air pollution analytical essay

Air Pollution

Our overview of indoor and outdoor air pollution.

By Hannah Ritchie and Max Roser

This article was first published in October 2017 and last revised in February 2024.

Air pollution is one of the world's largest health and environmental problems. It develops in two contexts: indoor (household) air pollution and outdoor air pollution.

In this topic page, we look at the aggregate picture of air pollution – both indoor and outdoor. We also have dedicated topic pages that look in more depth at these subjects:

Indoor Air Pollution

Look in detail at the data and research on the health impacts of Indoor Air Pollution, attributed deaths, and its causes across the world

Outdoor Air Pollution

Look in detail at the data and research on exposure to Outdoor Air Pollution, its health impacts, and attributed deaths across the world

Look in detail at the data and research on energy consumption, its impacts around the world today, and how this has changed over time

See all interactive charts on Air Pollution ↓

Other research and writing on air pollution on Our World in Data:

• Air pollution: does it get worse before it gets better?
• Data Review: How many people die from air pollution?
• Energy poverty and indoor air pollution: a problem as old as humanity that we can end within our lifetime
• How many people do not have access to clean fuels for cooking?
• What are the safest and cleanest sources of energy?
• What the history of London’s air pollution can tell us about the future of today’s growing megacities
• When will countries phase out coal power?

Air pollution is one of the world's leading risk factors for death

Air pollution is responsible for millions of deaths each year.

Air pollution – the combination of outdoor and indoor particulate matter and ozone – is a risk factor for many of the leading causes of death, including heart disease, stroke, lower respiratory infections, lung cancer, diabetes, and chronic obstructive pulmonary disease (COPD).

The Institute for Health Metrics and Evaluation (IHME), in its Global Burden of Disease study, provides estimates of the number of deaths attributed to the range of risk factors for disease. 1

In the visualization, we see the number of deaths per year attributed to each risk factor. This chart shows the global total but can be explored for any country or region using the "change country" toggle.

Air pollution is one of the leading risk factors for death. In low-income countries, it is often very near the top of the list (or is the leading risk factor).

Air pollution contributes to one in ten deaths globally

In recent years, air pollution has contributed to one in ten deaths globally. 2

In the map shown here, we see the share of deaths attributed to air pollution across the world.

Air pollution is one of the leading risk factors for disease burden

Air pollution is one of the leading risk factors for death. But its impacts go even further; it is also one of the main contributors to the global disease burden.

Global disease burden takes into account not only years of life lost to early death but also the number of years lived in poor health.

In the visualization, we see risk factors ranked in order of DALYs – disability-adjusted life years – the metric used to assess disease burden. Again, air pollution is near the top of the list, making it one of the leading risk factors for poor health across the world.

Air pollution not only takes years from people's lives but also has a large effect on the quality of life while they're still living.

Who is most affected by air pollution?

Death rates from air pollution are highest in low-to-middle-income countries.

Air pollution is a health and environmental issue across all countries of the world but with large differences in severity.

In the interactive map, we show death rates from air pollution across the world, measured as the number of deaths per 100,000 people in a given country or region.

The burden of air pollution tends to be greater across both low and middle-income countries for two reasons: indoor pollution rates tend to be high in low-income countries due to a reliance on solid fuels for cooking, and outdoor air pollution tends to increase as countries industrialize and shift from low to middle incomes.

A map of the number of deaths from air pollution by country can be found here .

How are death rates from air pollution changing?

Death rates from air pollution are falling – mainly due to improvements in indoor pollution.

In the visualization, we show global death rates from air pollution over time – shown as the total air pollution – in addition to the individual contributions from outdoor and indoor pollution.

Globally, we see that in recent decades, the death rates from total air pollution have declined: since 1990, death rates have nearly halved. But, as we see from the breakdown, this decline has been primarily driven by improvements in indoor air pollution.

Death rates from indoor air pollution have seen an impressive decline, while improvements in outdoor pollution have been much more modest.

You can explore this data for any country or region using the "change country" toggle on the interactive chart.

Interactive charts on air pollution

Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., ... & Borzouei, S. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 .  The Lancet ,  396 (10258), 1223-1249.

Here, we use the term 'contributes,' meaning it was one of the attributed risk factors for a given disease or cause of death. There can be multiple risk factors for a given disease that can amplify one another. This means that in some cases, air pollution was not the only risk factor but one of several.

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Analysis of Air Pollution Data in India between 2015 and 2019

1 Center for Policy Research on Energy and Environment, School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA 2 Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA

  Copyright  The Author(s). This is an open access article distributed under the terms of the  Creative Commons Attribution License (CC BY 4.0) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

• Download: PDF | Supplemental Material

Sharma, D., Mauzerall, D. (2022). Analysis of Air Pollution Data in India between 2015 and 2019. Aerosol Air Qual. Res. 22, 210204. https://doi.org/10.4209/aaqr.210204

• Analysis of PM 10 , PM 2.5 , SO 2 , NO 2 and O 3 measurements across India from 2015–2019.
• First comprehensive analysis of Indian government and US Air-Now data.
• More national ambient air quality standard exceedances in north than south India.
• Provides baseline for evaluation of mitigation measures and atmospheric models.

India suffers from among the worst air pollution in the world. In response, a large government effort to increase air quality monitoring is underway. We present the first comprehensive analysis of government air quality observations from 2015–2019 for PM 10 , PM 2.5 , SO 2 , NO 2 and O 3 from the Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring (CAAQM) network and the manual National Air Quality Monitoring Program (NAMP), as well as PM 2.5 from the US Air-Now network. We address inconsistencies and data gaps in datasets using a rigorous procedure to ensure data representativeness. We find particulate pollution dominates the pollution mix across India with virtually all sites in northern India (divided at 23.5°N) exceeding the annual average PM 10 and PM 2.5 residential national ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India exceeding the PM 10 standard by 50% and the PM 2.5 standard by 40%. Annual average SO 2 , NO 2 and MDA8 O 3 generally meet the residential NAAQS across India. Northern India has (~10%–130%) higher concentrations of all pollutants than southern India, with only SO 2 having similar concentrations. Although inter-annual variability exists, we found no significant trend of these pollutants over the five-year period. In the five cities with Air-Now PM 2.5 measurements - Delhi, Kolkata, Mumbai, Hyderabad and Chennai, there is reasonable agreement with CPCB data. The PM 2.5 CPCB CAAQM data compares well with satellite derived annual surface PM 2.5 concentrations (Hammer et al. , 2020), with the exception of the western desert region prior to 2018 when surface measurements exceeded satellite retrievals. Our reanalyzed dataset is useful for evaluation of Indian air quality from satellite data, atmospheric models, and low-cost sensors. Our dataset also provides a baseline to evaluate the future success of National Clean Air Programme as well as aids in assessment of existing and future air pollution mitigation policies.

Keywords: Air pollution, India, surface observations, CPCB, continuous and manual data, US AirNow

1 INTRODUCTION

Concerns over poor air quality in India have increased over the past few years with increasing evidence of the adverse impacts on health (Balakrishnan   et al. , 2014; Chowdhury and Dey, 2016; Balakrishnan   et al. , 2019), agricultural yields (Avnery   et al. , 2011, 2013; Ghude   et al. , 2014; Gao   et al. , 2020) and the economy (Pandey   et al. , 2021). Rapid growth and industrialization in India have resulted in some of the most polluted air in the world. Projections forecast further decreases in air quality and a 24% increase in PM 2.5   associated premature mortalities by 2050 relative to 2015 (GBD MAPS Working Group, 2018; Brauer   et al. , 2019). According to recent estimates based on the Global Exposure Mortality Model (GEMM), total premature mortality due to ambient PM 2.5   exposure in India increased approximately 47% between 2000 and 2015 (Chowdhury   et al. , 2020). Surface O 3   concentrations are also likely to increase with growing industrial emissions and increasing temperatures due to climate change resulting in additional stress on agricultural yields and public health (Avnery   et al. , 2011; Silva   et al. , 2017).

India has a national ambient surface monitoring network that started in 1987 and has become more extensive over time with a substantial increase in the number and spatial extent of continuous and manual monitoring stations between 2015 and 2019. At present, the Central Pollution Control Board (CPCB), along with the State Pollution Control Boards (SPCBs), run the most extensive monitoring network in the country under the National Air Quality Monitoring Program (NAMP). As of 2019, NAMP cooperatively operated (with CPCB and SPCBs) over 750 manual monitoring stations (compared with 20 in 1987 when monitoring first began and 450 in 2015 when our analysis starts) which publicly archive annual average concentrations of PM 10 , PM 2.5 , SO 2   and NO 2   ( https://cpcb.nic.in/namp-data/ ). As of 2019, over 220 Continuous Ambient Air Quality Monitoring (CAAQM) stations operated (compared with less than 50 stations in 2015 when our analysis starts). CPCB archives publicly available, real time data, every 15 minutes, from over 220 stations across India of an extensive list of criteria and non-criteria air pollutants and meteorological variables ( https://app.cpcbccr.com/ccr/ ). Stations vary in the air pollutant species and meteorological data they collect. The manual monitors provide better spatial coverage than the continuous monitors but provide data on fewer air pollutants at much lower temporal resolution (annual average values versus every 15 minutes). However, both sets of monitoring stations sample exclusively urban areas despite the fact that rural areas have significant emissions from households and agricultural waste burning (Balakrishnan   et al. , 2014; Venkatraman   et al. , 2018). Pant   et al.   (2019) and the Supplementary Information (SI) (Section 1) describe other Indian monitoring networks which are less extensive and are not publicly available. India has fewer monitoring stations than most south and east Asian countries, with ~1 monitor/6.8 million persons (Apte and Pant 2019; Brauer   et al. , 2019; Martin   et al. , 2019). Despite recent increases in urban monitoring stations across India, vast regions do not have monitors and except for satellite data for a few species, little information is available on surface concentrations of air pollutants in non-urban locations in India.

Recently, extreme levels of fine particulate air pollution in India, combined with a growing appreciation of the adverse impacts of elevated air pollution on health, led the Indian government to launch the National Clean Air Program (NCAP) in 2019 (Ministry of Environment, Forests and Climate Change NCAP, 2019). NCAP targets a reduction of 20–30% in PM 10   and PM 2.5   concentrations by 2024 relative to 2017 levels. One focus of NCAP is augmentation of the national monitoring network for which substantial financial support was announced in the 2020 Union Budget.

Despite a growing monitoring network and the need for analysis, prior to our work, no study holistically analyzed existing government surface air pollutant monitoring data across India. Most research studies analyzing ground monitoring data have focused on Delhi and the surrounding National Capital Region (NCR) (Guttikunda and Gurjar, 2012; Sahu and Kota, 2017; Sharma   et al. , 2018; Chowdhury   et al. , 2019; Guttikunda   et al. , 2019; Wang and Chen, 2019; Hama   et al. , 2020), and other major cities (Gurjar   et al. , 2016; Sreekanth   et al. , 2018, Yang   et al. , 2018; Chen   et al. , 2020). In addition, some studies also used ground observations to bias correct satellite measurements for India (Pande   et al. , 2018; Chowdhury   et al. , 2019; Navinya   et al. , 2020). However, a need remains for a comprehensive analysis of all surface data collected by manual NAMP and continuous CAAQM monitoring networks between 2015–2019 over which period monitoring increased substantially.

Here we provide the first national analysis of all available surface measurements of key criteria pollutants (PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 ) across India between 2015–2019. We use publicly available data from the NAMP manual and CAAQM real-time stations which have different spatial distributions and temporal resolutions. Collating spatio-temporal distributions of pollutant concentrations on inter-annual, annual, seasonal and monthly timescales, we present an overview of the variability in air pollution levels across the country and separately analyze pollution levels in northern (north of 23°N) and southern India. We conduct case studies of five cities in India in which U.S. State Department PM 2.5   monitors (Air-Now network) are present and, using additional data collected by CAAQM monitors, compare pollution status between these cities. We also compare analyzed annual average PM 2.5   from the CAAQM network with the satellite derived surface PM 2.5   (Hammer   et al. , 2020) and find good agreement between the two datasets. Our analysis will provide a valuable baseline to evaluate the future success of the NCAP in meeting its air pollution mitigation targets.

2 METHODOLOGY

  2.1 criteria pollutant data.

We analyze all open-source data available from the manual (NAMP) and continuous (CAAQM) networks, as well as from the US Embassy and consulates Air-Now network from 2015–2019 for five criteria pollutants—PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 .

Datasets from 2015-2018 were acquired for NAMP and were acquired from 2015–2019 for CPCB-CAAQM and Air-Now networks directly from the following sources:

• NAMP   manual monitoring network ( https://cpcb.nic.in/namp-data/ ): Annual average and annual maximum and minimum concentrations were obtained from a total of 730 manual stations. Higher resolution temporal measurements are not publicly reported by NAMP. We analyze data from 2015–2018 as datasets for 2019 were unavailable when our analysis was completed in December 2020.
• CAAQM   continuous monitoring network from the Central Control Room for Air Quality Management website ( https://app.cpcbccr.com/ccr/ ): One-hour averages were calculated from reported 15 minute average concentrations. Neither the continuous nor manual monitoring stations include geolocations. To obtain the latitude/longitude coordinates of each station, we used the monitoring station name and geolocated them using Google maps.
• S. State Department Air-Now network   ( https://www.airnow.gov/ ): One-hour average PM 2.5   concentrations were obtained for monitors located in Delhi, Mumbai, Hyderabad, Kolkata and Chennai.

