literature review on air pollution in india

Air pollution and public health: the challenges for Delhi, India

Affiliations.

1 Department of Community Medicine, University College of Medical Sciences, University of Delhi, Dilshad Garden, Delhi 110 095, India.
2 Department of Environmental Studies, University of Delhi, Delhi, India.
3 Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK.
PMID: 29267177
DOI: 10.1515/reveh-2017-0032

Mitigating the impact of pollution on human health worldwide is important to limit the morbidity and mortality arising from exposure to its effect. The level and type of pollutants vary in different urban and rural settings. Here, we explored the extent of air pollution and its impacts on human health in the megacity of Delhi (India) through a review of the published literature. The study aims at describing the extent of air pollution in Delhi, the magnitude of health problems due to air pollution and the risk relationship between air pollution and associated health effects. We found 234 published articles in the PubMed search. The search showed that the extent of air pollution in Delhi has been described by various researchers from about 1986 onwards. We synthesized the findings and discuss them at length with respect to reported values, their possible interpretations and any limitations of the methodology. The chemical composition of ambient air pollution is also discussed. Further, we discuss the magnitude of health problem with respect to chronic obstructive pulmonary diseases (COPD), bronchial asthma and other illnesses. The results of the literature search showed that data has been collected in last 28 years on ambient air quality in Delhi, though it lacks a scientific continuity, consistency of locations and variations in parameters chosen for reporting. As a result, it is difficult to construct a spatiotemporal picture of the air pollution status in Delhi over time. The number of sites from where data have been collected varied widely across studies and methods used for data collection is also non-uniform. Even the parameters studied are varied, as some studies focused on particulate matter ≤10 μm in aerodynamic diameter (PM10) and those ≤2.5 μm in aerodynamic diameter (PM2.5), and others on suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). Similarly, the locations of data collection have varied widely. Some of the sites were at busy traffic intersections, some on the terraces of offices and residential houses and others in university campuses or airports. As a result, the key question of the extent of pollution and its distribution across various parts of the city could be inferred. None of the studies or a combination of them could present a complete picture of the burden of diseases like COPD, bronchial asthma and other allergic conditions attributable to pollution in Delhi. Neither could it be established what fraction of the burden of the above diseases is attributable to ambient air pollution, given that other factors like tobacco smoke and indoor air pollution are also contributors to the causation of such diseases. In our discussion, we highlight the knowledge gaps and in the conclusion, we suggested what research can be undertaken to fill the these research gaps.

Keywords: air pollution; chronic obstructive pulmonary diseases; pollution exposure.

Publication types

Air Pollutants / adverse effects*
Air Pollution / adverse effects*
Cities / epidemiology
Environmental Exposure*
Environmental Monitoring*
India / epidemiology
Public Health*
Respiratory Tract Diseases / chemically induced*
Air Pollutants

Air pollution status and attributable health effects across the state of West Bengal, India, during 2016–2021

Published: 17 January 2024
Volume 196 , article number 165 , ( 2024 )

Cite this article

literature review on air pollution in india

Buddhadev Ghosh 1 ,
Harish Chandra Barman 1 ,
Sayoni Ghosh 1 ,
Md Maimun Habib 1 ,
Jayashree Mahato 1 ,
Lovely Dayal 1 ,
Susmita Mahato 1 ,
Priti Sao 1 ,
Atul Chandra Murmu 1 ,
Ayontika Deb Chowdhury 1 ,
Sourina Pramanik 1 ,
Rupsa Biswas 1 ,
Sushil Kumar 1 &
Pratap Kumar Padhy 1

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Air pollution is one of the most significant threats to human safety due to its detrimental health consequences worldwide. This study examines the air pollution levels in 22 districts of West Bengal from 2016 to 2021, using data from 81 stations operated by the West Bengal Pollution Control Board (WBPCB). The study assesses the short- and long-term impacts of particulate matter (PM) on human health. The highest annual variation of PM 10 was noted in 2016 (106.99 ± 34.17 μg/m 3 ), and the lowest was reported in 2020 (88.02 ± 13.61 μg/m 3 ), whereas the highest annual variations of NO 2 (μg/m 3 ) were found in 2016 (35.17 ± 13.55 μg/m 3 ), and lowest in 2019 (29.72 ± 13.08 μg/m 3 ). Similarly, the SO 2 level was lower (5.35 μg/m 3 ) in 2017 and higher in 2020 (7.78 μg/m 3 ). In the state, Bardhaman, Bankura, Kolkata, and Howrah recorded the highest PM 10 concentrations. The monthly and seasonal variations of pollution showed higher in December, January, and February (winter season) and lowest observed in June, July, and August (rainy season). The southern part of West Bengal state has recorded higher pollution levels than the northern part. The short- and long-term health impact assessment due to particulate matter shows that the estimated number of attributable cases (ENACs) for incidence of chronic bronchitis in adults and prevalence of bronchitis in children were 305,234 and 14,652 respectively. The long-term impact of PM 2.5 on human health ENACs for mortality due to chronic obstructive pulmonary disease for adults, acute lower respiratory infections in children aged 0–5, lung cancer, and stroke for adults were 21,303, 12,477, 25,064, 94,406, and 86,272 respectively. This outcome assists decision-makers and stakeholders in effectively addressing the air pollution and health risk concerns within the specified area.

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Acknowledgements

We would like to thank the West Bengal Pollution Control Board (WBPCB, http://emis.wbpcb.gov.in/airquality/filter_for_aqi.jsp ), which comes under the Central Pollution Control Board (CPCB), Ministry of Environment, Forest and Climate Change (MoEFCC), New Delhi for providing valuable data. The non-NET fellowship provided to one of the authors (BG) is gratefully acknowledged.

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Buddhadev Ghosh, Harish Chandra Barman, Sayoni Ghosh, Md Maimun Habib, Jayashree Mahato, Lovely Dayal, Susmita Mahato, Priti Sao, Atul Chandra Murmu, Ayontika Deb Chowdhury, Sourina Pramanik, Rupsa Biswas, Sushil Kumar & Pratap Kumar Padhy

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Contributions

BG contributed to the concept, study design, and statistical analysis, and wrote the original draft. HCB collected air quality data from Jhargram and South 24 Parganas districts. SG collected air quality data from North 24 Parganas district. MMH collected air quality data from Malda, Murshidabad, and Nadia districts. JM collected air quality data from Darjeeling, Kalimpong, and Jalpaiguri districts. LD collected air quality data from Kolkata district. SM collected air quality data from East and West Medinipur districts. PS collected air quality data from Purulia, Bankura, and Birbhum districts. ACM collected air quality data from Burdwan district. ADC collected air quality data from Cooch Behar and Alipurduar districts. SP collected air quality data from Howrah district. RB collected air quality data from Hooghly district. SK helped in review and editing the MS. PKP contributed to the concept, design of the study, writing the original draft, review, and final editing of the MS.

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Ghosh, B., Barman, H.C., Ghosh, S. et al. Air pollution status and attributable health effects across the state of West Bengal, India, during 2016–2021. Environ Monit Assess 196 , 165 (2024). https://doi.org/10.1007/s10661-024-12333-7

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DOI : https://doi.org/10.1007/s10661-024-12333-7

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Air Pollution in India: Major Issues and Challenges

This article foregrounds the challenges India is currently facing in reducing air pollution and bringing the level of air quality to a certain standard. It also discusses solutions that could be adopted to combat the national crisis.

Rising urbanisation, booming industrialisation, and associated anthropogenic activities are the prime reasons that lead to air pollutant emissions and poor air quality. It is expected that by 2030, around 50% of the global population will be residing in urban areas (Gurjar, Butler, Lawrence, et al. 2008). More than 80% of population in urban areas is exposed to emissions that exceed the standards set by World Health Organization (WHO 2016). Air pollution is one of the key global health and environmental concerns (Nagpure, Gurjar, Kumar, et al. 2016) and has been ranked among the top five global risk factors of mortality by the Health Effects Institute (HEI 2019). According to HEI's report, particulate matter (PM) pollution was considered the third important cause of death in 2017 and this rate was found to be highest in India. Air pollution was considered to cause over 1.1 million premature deaths in 2017 in India (HEI 2019), of which 56% was due to exposure to outdoor PM 2.5 concentration and 44% was attributed to household air pollution. As per WHO (2016), one death out of nine in 2012 was attributed to air pollution, of which around three million deaths were solely due to outdoor air pollution.

The rising trends in population growth and the consequent effects on air quality are evident in the Indian scenario. For example, the megacities of Delhi, Mumbai, and Kolkata combined holds a population exceeding 46 million (Gurjar, Ravindra, and Nagpure 2016). Over the years, there has been a massive-scale expansion in industries, population density, anthropogenic activities, and the increased use of automobiles has degraded the air quality in India (Gurjar and Lelieveld 2005). In the last few decades, the greenhouse gas (GHG) emissions and other emissions resulting from anthropogenic activities have increased drastically (Gurjar and Nagpure 2016).

As per WHO (2016) estimates, 10 out of the 20 most populated cities in the world are in India. Based on the concentrations of PM 2.5 emissions, India was ranked the fifth most polluted country by WHO (2019), in which 21 among the top 30 polluted cities were in India. The Indian cities, on average, exceeded the WHO threshold by an alarming 500%.

