in population-weighted concentrations (from 42.0 to 43.1. μg/m 3 ) contains lower values than for concentrations (from 50.7 to 52.6 μg/m 3 ). In this region, concentrations are inversely correlated with population, reflecting the high concentrations associated with desert dust in areas of lower population density.

Long-term policies to reduce air pollution have been shown to be effective and have been implemented in many countries, notably in Europe and the United States. However, even in countries with the cleanest air there are large numbers of people exposed to harmful levels of air pollution. Although precise quantification of the outcomes of specific policies is difficult, coupling the evidence for effective interventions with global, regional and local trends in air pollution can provide essential information for the evidence base that is key in informing and monitoring future policies. There have been major advances in methods that expand the knowledge base about impacts of air pollution on health, from evidence on the health effects 26 , modelling levels of air pollution 1 , 11 and quantification of health impacts that can be used to monitor and report on progress towards the air pollution-related indicators of the Sustainable Development Goals: SDG 3.9.1 (mortality rate attributed to household and ambient air pollution); SDG 7.1.2 (proportion of population with primary reliance on clean fuels and technology); and SDG 11.6.2 (annual mean levels of fine particulate matter (e.g., PM 2.5 and PM 10 ) in cities (population weighted)) 1 . There is a continuing need for further research, collaboration and sharing of good practice between scientists and international organisations, for example the WHO and the World Meteorological Organization, to improve modelling of global air pollution and the assessment of its impact on health. This will include developing models that address specific questions, including for example the effects of transboundary air pollution and desert dust, and to produce tools that provide policy makers with the ability to assess the effects of interventions and to accurately predict the potential effects of proposed policies.

Globally, the population exposed to PM 2.5 levels above the current WHO AQG (annual average of 10 μg/m 3 ) has fallen from 94.2% in 2010 to 90.0% in 2016, driven largely by decreases in North America and Europe (from 71.0% in 2010 to 48.6% in 2016). However, no such improvements are seen in other regions where the proportion has remained virtually constant and extremely high (e.g., greater than 99% in Central, Southern, Eastern and South-Eastern Asia Sustainable Development Goal (SDG) regions. See Supplementary Information for more details).

The problem, and the need for solutions, is not confined to cities: across much of the world the vast majority of people living in rural areas are also exposed to levels above the guidelines. Although there are differences when considering urban and rural areas in North America and Europe, in the vast majority of the world populations living in both urban and rural areas are exposed to levels that are above the AQGs. However, in other regions the story is very different (see Supplementary Information Fig. 7.1 and Supplementary Information Sections 7 and 8), for example population-weighted concentrations in rural areas in the Central and Southern Asia (55.5 μg/m 3 ), Sub-Saharan Africa (39.1 μg/m 3 ), Western Asia and Northern Africa (42.7 μg/m 3 ) and Eastern Asia and South-Eastern Asia (34.3 μg/m 3 ) regions (in 2016) were all considerably above the AQG. From 2010 to 2016 population-weighted concentrations in rural areas in the Central and Southern Asia region rose by approximately 11% (from 49.8 to 55.5 μg/m 3 ; see Supplementary Information Fig. 7.1 and Supplementary Information Sections 7 and 8). This is largely driven by large rural populations in India where 67.2% of the population live in rural areas 27 . Addressing air pollution in both rural and urban settings should therefore be a key priority in effectively reducing the burden of disease associated with air pollution.

Attempts to mitigate the effects of air pollution have varied according to its source and local conditions, but in all cases cooperation across sectors and at different levels, urban, regional, national and international, is crucial 28 . Policies and investments supporting affordable and sustainable access to clean energy, cleaner transport and power generation, as well as energy-efficient housing and municipal waste management can reduce key sources of outdoor air pollution. Interventions would not only improve health but also reduce climate pollutants and serve as a catalyst for local economic development and the promotion of healthy lifestyles.

Assessment of trends in global air pollution requires comprehensive information on concentrations over time for every country. This information is primarily based on ground monitoring (GM) from 9690 monitoring locations around the world from the WHO cities database for 2010–2016. However, there are regions in this may be limited if not completely unavailable, particularly for earlier years (see Supplementary Information). Even in countries where GM networks are well established, there will still be gaps in spatial coverage and missing data over time. The Data Integration Model for Air Quality (DIMAQ) supplements GM with information from other sources including estimates of PM2.5 from satellite retrievals and chemical transport models, population estimates and topography (e.g., elevation). Specifically, satellite-based estimates that combine aerosol optical depth retrievals with information from the GEOS-Chem chemical transport model 29 were used, together with estimates of sulfate, nitrate, ammonium, organic carbon and mineral dust 30 .

The most recent release of the WHO ambient air quality database, for the first time, contains data from GM for multiple years, where available The version of DIMAQ used here builds on the original version 11 , 30 by allowing data from multiple years to be modelled simultaneously, with the relationship between GMs and satellite-based estimates allowed to vary (smoothly) over time. The result is a comprehensive set of high-resolution (10 km × 10 km) estimates of PM2.5 for each year (2010–2016) for every country.

In order to produce population-weighted concentrations, a comprehensive set of population data on a high-resolution grid (Gridded Population of the World (GPW v4) database 31 ) was combined with estimates from DIMAQ. In addition, the Global Human Settlement Layer 32 was used to define areas as either urban, sub-urban or rural (based on land-use, derived from satellite images, and population estimates). A further dichotomous classification of whether grid-cells within a particular country were urban or rural (allocating sub-urban as either urban or rural) was based on providing the best alignment (at the country-level) to the estimates of urban-rural populations produced by the United Nations 27 .

It is noted that the estimates from DIMAQ used in this article may differ slightly from those used in the WHO estimates of the global burden of disease associated with ambient air pollution 1 , and the associated estimates of air pollution related SDG indicators, due to recent updates in the database and further quality assurance procedures.

Data availability

The estimates of PM 2.5 data that support the findings of this work are available from https://www.who.int/airpollution/data/en/ .

Ambient air pollution: Global assessment of exposure and BOD, update 2018. WHO (2020) (In press).

Krzyzanowski, M. & Cohen, A. Update of WHO air quality guidelines. Air Qual. Atmosphere Health 1 , 7–13 (2008).

Article   Google Scholar  

Zheng, Y. et al. Air quality improvements and health benefits from China’s clean air action since 2013. Environ. Res. Lett. 12 , 114020 (2017).

Turnock, S. T. et al. The impact of European legislative and technology measures to reduce air pollutants on air quality, human health and climate. Environ. Res. Lett. 11 , 024010 (2016).

Zhang, Y. et al. Long-term trends in the ambient PM 2.5 - and O 3 -related mortality burdens in the United States under emission reductions from 1990 to 2010. Atmos. Chem. Phys. 18 , 15003–15016 (2018).

Kuklinska, K., Wolska, L. & Namiesnik, J. Air quality policy in the U.S. and the EU – a review. Atmos. Pollut. Res 6 , 129–137 (2015).

Guerreiro, C. B. B., Foltescu, V. & de Leeuw, F. Air quality status and trends in Europe. Atmos. Environ. 98 , 376–384 (2014).

Byrne, A. The 1979 convention on long-range transboundary air pollution: assessing its effectiveness as a multilateral environmental regime after 35 Years. Transnatl. Environ. Law 4 , 37–67 (2015).

Crippa, M. et al. Forty years of improvements in European air quality: regional policy-industry interactions with global impacts. Atmos. Chem. Phys. 16 , 3825–3841 (2016).

Butt, E. W. et al. Global and regional trends in particulate air pollution and attributable health burden over the past 50 years. Environ. Res. Lett. 12 , 104017 (2017).

Shaddick, G. et al. Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. J. R. Stat. Soc. Ser. C. Appl. Stat. 67 , 231–253 (2018).

World Bank Country and Lending Groups—World Bank data. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (Accessed 3rd December 2018).

Guo, H. et al. Assessment of PM2.5 concentrations and exposure throughout China using ground observations. Sci. Total Environ. 601–602 , 1024–1030 (2017).

Ganor, E., Osetinsky, I., Stupp, A. & Alpert, P. Increasing trend of African dust, over 49 years, in the eastern Mediterranean. J. Geophys. Res. 115 , 1–7 (2010).

Google Scholar  

Goudie, A. S. & Middleton, N. J. Desert Dust in the Global System . (Springer Science & Business Media, 2006).

Mahowald, N. M. et al. Observed 20th century desert dust variability: impact on climate and biogeochemistry. Atmos. Chem. Phys. 10 , 10875–10893 (2010).

Stanelle, T., Bey, I., Raddatz, T., Reick, C. & Tegen, I. Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing. J. Geophys. Res. Atmosph 119 , 13526–13546 (2014).

Air quality in Europe (European Environment Agency, 2018). https://www.eea.europa.eu/publications/air-quality-in-europe-2018 .

Particulate Matter (PM2.5) Trends | National Air Quality: Status and Trends of Key Air Pollutants | US EPA. https://www.epa.gov/air-trends/particulate-matter-pm25-trends .

Chay, K., Dobkin, C. & Greenstone, M. The clean air act of 1970 and adult mortality. J. Risk Uncertain. 27 , 279–300 (2003).

Huang, J., Pan, X., Guo, X. & Li, G. Health impact of China’s air pollution prevention and control action plan: an analysis of national air quality monitoring and mortality data. Lancet Planet. Health 2 , e313–e323 (2018).

Heal, M. R., Kumar, P. & Harrison, R. M. Particles, air quality, policy and health. Chem. Soc. Rev. 41 , 6606–6630 (2012).

Chen, J. et al. A review of biomass burning: emissions and impacts on air quality, health and climate in China. Sci. Total Environ. 579 , 1000–1034 (2017).

Zhao, B. et al. Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005–2015. Proc. Natl. Acad. Sci . 201812955 (2018). https://doi.org/10.1073/pnas.1812955115 .

Shen, H. et al. Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Sci. Adv. 3 , e1700300 (2017).

Burnett, R. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. 115 , 9592–9597 (2018).

World Urbanization Prospects - Population Division - United Nations. https://population.un.org/wup/Download/ (Accessed: 10th December 2018).

Towards Cleaner Air Scientific Assessment Report 2016- UNECE (2016). https://www.unece.org/index.php?id=42861 .

van Donkelaar, A. et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 50 , 3762–3772 (2016).

Shaddick, G. et al. Data Integration for the assessment for population exposure to ambient air pollution for global burden of disease assessment. Environ. Sci. Technol. 52 , 9069–9078 (2018).

Center for International Earth Science Information Network (CIESIN) Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Count. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY. https://doi.org/10.7927/H4X63JVC . Accessed 3rd December 2018.

Pesaresi, M. et al. GHS Settlement grid following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4- multitemporal (1975-1990-2000-2015). European Commission, Joint Research Centre (JRC)[Dataset] http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a . Accessed: 3rd December 2018.

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Acknowledgements

The authors would like to thank the WHO Data Integration Task Force, a multi-disciplinary group of experts established as part of the recommendations from the first meeting of the WHO Global Platform for Air Quality, Geneva, January 2014. The Task Force developed the Data Integration Model for Air Quality and consists of the first author, Michael Brauer, Aaron van Donkelaar, Rick Burnett, Howard H. Chang, Aaron Cohen, Rita Van Dingenen, Yang Liu, Randall Martin, Lance A. Waller, Jason West, James V. Zidek and Annette Pruss-Ustun. The authors would like to give particular thanks to Michael Brauer who provided specialist expertise, together with data on ground measurements, and Aaron van Donkelaar and the Atmospheric Composition Analysis Group at Dalhousie University for providing estimates from satellite remote sensing. The authors would also like to thank Dan Simpson for technical expertise on implementing extensions to DIMAQ. Matthew L Thomas is supported by a scholarship from the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa), under the project EP/L015684/1. The views expressed in this article are those of the authors and they do not necessarily represent the views, decisions or policies to institutions with which they are affiliated.

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Shaddick, G., Thomas, M.L., Mudu, P. et al. Half the world’s population are exposed to increasing air pollution. npj Clim Atmos Sci 3 , 23 (2020). https://doi.org/10.1038/s41612-020-0124-2

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Research Article

Exposure to outdoor air pollution and its human health outcomes: A scoping review

Contributed equally to this work with: Zhuanlan Sun, Demi Zhu

Roles Writing – original draft

Affiliation Department of Management Science and Engineering, School of Economics and Management, Tongji University, Shanghai, China

Roles Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Comparative Politics, School of International and Public Affairs, Shanghai Jiaotong University, Shanghai, China

• Zhuanlan Sun, 

• Published: May 16, 2019
• https://doi.org/10.1371/journal.pone.0216550
• Reader Comments

Despite considerable air pollution prevention and control measures that have been put into practice in recent years, outdoor air pollution remains one of the most important risk factors for health outcomes. To identify the potential research gaps, we conducted a scoping review focused on health outcomes affected by outdoor air pollution across the broad research area. Of the 5759 potentially relevant studies, 799 were included in the final analysis. The included studies showed an increasing publication trend from 1992 to 2008, and most of the studies were conducted in Asia, Europe, and North America. Among the eight categorized health outcomes, asthma (category: respiratory diseases) and mortality (category: health records) were the most common ones. Adverse health outcomes involving respiratory diseases among children accounted for the largest group. Out of the total included studies, 95.2% reported at least one statistically positive result, and only 0.4% showed ambiguous results. Based on our study, we suggest that the time frame of the included studies, their disease definitions, and the measurement of personal exposure to outdoor air pollution should be taken into consideration in any future research. The main limitation of this study is its potential language bias, since only English publications were included. In conclusion, this scoping review provides researchers and policy decision makers with evidence taken from multiple disciplines to show the increasing prevalence of outdoor air pollution and its adverse effects on health outcomes.

Citation: Sun Z, Zhu D (2019) Exposure to outdoor air pollution and its human health outcomes: A scoping review. PLoS ONE 14(5): e0216550. https://doi.org/10.1371/journal.pone.0216550

Editor: Mathilde Body-Malapel, University of Lille, FRANCE

Received: December 15, 2018; Accepted: April 10, 2019; Published: May 16, 2019

Copyright: © 2019 Sun, Zhu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work received support from Major projects of the National Social Science Fund of China, Award Number: 13&ZD176, Grant recipient: Demi Zhu.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent years, despite considerable improvements in air pollution prevention and control, outdoor air pollution has remained a major environmental health hazard to human beings. In some developing countries, the concentrations of air quality far exceed the upper limit announced in the World Health Organization guidelines [ 1 ]. Moreover, it is widely acknowledged that outdoor air pollution increases the incidence rates of multiple diseases, such as cardiovascular disease, lung cancer, respiratory symptoms, asthma, negatively affected pregnancy, and poor birth outcomes [ 2 – 6 ].

The influence of outdoor air pollution exposure and its mechanisms continue to be hotly debated [ 7 – 11 ]. Some causal inference studies have been conducted to examine these situations [ 12 ]; these have indicated that an increase in outdoor air exposure affects people’s health outcomes both directly and indirectly [ 13 ]. However, few studies in the existing literature have examined the extent, range, and nature of the influence of outdoor air pollution with regard to human health outcomes. Thus, such research gaps need to be identified, and related fields of study need to be mapped.

Systematic reviews and meta-analyses, the most commonly used traditional approach to synthesize knowledge, use quantified data from relevant published studies in order to aggregate findings on a specific topic [ 14 ]; furthermore, they formally assesses the quality of these studies to generate precise conclusions related to the focused research question [ 15 ]. In comparison, scoping review is a more narrative type of knowledge synthesis, and it focuses on a broader area [ 16 ] of the evidence pertaining to a given topic. It is often used to systematically summarize the evidence available (main sources, types, and research characteristics), and it tends to be more comprehensive and helpful to policymakers at all levels.

Scoping reviews have already been used to examine a variety of health related issues [ 17 ]. As an evidence synthesis approach that is still in the midst of development, the methodology framework for scoping reviews faces some controversy with regard to its conceptual clarification and definition [ 18 , 19 ], the necessity of quality assessment [ 20 – 22 ], and the time required for completion [ 19 , 21 , 23 ]. Comparing this approach with other knowledge synthesis methods, such as evidence gap map and rapid review, the scoping review has become increasingly influential for efficient evidence-based decision-making because it offers a very broad topic scope [ 15 ].

To our knowledge, few studies have systematically reviewed the literature in the broad field of outdoor air pollution exposure research, especially with regard to related health outcomes. To fill this gap, we conducted a comprehensive scoping review of the literature with a focus on health outcomes affected by outdoor air pollution. The purposes of this study were as follows: 1) provide a systematic overview of relevant studies; 2) identify the different types of outdoor air pollution and related health outcomes; and 3) summarize the publication characteristics and explore related research gaps.

Materials and methods

The methodology framework used in this study was initially outlined by Arksey and O’Malley [ 23 ] and further advanced by Levac et al. [ 20 ], Daudt et al. [ 21 ], and the Joanna Briggs Institute [ 24 ]. The framework was divided into six stages: identifying the research question; identifying relevant studies; study selection; charting the data; collating, summarizing and reporting the results; and consulting exercise.

Stage one: Research question identification

As recommended, we combined broader research questions with a clearly articulated scope of inquiry [ 20 ]; this included defining the concept, target population, and outcomes of interest in order to disseminate an effective search strategy. Thus, an adaptation of the “PCC” (participants, concept, context) strategy was used to guide the construction of research questions and inclusion criteria [ 24 ].

Types of participants.

There were no strict restrictions on ages, genders, ethnicity, or regions of participants. Everyone, including newborns, children, adults, pregnant women, and the elderly, suffer from health outcomes related to exposure to outdoor air pollution; hence, all groups were included in the study to ensure that the inquiry was sufficiently comprehensive.

The core concept was clearly articulated in order to guide the scope and breadth of the inquiry [ 24 ]. A list of outdoor air pollution and health outcome related terms were compiled by reviewing potential text words in the titles or abstracts of the most pertinent articles [ 25 – 33 ]; we also read the most cited literature reviews on air pollution related health outcomes. To classify the types of air pollution and health outcomes, we consulted researchers from different air pollution related disciplines. The classified results are shown in Table 1 .

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https://doi.org/10.1371/journal.pone.0216550.t001

Our scoping review included studies from peer-reviewed journals. There were no restrictions in terms of the research field, time period, and geographical coverage. The intended audiences of our scoping review were researchers, physicians, and public policymakers.

Stage two: Relevant studies identification

We followed Joanna Briggs Institute’s instructions [ 24 ] to launch three-step search strategies to identify all relevant published and unpublished studies (grey literature) across the multi-disciplinary topic in an iterative way. The first step included a limited search of the entire database using keywords relevant to the topic and conducting an abstract and indexing categorizations analysis. The second step was a further search of all included databases based on the newly identified keywords and index terms. The final step was to search the reference list of the identified reports and literatures.

Electronic databases.

We conducted comprehensive literature searches by consulting with an information specialist. We searched the following three electronic databases from their inception until now: PubMed, Web of Science, and Scopus. The language of the studies included in our sample was restricted to English.

Search terms.

The search terms we used were broad enough to uncover any related literature and prevent chances of relevant information being overlooked. This process was conducted iteratively with different search item combinations to ensure that all relevant literature was captured ( S1 Table ).

The search used combinations of the following terms: 1) outdoor air pollution (ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide, PM 2.5 , PM 10 , total suspended particle, suspended particulate matter, toxic air pollutant, volatile organic pollutant, nitrogen oxide) and 2) health outcomes (asthma, lung cancer, respiratory infection, respiratory disorder, diabetes, chronic respiratory disease, chronic obstructive pulmonary disease, hypertension, heart rate variability, heart attack, cardiopulmonary disease, ischemic heart disease, blood coagulation, deep vein thrombosis, stroke, morbidity, hospital admission, outpatient visit, emergency room visit, mortality, DNA methylation change, neurobehavioral function, inflammatory disease, skin disease, abortion, Alzheimer’s disease, disability, cognitive function, Parkinson’s disease).

Additional studies search.

Key, important, and top journals were read manually, reference lists and citation tracing were used to collect studies and related materials, and suggestions from specialists were considered to guarantee that the research was as comprehensive as possible.

Bibliographies Management Software (Mendeley) was used to remove duplicated literatures and manage thousands of bibliographic references that needed to be appraised to check whether they should be included in the final study selection.

Our literature retrieval generated a total of 5759 references; the majority of these (3567) were found on the Scopus electronic database, which emphasized the importance of collecting the findings on this broad topic.

Stage three: Studies selection

Our study identification picked up a large number of irrelevant studies; we needed a mechanism to include only the studies that best fit the research question. The study selection stage should be an iterative process of searching the literature, refining the search strategy, and reviewing articles for inclusion. Study inclusion and exclusion criteria were discussed by the team members at the beginning of the process, then two inter-professional researchers applied the criteria to independently review the titles and abstracts of all studies [ 21 ]. If there were any ambiguities, the full article was read to make decision about whether it should be chosen for inclusion. When disagreements on study inclusion occurred, a third specialist reviewer made the final decision. This process should be iterative to guarantee the inclusion of all relevant studies.

Inclusion and exclusion criteria.

The inclusion criteria used in our scoping study ensured that the articles were considered only if they were: 1) long-term and short-term exposure, perspective or prospective studies; 2) epidemiological time series studies; 3) meta-analysis and systematic review articles rather than the primary studies that contained the main parameters we were concerned with; 4) economic research studies using causality inference with observational data; and 5) etiology research studies on respiratory disease, cancer, and cardiovascular disease.

Articles were removed if they 1) focused exclusively on indoor air pollution exposure and 2) did not belong to peer-reviewed journals or conference papers (such as policy documents, proposals, and editorials).

Stage four: Data charting

The data extracted from the final articles were entered into a “data charting form” using the database, programmed Excel, so that the following relevant data could be recorded and charted according to the variables of interest ( Table 2 ).

https://doi.org/10.1371/journal.pone.0216550.t002

Stage five: Results collection, summarization, and report

The extracted data were categorized into topics such as people’s health types and regions of diseases caused by outdoor air exposure. Each reported topic should be provided with a clear explanation to enable future research. Finally, the scoping review results were tabulated in order to find research gaps to either enable meaningful research or obtain good pointers for policymaking.

Stage six: Consultation exercise

Our scoping review took into account the consultation phase of sharing preliminary findings with experts, all of whom are members of the Committee on Public Health and Urban Environment Management in China. This enabled us to identify additional emerging issues related to health outcomes.

The original search was conducted in May of 2018; the Web of Science, PubMed, and Scopus databases were searched, resulting in a total of 5759 potentially relevant studies. After a de-duplication of 1451 studies and the application of the inclusion criteria, 3027 studies were assessed as being irrelevant and excluded based on readings of the titles and the abstracts. In the end, 1281 studies were assessed for in-depth full-text screening. To prevent overlooking potentially relevant papers, we manually screened the top five impact factor periodicals in the database we were searching. We traced the reference lists and the cited literatures of the included studies, and then we reviewed the newly collected literatures to generate more relevant studies. Further, after preliminary consultation with experts, we included studies on two additional health outcome categories, pregnancy and children and mental disorders. Hence, 214 more potential studies were included during this process. Besides, 379 original studies of the inclusive meta-analysis and systematic review studies were removed for duplication. In total, 1116 studies were included for in-depth full-text screening analysis and 799 eligible studies were included in the end. The detailed articles selection process was shown in Fig 1 .

https://doi.org/10.1371/journal.pone.0216550.g001

The included air pollution related health outcome studies increased between 1992 and 2018, as shown in Fig 2 . Most studies were published in the last decade and more than 75% of studies (614/76.9%) were published after 2011.

The included studies increased during this period, more than 75% of studies were published after 2011.

https://doi.org/10.1371/journal.pone.0216550.g002

The general characteristics summary of all included studies are shown in Table 3 . Most studies were carried out in Asia, Europe and North America (280/35.0%, 261/32.7% and 219/27.4%, respectively). According to the category system of journal citation reports (JCR) in the Web of Science, 323/40.4% of all studies on health outcomes came from environmental science, 213/26.7% came from the field of medicine, and 24/3.0% were from economics. The top three research designs of the included studies were cohort studies, systematic reviews and meta-analyses, and time series studies (116/14.5%, 107/13.4% and 76/9.5%, respectively). Almost all included studies were published in journals (794/99.4%). The lengths of the included studies ranged from four pages [ 34 ] to over thirty-nine pages [ 35 ].

https://doi.org/10.1371/journal.pone.0216550.t003

Regions of studies

Table 4 outlines the locations in which health outcomes were affected by outdoor air pollution. The continents of Asia (277/34.7%), Europe (219/27.4%), and North America (168/21.0%) account for most of these studies. As the word cloud in Fig 3 illustrates, most of the included studies had been mainly conducted in the United States and China. About 62.8% of the studies (502) had been especially conducted in developed countries.

https://doi.org/10.1371/journal.pone.0216550.t004

Word cloud representing the country of included studies, the size of each term is in proportion to its frequency.

https://doi.org/10.1371/journal.pone.0216550.g003

Most authors (573/799) evaluated the air pollution health outcomes of their own continent, at a proportion of 71.7%.

Types of air pollution and related health outcomes

We categorized the health outcomes, by consulting with experts, into respiratory diseases, chronic diseases, cardiovascular diseases, health records, cancer, mental disorders, pregnancy and children, and other diseases ( Table 5 ). We also divided the outdoor air pollution into general air pollution gas, fine particulate matter, other hazardous substances, and a mixture of them.

https://doi.org/10.1371/journal.pone.0216550.t005

Most of the health records showed that mortality (163/286; 57.0%) was the most common health outcome related to outdoor air pollution, as is visually represented in Fig 4 . Respiratory diseases (e.g., asthma and respiratory symptoms) and cardiovascular diseases (e.g., heart disease) that resulted from exposure to outdoor air pollution were also common (69/199, 63/199 and 23/90; or 34.7%, 31.7% and 25.6%; respectively).