  2.2 Data Quality Control

We directly utilize the data available from the NAMP and Air-Now networks, but process the data we use from the CAAQM network to ensure representative monthly, seasonal, and annual average air pollutant concentrations using the following method:

• Missing data is removed. Values in excess of the reported range (see Table S1) are assumed to be errors and are removed. Values of 999.99 for PM 10   and PM 5   are retained as they may represent concentrations above the upper detection limit of the instrument. The U.S. Air-Now network data in New Delhi report 1-hour average PM 2.5   concentrations between 1300 and 1486 µg m – 3   during Diwali for each year. As CAAQM does not report values in excess of 999.99 µg m – 3   for PM 2.5   our annual means based on CAAQM will likely be biased low in some locations. In sequences of 24 or more consecutive identical hourly values, only the first value out of the sequence is retained. Data were processed following the QA/QC procedure described below. The percentage of data removed due to this processing is provided in Tables S2(a) and S2(b).
• Diurnal mean values are calculated for criteria pollutants PM 10 , PM 5 , SO 2 , NO 2   and O 3   for each 12-hour day-night interval (between 6 am–6 pm and 6 pm–6 am (next day)), using a minimum of one hourly observation for each 12-hour period. Daily means are calculated only for days that have a daytime or nighttime mean value. For O 3 , daily mean (MDA8) values are calculated as the maximum of 8-hour moving averages over a 24-hour period using at least 6 hourly observations. For all pollutants, monthly mean values are calculated for months that have at least 8 daily mean values (at least 25% of observations). To obtain annual average concentrations, we calculate quarterly means and require at least one monthly mean value as input to each quarterly mean concentration. At least two quarterly mean values are used for calculating annual average concentrations. This procedure is followed to ensure representativeness of data in diurnal, daily, monthly, seasonal, annual and interannual timeseries.   Fig. 1   shows a flow chart describing the methodology for generating each step of the time-series.

  3 RESULTS

  3.1 strengths and weaknesses of available air quality datasets.

Until the start of 2018 the Indian monitoring network had limited extent. Very few stations have operated continuously from 2015 to the present. The number of stations in the continuous monitoring network has increased dramatically since 2017 ( Fig. 2 ) making it far more feasible now to evaluate air quality across India than in the past. However, spatial coverage is still limited with unequal distribution of monitors. All monitors are in cities, with a concentration in the largest cities, and none are in rural areas.   Fig. 3   shows the percentage of valid hourly observations, compared with total hours annually, from each CAAQM station between 2015 and 2019. Although the current data is sufficient to provide an overview of air quality across much of India, it is currently challenging to use air quality datasets to conduct long term trend analysis due to their limited spatial and temporal coverage.

  3.2 Spatial Distribution of Air Pollutants from 2015–2019

Figs. 4   and   5   show annual average concentrations of five criteria pollutants (PM 10 , PM 2.5 , SO 2 , NO 2   and O 3 ) at continuous and manual monitoring stations across India, from 2015 to 2019. The general distribution pattern of air pollution, showing higher pollution levels in northern than southern India, is captured in both the manual and continuous monitoring station data.

The number of continuous and manual monitoring stations have both increased substantially between 2015 and 2019 with 15 (147) CAAQM stations meeting our criteria for PM 10 , 33 (181) for PM 2.5 , 31 (163) for SO 2 , 34 (175) for NO 2   and 32 (168) for O 3   and in 2015 (2019) (see Figs. 4 and 5 for details of other years and manual stations). Of the total, nearly 60% of the CAAQM continuous monitoring stations are in northern India with 20% of the total stations in Delhi in 2019. Despite being a high pollution zone with nearly 15% of the Indian population ( http://up.gov.in/upstateglance.aspx ), the Indo Gangetic Plain has only 13% (9%) of total continuous (manual) monitoring stations. NAMP manual monitoring stations are more widely distributed than continuous monitors across India, with more monitors in the south and thus provide more representative spatial distributions of pollutants. However, they only provide annual average pollutant concentrations and thus cannot be used to analyze seasonal variations.

Elevated concentrations of PM 10   and PM 2.5   were recorded by both CAAQM and NAMP manual monitors across northern Indian states in all years, with particularly high concentrations across the Indo-Gangetic Plain (IGP). Ground observations of SO 2   are generally low across the country with high concentrations found at a few urban and industrial locations. This has been corroborated by previous studies (Guttikunda and Calori, 2013). The role of alkaline dust in scavenging SO 2   in India likely reduces ambient concentrations (Kulshrestha   et al. , 2003). In contrast, annual average NO 2   and MDA8 O 3   concentrations are highly variable depending on location with higher O 3   concentrations often seen in the IGP region.

  3.3 Annual Variation in Pollutant Concentrations in Northern and Southern India

The spatial distribution of pollutants is affected by meteorology, geography, topography, population density, location specific emission sources including industries, vehicular density, resuspended dust from poor land use management etc. In northern India (north of 23.5°N), higher population density and higher associated activities in industry, transport, power generation, seasonal crop residue burning, and more frequent dust storms contribute to higher particulate loads than in southern India (Sharma and Dixit, 2016; Cusworth   et al. , 2018). We observed significant differences between northern and southern India in the spatio-temporal patterns of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3 .

Fig. 6   shows annual average concentrations (µg m – 3 ) of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   respectively, for northern and southern India (divided at 23.5°N) from CAAQM stations. The number of stations used to calculate annual average values is shown in Fig. 4 for each species. Annual average concentrations of PM 10 , PM 2.5 , and NO 2   are higher in northern India, whereas SO 2   and MDA8O 3   are similar in the north and the south. Annual average concentrations from CAAQM continuous and NAMP manual monitoring stations, combined (S1 a), and only manual monitoring Stations (S1 b) are plotted separately in Fig. S1. We found inter-annual variability but no significant annual trend in the timeseries of these pollutants. Annual average concentrations over the five year period in northern (and southern) India were: 197 ± 84 µg m – 3   (93 ± 30 µg m – 3 ) for PM 10 , 109 ± 29 µg m – 3   (47 ± 16 µg m – 3 ) for PM 2.5 , 12 ± 7 µg m – 3   (12 ± 10 µg m – 3 ) SO 2 , 35 ± 21 µg m – 3   (27 ± 16 µg m – 3   ) for NO 2   and 73 ± 29 µg m – 3   (66 ± 31 µg m – 3 ) for MDA8 O 3 . In the five-year period, annual NAAQS were met at approximately 3% of all CAAQM stations measuring PM 10 , 13% of PM 2.5 , 70% of NO 2   and 98% of SO 2   (Table S3). MDA8 O 3   standard of 100 µg m – 3   (to be met 98% of the time within a year) was met at 77% of all CAAQM stations between 2015–2019, inclusive. Particulate matter dominates the pollution mix with national average annual mean concentrations exceeding the NAAQ standard for all analyzed years and in northern India more than double the allowed concentration.   Fig. 7   shows annual average concentrations of these pollutants from CAAQM stations that meet our analysis criteria and are available each year from 2015 through 2019. The change in annual concentrations relative to the annual average concentrations in 2015–2017 at the stations operational throughout this period is shown in Fig. S2 in order to provide a comparison useful for evaluating the success of the NCAP.

  3.5 Seasonal and Monthly Patterns of Air Pollutants

Seasonal concentrations of air pollutants in India are heavily influenced by meteorology and location. Influence of meteorology on spatio-temporal distributions of pollutants across India is described in Section S3. Fig. S3 shows the mean seasonal distribution of boundary layer height, surface pressure, precipitation, and omega/vertical and horizontal wind velocity. We calculate seasonal and monthly concentrations of PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   between 2015–2019 for northern and southern India in each season ( Fig. 8 ) and month ( Fig. 9 ) and show seasonal spatial distributions of these pollutants across India (Fig. S4). We analyze seasonal composites computed as averages for the spring or pre-monsoon period, March–April–May (MAM), the monsoon period, June–July–August (JJA), the autumn or post monsoon period, September–October–November (SON) and winter, December–January–February (DJF). In all seasons, substantially higher concentrations are observed for PM 10   and PM 2.5 , in northern India with concentrations of NO 2 , SO 2   and MDA8 O 3   only slightly more elevated in northern than southern India. The DJF average concentrations are highest for PM 10 , PM 2.5   and NO 2   in northern (southern) India: 270 ± 51 (137 ± 11) µg m –3 , 170 ± 26 (69 ± 2) µg m –3 , 47 ± 2 (35 ± 7) µg m –3 , respectively. Seasonal average concentrations of SO 2   peak in MAM in northern India (15 ± 3 µg m –3 ) and in DJF in southern India (16 ± 4 µg m –3 ), with highest concentrations in winter across the country. For DA8 O 3 , highest seasonal concentrations occur in MAM (DJF) in the north 71.8 ± 28 µg m –3   and south (84 ± 8 µg m –3 ).

Monthly variations in pollution are also a function of regional circulation patterns. The summer monsoon facilitates dilution of pollution via strong south-westerly winds from the Arabian Sea and wet scavenging of anthropogenic pollution (Zhu   et al. , 2012). Wet deposition removes PM 10 , PM 2.5   and water soluble SO 2   (Chin, 2012) leading to substantially lower ambient concentrations of these pollutants in JJA across India. Minimum concentrations of all pollutants occur in August.

Outside the monsoon, weak regional circulation and large scale high pressure systems result in accumulation of pollutants near the surface which is most pronounced in winter. Highest monthly concentrations are seen in November–January, inclusive, for PM 10 , PM 2.5 , SO 2   and NO 2 . For, MDA8O 3 , highest monthly concentrations are recorded in May (January) for northern (southern) India. Precursor emissions, surface temperature and solar insolation modulate a complex chemistry that drives the ozone cycle (Lu   et al. , 2018).

  3.6 Case studies of Delhi, Kolkata, Mumbai, Hyderabad and Chennai

Delhi, Kolkata, Mumbai, Hyderabad and Chennai are the five cities in India in which the U.S. State Department Air-Now network real time monitoring stations record PM 2.5   concentrations at the US embassy and consulates. In these five cities, we compare daily and monthly mean PM 2.5   measurements from the Air-Now and CAAQM networks.   Fig. 10   shows scatterplots between daily mean PM 2.5   from the Air-Now monitor located in each of the five cities with all CPCB CAAQM monitors in those cities for 2015–2019, inclusive. We find a good correlation between the daily average PM 2.5   concentrations from the two networks at all the cities (r > 0.8), except Chennai (r~0.47) where CPCB concentrations are biased higher than the Air-Now concentrations. On highly polluted days in Delhi, the Air-Now monitors report higher PM 2.5   concentrations than the CPCB monitors in part because Air-Now monitors are able to report hourly concentrations above 1000 µg m –3   while the CPCB monitors cannot.

We examine how concentrations of PM 10 , PM 2.5 , SO 2 , NO 2   and O 3   vary between cities in which Air-Now monitors exist from 2015–2019 (see Fig. 11).   Fig. 11   compares the monthly average concentrations of PM 2.5   between the two networks, examines the variation in concentrations over time for other species measured only by CPCB, and compares observed concentrations with the annual NAAQS for residential areas. Annual average concentrations from the stations combined in each city that meet our criteria is shown in Fig. S5 and a timeseries for each pollutant at each station is shown in Fig. S6. From CAAQM and Air-Now networks, we find Delhi has the highest daily, monthly mean and annual average concentrations of PM 10   and PM 2.5 , followed by Kolkata and Mumbai (Figs. 10, 11; Fig. S5).

For Delhi, between 2015 and 2019, annual average concentrations of PM 2.5   from the CAAQM station closest to the U.S. embassy (RK Puram, Delhi) greatly exceeded the residential NAAQS for PM 2.5   of 40 µg m –3   and ranged from 101 to 119 µg m –3   with the Air-Now station ranging from 95 to 124 µg m –3 . Chennai has the lowest monthly and annual average concentrations of PM 2.5 . The US state department annual average PM 2.5 values overall are consistent with the CAAQM stations and show a similar trend across cities. All five cities failed to meet the annual average CPCB PM 10   standard of 60 µg m –3   in all years.

Monthly and annual average SO 2   concentrations are far below the annual standard of 50 µg m –3   at all locations throughout the year in these five cities with Delhi reporting the highest annual average concentrations among the five cities followed by Mumbai. Starting in 2018 both Delhi and Mumbai had SO 2   concentrations lower than prior years.

Monthly average NO 2   concentrations are highest in Delhi in all years and starting in 2017, decrease from a peak over 100 µg m –3   in 2017 to a peak of 52 µg m –3   in 2019. Kolkata and Hyderabad also have relatively high concentrations of NO 2   with annual average concentrations exceeding the residential NAAQS of 40 µg m –3   starting in 2018.

Monthly MDA8 O 3   concentrations across all five cities are similar, particularly after 2018 and are generally falling below the residential 8-hour average NAAQS of 100 µg m 3 . Similar monthly tropospheric ozone concentrations in these cities, despite different levels of particulate matter, NO 2   and meteorology, make it a topic for further investigation.

  4 DISCUSSION

  4.1 growing dataset and existing gaps.

Prior to 2015 surface air quality monitoring data was available from only a few stations in India. Over the period we analyzed, 2015–2019, the number of monitoring stations across India increased dramatically. Our compilation and rigorous quality control of these data provide, for the first time, a comprehensive dataset of criteria pollutants that can be used to evaluate air pollutant concentrations simulated by atmospheric chemical transport models, satellite retrievals and reanalysis. Our dataset also provides a baseline for the NCAP. Previous studies have used ground observations from selected locations without transparently addressing existing data gaps and are not clear in their evaluation and quality assurance of surface observations. Here, we have carefully evaluated the archived data for completeness and accuracy, discarding values in excess of instrumental range, and requiring representative temporal coverage for each averaging period at each monitor. For example, for inclusion in our analysis a monitor measuring a species we analyze must report daily averages at least one hour per 12-hour daytime or night-time period, eight days for each monthly average, and one month per quarter and atleast two quarters for each annual average (see Tables S2(a), S2(b) and S3). However, spatial coverage remains spotty with monitoring stations predominantly located in large cities; smaller cities and rural locations lack coverage. Further expansion of the monitoring networks to facilitate an improved understanding of spatial distributions of pollutants across urban/rural India and to evaluate future trends in pollutant concentrations is needed. Very few stations provide valid observations continuously from 2015 onwards limiting our ability to analyze past trends in air quality. However, trend analyses starting in 2018 will be valuable and possible in the future.