The consistent population growth has led to an excessive strain on the energy consumption, thereby affecting the environment and the air quality of the megacities (Gurjar and Nagpure 2016). Kumar, Khare, Harrison, et al. (2015) calculated the increase in the total energy demand for both mobile and point sources and inferred that in Delhi, the energy demand had grown by 57.16% from 2001 to 230,222 TJ in 2011. A subsequent rise in energy consumption can be expected in the coming years, with no reliable sources available for monitoring the rate of energy consumption.

The continuous degradation of ambient air quality in the urban centres of India demands effective measures to curb air pollution. Though various policy measures are being introduced by the Government of India (GoI) to reduce the vehicular and industrial emissions, the extent to which these measures are implemented is questionable. The lack of infrastructural facilities, inadequacy of financial resources to implement advanced infrastructural innovations, difficulty in relocation of the industries from the urban centres even after mandatory court decisions, and above all, the behavioural patterns among people in accepting the green solutions are some of the crucial impediments on the road to environmental protection that our country seems to be struggling to overcome today.

There have been various efforts to study the air quality in Indian cities. The potential of the atmospheric carcinogenic emissions to put human health at risk has been studied by Gurjar, Mohan, and Sidhu (1996). Gurjar, Aardenne, Lelieveld, et al. (2004) framed a comprehensive emission inventory model to understand the emission trends in Delhi, India's capital, for a period from 1990 to 2000. A multi-pollutant index (MPI) rating scale was used by Gurjar, Butler, Lawrence, et al. (2008) to rank the megacities with respect to their ambient air quality. According to this study, out of 18 megacities considered worldwide, the Indian cities, namely, Delhi, Kolkata, and Mumbai were ranked 7, 9, and 11, respectively. Gurjar, Nagpure, Kumar, et al. (2010) evaluated the vehicular emissions in Kolkata between 2000 and 2010 and inferred that the older vehicles in the city contributed more to the pollution load and should be phased out. A Vehicular Air Pollution Inventory (VAPI) model was developed by Nagpure and Gurjar (2012) that could estimate the vehicular emissions from road traffic in Indian cities. Later, Gurjar, Nagpure, and Kumar (2015) evaluated the potential gaseous emissions from the agricultural wetlands of Delhi and inferred that man-made wetlands were responsible for 48–49% of the total GHG emissions in the capital city. The study intended to develop an emission inventory for agricultural activities to evaluate their contribution to pollution in Delhi.

Several policy measures have been taken by the Ministry of Environment, Forest and Climate Change (MoEFCC), GoI to tackle the adverse effects of air emissions in short and long terms. The government's decision to adopt compressed natural gas (CNG) as an alternative fuel to petrol and diesel, the odd-even measures introduced in Delhi, and the improvements in fuel and vehicle quality to lower emissions are some of the recent commendable steps towards curtailment of air pollution. Moreover, the increasing number of studies related to this field shows the importance of research on this subject. Several studies have assessed the trends of air pollutant emissions from different sources across several cities in India. However, there is an urgent need for a comprehensive review of the existing issues in the Indian scenario. More focus is needed on studying the impacts of these pollutant emissions on various forms, such as the ecosystem, biodiversity, buildings and materials, and primarily the health risks that people are vulnerable to due to breathing foul air.

A comprehensive review is done to understand the current scenario in the Indian context. The following section comprises a detailed review focusing on air pollution studies in India, the various sources, and the effects of the pollutants on the ecosystem, biodiversity, materials and buildings, and on human health, which are discussed in the later sections of this article. The various air quality standards followed by countries worldwide are included as well. The Discussion section of the article consists of the mitigation strategies adopted for emission control in India, the challenges posed by various sectors in the Indian scenario, and the research gaps that have been identified from the available literature. The key conclusions and a few recommendations form part of the last section.

Reviewing Literature

The present review is divided into three sub-sections: The first sub-section discusses the literature that focuses on air pollution in India on a national scale; the next segment highlights the various sources of air pollution and the effects of the pollutants. The major sources are categorised into seven sectors. Thereafter, the various effects of pollutant emissions are pointed out. The air quality standards adopted by various countries for controlling air pollution have been discussed in the later sections of this article.

Studies on air pollution in India

Though various studies have addressed the issue of air pollution and its impacts on urban Indian cities, most of these studies are limited to specific cities and do not necessarily give a complete picture of the situation. Some of the highlights of these studies are discussed in the following paragraphs.

Pandey and Venkataraman (2014) evaluated the effects of emissions from various modes of transport in India. Their study inferred that on-road transportation contributed over 97% of the estimated emissions in India, when compared to other modes of transport, such as railways, waterways, and airways. Gurjar, Ravindra, and Nagpure (2016) did a comprehensive study on various anthropogenic emission sources in Indian megacities, such as Delhi, Mumbai, and Kolkata. The global impact of urban pollution is also discussed in their study. Upadhyay, Dey, Chowdhury, et al. (2018) evaluated the major anthropogenic sources of PM 2.5 and the potential benefits to human health, if sufficient control measures are applied to curb emissions. A recent study by Jat, Gurjar, and Lowe (2021) examined the extent of pollution during the winter months in India. The study used a WRF-Chem model, that is, Weather Research and Forecasting (WRF) coupled with chemistry, to evaluate the concentrations of pollutants, such as PM 2.5 , oxides of sulphur (SOX), oxides of nitrogen (NOX), black and organic carbons, and non-methane volatile organic carbons (NMVOCs) that were identified for the winter months. The various sources of air pollution can be classified into seven major sources and the consequent effects are discussed in this article.

Sources of air pollution

The various sources of air pollution are classified into seven major sectors, which include transportation, industries, agriculture, power, waste treatment, biomass burning, residential, construction, and demolition waste.

Vehicular/Transport Emissions

The transportation sector is the main contributor of air pollutants in almost every city, but this phenomenon is worse in urban cities (Gurjar, Aardenne, Lelieveld, et al. 2004). This could be due to the increased number of vehicles when compared to the existing infrastructural facilities, e.g., roads, fuel stations, and the number of passenger terminals provided for public transport. In India, the amount of motorised transport increased from 0.3 million in 1951 to 159.5 million in 2012 (Gurjar, Ravindra, and Nagpure 2016). A significant share of vehicular emissions comes from urban cities, such as Delhi, Mumbai, Bengaluru, and Kolkata. Carbon monoxide (CO), NOX, and NMVOCs are the major pollutants (>80%) from vehicular emissions (Gurjar, Aardenne, Lelieveld, et al. 2004). Other trace emissions include methane (CH4), carbon dioxide (CO2), oxides of sulphur (SOx), and total suspended particles (TSPs).

In an urban environment, road traffic emissions are one of the prime contributors of air pollution. Road dust is a major contributor to PM emissions in Delhi (37%), Mumbai (30%), and Kolkata (61%). Road transport is the largest source of PM 2.5 in Bengaluru (41%), Chennai (34%), Surat (42%), and Indore (47%) (Nagpure, Gurjar, Kumar, et al. 2016). In the Indian context, some of the essential factors of high traffic emissions include extreme lack of exhaust measures, the highly heterogeneous nature of vehicles, and poor quality of fuel.

Industrial Processes

Over the last few decades, India has witnessed large-scale industrialisation. This has degraded the air quality in most urban cities. The Central Pollution Control Board (CPCB) has categorised the polluting industries into 17 types, which fall under the small and medium scale (Gurjar, Ravindra, and Nagpure 2016). Out of these categories, seven have been marked as 'critical' industries that include iron and steel, sugar, paper, cement, fertiliser, copper, and aluminium. The major pollutants comprise SPM, SOX, NOX, and CO2 emissions.

The small-scale industries, which are not regulated like the major industries, use several energy sources apart from the primary source of state-provided electricity. Some of these fuels include the use of biomass, plastic, and crude oil. These energy sources are neglected in the current emission inventory studies. In Delhi, after the intervention of the judiciary in 2000, many industries were relocated from urban areas to adjacent rural areas (Nagpure, Gurjar, Kumar, et al. 2016). In Delhi, a major fraction of the pollution load comes from the brick manufacturing industries, which are situated at the outskirts of the city. Rajkot (42%) and Pune (30%) are the two cities where industries play a prominent role in contributing to the highest amount of PM 2.5 (Nagpure, Gurjar, Kumar, et al. 2016).

Agriculture

Agricultural activities produce emissions, which have the potential to pollute the environment. Ammonia (NH3) and nitrous oxide (N2O) are the key pollutants released from agricultural activities. The other agricultural emissions include methane emissions from enteric fermentation processes, nitrogen excretions from animal manure, such as CH4, N2O, and NH3, methane emissions from wetlands, and nitrogen emissions from agricultural soils (N2O, NOX, and NH3) due to the addition of fertilisers and other residues to the soil (Gurjar, Aardenne, Lelieveld, et al. 2004). Agricultural processes, such as 'slash and burn' are prime reasons for photochemical smog resulting from the smoke generated during the process. Crop residue burning is another process that results in toxic pollutant emissions. This is how neighbouring cities of Delhi contribute to the agricultural pollution load. This is an example of how external sources contribute to the menace of air pollution in the city (Nagpure, Gurjar, Kumar, et al. 2016).