Word cloud representing the health outcomes of included studies, the size of each term is in proportion to its frequency.

https://doi.org/10.1371/journal.pone.0216550.g004

Types of affected groups

The population of included studies was categorized into seven subgroups: birth and infant, children, women and pregnancy, adults, elderly, all ages and not specified ( Table 6 ). The largest air pollution proportion fell under the groups of all ages and children (261/799; 32.7% and 165/799; 20.7%), health outcomes of respiratory diseases in children account for the largest groups (114/199; 57.3%).

https://doi.org/10.1371/journal.pone.0216550.t006

There were 121 research studies in the “Not Specified” group. As shown in Table 6 , the “Birth & Infant,” “Women & Pregnancy,” “Children,” and “elderly” groups occupied the subject areas of more than half of the total included studies, which means that air pollution affected these population groups more acutely. Moreover, age is a confounding factor for the prevalence of cancer and cardiovascular diseases. However, there were only 2 studies (2/38, 5.3%) on cancer and 14 studies (14/90, 15.6%) on cardiovascular diseases in the elderly group.

Summary of results

Of all included studies, 95.2% reported at least one statistically positive result, 4.4% were convincingly negative, and only 0.4% showed ambiguous results ( Table 7 ).

https://doi.org/10.1371/journal.pone.0216550.t007

There were 27 primary studies that showed no association between air pollution and disease, including cancer (n = 1), chronic diseases (n = 1), cardiovascular diseases (n = 6), health records (n = 7), pregnancy and children (n = 2), respiratory diseases (n = 6), mental disorders (n = 1), and other diseases (n = 3). Moreover, eight meta-analyses showed no evidence for any association between air pollution and disease prevalence (childhood asthma, chronic bronchitis, asthma, cardio-respiratory mortality, acute respiratory distress syndrome and acute lung injury, mental disorder, cardiovascular disease, and daily respiratory death). Three meta-analyses showed ambiguous results for mental health, venous thromboembolism, and hypertension.

Our scoping review provided an overview on the subject of outdoor air pollution and health outcomes. We adhered to the methodology outlined for publishing guidelines and used the six steps outlined by the scoping review protocol. The guiding principle ensured that our methods were transparent and free from potential bias. The strengths of the included studies are that they tend to focus on large sample sizes and broad geographical coverage. This research helped us to identify research gaps and disseminate research findings [ 23 ] to policymakers, practitioners, and consumers for further missing or potentially valuable investigations.

Principal findings

Among the included studies, we identified various health outcomes of outdoor air pollution, including respiratory diseases, chronic diseases, cardiovascular diseases, health records, cancer, mental disorders, pregnancy and children, and other diseases. Among them, asthma in respiratory diseases and mortality in health records were the most common ones. The study designs contained cohort, meta-analysis, time series, crossover, cross-sectional, and other qualitative methods. In addition, we included economically relevant studies [ 12 , 36 , 37 ] to investigate the causal inference of outdoor air pollution on health outcomes. Further, pregnancy and children, mental disorders, and other diseases are health outcomes that might have uncertain or inconsistent effects. For example, Kirrane et al. [ 38 ] reported that PM 2.5 had positive associations with Parkinson’s disease; however, some studies report that there is no statistically significant overall association between PM exposure and such diseases [ 39 ]. Overall, the majority of these studies suggested a potential positive association between outdoor air pollution and health outcomes, although several recent studies revealed no significant correlations [ 40 – 42 ].

Time frame of included studies

The time frame of included studies is one of the most important characteristics of air pollution research. Even in the same country or region, industrialization and modernization caused by air pollution is distinguished between different time periods [ 43 , 44 ]. In addition, the more the public understands environment science, the more people will take preventative measures to protect themselves. This is also influenced by time. Although air pollution should not be seen as an inevitable side effect of economic growth, time period should be considered in future studies. The publication trends with regard to air pollution related health outcome research increased sharply after 2010. In recent times, published studies have begun to pay more attention to controlling confounding factors such as socioeconomic factors and human behavior.

Population and country

More than 50% of the studies on the relationship between air pollution and health outcomes originated from high income countries. There was less research (<25%) from developing countries and poor countries [ 45 – 48 ], which may result from inadequate environmental monitoring systems and public health surveillance systems. Less cohesive policies and inadequate scientific research may be another reason. In this regard, stratified analysis by regional income will be helpful for exploring the real estimates. It is reported that the stroke incidence is largely associated with low and middle income countries rather than with high income countries [ 49 ]. More studies are urgently needed in highly populated regions, such as Eastern Asia and North and Central Africa.

It is worth noting that rural and urban differences in air pollution research have been neglected. There are only eight studies focused on the difference of spatial variability of air pollution [ 50 – 57 ]. Variation is common even across relatively small areas due to geographical, topographical, and meteorological factors. For example, an increase in PM 2.5 in Northern China was predominantly from abundant coal combustion used for heating in the winter months [ 58 ]. These differences should be considered with caution by urbanization and by region. Data analysis adjustment for spatial autocorrelation will provide a more accurate estimate of the differences in air. What’s more, in some countries such as China, migrants are not able to access healthcare within the cities; this has resulted in misleading conclusions about a “healthier” population and null based bias was introduced [ 59 ].

Other studies (including systematic reviews and economic studies) on outdoor air pollution

Our scoping review included a large number of systematic reviews and meta-analyses. Of the included 107 systematic review and meta-analyses, the most discussed topics were respiratory diseases influenced by mixed outdoor air pollution [ 60 – 62 ]. Little systematic review research focused on chronic diseases, cancer, and mental disorders, which are current research gaps and potential research directions. A large overlap remains between the primary studies included in the systematic reviews. However, some systematic reviews that focused on the same topic have conflicting results, which were mainly caused by different inclusion criteria and subgroup analyses [ 63 , 64 ]. To solve this problem, it is critical that reporting of systematic reviews should retrieve all related published systematic reviews and meta-analyses.

As for the 24 included economic studies, two kinds of health outcomes—morbidity [ 65 ] and economic cost [ 66 ]—were discussed separately using regression approaches. The economic methods were different from those used in the epidemiology; the study focused on causal inference and provided a new perspective for examining the relevant environmental health problems. Furthermore, meta-regression methodology, an economic synthesis approach, proved to be very effective for evaluating the outcomes in a comprehensive way [ 67 ].

Diagnostic criteria for diseases

The diagnostic criteria for diseases forms an important aspect of health-related outcomes. The diagnostic criteria for stroke and mental disorders might be less reliable than those for cancer, mobility, and cardiovascular diseases [ 68 ]. Few studies provided detailed disease diagnostic information on how the disease was measured. Thus, the overall effect estimation of outdoor air pollution might be overestimated. It is recommended that ICD-10 or ICD-11 classification should be adopted as the health outcome classification criterion to ensure consistency among studies in different disciplines considered in future research [ 69 ].

In spite of these broad disease definitions, studies in healthy people or individuals with chronic diseases were not conducted separately. People with chronic diseases were more susceptible to air pollution [ 70 ]. It is obvious that air pollution related population mobility might be underestimated. However, the obvious association of long-term exposure to air pollution with chronic disease related mortality has been reported by prospective cohort studies [ 71 ]. It should be translated to other diverse air pollution related effect research. The population with pre-existing diseases should be analyzed as subgroups.

Except for the overall population, subgroups of people with outdoor occupations and athletes [ 72 , 73 ], sensitive groups such as infants and children, older adults [ 74 , 75 ], and people with respiratory or cardiovascular diseases, should be analyzed separately.

Measurement of personal exposure

The measurement of personal exposure to air pollutants (e.g., measurement of errors associated with the monitoring instruments, heterogeneity in the amount of time spent outdoors, and geographic variation) was lacking in terms of accurate determination. There is a need for clear reporting of these measurements. The key criterion to determine if there is causal relationship between air pollution and negative health outcomes was that at least one aspect of these could be measured in an unbiased manner.

Pollutant dispersion factor

It is well known that the association between air pollution and stroke, and respiratory and cardiovascular disease subtype might be caused by many other factors such as temperature, humidity, season, barometric pressure, and even wind speed and rain [ 76 – 78 ]. These confounding factors related to aspects of energy, transportation, and socioeconomic status, may explain the varying effect size of the association between air pollution and diseases.

While the associations reported in epidemiological studies were significant, proving a causal relationship between the different air pollutants affected by any other factors and adverse effects has been more challenging. To avoid bias, these modifier effects should be compared with previous localized studies. In fact, how the confounding variables account for the heterogeneity should be explored by case-controlled study design or other causal interference research designs.

Study limitations

The following limitations should not be overlooked. First, scoping reviews are based on a knowledge synthesis approach that allows for the mapping of gaps in the existing literature; however, they lack quality assessment for the included studies, which may be an obstacle for precise interpretation. Some improvements have been made by adding a quality assessment [ 15 , 22 , 79 ] to increase the reliability of the findings, and other included studies control for quality by including only peer-reviewed publications [ 80 ]; however, this is not a requirement for scoping reviews. While our paper aimed to comprehensively present a broader range of global-level current published literatures related to outdoor air pollution health outcomes, we did not assess the quality of the analyzed literature. The conclusions of this scoping review were based on the existence of the selected studies rather than their intrinsic qualities.

Second, bias is an inevitable problem from the perspectives of languages, disciplines, and literatures in knowledge synthesis. We included literatures from electronic databases, key journals, and reference lists to avoid “selection bias” and then included unpublished literature to avoid “publication bias”; further, we also conscientiously sampled among the studies to ensure that there was a safeguard against “researcher bias.” We only took English language articles into account because of the cost and time involved in translating the material, which might have led to a potential language bias [ 23 ]. However, in scoping reviews, language restriction does not have the importance that it does in meta-analysis [ 81 ].

Conclusions

In all, the topic of outdoor air pollution exposure related health outcomes is discussed across multiple-disciplines. The various characteristics and contexts of different disciplines suggest different underlying mechanisms worth of the attention of researchers and policymakers. The presentation of the diversity of health outcomes and its relationship to outdoor exposure air pollution is the purpose of this scoping review for new findings in future investigations.

Supporting information

S1 table. literature search strategies..

https://doi.org/10.1371/journal.pone.0216550.s001

S2 Table. PRISMA-ScR checklist.

https://doi.org/10.1371/journal.pone.0216550.s002

Acknowledgments

The authors would like to thank Miaomiao Liu, an assistant professor in School of the Environment of Nanjing University, for her valuable advice with regard to this article.

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• 24. The Joanna Briggs Institute. The Joanna Briggs Institute Reviewers’ Manual 2015: Methodology for JBI scoping reviews. Joanne Briggs Inst. 2015; 1–24.

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Air pollution exposure—the (in)visible risk factor for respiratory diseases

• Review Article
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• Published: 04 March 2021
• Volume 28 , pages 19615–19628, ( 2021 )

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• Gabriel-Petrică Bălă   ORCID: orcid.org/0000-0002-1877-1327 1 ,
• Ruxandra-Mioara Râjnoveanu 2 ,
• Emanuela Tudorache 1 ,
• Radu Motișan 3 &
• Cristian Oancea   ORCID: orcid.org/0000-0003-2083-0581 1  

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There is increasing interest in understanding the role of air pollution as one of the greatest threats to human health worldwide. Nine of 10 individuals breathe air with polluted compounds that have a great impact on lung tissue. The nature of the relationship is complex, and new or updated data are constantly being reported in the literature. The goal of our review was to summarize the most important air pollutants and their impact on the main respiratory diseases (chronic obstructive pulmonary disease, asthma, lung cancer, idiopathic pulmonary fibrosis, respiratory infections, bronchiectasis, tuberculosis) to reduce both short- and the long-term exposure consequences. We considered the most important air pollutants, including sulfur dioxide, nitrogen dioxide, carbon monoxide, volatile organic compounds, ozone, particulate matter and biomass smoke, and observed their impact on pulmonary pathologies. We focused on respiratory pathologies, because air pollution potentiates the increase in respiratory diseases, and the evidence that air pollutants have a detrimental effect is growing. It is imperative to constantly improve policy initiatives on air quality in both high- and low-income countries.

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Introduction

Air pollution represents one of the biggest risk factors for human health. It is an invisible killer that hides around us, influencing both young and old generations. According to the World Health Organization (WHO), each year, 7 million people die due to air pollution. The most affected pathologies are chronic obstructive pulmonary disease, lung cancer, and respiratory infections, including pneumonia, stroke, and heart disease. Nine out of 10 individuals breathe air with polluted compounds, which penetrate deep into the lung tissue, and furthermore in the cardiovascular system (Ghebreyesus 2018 ) (World Health Organization 2018 ) (Tiotiu et al. 2020 ).

The most exposed individuals are elderly persons, infants, pregnant women, and persons with comorbidities (Mannucci et al. 2015 ). An estimated 43% of lung diseases and 24% of strokes are attributed to air pollution.

We performed an electronic search on PubMed for literature published in the last 5 years, with the last search date on February 15, 2020. The following terms were used: “air pollution,” “particulate matter,” “biomass,” “smoke,” “sulfur dioxide,” “nitrogen dioxide,” “carbon monoxide,” “ozone,” “chronic obstructive pulmonary disease,” “asthma,” “lung cancer,” “idiopathic pulmonary fibrosis,” “respiratory infections,” “bronchiectasis,” and “tuberculosis.” The terms were also searched in combination, such as “particulate matter and lung cancer” and “air pollution and chronic obstructive pulmonary disease.” The results were restricted to full-text studies of humans and mechanisms via animal experiments. Systematic reviews, meta-analyses, reviews, and publications from the WHO were included in the search. We additionally included older references if they had an important impact on the subject area, according to our knowledge.

General air pollution

Air pollutants are classified into two main categories: primary air pollutants (pollutants emitted directly into the atmosphere) and secondary air pollutants (pollutants that are formed within the atmosphere itself) (World Health Organization 2005 ) (Mannucci et al. 2015 ).

Primary air pollutants are those released from a direct source, such as exhaust pipes from a mobile vehicle, or from a stationary source, such as factory chimneys. At the same time, contaminated dust can also be distributed by the wind to uncontaminated areas. These pollutants can be calculated by measuring the amounts emitted by the source itself. Primary air pollutants are represented by oxides of nitrogen, carbon monoxide (CO), sulfur dioxide (SO 2 ), volatile organic compounds (VOCs), and carbonaceous and non-carbonaceous primary particles. The International Agency for Research on Cancer (IARC) has classified emissions from burning coal in an indoor environment as potentially carcinogenic to humans. These were observed with sufficient evidence in both animals and humans (Barone-Adesi et al. 2012 ).

There are many sources of primary air pollutants, but the most significant are road traffic and power plants. Additionally, industrial and residential heating based on wood, coal. or oil contributes to increasing the degree of air pollution (World Health Organization 2005 ) (Guarnieri and Balmes 2014 ) (Kravchenko and Lyerly 2018 ) (Minichilli et al. 2019 ).

Secondary air pollutants are formed through chemical reactions in the atmosphere, with natural components such as water and oxygen. Secondary air pollutants include ozone (O 3 ), oxides of nitrogen, and particulate matter (PM) (World Health Organization 2005 ) (Guarnieri and Balmes 2014 ) (Mannucci et al. 2015 )

The chemical composition of air pollutants is diverse and depends on the source. Additionally, a seasonal pattern is observed, with higher average daily concentration levels of nitrogen dioxide (NO 2 ), CO, PM10, and fine particulate matter (PM2.5) during the cold season, while O 3 concentration levels tended to be higher during the warm season (Bernardini et al. 2019 )

Air pollutants released by coal-fired power plants have raised concerns about their impact on public health. PM2.5 can have both short-term and long-term consequences on human health. In a study conducted by Cheng-Kuan Lin et al., a strong association was observed between the increase in coal capacity per person and an increase in the relative risk for lung cancer, both in men and women. These were observed by a factor of 85% among women and 59% among men. Based on these data, it is predicted that in 2025, a total of 1.37 million cases of lung cancer will be correlated with coal-fired power plants (Lin et al. 2019 ).

Outdoor (ambient) air pollutants

Sulfur dioxide.

Sulfur dioxide (SO 2 ) and PM come from the process of burning fossil fuels and represent the essential components of air pollution. Sulfurous and sulfuric acids are formed as a result of the oxidation process of SO 2 . Natural sources include volcanoes, but significant concerns have been encountered in large metropolitan regions where coal is being used for domestic heating or for poorly controlled combustion for industrial installations (World Health Organization 2005 ).

Exacerbation of respiratory symptoms has been shown to be related to exposure to SO 2 emitted by coal-burning power plants, and lower concentrations were associated with respiratory deaths. The major anthropogenic sources of SO 2 are found in developing countries and come from burning fossil fuels that contain sulfur. The reason for burning fossil fuels is due to heating homes, use in power plants, and powering vehicles (Kravchenko and Lyerly 2018 ). Sulfur dioxide concentrations are lower since indoor concentrations are absorbed by walls, furniture, and inhalation systems (World Health Organization 2005 ).

Nitrogen dioxide

There are many species of nitrogen oxides, but the one with the most important effect on human health is NO 2 . NO 2 is a gas with a brown color, having a distinctive powerful scent. Nitric oxide spontaneously produces dioxide when it is exposed to air. It is a powerful oxidant that produces nitric acid and nitric oxide by reacting with water, and it is an important trace gas affecting human health. It absorbs solar radiation, contributing to low visibility in the atmosphere and plays a direct role in global climate change.

NO 2 undergoes further transformations, and after the photochemical reaction sequence is initiated by its solar radiation-induced activation, newly generated pollutants are created, containing organic, nitrate, and sulfate particles, all measured at PM2.5 and PM10. Among natural sources, the represented sources include lightning, inclusion of stratospheric nitrogen oxides, and bacterial and volcanic actions. The major anthropogenic sources are mobile sources (combustion engines) and stationary combustion sources (power generation sources) (World Health Organization 2005 ) (Kravchenko and Lyerly 2018 )

Patients with asthma and chronic obstructive pulmonary disease (COPD) have been associated with an increased risk of respiratory hospitalization after exposure to NO 2 (Kravchenko and Lyerly 2018 ). In addition, exposure to air pollution due to traffic vehicles increases the risk of developing bronchiolitis obliterans post-lung transplant syndrome (Johannson et al. 2015 ).

In China, a systematic review and meta-analysis by Sun et al. identified a positive correlation between short-term ambient exposure to NO 2 and pulmonary diseases. A 10-μg/m 3 increase in NO 2 concentration was associated with an increase of 1.4% in mortality due to respiratory disease and 1.0% in hospital admission. Elderly individuals had an even higher susceptibility (Sun et al. 2017 ).

Carbon monoxide

The most important source of environmental CO is incomplete combustion of traffic-related fossil fuels, leading to >50% of emissions in urban areas, other sources (such as manufacturing and natural processes, etc.) being less prominent (World Health Organization 2005 ).

Carbon monoxide is considered to be a “silent killer” due to its toxicity arising from its ability to bind hemoglobin more strongly than oxygen, increasing the risk of asphyxia-related deaths at high levels of exposure or hypoxic tissue damage at low levels of exposure (World Health Organization 2005 ).

Asthma, bronchiectasis, and pneumonia have been associated with ambient short-term exposure to CO (Zhao et al. 2019 ). The study of Zhao et al. conducted over 4 and 1/2 years, with a daily mean ambient CO of 0.88 mg/m 3 , varying from 0.40 to 3.13 mg/m 3 , reported an increased risk for daily outpatient visits for respiratory disease, with a higher effect on women and elderly patients (Zhao et al. 2019 ).

In different studies, a positive association between daily exposure to PM2.5, SO 2 , and CO and an increased risk of mortality from respiratory diseases and lung cancer was reported. For a 1-mg/m 3 increase in CO and a 10-μg/m 3 increase in PM10, there has been a 1.9% and 4.8% increase in total deaths, respectively (Xue et al. 2018 ) (Table 1 ).

Volatile organic compounds

Volatile organic compounds (VOCs) are compounds with a high vapor pressure of one or more carbon atoms, which will lead to their release in the atmosphere (Ciganek and Neca 2008 ). Compounds from the atmosphere, in a state of a vapor phase, such as oxygenates, hydrocarbons, halogenates, and other carbon compounds, are the main components of VOCs (World Health Organization 2005 ).

There are different sources of VOCs. They can arise from natural causes, such as forest fires, vegetation, and animals, but also from artificial causes, such as vehicles. Natural sources of VOCs represent a higher percentage, but anthropogenic sources contribute significantly to reducing air quality.

The most important sources of VOCs are released by industrial and agricultural sources. At the same time, handling solvents or solvent-based products contributes significantly to VOC concentrations. Samples that were collected from road dust or soil were based on volatile organic compounds, such as benzene, toluene, styrene, ethylbenzene, and xylene, as well as aliphatic hydrocarbons (mostly n -alkanes), dichloromethane, and disulfide carbon (Ciganek and Neca 2008 ).

Oxidative stress and decreased lung function are related to exposure to low levels of VOCs. Additionally, airway inflammation could be related to exposure to increased levels of VOCs in everyday life (Kwon et al. 2018 ).

In a national cross-sectional representative survey that was conducted by the Indoor Air Quality Observatory, N -undecane and 1,2,4-trimethylbenzene were correlated with asthma in 8.6% of cases, while trichloroethylene, ethylbenzene, and m/p- and o-xylene were associated with rhinitis (Billionnet et al. 2011 ).

In a French cross-sectional study on farmers, indoor mean VOC concentrations were smaller in workplaces than in dwellings. Working in a rural environment involves a degree of exposure to various risk factors such as agricultural machinery and fires, exposure to capricious weather, agricultural land working, and exposure to various organic compounds. Following this study, individuals mentioned that respiratory symptoms, such as dyspnea, cough, sneezing, and wheezing, were the most common. They were present in 44% of people when manipulating plants that had been harvested. Asthma and early airway obstruction were linked with exposure to VOCs and PM and in farmers (Maesano et al. 2019 ).

Ozone (O 3 ) is a chemical compound that is not directly emitted into the air but is formed through a series of complex reactions. Atomic oxygen and nitric oxide are formed after NO 2 splits. Atomic oxygen later combines with oxygen-forming ozone. Ozone is disintegrated by reacting with nitric oxide, resulting in NO 2 and oxygen. Ambient concentrations depend on several factors: the concentration of NO 2 and VOCs, sunshine intensity, atmospheric convection, and the proportion of VOCs to nitrogen oxides (World Health Organization 2005 ) (Guarnieri and Balmes 2014 ).

Daily concentrations of air pollutants are higher in the cool seasons than in the warm seasons, except for O 3 , which is higher during warm seasons (Wang et al. 2019d ). Ground-level ozone (O 3 ) is considered one of the most dangerous air pollutants in the USA and the European Union, being a strong oxidizing compound. In recent years, O 3 levels have remained high without showing any decline and will remain a constant public health problem, especially with the progression of global warming (Guarnieri and Balmes 2014 ) (Wang et al. 2019c ).

Concentrations of O 3 can increase during late spring and summer months due to photochemical reactions, along with its precursors, such as VOCs. High concentrations of O 3 can be associated with various local and long-range transports of anthropogenic emissions. In winter, lower photochemical processes result in a smaller contribution of this factor to PM2.5 mass (18%) (Bari and Kindzierski 2017 ). Human exposure to ozone is correlated with a high risk of respiratory disorders, such as asthma exacerbation and lung inflammation, loss of lung function, and cystic fibrosis (Johannson et al. 2014 ). Additionally, it has been shown to interact with cerebral blood vessels by modulating the expression of genes involved in brain vasoreactivity, irritating mucous membranes, altering the levels of serotonin, and affecting the immune system (Bernardini et al. 2019 ).

• Particulate matter

According to the World Health Organization (WHO), the standard for daily PM2.5 concentration is 25 μg/m 3 , while the annual average is 10 μg/m 3 . Approximately 92% of the world’s population lives in locations where the mean PM2.5 mass concentration surpasses this amount. Approximately 3 million persons die from outdoor air pollution each year (Wang et al. 2019b ).

Particulate matter (PM) is represented by a complex mixture containing components with diverse physical and chemical characteristics. The potential for these particles to cause injury varies due to their chemical composition and source. Additionally, their size and physical characteristics represent major concerns for public health (World Health Organization 2005 ).

Particles are classified in general by their aerodynamic diameter. PM can generally be classified into three major fractions: coarse particles, exceeding 2.5 μm in aerodynamic diameter; fine particles, which are smaller than 2.5 μm; and ultrafine particles, which are smaller than 0.1 μm (100 nm). PM10 contains PM2.5 and thoracic coarse mass (the distinction between PM10 and PM2.5 is generally presented as coarse mass). In general, PM10 mass contains 40–90% of the PM2.5, the rest being considered coarse PM (World Health Organization 2005 ) (Guarnieri and Balmes 2014 ) (Mannucci et al. 2015 ).

PM with a 2.5–10-μm aerodynamic diameter, also known as coarse PM, is stored particularly in the head and in the upper respiratory tract. PM2.5 is usually stored in the deep respiratory airways, primarily in the small airways and alveoli, while ultrafine PM (<0.1μm) is stored in the alveoli (World Health Organization 2005 ) (Guarnieri and Balmes 2014 ) (Liang et al. 2019 ).