  4.2 Differences in Air Quality Observations

We compare monthly, seasonal and annual mean concentrations of air pollutants we analyze with other studies that have analyzed surface measurements of the same pollutants, cities and time periods across India (Table S5). We find that the range of concentrations of criteria pollutants reported in our analysis of CPCB data are similar to the values presented in research studies using ground observations during the same period (Kota   et al. , 2018; Sreekanth   et al. , 2018; Guttikunda   et al. , 2019; Mahesh   et al. , 2019; Ravinder   et al. , 2019; Jain   et al. , 2020; Tyagi   et al. , 2020; Jat   et al. , 2021). However, as shown in Table S5, in case studies covering extreme events and studies in bigger cities and more polluted regions, like Delhi and the IGP, differences exist between the CPCB concentrations we calculate and those reported in the literature from surface monitoring stations, models and satellite data (Kota   et al. , 2018; Tyagi   et al. , 2019; Jat   et al. , 2021).

In   Fig. 12 , we compare the spatial patterns of annual average surface PM 2.5   concentrations derived from satellite data with measurements from the CPCB continuous network. The surface satellite concentrations were obtained by combining data from Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging Spectroradiometer), MAIAC (Multi Angle Implementation of Satellite Correction) and SeaWiFS (Sea Viewing Wide Field of View Sensor) satellite products and using the GEOS-Chem model to obtain gridded surface PM 2.5   concentrations at 0.05° × 0.05° (Hammer   et al. , 2020). The product we use is V4.GL.03 available at   https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.GL.03 . Reasonable agreement is seen between the annual mean surface concentrations of PM 2.5   derived from the satellite data and from the CPCB CAAQM observations from 2015-2019. Agreement is particularly good over the IGP and in central and southern India. However, along the western desert region (near Thar desert in Rajasthan), satellite concentrations of surface PM 2.5   (~40–50 µg m –3 ) were substantially lower than concentrations obtained from the CPCB CAAQM monitors (~80–100 µg m –3 ) for 2015–2017. In 2018 and 2019 the correspondence between the two datasets improved with most annual mean PM 2.5   concentrations in the western desert region generally between ~40 and 60 µg m –3 .

  5 CONCLUSIONS

This study provides the first comprehensive analysis of all existing government monitoring data available for PM 10 , PM 2.5 , SO 2 , NO 2   and MDA8 O 3   using the continuous (CAAQM) and manual (NAMP) monitoring networks in India as well as the data from the US State Department Air-Now network, between 2015 and 2019 (2018 for NAMP). Our analysis shows that the Indian data record, in terms of number of monitoring stations, observations and quality of data, has improved significantly over this period. Despite the effort to augment surface monitoring infrastructure, gaps remain in spatial and temporal coverage and additional monitoring stations in small cities and rural areas are needed. Monitoring stations located in bigger cities (e.g., five Air-Now cities) have better data quality, from more widely distributed stations within the city, than is available for smaller cities. Pollution hotspots are occasionally found in smaller cities where monitoring stations are sparse. No stations have yet been placed in rural areas and are needed there in order to better characterize air quality and pollution sources across India (e.g., the effect of agricultural waste burning on air quality).

We find that fine particulate pollution dominates the pollution mix across India with virtually all sites in northern India (north of 23.5°N) exceeding the annual average PM 10   and PM 2.5   national residential ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India (south of 23.5°N) exceeding the PM 10   standard by 50% and PM 2.5   standard by 40%. Comparison of PM 2.5   surface observations from the CPCB continuous monitoring network with surface satellite concentrations finds good agreement across India, particularly for 2017 and 2018. Prior to 2017 CAAQM concentrations were substantially higher than indicated by the satellite data over the western desert region. Annual average SO 2 , NO 2   and MDA8 O 3   generally meet the residential NAAQS across India. We find that northern India has (~10%–130%) higher average concentrations of all pollutants than southern India, except for SO 2   where the concentrations are similar. Although inter-annual variability exists, no significant trend of these pollutants was observed over the five-year period except for a small decrease over time in PM 10   and PM 2.5   in winter, which is more pronounced in the stations in northern and central India.

Our analysis of surface measurements is valuable for evaluating air pollutant concentrations simulated in atmospheric chemistry models. We found good agreement between the annual average CAAQM PM 2.5   we analyzed and satellite derived surface PM 2.5   from Hammer   et al.   (2020). Our data set can also be used to evaluate satellite retrievals of NO 2   and O 3   as well as seasonal variability in PM 2.5   concentrations. Finally, India is targeting a reduction of 20–30% in particulate pollution under NCAP by 2024 relative to 2017. Our analysis from 2015–2019 at different spatial and temporal scales of surface pollution provides a baseline to evaluate the future success of the programme as well as aids in the assessment of existing and future air pollution mitigation policies.

  ADDITIONAL INFORMATION

  data access.

The raw data from the continuous CPCB monitors used in our analyses along with the code for data quality control and the calculation of various temporal averages is available at   https://doi.org/10.34770/60j3-yp02

  ACKNOWLEDGEMENTS

We thank Mi Zhou for early assistance in data processing and two anonymous reviewers for helpful suggestions to improve our manuscript. Funding for D.S. was provided by a Science, Technology and Environmental Policy fellowship at the Center for Policy Research on Energy and Environment at Princeton University.

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Air pollution prediction with machine learning: a case study of Indian cities

• Original Paper
• Published: 15 May 2022
• Volume 20 , pages 5333–5348, ( 2023 )

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• K. Kumar   ORCID: orcid.org/0000-0002-1884-0548 1 &
• B. P. Pande   ORCID: orcid.org/0000-0001-9085-6731 2  

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The survival of mankind cannot be imagined without air. Consistent developments in almost all realms of modern human society affected the health of the air adversely. Daily industrial, transport, and domestic activities are stirring hazardous pollutants in our environment. Monitoring and predicting air quality have become essentially important in this era, especially in developing countries like India. In contrast to the traditional methods, the prediction technologies based on machine learning techniques are proved to be the most efficient tools to study such modern hazards. The present work investigates six years of air pollution data from 23 Indian cities for air quality analysis and prediction. The dataset is well preprocessed and key features are selected through the correlation analysis. An exploratory data analysis is exercised to develop insights into various hidden patterns in the dataset and pollutants directly affecting the air quality index are identified. A significant fall in almost all pollutants is observed in the pandemic year, 2020. The data imbalance problem is solved with a resampling technique and five machine learning models are employed to predict air quality. The results of these models are compared with the standard metrics. The Gaussian Naive Bayes model achieves the highest accuracy while the Support Vector Machine model exhibits the lowest accuracy. The performances of these models are evaluated and compared through established performance parameters. The XGBoost model performed the best among the other models and gets the highest linearity between the predicted and actual data.

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Introduction

Energy consumption and its consequences are inevitable in modern age human activities. The anthropogenic sources of air pollution include emissions from industrial plants; automobiles; planes; burning of straw, coal, and kerosene; aerosol cans, etc. Various dangerous pollutants like CO, CO 2 , Particulate Matter (PM), NO 2 , SO 2 , O 3 , NH 3 , Pb, etc. are being released into our environment every day. Chemicals and particles constituting air pollution affect the health of humans, animals, and even plants. Air pollution can cause a multitude of serious diseases in humans, from bronchitis to heart disease, from pneumonia to lung cancer, etc. Poor air conditions lead to other contemporary environmental issues like global warming, acid rain, reduced visibility, smog, aerosol formation, climate change, and premature deaths. Scientists have realized that air pollution bears the potential to affect historical monuments adversely (Rogers 2019 ). Vehicle emissions, atmospheric releases of power plants and factories, agriculture exhausts, etc. are responsible for increased greenhouse gases. The greenhouse gases adversely affect climate conditions and consequently, the growth of plants (Fahad et al. 2021a ). Emissions of inorganic carbons and greenhouse gases also affect plant-soil interactions (Fahad et al. 2021b ). Climatic fluctuations not only affect humans and animals but agricultural factors and productivity are also greatly influenced (Sönmez et al. 2021 ). Economic losses are the allied consequences too. The Air Quality Index (AQI) , an assessment parameter is related to public health directly. A higher level of AQI indicates more dangerous exposure for the human population. Therefore, the urge to predict the AQI in advance motivated the scientists to monitor and model air quality. Monitoring and predicting AQI, especially in urban areas has become a vital and challenging task with increasing motor and industrial developments. Mostly, the air quality-based studies and research works target the developing countries, although the concentration of the most deadly pollutant like PM 2.5 is found to be in multiple folds in developing countries (Rybarczyk and Zalakeviciute 2021 ). A few researchers endeavored to undertake the study of air quality prediction for Indian cities. After going through the available literature, a strong need had been felt to fill this gap by attempting analysis and prediction of AQI for India.

Various models have been exercised in the literature to predict AQI, like statistical, deterministic, physical, and Machine Learning (ML) models. The traditional techniques based on probability, and statistics are very complex and less efficient. The ML-based AQI prediction models have been proved to be more reliable and consistent. Advanced technologies and sensors made data collection easy and precise. The accurate and reliable predictions through such huge environmental data require rigorous analysis which only ML algorithms can deal with efficiently. Al-Jamimi et al. ( 2018 ) thoroughly discussed the importance of supervised ML algorithms for applied environment protection issues. The present work investigates six years of air pollution data of the Indian cities and analyzes twelve air pollutants and AQI. The dataset is preprocessed and cleaned first, then methods of data visualization are applied to develop better insights and to investigate hidden patterns and trends. This work exploits the essence of correlation coefficient with ML models which has been exercised by very few scholars in the literature (Alade et al. 2019a ). The data imbalance is identified and addressed with a resampling technique. Five popular ML models are exercised in context with this resampling technique. Their performances are then compared through standard metrics. These metrics are utilized by many scholars of the realm (see Table 1 ) and some other authors of ML applications like Ayturan et al. ( 2020 ), Alade et al. ( 2019b ), Al-Jamimi et al ( 2019 ), and Al-Jamimi and Saleh ( 2019 ), etc.

Section  2 presents the literature survey with a comparative analysis of the literary works in the realm of air quality prediction with ML. Section  3 describes the dataset being studied, preprocessing, and feature selection techniques applied. Section  4 deals with observing hidden patterns in the dataset through data visualisation. Section  5 is dedicated to the experimental design, analysis of seasonal trends, empirical results, and discussions. The final section concludes the present work.

Date: 17 February 2022.

Place: Qadian, Punjab and Pithoragarh, Uttarakhand, India.

A brief literature review

Gopalakrishnan ( 2021 ) combined Google’s Street view data and ML to predict air quality at different places in Oakland city, California. He targeted the places where the data were unavailable. The author developed a web application to predict air quality for any location in the city neighborhoods. Sanjeev ( 2021 ) studied a dataset that included the concentration of pollutants and meteorological factors. The author analyzed and predicted the air quality and claimed that the Random Forest (RF) classifier performed the best as it is less prone to over-fitting.

Castelli et al. ( 2020 ) endeavored to forecast air quality in California in terms of pollutants and particulate levels through the Support Vector Regression (SVR) ML algorithm. The authors claimed to develop a novel method to model hourly atmospheric pollution. Doreswamy et al. ( 2020 ) investigated ML predictive models for forecasting PM concentration in the air. The authors studied six years of air quality monitoring data in Taiwan and applied existing models. They claimed that predicted values and actual values were very close to each other. Liang et al. ( 2020 ) studied the performances of six ML classifiers to predict the AQI of Taiwan based on 11 years of data. The authors reported that Adaptive Boosting (AdaBoost) and Stacking Ensemble are most suitable for air quality prediction but the forecasting performance varies over different geographical regions. Madan et al. ( 2020 ) compared twenty different literary works over pollutants studied, ML algorithms applied, and their respective performances. The authors found that many works incorporated meteorological data such as humidity, wind speed, and temperature to predict pollution levels more accurately. They found that the Neural Network (NN) and boosting models outperformed the other eminent ML algorithms. Madhuri et al. ( 2020 ) mentioned that wind speed, wind direction, humidity, and temperature played a significant role in the concentration of air pollutants. The authors employed supervised ML techniques to predict the AQI and found that the RF algorithm exhibited the least classification errors. Monisri et al. ( 2020 ) collected air pollution data from various sources and endeavored to develop a mixed model for predicting air quality. The authors claimed that the proposed model aims to help people in small towns to analyze and predict air quality. Nahar et al. ( 2020 ) developed a model to predict AQI based on ML classifiers. Their authors studied the data collected over the tenure of 28 months by the ministry of environment, Jordan, and identified the concentrations of pollutants. Their proposed model detected the most contaminated areas with satisfying accuracy. Patil et al. ( 2020 ) presented some literary works on various ML techniques for AQI modeling and forecasting. The authors found that Artificial Neural Network (ANN) , Linear Regression (LR), and Logistic Regression (LogR) models were exploited by most of the scholars for AQI prediction.