Power Plants

The contribution of power plants to air emissions in India is both immense and worrisome. The thermal power plants manufacture around 74% of the total power generated in India (Gurjar, Ravindra, and Nagpure 2016). According to The Energy and Resources Institute (TERI), the emissions of SO2, NOX, and PM increased over 50 times from 1947 to 1997. Thermal power plants are the main sources of SO2 and TSP emissions (Gurjar, Aardenne, Lelieveld, et al. 2004), thereby contributing significantly to the emission inventories. In Delhi, power plants contributed 68% of SO2 emissions and 80% of PM10 concentrations over a period from 1990 to 2000 (Gurjar, Aardenne, Lelieveld, et al. 2004). Thus, there is an urgent need to adopt alternative power sources including green and renewable resources for meeting the energy requirements.

Waste Treatment and Biomass Burning

In India, about 80% of municipal solid waste (MSW) is still discarded into open dumping yards and landfills, which leads to various GHG emissions apart from the issues of foul odour and poor water quality at nearby localities. The lack of proper treatment of MSW and biomass burning has been responsible in causing air pollution in urban cities. In Delhi alone, around 5300 tonne of PM10 and 7550 tonne of PM 2.5 are generated every year from the burning of garbage and other MSW (Nagpure, Gurjar, Kumar, et al. 2016).

Methane (CH4) is the major pollutant released from landfills and wastewater treatment plants. Ammonia (NH3) is another by-product, which is released from the composting process. The open burning of wastes, including plastic, produces toxic and carcinogenic emissions, which are a grave concern (Gurjar, Aardenne, Lelieveld, et al. 2004).

Domestic Sector

Households are identified as a major contributor of air pollution in India. The emissions from fossil fuel burning, stoves or generators come under this sector, thereby affecting the overall air quality. Domestic energy is powered by fuels, such as cooking gas, kerosene, wood, crop wastes or cow dung cakes (Gurjar, Aardenne, Lelieveld, et al. 2004).

Though liquefied petroleum gas (LPG) is used as an alternative source of cooking fuel in most urban cities, the major share of India's rural population continues to rely on cow dung cakes, biomass, charcoal or wood as the primary fuel for cooking and other energy purposes and demands. These lead to severe implications on air quality, especially the indoor air quality, which could directly affect human health. According to HEI (2019), about 60% of India's population was exposed to household pollution in 2017.

Construction and Demolition Waste

Another major source of air pollution in India is waste, which is an outcome of construction and demolition activities. Guttikunda and Goel (2013) inferred from their study that around 10,750 tonne of construction waste is generated in Delhi every year. Even after the construction phase, these buildings have the potential to be the major contributors of GHG emissions. Nowadays, the increasing interest in green building technologies and the application of green infrastructure and materials during construction could tackle this issue to a large extent, thereby preserving our biodiversity and maintaining cleaner air quality.

On the Ecosystem

The terrestrial ecosystem is widely affected by ground air pollution. The ill-effects include respiratory and pulmonary disorders in animals and humans (Stevens, Bell, Brimblecombe, et al. 2020). The effects on the marine ecosystem include acidification of lakes, eutrophication, and mercury accumulation in aquatic food (Lovett, Tear, Evers, et al. 2009). These processes may indirectly affect the health of the living beings. Similarly, soil acidification is another phenomenon that is common in forest ecosystems as a result of long-term pollutant accumulation. The deposition of sulphate, nitrate, and ammonium is the main reason for soil acidification. Bignal, Ashmore, Headley, et al. (2007) inferred that traces of heavy metals were found in soil samples in areas adjacent to roadways due to cumulative deposition of pollutants. Soil pollution indirectly affects the ecosystems of plants and animals that are reliant on soil for nutritional intake. Nitrogen deposition in wet and dry forms on vegetation and soil surfaces can occur from vehicular and agricultural activities (Driscoll, Whital, Aber, et al. 2003). The results of these activities on the ecosystem have long-term environmental implications, such as global warming and climate change (Lovett, Tear, Evers, et al. 2009). A recent study by Stevens, Bell, Brimblecombe, et al. (2020) discussed four threats to the global ecosystem from pollution, namely, the effects of primary pollutants, such as SO2 and NO2 in a gaseous state, the consequences of wet and dry depositions from SOX and NOX emissions, effects of eutrophication by nitrogen deposition, and the impact of ground-level ozone concentrations.

On Biodiversity

The ill-effects of air pollutant emissions could impact the biological diversity. Though it is evident that air pollution contributes to ground-level emissions, limited studies have been conducted to address the effects on our biodiversity. Acid rain, which is a result of air pollution, is caused by the oxidation and wet deposition of SO2 and NOX emissions in the atmosphere (Rao, Rajasekhar, and Rao 2016). Therefore, acid rain can have harmful effects on our biodiversity.

Nitrogen deposition on plants is a serious outcome of air pollution (Lovett, Tear, Evers, et al. 2009). Bignal, Ashmore, Headley, et al. (2007) investigated three sites adjacent to roadways in the UK to study the impact of pollution on the health of oak and beech trees. Several damages, such as increased defoliation, discolouration, poorer crown condition, and increased pest attacks were observed during the study. It was inferred that significant effects on plants could be found within 100 m from the roadways due to NO2 emissions.

Ozone is another pollutant which is toxic to both plants and animals. Ozone results in reduced photosynthesis and slower growth in plants. In animals and humans, ozone can affect the lung tissues causing respiratory conditions, such as asthma (Stevens, Bell, Brimblecombe, et al. 2020). The effect of ground-level ozone on the crop yield was studied by Sharma, Ojha, Pozzer, et al. (2019), where the researchers evaluated the pan India losses in crop yield and financial problems incurred during 2014–15 due to the ozone. Poor air quality and exposure to anthropogenic pollution had a negative effect on the health of animals as well (Isaksson 2010).

Moreover, the reproductive performance of animals also gets affected due to increased oxidative stress (Isaksson 2010), thereby impacting the population of any species. This may not prove healthy especially for the endangered species. Considering the rapid urbanisation, more focus should be given to this study area in the future.

On Materials and Buildings

SOX and NOX emissions can harm the flora, fauna, material surfaces, and even damage buildings and structures. The negative effects may be in the form of discolouration, loss of material, structural failing, and soiling. This can reduce the service life of buildings and can severely damage historical monuments and structures. One such example is India's white-marble Taj Mahal, which is turning yellow as a result of being exposed to SOX emissions from industries and acid rain. Another historical monument in India is Hyderabad's Charminar, which is turning black due to it being situated in a highly polluted area (Rao, Rajasekhar, and Rao 2016). The erosion of such heritage zones poses a grave concern.

On Human Health

People residing in areas exposed to poor air quality and high pollution levels are prone to hazardous health risks. Such deleterious implications can lead to both minor respiratory disorders and fatal diseases (Gurjar, Jain, Sharma, et al. 2010). Molina, Molina, Slott, et al. (2004) inferred that the studies conducted worldwide had similar conclusions regarding the impact of pollutants on humans. Emissions such as PM, O3, SOX, and NOX have the potential to damage the cardiovascular and respiratory systems of humans. In recent years, the study of human health risks as an outcome of poor air quality has been of prime focus. Gurjar, Jain, Sharma, et al. (2010) evaluated the health risks people in urban areas were prone to due to air pollution in terms of mortality and morbidity. However, there are several limitations associated with the application of this health risk assessment methodology, which must be addressed in the future studies. The HEI (2019) assessed the impact of PM 2.5 concentrations in India and concluded that around 1.1 million deaths in 2015 were a result of being exposed to air pollution. Upadhyay, Dey, Chowdhury, et al. (2018) inferred that a total of 92,380 lives would have been saved if control measures were applied in the transport, residential, industries, and energy sectors, which are some of the prominent contributors of air pollution.

Gurjar, Ravindra, and Nagpure (2016) concluded in their study that around 30% of Delhi's population complained of respiratory issues due to air pollution in the selected year. Another study by Nagpure, Gurjar, and Martel (2014) evaluated that the mortality rate due to air pollution had doubled between 1990 and 2010 in the capital city. According to Gurjar, Mohan, and Sidhu (1996), the number of premature deaths in Mumbai due to air pollution was recorded at 2800 in 1995, which later increased exponentially to 10,800 in 2010 (Gurjar, Ravindra, and Nagpure 2016). In Kolkata, the premature deaths were estimated to be around 13,500 in 2010. Similarly, Delhi reported about 18,600 premature deaths per year (Lelieveld, Evans, Fnais, et al. 2015).

Air quality standards

The acceptable threshold level of air pollution in terms of its potential impacts on health and environment is defined as the ambient air quality standards. These standards are adopted and enforced by a regulatory body or authority. Every standard should have a standalone definition and its threshold values should be justified appropriately (Molina, Molina, Slott, et al. 2004). The air quality standards may vary for different countries due to various factors, such as economic conditions, technological know-how, and indigenous air pollution-related epidemiological studies. These are known as the National Ambient Air Quality Standards (NAAQS) in countries, such as India, China, and the US. However, in Canada and the European countries, the limit values are predefined (WHO 2005). Table 1 gives a representation of the different standards adopted by different countries (WHO 2005).