PM2.5 are primarily formed from gases. These particles usually emerge as ultrafine particles created by the formation of very small particles (nuclei) by condensation-nucleation of low vapor pressure substances generated by chemical reaction into the atmosphere or by high-temperature vaporization (World Health Organization 2005 ) (Mannucci et al. 2015 ) (Kravchenko and Lyerly 2018 ). The principal precursor gases are represented by nitrogen oxides, ammonia, SO 2 , and VOCs. At the same time, fluctuations in the concentrations of these compounds may alter ambient PM concentrations. On the days when PM10 concentration exceeds 50 μg/m 3 PM, nitrate becomes the main compound of PM10 and PM2.5 (World Health Organization 2005 )(Kravchenko and Lyerly 2018 ).

There are numerous sources of both human activity and natural source-related particles. Specific sources impact different regions of the world, but more than two-thirds of PM 2.5 is due to industrialization in developed areas.

The origin of PM2.5 is variable. It can come from several sources, such as vehicle traffic, followed by dust generation, aerosols from regional transport, agricultural activities, and the burning of biomass for cooking or heating. It is difficult to appreciate the contribution of each source and to further recognize the PM formation mechanism (Zhang and Cao 2015 ).

Photochemical conversion of secondary pollutants (SO 4 2− , NO 3 − , and NH4+) represents 3.7% of PM2.5 and 2.4% of PM10 (Lee et al. 2018 ). Concentrations of NH 4 + , NO 3 − , and SO 4 2− on days with higher pollution can be two to four times higher than on unpolluted days ( p < 0.01), according to the study of Pan et al, where PM2.5 samples were gathered from two metropolitan areas (Beijing and Shanghai) on polluted and unpolluted days in the fall of 2017 (Pan et al. 2019 ). Additionally, seasonal variations in PM2.5 have been described in different regions of the world.

In a Canadian study conducted by Md. Aynul Bari from May 2009 to December 2015, the overall mean and median concentrations of PM2.5 were comparatively higher in winter than in summer (Bari and Kindzierski 2017 ). The same observation was reported in the Chinese study of Yan-Lin Zhang et al., with remarkable seasonal variability in PM2.5, which was highest during winter and lowest during summer. On the other hand, increased levels of PM2.5 are also found in the spring and autumn due to the contribution of dust particles and the start of burning biomass. In addition, the lowest and highest PM2.5 concentrations frequently occur in the afternoon and evening hours (Johannson et al. 2014 ).

An investigation of the origin of wintertime high PM2.5 pollution days revealed that in addition to traffic emissions, another significant source that helped increase PM2.5 in winter was a mixed factor represented by local industry and agriculture (deduced as gas emission sources and upstream oil) (Bari and Kindzierski 2017 ).

Indoor air pollutants

Biomass smoke.

Biomass smoke is a major public health problem (Balcan et al. 2016 ). Coal and biomass fuels are used by almost 3 billion people worldwide. A large part of the world’s population still depends on solid fuel for cooking, firewood, and charcoal (Nsoh et al. 2019 ). Individuals generally use different types of fuels for heating and cooking, such as “smoky coal” (bituminous), “smokeless coal” (anthracite), and wood (Barone-Adesi et al. 2012 ).

Compounds emitted from smoky coal combustion are present in abundance, such as polycyclic aromatic hydrocarbons (PAHs), methylated PAHs, and heterocyclic aromatic compounds containing nitrogen. Following an incomplete combustion process, solid fuel releases a significant amount of toxic particles. These will then be inhaled and cause multiple respiratory symptoms. Symptoms may include upper respiratory tract conditions, such as cough, nasal obstruction, vocal dysfunction, rhinorrhea, laryngeal spasm, and lower respiratory tract symptoms, such as dyspnea, wheezing, and cough (Nsoh et al. 2019 ).

Approximately half of the world’s population cooks and heats using unprocessed biomass fuels and coal. Several diseases are associated with exposure to solid fuel smoke, including lung cancer, chronic obstructive pulmonary disease, and respiratory infections (Barone-Adesi et al. 2012 ). In the study by Barone-Ades et al., it was observed that mortality due to lung cancer was higher in people who used smoked charcoal than in those who used no-smoke charcoal throughout their lives. In the study, 9962 people used smokeless coal, and 27,310 used smoked coal. The absolute risk of death from lung cancer in individuals who used smoked charcoal was higher for women (20%) than men (18%). As a comparison, the percentage of people who used smokeless charcoal and developed lung cancer was only 0.5%. These values are similar to those observed in heavy smokers in Western countries, with a value between 20 and 26% (Barone-Adesi et al. 2012 ).

Lung function begins to deteriorate after exposure to smoke for more than 15 years. The chance of having modified pulmonary function increases as the duration of exposure increases. In rural areas, women are generally more exposed due to their conventional lifestyle. In a case-control study, from a total of 115 women exposed to biomass smoke, 23.8% had small airway disease, 19.1% had obstruction, and 17.3% had a restriction pattern on pulmonary function tests (Balcan et al. 2016 ).

Public health focus on respiratory disease

• Chronic obstructive pulmonary disease

COPD is a multifactorial condition characterized by chronic airway obstruction that is incompletely reversible, progressive, and associated with an abnormal inflammatory response of the lung to harmful particles or gases (Singh et al. 2019 ).

Ambient air pollution is associated with COPD morbidity and mortality. From systemic analyses, it was observed that morbidity from COPD is correlated with a short-term increase in air pollution (Adar et al. 2014 )(Zhang et al. 2016 ) (Tian et al. 2018 ).

The body’s response may differ from person to person. The ability of each person to react to air pollution may differ in the Chinese population compared with the North American or European population due to differences in air pollution concentration and the composition of the polluted air. At the same time, the pre-existing pathology of one population may be different from another. In a study conducted in Beijing, China, a reduction in the average concentration of PM2.5 up to 58 μg/m 3 was observed in 2017 compared with 2013 when the average concentration was 87 μg/m 3 . Although a significant reduction was observed, the value was still high, given that the reference value was 10 μg/m 3 according to the WHO. However, it was observed that there were 161,613 hospitalizations for exacerbations of COPD (most patients were men over 65 years of age). Short-term exposure to air pollutants was correlated with hospital visits in the COPD emergency sections, resulting in subsequent hospitalizations and mortality (Liang et al. 2019 ).

In a population-based study involving 3941 nonsmoking Taiwanese adults, 791 had COPD. Exposure to PM2.5 at concentrations higher than 38.98 μg/m 3 was associated with increased predisposition to COPD among nonsmokers in Taiwan. However, exposures to concentrations of 32.07–38.98 μg/m 3 and 29.38–32.07 μg/m 3 were not significant (Huang et al. 2019 ).

From the multitude of studies that have shown a link between COPD and air pollution, we selected this study.

At high concentrations, air pollutants have a direct inflammatory effect on airway neuroreceptors and the epithelium. In addition, oxidative stress has been associated with pollutant exposure (O 3 , NO 2 , PM2.5) (Johannson et al. 2014 ). Airway inflammation can be induced by specific pollutants (O 3 , NO 2 , PM2.5), while airway hyperresponsiveness can be induced by O 3 and NO 2 (Johannson et al. 2014 ) (Kravchenko and Lyerly 2018 ).

The EGEA study conducted on 204 adult asthmatic patients revealed important data about the role of oxidative stress in the association between air pollution and asthma. The levels of fluorescent oxidation products (FlOPs), an oxidative stress-related biomarker, increased with PM10 and O 3 , and the risk of persistent asthma increased with plasma FlOP levels (Havet et al. 2019 ).

Air pollution represents one of the most important factors aggravating asthma in children, with higher incidences in European and Caribbean regions (Cadelis et al. 2014 )(Akpinar-Elci et al. 2015 ). One of the contributing factors is Saharan dust (Gyan et al. 2005 ). Saharan particles are composed of mineral origins. They are composed of a multitude of particles, such as clay, quartz, silicon oxide, and carbonates. They are lined with organic matter represented by bacteria and spores or pollen grains. Saharan dust contains PM10 and PM2.5–10, which can further predispose to an increase in visits to the emergency service for patients aged 5–15 years (Cadelis et al. 2014 ).

In a retrospective study of 5 years conducted by Muge Akpinar-Elci, the relationship between Saharan dust and exposure, climatic variables, and asthma was analyzed. There were 4411 recorded asthma-related emergency visits, and variation in asthma was correlated with dust concentration (Akpinar-Elci et al. 2015 ). Additionally, in a study conducted by Cadelis et al., there were 836 visits for asthma, with 514 boys and 322 girls (Cadelis et al. 2014 ).

In a study that took place in 10 European cities, the incidence of asthma among children was 14%, and after exposure to air with polluting compounds from road traffic, children with exacerbated asthma constituted 15% of the cohort. Asthma symptoms have been correlated with short-term exposure to ambient PM2.5 and PM10 in prospective cohorts, particularly in children with allergic sensitivity (Guarnieri and Balmes 2014 ). In a cohort study conducted by Bowatte et al., exposure to traffic-related air pollution (TRAP) was associated with both persistent and new-onset asthma in adults. Living < 200 m from a major road was correlated with greater odds of new asthma for middle-aged persons who never had asthma by 45 years. Asthmatic participants at 45 years had an increased risk of persistent asthma up to 53 years if they were living < 200 m from a major road compared with asthmatic participants living > 200 m from a major road (Bowatte et al. 2018 ).

• Lung cancer

Lung cancer represents one of the most common types of cancer and has a poor prognosis. The most important risk factor incriminated in developing lung cancer is active smoking, but exposure to environmental occupational carcinogens, residential radon, and passive smoke is also recognized as risk factors (Raaschou-Nielsen et al. 2013 ).

Although the association between lung cancer and long-term exposure to air pollution has been clarified, the link between lung cancer mortality and short-term exposure to air pollution remains unknown. The number of lung cancer cases is expected to increase due to continuous exposure to air pollution in regard to massive industrialization, an aging population and constant high smoking prevalence (Wang et al. 2019d ). PM2.5, PM10, and O 3 contribute to oxidative stress within the respiratory system and therefore potentially facilitate pulmonary inflammation and could initiate or promote the mechanisms of carcinogenesis (Xing et al. 2019 ). PM2.5 is considered the most relevant pollutant (Hamra et al. 2014 ).

In 2010, cancers of the trachea, bronchial tree, or lungs attributable to exposure to PM2.5 accounted for approximately 7% of total mortality. The mechanisms that have been incriminated in the association between PM2.5 and lung cancer include DNA deterioration and cell cycle changes (Longhin et al. 2013 ). PM2.5 was also related to increased production of reactive oxidative species.

A correlation was observed between the aerodynamic diameter of the fine particles in the medium ≤ 2.5 (PM2.5) and the incidence and mortality from lung cancer. Based on a meta-analysis of 18 studies, the correlation between PM2.5 and PM10 and the incidence and mortality of lung cancer were studied. Following the analysis, it was observed that the meta-relative risk was 1.09 for lung cancer related to PM2.5 and 1.09 for PM10. Additionally, the risk of adenocarcinoma associated with PM 10 was 1.29, while for PM2.5, it was 1.4. These results can help us better analyze the pathology of bronchopulmonary cancer in connection with air pollution (Hamra et al. 2014 ).

In the Ahsmong-2 study, it was shown that for each 10 μg/m 3 increase in ambient PM2.5, the incidence of lung cancer increased, although the individuals from the study were exposed to low levels of ambient PM2.5 and had never smoked. The percentage was higher for individuals who had a longer period of residence and who had spent more than 1 h/day outside. The predominant type of cancer was adenocarcinomas, with a percentage of 66.4% (Gharibvand et al. 2017 ).

Wang et al. suggested that the carcinogenic effects of PM2.5 vary by gender as well as by the environment in which individuals live, i.e., rural or urban. It has also been observed that younger people have a lower sensitivity than elderly people. For individuals in rural areas, it was observed that with a growth level of the average concentration of PM2.5 by 10 μg/m 3 , the incidence and mortality from lung cancer were 15% and 23% among men, compared with 22% and 24% among women, respectively. Thus, following this study, the results showed that women have a meaningful risk of developing lung cancer in correlation to PM2.5 exposure (Wang et al. 2019a ).

Idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is defined as a specific form of chronic, progressive fibrosing interstitial pneumonia of unknown cause, occurring primarily in older adults, and limited to the lungs. It is a progressive lung disease with a complex etiology (Johannson et al. 2018 ) characterized by progressive worsening of dyspnea and lung function and is associated with poor prognosis (Raghu et al. 2011 ).

There are not enough studies to certify the effects of air pollution on interstitial lung disease. However, in a study conducted by Johansson, it was shown that acute exacerbations of IPF were associated with an increase in the mean level, the maximum level, and the number of exceedances above accepted standards of O 3 and NO 2 (Johannson et al. 2014 ). Of the six criteria regulated by the US Environmental Protection Agency, particulate matter (PM), ground level (O 3 ), and NO 2 were strongly related to adverse respiratory effects (Johannson et al. 2015 ).

One potential mechanism by which ambient air pollution may cause interstitial lung disease is oxidative stress through the generation of excess reactive oxygen species (ROS), such as radical hydroxide and superoxide anion. IPF patients exhibit evidence of reduced antioxidant capacity, suggesting that they may have an increased vulnerability to excess ROS (Johannson et al. 2015 ). Another explanation for the progressive evolution of the disease was highlighted by the study of Winterbottom et al. on 135 subjects evaluated between 2007 and 2013. The results showed a strong association between PM10 levels and a decrease in forced vital capacity (FVC). With each μg/m 3 increase in PM10, there was an additional 46 cc/year decline in FVC. The significant relationship observed between the exposure to coarse (PM10) and the decline rate of FVC was not reported for PM2.5, showing an inverse relationship between the diameter size of the particle and penetration into the airways. Each 5 μg/m 3 increase in ambient PM2.5 concentration at residences corresponded with an additional 1.15 L/year increase in oxygen use on the 6-min walking test (6MWT) (Winterbottom et al. 2018 ). The results are equivocal, because the study conducted by Kerri A. Johannson et al. showed that PM10, PM2.5, and NO2 were associated with reduced lung function, but the changes were independent of air pollution exposure (Johannson et al. 2018 ).

There were no significant relationships between mean weekly change in air pollutant levels and concurrent weekly changes in forced vital capacity (FVC), forced expiratory volume during the first second (FEV1), University of California San Diego Shortness of Breath Questionnaire (UCSD-SOBQ), or visual analog scale (VAS) scores. Nevertheless, regarding the duration or interval of assessment periods, there was no significant association between the mean level of air pollutants and subsequent changes in lung function. Additionally, neither higher cumulative mean exposures nor maximal exposures to air pollution were associated with a more rapid decline in FVC or FEV1 over the study period. However, in patients with IPF, average exposures to NO 2 , PM2.5, and PM10 were associated with lower FVC, indicating that air pollution may influence the severity of disease in some individuals (Johannson et al. 2018 ).

In a study with 436 patients performed by Johannson et al., 75 of them had at least one acute exacerbation, and a subgroup of 13 patients had more than one exacerbation. During the exposure period, acute exacerbation of IPF was correlated significantly with increased mean rates, maximum levels, and amounts of O 3 and NO 2 exceedances. Increased exposure to O 3 and NO 2 over the preceding 6 weeks was associated with a high risk of acute exacerbation of IPF. This suggests that air pollution could be correlated with the development of this clinically significant event. At the same time, there were no consistent relationships between PM10, SO 2 , or CO and acute exacerbation of IPF compared with NO 2 , O 3 , and PM2.5 (Johannson et al. 2014 ).

Respiratory infections

In the cross-sectional study of Nsoh et al., 1849 patients diagnosed from January 2013 to April 2016 with acute respiratory infections (ARIs) were registered. Of the selected patients, more than 70% used at least one form of solid fuel for cooking. In poorly ventilated homes, the impact of this exposure was irritation of the respiratory tract and eyes and an increased risk of cancers related to long-term inhalation of this poor-quality air. The probability of developing ARI was 3.62 times higher for people who were exposed to cooking indoors than for those who were not exposed. Additionally, the chances of developing ARI were 1.91 times higher for those exposed to open fire than for those who were not exposed. Thus, PM2.5 values were 13.2 times higher than what the WHO recommends. Dry weather and dust also increase the risk of developing ARI (Nsoh et al. 2019 ).

A study conducted in China found that with increasing concentrations of PM2.5 and PM2.5–10 compounds, the number of hospital visits for upper airway infections and pneumonia meaningfully increased. The increase in the average concentration, which accumulated over 6 days, was 10 μg/m 3 (Z. Zhang et al. 2019 ).

Zheng P et al. constructed a seasonal model of cases of respiratory infections, revealing a higher preponderance in the period with lower temperatures. While children aged 5–14 years had a higher chance of developing acute respiratory infections (55.1%), those under 5 years had a higher chance (60.5%) of developing lower acute respiratory infections. The concentrations of air pollutants PM10, NO 2 , and SO 2 exhibited lower values in the warmer period. Young children have a higher degree of susceptibility than older individuals due to their less developed immune system, tighter airways, higher frequency of respiration, and higher long-term exposure to air pollutants of the lower respiratory tract. Due to the excessive use of coal for heating during the colder season, winds also contribute to increased concentrations of air pollutants (P. Zheng et al. 2017 ).

Bronchiectasis

Bronchiectasis is defined as inappropriate and permanent dilatation of the bronchi. It is a chronic respiratory disease, with many patients having frequent exacerbations. Due to their exacerbations, lung function will subsequently decrease, furthermore, increasing mortality (Garcia-Olivé et al. 2018 ).

Infectious pathogens are often incriminated in the majority of bronchiectasis exacerbations, but frequently, no pathogen can be identified. In a study conducted by C. Pieter Goeminne et al. on 432 patients diagnosed through high-resolution computed tomography (HRCT) and clinically confirmed bronchiectasis, for a 10 μg/m 3 increase in PM10 and NO 2 , the chance of developing an exacerbation in that same day increased by 4.5% and 3.2%. In total, 11.2% for PM10 and 4.7% for NO 2 were the risk of having an exacerbation for a 10-μg/m 3 increase in the concentration of air pollutants. Subanalysis showed considerably higher relative risks through spring and summer due to increased expected outdoor air pollution exposure (Goeminne et al. 2018 ).

Additionally, in a retrospective observational study conducted in Badalona, SO 2 was considerably related to an increase in the hospitalization number (Garcia-Olivé et al. 2018 ). Through our search of the literature, we noticed that there are few studies on the connection between air pollution and bronchiectasis.

Tuberculosis

According to the WHO, in the 2019 Global Tuberculosis Report, approximately 10 million people worldwide fell ill with tuberculosis in 2018, and it is the leading cause of a single infectious agent. Worldwide, tuberculosis is considered to be the 10th leading cause of death (WHO-Global Tuberculosis Report 2019 n.d. ) Tuberculosis (TB) is a disease whose prevalence has been associated with socioeconomic risk factors that has a stronger association with urban settings, where there is greater exposure to air pollution (Jassal et al. 2012 ).

There is a direct correlation between air quality and tuberculosis incidence. Precipitation, atmospheric pressure, and relative humidity affect the incidence of tuberculosis by indirectly reducing the quantity of inhalable PM and SO 2 concentrations. On the other hand, wind plays a major role by increasing the incidence of tuberculosis by spreading pathogens (Zhang and Zhang 2019 ). Fine particulate matter and traffic-related air pollution might be associated with an increased risk of developing tuberculosis. This association is not due to direct exposure but rather to the impairment of the individual’s immunity (Lai et al. 2016 ). Popovic et al. showed in a systematic review that the pollutant most frequently associated with tuberculosis is PM2.5 (Popovic et al. 2019 ).

The Chengdu study also documented that exposure to ambient PM10, NO 2 , and SO 2 was linked to increased tuberculosis morbidity in China, but the lag time was 28 days for PM10 and 5 days for SO 2 and NO 2 , which can only be attributed to short-term effects (Zhu et al. 2018 ). Another study conducted by Lai et al. highlighted that an increased risk of active tuberculosis is related to exposure to fine particulate matter PM2.5. Furthermore, traffic-related air pollution, including nitrogen dioxide, nitrogen, and carbon monoxide, was associated with tuberculosis risk. On the other hand, PM10 was not linked with active tuberculosis, and O 3 was inversely associated with the risk of TB (Lai et al. 2016 ).

Similar to the last results, O 3 levels could not be significantly correlated with acid fast bacilli (AFB)-positive smears in the retrospective study of Jamal et al. Medical records of 196 individuals diagnosed with TB positivity at Los Angeles County and University of Southern California Medical Center Hospital were analyzed. A total of 111 had smear positivity, while 85 had smear negativity. There was a significant correlation in single pollutant models analyzing PM2.5 levels and smear-positive TB (Jassal et al. 2012 ).

The link between PM2.5 concentration, notably a 10 μg/m 3 increase in PM2.5 levels, and active TB was also noted in a study conducted from 2014 to 2017 in Lianyungang. For the single-pollutant model, the association between a 10-μg/m 3 increase in PM10 concentration and SO 2 concentration and active TB was significant. Additionally, a potential correlation between relatively long-term outdoor exposure to PM2.5, PM10, SO 2 , and NO 2 and active TB was observed in the time-series study conducted in the northeastern region of Jiangsu Province, China. In the multipollutant models, ambient PM10 and NO 2 remained significant, and the association was not altered in subgroups of different genders or ages (Li et al. 2019 ).

In addition, exposure to pollution over different periods of time may be associated with drug resistance. Exposure to PM2.5, PM10, O 3 , and CO has been associated with drug-resistant TB, including first-line monodrug resistance, polydrug resistance, and multidrug resistance (MDR), in both single- and multiregression models. In the retrospective study of Yao et al., conducted in Jinan city, China, from January 1, 2014 to December 31, 2015, 752 new culture-confirmed TB cases reported in TB prevention and control institutions of Jinan were included. The results showed significant monodrug resistance, and polydrug resistance increased the risk for ambient PM2.5, PM10, O 3 , and CO exposure. The most significant association for PM2.5 was noticed at 540 days of exposure, for O 3 was noticed at 180 days of exposure, and for PM10 and CO, it was noted from 90 to 540 days of exposure. Of the 752 cases, 18.8% were first-line drug-resistant cases with streptomycin having the highest rate of resistance (15.3%), 13% were second-line resistant, fluoroquinolones having the highest rate of resistance (11.3%), 12.3% were resistant to more than 1 drug but not MDR, and 3.3% were MDR-TB (Yao et al. 2019 ).

NO 2 nitrogen dioxide, SO 2 sulfur dioxide, PM2.5 particulate matter with diameter < 2.5μm in diameter, PM10 particulate matter with diameter < 10μm in diameter, O 3 ozone, CO carbon monoxide, COPD chronic obstructive pulmonary disease, IPF idiopathic pulmonary fibrosis

Animal experiments

Animal experiments have opened up new perspectives on air pollution (Edwards et al. 2020 ). Air pollution contributes to increased inflammation. When polluted air is inhaled, its first stop is the lungs. This is where oxidation-reduction first occurs (Gangwar et al. 2020 ). Oxidative stress arises from altering the balance between oxidants and antioxidants. Altering this balance will increase oxidative stress and cause the increase of lung pathologies through promoting inflammation of the airways. PM consists of a number of components capable of generating ROS, which subsequently increase inflammatory mediators in the lungs (Valavanidis et al. 2013 ).

In a study conducted by Edward et al., it was observed that rats exposed to TRAP, compared with those not exposed, exhibited increased gene expression changes related to oxidative stress, inflammation, aging, and fibrosis in the heart (Edwards et al. 2020 ). While Zheng et al. showed that following exposure to tracheal diesel particles, mice presented an increase in transient oxidative stress in the lungs, Sun et al. showed that PM2.5 accentuates the degree of atherosclerosis, degrades vasomotor tone, and determines vascular inflammation in mice that have been chronically exposed to low concentrations of PM2.5 (Q. Sun et al. 2015 ) (X. Zheng et al. 2019 ) (Gangwar et al. 2020 ). Rats that were exposed to ozone showed 8-hydroxy-2′-deoxyguanosine (8-OHdG) and heme oxygenase-1 (HO-1) in macrophages, developing rigid lungs with reduced function (Sunil et al. 2013 ) (Valavanidis et al. 2013 ).

Fibrosis and reversible cardiac dysfunction were observed in mice after intratracheal exposure to PM2.5 (Gangwar et al. 2020 ). Oxidative stress in the myocardium is increased in those exposed to ultrafine particles (Cozzi et al. 2021 ). Qin and all demonstrated that after intratracheal exposure to PM2.5, the most sensitive were the extremes of age compared with adult animals, developing heart dysfunction and reversible fibrosis (Qin et al. 2018 ) (Gangwar et al. 2020 ). Cozzi et al. showed that myocardial damage in mice exposed to ultrafine particles is double that in those not exposed (Cozzi et al. 2021 ).

General information

When the level of pollution is high, informing the population should be a priority. This information should be free and easy to access so that outdoor activity is reduced during periods with higher air pollution (Tiotiu et al. 2020 ).

Air quality alerts are beneficial to the population. The population is notified when air quality alerts occur (Wen et al. 2009 ). In a study conducted by Graff and Neidell, it was found that when the population was alerted by smog alerts, outdoor physical activity on visits to the Griffith Park Observatory and Los Angeles Zoo decreased by 8% and 15%, respectively. However, when alerts were repeated on days 2 and 3, people did not take into account the smog alerts, with values of 0% and 5%, respectively (Graff and Neidell 2009 ). These warnings should be repeated as often as possible and should be of particular interest for patients with cardiopulmonary pathology, as well as healthy patients who may subsequently develop chronic diseases (Wen et al. 2009 ).