Bhalgat et al. ( 2019 ) applied the ML technique to predict the concentration of SO 2 in the environment of Maharashtra, India. The authors concluded that being highly polluted, some cities of this Indian province require grave attention. The authors mentioned that their model was not capable of exhibiting expected outputs. Mahalingam et al. ( 2019 ) developed a model to predict the AQI of smart cities and tested it in Delhi, India. The authors reported that the medium Gaussian Support Vector Machine (SVM) exhibited maximum accuracy. The authors claim that their model can be used in other smart cities too. Soundari et al. ( 2019 ) developed a model based on NNs to predict the AQI of India. The authors claimed that their proposed model could predict the AQI of the whole county, of any province, or of any geographical region when the past data on concentration of pollutants were available.

Sweileh et al. ( 2018 ) came up with a very interesting study about the analysis of global peer-reviewed literature about air pollution and respiratory health. The authors extracted 3635 documents from the Scopus database published between 1990 and 2017. They observed that there was a substantial increase in publications from 2007 to 2017. The authors reported active countries, institutions, journals, authors, international collaborations in the realm and concluded that research works on air pollution and respiratory health had been receiving a lot of attention. They suggested securing public opinions about mitigation of outdoor air pollution and investment in green technologies. Zhu et al. ( 2018 ) refined the problem of AQI prediction as a multi-task learning problem. The authors utilized large-scale optimization techniques and endeavored to reduce the number of parameters. Based on their empirical results, they claimed that the proposed model exhibited better results than existing regression models.

Bellinger et al. ( 2017 ) carried out a detailed literature analysis on the application of ML and data mining methods toward air pollution epidemiology. The authors found that the researchers from Europe, China, and the USA were very active in this realm and the following classifiers had been widely applied: Decision Tree (DT) , SVMs , K-means clustering, and the APRIORI algorithm. Rybarczyk and Zalakeviciute ( 2017 ) endeavored to develop a model that correlated traffic density with air pollution. The author mentioned that such traffic data collection was economical, and integrating it with meteorological features boosted accuracy. The authors found that the hybrid model performed the best and accuracy based on morning time data was the highest.

Table 1 shown below presents a concise and comparative analysis of the literary works in the realm of AQI prediction.

It has been observed that research works in air quality analysis and prediction for Indian cities acquired lesser attention from scholars. In spite of the fact that out of the ten most polluted cities in the world, nine cities are Indian (Deshpande 2021 ), very few researchers investigated AQI prediction from the Indian perspective. The present work endeavors to fill this gap by studying 5 years of substantial air pollution data from twenty-three Indian cities. The current study is an earnest attempt to contribute to the literature with novel ideas of data visualizations, exploiting correlation coefficient-based statistical outliers for analytics, and comparison of five key ML models over standard performance metrics.

Material and methods

Some Indian cities fall in the array of the most polluted cities in the world, and the threat of air pollution is being raised day by day. Poor air quality in India is now considered a significant health challenge and a major obstacle to economic growth. According to a new study released jointly by a UK-based non-profit management firm, Dalberg Advisors and Industrial Development Corporation , air pollution in India caused annual losses of up to Rs 7 lakh crore ($95 billion) (Dalberg 2019 ). The main pollutant emissions in India are due to the energy production industry, vehicle traffic on roads, soil and road dust, waste incineration, power plants, open waste burning, etc. The present research investigates air pollution data extracted from the Central Pollution Control Board (CPCB) , India. Footnote 1 This dataset possesses observations from January 2015 to July 2020 and it is comprised of 12 features with 29,531 instances from 23 different Indian cities. Table 2 presented below provides brief descriptive statistics of the pollutants/particles and AQI from this dataset.

Analysis of some major air pollutants such as PM 2.5 , PM 10 , NO 2 , CO, SO 2 , O 3, etc. and prediction of AQI are the essence of the current work. The methodological steps of the adopted process are presented in the following figure (Fig. 1 ).

Data preprocessing

Quality of data is the first and most important prerequisite for effective visualization and creation of efficient ML models. The preprocessing steps help in reducing the noise present in the data which eventually increases the processing speed and generalization capability of ML algorithms. Outliers and missing data are the two most common errors in data extraction and monitoring applications. The data preprocessing step performs various operations on data such as filling out not-a-number (NAN) data, removing or changing outlier data, etc. Figure  2 shown below presents a view of the missing values in each feature of the dataset. Observe that among all other features, Xylene has the most missing values and CO has the least missing values. A large number of missing values may be existing due to a variety of factors, such as a station that can sense data but does not possess a device to record it.

Flowchart of the proposed model

Missing values of the features and their percentages

All the missing values are filled with the median values against each feature to solve the missing data problem. Next, a normalisation process has been applied to standardize the data, ensuring that the significance of variables is unaffected by their ranges or units. The data normalisation process helps to bring different data attributes into a similar scale of measurement. This process plays a vital role in the stable training of ML models and boosts performance. The datatypes of all the variables are also examined during normalisation. For example, the dataset is collected from different monitoring stations which deal with different representations of dates. Thus, the date ‘Monday, May 17, 2021’ may be represented as ‘17/5/2021’ or as ‘17–05-2021’ etc. Such date feature has been normalised through the datetime Python library.

Feature selection

The CPCB dataset under study involves a specific parameter viz, AQI and government agencies use this parameter to alert people about the quality of the air and also practice forecasting it. According to the National Ambient Air Quality Standards , there are six AQI categories: good (0–50), satisfactory (51–100), moderate (101–200), poor (201–300), very poor (301–400), and severe (401–500). Scholars in the realm suggest that reducing input variables lowers the computational cost of modeling and enhances prediction performance. A correlation-based feature selection method has been exploited in the present work to determine the optimal number of input variables (pollutants) when developing a predictive model. Statistical correlation-based feature selection algorithms compute correlations between every pair of the input variable and the target variable. The variables possessing the strongest correlation with the target variable are then filtered for further study. Since many ML algorithms are sensitive to outliers, any feature in the input dataset which does not follow the general trend of that data must be found. For the present dataset, a correlation-based statistical outliers detection method has been applied to identify the outliers. To select significant features, the correlation analysis of the AQI feature has been exercised with features of other pollutants. Figure  3 , shown below clearly reveals that pollutants PM 10 , PM 2.5 , CO, NO 2 , SO 2 , NO X, and NO are generally responsible for the AQI to attain higher values. These pollutants are correlated with AQI based on the correlation values above the threshold of 0.4.

Correlation heatmap of AQI with other pollutants (Threshold: 0.4)

Table 3 given below shows the exact correlation values of each pollutant of the dataset with AQI.

Many ML models function better when data have a normal distribution and underperform when data have a skewed distribution. Therefore, it is necessary to identify the skewness being present in the features and to perform some transformations and mappings which convert the skewed distribution into a normal distribution. Figure  4 , given below shows that the features of Benzene , Toluene , CO, and Xylene are highly skewed. To make these skewed features more normal, the logarithmic transformations have been used to reduce the impact of outliers by normalising magnitude differences.

Skewness present in dataset features

• Exploratory data analysis

This section of the present study deals with data exploration and analysis for finding various hidden patterns present in the dataset. Exploratory data analysis is the first step in data analytics which is performed before applying any ML model. Under this, the following important things are being analyzed: (a) exploring statuses and trends of air pollutants over the past six years i.e. from 2015 to 2020; (b) exploring the distribution of pollutants in the air along with top-six polluted cities with their average AQI values; and (c) estimating top four pollutants which are directly involved in increasing the AQI values.

Exploring the trends of air pollutants over the last six years

India has become one of the few countries having the most severe air pollution resulting from rapid industrialization and booming urbanization over the last several years. Air pollution is among grave public health and environmental issues, and the Health Effects Institute (HEI) ranks it among the top five global risk factors for mortality (IHME 2019 ). According to the HEI research, the emission of PM was the third leading cause of death in 2017, and this rate was highest in India. Based on the emissions of PM 2.5 and other pollutants, the World Health Organization (WHO) ranked India as the fifth most polluted country (Gurjar, 2021 ). The trends of various pollutants from 2015 to 2020 are observed and shown in the figure below (Fig.  5 ). Observe that except for O 3 and Benzene , all other pollutants exhibited a significant fall in 2020. The year 2020 witnessed the most strict lockdown in the history of mankind and ceased industrial, automobile, and aviation activities in India and the world served as some ambrosia for the ailing environment and air.

Intensities of various pollutants from 2015 to 2020

Figure  6 shown below depicts the average AQI values over the aforementioned tenure for the six most polluted cities in India.

The six most polluted Indian cities with their average AQI values from 2015 to 2020

Pollutants that are directly involved in increasing AQI values

The correlation values between different pollutants and AQI have been exercised and the pollutants for which this correlation value is greater than the threshold of 0.5, i.e. the correlation is strongly positive have been identified. Figure  7 shown below depicts the concentration of four such pollutants in various cities in India.

Pollutants governing AQI directly

Results and discussion

This section deals with the experimental design and empirical analysis for predicting AQI values through the pollutants present in the air. The air pollution dataset is split into training (75%) and testing (25%) subsets before evaluating ML models. The Google Colab Pro cloud platform with Intel(R) Xeon(R) CPU @ 2.30 GHz, Tesla P100-PCIE-16 GB, 12.8 GB RAM, and 180 GB of disc space has been utilized for executing Python scripts. The Python libraries like Scikit-learn , NumPy , Pandas , Seaborn , etc. are exploited for various data processing tasks. Next, the dataset is explored with the motive to find the overall value of the AQI with respect to those pollutants which have a significant role in raising the AQI value. In Fig.  8 shown below, a timeline graph of AQI is depicted over some particular pollutants which are directly responsible for higher values of AQI. From Fig.  8 , it is clear that each pollutant grows and drops year after year, and their values do not remain constant every year. PM 2.5 and PM 10 have seasonal effects, with higher pollution levels in the winter than in the summer. After 2018, the level of SO 2 began to rise, but the level of O 3 stayed unchanged from 2018 to 2020. The same trend can be seen in BTX Footnote 2 levels as well. Except for CO, practically every pollutant has exhibited seasonal variations.

Timeline graph of AQI with respect to specific pollutants

To examine the seasonality of the data thoroughly, Box plot visualizations are employed. Box plots categorise data into different periods by grouping the entire information in years and months. Figure 9 presents the Box plots of various pollutants over time, both annually and monthly. Notice that pollution levels in India decrease between June and August. It may be the consequence of the inception of the Monsoon in the Indian subcontinent during this tenure. BTX levels exhibit a significant drop between March and April, a modest rise from May to September, and a sharp surge from October to December. The median values for 2020 are lower than those for previous years, indicating that pollution may have decreased substantially in 2020. Strict lockdown ceased human and industrial activities in India during the COVID-19 pandemic are the obvious reasons for this observed phenomenon.

Variation analysis of pollutants through Box plots

Next, the detailed development of ML-based AQI prediction models is discussed. Finally, the performance of the AQI forecasting models is evaluated. The target attribute, AQI_Bucket has some missing values which result in the unequal splitting of the classes. Many ML models ignore this imbalanced datasets problem which may lead to poor classification and prediction performances. To overcome this data imbalance problem, the SMOTE (Synthetic Minority Oversampling Technique) has been applied. In this technique, the algorithm synthesizes new elements for minority classes rather than creating copies of already existing elements. It functions by randomly choosing a point from the minority class and computing the k-nearest neighbor distances for the selected point. The newly created synthetic points are added between the chosen point and its neighbors. To implement SMOTE for class imbalance, we have used an imbalanced-learn Python library in the SMOTE class. Now, five popular ML models, KNN , Gaussian Naive Bayes (GNB) , SVM , RF , and XGBoost have been employed to predict the AQI level with SMOTE and without SMOTE resampling technique. Table 4 shown below presents the results of used ML models in terms of accuracy, precision, recall, and F1-score during the training phase. Precision tells the fraction of relevant instances present in the retrieved instances, while recall is the fraction of relevant instances that have been retrieved. Accuracy is the ratio of the correctly labeled attributes to the whole pool of variables. F1-score is a weighted average of precision and recall. Note that the XGBoost model achieved the highest accuracy, while the SVM model exhibited the lowest accuracy.

The performances of the ML models for the training set are evaluated against the standard performance parameters, viz MAE , RMSE , Root Mean Squared Logarithmic Error (RMSLE) , and coefficient of determination, i.e. R 2 (Table 5 ). These performance measures have been exploited extensively in the literature. Table 5 given below provides error statistics of the ML models applied with and without SMOTE resampling technique on the training set. The XGBoost model outperformed other models in terms of error statistics when exercised without the SMOTE technique. On the other hand, the RF model performed relatively good among others in terms of error statistics when exercised with the SMOTE technique. The XGBoost model performed equally good in this area in terms of MAE and RMSLE. These observations are marked bold in Table 5 .

Table 6 shown below presents the results of employed ML models obtained during the testing phase. It is evident from Table 6 that the XGBoost model surpassed the other models again, whereas the SVM model attained the lowest accuracy in the testing phase too.

The performances of the ML models for the testing set are evaluated against the standard performance parameters as above (Table 7 ).

The above table summarizes the performances of various ML models applied with and without SMOTE resampling technique on the testing set. It is observed that all ML models exhibited improvement in almost all assessment metrics when applied with SMOTE resampling technique. The GNB model attained the best values of R 2 in both cases. The XGBoost model performed the best in terms of error statistics and attained the most optimum values in both experimental genres. These observations are marked bold in Table 7 .