For India, the NAAQS developed by the Central Pollution Control Board (CPCB 2009) are given in Table 2.

Mitigation strategies for emission control in India

In India, the central and state governments have taken several steps to control air pollution and improve the ambient air quality. Various initiatives, such as the use of compressed natural gas (CNG) as an alternative fuel, the odd-even measures implemented in Delhi, the introduction of Bharat Stage VI vehicle and fuel standards, the Pradhan Mantri Ujjwala Yojana (PMUY), and the National Clean Air Programme (NCAP) are some examples in this endeavour. The CPCB ensures the monitoring and regulation of the NAAQS in the cities, towns, and industrial areas with the cooperation of the respective state pollution control boards (SPCBs). Under these plans, various sector-wise measures have been implemented in the urban cities of India. For the transport sector, for instance, some of these measures include the use of electric vehicles (EVs) as a mode of public transportation, development of cycling infrastructure, use of bioethanol as fuel, and the construction of multi-level car parking facilities and peripherals to tackle congestion. Within the industrial sector, some of the measures undertaken comprise the implementation of zig-zag technology for the stack emissions from brick kilns, online monitoring of discharges through the Online Continuous Emission Monitoring Systems (OCEMS), and the installation of web cameras in highly polluting industries. To tackle the problem of open burning of garbage and household wastes, door-to-door collection of segregated wastes has been introduced and several compost pits have been established in urban cities. In the residential sector, the government has set a target of achieving 100% usage of LPG for cooking purposes. Further, to control the concentrations of particulate matter (PM) and dust particles, various steps, such as the green buffer around cities, maintenance of 33% green cover around urban areas, installation of water fountains across the cities have been taken over the years (Ganguly, Kurinji, and Guttikunda 2020; Sharma, Mallik, Wilson, et al. 2018; Sharma, Rehman, Ramanathan, et al. 2016).

Other potential mitigation strategies

Air quality management in megacities is a four-stage process that involves problem identification, formulation of policies, their implementation, and control strategies (Molina, Molina, Slott, et al. 2004). The various management tools to ensure emission control and attainment of air quality standards include, air quality modelling, emission inventories, monitoring the concentration of pollutants, and source apportionment studies. These methodologies involve a complex analysis of extensive data sets for the effective management of air quality standards. Due to the lack of transparency and unavailability of data, uncertainties are introduced in the estimation of atmospheric concentrations. Minimising these uncertainties with our scientific understanding is one of the major challenges towards addressing the issues related to air quality (Gurjar and Ojha 2016).

The increase in private vehicles is the prime contributor of air pollution in Indian cities (Molina, Molina, Slott, et al. 2004). Therefore, there should be some policy norms that would set a certain limit to private vehicle ownership. Second, the age of vehicles degrades the air quality and such ageing vehicles should be phased out over a period of 10 years or so. Threshold limits should be imposed on emissions from all sources, primarily vehicles and industries, and the violators should be penalised.

Infrastructural modifications to limit traffic in polluted areas, development of efficient public transport facilities, such as the Bus Rapid Transit (BRT) system or other public transit systems, improved facilities for walking, biking, and public transport, and relocation of point sources out of urban centres could help curb emissions significantly.

Technology modifications, such as the introduction of hybrid vehicles or fuel cell vehicles or fuel modifications, such as ultra-low sulphur fuels, or alternative fuels like CNG, methanol in Brazil or hydrogen fuel in Japan (Molina, Molina, Slott, et al. 2004) could reduce air pollution levels. In recent years, owing to the reduced sulphur fractions in the fuels, decreasing trends in SOX have been observed (Gurjar, Ravindra, and Nagpure 2016) and such a development could further control air pollutant emissions.

Control points should be identified and prioritised in urban areas that would help reduce pollutant emissions significantly. The development of sustainability matrices could help monitor and regulate the emissions. Emission trading, also known as cap and trade, is another control strategy that could be applied in urban cities, a practice already prevalent in the US, where economic incentives are offered to reduce the pollutant concentrations (Molina, Molina, Slott, et al. 2004). Congestion pricing, as followed in London, where a driver is charged each time they enter the peak zones of a city could be another avenue to explore within the Indian context as well. However, such a strategy would require strong public awareness and support to become successful.

A combination of effective policies, technologies, and land-use planning could help develop a control strategy for emission control. Stricter emission standards, cleaner fuels, advancements in engines, manufacture of cleaner and green vehicles, and post-emission treatment technologies could curtail pollution levels in urban areas to a great extent. Concrete policy measures could be imposed that would further limit the exposure of people to pollutant emissions. Relocation of industries to the outskirts of the city is a fine example (Molina, Molina, Slott, et al. 2004) to consider in this regard.

Limiting the emissions from combustion sources could curb pollution. One such example was the use of CNG-fuelled vehicles in Delhi from 2001 to 2006, which had reduced the emissions of PM, CO, NOX, and SO2 levels considerably.

Challenges in the Indian scenario

Air pollution poses serious risks to human health, economic assets, and the overall environment (Gurjar, Butler, Lawrence, et al. 2008). In the current Indian scenario, urban cities are mostly polluted by vehicular emissions, industries, and thermal power plants (Gurjar, Ravindra, and Nagpure 2016). Nagpure, Gurjar, Kumar, et al. (2016) studied and inferred that vehicular emission is the major contributor of increasing pollution in Delhi. Gurjar, Aardenne, Lelieveld, et al. (2004) had earlier indicated that there is a lack of India-specific emission factors for several air pollutants, which could be a major concern towards developing realistic emission inventories for Indian cities. Further, Nagpure, Sharma, and Gurjar (2013) observed that neither ratio nor realistic numbers are available for two-stroke and four-stroke two-wheelers or for light and heavy commercial vehicles. Similarly, for evaluating the utilisation factors for vehicles, which indicate how frequently a vehicle is being used in a given period of time, the escalating travel demand in the country is not considered. This results in uncertainties in the estimated emissions of air pollutants.

Over the last decades, industrialisation has boomed and India ranks among the top 10 industrialised countries, globally (Gurjar, Ravindra, and Nagpure 2016). Guttikunda and Calori (2013) studied and listed the improvements that could be made in the emission estimates from Indian cities by monitoring capacity, regular documentation of pollutant sources, fuel usage patterns, and receptor modelling studies.

In India, the methodologies associated with emission estimation from biomass burning have certain limitations. For instance, Gurjar, Aardenne, Lelieveld, et al. (2004) exempted several sources from estimation due to non-reliable data sets pertaining to biogenic emissions. Guttikunda and Calori (2013) estimated that the burning of roadside garbage and the landfill fires have an uncertainty of ±50%. The study also estimated that the data on fuel used for cooking and heating in the domestic sector have an uncertainty of ±25%. This uncertainty of fuel usage data for the in-situ generators used in large institutions, hospitals, and hotels was ±30% for the year 2010.

As discussed in the previous section, the implementation of strict policy measures and the use of advanced technologies and infrastructure could tackle the problem of air pollution to a great extent. Though stringent measures and policies are being adopted to curb vehicular and stack emissions, most Indian cities lack the technological and infrastructural wherewithal. In a developing country like India, financial constraints faced during the timely planning and implementation of advanced urban infrastructural changes could pose a serious hurdle to air pollution mitigation strategies (Gurjar and Nagpure 2016).

Irresponsible human behaviour is another major issue that makes the existing challenges difficult to overcome. The lack of public interest in the emission control measures and inefficient traffic management system are major hurdles to realising the goal of clean air. The lack of public interest in certain measures taken by the government could result in significant losses of investments in infrastructural facilities (Gurjar and Nagpure 2016).

Research Gaps

Several studies focus on air pollutant emissions in Indian urban cities and industrial clusters. However, India-specific emission factors are either unavailable or difficult to interpret for various sources in most cases (Gurjar, Aardenne, Lelieveld, et al. 2004). Also, there is a lack of adequate research on the extent of pollution concentrations in medium-scale cities, which are likely to expand in the near future. For a country like India, nearly 68% of population (Chandramouli 2014) resides in rural areas and is dependent on domestic cooking fuels, such as wood and cow dung cakes. Moreover, practices such as biomass and crop burning create additional point sources of air pollution. This further gives an opportunity to evaluate the strategies to reduce emissions from such sources.

A recent national-level emission inventory for India at fine resolution is not available in the public domain and research on policy measures using regional air quality modelling mostly depends on global emissions inventories, which are at coarser resolutions. For Indian cities with limited or no air quality monitoring infrastructure, researchers and authorities are dependent on the data available through secondary sources. However, these data sets are non-reliable and the accuracy of such data is also uncertain (Gurjar, Jain, Sharma, et al. 2010)Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP).

With the increasing rate of industrialisation, Gurjar, Aardenne, Lelieveld, et al. (2004) discussed the lack of factual data on industrial production and fuel statistics for Indian cities.