From a real estate point of view, both large cities and those with fewer inhabitants should be channeled on development so that the degree of pollution does not affect the quality of life of the population. This should be done from the beginning and not developed later, after the urbanization plan has been made. This would help reduce air pollution from the start. Public institutions, as well as the community, must contribute to reducing the degree of pollution. However, although institutions should play a key role in reducing pollution, it can also be reduced by individual freewill (Carlsten et al. 2020 ).

As medical staff inform asthmatics to avoid aeroallergens, patients with chronic cardiopulmonary disease should also be informed by the degree of air pollution and how it may affect their health. Otherwise, they may develop new symptoms or experience worsening of pre-existing symptoms. The air quality index (AQI) should be consulted frequently by patients to cancel outdoor activities when air quality is poor (Shofer et al. 2007 ) (Wen et al. 2009 ).

The use of masks helps reduce the degree of inhalation of noxious substances. However, not all masks are equally effective, and this depends on both the type of mask and the filter it has (Carlsten et al. 2020 ). In a study conducted by Shakya et al., masks made from material were beneficial to a low degree in protecting particles with a diameter of 2.5 μm, while surgical masks were more effective. The most efficient in eliminating most tested particles was N95 masks. The material masks have a higher comfort but are much weaker than N95 masks (Shakya et al. 2016 ).

Conclusions

Today, although we know the impact of pollution on the respiratory system, we have tried to describe up-to-date information on how pollution affects the respiratory system and the pathologies associated with it (Fig. 1 ). This depends on the type of pollutant, its concentration in the environment, and its size. Air pollution potentiates the increase in respiratory pathology. It is important to constantly measure the quality of the air, both in developed and less-developed countries to ensure continued improvement.

NO 2− nitrogen dioxide, SO 2 sulfur dioxide, VOCs volatile organic compounds, CO carbon monoxide, PM2.5 particulate matter with diameter < 2.5 μm in diameter, PM10 particulate matter with diameter < 10 μm in diameter

Strength of this review

The characteristics of this review refer in particular to the lung diseases caused by air pollutants. The lung is one of the main human organs that have direct contact with the air and is able to filter inhalable pollutants. Lung damage by any other pathology corroborated with inhalable pollutants can later affect other organs and the whole body. For this reason, we considered it of major importance to classify the air pollutants and to present how each pollutant influences lung pathologies and can later affect the whole body.

Abbreviations

World Health Organization

sulfur dioxide

nitrogen dioxide

carbon monoxide

volatile organic compounds

International Agency for Research on Cancer

particulate matter

particulate matter with diameter < 2.5 μm in diameter

particulate matter with diameter < 10 μm in diameter

chronic obstructive pulmonary disease

fluorescent oxidation products

traffic-related air pollution

idiopathic pulmonary fibrosis

reactive oxygen species

forced vital capacity

6-min walking test

forced expiratory volume during the first second

University of California San Diego Shortness of Breath Questionnaire

visual analog scale

acute respiratory infections

high-resolution computed tomography

tuberculosis

multiresistance drug resistance

Adar SD, Filigrana PA, Clements N, Peel JL (2014) Ambient coarse particulate matter and human health: a systematic review and meta-analysis. Current Environmental Health Reports 1(3):258–274. https://doi.org/10.1007/s40572-014-0022-z

Article   Google Scholar  

Akpinar-Elci M, Martin FE, Behr JG, Diaz R (2015) Saharan dust, climate variability, and asthma in Grenada, the Caribbean. International Journal of Biometeorology 59(11):1667–1671. https://doi.org/10.1007/s00484-015-0973-2

Balcan B, Aakan S, Ugurlu AO, Hhandemir BO, Ceyhan BB, Ozkaya S (2016) Effects of biomass smoke on pulmonary functions: a case control study. International Journal of COPD 11(1):1615–1622. https://doi.org/10.2147/COPD.S109056

Bari MA, Kindzierski WB (2017) Characteristics of air quality and sources affecting fine particulate matter (PM2.5) levels in the City of Red Deer, Canada. Environmental Pollution 221:367–376. https://doi.org/10.1016/j.envpol.2016.11.087

Article   CAS   Google Scholar  

Barone-Adesi F, Chapman RS, Silverman DT, He X, Hu W, Vermeulen R, Ning B, Fraumeni JF, Rothman N, Lan Q (2012) Risk of lung cancer associated with domestic use of coal in Xuanwei, China: Retrospective Cohort Study. BMJ (Online) 345(7874):1–10. https://doi.org/10.1136/bmj.e5414

Bernardini F, Attademo L, Trezzi R, Gobbicchi C, Balducci PM, Del Bello V, Menculini G et al (2019) Air pollutants and daily number of admissions to psychiatric emergency services: evidence for detrimental mental health effects of ozone. Epidemiology and Psychiatric Sciences 29:1–7. https://doi.org/10.1017/S2045796019000623

Billionnet C, Gay E, Kirchner S, Leynaert B, Annesi-Maesano I (2011) Quantitative assessments of indoor air pollution and respiratory health in a population-based sample of French dwellings. Environmental Research 111(3):425–434. https://doi.org/10.1016/j.envres.2011.02.008

Bowatte G, Lodge CJ, Knibbs LD, Erbas B, Perret JL, Jalaludin B, Morgan GG et al (2018) Traffic related air pollution and development and persistence of asthma and low lung function. Environment International 113(January):170–176. https://doi.org/10.1016/j.envint.2018.01.028

Cadelis G, Tourres R, Molinie J (2014) Short-term effects of the particulate pollutants contained in Saharan dust on the visits of children to the emergency department due to asthmatic conditions in Guadeloupe (French Archipelago of the Caribbean). PLoS ONE 9(3):e91136. https://doi.org/10.1371/journal.pone.0091136

Carlsten C, Salvi S, Wong GWK, Chung KF (2020) Personal strategies to minimise effects of air pollution on respiratory health : advice for providers , Patients and the Public. https://doi.org/10.1183/13993003.02056-2019

Ciganek M, Neca J (2008) Chemical characterization of volatile organic compounds on animal farms. Veterinarni Medicina 53(12):641–651. https://doi.org/10.17221/1969-VETMED

Cozzi E, Hazarika S, Stallings Iii HW, Cascio WE, Devlin RB, Lust RM, Wingard CJ et al (2021) Ultrafine particulate matter exposure augments ischemia-reperfusion injury in mice. 27834:894–903. https://doi.org/10.1152/ajpheart.01362.2005

Edwards S, Zhao G, Tran J, Patten KT, Valenzuela A, Wallis C, Bein KJ, Wexler AS, Lein PJ, Rao X (2020) Pathological cardiopulmonary evaluation of rats chronically exposed to traffic- related air pollution 128 (December): 1–14.

Gangwar RS, Bevan GH, Palanivel R, Das L (2020) Redox biology oxidative stress pathways of air pollution mediated toxicity : recent insights. Redox Biology 34(March):101545. https://doi.org/10.1016/j.redox.2020.101545

Garcia-Olivé I, Stojanovic Z, Radua J, Rodriguez-Pons L, Martinez-Rivera C, Manzano JR (2018) Effect of air pollution on exacerbations of bronchiectasis in Badalona, Spain, 2008-2016. Respiration 96(2):111–116. https://doi.org/10.1159/000488646

Gharibvand L, Shavlik D, Mark G, Lawrence Beeson W, Soret S, Knutsen R (2017) The association between ambient fine particulate air pollution and lung cancer incidence : results from the. AHSMOG-2 Study 378(3):378–384

Google Scholar  

Ghebreyesus TA (2018) 9 out of 10 people worldwide breathe polluted air , but more countries are taking actioN 39 (May): 641–43.

Goeminne PC, Cox B, Finch S, Loebinger MR, Bedi P, Hill AT, Fardon TC, de Hoogh K, Nawrot TS, Chalmers JD (2018) The impact of acute air pollution fluctuations on bronchiectasis pulmonary exacerbation: a case-crossover analysis. European Respiratory Journal 52(1):1702557. https://doi.org/10.1183/13993003.02557-2017

Graff J, Neidell M (2009) Days of haze : environmental information disclosure and intertemporal avoidance behavior. Journal of Environmental Economics & Management 58(2):119–128. https://doi.org/10.1016/j.jeem.2009.03.001

Guarnieri M, Balmes JR (2014) Outdoor air pollution and asthma. The Lancet 383(9928):1581–1592. https://doi.org/10.1016/S0140-6736(14)60617-6

Gyan K, Henry W, Lacaille S, Laloo A, Lamsee-Ebanks C, McKay S, Antoine RM, Monteil MA (2005) African dust clouds are associated with increased paediatric asthma accident and emergency admissions on the Caribbean Island of Trinidad. International Journal of Biometeorology 49(6):371–376. https://doi.org/10.1007/s00484-005-0257-3

Hamra GB, Guha N, Cohen A, Laden F, Raaschou-Nielsen O, Samet JM, Vineis P, Forastiere F, Saldiva P, Yorifuji T, Loomis D (2014) Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environmental Health Perspectives 122(9):906–911. https://doi.org/10.1289/ehp.1408092

Havet A, Li Z, Zerimech F, Sanchez M, Siroux V, Le Moual N, Brunekreef B et al (2019) Does the oxidative stress play a role in the associations between outdoor air pollution and persistent asthma in adults? Findings from the EGEA study. Environmental Health: A Global Access Science Source 18(1):1–9. https://doi.org/10.1186/s12940-019-0532-0

Huang HC, Lin FCF, Wu MF, Nfor ON, Hsu SY, Lung CC, Ho CC, Chen CY, Liaw YP (2019) Association between chronic obstructive pulmonary disease and PM2.5 in Taiwanese nonsmokers. International Journal of Hygiene and Environmental Health 222(5):884–888. https://doi.org/10.1016/j.ijheh.2019.03.009

Jassal MS, Bakman I, Jones B (2012) Correlation of ambient pollution levels and heavily-trafficked roadway proximity on the prevalence of smear-positive tuberculosis. Public Health 127(3):268–274. https://doi.org/10.1016/j.puhe.2012.12.030

Johannson KA, Balmes JR, Collard HR (2015) Air pollution exposure: a novel environmental risk factor for interstitial lung disease? Chest 147(4):1161–1167. https://doi.org/10.1378/chest.14-1299

Johannson KA, Vittinghoff E, Lee K, Balmes JR, Ji W, Kaplan GG, Kim DS, Collard HR (2014) Acute exacerbation of idiopathic pulmonary fibrosis associated with air pollution exposure. European Respiratory Journal 43(4):1124–1131. https://doi.org/10.1183/09031936.00122213

Johannson KA, Vittinghoff E, Morisset J, Wolters PJ, Noth EM, Balmes JR, Collard HR (2018) Air pollution exposure is associated with lower lung function, but not changes in lung function, in patients with idiopathic pulmonary fibrosis. Chest 154(1):119–125. https://doi.org/10.1016/j.chest.2018.01.015

Kravchenko J, Lyerly HK (2018) The impact of coal-powered electrical plants and coal ash impoundments on the health of residential communities. North Carolina Medical Journal 79(5):289–300. https://doi.org/10.18043/ncm.79.5.289

Kwon JW, Park HW, Kim WJ, Kim MG, Lee SJ (2018) Exposure to volatile organic compounds and airway inflammation. Environmental Health: A Global Access Science Source 17(1):1–8. https://doi.org/10.1186/s12940-018-0410-1

Lai TC, Chiang CY, Wu CF, Yang SL, Liu DP, Chan CC, Lin HH (2016) Ambient air pollution and risk of tuberculosis: a cohort study. Occupational and Environmental Medicine 73(1):56–61. https://doi.org/10.1136/oemed-2015-102995

Lee Y, Lee YC, Kim T, Choi JS, Park D (2018) Sources and characteristics of particulate matter in subway tunnels in Seoul, Korea. International Journal of Environmental Research and Public Health 15(11):1–17. https://doi.org/10.3390/ijerph15112534

Li Z, Mao X, Liu Q, Song H, Ji Y, Xu D, Qiu B, Tian D, Wang J (2019) Long-term effect of exposure to ambient air pollution on the risk of active tuberculosis. International Journal of Infectious Diseases 87:177–184. https://doi.org/10.1016/j.ijid.2019.07.027

Liang L, Cai Y, Barratt B, Lyu B, Chan Q, Hansell AL, Xie W, Zhang D, Kelly FJ, Tong Z (2019) Associations between daily air quality and hospitalisations for acute exacerbation of chronic obstructive pulmonary disease in Beijing, 2013–17: an ecological analysis. The Lancet Planetary Health 3(6):e270–e279. https://doi.org/10.1016/S2542-5196(19)30085-3

Lin CK, Lin RT, Chen T, Zigler C, Wei Y, Christiani DC (2019) A global perspective on coal-fired power plants and burden of lung cancer. Environmental Health: A Global Access Science Source 18(1):1–11. https://doi.org/10.1186/s12940-019-0448-8

Longhin E, Holme JA, Gutzkow KB, Arlt VM, Kucab JE, Camatini M, Gualtieri M (2013) Cell cycle alterations induced by urban PM2.5 in bronchial epithelial cells: characterization of the process and possible mechanisms involved. Particle and Fibre Toxicology 10(1):1–19. https://doi.org/10.1186/1743-8977-10-63

Maesano CN, Caillaud D, Youssouf H, Banerjee S, Homme JP, Audi C, Horo K, Toloba Y, Ramousse O, and Annesi-maesano I (2019) Indoor exposure to particulate matter and volatile organic compounds in dwellings and workplaces and respiratory health in French farmers 3: 1–12.

Mannucci PM, Harari S, Martinelli I, Franchini M (2015) Effects on health of air pollution: a narrative review. Internal and Emergency Medicine 10(6):657–662. https://doi.org/10.1007/s11739-015-1276-7

Minichilli F, Gorini F, Bustaffa E, Cori L, Bianchi F (2019) Mortality and hospitalization associated to emissions of a coal power plant: a population-based cohort study. Science of the Total Environment 694:133757. https://doi.org/10.1016/j.scitotenv.2019.133757

Nsoh M, Olga B, Mankollo Y, Ebongue M, Cyprien KN, Louise J, Likeng N et al (2019) Acute respiratory infection related to air pollution in Bamenda, North West Region of Cameroon. 8688:1–8. https://doi.org/10.11604/pamj.2019.32.99.15228

Pan Y, Pan X, Xiao H, Xiao H (2019) Structural Characteristics and functional implications of PM2.5 bacterial communities during fall in Beijing and Shanghai, China. Frontiers in Microbiology 10(October):1–11. https://doi.org/10.3389/fmicb.2019.02369

Popovic I, Soares RJ, Magalhaes EG, Marks GB, Dong GH, Wei X, Knibbs LD (2019) A systematic literature review and critical appraisal of epidemiological studies on outdoor air pollution and tuberculosis outcomes. Environmental Research 170(November 2018):33–45. https://doi.org/10.1016/j.envres.2018.12.011

Qin G, Jin X, Zhang Y, Guo L, Chen R, Sang N (2018) Ambient fine particulate matter exposure induces reversible cardiac dysfunction and fibrosis in juvenile and older female mice, 1–14.

Raaschou-Nielsen O, Andersen ZJ, Beelen R, Samoli E, Stafoggia M, Weinmayr G, Hoffmann B, Fischer P, Nieuwenhuijsen MJ, Brunekreef B, Xun WW, Katsouyanni K, Dimakopoulou K, Sommar J, Forsberg B, Modig L, Oudin A, Oftedal B, Schwarze PE, Nafstad P, de Faire U, Pedersen NL, Östenson C-G, Fratiglioni L, Penell J, Korek M, Pershagen G, Eriksen KT, Sørensen M, Tjønneland A, Ellermann T, Eeftens M, Peeters PH, Meliefste K, Wang M, Bueno-de-Mesquita B, Key TJ, de Hoogh K, Concin H, Nagel G, Vilier A, Grioni S, Krogh V, Tsai M-Y, Ricceri F, Sacerdote C, Galassi C, Migliore E, Ranzi A, Cesaroni G, Badaloni C, Forastiere F, Tamayo I, Amiano P, Dorronsoro M, Trichopoulou A, Bamia C, Vineis P, Hoek G (2013) Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). The Lancet Oncology 14(9):813–822. https://doi.org/10.1016/S1470-2045(13)70279-1

Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Colby TV et al (2011) An Official ATS/ERS/JRS/ALAT Statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. American Journal of Respiratory and Critical Care Medicine 183(6):788–824. https://doi.org/10.1164/rccm.2009-040GL

Shakya KM, Noyes A, Kallin R, Peltier RE (2016) Evaluating the ef fi cacy of cloth facemasks in reducing particulate matter exposure. 27(3):352–357. https://doi.org/10.1038/jes.2016.42

Shofer S, Chen T-m, Gokhale J, Kuschner WG (2007) Outdoor air pollution : counseling and exposure risk reduction. The Medical Science 333(4):257–260. https://doi.org/10.1097/MAJ.0b013e31803b913c

Singh D, Agusti A, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, Criner GJ et al (2019) Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: The GOLD Science Committee Report 2019. The European Respiratory Journal 53(5):1900164. https://doi.org/10.1183/13993003.00164-2019

Wen X-j, Balluz L, Mokdad A (2009) Association between media alerts of air quality index and change of outdoor activity among adult asthma in six states, BRFSS, 2005. J Community Health 34(1):40–46. https://doi.org/10.1007/s10900-008-9126-4

Sun J, Barnes AJ, He D, Wang M, Wang J (2017) Systematic review and meta-analysis of the association between ambient nitrogen dioxide and respiratory disease in China. International Journal of Environmental Research and Public Health 14(6). https://doi.org/10.3390/ijerph14060646

Sun Q, Wang A, Jin X, Natanzon A, Duquaine D, Brook RD (2015) Acceleration of atherosclerosis and vascular inflammation in an animal model 294 (23): 3003–10.

Sunil VR, Vayas KN, Massa CB, Gow AJ, Laskin JD, Laskin DL (2013) Ozone-induced injury and oxidative stress in bronchiolar epithelium are associated with altered pulmonary mechanics. 133(2):309–319. https://doi.org/10.1093/toxsci/kft071

Tian Y, Xiang X, Juan J, Song J, Cao Y, Huang C, Li M, Yonghua H (2018) Short-term effects of ambient fine particulate matter pollution on hospital visits for chronic obstructive pulmonary disease in Beijing, China. Environmental Health: A Global Access Science Source 17(1):1–8. https://doi.org/10.1186/s12940-018-0369-y

Tiotiu AI, Novakova P, Nedeva D, Chong-neto HJ, Novakova S (2020) Paschalis Steiropoulos, and Krzysztof Kowal. Impact of air pollution on asthma outcomes, 1–29.

Valavanidis A, Vlachogianni T, Fiotakis K, Loridas S (2013) Pulmonary oxidative stress , inflammation and cancer : respirable particulate matter , fibrous dusts and ozone as major causes of lung carcinogenesis through reactive oxygen species mechanisms:3886–3907. https://doi.org/10.3390/ijerph10093886

Wang H, Gao Z, Ren J, Liu Y, Chang L T-c, Cheung K, Feng Y, Li Y (2019a) Chemosphere an urban-rural and sex differences in cancer incidence and mortality and the relationship with PM 2 . 5 exposure: an ecological study in the southeastern side of Hu Line. Chemosphere 216:766–773. https://doi.org/10.1016/j.chemosphere.2018.10.183

Wang L, Xiong Q, Wu G, Gautam A, Jiang J, Liu S, Zhao W, Guan H (2019b) Spatio-temporal variation characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018. International Journal of Environmental Research and Public Health 16(21). https://doi.org/10.3390/ijerph16214276

Wang M, Sampson PD, Sheppard LE, Stein JH, Vedal S, Kaufman JD (2019c) Long-term exposure to ambient ozone and progression of subclinical arterial disease : the multi-ethnic study of atherosclerosis and air pollution 127 (May): 1–8.

Wang N, Mengersen K, Tong S, Kimlin M, Zhou M, Wang L, Yin P et al (2019d) Short-term association between ambient air pollution and lung cancer mortality. Environmental Research 179(June):108748. https://doi.org/10.1016/j.envres.2019.108748

WHO (n.d.) Global Tuberculosis Report 2019

Winterbottom CJ, Shah RJ, Patterson KC, Kreider ME, Panettieri RA, Rivera-Lebron B, Miller WT et al (2018) Exposure to ambient particulate matter is associated with accelerated functional decline in idiopathic pulmonary fibrosis. Chest 153(5):1221–1228. https://doi.org/10.1016/j.chest.2017.07.034

World Health Organization, WHO (2005) WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: global update 2005, 1–21. 10.1016/0004-6981(88)90109-6.

World Health Organization, WHO (2018) 9 out of 10 people worldwide breathe polluted air, but more countries are taking action. WHO.INT. 2018. https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action .

Xing DF, Xu CD, Liao XY, Xing TY, Cheng SP, Hu MG, Wang JX (2019) Spatial association between outdoor air pollution and lung cancer incidence in China. BMC Public Health 19(1):1–11. https://doi.org/10.1186/s12889-019-7740-y

Xue X, Chen J, Sun B, Zhou B, Li X. (2018) Temporal trends in respiratory mortality and short-term effects of air pollutants in Shenyang , China, 11468–79.

Yao L, Cui LL, Liu JY, Song WM, Su L, Li YF, Li HC (2019) Ambient air pollution exposures and risk of drug-resistant tuberculosis. Environment International 124(January):161–169. https://doi.org/10.1016/j.envint.2019.01.013

Zhang CY, Zhang A (2019) Climate and air pollution alter incidence of tuberculosis in Beijing, China. Annals of Epidemiology 37(2):71–76. https://doi.org/10.1016/j.annepidem.2019.07.003

Zhang S, Li G, Lin T, Guo Q, Pan X (2016) Short-term exposure to air pollution and morbidity of COPD and asthma in East Asian area: a systematic review and meta-analysis. Environmental Research 148:15–23. https://doi.org/10.1016/j.envres.2016.03.008

Zhang YL, Cao F (2015) Fine particulate matter (PM 2.5) in China at a city level. Scientific Reports 5(2014):1–12. https://doi.org/10.1038/srep14884

Zhang Z, Chai P, Wang J, Ye Z, Shen P, Lu H, Jin M, et al. (2019) Association of particulate matter air pollution and hospital visits for respiratory diseases : a time-series study from China.”

Zhao Y, Hu J, Tan Z, Liu T, Zeng W, Li X, Huang C, Wang S, Huang Z, Ma W (2019) Ambient carbon monoxide and increased risk of daily hospital outpatient visits for respiratory diseases in Dongguan, China. Science of the Total Environment 668:254–260. https://doi.org/10.1016/j.scitotenv.2019.02.333

Zheng P-w, Wang J-b, Zhang Z-y, Shen P, Chai P-f, Li D, Jin M-j, Tang M-L, Lu H-c, Lin H-b, Chen K (2017) Air pollution and hospital visits for acute upper and lower respiratory infections among children in Ningbo, China: A Time-Series Analysis. 24:18860–18869. https://doi.org/10.1007/s11356-017-9279-8

Zheng X, Wang G, Bin P, Meng T, Niu Y, Yang M, Zhang L (2019) Time-course e ff ects of antioxidants and phase II enzymes on diesel exhaust particles-induced oxidative damage in the mouse lung. Toxicology and Applied Pharmacology 366(January):25–34. https://doi.org/10.1016/j.taap.2019.01.010

Zhu S, Xia L, Wu J, Chen S, Chen F, Zeng F, Chen X, Chen C, Xia Y, Zhao X, Zhang J (2018) Ambient air pollutants are associated with newly diagnosed tuberculosis: a time-series study in Chengdu, China. Science of the Total Environment 631–632(17):47–55. https://doi.org/10.1016/j.scitotenv.2018.03.017

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Bălă, GP., Râjnoveanu, RM., Tudorache, E. et al. Air pollution exposure—the (in)visible risk factor for respiratory diseases. Environ Sci Pollut Res 28 , 19615–19628 (2021). https://doi.org/10.1007/s11356-021-13208-x

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This article is part of the research topic.

Crucial Air Quality, Atmospheric Environment, and Climate Change in Low- and Middle-Income Countries

Crucial Air Quality, Atmospheric Environment, and Climate Change in Low-and Middle-Income Countries Provisionally Accepted

• 1 Ministry of Natural Resources and Environment, Thailand
• 2 Thammasat University, Thailand
• 3 Kasetsart University, Thailand
• 4 North South University, Bangladesh
• 5 Bandung National Institute of Technology (Itenas), Indonesia

The final, formatted version of the article will be published soon.

Management of air pollution and climate change is an urgent issue that requires tackling both 13 problems simultaneously. It was addressed to be a significant milestone in the meeting of Conference 14 of the Parties (COP). Most countries have agreed to transition away from fossil fuels (Arora, 2024). 15The relationship between air quality and climate change is complex. Pollutants emitted into the 16 atmosphere not only reduce air quality but also exacerbate global warming by increasing greenhouse 17 gas concentrations. Reducing emissions like carbon dioxide and particulate matter is crucial for both 18 improving air quality and mitigating the impacts of climate change (Im et al., 2022). Many developing 19 countries in South Asia and Southeast Asia have faced severe air pollution problems, particularly with 20 PM2.5 exceeding the World Health Organization (WHO) air quality guideline value in Asian cities 21 (Sooktawee et al., 2023;Verma et al., 2023;Jawaa et al., 2024). They have also experienced climate 22 change impacts, both extreme and slow-onset events such as warm days and nights, intense heat waves, 23unpredictable floods, sea level rise, saltwater intrusion (Hussain et al., 2020;Limsakul et al., 2023). 24This Research Topic aims to collect original research papers on air quality, the atmospheric 25 environment, and climate change that focuses on low-to upper-middle-income economy countries. 26The content of this issue is mostly research related to air pollution, particulate matter with diameter 27 less than 2.5 micron (PM2.5), bio-particle, remote sensing, and its relationship with climatic factors. 28Seven articles are published in this research topic with contributions from 35 authors. We briefly 29 selected their findings and highlights from each paper summarized as follows: 30Ratanavalachai and Trivitayanurak carried out air quality dispersion modeling to reveal source 31 contributions of PM2.5 for the central business area in Bangkok, Thailand. Emission inventory of 32 mobile sources in this study was developed in hourly variation. Interestingly, PM2.5 from outside the 33 study domain was an input and declared as advected-in PM2.