Prediction of air quality is a challenging task because of the dynamic environment, unpredictability, and variability in space and time of pollutants. The grave consequences of air pollution on humans, animals, plants, monuments, climate, and environment call for consistent air quality monitoring and analysis, especially in developing countries. However, lesser attention for researchers has been observed for AQI prediction for India. In the present work, air pollution data of 23 Indian cities for a tenure of six years are investigated. The dataset is cleaned and preprocessed first by filling NAN values, addressing outliers, and normalising data values. Then correlation-based feature selection technique is exercised to filter AQI affecting pollutants for further study and logarithmic transformations are applied to the skewed features. The exploratory data analysis methods are exercised to find various hidden patterns present in the dataset. It was found that almost all pollutants exhibited a significant fall in 2020. The data imbalance problem is addressed by the SMOTE analysis. The dataset is split into train-test subsets by the ratio of 75–25% respectively. ML-based AQI prediction is carried out with and without SMOTE resampling technique and a comparative analysis is presented. The results of ML models for both the train-test subsets are presented in terms of standard metrics like accuracy, precision, recall, and F1-Score. For both the train-test sets, the XGBoost model attained the highest accuracy and the SVM model exhibited the lowest accuracy. The classical statistical error metrics, namely MAE, RMSE, RMSLE, and R 2 are then evaluated to assess and compare the performances of ML models. The XGBoost model comes out to be the overall best performer by attaining the optimum values in both training and testing phases. For the training phase, the RF model performed relatively good when exercised with SMOTE. On the other hand, almost all ML models exhibited improvements in the testing phase. In this phase, the GNB model attained the best results for R 2 in target predictions. The present research endeavors to contribute to the literature by addressing air quality analysis and prediction for India which might have not been properly studied. This work can be extended by employing deep learning techniques for AQI prediction.

The dataset can be downloaded from: https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing/data .

BTX is the combined name given to Benzene , Toluene , and Xylene.

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Kumar, K., Pande, B.P. Air pollution prediction with machine learning: a case study of Indian cities. Int. J. Environ. Sci. Technol. 20 , 5333–5348 (2023). https://doi.org/10.1007/s13762-022-04241-5

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DOI : https://doi.org/10.1007/s13762-022-04241-5

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“National Civilized City” (NCC) is regarded as China’s highest honorary title and most valuable city brand. To win and maintain the “golden city” title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO 2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO 2 pollution. We find that SO 2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO 2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran’s I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO 2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO 2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO 2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO 2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

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Introduction

China’s rapid economic growth, which is characterized by extensive development, has depended largely on natural resources, particularly fossil fuels [ 1 ]. In 2009, China became the largest energy consumer in the world [ 2 ], with coal being the dominant energy source for over 40 years. In 2021, in terms of coal consumption, China accounted for more than 50% of the world total [ 3 ]. A main pollutant of coal consumption is sulfur dioxide (SO 2 ), which has led to widespread and serious environmental pollution problems [ 4 ], SO 2 is a toxic gas that not only poses a threat to people’s health but also damages sustainable development [ 5 ]. In other words, China’s economic growth has been accompanied by serious environmental degradation at the expense of people’s health and the environment. In response, both the central and local governments have taken steps to strengthen various policies and measures to mitigate pollution, notably SO 2 pollution [ 6 ]. For example, in the 11th Five-year-plan period (2006–2010), central government set up a 5-year short-term binding and quantitative target for SO 2 emissions reduction. Specifically, national SO 2 emissions were enforced to reduce by 10% [ 7 ]. The target was reinforced in the 12th FYP with a further 8% reduction than that of 2010. The two targets were successfully achieved by reducing 14.29% and 18% by the end of the 11th and 12th FYPs [ 8 ], signaling strong environmental awareness and dedication by governments to tackle the environmental degradation, reduce SO 2 pollution, and realize the goal of sustainable development in Chinese cities.

To promote harmonious and sustainable development and enhance environmental governance, the Central Commission for Guiding Cultural and Ethical Progress published the “ National Civilized City Appraisal Systems (Trial) ,” to motivate local government participation in a competition in a non-economic field, e.g., environmental protection, with the aim to achieve greater public service [ 9 ]. It implies a transition of the emphasis of government on public services rather than only economic performance, e.g., GDP tournaments [ 10 ]. Environmental performance has become one of the critical indicators, e.g., an environmental policy tool, of central government to promote an ecological civilization. Notably, one of its goals is to reduce pollution and accelerate sustainable development. Since it is a high-profile program run by the Central Commission, the NCC is regarded to date as the highest honor among Chinese cities and the most valuable city brand in China [ 1 ], it is also the most difficult award to receive [ 9 ]. Cities winning the award are usually referred to as civilized cities . Municipal officials of all Chinese cities therefore pay close attention to this phenomenon. On the one hand, it will greatly enhance political performance of the municipal officials and is beneficial to prestige and promotion in economically developed cities that have already won the golden title. Liu [ 11 ] reports that more than a half of mayors of civilized cities have received promotions, which are generally referred to as a promotion tournament [ 12 ].

On the other hand, since the weight of economic indicators accounts for less than 3%, much lower than that of environmental management and quality (7.89%), it provides a novel competition model that signals a clear shift from “competition for rapid growth” to “competition for harmonious development.” In this sense, those officials of less economically developed cities but with high level of environmental protection and sustainability, also have opportunities to demonstrate their governance practices and performance. Hence, the NCC policy can also mobilize the initiatives of local governments to improve cities. In fact, this place-based competition is prevalent in China. More specifically, it is an institutionalized competition contest model, where the top central commission establishes measurable, easy-to-evaluate, and easy-to-compare criteria and sets clear-cut goals to make lower local governments compete. Afterwards, the central commission assesses and awards the winners [ 13 ] in what is usually referred to as the top-down place-competition and award model [ 14 ]. This practice still maintains a sufficient incentive effect on local officials and has become a larger contributor to tenure experience and political promotion than that of economic performance. In 2005, the first batch of cities with the NCC title was evaluated and recognized by the Central Commission [ 15 ]. Since then, a total of 146 cities (including 25 provincial capital cities and sub-provincial cities, and 121 prefecture-level cities) have been selected in the NCC campaign and the competition for this honorary title has intensified year on year.

The appraisal system for the NCC has been well-developed after several revisions over the past 20 years. At present it consists of 3 modules, 12 indicators and 90 sub-indicators [ 12 ], and emphasizes public service sectors, like the ecological environment. According to the “National Civilized City Appraisal System (2007 Edition),” the basic indicators in public service concentrate on two major aspects, namely, the environment for citizens’ work and life, and the ecological civilization for sustainable development. In the 11th version of 2015, the environment-related indicators in the appraisal system expanded to include nine green and low-carbon sub-indicators, e.g., urban air quality [ 16 ]. In other words, sustainable development is the most important among the evaluation indicators [ 17 ] because the problem of environmental degradation has become increasingly prominent in recent decades, which has had a remarkable influence on the selection of the NCC. Also, the Central Commission inspects the progress of environmental improvements in field visits, making local governments focus on livelihood and environmental indicators. Most importantly, the golden title cannot be permanently held by the civilized cities, after receiving the award, they are reviewed in each subsequent assessment. The title is revoked if indicators such as environmental concerns fail to meet the standards. Given that the successful rating of civilized city could significantly increase the likelihood of promotion, during the tournament, local governments must strenuously improve all the indicators, e.g., environmental quality, to maintain the honorary city brand. Otherwise, local governments of civilized cities are likely to have their golden title withdrawn. The implication here is that the competition carries on long-term and will continue until there is no room for improvement. Also, the NCC appraisal has the unique characteristic of not always being a one-time process. The cities participating in the competition must undergo several selection processes before finally winning the honorary title [ 18 ], this can fully motivate local officials to stay competitive. In this sense, it is safe to say that this institutional design is robust because it addresses and copes with problems such as environmental pollution which cannot be solved in the short-term.

We inquire whether cities winning the NCC title may significantly mitigate SO 2 pollution of their own city and also their neighbors, since the golden title is an incentive to resolve environmental pollution and improve the environmental quality for the purpose of political promotion of municipal officials. However, the performance contest does not work across the full range of China’s political systems; it is applicable only to certain institutional arenas. Hence, it is necessary to assess the effect of the NCC policy on the environment. Whereas the NCC contest can also be regarded as another promotion tournament, in which place-based officials win the honorary title through mutual competition for promotion opportunities. In this way, a civilized city may stimulate its neighbors to participate in the tournament and improve assessment indicators, such as environmental problems, which are referred to as demonstration effects. To answer this question and to capture the demonstration effect, we treat the NCC as a quasi-experiment and introduce the spatial Durbin DID model to verify this hypothesis of the local and spillover effects of the NCC policy on mitigating SO 2 pollution.

The research is organized such that " Literature review " section gives a literature review and " Methods and data sources " section introduces methods and data sources. " Empirical results and discussion " section presents empirical results and robustness checks while " Discussion " section summarizes our conclusions and proposes policy implications.

Literature review

Evidence indicates that the mutual competition to gain the civilized city title, as a public service motivation tool, is advantageous in coping with public issues [ 14 ]. The NCC as a policy tool is therefore increasingly being applied in the literature, especially towards quantifying its impacts. Studies can be divided into three strands. In the first strand some researchers focus on the impact of the NCC in the competition of economic performance. For example, Huang and Zhou [ 19 ] chose two civilized cities, both having received five consecutive honorary titles: Baotou and Yantai, and adopted a synthetic control method. Results show that the NCC has had a long-term and positive impact on economic growth. In another study, Chen and Mao [ 20 ] used a quasi-experiment approach to evaluate the NCC on tourism. The findings indicate that the NCC title indeed contributes to growth in the tourism economy of civilized cities. The Li et al. [ 12 ] study showed that civilized cities exhibit significant improvements in public services as well as the subjective perceptions of citizens. The NCC award was also shown to have a positive effect on the promotion of local leaders. Wang and Wang [ 21 ] found that civilized cities substantially increase the scale of municipal investment debt. Further studies found evidence that taking the NCC into consideration also contributes to increasing house prices [ 22 ], attracting migrants [ 23 ], industrial upgrading [ 24 ], urban economic growth [ 25 ], and urban green innovation [ 9 ], among others.

The second strand focuses on evaluating the effect of the NCC award on local enterprise performance. For example, Wu et al. [ 26 ] found that having NCC status lowers transaction costs, thereby increasing the profitability of local privately listed firms. Shi et al. [ 27 ] concluded that the NCC promotes the total factor productivity and labor productivity of local firms. Qi et al. [ 28 ] confirmed that the NCC policy increases the corporate environmental, the social governance performance of civilized cities. Zhao et al. [ 29 ] applied a staggered DID model and investigated whether the NCC decreased the risk of stock price crash for local firms. Their results indicated that it could significantly reduce future crash risk. Apart from the objective indicators of firms, the NCC may also have an impact on subjective perceptions since it emphasizes the construction of a spiritual civilization. For example, a study by Chai et al. [ 30 ] revealed that the honorary title enhances the sense of corporate social responsibility.

The third strand identifies the effect of the NCC in non-economic areas, such as environmental performance and green development. The mechanism of the NCC competition is beneficial for solving the environmental degradation problem caused by the traditional GDP tournament [ 13 ]. For example, Lu et al. [ 31 ] adopted a propensity score matching and difference-in-differences (PSM-DID) approach to examine the impact of NCC on environmental pollution and find that it could contribute to improving urban environments. Zhang et al. [ 15 ] also applied the PSM-DID approach based on a quasi-experiment of participating in the NCC campaign,they evaluated the effect of government intervention on corporate environmental performance. Their results show that the environmental performance of firms in civilized cities is higher than in non-civilized ones. Similarly, Shi et al. [ 32 ] also adopted the PSM-DID approaches, using panel data of 281 cities from 2000 to 2018 and find that the NCC designation of the cities is likely to greatly improve the green total factor productivity. Li et al. [ 12 ] highlighted the impact of NCC on the energy efficiency of resource-based cities in China and found that NCC is beneficial to energy efficiency improvements. Lastly, Yang et al. [ 17 ] emphasized the effect of NCC on green innovation in Chinese cities. Green innovation is deemed capable of mitigating environmental pollution by increasing production efficiency to achieve a double dividend effect between urban economic growth and environmental sustainability. Yang et al. [ 17 ] found that NCC could contribute to increasing the level of urban green innovation.

From the literature we find that the NCC helps make cities better by enhancing the performance of local firms and improving the urban environment. However, very few studies pay direct attention to the impact of the NCC on pollutant reduction. One exception is a study by Shen et al. [ 33 ] who use panel data of manufacturing firms from 1999 to 2008 in China to calculate the environmental effect of the NCC at firm level from a micro perspective. Their results showed that firms in civilized cities have reduced more COD discharge than firms in non-civilized cities. In another study, Huang et al. [ 16 ] have evaluated the reduction effect of the NCC on carbon emissions, and found that it significantly lowered emissions with the reduction effect strengthening over time. However, we observe that spatial spillovers have not yet been considered in empirical studies when examining the impact of the NCC on pollution reduction. The two studies also suffer from two shortcomings. One is that spatial spillovers of pollutants was omitted in the Shen et al.’s [ 33 ] study. Since pollution in Chinese cities is not randomly distributed, the omission of spatial spillovers may also lead to biased conclusions. The second is that statistical data may not objectively and accurately measure the pollution level of Chinese cities, since pollutant data, one of the key performance indictors to determine the promotion of local officials, could be modified or falsified during data collection [ 8 ]. Recently, satellite observed pollutant data, such as SO 2 concentrations, have been widely studied thanks to two main advantages, namely, the ability to objectively describe SO 2 pollution, and full coverage. In this regard, we introduce satellite observed SO 2 pollution to perform a robustness check.