The urban population in India is anticipated to increase exponentially and the number of cities will grow as well. This suggests that the MSW generation will also increase, which must be managed efficiently. However, in India, proper MSW management and treatment techniques need to be implemented other than the current practices of landfilling and composting. Moreover, data sets on detailed MSW statistics regarding the amount of wastes collected, segregated, stored, and treated were absent (Gurjar, Aardenne, Lelieveld, et al. 2004).

Over the years, indoor air quality (IAP) has become an area of scientific interest and researchers worldwide are studying the threats IAP poses to human life. However, in the Indian context, there are limited studies which have stressed on the impact of indoor air pollution concentrations.

Conclusion and Recommendations

An effective and successful emission control strategy should be holistic (Molina, Molina, Slott, et al. 2004). It must be a combination of successfully applied scientific ideas and technological advancements; should support the economy and be supported by the public. Various steps taken by the Government of India to control air pollution in Indian cities have been highlighted in the previous sections. These measures have the potential to tackle pollution only if implemented successfully in the coming years.

India is facing serious issues of poor air quality in many urban areas. Apart from the much discussed megacities, like Delhi, various reports suggest that several medium-scale cities are equally at the brunt of filthy air. The ill-effects could impact human health in a negative way, also affecting the biodiversity, other life forms, heritage, cultural buildings and even climate in the longer term. It is about time that the government comes forward to support cities for the development of infrastructure and treatment facilities.

The control strategies adopted to tackle air pollution must be sustainable in nature. For example, the urban air pollution control strategy should depend mainly on sustainable means of public transportation modes, such as BRTs, metros, trams, cycle lanes and well-connected pedestrian facilities, which can further ensure minimum use of private vehicles, thereby reducing air pollution levels. People must be motivated to opt for an efficient public transport system instead of relying on private vehicles. Similarly, some strict laws must be enforced, such as emission trading and congestion pricing, which have the potential to reduce emissions drastically. Apart from these, the use of alternate fuels and e-cars, e-bikes and hybrid vehicle types must be promoted by the government. All these measures could reduce city emissions significantly.

The residents of rural areas are seldom aware of the harmful effects of air-borne pollutants and their consequence to human health. Public awareness programmes should be initiated by the government in every city, both rural and urban, highlighting the importance of managing air pollution at source and the various control measures that could be adopted to reduce pollutant emissions. Such initiatives could significantly reduce the activities, such as open burning of wastes, crop burning, use of biomass as a fuel for cooking and burning of plastic and rubber materials during winters. A holistic approach incorporating all of the mentioned measures could be beneficial to attain cleaner air quality in Indian cities and guarantee a healthier place to inhabit.

In this context, the NCAP launched by the Government of India appears to be a timely intervention. It is based on a long-term, time-bound, national-level strategy to tackle air pollution in a comprehensive manner with targets to achieve 20–30% reduction in particulate matter (PM) concentrations by 2024, keeping 2017 as the base year for the comparison of concentration levels. A total of 122 non-attainment cities have been identified across the country based on the 'Air Quality' data obtained for the period 2014–2018 under NCAP. The city-specific action plans are being prepared which, inter-alia, include measures for strengthening the monitoring network, developing emission inventories, carrying out source apportionment studies, reducing vehicular/industrial emissions, and generating public awareness, among others. It is expected that such initiatives by the central and state governments along with the participation of local bodies and other stakeholders comprising academia, research institutions, and public interest groups would result in ensuring better air quality in India.

Dr Bhola Ram Gurjar is Professor of Civil (Environmental) Engg., and Dean of Resources & Alumni Affairs (DORA), Indian Institute of Technology, Roorkee. He can be reached at [email protected]. This article was originally published in the January to March 2021 issue of Energy Futures magazine .

Acknowledgements

I thank my students, who have helped me in conducting the literature survey and compiling the necessary information from various bibliographical resources.

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Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

Lu Liang 1 &
Peng Gong 2 , 3 , 4

Scientific Reports volume 10 , Article number: 18618 ( 2020 ) Cite this article

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Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM 2.5 ) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM 2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM 2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM 2.5 level increases. We suggest that, depending on the city’s developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.

Impact of urban agglomeration construction on urban air quality–empirical test based on PSM–DID model

Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019

Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide

Introduction.

Air pollution represents a prominent threat to global society by causing cascading effects on individuals 1 , medical systems 2 , ecosystem health 3 , and economies 4 in both developing and developed countries 5 , 6 , 7 , 8 . About 90% of global citizens lived in areas that exceed the safe level in the World Health Organization (WHO) air quality guidelines 9 . Among all types of ecosystems, urban produce roughly 78% of carbon emissions and substantial airborne pollutants that adversely affect over 50% of the world’s population living in them 5 , 10 . While air pollution affects all regions, there exhibits substantial regional variation in air pollution levels 11 . For instance, the annual mean concentration of fine particulate matter with an aerodynamic diameter of less than 2.5 \(\upmu\mathrm{m}\) (PM 2.5 ) in the most polluted cities is nearly 20 times higher than the cleanest city according to a survey of 499 global cities 12 . Many factors can influence the regional air quality, including emissions, meteorology, and physicochemical transformations. Another non-negligible driver is urbanization—a process that alters the size, structure, and growth of cities in response to the population explosion and further leads to lasting air quality challenges 13 , 14 , 15 .

With the global trend of urbanization 16 , the spatial composition, configuration, and density of urban land uses (refer to as urban form) will continue to evolve 13 . The investigation of urban form impacts on air quality has been emerging in both empirical 17 and theoretical 18 research. While the area and density of artificial surface areas have well documented positive relationship with air pollution 19 , 20 , 21 , the effects of urban fragmentation on air quality have been controversial. In theory, compact cities promote high residential density with mixed land uses and thus reduce auto dependence and increase the usage of public transit and walking 21 , 22 . The compact urban development has been proved effective in mitigating air pollution in some cities 23 , 24 . A survey of 83 global urban areas also found that those with highly contiguous built-up areas emitted less NO 2 22 . In contrast, dispersed urban form can decentralize industrial polluters, improve fuel efficiency with less traffic congestion, and alleviate street canyon effects 25 , 26 , 27 , 28 . Polycentric and dispersed cities support the decentralization of jobs that lead to less pollution emission than compact and monocentric cities 29 . The more open spaces in a dispersed city support air dilution 30 . In contrast, compact cities are typically associated with stronger urban heat island effects 31 , which influence the availability and the advection of primary and secondary pollutants 32 .

The mixed evidence demonstrates the complex interplay between urban form and air pollution, which further implies that the inconsistent relationship may exist in cities at different urbanization levels and over different periods 33 . Few studies have attempted to investigate the urban form–air pollution relationship with cross-sectional and time series data 34 , 35 , 36 , 37 . Most studies were conducted in one city or metropolitan region 38 , 39 or even at the country level 40 . Furthermore, large cities or metropolitan areas draw the most attention in relevant studies 5 , 41 , 42 , and the small- and mid-sized cities, especially those in developing countries, are heavily underemphasized. However, virtually all world population growth 43 , 44 and most global economic growth 45 , 46 are expected to occur in those cities over the next several decades. Thus, an overlooked yet essential task is to account for various levels of cities, ranging from large metropolitan areas to less extensive urban area, in the analysis.

This study aims to improve the understanding of how the urban form evolution explains the decadal-long changes of the annual mean PM 2.5 concentrations in 626 cities at the county-level and above in China. China has undergone unprecedented urbanization over the past few decades and manifested a high degree of heterogeneity in urban development 47 . Thus, Chinese cities serve as a good model for addressing the following questions: (1) whether the changes in urban landscape patterns affect trends in PM 2.5 levels? And (2) if so, do the determinants vary by cities?

City boundaries

Our study period spans from the year 2000 to 2014 to keep the data completeness among all data sources. After excluding cities with invalid or missing PM 2.5 or sociodemographic value, a total of 626 cities, with 278 prefecture-level cities and 348 county-level cities, were selected. City boundaries are primarily based on the Global Rural–Urban Mapping Project (GRUMP) urban extent polygons that were defined by the extent of the nighttime lights 48 , 49 . Few adjustments were made. First, in the GRUMP dataset, large agglomerations that include several cities were often described in one big polygon. We manually split those polygons into individual cities based on the China Administrative Regions GIS Data at 1:1 million scales 50 . Second, since the 1978 economic reforms, China has significantly restructured its urban administrative/spatial system. Noticeable changes are the abolishment of several prefectures and the promotion of many former county-level cities to prefecture-level cities 51 . Thus, all city names were cross-checked between the year 2000 and 2014, and the mismatched records were replaced with the latest names.

PM 2.5 concentration data

The annual mean PM 2.5 surface concentration (micrograms per cubic meter) for each city over the study period was calculated from the Global Annual PM 2.5 Grids at 0.01° resolution 52 . This data set combines Aerosol Optical Depth retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The global 3-D chemical transport model GEOS-Chem is further applied to relate this total column measure of aerosol to near-surface PM 2.5 concentration, and geographically weighted regression is finally used with global ground-based measurements to predict and adjust for the residual PM 2.5 bias per grid cell in the initial satellite-derived values.