Keywords: Air Quality, Atmospheric pollutant, PM2.5, climate, Developing country

Received: 05 Apr 2024; Accepted: 11 Apr 2024.

Copyright: © 2024 Sooktawee, Kanabkaew, Lalitaporn, Khan, Permadi and Limsakul. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Pichnaree Lalitaporn, Kasetsart University, Bangkok, Thailand

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Atmospheric and economic drivers of global air pollution

Carbon monoxide emissions from industrial production have serious consequences for human health and are a strong indicator of overall air pollution levels. Many countries aim to reduce their emissions, but they cannot control air flows originating in other regions. A new study from the University of Illinois Urbana-Champaign looks at global flows of air pollution and how they relate to economic activity in the global supply chain.

"Our study is unique in combining atmospheric transport of air pollution with supply chain analysis as it tells us where the pollution is coming from and who is ultimately responsible for it," said lead author Sandy Dall'erba, professor in the Department of Agricultural and Consumer Economics (ACE) and director of the Center for Climate, Regional, Environmental and Trade Economics (CREATE), both part of the College of Agricultural, Consumer and Environmental Sciences (ACES) at Illinois.

"There is a direct link between a country's level of production and how much air pollution is emitted. But production may be driven by demand from consumers in other countries. We use supply chain analysis to quantify the links between production and consumption. This helps us to understand how production in one country is linked to domestic and foreign demand," he added.

The researchers traced the movement of pollutants through the atmosphere to understand the flow of emissions, using simulations developed by Nicole RIemer, professor in the Department of Climate, Meteorology & Atmospheric Sciences, College of Liberal Arts & Sciences at Illinois. For analytical purposes, they divided the world into five sections: the United States, Europe, China, South Korea, and the rest of the world. South Korea is located downwind of China, and it serves as an example of how a small country can be affected by pollution from a much larger upwind neighbor.

"Over recent years, South Korea has taken several measures to reduce its own pollution, yet it has experienced worsening air quality. Why? The answer is to be found in its upwind neighbor, China. Yet, a large amount of the goods manufactured in China are destined for foreign consumers in the U.S. and in Europe, among other places. As such, who is to be blamed for the increase in air pollution in South Korea? That is the challenge we embarked on with this study," Dall'erba stated.

The researchers found the amount of carbon monoxide emissions coming from China to South Korea increased from 30 teragrams (Tg) in 1990 to 42 Tg in 2014.

"To put these numbers in perspective, 5 Tg of carbon monoxide corresponds to the emissions from all of the cars in the U.S. -- roughly 274 million -- each driving 13,500 miles per year. So it's definitely not a small increase. We conclude that South Korea has, in effect, lost control of their own air quality," Dall'erba explained.

Dall'erba and his colleagues conducted a structural decomposition analysis to identify the economic drivers of carbon monoxide emissions in the five study regions. They found that while China's technological processes to reduce pollution have improved, overall carbon monoxide emissions have gone up because the country's production has increased.

Next, the researchers sought to identify where the demand that drives the increased production comes from. In China's case, some of the increase can be attributed to U.S. and European demand, but it is primarily driven by households in China. The Chinese population grew considerably between 1990 and 2014, and the country became wealthier, leading to higher consumption, Dall'erba noted.

"Our findings show that pollution is a global concern that can't be solved by individual countries. The world is connected, and we're all in this together," said co-author Yilan Xu, associate professor in ACE. "Pollution in one country can result from economic activities in neighboring countries, which in turn is influenced by who's demanding the goods produced in that country. Pollution emitted anywhere in the world is going to have consequences all over the world to varying degrees."

Dall'erba, Riemer, and Xu emphasize that everybody can play a part in reducing emissions. Producers can implement technological change; policymakers can issue regulations or provide incentives; and consumers can make choices that favor sustainable products.

• Air Quality
• Environmental Issues
• Air Pollution
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• Resource Shortage
• Public Health
• World Development
• Air pollution
• Automobile emissions control
• Climate change mitigation
• Environmental impact assessment
• Global warming
• Climate engineering

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Materials provided by University of Illinois College of Agricultural, Consumer and Environmental Sciences . Original written by Marianne Stein. Note: Content may be edited for style and length.

Journal Reference :

• Sandy Dall’erba, Nicole Riemer, Yilan Xu, Ran Xu, Yu Yao. Identifying the key atmospheric and economic drivers of global carbon monoxide emission transfers . Economic Systems Research , 2024; 1 DOI: 10.1080/09535314.2023.2300787

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What is air pollution?

What causes air pollution, effects of air pollution, air pollution in the united states, air pollution and environmental justice, controlling air pollution, how to help reduce air pollution, how to protect your health.

Air pollution  refers to the release of pollutants into the air—pollutants that are detrimental to human health and the planet as a whole. According to the  World Health Organization (WHO) , each year, indoor and outdoor air pollution is responsible for nearly seven million deaths around the globe. Ninety-nine percent of human beings currently breathe air that exceeds the WHO’s guideline limits for pollutants, with those living in low- and middle-income countries suffering the most. In the United States, the  Clean Air Act , established in 1970, authorizes the U.S. Environmental Protection Agency (EPA) to safeguard public health by regulating the emissions of these harmful air pollutants.

“Most air pollution comes from energy use and production,” says  John Walke , director of the Clean Air team at NRDC. Driving a car on gasoline, heating a home with oil, running a power plant on  fracked gas : In each case, a fossil fuel is burned and harmful chemicals and gases are released into the air.

“We’ve made progress over the last 50 years in improving air quality in the United States, thanks to the Clean Air Act. But climate change will make it harder in the future to meet pollution standards, which are designed to  protect health ,” says Walke.

Air pollution is now the world’s fourth-largest risk factor for early death. According to the 2020  State of Global Air  report —which summarizes the latest scientific understanding of air pollution around the world—4.5 million deaths were linked to outdoor air pollution exposures in 2019, and another 2.2 million deaths were caused by indoor air pollution. The world’s most populous countries, China and India, continue to bear the highest burdens of disease.

“Despite improvements in reducing global average mortality rates from air pollution, this report also serves as a sobering reminder that the climate crisis threatens to worsen air pollution problems significantly,” explains  Vijay Limaye , senior scientist in NRDC’s Science Office. Smog, for instance, is intensified by increased heat, forming when the weather is warmer and there’s more ultraviolet radiation. In addition, climate change increases the production of allergenic air pollutants, including mold (thanks to damp conditions caused by extreme weather and increased flooding) and pollen (due to a longer pollen season). “Climate change–fueled droughts and dry conditions are also setting the stage for dangerous wildfires,” adds Limaye. “ Wildfire smoke can linger for days and pollute the air with particulate matter hundreds of miles downwind.”

The effects of air pollution on the human body vary, depending on the type of pollutant, the length and level of exposure, and other factors, including a person’s individual health risks and the cumulative impacts of multiple pollutants or stressors.

Smog and soot

These are the two most prevalent types of air pollution. Smog (sometimes referred to as ground-level ozone) occurs when emissions from combusting fossil fuels react with sunlight. Soot—a type of  particulate matter —is made up of tiny particles of chemicals, soil, smoke, dust, or allergens that are carried in the air. The sources of smog and soot are similar. “Both come from cars and trucks, factories, power plants, incinerators, engines, generally anything that combusts fossil fuels such as coal, gasoline, or natural gas,” Walke says.

Smog can irritate the eyes and throat and also damage the lungs, especially those of children, senior citizens, and people who work or exercise outdoors. It’s even worse for people who have asthma or allergies; these extra pollutants can intensify their symptoms and trigger asthma attacks. The tiniest airborne particles in soot are especially dangerous because they can penetrate the lungs and bloodstream and worsen bronchitis, lead to heart attacks, and even hasten death. In  2020, a report from Harvard’s T.H. Chan School of Public Health showed that COVID-19 mortality rates were higher in areas with more particulate matter pollution than in areas with even slightly less, showing a correlation between the virus’s deadliness and long-term exposure to air pollution. 

These findings also illuminate an important  environmental justice issue . Because highways and polluting facilities have historically been sited in or next to low-income neighborhoods and communities of color, the negative effects of this pollution have been  disproportionately experienced by the people who live in these communities.

Hazardous air pollutants

A number of air pollutants pose severe health risks and can sometimes be fatal, even in small amounts. Almost 200 of them are regulated by law; some of the most common are mercury,  lead , dioxins, and benzene. “These are also most often emitted during gas or coal combustion, incineration, or—in the case of benzene—found in gasoline,” Walke says. Benzene, classified as a carcinogen by the EPA, can cause eye, skin, and lung irritation in the short term and blood disorders in the long term. Dioxins, more typically found in food but also present in small amounts in the air, is another carcinogen that can affect the liver in the short term and harm the immune, nervous, and endocrine systems, as well as reproductive functions.  Mercury  attacks the central nervous system. In large amounts, lead can damage children’s brains and kidneys, and even minimal exposure can affect children’s IQ and ability to learn.

Another category of toxic compounds, polycyclic aromatic hydrocarbons (PAHs), are by-products of traffic exhaust and wildfire smoke. In large amounts, they have been linked to eye and lung irritation, blood and liver issues, and even cancer.  In one study , the children of mothers exposed to PAHs during pregnancy showed slower brain-processing speeds and more pronounced symptoms of ADHD.

Greenhouse gases

While these climate pollutants don’t have the direct or immediate impacts on the human body associated with other air pollutants, like smog or hazardous chemicals, they are still harmful to our health. By trapping the earth’s heat in the atmosphere, greenhouse gases lead to warmer temperatures, which in turn lead to the hallmarks of climate change: rising sea levels, more extreme weather, heat-related deaths, and the increased transmission of infectious diseases. In 2021, carbon dioxide accounted for roughly 79 percent of the country’s total greenhouse gas emissions, and methane made up more than 11 percent. “Carbon dioxide comes from combusting fossil fuels, and methane comes from natural and industrial sources, including large amounts that are released during oil and gas drilling,” Walke says. “We emit far larger amounts of carbon dioxide, but methane is significantly more potent, so it’s also very destructive.” 

Another class of greenhouse gases,  hydrofluorocarbons (HFCs) , are thousands of times more powerful than carbon dioxide in their ability to trap heat. In October 2016, more than 140 countries signed the Kigali Agreement to reduce the use of these chemicals—which are found in air conditioners and refrigerators—and develop greener alternatives over time. (The United States officially signed onto the  Kigali Agreement in 2022.)

Pollen and mold

Mold and allergens from trees, weeds, and grass are also carried in the air, are exacerbated by climate change, and can be hazardous to health. Though they aren’t regulated, they can be considered a form of air pollution. “When homes, schools, or businesses get water damage, mold can grow and produce allergenic airborne pollutants,” says Kim Knowlton, professor of environmental health sciences at Columbia University and a former NRDC scientist. “ Mold exposure can precipitate asthma attacks  or an allergic response, and some molds can even produce toxins that would be dangerous for anyone to inhale.”

Pollen allergies are worsening  because of climate change . “Lab and field studies are showing that pollen-producing plants—especially ragweed—grow larger and produce more pollen when you increase the amount of carbon dioxide that they grow in,” Knowlton says. “Climate change also extends the pollen production season, and some studies are beginning to suggest that ragweed pollen itself might be becoming a more potent allergen.” If so, more people will suffer runny noses, fevers, itchy eyes, and other symptoms. “And for people with allergies and asthma, pollen peaks can precipitate asthma attacks, which are far more serious and can be life-threatening.”

More than one in three U.S. residents—120 million people—live in counties with unhealthy levels of air pollution, according to the  2023  State of the Air  report by the American Lung Association (ALA). Since the annual report was first published, in 2000, its findings have shown how the Clean Air Act has been able to reduce harmful emissions from transportation, power plants, and manufacturing.

Recent findings, however, reflect how climate change–fueled wildfires and extreme heat are adding to the challenges of protecting public health. The latest report—which focuses on ozone, year-round particle pollution, and short-term particle pollution—also finds that people of color are 61 percent more likely than white people to live in a county with a failing grade in at least one of those categories, and three times more likely to live in a county that fails in all three.

In rankings for each of the three pollution categories covered by the ALA report, California cities occupy the top three slots (i.e., were highest in pollution), despite progress that the Golden State has made in reducing air pollution emissions in the past half century. At the other end of the spectrum, these cities consistently rank among the country’s best for air quality: Burlington, Vermont; Honolulu; and Wilmington, North Carolina. 

No one wants to live next door to an incinerator, oil refinery, port, toxic waste dump, or other polluting site. Yet millions of people around the world do, and this puts them at a much higher risk for respiratory disease, cardiovascular disease, neurological damage, cancer, and death. In the United States, people of color are 1.5 times more likely than whites to live in areas with poor air quality, according to the ALA.

Historically, racist zoning policies and discriminatory lending practices known as  redlining  have combined to keep polluting industries and car-choked highways away from white neighborhoods and have turned communities of color—especially low-income and working-class communities of color—into sacrifice zones, where residents are forced to breathe dirty air and suffer the many health problems associated with it. In addition to the increased health risks that come from living in such places, the polluted air can economically harm residents in the form of missed workdays and higher medical costs.

Environmental racism isn't limited to cities and industrial areas. Outdoor laborers, including the estimated three million migrant and seasonal farmworkers in the United States, are among the most vulnerable to air pollution—and they’re also among the least equipped, politically, to pressure employers and lawmakers to affirm their right to breathe clean air.

Recently,  cumulative impact mapping , which uses data on environmental conditions and demographics, has been able to show how some communities are overburdened with layers of issues, like high levels of poverty, unemployment, and pollution. Tools like the  Environmental Justice Screening Method  and the EPA’s  EJScreen  provide evidence of what many environmental justice communities have been explaining for decades: that we need land use and public health reforms to ensure that vulnerable areas are not overburdened and that the people who need resources the most are receiving them.

In the United States, the  Clean Air Act  has been a crucial tool for reducing air pollution since its passage in 1970, although fossil fuel interests aided by industry-friendly lawmakers have frequently attempted to  weaken its many protections. Ensuring that this bedrock environmental law remains intact and properly enforced will always be key to maintaining and improving our air quality.

But the best, most effective way to control air pollution is to speed up our transition to cleaner fuels and industrial processes. By switching over to renewable energy sources (such as wind and solar power), maximizing fuel efficiency in our vehicles, and replacing more and more of our gasoline-powered cars and trucks with electric versions, we'll be limiting air pollution at its source while also curbing the global warming that heightens so many of its worst health impacts.

And what about the economic costs of controlling air pollution? According to a report on the Clean Air Act commissioned by NRDC, the annual  benefits of cleaner air  are up to 32 times greater than the cost of clean air regulations. Those benefits include up to 370,000 avoided premature deaths, 189,000 fewer hospital admissions for cardiac and respiratory illnesses, and net economic benefits of up to $3.8 trillion for the U.S. economy every year.

“The less gasoline we burn, the better we’re doing to reduce air pollution and the harmful effects of climate change,” Walke explains. “Make good choices about transportation. When you can, ride a bike, walk, or take public transportation. For driving, choose a car that gets better miles per gallon of gas or  buy an electric car .” You can also investigate your power provider options—you may be able to request that your electricity be supplied by wind or solar. Buying your food locally cuts down on the fossil fuels burned in trucking or flying food in from across the world. And most important: “Support leaders who push for clean air and water and responsible steps on climate change,” Walke says.

• “When you see in the news or hear on the weather report that pollution levels are high, it may be useful to limit the time when children go outside or you go for a jog,” Walke says. Generally, ozone levels tend to be lower in the morning.
• If you exercise outside, stay as far as you can from heavily trafficked roads. Then shower and wash your clothes to remove fine particles.
• The air may look clear, but that doesn’t mean it’s pollution free. Utilize tools like the EPA’s air pollution monitor,  AirNow , to get the latest conditions. If the air quality is bad, stay inside with the windows closed.
• If you live or work in an area that’s prone to wildfires,  stay away from the harmful smoke  as much as you’re able. Consider keeping a small stock of masks to wear when conditions are poor. The most ideal masks for smoke particles will be labelled “NIOSH” (which stands for National Institute for Occupational Safety and Health) and have either “N95” or “P100” printed on it.
• If you’re using an air conditioner while outdoor pollution conditions are bad, use the recirculating setting to limit the amount of polluted air that gets inside. 

This story was originally published on November 1, 2016, and has been updated with new information and links.

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• v.9(4); 2023 Jul
• PMC10375903

Air quality and mental health: evidence, challenges and future directions

Kamaldeep bhui.

Department of Psychiatry, University of Oxford, UK; Nuffield Department of Primary Care Health Sciences, Medical Sciences Division, Wadham College, University of Oxford, UK; World Psychiatric Association Collaborating Centre, UK; Oxford Health NHS Foundation Trust, UK; East London Foundation NHS Trust, UK; and Oxford Health NIHR Biomedical Research Centre, UK

Joanne B. Newbury

Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, UK; and MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, UK

Rachel M. Latham

Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; and ESRC Centre for Society and Mental Health, King's College London, UK

Marcella Ucci

UCL Institute for Environmental Design and Engineering, University College London, UK

Zaheer A. Nasir

School of Water, Energy and Environment, Cranfield University, UK

Briony Turner

National Centre for Earth Observation, Department of Meteorology, University of Reading, UK

Catherine O'Leary

Helen l. fisher, emma marczylo.

Radiation, Chemical and Environmental Hazards, UK Health Security Agency, UK; and Centre for Environmental Health and Sustainability, University of Leicester, UK

Philippa Douglas

Radiation, Chemical and Environmental Hazards, UK Health Security Agency, UK; Environment Agency, UK; Chief Scientist's Group, Environment Agency, UK; and Centre for Environmental Health and Sustainability, University of Leicester, UK

Stephen Stansfeld

Centre for Psychiatry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, UK

Simon K. Jackson

School of Biomedical Science, Faculty of Health Sciences, University of Plymouth, UK

Sean Tyrrel

Andrey rzhetsky.

Department of Medicine, The University of Chicago, USA; Department of Human Genetics, The University of Chicago, USA; and Institute for Genomics and Systems Biology, The University of Chicago, USA

Rob Kinnersley

Chief Scientist's Group, Environmental Agency, UK

Prashant Kumar

Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, UK

Caroline Duchaine

Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Canada; and Quebec Heart and Lung Institute, Université Laval, Canada

Frederic Coulon

Associated data.

Data availability is not applicable to this article as no new data were created or analysed in this study.

Poor air quality is associated with poor health. Little attention is given to the complex array of environmental exposures and air pollutants that affect mental health during the life course.

We gather interdisciplinary expertise and knowledge across the air pollution and mental health fields. We seek to propose future research priorities and how to address them.

Through a rapid narrative review, we summarise the key scientific findings, knowledge gaps and methodological challenges.

There is emerging evidence of associations between poor air quality, both indoors and outdoors, and poor mental health more generally, as well as specific mental disorders. Furthermore, pre-existing long-term conditions appear to deteriorate, requiring more healthcare. Evidence of critical periods for exposure among children and adolescents highlights the need for more longitudinal data as the basis of early preventive actions and policies. Particulate matter, including bioaerosols, are implicated, but form part of a complex exposome influenced by geography, deprivation, socioeconomic conditions and biological and individual vulnerabilities. Critical knowledge gaps need to be addressed to design interventions for mitigation and prevention, reflecting ever-changing sources of air pollution. The evidence base can inform and motivate multi-sector and interdisciplinary efforts of researchers, practitioners, policy makers, industry, community groups and campaigners to take informed action.

Conclusions

There are knowledge gaps and a need for more research, for example, around bioaerosols exposure, indoor and outdoor pollution, urban design and impact on mental health over the life course.

Aim, scope and methodological approach

The purpose of this rapid narrative review is to gather expert opinions and summarise the existing body of knowledge on air quality and the long-term effects on mental health, highlight methodological challenges and knowledge gaps and identify future research directions. The perspective we take is broad, interdisciplinary and adopts a ‘life-course’ approach, considering psychiatric, cognitive and neurodevelopmental pathways and a wide spectrum of both indoor and outdoor air pollutants, including bioaerosols, heavy metal ions, inorganic particulate matter (PM) and gaseous pollutants.

Existing reviews have mostly focused on associations between air pollution and one type of mental health problem, using multiple study designs. For example, an excellent recent systematic review shows convincing evidence of associations between depression and PM 2.5 . 1 This included five cohort studies and mostly cross-sectional and time-series studies from high- and low-income countries; the authors report significant heterogeneity and potential selection biases, but find convincing evidence of links between particulate matter and depression. There is much less research on psychoses, and specific conditions such as schizophrenia or personality disorders. One review argues cogently that exposure to xenobiotic heavy metals (such as lead and cadmium), particulate matter and nitrogen and sulphur oxides, organic solvents and other constituents of environmental pollution could be component causes of neurodevelopmental disorders such as schizophrenia. 2

The work is undertaken by BioAirNet, a network funded by the UK Research & Innovation (UKRI) agency, bringing together diverse disciplines to advance research, practice and policy. We aimed to provide an umbrella review including multiple mental problems, and the broadest range of literature in a very complex field, which necessarily brings together multiple disciplinary perspectives and contradictory positions. Although narrative reviews can undertake systematic searches, for complex and interdisciplinary narrative reviews, where the evidence is scattered across disciplinary journal, snowballing is considered a more appropriate approach and yields a fuller body of evidence. 3 , 4 Even when systematically analysed, the conclusions of conventional reviews for complex areas often suggest there is inadequate evidence to draw firm conclusions. Consequently, the BioAirNet team identified suitable literature through their existing work and networks. We added our interdisciplinary dialogue through workshops as an additional source of synthesis. This is a critical step, as assumed knowledge in one discipline is not necessarily that in another; furthermore, epistemic processes in each discipline and across academic–community partnerships often lead to some knowledge being valued and some being dismissed. 5 , 6 Hence, in BioAirNet and related UKRI-funded air quality networks, our desire is to establish a cross-disciplinary scaffolding and opportunity for progressing research optimally so it can have a greater impact on public health, offering a starting point and foundation for future research efforts.

We present our review findings by examining the health burden of air pollution and the influence of outdoor and indoor environments; outdoor and indoor air pollution and mental health; and the impact of air pollution on mental health over the life course, from pregnancy, through childhood, to adult and older populations. We also consider research methodological challenges.

Then, we present knowledge gaps and recommendations for research emerging from the literature and from our early workshops. Finally, of all the possible studies, we propose priority research topics and related methods, taking account of the earlier learning from the reviewed literature and workshops.

The health burden of air pollution

The World Health Organization (WHO) has ranked air pollution as one of the major environmental health risks, and the single biggest environmental threat to human health. 7 Worldwide, it is estimated that 4.2 million and 3.8 million premature deaths were attributable to outdoor and indoor air pollution, respectively. 7 There is more evidence of the adverse health effects of particulate matter. 8 Particulate matter has diverse sources (natural/anthropogenic, indoor/outdoor), formation processes, composition (organic/inorganic) and sizes (ultrafine: PM 0.1 , particles that are <0.1 μm in diameter; fine: PM 2.5 , particles that are <2.5 μm in diameter; coarse: particles that are >PM 2.5 and <PM 10 in diameter).

The WHO guidelines implicate particulate matter with aerodynamic diameters of ≤2.5 μm (PM 2.5 ) and ≤10 μm (PM 10 ), ozone, nitrogen dioxide, sulphur dioxide and carbon monoxide in poor air quality. The particle size can influence whether particulate matter can cross the blood–brain barrier and, along with duration of exposure, increase the risk of adverse health effects. Smaller particles are inhaled more deeply into the lung, leading to greater effects on health. The strongest evidence for adverse effects on health is for PM 2.5 , with an extensive body of evidence linking outdoor PM 2.5 exposure to mortality, cardiovascular diseases, pulmonary diseases and cancer. 8 , 9 Therefore, modifying exposure to poor air quality in indoor and outdoor environments could reduce the population-level burden of poor health.