Correspondingly, the contributions of this research may be threefold. Firstly, we incorporate spatial spillovers of both SO 2 pollution and the NCC and then make use of the NCC campaign to build a spatial Durbin DID model to evaluate the direct and demonstration effects of the NCC on SO 2 pollution reduction. Doing so provides evidence for understanding the environmental impact of government policies, e.g., the selection of civilized city, on non-economic performance indicators. In addition, the present research better clarifies policy boundaries and demonstration effects of the NCC policy and provides an underlying implication that the widening effect should be considered when designing and implementing an institutional policy. Secondly, in general, rook contiguity spatial weights matrix is the first to consider. When introducing spatial demonstration effects of the NCC in the model, it is a strong assumption that only cities sharing the common borders with civilized cities will be affected. In fact, it is likely that more neighboring cities may be affected due to demonstration effects, which also should be incorporated in the model. Hence, this research introduces different spatial weights matrices and constructs a multi-period spatial Durbin DID model to correct the possible multi-period bias from differential trends. Thirdly, we introduce satellite observed SO 2 concentrations data to again confirm the environmental effects of the NCC on pollution reduction. We aim for our robust and convincing conclusions to contribute to eliminating underlying bias and formulating effective policies and measures. The main findings of this research also further clarify the mutual competition of the NCC campaign, thereby enriching the literature on the nexus between local governance and environmental performance. Lastly, the conclusions of this research may provide a novel insight into designing and implementing effective policies to improve environmental quality in China from the perspective of mutual competition.

Methods and data sources

Classical did model.

The NCC can be regarded an environmental policy tool. In this sense, we conduct a quasi-experiment and evaluate the impact of the NCC program on SO 2 pollution. Specifically, our selected treatment group is cities that won the title of the NCC. Whereas the control group encompasses non-civilized cities. Hence, to ascertain the policy effect, we analyze if the control group (non-civilized cities) can be significantly distinguished from the treatment group (civilized cities) in terms of SO 2 pollution reduction. To this end, we introduce the DID model because it can remove the bias between the control group and the treatment group [ 34 ].

Since the selection of NCC is a dynamic cycle process and program has been conducted for five times within our sample period, this provides us to use a staggered DID method. The key explanatory variable, DID it , is set to 1 if city i was a civilized city in year t , but otherwise, 0. The DID model can be written as

where Ln denotes natural logarithm transformation. SO 2 is the dependent variable, SO 2 emissions. DID is the core variable. \(\zeta\) is an unknown parameter to be estimated. If it is highly significant and negative, we can conclude that civilized city is able to significantly reduce SO 2 pollution and captures the policy effect of the NCC. Furthermore, \({u}_{i}\) and \({\lambda }_{t}\) represent city fixed effects and time fixed effects, respectively. Lastly, \(\varepsilon\) is an error term.

To avoid omitted variables biased conclusions, we also incorporate several control variables in the DID model, which can be re-written as

where X denotes a set of control variables, including gross urban product ( GUP ), GUP growth rate ( GUPr ), population ( POP ), and foreign direct investment ( FDI ). The other variables are the same as Eq. ( 1 ).

Spatial DID model

The classical DID model assumes that observations are independent from each other. However, findings indicate that the observations of proximate spatial units tend to have similar values in which, technically speaking, there may be spatial spillovers. In other words, a city suffering SO 2 pollution is deeply affected by neighbors. The potential spatial spillovers of the observations should therefore be considered in the model; not doing so could lead to biased conclusions.

In general, two spatial econometric models are commonly used in empirical studies, namely, spatial lag model and spatial error model. The former considers spatial spillovers of our dependent variable, namely, SO 2 pollution which is expressed as

where WLnSO 2it is the spatially lagged dependent variable used to capture spatial spillovers of SO 2 pollution. \(\rho ,\) also known as spatial autoregressive coefficient, is an unknown parameter to be estimated. W is a n × n spatial weights matrix used to describe the arrangement of spatial units (i.e., Chinese cities). In this study we adopt two types of commonly used distance-based matrices, e.g., inverse distance matrix and k-nearest matrix. The former refers to a matrix with element \({w}_{ij}=1/{d}_{ij}\) , where d is the distance between pairs of Chinese cities. However, in practice its disadvantage is that large distances will yield small values of the matrix, and vice versa. To overcome this shortcoming, we set a cut-off criterion, δ , such that \({w}_{ij}=0\) for \({d}_{ij}>\delta\) . We consider four cut-off values, namely, 300, 350, 400, and 450 km. The four inverse distance matrices are labeled inv300, inv350, inv400, and inv450, respectively. The latter is an alternative type of distance-based matrix. The difference is that k-nearest neighbor matrix considers that the closest cities can have a spatial spillover effect. k indicates the number of nearest neighbors. However, there is no pre-defined statistical method to help us determine the most favorable value of k. To obtain robust conclusions, we thus also consider four values, e.g., 5, 6, 7, and 8. Hence, the four k-nearest neighbor matrices are labeled k5, k6, k7, and k8, respectively.

The spatial error model highlights spatial dependence among disturbances by incorporating a spatially autocorrelated errors in the model. Similarly, the spatial spillovers of exogenous control variables should also be considered in the model because in econometrics, the exclusion of spatially lagged explanatory variables may also lead to omitted variables bias and even undermine the foundation of the research [ 35 ]. Hence, based on the spatial lag model, we can build another model (spatial Durbin) that includes spatially lagged explanatory variables. Another advantage of the spatial Durbin model is that it will not produce biased estimates even though data generation processes point to the spatial lag model or spatial error model [ 36 ]. Hence, it is more appropriate in empirical studies. Here, we adopt the spatial Durbin DID model to capture both spatial spillovers of SO 2 pollution and exogenous control variables by adding the spatially lagged dependent and independent variables in the model. The spatial Durbin DID Model can be expressed as

where WX denotes the spatially lagged independent variables, and \(\theta\) is unknown parameters to be estimated.

It is worth noting that, apart from WX , WDID in the spatial Durbin DID model captures the spatial spillover effects of the NCC. If the coefficient is statistically significant and negative, we can conclude that neighboring civilized cities also have impacts on the SO 2 pollution reduction of the local city, indicating that the policy not only has a reduction effect on SO 2 pollution of the local civilized city, but also exhibits a spatial spillover effect on SO 2 pollution of neighboring cities.

Data sources

The data for all variables is derived from China City Statistical Yearbooks. To obtain robust conclusions we also conduct a robustness check. Specifically, we replace the dependent variable, SO 2 emissions, with satellite observed SO 2 concentrations known as planetary boundary layer SO 2 vertical column densities. These are retrieved from the Ozone Measurement Instrument onboard the EOS-Aura satellite [ 8 , 37 ]. The description, units and descriptive statistics for the variables involved in this study, including mean, standard deviation (S.D.), Min (minimum), and Max (maximum) are summarized in Table  1 .

Empirical results and discussion

Spatial distribution of so 2 emissions.

To better understand our application of the spatial Durbin DID model, the distribution of SO 2 emissions of Chinese cities and the civilized cities are geo-visualized in Fig.  1 for years 2005, 2009, 2011, 2015, and 2017.

Spatial distribution of SO 2 emissions and civilized cities in 2005, 2009, 2011, 2015, and 2017 as well as average emissions and total civilized cities

We can observe that in 2005 the SO 2 emissions were not evenly distributed in China (Fig.  1 a). The cities with the highest values mainly concentrated in the North China Plain, specifically, most of Hebei province, northern Henan province, and western Shandong province, as well as Baotou in Inner Mongolia; these highly polluted regions were characterized by heavy industries such as steel, which consumed enormous amounts of coal and emitted a million tons of SO 2 . Notably, Chongqing in the southwest had the highest emissions due to its large-scale industrial system. Conversely, only seven cities received the very first NCC award in 2005, mostly eastern coastal cities, e.g., Yantai, Dalian, Qingdao, Ningbo, Xiamen, and Zhongshan, along with the western city of Baotou.

We can observe that in 2009, SO 2 pollution in the North China Plain reduced slightly (Fig.  1 b). At this time SO 2 emissions were tightly restricted to the 5-year quantitative and binding reduction target formulated in the 11th Five-year-plan (FYP). The national SO 2 emissions were enforced to reduce by 10% relative to the emissions by 2010 (end of 10th FYP). Most cities witnessed a reduction in SO 2 emissions, but Chongqing faced an even larger SO 2 pollution challenge. In the meantime, the number of civilized cities increased; newly added civilized cities included three provincial capital cities and six prefecture-level cities, mostly in the east and south.

In 2011, however, the decreasing trend of SO 2 emissions reversed and highly polluted regions such as the North China Plain, the Yangtze River Delta and the Pearl River Delta began to worsen sharply (Figs. 1 c-d). One possible explanation is that a series of preferential policies were formulated during the 12th FYP (2011–2015), causing a sharp increase in SO 2 pollution in the first years of the 12th FYP. In fact, SO 2 pollution dropped substantially during the latter period of the 12th FYP due to stricter targets for SO 2 emissions reduction. Interestingly, the third batch of NCC titles was issued in early 2011, expanding civilized cities to 23 (9 provincial capitals and 14 prefecture-level cities). Later in 2011, the fourth batch was granted, increasing civilized cities by 6 provincial capitals and 22 prefecture-level cities, situated in the east, central and western China.

In 2017, SO 2 pollution was substantially mitigated, indicating that the binding reduction targets were successfully achieved nationwide (Fig.  1 e). Notably, the highly polluted North China Plain also witnessed a noteworthy reduction, which was primarily attributed to joint efforts of both sides that local governments reinforced the stringency of environmental regulation and local heavy industries upgraded their pollution control technologies. Hence, SO 2 has not yet been a main source of environmental pollutants in China so far. Whereas by 2017, newly added civilized cities increased to 35 (5 capital cities and 30 prefecture-level). A few western cities were also granted the honorary title due to their improved environments and relatively high economic levels.

From Fig.  1 we can infer that SO 2 emissions of 283 Chinese cities are not randomly distributed in geography. In other words, we observe that cities with high SO 2 emissions are clustered, for example, in the North China Plain, and cities with low emissions are in the south. We next performed a Moran’s I test with k-nearest neighbors matrix (i.e., k6) [ 38 ] since it is the most appropriate matrix in the spatial Durbin DID model. Results of Moran’s I are presented in Table  2 .

From Table  2 , we notice that all Moran’s I values are statistically significant and positive, indicating strong spatial spillovers during the sample period, that is, the observations are not independent from each other, which violates the assumptions of classical econometrics. Besides, we also perform panel Moran’s I with different matrices and the results show that they are all significant and positive. Hence, spatial spillovers should be controlled for; we suggest that the spatial Durbin DID model will be more appropriate than the classical DID model, especially when evaluating the demonstration effect between Chinese cites. Similarly, we note that the civilized cities tend to be clustered in specific regions, such as urban agglomerations, provincial capital cities and their satellite cities. Demonstration effects is exemplified by the civilized cities which initially won the NCC award and would then stimulate neighboring cities to actively participate in the campaign and resultingly mitigate SO 2 pollution.

Results of classical and spatial Durbin DID models

We first estimated the classical DID models and give summarized results in Table  3 . Model (1) in the second column of Table  3 only controls for city fixed effects. Model (2) in the third column consists of the two-way fixed effects classical DID model. Model (3) incorporates control variables based on Model (2). Given the low number of first and second batches of civilized cities, this may lead to biased conclusions. To conduct a robustness check, we have deleted the first batch of civilized cities from Model (4) to avoid biased estimates in the classical DID models with multiple time periods. Similarly, we deleted both the first and second batches of civilized cities from Model (5).

From the results of city fixed effects model (Model (1)), we observe that the estimated coefficient of the DID term (-0.828) is highly significant and negative. Model (2) also exhibits a significant and negative coefficient. However, it has a smaller reduction effect (-0.160) than that of Model (1), implying that the omission of time fixed effects leads to upward bias in Model (1). It still has significant and negative estimated coefficients in Models (3)-(5) when controlling for explanatory variables or deleting the first and second batches of civilized cities. From the above estimation results, we can conclude that the civilized cities can significantly reduce SO 2 emissions.

The Moran’s I test results in Table  2 show strong spatial spillovers, indicating that the classical DID model may not be suitable for our study. Instead, the spatial Durbin DID models may be a better fit since they can capture spatial spillovers by incorporating spatially lagged dependent variables and spatially lagged control variables. However, since we do not yet know which model is the best fitted among spatial lag DID model, spatial error DID model, and spatial Durbin DID model, we have considered different spatial weights matrices and performed Lagrange multiplier (LM) tests to select the best one. The results of the LM tests are summarized in Table  4 .

From Table  4 we can observe that the results of the LM test statistics with inv300, inv350, inv400, and inv450 matrices support spatial lag models and reject spatial error models. However, the four LM test statistics with k5-k8 matrices are highly significant, indicating that both spatial lag model and spatial error model are appropriate; this finding is not in line with the results with inverse distance matrices.

We therefore conducted a Wald test to examine if the spatial Durbin model is better than the two above models. The Wald test results showed that the null hypotheses that: spatial Durbin model can be simplified to spatial lag model or spatial error model, can be strongly rejected at a 1% significance level, indicating that the spatial Durbin model is the best fit. Estimation results of two-way fixed effects spatial Durbin DID model with different spatial weights matrices are presented in Table  5 .

From Table  5 , we can observe that the estimated spatial autoregressive coefficients (i.e., ρ ) of all spatial Durbin DID models are highly significant and positive, despite slight differences in magnitude. The indication here is that increases in SO 2 emissions of neighboring cities lead to the rise of SO 2 emissions in own city. In other words, the highly SO 2 polluted cities are surrounded by cities with high SO 2 emissions, while cities with low SO 2 emissions tend to be clustered. We also performed a log-likelihood ratio test to examine the spatial Durbin DID model against the classical DID model. The null hypothesis can be strongly rejected at the 1% significance level, indicating that the spatial Durbin DID model is a better fit.