Human settlement layer

The urban forms were quantified with the 40-year (1978–2017) record of annual impervious surface maps for both rural and urban areas in China 47 , 53 . This state-of-art product provides substantial spatial–temporal details on China’s human settlement changes. The annual impervious surface maps covering our study period were generated from 30-m resolution Landsat images acquired onboard Landsat 5, 7, and 8 using an automatic “Exclusion/Inclusion” mapping framework 54 , 55 . The output used here was the binary impervious surface mask, with the value of one indicating the presence of human settlement and the value of zero identifying non-residential areas. The product assessment concluded good performance. The cross-comparison against 2356 city or town locations in GeoNames proved an overall high agreement (88%) and approximately 80% agreement was achieved when compared against visually interpreted 650 urban extent areas in the year 1990, 2000, and 2010.

Control variables

To provide a holistic assessment of the urban form effects, we included control variables that are regarded as important in influencing air quality to account for the confounding effects.

Four variables, separately population size, population density, and two economic measures, were acquired from the China City Statistical Yearbook 56 (National Bureau of Statistics 2000–2014). Population size is used to control for the absolute level of pollution emissions 41 . Larger populations are associated with increased vehicle usage and vehicle-kilometers travels, and consequently boost tailpipes emissions 5 . Population density is a useful reflector of transportation demand and the fraction of emissions inhaled by people 57 . We also included gross regional product (GRP) and the proportion of GRP generated from the secondary sector (GRP2). The impact of economic development on air quality is significant but in a dynamic way 58 . The rising per capita income due to the concentration of manufacturing industrial activities can deteriorate air quality and vice versa if the stronger economy is the outcome of the concentration of less polluting high-tech industries. Meteorological conditions also have short- and long-term effects on the occurrence, transport, and dispersion of air pollutants 59 , 60 , 61 . Temperature affects chemical reactions and atmospheric turbulence that determine the formation and diffusion of particles 62 . Low air humidity can lead to the accumulation of air pollutants due to it is conducive to the adhesion of atmospheric particulate matter on water vapor 63 . Whereas high humidity can lead to wet deposition processes that can remove air pollutants by rainfall. Wind speed is a crucial indicator of atmospheric activity by greatly affect air pollutant transport and dispersion. All meteorological variables were calculated based on China 1 km raster layers of monthly relative humidity, temperature, and wind speed that are interpolated from over 800 ground monitoring stations 64 . Based on the monthly layer, we calculated the annual mean of each variable for each year. Finally, all pixels falling inside of the city boundary were averaged to represent the overall meteorological condition of each city.

Considering the dynamic urban form-air pollution relationship evidenced from the literature review, our hypothesis is: the determinants of PM 2.5 level trends are not the same for cities undergoing different levels of development or in different geographic regions. To test this hypothesis, we first categorized city groups following (1) social-economic development level, (2) spatial autocorrelation relationship, and (3) population size. We then assessed the relationship between urban form and PM 2.5 level trends by city groups. Finally, we applied the panel data models to different city groups for hypothesis testing and key determinant identification (Fig. 1 ).

Methodology workflow.

Calculation of urban form metrics

Based on the previous knowledge 65 , 66 , 67 , fifteen landscape metrics falling into three categories, separately area, shape, and aggregation, were selected. Those metrics quantify the compositional and configurational characteristics of the urban landscape, as represented by urban expansion, urban shape complexity, and compactness (Table 1 ).

Area metrics gives an overview of the urban extent and the size of urban patches that are correlated with PM 2.5 20 . As an indicator of the urbanization degree, total area (TA) typically increases constantly or remains stable, because the urbanization process is irreversible. Number of patches (NP) refers to the number of discrete parcels of urban settlement within a given urban extent and Mean Patch Size (AREA_MN) measures the average patch size. Patch density (PD) indicates the urbanization stages. It usually increases with urban diffusion until coalescence starts, after which decreases in number 66 . Largest Patch Index (LPI) measures the percentage of the landscape encompassed by the largest urban patch.

The shape complexity of urban patches was represented by Mean Patch Shape Index (SHAPE_MN), Mean Patch Fractal Dimension (FRAC_MN), and Mean Contiguity Index (CONTIG_MN). The greater irregularity the landscape shape, the larger the value of SHAPE_MN and FRAC_MN. CONTIG_MN is another method of assessing patch shape based on the spatial connectedness or contiguity of cells within a patch. Larger contiguous patches will result in larger CONTIG_MN.

Aggregation metrics measure the spatial compactness of urban land, which affects pollutant diffusion and dilution. Mean Euclidean nearest-neighbor distance (ENN_MN) quantifies the average distance between two patches within a landscape. It decreases as patches grow together and increases as the urban areas expand. Landscape Shape Index (LSI) indicates the divergence of the shape of a landscape patch that increases as the landscape becomes increasingly disaggregated 68 . Patch Cohesion Index (COHESION) is suggestive of the connectedness degree of patches 69 . Splitting Index (SPLIT) and Landscape Division Index (DIVISION) increase as the separation of urban patches rises, whereas, Mesh Size (MESH) decreases as the landscape becomes more fragmented. Aggregation Index (AI) measures the degree of aggregation or clumping of urban patches. Higher values of continuity indicate higher building densities, which may have a stronger effect on pollution diffusion.

The detailed descriptions of these indices are given by the FRAGSTATS user’s guide 70 . The calculation input is a layer of binary grids of urban/nonurban. The resulting output is a table containing one row for each city and multiple columns representing the individual metrics.

Division of cities

Division based on the socioeconomic development level.

The socioeconomic development level in China is uneven. The unequal development of the transportation system, descending in topography from the west to the east, combined with variations in the availability of natural and human resources and industrial infrastructure, has produced significantly wide gaps in the regional economies of China. By taking both the economic development level and natural geography into account, China can be loosely classified into Eastern, Central, and Western regions. Eastern China is generally wealthier than the interior, resulting from closeness to coastlines and the Open-Door Policy favoring coastal regions. Western China is historically behind in economic development because of its high elevation and rugged topography, which creates barriers in the transportation infrastructure construction and scarcity of arable lands. Central China, echoing its name, is in the process of economic development. This region neither benefited from geographic convenience to the coast nor benefited from any preferential policies, such as the Western Development Campaign.

Division based on spatial autocorrelation relationship

The second type of division follows the fact that adjacent cities are likely to form air pollution clusters due to the mixing and diluting nature of air pollutants 71 , i.e., cities share similar pollution levels as its neighbors. The underlying processes driving the formation of pollution hot spots and cold spots may differ. Thus, we further divided the city into groups based on the spatial clusters of PM 2.5 level changes.

Local indicators of spatial autocorrelation (LISA) was used to determine the local patterns of PM 2.5 distribution by clustering cities with a significant association. In the presence of global spatial autocorrelation, LISA indicates whether a variable exhibits significant spatial dependence and heterogeneity at a given scale 72 . Practically, LISA relates each observation to its neighbors and assigns a value of significance level and degree of spatial autocorrelation, which is calculated by the similarity in variable \(z\) between observation \(i\) and observation \(j\) in the neighborhood of \(i\) defined by a matrix of weights \({w}_{ij}\) 7 , 73 :

where \({I}_{i}\) is the Moran’s I value for location \(i\) ; \({\sigma }^{2}\) is the variance of variable \(z\) ; \(\bar{z}\) is the average value of \(z\) with the sample number of \(n\) . The weight matrix \({w}_{ij}\) is defined by the k-nearest neighbors distance measure, i.e., each object’s neighborhood consists of four closest cites.

The computation of Moran’s I enables the identification of hot spots and cold spots. The hot spots are high-high clusters where the increase in the PM 2.5 level is higher than the surrounding areas, whereas cold spots are low-low clusters with the presence of low values in a low-value neighborhood. A Moran scatterplot, with x-axis as the original variable and y-axis as the spatially lagged variable, reflects the spatial association pattern. The slope of the linear fit to the scatter plot is an estimation of the global Moran's I 72 (Fig. 2 ). The plot consists of four quadrants, each defining the relationship between an observation 74 . The upper right quadrant indicates hot spots and the lower left quadrant displays cold spots 75 .

Moran’s I scatterplot. Figure was produced by R 3.4.3 76 .

Division based on population size

The last division was based on population size, which is a proven factor in changing per capita emissions in a wide selection of global cities, even outperformed land urbanization rate 77 , 78 , 79 . We used the 2014 urban population to classify the cities into four groups based on United Nations definitions 80 : (1) large agglomerations with a total population larger than 1 million; (2) mid-sized cities, 500,000–1 million; (3) small cities, 250,000–500,000, and (4) very small cities, 100,000–250,000.

Panel data analysis

The panel data analysis is an analytical method that deals with observations from multiple entities over multiple periods. Its capacity in analyzing the characteristics and changes from both the time-series and cross-section dimensions of data surpasses conventional models that purely focus on one dimension 81 , 82 . The estimation equation for the panel data model in this study is given as:

where the subscript \(i\) and \(t\) refer to city and year respectively. \(\upbeta _{{0}}\) is the intercept parameter and \(\upbeta _{{1}} - { }\upbeta _{{{18}}}\) are the estimates of slope coefficients. \(\varepsilon \) is the random error. All variables are transformed into natural logarithms.