Outdoor air pollution

Outdoor air pollution, particularly particulate matter, is classified by the International Agency for Research on Cancer as carcinogenic to humans (a Group 1 carcinogen) and causes lung cancer. 10 Given the high levels of incident serious mental illness in urban areas where air pollution is greatest, and reverse causal relationships between cancer and serious mental illness (see the section ‘Air quality and mental health over the life course'), there may be common aetiological and mutually reinforcing pathways of risk involving air pollution and inflammation, 11 and oncogenic impacts. 12

Bioaerosols are the biological fraction of particulate matter and are a complex mixture of bacteria, viruses and fungi, or parts of living organisms, like pollen, spores, endotoxins from bacterial cells and mycotoxins from fungi. 13 , 14 Bioaerosol exposure is associated with chronic and acute respiratory illness (via both atopic and non-atopic allergic mechanisms, and non-allergic pathways like infection), and other diseases including gastrointestinal disturbance, dermatological conditions, general malaise and fatigue. 13 , 14 However, the role of biological particulate matter in health burden, their mechanisms of toxicity and impact on human health and well-being across the indoor–outdoor continuum of exposure is not yet clear. Conclusive evidence linking the exact mode of action between pollution, including bioaerosol exposure and its related toxicity, is lacking. However, airway inflammation and oxidative stress are recognised as major mechanisms of the diseases because of particulate matter and associated microbe exposure. 15 , 16 In particular, bacterial endotoxin (lipopolysaccharides) and fungi are linked with inflammatory responses and hypersensitivity in airway models. 16 , 17

Inflammation is implicated in pathways to poor health, for which the emerging evidence is credible for both mental and physical conditions. 18 – 20 The exact process by which inflammation (peripheral and brain tissue) leads to neurotoxic effects is dynamic, complex and subject to numerous self-regulatory processes; 21 for example, internalising symptoms of anxiety and depression, fronto-limbic brain areas responsible for emotional regulation, and neuroinflammation and oxidative stress are implicated. 22 , 23 In a systematic review by Zundel et al, air pollution was consistently associated with neurostructural and neurofunctional effects such as inflammation and oxidative stress, changes to neurotransmitters, neuromodulators and their metabolites, within multiple brain regions (24% of papers), the hippocampus (66%), prefrontal cortex (7%) and amygdala (1%). 22

Shared inflammatory mechanisms of disease aetiology, if confirmed, offer hope for new forms of prevention and treatment that target inflammation by repurposing well-established and relatively safe anti-inflammatory drugs. 24 Furthermore, in children exposed to fine and ultrafine particulate matter, there is evidence of the hallmarks of Alzheimer's and Parkinson's diseases, namely hyperphosphorylated tau, amyloid plaques and misfolded α-synuclein. 23 There is emerging evidence of air pollution in cognitive function and dementia. 25 Brain imaging and animal studies could help to further elucidate relevant mechanisms. A recent systematic review suggests that depression, suicide and neurodevelopmental disorders (such as autism for pregnancy-related exposures) may be more common among those exposed to air pollution. 26

Urban design and indoor environments

The effect of outdoor air quality on indoor exposures is a controversial area, with contradictory findings. 27 Research follows two main lines, either assuming linear correlations between indoor and outdoor pollution, or the need for measurement of dynamic indoor and outdoor pollution ratios. 27 External levels of bioaerosols and particulate matter influence indoor levels, yet there may be additional indoor sources. Indoor air quality can be poorer because of ventilation systems drawing in highly polluted air; for example, from diesel fumes from lorries parked near vents. Concentrations of pollutants are worse where residents are smokers, use open fires for warming the house and where cooking fats and emissions are not cleared.

Re-design of environments and buildings may have several benefits. 28 In areas of high deprivation and urbanicity, several co-occurring risk factors are common: poverty and lack of affordable housing, unemployment and lack of green space, and unsafe neighbourhoods. These multiple and chronic adversities are associated with inflammation and, through interactions with air quality, can lead to more physical and mental ill health (see Fig. 1 ). 29 , 30

Complex webs of causation linking air quality with health.

Mental health and air pollution

Alongside effects on cardiovascular and respiratory health, there is emerging evidence that exposure to air pollutants (both indoors and outdoors) may lead to neurocognitive disorders and affect mental health (directly and indirectly) through a range of potential causal pathways (see Figs 1 and ​ and2 2 ). 1 , 31 – 35 Observational evidence has implicated outdoor air pollutants as risk factors for a variety of mental health problems, including depression, anxiety, personality disorders and schizophrenia. 36 – 40 In contrast, there is less research on the effects of indoor air quality and exposures to air pollutants on mental health. Yet, some aspects will be common; for example, inadequate housing is more common in urban spaces, where outdoor and indoor air quality is poorer.

Potential pathways from particulate matter/biological particulate matter to adverse effects on brain health.

Outdoor air quality

Much of the existing literature on outdoor air pollution and mental health is based on cross-sectional observations, aggregated air quality data and studies performed only in adults. In addition, studies often do not rule out alternative interpretations, such as individuals with a greater liability to mental health problems self-selecting into neighbourhoods with poorer air quality or not being able to leave those areas. Furthermore, other disadvantageous aspects of the environment are also associated with poor air quality and poor mental health; these include deprivation, crime and noise, which each might affect mental health. However, to understand the causal role of air pollution in the development of mental health problems, longitudinal studies are needed to ensure exposure to air pollution does occur before the emergence of mental health problems. Ideally, these studies should collect information on individual- and neighbourhood-level deprivation. Causal inferences are strengthened by such designs, although confounding influences do still need consideration.

Indoor environments

Since a great proportion of time is spent indoors, especially in the winter and autumn months, it is reasonable to assume that some of the effects attributed to outdoor air pollutants result from indoor exposures. 41 Indoor environments can have diverse pollutants (e.g. particulate matter, nitrogen dioxide, carbon monoxide) of outdoor and indoor origin, and highly varying source strength for each pollutant across different indoor environments. Yet indoor environments may also be a more significant source of specific chemical exposures (e.g. volatile and semi-volatile organic compounds). Much built environment research is model based, with less real-world sampling in diverse geographical contexts. The health evidence often lacks housing and geolocation information, making it difficult to review historical data for causal trends retrospectively. Both types of approach would benefit from greater interaction with chemists so that we can better understand pollutants, and potential webs of causation. For example, the impact of cooking emissions on human and environmental health can be reduced by better-designed research that might help to reconsider open-plan kitchen and living spaces. 42 Different factors related to design, construction and occupants’ activities can help to determine occupants’ exposure to different pollutants indoors. 43 Additionally, the growing focus on energy-efficient built environments may lead to increased exposure to an array of air pollutants of indoor origin, because of the decreased ventilation and potential increase in pollutant concentrations. 28 , 44 Sound insulation may also reduce ventilation and more heat efficient, but raise indoor temperatures that influence the composition of particulate matter; hence, there is a need for building designs that tackle multiple environmental factors. These design issues may explain low-grade fatigue and poor mental health found in certain home and work environments that lack ventilation, daylight and good air quality; ‘sick building syndrome’ may be partially explained by air quality. 45 , 46

The Royal College of Paediatrics and Child Health and the Royal College of Physicians considered indoor air quality and found that emissions from construction materials, building design (e.g. ventilation and heating systems) and activities inside buildings (e.g. cooking, fireplaces, cleaning products, moisture production) all affect indoor air quality and affect health. 47 Some activities can lead to elevated moisture levels indoors, resulting in dampness and related pollutants such as mould and house dust mites, which in turn affect health. Although this report did not specifically consider mental health outcomes, the underpinning studies found links between poor indoor air quality and neurological and psychological symptoms, with cognitive and behavioural effects. For example, higher carbon dioxide levels and indoor air pollutants associated with carbon dioxide, can negatively affect cognitive function and concentration. Of course, other indoor air pollutants that accumulate with carbon dioxide may be partially responsible. There is ongoing research and debate on the effects of carbon dioxide concentrations on cognitive performance in settings such as schools and offices. 48 , 49 Some researchers have emphasised that fossil fuel combustion ‘is driving indoor CO 2 [carbon dioxide] towards levels harmful to human cognition’, 50 and that associations between cognitive performance and indoor levels of carbon dioxide, and volatile organic compounds, are independent of ventilation rates. 49 A recent systematic review raises significant questions about the quality of this evidence, and whether any associations between carbon dioxide and health can yet be inferred, although several of the reviewed studies suggested that high concentrations of carbon dioxide was associated with increased mental effort and fatigue. 51

Although, there is limited evidence on the impact of indoor air quality on mental health, some studies have found an association between depression and dampness and mould in the home; this tentatively suggested that a lack of control over the home environment was a potential mechanism leading to poor health. 52 Furthermore, poverty and cockroach infestations are associated with elevated levels of endotoxins, also leading to inflammatory responses. 53 A report by Shelter, a non-governmental organisation in the UK, concluded that 26% of households complain of significant dampness, mould and condensation. 54 A recent government analysis following the death of a child because of damp conditions (coroner's verdict) estimates that 4% of social housing has notable damp and mould, and 0.2% has the most serious conditions that would fail the safe home standards. 55 Associations between mould exposure and various non-specific symptoms such as fatigue, ‘brain fog’ and anxiety have been reported. Yet, overall, the evidence is mixed, and underlying mechanisms are not clear. More recently, studies using animal models suggested that mould inhalation affects the central nervous system and immune activation, with concomitant neural effects and cognitive, emotional and behavioural symptoms. 56 , 57 Furthermore, flame retardants and plasticisers are common in indoor environments, and can cause adverse neurological effects and negative behavioural outcomes, including impaired learning and spatial memory. 58 , 59

Poor socioeconomic status is known to be associated with both poor mental health and poor living conditions, including overcrowding, unstable housing, dampness, poor nutrition and health risk behaviours such as smoking, alcohol use, substance misuse and adverse childhood experiences (e.g. poverty, loss events and neglect). 60 , 61 Consistent with this evidence, are studies of exposures to poor indoor air quality by low- and high-income settings; in low-income settings, burning unclean and solid biomass fuels dominate as contributors to air pollution. 62

All of these influences might combine to create a pro-inflammatory exposome that includes poor air quality and affects health – both in the onset of new illnesses as well as the compounding of existing disabilities for pre-existing illnesses. Linking the associations with mechanisms is challenging (see Figs 1 and ​ and2 2 for potential explanations). Yet, there are plausible pathways from pollution to poor health and poor mental health, especially if shared aetiologies (e.g. inflammatory processes as one example) are triggered. One approach to understanding prevention and care is to look at the individual life course, from pregnancy to youth, adulthood and old age.

Air quality and mental health over the life-course

Pregnancy and early years.

Studies of associations between early exposure to air pollution and mental health are scarce, and the findings are somewhat mixed. Prenatal air pollution exposure has been linked with cognitive impairments at 5 years of age, 63 but there is no greater risk of anxiety and depressive symptoms. 64 In a Spanish study of 1889 children, exposure to nitrogen dioxide and benzene were inversely associated with mental development, but this did not remain a statistically significant finding after adjusting for confounders. 65 There are some details of relevance: stronger inverse associations were estimated for pollutants among infants whose mothers reported low intakes of fruits/vegetables during pregnancy, in non-breastfed infants and infants with low maternal vitamin D; however, these interesting interactions were not statistically significant. During pregnancy, exposure to PM 10 , PM 2.5 , nitrogen dioxide and nitrogen oxides were associated with a 29–74% increased odds of unspecified mental disorders that complicated pregnancy. 66 Exposure pathways in utero and early childhood also differ from those in adulthood; for example, in utero , neo-natal and infancy-related pathways may include ingestion (non-nutritional as well as nutritional), inhalation, transplacental, transdermal and breastfeeding. 62

Adolescents

Prevention of mental illness early in the life course is critical, given that half of adults with mental illnesses show signs and symptoms by 11 years of age, and 75% do so by 24 years of age. 67 , 68 In addition to the human suffering and functional impairment caused by chronic mental health problems, adults with mental illnesses face premature mortality of 15–20 years as a result of cancer, heart disease, lung disease and obesity-related conditions. 69 – 71 Identifying modifiable risk factors for mental health problems is, therefore, a crucial research challenge of the 21st century. A long-standing finding that has not been fully explained is the higher incidence rates of psychoses in inner city and urban areas, and these usually arise during adolescence and early adulthood. 72 , 73 Could air quality be a relevant aetiological factor? Among 2063 adolescents, psychotic experiences were significantly more common among adolescents with the highest (top quartile) level of annual exposure to nitrogen dioxide and PM 2.5 . 74 Together, nitrogen dioxide and nitrogen oxides explained 60% of the variance. There was no evidence of confounding by family socioeconomic status, family psychiatric history, maternal psychosis, childhood psychotic symptoms, adolescent smoking and substance dependence. There is also evidence of associations with depression. 75 In the Environmental Risk Longitudinal Twin Study of 2039 participants, after adjustment for family and individual factors, interquartile range increments in exposure to nitrogen oxides were associated with a 1.4-point increase in general psychopathology. 76 There was no association between continuously measured PM 2.5 and general psychopathology. However, those in the highest quartile of PM 2.5 exposure scored higher than those in the bottom three quartiles. There were also statistically significant findings for nitrogen oxides. Exposure to nitrogen oxides was associated with all secondary outcomes, but associations were weakest for internalising and strongest for ‘thought disorder’, a symptom of psychosis. Studies to replicate this and evaluate the source of these differential effects are needed. Despite nitrogen oxide concentrations being highest in neighbourhoods with worse physical, social and economic conditions, adjusting estimates for neighbourhood characteristics did not change the results, suggesting other neighbourhood characteristics may be driving the associations.

A study in the USA and Denmark assessed air pollution on an air quality index of 87 potential air pollutants in the USA and 14 in Denmark. PM 10 and PM 2.5 , diesel emissions, nitrogen dioxide and organic substances (such as polycyclic aromatic hydrocarbons) were significantly associated with an increased risk of psychiatric disorders. 77 The country-specific data showed pollution exposure to be associated with bipolar disorder in both countries, and depression, schizophrenia and personality disorder in Denmark. A number of studies show associations between air pollution and service use for mental disorders. 31 , 78 – 80

A recent systematic review and meta-analysis showed associations of PM 2.5 and PM 10 with depression, anxiety, bipolar disorder, psychosis and suicide in adults. The most apparent association was between long-term (>6 months) exposure to PM 2.5 and depression. 81 Depression and suicide were the most studied outcomes; however, there were no studies of long-term particulate matter exposure and suicide, nor of particulate matter exposure and bipolar disorder. The review highlighted a need for larger-scale longitudinal studies using representative samples, and adjustments for area-level factors such as traffic noise, access to green space and socioeconomic status, to help better understand potential causality in observed associations. Further research is needed on the mechanisms involved in these observed associations.

Methodological issues in air pollution and mental health research

Outdoor air pollution can be measured and estimated in numerous ways. 82 , 83 Here, we describe some of the main methods used in the field of air pollution and mental health, in roughly chronological order.

From Tuke in Victorian Britain, through to Durkheim, Jarvis, and Faris and Dunham in the 1930s, the social and geographical distribution of mental illnesses have been investigated so as to understand how urbanisation and rurality might affect mental health. 84 The manner in which the social and ecological environment shape neurodevelopment and mental illness in early adolescence and adulthood are important, but are often neglected in favour of a more deterministic approach to understanding the causes of mental illnesses; rather, we propose place and contextual bodies of evidence are important, on which studies of air quality can naturally build. 85 Furthermore, new studies must accommodate existing research on urban environments, including building design, and how neurodevelopment and social meaning interact. 86 , 87

Dating back to the seminal work by Faris and Dunham (1939), 88 a precursor to the air pollution and mental health field is the body of research demonstrating associations between the urban environment and mental health, often by using population density or urban–rural comparisons. Air pollution has been speculated as a potential driver of this relationship. 89 However, cities are complex environments comprising multiple correlated risk factors that could affect mental health, making urbanicity only a crude proxy for air quality. Nevertheless, a series of comparative studies based in Mexico used a similar design, comparing Mexico City to less polluted areas. Among these, one post-mortem study 90 compared prefrontal white matter between children and teenagers who had lived in Mexico City versus a less urbanised area, and found that ultrafine particulate matter was found in the brain cells of those from Mexico City, but not those from the less urbanised area.

Among the earliest studies are some exploring associations between people's perceptions of air pollution and mental health. For example, Evans et al 91 asked residents of Los Angeles to rate the level of smog that day, from 1 (no smog) to 10 (heavy smog), and examined correlations of these responses with depression, anxiety and hostility. Although relatively economical to conduct and complete individual-level analysis, this type of design does not examine a direct biological effect of air pollution on the brain. In addition, this design is only suited to air pollutants that can be seen or smelled, which excludes many types such as carbon monoxide, which can have effects on neurological functions. Research on the self-reported perception of indoor air quality also shows that factors other than indoor pollutant concentrations can affect perceptions, such as occupational status or thermal sensation. Similar findings exist for environmental noise (in particular traffic noise) 92 , 93 and various components of air pollution (in particular, particulate matter), which cluster with poor air quality. 94 Thus, it is difficult to disentangle the effects of noise and air pollution. Noise can also increase the risk of mental disorders such as depression, anxiety disorders, psychoses and suicide. 95 These place effects and other different potential causal factors need to be considered in future studies. Proximity to roads, for example, is also used as a proxy for air pollution exposure and noise. 96 , 97 For instance, using data from the Danish Civil Registration System, Pedersen and Mortensen 97 examined the association between distance to major roads and schizophrenia. The authors used official classifications of road types and geographic information system software to calculate the distance between households and the nearest major road. This innovative methodology still does not factor in other sources of air pollution, meteorological patterns or urban morphometric features (e.g. pockets of air pollution trapped between high-rise buildings).

One of the most common methods to measure air pollution concentrations directly at monitoring sites is to use passive diffusion tubes. Some studies have also set up monitoring stations at the locations of interest to measure real-time concentrations. For instance, Wang et al 98 installed nitrogen dioxide and PM 10 monitors at various locations within two schools in Quanzhou, China, and examined associations with neurologic functioning. The most common design is to use data from existing, permanent monitoring stations, often in conjunction with a time-series analysis design. For instance, Gu et al 99 obtained data on daily average pollution concentrations for 75 Chinese cities from China's National Air Quality Monitoring System, and examined correlations with daily hospital admissions for depression. Although this time-series design is powerful in terms of understanding potential short-term effects of air pollution, monitoring stations are often very sparse, making it inappropriate to infer individual long-term exposure from the data.

The measurement techniques used for sampling outdoor pollution can also be used in indoor settings. 83 Usually, measurements from static loggers and/or passive samplers placed within representative rooms and/or locations within a room are used as proxy of exposure to indoor pollutants. Some industry and International Organization for Standardization standards exist for the monitoring of specific indoor air pollutants. 100 Pollutants such as carbon dioxide or total volatile organic compounds are sometimes used as a proxy of air quality and ventilation in indoor settings. Indoor concentrations can differ even when building layout/location is similar, because of variations in indoor sources and activities. Therefore, it is not always possible to deduce indoor pollution levels via limited sampling sites. Overall, monitoring indoor air quality at scale can be time-consuming and relatively expensive, requiring access to several properties/participants. On the other hand, low-cost sensing technologies also have the potential to provide high-density spatial–temporal information on air quality across the indoor–outdoor continuum, although accuracies can vary. 101 , 102 Better assessment methods of indoor air quality with standardised and validated measures of built design may benefit from the expertise of engineers. 103

Recently, more sophisticated methods of modelling outdoor air pollution concentrations have enabled much higher resolution estimates to be achieved, thereby facilitating more precise, individual-level exposure based on, for example, residential addresses. One dominant method is land-use regression modelling, which factors in environmental characteristics with predictable influences on pollution concentrations, such as road, factories and forests, to estimate pollution concentrations in a given area. 104 Another method, called dispersion modelling, additionally factors in the atmospheric chemistry of air pollutants together with meteorological data, to estimate pollution concentrations. 105 Dispersion models now achieve good predictions against ground-based measurements, as well as high temporal (e.g. hourly) and spatial (e.g. 20 m × 20 m) precision. 106

The power of these models in understanding links between outdoor air quality and mental health lies in the ability to link this exposure data with large-scale epidemiological cohort studies. There are several important benefits of this large multidisciplinary consortium approach. First, the large sample sizes afford the statistical power to detect small effects, which may be needed in contexts (such as Europe and the USA) where pollution levels and variability are relatively low. 81 Second, together with their large samples, the comprehensive assessment of a wide range of measures provides a valuable opportunity to adjust for multiple confounders and rule out threats to causal inference. These approaches can enable investigations of the role of important social and biological factors as mediators or moderators of associations, including psychosocial adversity, social deprivation, noise pollution, genetic risk and inflammation. Third, the prospective longitudinal design of cohort studies can help establish the temporality of associations, and therefore move the research on from reliance on cross-sectional observations that severely limit causal inference. Fourth, depending on the age and duration of the particular cohort, the impact of outdoor air pollutants on mental health can be explored across developmental periods across the life span, and consider residential mobility and distinct geographical contexts.

There is a need for more research focused on early-life exposure as in utero and childhood may be a time of particular vulnerability because the lungs, brain and immune system are developing. Indeed, a focus on such early pollution exposure and its later effects is particularly necessary to elucidate its role in the development of mental health problems, given the common onset of symptoms in childhood and adolescence. 107 In the UK, birth cohort studies such as the Avon Longitudinal Study of Parents and Children (ALSPAC) and Environmental Risk (E-Risk) Longitudinal Twin Study have linked high-resolution air pollution models to their data. 74 , 76 , 108 – 110 Combining epidemiological approaches with air pollution modelling in this way has yielded important insights. However, this methodology is not without limitations. Exposure estimates are modelled rather than measured directly. Typically, these are linked to just one or a few addresses commonly visited by the study participants (e.g. home, school/college, shops). To better quantify levels of air pollution exposure, multiple different locations are needed, as well as several different time points.

Although some data on indoor/outdoor ratios exist for some pollutants, indoor concentrations are not solely driven by outdoor levels. Therefore, air pollution modelling of outdoor levels could be combined with indoor modelling (or monitoring), to better understand patterns of exposure, and provide more representative exposure estimates accounting for the time spent indoors versus outdoors. Various approaches to modelling indoor air quality exist, including mass balance or computational fluid dynamic models. 100 These can be used to estimate respective pollutant concentrations and their spatial distribution, and can be combined with meta-models of the building stock to estimate indoor air quality at scale. 111 However, models of indoor air quality rely on assumptions about indoor sources and human behaviours, for which there are limited empirical data.

Wearable, personal monitoring devices that measure pollutant concentrations close to the person's breathing zone offer a promising alternative. These enable individuals’ exposure to be directly measured in real time as they go about their usual activity. As people spend time in and move between spaces with varying concentrations of pollution, these devices more accurately capture their unique exposure. Although currently expensive – prohibitively so for large samples – future studies should consider utilising new technologies that allow personal monitoring to move toward accurately capturing air pollution exposure in everyday life. The emergence of low-cost sensing technologies has the potential to provide high-density spatial–temporal information on air quality and personal exposure across indoor–outdoor continuum. 112 , 113

Research gaps and challenges

In the early phase of the BioAirNet ( https://bioairnet.co.uk/ ) research network, a sandpit event involving multidisciplinary experts and a range of stakeholders was held. This sought to identify key research questions, knowledge gaps and methodological challenges. In combination with the literature we identified, the following priority research questions and knowledge gaps warrant future attention:

• Could air pollutant exposure and inflammatory mechanisms explain higher rates of mental illnesses (psychoses and affective disorders) in urban areas; variations of incident mental illnesses by age, gender, sexuality, ethnicity and deprivation; and greater risk for chronic health conditions into adulthood, including psychoses, common mental illnesses and comorbid medical conditions?
• What future environmental designs and practices (outdoor, indoor, buildings and institutions) might prevent and reduce the risks of poor health, especially in specific at-risk populations?
• How might specific interventions be developed and tested for impact on the mechanisms?
• How do we evaluate policy interventions and major policy re-designs, such as the introduction of low emission zone restrictions, which are being adopted in many cities? 114
• Urban design evaluations also need methodologies that are feasible, adopting quasi-experimental designs, and collaboration between local government, building designer, epidemiologists/public health professionals, built environment architects and local residents.
• What constitutes an ‘anti-inflammatory’ environment that benefits young people in their worlds and adults at risk of or already experiencing mental illnesses and other health conditions?
• What role do social and behavioural factors have for creating or concentrating harmful exposomes in specific places and indoor environments, and mitigating these drivers of poor health?
• How do structural (socioeconomic, deprivation, poverty, geographical) and behavioural influences interact to promote or militate against a harmful exposome?
• How is child health and mental health affected, and what is the impact over the life course?
• How are specific high-risk groups affected: those with early psychoses, chronic depression and multimorbidity, including poor mental health?
• What are the implications for care environments for children and for those with mental illness?

In addition, specific approaches were identified to better quantify levels of exposure to indoor/outdoor pollution and links with impact on health in different scenarios (see Appendix 1); approaches to understand the mechanisms of harm to human health and well-being (see Appendix 2); and the need to specify more carefully which health conditions and causal models were being investigated (see Appendix 3).

These research gaps are broadly aligned with the six priorities proposed in a recent review of Environmental Science and Mental Health, including over 200 publications and six case studies. 115 In this report, five areas of opportunity were identified, which consider both the research approach and topics warranting further investigation: exploit large-scale data-sets, longitudinal approaches, integrative complex systems research, mixed-methods approach and community of practice.

Research design for a way forward

These priority research topics require advances in complex systems and mixed-methods research, and more capability to collect, analyse and use new data for policy actions. Research in this area needs to be interdisciplinary, and the methodologies selected will also need careful co-design and review to address the full range of research questions and knowledge gaps. The following section considers the potential study designs and recruitment venues for interdisciplinary research with health outcomes.

School studies

Experience-based sampling is possible through mobile phone applications and wearable devices or by websites and self-report measures. The volume of data would not be sufficiently high, perhaps compared with school-based studies where young people usually complete the questionnaires in classes. A whole-school approach to support studies will be needed to ensure data quality, engagement and participation, and to ensure the research process itself is of value and beneficial and aligned with other priorities in schools.

Establishing partnerships and rapport with schools, higher education institutions and other stakeholders alongside developing appropriate teacher, community and parent panels, will support recruitment into studies and offer information about the acceptability of potential interventions and policy options.