Whereas we also notice that the estimated spatial autoregressive coefficients increase as spatial weights matrices vary with distance. Specifically, the coefficients in Models (6)-(9) range from 0.407 to 0.482 when the cut-off distance increases from 300 to 450 km. This is because the longer the distance, the more that cities are enclosed, and the stronger are the spatial spillover effects. Similar conclusions are reached for Models (10)-(13), in which the coefficients get larger as the number of neighbors increases. Since Models (6)-(13) have similar conclusions, the log-likelihood statistic can help us determine which one is the best. We have selected Model (11) as the best fit, which we will discuss later.

With regard to the estimated DID coefficient in the spatial Durbin DID model (Model 11), we find that it is still significant and negative (-0.114) and is smaller than that (-0.153) in the classical DID model because the omission of spatial spillovers can lead to biased estimates, specifically upward bias. The estimation result indicates that the civilized cities could significantly reduce SO 2 emissions because they are highly aware of the honorary title and tend to implement strict environmental regulation to mitigate SO 2 pollution and improve the environment for the purpose of maintaining the award in the process of review in subsequent evaluations. Otherwise, the title will be revoked if they do not meet the environmental standards. Specifically, in the campaign to maintain the golden title, the local government reinforces environmental regulations, notably for highly polluting firms, to curb emissions and strengthen public environmental facilities for pollution control. Local citizens are also encouraged to participate in environmental management to avoid environmental worsening or occurrence of environmental events. In sum, the place-based NCC policy can stimulate local governments to allocate resources effectively and implement regulation through policy intervention [ 39 ] to safeguard ‘the public good’, by reducing environmental hazards that have long been ignored [ 40 ]. It is therefore conducive to focus on SO 2 reduction and the environment and realize a win–win situation for the promotion probability of local officials and environmental benefits of the city.

More importantly, we notice that the spatial lag of DID , namely, W*DID also has a significant and negative coefficient, indicating that neighboring civilized cities also contribute to reducing SO 2 emissions of their own city. We can verify that the NCC policy can generate positive externalities, as has been confirmed by other researchers (see, amongst others, [ 18 , 41 ]). One possible explanation is that neighboring cities receiving NCC award will also stimulate their own city to take stringent action to reduce SO 2 emissions and protect the environment due to demonstration and competition effects. If a municipal government is finally granted the title, it can serve as a benchmark for neighboring municipal governments [ 12 ]. Spatial spillovers are effective in the yardstick competition and exert powerful influence towards total reductions in SO 2 emissions. All in all, the exemplary role is beneficial to environmental quality improvements across the administrative regions.

• Robustness check

Replacing the dependent variable

To confirm our conclusions in a robustness check, we replace the dependent variable, SO 2 emissions, with satellite observed SO 2 concentrations and repeat the estimation of the spatial Durbin DID models with different spatial weights matrices. Results are reported in Table  6 .

In Table  6 the spatial autoregressive coefficients become larger than those in Table  5 because SO 2 concentrations of Chinese cities present much stronger spatial spillovers compared with SO 2 emissions. However, we also notice that both the DID and W*DID terms are still significant and negative, and consistent with those in Table  6 , although the estimated spatial autoregressive coefficients (e.g., ρ ) in the two models with two types of the dependent variable are differentiated in magnitude because they have different units. From the above analysis, it follows that the conclusions are robust and convincing.

Apart from SO 2 pollution, to confirm the robust conclusions again we introduce satellite-observed NO 2 concentration data as the dependent variable and then repeat the estimation of the spatial Durbin DID models. The results are presented in Table  7 .

As shown in Table  7 , we observe that both the DID and W*DID terms are still significant and negative, indicating that the NCC policy can also contribute to reducing NO 2 pollution. In other words, the conclusions have been confirmed, again. Besides, we notice that the spatial autoregressive coefficients in Table  7 are larger than those in Table  6 , implying stronger spatial spillovers of NO 2 pollution.

Controlling for fiscal autonomy

On the basis of Fig.  1 , economically developed cities are more likely to be granted the honorary title, since the economy is somewhat of a prerequisite for candidacy, even though economy-related indicators account for less than 3% of the total. Better economic and fiscal conditions enable these municipal governments to have more resources and stronger incentives to participate in the campaign and higher abilities to cope with environmental problems like SO 2 pollution. To rule out the possibility of the impact of fiscal conditions on SO 2 pollution, we follow Chen’s [ 42 ] study and introduce a fiscal autonomy variable ( Fisc ) to control for the effect, which is defined as the ratio of fiscal revenue to fiscal expenditure. In addition, the higher the fiscal autonomy, the better are fiscal conditions. If the DID and W*DID terms turn out to be insignificant when the Fisc variable and its spatial lag are incorporated in the spatial Durbin DID model, the implication is that fiscal conditions are the key contributors to SO 2 pollution mitigation rather than the effect of the NCC policy. The estimation results are presented in Table  8 .

From Table  8 , we observe that the variable of Fisc is highly significant and positive, indicating that higher fiscal autonomy means more pollution. One possible interpretation is that high fiscal autonomy not only indicates that local fiscal revenue dominates the fiscal structure of the own city, but also implies less dependence on subsidies from higher level governments. In this regard, facing the trade-off between economic growth and the environment, local governments may have fewer incentives to further mitigate SO 2 pollution. Instead, they have more incentives to pursue better economic performance. In this way preferential policies are implemented to attract more businesses in a bid to garner more taxes, which increases the number of businesses and leads to higher SO 2 emissions. Whereas the spatial lag of Fisc , namely, W*Fisc , is found to have a significant and negative impact; the implication here is that an increase in fiscal autonomy of neighboring cities leads to the decline of SO 2 pollution of the own city. One possible reason is that the number of businesses in nearby cities decreases accordingly due to the competition effect, so that SO 2 emissions also decline.

Most importantly, we notice that both the DID term and its spatial lag ( W*DID ) are still significant and negative after controlling for the fiscal autonomy variable, once again confirming the effective impact of the NCC policy on SO 2 reduction. In other words, the above conclusions are robust and convincing.

Excluding interference from other policies

Considering that other possible environmental policies may work during the same period that interfere with our conclusions, we consider another policy implemented by the government. Specifically, 81 cities were officially granted as the new energy demonstration cities in January 2014 [ 43 ]. We refer to it as the new energy demonstration city pilot policy. Then, we control for these dummy variables ( DID_NED ) to verify if it may interact with the NCC policy to affect SO 2 pollution. The results are presented in Table  9 .

We observe from Table  9 that the new variable DID_NED is highly significant. Moreover, the DID term remains significant and negative, indicating that our conclusions are robust.

Parallel trend test

A prerequisite for applying the DID model is that both control and treatment groups comply with the parallel trend hypothesis. In other words, the differences of SO 2 pollution between the two groups are fixed before the NCC policy implementation. Then, the gaps began to happen after the policy was implemented. To verify the hypothesis, we follow Beck et al. [ 44 ] by introducing a set of dummies into the spatial Durbin DID model to observe the variations between control and treatment groups. The model can be rewritten as follows.

where \({DID}_{it}^{k}\) represents a set of dummies. k is the k-th year before or after the NCC policy. If the trend of the estimated coefficients \({\zeta {\prime}}_{k}\) fluctuates around 0 when k < 0, the common trend assumption is satisfied. In contrast, if the trend is decreasing when k > 0, it indicates that a striking difference between control and treatment groups can be found after the policy. In other words, the policy began to work. The parallel trend test results are plotted in Fig.  2 .

Parallel trend test results: K6 matrix vs. inv300 matrix

As shown in Fig.  2 , the estimated coefficients \({{\zeta }{\prime}}_{k}\) fluctuated around 0 when k < 0. In other words, they are insignificant before the NCC policy was implemented. It implies that the common trend assumption is confirmed. After the policy was implemented (k > 0), the estimated coefficients are negative and significant, indicating that it works well in reducing SO 2 pollution.

Placebo test

A further alternative robustness check to confirm our conclusions is to perform a placebo test. We followed the practice of Ferrara et al. [ 45 ] to randomly generate a new DID variable ( DID_P ) by randomly selecting the treatment group from the total samples while keeping the ratio of treatment observations the same as estimation results in Table  5 . In the pseudo treatment group these cities did not really win the NCC title; the main aim of the placebo test is to confirm if the variable of DID_P is highly insignificant. In this test, random sampling and re-estimation of spatial DID models is repeated 1000 times. On the one hand, if it remains significant, the results in Table  5 could have happened by chance. On the other hand, if it is insignificant, the reduction effect of the NCC policy on SO 2 can be confirmed. We applied the kernel density estimation method to plot the densities of the coefficients of the DID and W*DID variables for 1000 times, as shown in Fig.  3 .

Placebo test results: K6 matrix vs. inv300 matrix

The blue and red dash lines are the estimated coefficients of the DID and W*DID variables, respectively. We find that the DID coefficient of sampling regression is close to 0. Furthermore, the p values are all greater than 10%, indicating that the pseudo civilized cities cannot significantly reduce local SO 2 pollution. Similarly, we notice that the W*DID coefficient is also close to 0. However, the far-left side of the density curve of W*DID is approaching the estimated coefficient. The main reason for this is that low probability events have happened, but they do not undermine the main conclusions. In this sense, we can conclude that civilized cities can also contribute to mitigating SO 2 pollution of neighboring cities.

To better understand the random selection of the placebo test, the results of one random sampling regression are presented in Table  10 .

We notice that the estimated coefficients of the DID_P variable in Model (30)-(37) are all highly insignificant. In addition, the spatial lag ( W*DID_P ) is also found to be statistically insignificant. Thus, we can rule out the possibility of a random event: the findings of the present research are robust and convincing.

Mechanism analysis

The above conclusions indicate that the NCC policy can contribute to reducing pollution. Then, a question may arise through which channels does it mitigate pollution? From the above analysis, civilized cities reduce pollution by lowering the share of the secondary industry since it is the largest pollutant emitter in China [ 46 ]. In other words, industrial structure upgrading is the key channel for civilized cities to reduce pollution. Hence, we focus on testing for the influence path of industrial structure upgrading on SO 2 pollution. Specifically, it is measured by the share of the secondary industry to GDP ( Second ). The estimation results are presented in Table  11 .

From Model (46), we find that the variable Second is significant and positive, indicating that a decline in the share of the secondary industry can help reduce SO 2 pollution. On the other hand, we notice that its spatial lag W*Second in Model (47) is significantly negative, suggesting that a decrease in the share in neighboring cities could exacerbate SO 2 pollution. One possible interpretation is that industries cities subjected to strict environmental regulation move to neighboring cities, leading to increasing both the share of the industry and SO 2 pollution of neighbors. Regarding the DID term, we find that in both Models (46) and (47) it is still significant and negative, implying that the NCC policy works. Hence, the influence channel is confirmed.

Conclusions and policy implications

In this research we adopted the spatial Durbin DID model and panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the National Civilized City (NCC) policy on SO 2 pollution. We found that overall SO 2 pollution presented a decreasing trend from 2003 to 2018, but with two breaks in 2007 and 2011, when the declines reversed. Meanwhile, we found that the number of civilized cities continued to increase in five batches from 2005 to 2017, with most located in the economically developed eastern region. Moreover, among 31 capital cities, 25 had won the honorary title. The results of the spatial Durbin DID model revealed that civilized cities could significantly reduce local SO 2 emissions. In addition, neighboring civilized cities were also conducive to mitigating SO 2 pollution of the own city, indicating that the spatial spillover effect of the NCC policy had worked. Robustness checks, including replacing the dependent variable with satellite observed SO 2 concentrations, controlling for fiscal autonomy and the placebo test, confirmed robust and convincing conclusions.

Based on the findings of this study, policy implications can be proposed as follows. Since the NCC policy exhibits strong environmental effects, the criteria for assessing environmental quality of civilized cities should be reinforced or further improved. In addition, central government should strictly implement the selection criteria for the title and rule out those under-qualified cities with major environmental problems by revoking their titles. Given that municipal governments are incentivized to take measures to protect the environment, controlling the number of civilized cities can significantly intensify competition for the title and instigate environmental improvements. Central government should make full use of the NCC policy to push local governments to focus on reducing pollution, improving the environment, and finally making the cities better. Apart from the NCC policy, the policymakers should also develop more appropriate policies that work for different types of cities to push them to reduce pollution. For example, appropriate incentive mechanisms should be designed and implemented for those non-civilized cities to encourage them to mitigate pollutants and improve the local environment, although they are not granted the honorary title.

It should be highlighted that the spatial spillover demonstration effect of civilized cities works. Not only do civilized cities reduce local pollution, but they also exhibit a demonstration effect. After a city has won the title, neighboring cities subsequently tend to adopt similar strategies and measures for environmental protection in order to win the performance contest and be granted the golden title. However, we observed that most of the civilized cities are located in the economically developed eastern regions, which is closely related to economic levels. A better way to promote the widest range of environmental improvements in western cities is to relatively relax the selection criteria and grant the golden title to cities with high environmental standards; in so doing the demonstration effect and spillovers may be maximized to stimulate neighboring cities to also improve the environment.