Two methods can be used to obtain model estimates, separately fixed effects estimator and random effects estimator. The fixed effects estimator assumes that each subject has its specific characteristics due to inherent individual characteristic effects in the error term, thereby allowing differences to be intercepted between subjects. The random effects estimator assumes that the individual characteristic effect changes stochastically, and the differences in subjects are not fixed in time and are independent between subjects. To choose the right estimator, we run both models for each group of cities based on the Hausman specification test 83 . The null hypothesis is that random effects model yields consistent and efficient estimates 84 : \({H}_{0}{:}\,E\left({\varepsilon }_{i}|{X}_{it}\right)=0\) . If the null hypothesis is rejected, the fixed effects model will be selected for further inferences. Once the better estimator was determined for each model, one optimal panel data model was fit to each city group of one division type. In total, six, four, and eight runs were conducted for socioeconomic, spatial autocorrelation, and population division separately and three, two, and four panel data models were finally selected.

Spatial patterns of PM 2.5 level changes

During the period from 2000 to 2014, the annual mean PM 2.5 concentration of all cities increases from 27.78 to 42.34 µg/m 3 , both of which exceed the World Health Organization recommended annual mean standard (10 µg/m 3 ). It is worth noting that the PM 2.5 level in the year 2014 also exceeds China’s air quality Class 2 standard (35 µg/m 3 ) that applies to non-national park places, including urban and industrial areas. The standard deviation of annual mean PM 2.5 values for all cities increases from 12.34 to 16.71 µg/m 3 , which shows a higher variability of inter-urban PM 2.5 pollution after a decadal period. The least and most heavily polluted cities in China are Delingha, Qinghai (3.01 µg/m 3 ) and Jizhou, Hubei (64.15 µg/m 3 ) in 2000 and Hami, Xinjiang (6.86 µg/m 3 ) and Baoding, Hubei (86.72 µg/m 3 ) in 2014.

Spatially, the changes in PM 2.5 levels exhibit heterogeneous patterns across cities (Fig. 3 b). According to the socioeconomic level division (Fig. 3 a), the Eastern, Central, and Western region experienced a 38.6, 35.3, and 25.5 µg/m 3 increase in annual PM 2.5 mean , separately, and the difference among regions is significant according to the analysis of variance (ANOVA) results (Fig. 4 a). When stratified by spatial autocorrelation relationship (Fig. 3 c), the differences in PM 2.5 changes among the spatial clusters are even more dramatic. The average PM 2.5 increase in cities belonging to the high-high cluster is approximately 25 µg/m 3 , as compared to 5 µg/m 3 in the low-low clusters (Fig. 4 b). Finally, cities at four different population levels have significant differences in the changes of PM 2.5 concentration (Fig. 3 d), except for the mid-sized cities and large city agglomeration (Fig. 4 c).

( a ) Division of cities in China by socioeconomic development level and the locations of provincial capitals; ( b ) Changes in annual mean PM 2.5 concentrations between the year 2000 and 2014; ( c ) LISA cluster maps for PM 2.5 changes at the city level; High-high indicates a statistically significant cluster of high PM 2.5 level changes over the study period. Low-low indicates a cluster of low PM 2.5 inter-annual variation; No high-low cluster is reported; Low–high represents cities with high PM 2.5 inter-annual variation surrounded by cities with low variation; ( d ) Population level by cities in the year 2014. Maps were produced by ArcGIS 10.7.1 85 .

Boxplots of PM 2.5 concentration changes between 2000 and 2014 for city groups that are formed according to ( a ) socioeconomic development level division, ( b ) LISA clusters, and ( c ) population level. Asterisk marks represent the p value of ANOVA significant test between the corresponding pair of groups. Note ns not significant; * p value < 0.05; ** p value < 0.01; *** p value < 0.001; H–H high-high cluster, L–H low–high cluster, L–L denotes low–low cluster.

The effects of urban forms on PM 2.5 changes

The Hausman specification test for fixed versus random effects yields a p value less than 0.05, suggesting that the fixed effects model has better performance. We fit one panel data model to each city group and built nine models in total. All models are statistically significant at the p < 0.05 level and have moderate to high predictive power with the R 2 values ranging from 0.63 to 0.95, which implies that 63–95% of the variation in the PM 2.5 concentration changes can be explained by the explanatory variables (Table 2 ).

The urban form—PM 2.5 relationships differ distinctly in Eastern, Central, and Western China. All models reach high R 2 values. Model for Eastern China (refer to hereafter as Eastern model) achieves the highest R 2 (0.90), and the model for the Western China (refer to hereafter as Western model) reaches the lowest R 2 (0.83). The shape metrics FRAC and CONTIG are correlated with PM 2.5 changes in the Eastern model, whereas the area metrics AREA demonstrates a positive effect in the Western model. In contrast to the significant associations between shape, area metrics and PM 2.5 level changes in both Eastern and Western models, no such association was detected in the Central model. Nonetheless, two aggregation metrics, LSI and AI, play positive roles in determining the PM 2.5 trends in the Central model.

For models built upon the LISA clusters, the H–H model (R 2 = 0.95) reaches a higher fitting degree than the L–L model (R 2 = 0.63). The estimated coefficients vary substantially. In the H–H model, the coefficient of CONTIG is positive, which indicates that an increase in CONTIG would increase PM 2.5 pollution. In contrast, no shape metrics but one area metrics AREA is significant in the L–L model.

The results of the regression models built for cities at different population levels exhibit a distinct pattern. No urban form metrics was identified to have a significant relationship with the PM 2.5 level changes in groups of very small and mid-sized cities. For small size cities, the aggregation metrics COHESION was positively associated whereas AI was negatively related. For mid-sized cities and large agglomerations, CONTIG is the only significant variable that is positively related to PM 2.5 level changes.

Urban form is an effective measure of long-term PM 2.5 trends

All panel data models are statistically significant regardless of the data group they are built on, suggesting that the associations between urban form and ambient PM 2.5 level changes are discernible at all city levels. Importantly, these relationships are found to hold when controlling for population size and gross domestic product, implying that the urban landscape patterns have effects on long-term PM 2.5 trends that are independent of regional economic performance. These findings echo with the local, regional, and global evidence of urban form effect on various air pollution types 5 , 14 , 21 , 22 , 24 , 39 , 78 .

Although all models demonstrate moderate to high predictive power, the way how different urban form metrics respond to the dependent variable varies. Of all the metrics tested, shape metrics, especially CONTIG has the strongest effect on PM 2.5 trends in cities belonging to the high-high cluster, Eastern, and large urban agglomerations. All those regions have a strong economy and higher population density 86 . In the group of cities that are moderately developed, such as the Central region, as well as small- and mid-sized cities, aggregation metrics play a dominant negative role in PM 2.5 level changes. In contrast, in the least developed cities belonging to the low-low cluster regions and Western China, the metrics describing size and number of urban patches are the strongest predictors. AREA and NP are positively related whereas TA is negatively associated.

The impacts of urban form metrics on air quality vary by urbanization degree

Based on the above observations, how urban form affects within-city PM 2.5 level changes may differ over the urbanization stages. We conceptually summarized the pattern in Fig. 5 : area metrics have the most substantial influence on air pollution changes at the early urban development stage, and aggregation metrics emerge at the transition stage, whereas shape metrics affect the air quality trends at the terminal stage. The relationship between urban form and air pollution has rarely been explored with such a wide range of city selections. Most prior studies were focused on large urban agglomeration areas, and thus their conclusions are not representative towards small cities at the early or transition stage of urbanization.

The most influential metric of urban form in affecting PM 2.5 level changes at different urbanization stages.

Not surprisingly, the area metrics, which describe spatial grain of the landscape, exert a significant effect on PM 2.5 level changes in small-sized cities. This could be explained by the unusual urbanization speed of small-sized cities in the Chinese context. Their thriving mostly benefited from the urbanization policy in the 1980s, which emphasized industrialization of rural, small- and mid-sized cities 87 . With the large rural-to-urban migration and growing public interest in investing real estate market, a side effect is that the massive housing construction that sometimes exceeds market demand. Residential activities decline in newly built areas of smaller cities in China, leading to what are known as ghost cities 88 . Although ghost cities do not exist for all cities, high rate of unoccupied dwellings is commonly seen in cities under the prefectural level. This partly explained the negative impacts of TA on PM 2.5 level changes, as an expanded while unoccupied or non-industrialized urban zones may lower the average PM 2.5 concentration within the city boundary, but it doesn’t necessarily mean that the air quality got improved in the city cores.