The balance between entire school surveys and recruitment of young people experiencing specific conditions or vulnerabilities needs some debate; there are tensions in terms of acceptability, the ethical process for recruitment and consent, concerns about stigma and confidentiality, and methodological challenges of screening people into specific studies. A whole-school approach would permit a series of nested case–control studies for specific conditions and contexts.

Research studies will need appropriate ethical and safeguarding frameworks, especially for young people, but generally for any proposed intervention studies.

Some young people will not be in school, or will have been excluded, perhaps directly because of health problems and linked with adverse social circumstances that are likely to affect their health status; these groups may well be those most likely to be exposed to poor air quality. Thus, additional samples of excluded groups will need to be considered, alongside creative and innovative methods for including them. There are likely to be age-, gender-, sexuality- and ethnicity-related intersectional forces that are associated with exclusion and poor mental health. Specific consultation and sampling strategies will need to be devised.

Longitudinal cohort studies with linkage

There are a number of existing cohorts that can be linked to data on air pollution. These offer opportunities to measure pollutants and mental health outcomes at multiple time points, resolving the temporal ordering and identifying critical periods for exposures and specific outcomes; the approach can also identify variation across multiple venues, and test generalisability and causal effects where exposures vary by geography. The linkage process takes time, yet some research groups have successfully done so and are generating new evidence in real time and gathering evidence on the entire exposome (e.g. the Equal-Life project, a European Union-funded programme; grant number 874724). 116 The alternative approach is to design and establish new cohorts, with appropriate measures of air quality as well as the total hypothesised exposome.

In-depth qualitative cohorts

This study design may be especially suited to exploring complex social, psychological and spatial mechanisms, generating new hypotheses and in-depth information about contexts and health status. Realist methodologies and ethnographies, for example, may reveal context, mechanism and outcome relationships, which can be tested in epidemiological cohorts. The approach is also suited to recruiting those at risk of not being represented in surveys and population, school-based and cohort studies. Obviously, this approach may not help identify or verify biological mechanisms unless biodata are collected alongside it. Such data could include functional brain scans, inflammatory markers, epigenetic effects and genetic liability through polygenic risk scores.

Future directions and challenges

Air pollution and mental health are both major challenges that the world must grapple with, now and for years to come. This makes their intersection a doubly vital public health priority. This paper outlines evidence on the importance of indoor and outdoor air quality on mental health, research needs, challenges and future directions. There remain methodological challenges that must be overcome to provide insights into critical time points; place-based hot-spots for poor air quality; biological, psychological and social mechanisms; and strategies for prevention and mitigation. The clinical, public health and societal (well-being and economic) effects need to be modelled. Better quality primary research and longitudinal cohorts, especially for young people at critical points of maturation, are needed, alongside well-specified systematic reviews and network analyses. Specifically, areas of focus include evidence of links between pollution, specifically bioaerosols, and mental health, and better exposure measurement. An important linked but distinct subject that we have not addressed is climate change. The pathways between global warming, poor air quality, climate change and poor mental health may be mediated through natural disasters and social disruption, biodiversity loss and ecosystem destruction. 62 Furthermore, there is little data on the contrasting effects of climate change on low- versus high-income countries. We know natural disasters and climate change will have more of an effect on low-income countries and poorer populations living in countries with less infrastructure, appropriate building design and protections around health and environmental policy. 117 Furthermore, rising global temperatures are associated with more air pollution, 118 including stagnation and less ventilation, and greater production of particulate matter (wildfire smoke, airborne soil dust, ozone and PM 2.5 ). These affect long-term medical conditions (heart, lung and kidney disease) via raised body temperature and inflammation. Heat also leads to more anxiety and depressive symptoms, and suicidal behaviours; those with pre-existing mental illnesses are more likely to die in hot spells than those without mental illnesses. 119 Engagement of policy stakeholders from diverse sectors is necessary to translate emergent findings into actions. We anticipate this paper and related publications from a number of networks will help bridge knowledge gaps to stimulate a new wave of research, practice and policy actions. This will enable us to gain deeper understanding of the intricate and interconnected relationship among individuals, air pollution exposure and resulting mental health and well-being effects, amid the continuously changing air pollution sources and exposure patterns. Ultimately, the knowledge gained should be used to inform policies concerning air pollution interventions, urban and built environment design, land use planning and behaviour change.

BioAirNet organised a series of thematic workshops focused on exploring the potential effects of biological particulate matter on human health, behaviour and well-being. Information on these workshops can be found on the BioAirNet website at https://bioairnet.co.uk/past-events/ . During the workshops, lead researchers delved into the associations between biological particulate matter and respiratory and neurological disorders, and examined the approaches that can be used to understand the biological mechanisms underlying these associations. On average, over 30 delegates attended each workshop, indicating a significant interest and engagement with the topics. Because of the General Data Protection Regulation, we cannot disclose the names of individuals who attended these workshops publicly. Nevertheless, BioAirNet remains committed to involving participants from diverse backgrounds and interests in advancing our understanding of the factors that shape and support healthy natural and built environments, with a key focus on biological particulate matter. The Network is open to new members and collaborations. The following priorities were identified:

• Data-sets – matching (homogeneous) longitudinal exposure and health data-sets with comprehensive metadata for confounders;
• Models of exposure – personal versus population;
• New technologies (e.g. real-time pollution measurement, high-throughput sequencing, biosensors, personal devices) to inform exposure assessment;
• Proxy/biomarkers of exposure to inform exposure assessment;
• Statistical methods and study designs for linking exposure(s) to outcome(s) with appropriate power;
• Methods of measuring and understanding: the human microbiome and its role in inducing health outcomes; individual genetic diversity and potential impact on pollution-induced health outcomes; benefits versus harms, which particles are beneficial or harmful and in what contexts; background levels and exposure thresholds above which health is affected; vulnerable populations (e.g. by age, respiratory disease).

Study designs for future research:

• Literature reviews – linking exposure(s) to outcome(s);
• Knowledge mobilisation – shared data-sets, best practice, multidisciplinary partnerships;
• Models of pollution-related harms: e.g. in vivo , in vitro , in silico for establishing levels of exposure and exploring biological causation pathways in whole systems versus specific mechanisms;
• Causally informed Mendelian randomisation studies;
• Interdisciplinary collaboration and communication between exposure scientists, health professionals, toxicologists, government and industry;
• Assessing co-exposures – interactions between distinct air pollutants and the resulting effects on the viability, allergenicity, toxicity and pathogenicity;
• What core measurements are required: what, in how much detail, for how long and for what purpose(s)?
• More longitudinal cohorts are needed.

Measures of outcome for specific medical conditions.

• These may include risk phenotypes of conditions of interest (e.g. low mood, psychosis experiences, ischaemic heart disease, asthma in childhood).
• Outcomes of mental health effects: individually, in combination with medical conditions; data including the timing of one or the other condition suitable for longitudinal data analyses.
• Related and relevant demographic (age/gender/ethnicity), and psychosocial variables (social support, relationships, emotional dysregulation, adversity, poverty, trauma, poorer places and environments).
• Exploring which biopsychosocial and eco-social and bio-bio interfaces are relevant for creating disease vulnerability to bioparticles, as well as which psychosocial variables lead to greater exposure: poorer places have poorer housing, poorer food outlets, more crime, violence, less safety, smaller houses, less green space). What are the types of interactions between these? To what extent are gene×environment interactions relevant versus direct toxic effects?
• Which poor health outcomes resulting from poor air quality can lead to poor mental health: e.g. respiratory disease or obesity being associated with depression?
• Multimorbidity tends to occur in poorer places and is driven by both biological vulnerability and psychosocial adversity over the life course and in contemporary environments; how does air quality interact with this causal web of poor health and premature mortality for a subset of the population who are most likely to be exposed to poor air quality?
• What is the role of inflammation and oxidative stress?

Data availability

Author contributions.

Conceptualisation: K.B. Writing, original draft preparation: K.B. Writing, review and editing: all authors. Funding acquisition: F.C., K.B., P.D., S.K.J., S.T., R.K. and Z.A.N.

The development of this paper was supported via activities funded by UK Research and Innovations, through the following grants: BioAirNet (NE/V002171/1), COVAIR (EP/V052462/1), COTRACE (EP/W001411/1) and INHALE (EP/T003189/1) projects. J.B.N. is supported by a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust (grant number 218632/Z/19/Z). R.M.L. and H.L.F. are supported by the Economic and Social Research Council (ESRC) Centre for Society and Mental Health at King's College London (grant number ES/S012567/1). The views expressed are those of the authors and not necessarily those of the ESRC or King's College London.

Declaration of interest

K.B. is College Editor for the Royal College of Psychiatrists and serves on the international board for BJPsych Open , but had no part in the processing or decision-making around this paper. All remaining authors declare no competing interests.

116 Air Pollution Essay Topics

🏆 best essay topics on air pollution, ✍️ air pollution essay topics for college, 🎓 most interesting air pollution research titles, 💡 simple air pollution essay ideas, ❓ research question about air pollution.

• Effects of Air Pollution on Health
• Air Pollution: Effects
• Air Pollution Solutions: How to Improve Air Quality
• Community’s Role in Fighting Air Pollution
• Air Pollution: Conducting a Quantitative Study
• Electric Vehicles: Addressing Air Pollution
• Air Pollution in New York City
• Environmental Wellness and Problem of Air Pollution Air pollution is one of the main factors affecting the environment. It can be considered as any change in its properties that has a deleterious effect.
• Air Pollution in the UAE and Its Management The following project focuses on investigating the problem of air pollution in the UAE and how it can be managed.
• Beijing Looks for Answers to Air Pollution Beijing has undertaken various projects aimed at improving the city’s infrastructure, reducing pollution from coal-fired power plants, and reducing vehicle emissions.
• Environmental Issues: Air Pollution One of the central environmental problems in today’s world is air pollution. With the development of cities, people expand the reach of their technology.
• Air Pollution in South Carolina In South Carolina, one of the most urgent environmental problems is air pollution with ozone and particles, which is hazardous to human health due to deadly diseases likelihood.
• The Impact of COVID-19 on Air Pollution Mobility changes in all types of indoor and outdoor settings have a substantial long-term influence on CO emissions at the national and regional levels.
• The Aerodyne Research Firm: Air Pollution Studies Aerodyne Research is a limited liability company that researches air and air pollution levels, one of the world’s most pressing environmental issues today.
• Electric Vehicles: The Roles in Air Pollution The main purpose of electric vehicles is to eliminate the direct contribution to air pollution through emissions.
• Air Pollution Crisis and Climate Change in China Air pollution is a serious problem in many countries, including China. The main source of air pollutants is fumes from burning fuels in industries or vehicles.
• Methodological Flaws in Studies of Air Pollution and COVID-19 Death Rates The research reviews the considerations related to studying the correlation between ambient air pollution and its effects on the symptoms of COVID-19.
• Air Pollution in the United States Environmental problems affect climatic conditions negatively. In this case, we will discuss air pollution. Air pollution introduces harmful substances into the air.
• Air Pollution Crisis in China and Its Impact on Economy In large industrialized countries such as China, the emission of carbon dioxide has a negative impact on climate conditions, which is hitting the national economy.
• The Effects of Air Pollution The paper addresses air pollution, its causes, significant pollutants, adverse effects of indoor pollutants and air pollution, and air pollution control.
• Outdoor Air Pollution and Uncontrolled Asthma in the San Joaquin Valley, California The study’s purpose was to examine the relationship between air pollution and cases of uncontrolled asthma in the San Joaquin Valley.
• Air Pollution and Its Consequences The paper states that air pollution has been an increasingly major problem affecting the economy, people’s health, and the environment.
• Role of Small Gas-Powered Engines in Air Pollution The purpose of this paper is to discuss the role of small gas-powered engines in air pollution and the associated controversy.
• The Correlation Between Air Pollution and Health The sampled study analyzes and explains how air pollution affect life expectancy and other measures of health.
• Air Pollution Resulting From Small Gas Powered Engines The paper seeks to discuss the effects of small gas-powered engines on air pollution and suggest possible solutions to reduce the levels of air pollution.
• How Air Pollution Impacts Health Air pollution causes a wide range of serious health abnormalities in one’s body. It severely affects the respiratory system, leading to a number of complications.
• The Impact of Air Pollution on Human Health and Well-Being Air pollution causes a wide range of health abnormalities in one’s body. A number of pollutants can cause lung cancer and even some non-lung cancer forms.
• Air Pollution as a Health Risk Factor: Policy Proposal Air pollution is one of the most critical health risk factors. Prolonged exposure to polluted air can result in cancer and respiratory and cardiovascular diseases.
• Providing Solutions for Air Pollution The reasons for air pollution regulations, explaining the concept of averaging time in the U.S. National Ambient Air Quality Standards (NAAQS), explaining emission offsets.
• The Alleviation Plans to Air Pollution Throughout the World Air Quality Mitigation Plan is a proposed project which aims at reducing the emissions that affect the air quality by at least fifteen percent.
• Air Pollution: Effects and Regulations This essay analyzes the air pollution effects and regulations based on a simple observation of a smoke coming from a large smokestack.
• Evaluating the Efficacy of Government Spending on Air Pollution Control: A Case Study From Beijing While living in a city often means better conditions and access to goods and services, rapid urban development has been associated with adverse health outcomes due to air pollution.
• Beijing’s Air Pollution Crisis Resolution Beijing’s struggle with poor air quality is far from over. Nevertheless, the government demonstrated its commitment to reducing particulate matter in the atmosphere.
• Clean Air Act: Overall Air Pollution Reducing The problem of environmental pollution and, in particular, the air became especially urgent after the emergence of large industrial enterprises operating on harmful fuel.
• Air Pollution Threats: Parent Education The purpose of this pamphlet is to educate parents on the dangers of air pollution and suggest preventive strategies to keep their children safe.
• Air Pollution Health Risks Information Campaign This paper is dedicated to developing and planning an information campaign about Air Pollution Health Risks in a suburban community with a population of 20,000.
• International Trade and Air Pollution: The Economic Costs of Air Emissions From Waterborne Commerce Vessels in the United States
• Acid Rain Formula and Air Pollution Problem
• Chronic Respiratory Problems: The Link With Air Pollution and Considerations in Medical Care
• Air Pollution and Its Effects on Cancer Risks
• Toxins That Contribute to Air Pollution and Their Effect on Humans
• Adaptive Policy Mechanisms for Transboundary Air Pollution Regulation: Reasons and Recommendations
• Interaction Between Local Air Pollution and Global Warming Policy and Its Policy Implications
• Main Problems for Planet Earth: Air Pollution and Water Pollution
• Addressing Air Pollution Threats With Air Purifiers
• Economic Instruments for Controlling Air Pollution
• Air Pollution and Climate Change in Tanzania
• Demand for Indoor Air Pollution Abatement Interventions
• Protecting China’s Children: Valuing the Health Impacts of Reduced Air Pollution in Chinese Cities
• How Cities Around the World Combat Air Pollution?
• Managing Air Pollution With Urban Transportation
• Controlling Urban Air Pollution Caused by Households: Uncertainty, Prices, and Income
• Measuring Health Benefits From Air Pollution Reduction in Kathmandu Valley
• Air Pollution and ‘Dirty’ Industries: How and Why Does the Composition of Manufacturing Output Change With Economic Development
• Intra-Country Health Inequalities and Air Pollution in Developing Countries
• Air Pollution and Its Effect on Our Health
• Using Clean Coal Technologies to Reduce Air Pollution
• Environment and Happiness: Valuation of Air Pollution in Ten European Countries
• Compliance and Enforcement: Air Pollution Regulation in the U.S. Steel Industry
• State Responsibility for Transboundary Air Pollution in International Law
• Air Pollution: Anthropogenic and Natural Sources, and Conditions in Thailand
• Integrated Air Pollution Management in China: Developing Particulate Matter Control
• Air Pollution and Acute Respiratory Illness: Evidence From Taiwan and Los Angeles
• China’s Air Pollution and Its Effect on COPD Patients
• Air Pollution and Breathing That Kills You
• Educational and Technological Solutions to Air Pollution
• Politics and the True Effects of Air Pollution
• Air Pollution and Energy Loss Due to Construction Activities
• Does Inequality Matter Air Pollution and Health Relationship?
• Air Pollution and Respiratory Ailments Among Children in Urban India: Exploring Causality
• Lung Cancer and CVD Mortality Associated With Ambient Air Pollution
• Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?
• Clearing the Air: The Health and Economic Damages of Air Pollution in China
• Reducing Air Pollution Through the Use of Oxygenated Gasoline
• Air Pollution and How It Affects Plants and Animals
• Forest Fires, Air Pollution, and Mortality in Southeast Asia
• Air Pollution and Mortality: Estimating Regional and National Dose-Response Relationships
• Handle With Care: The Local Air Pollution Costs of Coal Storage
• Air Pollution and Infant Health: Lessons From New Jersey
• The Correlation Between Air Pollution and Human Health
• Cost-Effective Control Strategies for Energy-Related Transboundary Air Pollution in Western Europe
• Air Pollution, Children’s Health, and Socio-Economic Status: The Effect of Outdoor Air Quality on Asthma
• The Effects and Costs of Air Pollution on Health Status in Great Britain
• Indoor Air Pollution: Home Is Where the Hazard Is
• Air Pollution and Some of the Diseases and Problems It Causes
• Creating Markets for Air Pollution Control in Europe and the USA
• What Are the Major Sources of Outdoor Air Pollution?
• How Does Air Pollution Lead to Ocean Acidification?
• How Does Air Pollution Affect Biodiversity?
• How Does Urban Sprawl Contribute to Air Pollution?
• Which Gas in the Air Pollution Can Affect Blood Stream Causing to Death?
• How Does Air Pollution Affect Marine Life?
• What Ecosystem Services Are Disrupted by Air Pollution?
• How Does Air Pollution Affect the Lithosphere?
• What Is the Current U.S. Air Pollution Policy?
• What Are the Ways to Reduce Air Pollution and Slow Climate Change?
• How Does Wind Erosion Cause Air Pollution?
• How Does Air Pollution Compromise Human Health?
• What Are Chemicals Typically Found in Air Pollution?
• How Does Air Pollution Affect the Hydrosphere?
• What Are the Natural Sources of Air Pollution?
• How Does Air Pollution Affect Climate Change?
• How Extraction and Combustion of Fossil Fuel Affect Air Pollution?
• How Can Air Pollution and Animal Agriculture Link Together?
• How Do Scientists Studying Air Pollution Affect the Politics and Society?
• What Are the Global Effects of Air Pollution?
• How Does Atmospheric Circulation Affect Air Pollution?
• What Is China Doing About Air Pollution?
• How Does Air Pollution Affect the Carbon Cycle?
• How Could Chemists Be Involved in Addressing Concerns About Air Pollution?
• Do Nuclear Power Plants Cause Air Pollution?
• Are the Most Common Air Pollutants Caused by Chemical Processes?
• How Do Smokers Contribute to Air Pollution?
• What Is the Cost-Effective Means of Controlling Air Pollution?
• What Cardiovascular Diseases Are Caused by Air Pollution?
• Is Air Pollution Mainly a Local Problem or Can It Travel Long Distances?

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April 12, 2024

14 min read

How Air Pollution Damages the Brain

The new science of "exposomics" shows how air pollution contributes to Alzheimer’s, Parkinson’s, bipolar disorder and other brain diseases

By Sherry Baker & OpenMind Magazine

Indian schoolgirls walk to school after days off due to heavy smog in Amritsar.

Narinder Nanu/AFP via Getty Images

By 1992, burgeoning population, choking traffic, and explosive industrial growth in Mexico City had caused the United Nations to label it the most polluted urban area in the world. The problem was intensified because the high-altitude metropolis sat in a valley trapping that atmospheric filth in a perpetual toxic haze. Over the next few years, the impact could be seen not just in the blanket of smog overhead but in the city’s dogs, who had become so disoriented that some of them could no longer recognize their human families. In a series of elegant studies, the neuropathologist Lilian Calderón-Garcidueñas compared the brains of canines and children from “Makesicko City,” as the capital had been dubbed, to those from less polluted areas. What she found was terrifying: Exposure to air pollution in childhood decreases brain volume and heightens risk of several dreaded brain diseases, including Parkinson’s and Alzheimer’s, as an adult.

Calderón-Garcidueñas, today head of the Environmental Neuroprevention Laboratory at the University of Montana, points out that the damaged brains she documented through neuroimaging in young dogs and humans aren’t just significant in later years; they play out in impaired memory and lower intelligence scores throughout life. Other studies have found that air pollution exposure later in childhood alters neural circuitry throughout the brain, potentially affecting executive function, including abilities like decision-making and focus, and raising the risk of psychiatric disorders.

The stakes for all of us are enormous. In places like China, India, and the rest of the global south, air pollution, both indoor and outdoor, has steadily soared over the course of decades. According to the United Nations Foundation , “nearly half of the world’s population breathes toxic air each day, including more than 90 percent of children.” Some 2.3 billion people worldwide rely on solid fuels and open fires for cooking, the Foundation adds, making the problem far worse. The World Health Organization calculates about 3 million premature deaths, mostly in women and children, result from air pollution created by such cooking each year.

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In the United States, meanwhile, average air pollution levels have decreased significantly since the passage of the Clean Air Act in 1970. But the key word is average . Millions of Americans are still breathing outdoor air loaded with inflammation-triggering ozone and fine particulate matter. These particles, known as PM 2.5 (particles less than 2.5 micrometers in diameter), can affect the lungs and heart and are strongly associated with brain damage . Wildfires—like the ones that raged across Canada this past summer—are a major contributor of PM 2.5 . A recent study showed that pesticides, paints, cleaners, and other personal care products are another major—and under-recognized—source of PM 2.5 and can raise the risk for numerous health problems, including brain-damaging strokes.

Untangling the relationship between air pollution and the brain is complex. In the modern industrial world, we are all exposed to literally thousands of contaminants. And not every person exposed to a given pollutant will develop the same set of symptoms, impairments, or diseases—in part because of their genes, and in part because each exposure may occur at a different point in development or impact a different area of the body or brain. What’s more, social disparities are at play: Poorer populations almost always live closer to factories, toxins, and pollutants.

The effort to figure it out and intervene has sparked a new field of study: exposomics, the science of environmental exposures and their effects on health, disease, and development. Exposomics draws on enormous datasets about the distribution of environmental toxins, genetic and cellular responses, and human behavioral patterns. There is a huge amount of information to parse, so researchers in the field are turning to another emerging science, artificial intelligence, to make sense of it all.

“Anything from our external environment—the air we breathe, food we eat, the water we drink, the emotional stress that we face every day—all of that gets translated into our biology,” says Rosalind Wright , professor of pediatrics and co-director of the Institute for Exposomic Research at the Icahn School of Medicine at Mount Sinai in New York. “All these things plus genes themselves explain the patterns of risk we see.” When an exposure is constant and cumulative, or when it overwhelms our ability to adapt, or “when you’re a fetus in utero, when you’re an infant or in early childhood or in a critical period of growth,” it can have a particularly powerful effect on lifelong cognitive clarity and brain health.

Neuroscientist Megan Herting at the University of Southern California (USC) has been studying the impact of air pollution on the developing brain. “Over the past few years, we have found that higher levels of PM 2.5 exposure are linked to a number of differences in the shape, neural architecture, and functional organization of the developing brain, including altered patterns of cortical thickness and differences in the microstructure of gray and white matter,” she says. On the basis of neuroimaging of exposed youngsters, Herting and fellow researchers suspect the widespread differences in brain structure and function linked with air pollution may be early biomarkers for cognitive and emotional problems emerging later in life.

That suspicion gains support from an international meta-analysis (a study of other studies) published in 2023 that correlated exposure to air pollution during critical periods of brain development in childhood and adolescence to risk of depression and suicidal behavior. The imaging parts of the studies showed changes in brain structure, including neurocircuitry potentially involved in movement disorders like Parkinson’s, and white matter of the prefrontal lobes, responsible for executive decision-making, attention, and self-control.

In a 2023 study , Herting and colleagues tracked children transitioning into adolescence, when brains are in a sensitive period of development and thus especially vulnerable to long-term damage from toxins. Among brain regions developing during this period is the prefrontal cortex, which helps with cognitive control, self-regulation, decision-making, attention, and problem-solving, Herting says. “Your emotional reward systems are also still being refined,” she adds.

Looking at scan data from more than 9,000 youngsters exposed to air pollution between ages 9 and 10 and following them over the next couple of years, the researchers found changes in connectivity between brain regions, with some regions having fewer connections and others having more connections than normal. Herting explains that these structural and functional connections allow us to function in our daily lives, but how or even whether the changes in circuitry have an impact, researchers do not yet know.

The specific pollutants involved in the atypical brain circuits appear to be nitrogen dioxide, ozone, and PM 2.5 —the small particles that worry many researchers the most. Herting explains: Limits set on fine particulate matter are stricter in the United States than in most other countries but still inadequate. The U.S. Environmental Protection Agency currently limits annual average levels of the pollutant to 12 micrograms per cubic meter and permits daily spikes of up to 35 micrograms per cubic meter. Health organizations, on the other hand, have called for the agency to lower levels to 8 micrograms and 25 micrograms per cubic meter, respectively. Thus, even though it may be “safe” by EPA standards, “air quality across America is contributing to changes in brain networks during critical periods of childhood,” Herting says. And that may augur “increased risk for cognitive and emotional problems later in life.” She plans to follow her group of young people into adulthood, when advances in science and the passage of time should reveal more about the effect of air pollution exposure during adolescence.

Other research shows that air pollution increases risk of psychiatric disorder as years go by. In work based on large datasets in the United States and Denmark, University of Chicago computational biologist Andrey Rzhetsky and colleagues found that bad air quality was associated with increased rates of bipolar disorder and depression in both countries, especially when exposure occurs early in life. Rzhetsky and his team used two major sources: in Denmark, the National Health Registry, which contains health data on every citizen from cradle to grave; and in the United States, insurance claims with medical history plus details such as county of residence, age, sex, and importantly, linkages to family—specifics that helped reveal genetic predisposition to develop a psychiatric condition during the first 10 years of life.

“It's possible that the same environment will cause disease in one person but not in another because of predisposing genetic variants that are different in different people,” Rzhetsky says. “The different genetic predisposition, that’s one part of the puzzle. Another part is varying environment.”

Indeed, these complex diseases are spreading much faster than genetics alone seems to explain. “We definitely don’t know for sure which pollutant is causal. We can’t really pinpoint a smoking gun,” Rzhetsky says. But one pesky culprit continues to prove statistically significant: “It looks like PM 2.5 is one of those strong signals.” To figure it out specifically, we’ll need much more data, and exposomics will play a vital role.

"This is a wake-up call,” Frances Jensen told her fellow physicians at the American Neurological Society’s symposium on Neurologic Dark Matter in October 2022. The meeting was an exploration of the exposome –the sum of external factors that a person is exposed to during a lifetime— driving neurodegenerative disease. It was focused in no small part on air pollution. Jensen, a University of Pennsylvania neurologist and president of the American Neurological Association, argued that researchers need to pay more attention to contaminants because the sharp rise in the number of Parkinson’s diagnoses cannot be explained by the aging population alone. “Environmental exposures are lurking in the background, and they’re rising,” she said.

Parkinson’s disease is already the second-most common neurodegenerative disease after Alzheimer’s. Symptoms, which can include uncontrolled movements, difficulty with balance, and memory problems, generally develop in people age 60 and older , but they can occur, though rarely, in people as young as 20. Could something in the air explain the increasing worldwide prevalence of Parkinson’s? Researchers have not identified one specific cause, but they know Parkinson’s symptoms result from degeneration of nerve cells in the substantia nigra, the part of the brain that produces dopamine and other signal-transmitting chemicals necessary for movement and coordination.

A host of air pollution suspects are now thought to play a role in the loss of dopamine-producing cells, according to Emory University environmental health scientist W. Michael Caudle , who uses mass spectrometry to identify chemicals in our bodies. One suspect he’s looking at are lipopolysaccharides, compounds often found in air pollution and bacterial toxins. Although lipopolysaccharides cannot directly enter the brain, they inflame the liver. The liver then releases inflammatory molecules into the bloodstream, which interact with blood vessels in the blood-barrier. “Then the inflammatory response in the brain leads to loss of dopamine neurons, like that seen in Parkinson’s disease,” Caudle says.

More evidence comes from neuroepidemiologist Brittany Krzyzanowski , based at the Barrow Neurological Institute in Phoenix. Krzyzanowski had an “aha!” moment when she saw a map highlighting the high risk of Parkinson’s disease in the Mississippi–Ohio River Valley, including areas of Tennessee and Kentucky. At first she wondered whether the Parkinson’s hotspot was due to pesticide use in the region. But then it hit her: The area also had a network of high-density roads, suggesting that air pollution could be involved. “The pollution in these areas may contain more combustion particles from traffic and heavy metals from manufacturing, which have been linked to cell death in the part of the brain involved in Parkinson’s disease,” she said.

In a study published in Neurology in October 2023, Krzyzanowski and colleagues, using sophisticated geospatial analytic techniques, went on to show that those with median levels of air pollution have a 56 percent greater risk of developing Parkinson’s disease compared to those living in regions with the lowest level of air pollution. Along with the Mississippi-Ohio River Valley, other hotspots included central North Dakota, parts of Texas, Kansas, eastern Michigan, and the tip of Florida. People living in the western half of the U.S. are at a reduced risk of developing Parkinson’s disease compared with the rest of the nation.

As to the hotspot in the Mississippi-Ohio River Valley, Parkinson’s there is 25% higher than in areas with the lowest air particulate matter. Aside from that, Krzyzanowski and her research team noted something especially odd: Frequency of the disease rose with the level of pollution, but then it plateaued even as air pollution continued to soar. One reason could be that other air pollution-linked diseases, including Alzheimer’s, are masking the emergence of Parkinson’s; another reason could be an unusual form of PM 2.5 . “Regional differences in Parkinson’s disease might reflect regional differences in the composition of the particulate matter, and some areas may have particulate matter containing more toxic components compared to other areas,” Krzyzanowsk says. Tapping the tenets of exposomics, she expects to explore these issues in the months and years ahead.

The hunt is on for the connections between environmental factors and Alzheimer’s as well. USC neurogerontologist Caleb Finch has spent years studying dementia, especially Alzheimer’s disease, which affects more than six million Americans. As with Parkinson’s, Alzheimer’s numbers are rising in the United State and much of the world. Degenerative changes in neurons become increasingly frequent after the age of 60, yet half of the people who make it to 100 will not get dementia. Many factors could explain those discrepancies. Air pollution may be an important one, Finch says.

Researchers like Finch and his USC colleague Jiu-Chiuan Chen are joining forces to explore the connections between environmental neurotoxins and decline in brain health. It’s a challenging project, since air pollution levels and specific pollutants vary on fine scales and can change from hour to hour in many areas of the globe. On the basis of brain scans of hundreds of people over a range of geographic areas, this much we know: “People living in areas of high levels of air pollution and who have been studied on three continents showed accelerated arterial disease, heart attacks, and strokes, and faster cognitive decline,” Finch says.

Not everyone reacts the same way when exposed to pollutants, of course. Greatest risk for Alzheimer’s seems to hit people who have a genetic variant known as apolipoprotein E (APOE4), which is involved in making proteins that help carry cholesterol and other types of fat in the bloodstream. About 25 percent of people have one copy of that gene, and 2 to 3 percent carry two copies. But inheriting the gene alone doesn’t determine a person’s Alzheimer’s risk. Environmental exposures count too.

A recent study by Chen, Finch, and colleagues published in the Journal of Alzheimer’s Disease looked at associations between air pollution exposure and early signs of Alzheimer’s in 1,100 men, all around age 56 when the study began. By age 68, test subjects with high PM 2.5 exposures had the worst scores in verbal fluency. People exposed to high levels of nitrogen dioxide (NO 2 ) air pollution were also linked to worsened episodic memory. The men who had APOE4 genes had the worst scores in executive function. The evidence indicates that the process by which air pollution interacts with genetic risk to cause Alzheimer’s in later life may begin in the middle years, at least for men.

A separate USC study of more than 2,000 women found that when air quality improved, cognitive decline in older women slowed. When exposure to pollutants like PM 2.5 and NO 2 dropped by a few micrograms per cubic foot a year over the course of six years, the women in the study tested as being a year or so younger than their real age. This suggests that when exposure air pollution is lowered, dementia risk can go down.

In parallel, an international study by the Lancet Commission concluded that the risk of dementia, including Alzheimer’s, can be lowered by modifying or avoiding 12 risk factors: hypertension, hearing impairment, smoking, obesity, depression, low social contact, low level of education, physical inactivity, diabetes, excessive alcohol consumption, traumatic brain injury—and air pollution. Together, the 12 modifiable risk factors account for around 40 percent of worldwide dementias, which theoretically could be prevented or delayed.

In light of all this, Finch and Duke University social scientist Alexander Kulminski have proposed the “ Alzheimer’s disease exposome ” to assess environmental factors that interact with genes to cause dementia. Where medicines have failed, exposomics just might help. Studies of Swedish twins show that half of individual differences in Alzheimer’s risk may be environmental, and thus modifiable; and while vast sums of research funding have been poured into the genetic roots of the disease, it could be that altering the exposome would provide a better preventive than all the ongoing drug trials to date. Environmental toxins broadly disrupt cell repair and protective mechanisms in the brain, the researchers point out. And factors like obesity and stress contribute to chronic inflammation, which likely damages neurons’ ability to function and communicate. The research framework of the Alzheimer's disease exposome offers a comprehensive, systematic approach to the environmental underpinnings of Alzheimer's risk over individuals’ lifespans—from the time they are pre-fertilized gametes to life as a fetus in the womb to childhood and beyond.

For three decades, Rosalind Wright at Mount Sinai has wanted to trace critical problems in neurodevelopment and neurodegeneration to pollutants—from highway emissions to heavy metals to specific household chemicals and a host of other factors—but the mass of data has been overwhelming. With the advent of artificial intelligence (AI) and sophisticated neuroimaging technology, high-precision research using vast genomic databanks is finally possible. “I knew we needed to ask these kinds of questions, but I didn't have the tools to do it. Now we do and it’s very exciting,” Wright says.

Using machine learning—an AI approach to data analysis—Wright looks at giant datasets that include the precise location of an individual’s residence as well as the myriad of pollutants he or she encounters. “It's no different fundamentally from other statistical models we use,” she says. “It’s just that this one has been developed to be able to take in bigger and bigger data, more and more types of exposures.” The resulting data breakdown should tell us which factors drive which types of risk for which people. That information will help people know where they should target their efforts to reduce exposures to risky pollutants, and ultimately how to lower risk of impairment and disease, brain or otherwise.

The tools used by Wright and her colleagues are being trained on diseases like Alzheimer’s. If you put genes and the environment together, “you start to see who might be at higher risk and also what underlying mechanisms might be driving it in different ways in different populations,” Wright says. The exposome could also explains more subtle cognitive effects of pollution that may emerge over long periods, such as harms to attention, intelligence, and performance.

To address environmental brain risks, it’s important to know which pollutants are present—another target of exposomic research. In the United States, the EPA has placed stationary environmental monitors all over our major cities, conducting daily measurements of small particulates from traffic and industry, along with secondary chemicals that emerge as a result. There are also thousands of satellites all over the globe calibrating heat waves that can alter how the pollutants react with each other.

Pioneers like Wright are just starting to chart the terrain of environmental exposures that affect the brain. “As we measure more and more of the exposome, we may be able to tailor prevention and intervention strategies. New weapons include a silicone bracelet that we have in the laboratory. You wear it and it will tell us what pollutants you are exposed to,” Wright says. She also is exploring more ways to collect data on the toxins people have already encountered: “With a single strand of hair, we can tell you what you’ve been exposed to. Hair grows about a centimeter a month, so if we get a hair from a pregnant woman and she has nine centimeters of hair, we can go back a full nine months, over the entire life of the fetus. Or we can create a life-long exposome history when a child loses a tooth at age six.”

“We're designed to be pretty resilient,” Wright adds. The problem comes when the exposures are chronic and accumulative and overwhelm our ability to adapt. We’re not going to fix everything, “but if I know more about myself than before, that empowers me to think, ‘I’m optimizing the balance, and I’m intervening as best I can.’ ”

Additional reporting and editing was done by Margaret Hetherman.

This story is part of a series of OpenMind essays, podcasts, and videos supported by a generous grant from the Pulitzer Center 's Truth Decay initiative.

This story originally appeared on OpenMind , a digital magazine tackling science controversies and deceptions.

92 Air Pollution Essay Topic Ideas & Examples

🏆 best air pollution topic ideas & essay examples, 👍 good essay topics on air pollution, 💡 interesting topics to write about air pollution, ❓air pollution research questions.

• Air Pollution and Its Impact on Human Health Community needs assessment is a systematic process in which the health educator, the nurse and other health care professionals together with the members of the community determine the health problems & needs of the community […]
• Car Air Pollution Further, NO2 can prevent the flow of oxygen in the blood to other parts of the body like the brain. These toxic substances settle in the lungs and disrupt the normal flow of air in […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Nurse Associate’s Role in Air Pollution Prevention This paper analyzes current health promotion strategies in Somerset and the United Kingdom, obstacles to preventative health strategies, health screening programs, the impact of psycho-social, economic, and behavioral factors, epidemiology and genomics, vaccination and immunization […]
• Air Pollution Impacts on Weather and Climate Air pollution is rated to be the major cause of discomfort in the living creatures of the world for air is essential for the survival of every living creature.
• Air Pollution in Beijing and Its Effects on Society It is worth noting that different regions/countries/cities in the world have different levels of air pollution depending on the intensity/presence of causing agents and the techniques applied in dealing with air pollution.
• Environmental Factors and Health Promotion: Indoor and Outdoor Air Pollution This presentation offers some information about the damage of air pollution and presents a health promotion plan with helpful resources and evidence from research.
• Air Pollution in Beijing and the Decision-Making Bias Severe air pollution in Beijing did not become a subject of worldwide concern and discussion until the 2008 Beijing Olympics, which brought the issue to the attention of the global public due to the immense […]
• Smog and Air Pollution in Los Angeles The city is often covered with a yellow veil in the sky, so the problem of smog is an actual problem of the state.
• The Ecogeographical Impact of Air Pollution The weakness of the text is that the safety of NPs and their probable toxic effects on human health and the environment are not evaluated.
• Air Pollution and Impact of Transportation Emissions of greenhouse gases, air pollution, the release of ballast water, aquatic invasive species, and oil and chemical leaks are only some of the environmental problems that marine transportation continues to cause.
• Air Pollution and Lung Disease To design a study in order to explore the link between lung disease and air pollution, it would be possible to follow a four-step process started by identifying the level or unit of analysis.
• Air Pollution in China: Atmospheric Chemistry and Physics One of the most acute environmental problems in China is air pollution, which the authorities are trying to solve, but still, many people, factories, and active processes of globalization do not allow environmental programs to […]
• Air Pollution and Child Health Conducting research, leading scholars, and summarizing the results of their efforts allowed the organization to investigate the numerous ways air pollution damages the human body.
• Air Pollution and Vulnerability to Covid-19 In other words, the findings will be used as one of the key arguments for showing that air pollution is detrimental to both individual and societal health.
• Fundamentals of Air Pollution The components of secondary air pollution include ozone and nitrogen oxides. Smog occurs when “car exhausts are exposed to direct sunlight”.
• Air Pollution: The Problem’ Review Indoor pollution and related conditions are a big burden to the already suffering world according to the reports of the world health organization that it’s the 8th most important risk factor and is perceived to […]
• Law, Property Rights, and Air Pollution In the law of torts, ‘harm’ is considered when there is physical invasion to a person there fore in the case there was no violation of this law as the secretary was not harmed by […]
• Air Pollution in Middle East: Saudi Arabia The rate of air pollution in the world has increased gradually since the advent of the industrial revolution in the early 1800s.
• Point vs. Non-Point Air Pollution To determine the air pollution source of a large smoke stack, one has to assess the physical characteristics of the smoke; description of the color concentration intensity is it grey or extremely dark?
• Air Pollution and Health Issues in the US The industry of health care is closely connected to the industrial activities sector, which has the largest impact on the atmosphere through polluting the air, soil, and waters.
• Air Pollution Externalities and Possible Solutions In order to fully integrate public utility, power generation, policy and use of nuclear power in light of the growing concerns on the depletion of natural forms of energy as well as degradation of the […]
• Air Pollution and Ecological Perspectives of the Atmosphere The major contributors to CO2, one of the main pollutants in the atmosphere, are the burning of fossil fuels and deforestation.
• Air Pollution and Its World History From the times of industrial revolution, smoke pollution was a concern and continues to be one with vehicles and industries replacing coal and wood.
• Construction Technology and Air Pollution Hot-list section has new and transferable technology and highlights the features that appeal to construction companies, specifies and designers, owners of the building and end users.
• The Public Perceptions of Air Pollution and Related Policies in London The primary questions for consideration are the public perceptions of air pollution and related policies in London and other cities of the United Kingdom, previous surveys regarding existing policies related to the environment or air […]
• How China Cuts Its Air Pollution 5, which is the smallest and one of the most harmful polluting particles, were 54 percent lower in the last quarter of 2017 as compared to the same period in 2016, specifically in Beijing.
• Climate Change: Reducing Industrial Air Pollution One of the most effective measures of air quality in the USA is the Air Quality Index, which estimates air conditions by concentrations of such pollutants as particle solution, nitrogen and sulfur dioxide, carbon monoxide, […]
• Air Pollution, Its Constituents and Health Effects The National Ambient Air Quality Standards are the regulations or policies that are adopted by states to ensure the safety of the environment.
• Air Pollution in the United Arab Emirates’ Cities In the article called Evaluating the Potential Impact of Global Warming on the UAE Residential Buildings, the author focuses on the negative consequences of global warming on the situation in the United Arab Emirates.
• Climate Change, Air Pollution, Soil Degradation Then followed by outdoor air pollution, soil degradation which can also be called as soil contamination, global overpopulation, drinking water pollution, nuclear waste build-up, disappearing of the water supplies, indoor air pollution, depletion of the […]
• Air Pollution in Washington State and Healthy Living of People The problem of air pollution is closely related to the issue of the energy supply of the US. Due to the high level of air pollution in Washington state, there is a growing threat to […]
• Air Pollution as a Factor for Renal Cancer Therefore, to prevent renal cancer, it is crucial to examine the primary causes and look for better strategies to curb the issue.
• Indoor Air Pollution: The Silent Killer in Rural India The video “Indoor air pollution: The silent killer” discusses the detrimental impact of indoor air pollution in rural Indian households on people’s health. The problem of indoor air pollution is rather significant, and people should […]
• Air Pollution and China’s Governmental Measures The consequences of air pollution in China are already becoming evident, and not only they are the reason for environmental problems, but also they have a significant influence on the health of Chinese people living […]
• “Fort McMurray Fires Cause Air Pollution” by McDiarmid As a rule, the air in Canada is clean and rich in oxygen; however, when the wildfire burst, it affected the ozone layer to a significant degree.
• Air Pollution as the Trigger of the Ecological Catastrophe The key data collection tool is a survey that is targeted at determining the main factors of air pollution, finding out the social opinion regarding the quality of air in different cities, and estimating the […]
• Water & Air Pollution and Health Issues in Brazil The main environmental effects of pollution include the destruction of marine habitats, water scarcity, and anoxia. The conclusion is informative because the writer includes strategies to alleviate the problem of air and water pollution in […]
• Air Pollution and Respiratory Illnesses in Nigeria The purpose of the article presented was to test the relationship of the respiratory system illness and air pollution in developing countries, especially in Africa.
• Air Pollution Impact on Children’s Health in the US In these parts of the country, the level of air pollution is much higher. Nevertheless, the growing number of vehicles in the United States contributes to air pollution.
• Traffic-Related Air Pollution and Health Effects It emphasizes the fact that air contamination has a negative influence on the health of the representatives of the general public.
• Air Pollution in Los Angeles The escalation of congestion in the city has worsened the problem of air pollution because of the volume of unhealthy air emitted in the atmosphere.
• Environmental Revolution: Air Pollution in China For instance, a case study of the current pollution levels in China reveals that the country is struggling with the management of hazy weather.
• Environmental Behavior and Air Pollution in Ohio Once people become aware of the harmful effects of air pollution on the environment and health, it is likely that they will adopt positive behaviors, reduce behaviors and activities that contribute to air pollution and […]
• The New York City Air Pollution As the reports say, the state of health of some of the New York residents has grown increasingly worse, mostly due to the air pollution and the diseases that it has triggered.
• Air Pollution Effects on the Health and Environment According to the National Ambient Air Quality Standards, there are six principal air pollutants, the excess of which critically affects the health, lifestyle, and welfare of the population. Still, to my mind, the priority should […]
• Dealing With Air Pollution Polluted air contains nitrogen oxides and other toxic substances that dissolve in the atmosphere to return to the Earth in the form of acid rain, which is detrimental to the ecosystem.
• Environmental Justice and Air Pollution in Canada One of the best ways is to explain that air pollution is a major contributor to the burgeoning problem of global warming.
• Principles of Air Pollution Control and Analysis The increased attention to air quality is a recent development as people were previously not concerned about the quality of air in the atmosphere.
• New York City Air Pollution Problem One positive impact of technological advancements on the environment in New York is the ability to provide communication options that are friendly to the environment.
• China’s Air Pollution Problem The fact that we do not know the rate at which the economy is slowing down denotes that we cannot tell the rate at which air pollution in the country is reducing and those who […]
• China’s Air Pollution Is Not Unique China and the United States of America have adversely been mentioned to be the leading polluters of the atmosphere. The recent statistics indicate that the gap between the level of pollution by China and that […]
• An Investigation of Green Roofs to Mitigate Air Pollution With Special Reference to Tehran, Iran Thus, the aim of the research is to inquire into the basic information on the concept of green roofs, to answer the research questions on different attributes of green roofs, methods used to construct green […]
• Air Pollution: Human Influence on Environment For these reasons, the emission of aerosols in the air has become a major issue of concern allover the world and it is one of the many issues that need to be addressed and controlled […]
• Air Pollution Sources in Houston Though pollution is virtually everywhere, the paper focuses on Houston, one of the major cities is the US that have unacceptable levels of pollutants that pose health risks to the lives of people, plants, and […]
• Air Pollution: Public Health Impact In this regard, the paper explores various articles on opencast coals mining, aviation emissions, and geological storage of carbon dioxide and public health concerns in air pollution.
• Environmental Impacts of Air Pollution The current high growth rates have contributed to high concentration of particulates and pollutants in the atmosphere owing to the population’s over reliance on various sources of energy.
• Does Air Pollution in Schools Influence Student Performance? When the quality of the air is poor, allergens are likely to be present in the air. To this end, the paper has revealed that poor IAQ may cause a number of short and long-term […]
• Air Pollution Characteristics and Effect Accumulation and deposition of this substance can damage the ozone layer and affect the visibility of the environment. The accumulation of this pollutant in the atmosphere can cause severe damage to the reproductive organs, lungs […]
• Impact of Blowing Drums on Air Pollution According to the CSB report, BP failed to “implement or heed all the safety recommendations regarding the blowdown drums before the blast”.
• Air Pollution Sources, Effects and Ways of Minimizing This paper discusses the various sources of air pollution, the effects of air pollution, and ways of minimizing air pollution. Definitely, the destruction of the atmosphere is a serious issue of concern to many people, […]
• Air Pollution Effects on the Health in China The justification of the study is premised on the fact that China is one of the world’s largest coal producers and consumers, hence the need to evaluate the health implications of coal pollution on the […]
• Air Pollution and Health Policy in China The proposed study aims to critically assess the health impact of various forms of air pollution arising from overreliance on coal so as to inform current and future health policy directions in China.
• Air Pollution and Its Consequences Air pollution refers to the infusion of chemicals, particles and biological matter that are hazardous and are the cause of discomfort to humanity and other living organisms into the atmosphere.
• Air Pollution by Automobiles This paper shall address specific automobile pollutants in relation to causes and public health, to draft possible recommendations to the obstacles, in order to manage the problem.
• Preposition 23: Suspension of Air Pollution Control Act On the one hand, it was approved by the California Air Resources Board that considered it more realistic to suspend the implementation of this law due to the existing $ billion deficit leading to the […]
• Climate and Air Pollution The earth has a number of climatic systems that ensure the distribution of heat across the face of the earth. Global warming is the result of retention of heat by the earth’s atmosphere originally from […]
• A Discussion of Air Pollution & Related Health Implications on the Community The first task in the multidisciplinary team should be to identify the leading sources of air pollution within the community and the nature of the specific toxics or hazardous chemicals associated with the pollutants.
• Are Land Values Related to Ambient Air Pollution Levels?
• What Are the Main Causes of Air Pollution?
• How Pollution Affects the Environment?
• Could Air Pollution Have a Negative Impact on Water Quality?
• Does Air Pollution Affect Consumption Behavior?
• How Can Air Pollution Affect an Individual’s Health?
• What Are the Natural Causes of Air Pollution?
• Does Air Pollution Affect Global Warming?
• Is Air Pollution Getting Worse?
• How Are Air Pollution Harmful to Plants?
• When Did Air Pollution Start?
• How Do American Cities Handle Air Pollution?
• Does Expanding Regional Train Service Reduce Air Pollution?
• What Affects Air Pollution the Most?
• How Does Outdoor Air Pollution Affect the Quality of Indoor Air?
• Why Can Air Pollution Harm the Environment Dramatically?
• How Do Trees Prevent Air Pollution?
• What Are the Biggest Air Pollution Sources?
• Does Air Pollution Affect Animals?
• How Can We Prevent Air Pollution at Home?
• Where Is Air Pollution the Worst?
• How Does Air Pollution Affect Humans?
• What Are the Best Ways to Reduce Air Pollution?
• Is Air Pollution the Biggest Killer?
• How Does Acid Rain Affect Air Pollution?
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Weatherwatch: how reducing air pollution adds to climate crisis

Aerosols produced by pollution cool the planet; the crusade for clean air is removing this protection

C urbs on the amount of greenhouse gases being pumped into the atmosphere are just one of the ways that politicians are reacting to the scientific evidence that we are damaging our health and our planet. An even greater threat to human life in the short term has been air pollution in the form of particulate matter from the burning of fossil fuels, agriculture and many industrial processes.

Clean air has become a crusade everywhere from Beijing to London, to save lives and improve economic output. At the same time as damaging lungs and hearts, the aerosols produced by pollution increase cloud formation, change rainfall patterns and reflect sunlight back into space, thus cooling the planet.

Scientists studying the significant progress that has been made in cutting air pollution this century concluded that this success had also “added considerably to amount of global warming we can expect”. The removal of pollution is already partly responsible for the “unprecedented rate of human-induced warming in the last decade”, they said.

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The problem is that carbon dioxide, methane and nitrous oxide, the main greenhouse gases, remain in the atmosphere for long periods, continuing to add to global heating, while the disappearance of aerosols instantly removes any protection. So, they say, less air pollution means we may expect continued acceleration of surface temperatures this decade.

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