Aggregation metrics at the landscape scale is often referred to as landscape texture that quantifies the tendency of patch types to be spatially aggregated; i.e., broadly speaking, aggregated or “contagious” distributions. This group of metrics is most effective in capturing the PM 2.5 trends in mid-sized cities (population range 25–50 k) and Central China, where the urbanization process is still undergoing. The three significant variables that reflect the spatial property of dispersion, separately landscape shape index, patch cohesion index, and aggregation index, consistently indicate that more aggregated landscape results in a higher degree of PM 2.5 level changes. Theoretically, the more compact urban form typically leads to less auto dependence and heavier reliance on the usage of public transit and walking, which contributes to air pollution mitigation 89 . This phenomenon has also been observed in China, as the vehicle-use intensity (kilometers traveled per vehicle per year, VKT) has been declining over recent years 90 . However, VKT only represents the travel intensity of one car and does not reflect the total distance traveled that cumulatively contribute to the local pollution. It should be noted that the private light-duty vehicle ownership in China has increased exponentially and is forecast to reach 23–42 million by 2050, with the share of new-growth purchases representing 16–28% 90 . In this case, considering the increased total distance traveled, the less dispersed urban form can exert negative effects on air quality by concentrating vehicle pollution emissions in a limited space.

Finally, urban contiguity, observed as the most effective shape metric in indicating PM 2.5 level changes, provides an assessment of spatial connectedness across all urban patches. Urban contiguity is found to have a positive effect on the long-term PM 2.5 pollution changes in large cities. Urban contiguity reflects to which degree the urban landscape is fragmented. Large contiguous patches result in large CONTIG_MN values. Among the 626 cities, only 11% of cities experience negative changes in urban contiguity. For example, Qingyang, Gansu is one of the cities-featuring leapfrogs and scattered development separated by vacant land that may later be filled in as the development continues (Fig. 6 ). Most Chinese cities experienced increased urban contiguity, with less fragmented and compacted landscape. A typical example is Shenzhou, Hebei, where CONTIG_MN rose from 0.27 to 0.45 within the 14 years. Although the 13 counties in Shenzhou are very far scattered from each other, each county is growing intensively internally rather than sprawling further outside. And its urban layout is thus more compact (Fig. 6 ). The positive association revealed in this study contradicts a global study indicating that cities with highly contiguous built-up areas have lower NO 2 pollution 22 . We noticed that the principal emission sources of NO 2 differ from that of PM 2.5. NO 2 is primarily emitted with the combustion of fossil fuels (e.g., industrial processes and power generation) 6 , whereas road traffic attributes more to PM 2.5 emissions. Highly connected urban form is likely to cause traffic congestion and trap pollution inside the street canyon, which accumulates higher PM 2.5 concentration. Computer simulation results also indicate that more compact cities improve urban air quality but are under the premise that mixed land use should be presented 18 . With more connected impervious surfaces, it is merely impossible to expect increasing urban green spaces. If compact urban development does not contribute to a rising proportion of green areas, then such a development does not help mitigating air pollution 41 .

Six cities illustrating negative to positive changes in CONTIG_MN and AREA_MN. Pixels in black show the urban areas in the year 2000 and pixels in red are the expanded urban areas from the year 2000 to 2014. Figure was produced by ArcGIS 10.7.1 85 .

Conclusions

This study explores the regional land-use patterns and air quality in a country with an extraordinarily heterogeneous urbanization pattern. Our study is the first of its kind in investigating such a wide range selection of cities ranging from small-sized ones to large metropolitan areas spanning a long time frame, to gain a comprehensive insight into the varying effects of urban form on air quality trends. And the primary insight yielded from this study is the validation of the hypothesis that the determinants of PM 2.5 level trends are not the same for cities at various developmental levels or in different geographic regions. Certain measures of urban form are robust predictors of air quality trends for a certain group of cities. Therefore, any planning strategy aimed at reducing air pollution should consider its current development status and based upon which, design its future plan. To this end, it is also important to emphasize the main shortcoming of this analysis, which is generally centered around the selection of control variables. This is largely constrained by the available information from the City Statistical Yearbook. It will be beneficial to further polish this study by including other important controlling factors, such as vehicle possession.

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Acknowledgements

Lu Liang received intramural research funding support from the UNT Office of Research and Innovation. Peng Gong is partially supported by the National Research Program of the Ministry of Science and Technology of the People’s Republic of China (2016YFA0600104), and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University.

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Liang, L., Gong, P. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep 10 , 18618 (2020). https://doi.org/10.1038/s41598-020-74524-9

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Air pollution is one of the most significant threats to human safety due to its detrimental health consequences worldwide. This study examines the air pollution levels in 22 districts of West Bengal from 2016 to 2021, using data from 81 stations operated by the West Bengal Pollution Control Board (WBPCB). The study assesses the short- and long-term impacts of particulate matter (PM) on human ...
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The present review is divided into three sub-sections: The first sub-section discusses the literature that focuses on air pollution in India on a national scale; the next segment highlights the various sources of air pollution and the effects of the pollutants. The major sources are categorised into seven sectors.
(PDF) Air Quality analysis
pollution in India (2018) reported that air pollution was responsible for 1.1 million deaths in India in 2015 [3]. 1.1 Causes of Air Pollution Some major causes of air pollution are discussed below.
Air pollution: A systematic review of its psychological, economic, and
Air pollution is a grave problem that impacts billions of people across the globe. For example, it is the primary cause of death in India, killing over 1.6 million people a year [1].According to the Environmental Protection Agency (EPA), in 2017 about 111 million Americans (about 35% of the U.S. population) were living in counties with unhealthy air [2].
Analysis Of Air Pollution In Indian Cities-A Literature Review
Air pollution in India has increased rapidly due to population growth, increase in the numbers of vehicles, use of fuels , bad transportation systems , poor land use pattern, industrialization, and above all, ineffective environmental regulations. Sulphur Dioxide, Nitrogen Dioxide, Particulate Matter are some of the pollutants which are contributing to environmental pollution.Purpose of this ...
Air Pollution, Climate Change, and Human Health in Indian Cities: A
The present review has been carried out over the Indian cities with significant impacts of both the climate change and air pollution on human health to serve as a baseline data for policy makers in analyzing vulnerable regions and implementing mitigation plans for tackling air pollution. Climate change and air pollution have been a matter of serious concern all over the world in the last few ...
Urban and air pollution: a multi-city study of long-term ...
Considering the dynamic urban form-air pollution relationship evidenced from the literature review, our hypothesis is: the determinants of PM 2.5 level trends are not the same for cities ...
PDF Air Pollution in Delhi: a Review of Past and Current Policy Approaches
air pollution in delhi: a review of past and current policy approaches laura de vito1, tim chatterton1, anil namdeo2, shiva nagendra4, sunil gulia5, sanjiv goyal5, margaret bell2, paul goodman2, james longhurst1, enda hayes1, rakesh kumar5, virendra sethi3, sengupta b. gitakrishnan ramadurai4, shoban majumder4, jyothi s menon4, mallikarjun ngappa turamari4 & jo barnes1
A critical review and prospect of NO2 and SO2 pollution over Asia
The literature review demonstrates that measurements of these concentrations are generally used to evaluate air quality, air pollutants, and health risks. ... Significant impacts of COVID-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environmental health. Environ. Dev. Sustain., 23 (5) (2021), pp. 6913-6940, 10.1007 ...
Remote Sensing
The accuracy of cosmic ray observations by the Large High Altitude Air Shower Observatory Wide Field-of-View Cherenkov/Fluorescence Telescope Array (LHAASO-WFCTA) is influenced by variations in aerosols in the atmosphere. The solar photometer (CE318-T) is extensively utilized within the Aerosol Robotic Network as a highly precise and reliable instrument for aerosol measurements. With this ...
PDF Analysis Of Air Pollution In Indian Cities
Naveen Kishore* and Surinder Deswal**. ABSTRACT: Air pollution in India has increased rapidly due to population growth, increase in the numbers of vehicles, use of fuels , bad transportation systems , poor land use pattern, industrialization, and above all, ineffective environmental regulations. Sulphur Dioxide, Nitrogen Dioxide, Particulate ...

Air pollution and public health: the challenges for Delhi, India

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Air pollution status and attributable health effects across the state of West Bengal, India, during 2016–2021

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Annual and Periodic Variations of Particulates and Selected Gaseous Pollutants in Astana, Kazakhstan: Source Identification via Conditional Bivariate Probability Function

Policy Interventions and Their Impact on Air Quality in Delhi City — an Analysis of 17 Years of Data

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Air Pollution in India: Major Issues and Challenges

Reviewing Literature

Vehicular/Transport Emissions

Industrial Processes

Agriculture

Power Plants

Waste Treatment and Biomass Burning

Domestic Sector

Construction and Demolition Waste

On the Ecosystem

On Biodiversity

On Materials and Buildings

On Human Health

Air quality standards

Mitigation strategies for emission control in India

Other potential mitigation strategies

Challenges in the Indian scenario

Research Gaps

Conclusion and Recommendations

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Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

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Impact of urban agglomeration construction on urban air quality–empirical test based on PSM–DID model

Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019

Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide

City boundaries

PM 2.5 concentration data

Human settlement layer

Control variables

Calculation of urban form metrics

Division of cities

Division based on spatial autocorrelation relationship

Division based on population size

Panel data analysis

Spatial patterns of PM 2.5 level changes

The effects of urban forms on PM 2.5 changes

Urban form is an effective measure of long-term PM 2.5 trends

The impacts of urban form metrics on air quality vary by urbanization degree

Conclusions

Acknowledgements

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