Apart from measures and regulation by local governments, another way to build civilized cities with better environmental quality is to widen channels to encourage citizens to participate in environmental management. Building high quality civilized cities and solving environmental problems is a long-term campaign which cannot merely be heavily dependent on “command and control” environmental measures. We noticed that some civilized cities also suffer from environmental pollution. In response to the challenges, local governments should emphasize the importance of public participation in environmental management because it helps promote the effective implementation of environmental regulation [ 47 ]. In addition, monitoring and reporting environmental violations by the public should be encouraged. It has been evidenced that it is an effective way to address environmental problems because responding to environmental complaints from local governments has become a priority in most Chinese cities, including the NCC ones. Joint cooperation between local government and the public not only helps build environmentally friendly civilized cities, but also contributes to achieving the sustainable development goals of Chinese cities. 

Availability of data and materials

The datasets generated and/or analyses during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

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This work is supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project (Grant No. 20NDJC136YB and 23YJRC08ZD-2YB), and National Natural Science Foundation of China (42371228).

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Lei Jiang: Conceptualization, investigation, supervision, writing—review and editing. Zinan Zhang: Conceptualization, formal analysis, software, writing—review and editing. Bo Zhang: Data Curation, software. Shixiong He: Data Curation, resources, visualization, investigation.

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Jiang, L., Zhang, Z., Zhang, B. et al. Does “National Civilized City” policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis. BMC Public Health 24 , 1234 (2024). https://doi.org/10.1186/s12889-024-18671-y

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Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

• Ling Tang   ORCID: orcid.org/0000-0002-2522-9675 1 , 2 ,
• Min Jia   ORCID: orcid.org/0000-0002-8317-9565 1 ,
• Junai Yang 1 ,
• Ling Li   ORCID: orcid.org/0000-0001-5738-1551 3 ,
• Xin Bo 4 , 5 &
• Zhifu Mi   ORCID: orcid.org/0000-0001-8106-0694 6  

Scientific Data volume  10 , Article number:  153 ( 2023 ) Cite this article

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As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO 2 and NO x emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

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Background & summary.

China has been suffering from severe air pollution 1 . Industrial sectors contributed the majority of China’s air pollutant emissions, representing 72.8–86.1%, 74.3–91.0% and 40.7–79.1% of national anthropogenic particulate matter (PM, comprising all PM particle sizes) 1 , 2 , sulphur dioxide (SO 2 ) 1 , 2 , 3 and nitrogen oxide (NO X ) 1 , 2 , 3 emissions, respectively, between 2010 and 2018. These air pollutants constituted the primary precursors of PM 2.5 (PM with an aerodynamic diameter within 2.5 μm) pollution, which poses severe environmental problems and public health burden 1 .

To control the industrial emissions, a nationwide, dynamic and up-to-date emission inventory is critical for accurately analysing industrial emission characteristics and targeted policymaking. There are some bottom-up emission inventories of atmospheric pollutants for China’s industrial emissions, including the Multi-resolution Emission Inventory for China (MEIC) 1 , 4 , the Regional Emission inventory in ASia (REAS) 5 , the Community Emissions Data System (CEDS) 6 , 7 , 8 and other emission datasets 9 , 10 , 11 , 12 , 13 , 14 . However, due to the lack of actual monitoring data, these databases resort to average emission factors (i.e., atmospheric pollutant emissions per unit of production or fossil fuel combustion) compiled by previous studies 15 , 16 or official guidebooks 17 , 18 (such as the National Pollution Source Census 17 published by the Council of State Governments (CSG)), which bears several shortages. First, these average emission factors do not entail direct CEMS-monitored observations but are proxies for various assumptions or indirect parameters (about operational conditions and control measures), which result in high uncertainty 19 . Second, based on many indirect parameters and associated assumptions, the emissions factors employed in previous inventories are assumed invariable within a given province 12 , 14 , region 13 or nation 1 , 4 , thereby failing to reflect individual heterogeneities throughout industrial facilities 20 . Third, available emissions factors have been evaluated up to 2017 21 , and the effect of latest mitigation policies 22 , 23 on industrial sectors through technology upgrading and operational adjustment has not been considered. Therefore, introducing direct and real-time CEMS-monitored observations can significantly reducing the estimation uncertainty due to the application of indirect and constant average emission factors.

China has started building a national continuous emissions monitoring system (CEMS) network ( http://www.envsc.cn/ ) for high-emitting industrial stationary sources (such as operating units, machines, facilities or boilers for production) since 2007 24 , to directly measure hourly and source-level PM, SO 2 and NO X concentrations 20 , 25 , 26 , 27 , 28 , 29 , 30 . During 2015–2018 period, the CEMS network involved comprehensive industrial sectors, particularly energy- and emission-intensive sectors such as thermal power industry (representing 57.7%–77.1% of plants and 95.9–97.4% of total national capacity), iron and steel industry (representing 62.9%–71.6% of plants and 74.2–88.3% of crude steel production) and cement industry (representing 63.5%–77.2% of plants and 78.9–87.6% of total clinker production). To improve the quality and reliability of CEMS system, China has implemented a number of policy actions: developing detailed specifications and technical guidelines for CEMS’ proper operation, preservation and regulation 27 , 31 ; conducting quarterly random inspections to avoid data manipulation 32 ; and comparing monitoring values among emission sources to determine outliers 33 . To date, some research has employed CEMS data to analyse industrial emissions from limited industries, including thermal power industry 20 , 26 , 34 , iron and steel industry 28 and cement industry 35 . However, these data based on actual monitoring measurements have not yet been extended to other industrial sectors, and a comprehensive analysis across different industry sectors has not been performed.

Here, we contribute to addressing above research gap by developing a new nationwide database of industrial emissions based on CEMS measurements, named Chinese Industrial Emissions Database (CIED). The CIED database considers comprehensive industrial sectors in China from 2015 to 2018, adding up to 10,933 plants and 19,032 facilities. In particular, the database introduces all available actual monitoring data of smokestack concentrations from CEMS network (exclusively provided by the China’s Ministry of Ecology and Environment (MEE), http://www.envsc.cn/ ) for PM, SO 2 and NO X from industrial plant stacks across China during 2015–2018, and estimates nationwide, real-time and dynamic industrial emission factors and absolute emissions on a source and monthly basis. The CEMS data can sufficiently provide a direct, simple estimates of nationwide, source-level and dynamic emission factors and absolute emissions for Chinese industrial sectors, which can address the above three limitations of using average emission factors. First, the CEMS database offers real-time measurements that avoid using diverse assumptions and indirect parameters employed in average emission factors of previous emission inventories and thus reduce the estimation uncertainty 36 . Second, the hourly, source-level actual CEMS measurements enhance the spatio-temporal resolutions of emission factors and absolute emissions, which can effectively highlight the heterogeneous and dynamic characteristics of industrial emissions over periods 26 , 37 . Third, the CEMS-monitored observations for the 2015–2018 period are applied, and the detailed, up-to-date emission factors and emission inventory are updated, directly reflecting the potential emission reduction effects of recent air pollution control policies 22 , 23 . Moreover, the CIED dataset also encompasses other stack specific information derived from the MEE ( http://permit.mee.gov.cn/ ), regarding geographic allocations (e.g., latitude and longitude), physical parameter (e.g., diameter, height and temperature) and so on. In addition, uncertainty analyses for total emissions of PM, SO 2 and NO X across 2015–2018 show that our estimates are more robust (with 95% confidence interval (CI) of [−0.2%, 0.1%]) relative to prior studies (with 95% CI of [−76.0%, 136.0%]) 10 , 13 , 38 , 39 , 40 , 41 , 42 ). This CEMS-based CIED dataset can be employed to conduct a more accurate analysis of overall, detailed and dynamic characteristics of industrial emissions, serve mitigation policy making for China, and offer insights for other countries looking to control industrial emissions 43 , 44 .

Scopes and databases

The CIED database encompasses comprehensive industrial sources in mainland China from 2015 to 2018 in all the provinces and municipalities (totaling 26 and 5, respectively) in mainland China. According to the Industrial Classification for National Economic Activities (GB/T 4754–2017) 45 , these industrial emission sources can be aggregated into 10 sectors or 33 subsectors (deposited at figshare 46 ). Thereafter, these sectors can be further divided into 170 subcategories (by fuel types, processes or products; deposited at figshare 46 ). Specifically, by fuel type, thermal power industry is classified into 5 subcategories (i.e., coal, gas, oil, biomass and other fuels-based burning thermal power industries), according to the varieties of fossil energies used in the power generation. By process, iron and steel industry is allocated to 7 production processes (i.e., sintering, pelletizing, coking, ironmaking, steelmaking via a basic oxygen furnace, steelmaking via an electric arc furnace and steel rolling) or 22 associated processes (e.g., sinter machine heads in sintering); and cement industry can fall into 2 processes of kiln heads and kiln tails. By product, other subsectors are classified into 141 subcategories.

The CIED dataset is a new dataset that offers nationwide, detailed, dynamic emission factors and total emissions of PM, SO 2 and NO X between 2015 and 2018 for Chinese industrial sources across different fuel types, production processes or products categories. Compared with existing inventories 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , the CIED dataset has the unique advantage in reducing estimation uncertainty by using real CEMS-monitoring data rather than average emission factor (and many assumption and uncertain parameters thereof).

In particular, the CIED dataset incorporates two databases, i.e., the CEMS database and source-specific information. The CEMS database—actual, source-level and hourly monitoring data of smokestack concentrations of PM, SO 2 and NO X for stationary industrial emission sources—are recorded by Chinese CEMS network and released by the MEE. Overall, a total of 17,134 sources (associated with 7,708 generating plants) across different industrial sectors are encompassed in the CEMS network from 2015 to 2018.

Source-specific information is provided by national facility-level database for Chinese industrial sources. Specifically, the facility-specific information for each individual industrial source involves activity level (industrial production or power generation, inputs and fuel consumption; yearly), unit or facility property (geographical location, production processes involved, emission sources associated, facility type, facility scale and age), quality of fuel and raw material and pollution control technology (category and removal efficiency), which are derived from the MEE 20 , 29 , 35 .

Pre-processing of CEMS data

The CEMS includes a sampling system (to filter and sample flue gases), an analysis system (to evaluate flue gas indicators, especially smokestack concentrations) and a data-processing system (to collect, process and report real-time measurements) 24 , 27 . These three systems of CEMS should be carefully operated, maintained and examined, in order to prevent observation biases mainly in the sampling and analysis systems (in terms of zero drift, span drift and indication errors) and invalid data communication and data loss in data-processing system (leading to null and invalid values) 27 .

To ensure the quality of CEMS data, Chinese governments have promulgated a series of measures to prevent systematic biases, including: formulating detailed specifications and technical requirements for local government agencies and industrial plants to perform and superintend the proper operation, maintenance and regulation of CEMS network 27 , 31 ; performing quarterly random examinations to prevent data manipulation 32 ; comparing data among emission sources to identify outliers 33 ; mandating plants to regularly calibrate, maintain and verify CEMS instruments 24 , 47 ; and employing third parties to conduct technical validation for the CEMS network 24 . According to these official documents, all state-monitored firms are required to post their CEMS measurements to the local authorities via associated provincial online platforms. Then, the local governments randomly check the authenticity of the reported data at least once per quarter 20 , 27 , 32 , and publicly disclose the relevant inspection results through the same online platforms 20 , 48 , 49 . The firms that engage in data manipulation (including deletion, distortion and falsification of CEMS data, etc.) are subject to strict financial and criminal penalties 50 , 51 .

Even with all the above efforts, there are still a small number of invalid values in the CEMS database (accounted for 2.6%–3.3% of the total data from 2015 to 2018, deposited at figshare 46 ), including missing data (nulls), zeros and abnormal (or extreme) observations, which should be seriously handled in accordance with relevant official documents and guidelines issued by the Chinese government. In general, nulls or zeros can be treated in three different methods depending on the duration 24 . First, we treat the nulls or zeros that last below 5 day(s) as invalid values and set nulls or zeros successive from 1 to 24 hour(s) to the averages of the two closest valid values before and after them 24 , 52 :

where C f, i, y, m, h is the stack gas concentration (g m −3 ) monitored by CEMS network, which denotes the real-time measurements of pollutant f produced by facility i in year y , month m and hour h ; \({\widehat{C}}_{f,i,y,m,h}\) represents the estimated value for the nulls or zeros C f, i, y, m, h ; C f, i, y, m, h - p and C f, i, y, m, h + j indicate the closest last valid values ( p hour(s) before) and next valid values ( j hour(s) after), respectively, of the nulls or zeros C f, i, y, m, h . Second, the nulls or zeros consecutive for more than 24 hours but less than 5 days are interpolated with the effective monthly averages near the time 24 :

where \({\overline{C}}_{f,i,y,m,\bullet }\) means the average of the hourly known values for the same atmospheric pollutant, production facility, operating year and month as C f, i, y, m, h . Conversely, we consider nulls or zeros successive for more than 5 days as an overhaul and ignore them 24 , in the light of estimation regulation. Furthermore, we use a data visualization to identify extreme data (in terms of the values outside the CEMS instruments’ measurement range) as outliers and treat those data in a same way as nulls (or zeros) in accordance with the authoritative regulations 24 .

Estimation of emission factors and emissions

Using actual CEMS-monitored observations for nationwide industrial sources, we can directly measure the emission factors for PM, SO 2 and NO X on a source and hourly basis, which is the main contribution of this work and can enhance the estimate accuracy and avoids the use of various assumptions or indirect parameters that are common in existing research 20 , 26 , 29 , 35 :

where EF f, i, y .m, h stands for the emission factor (g per activity data), expressed as the emissions mass per unit of production or fuel consumption; V i, y denotes the theoretical flue gas rate (m 3 per activity data), defined as the volume of flue gas per unit of product or fuel consumption. Given that the CEMS equipment installed at smokestacks are required to monitor the abated smokestack concentrations after the effect of pollution control technology (if any), the abated emission factors can be estimated in a direct way without considering the removal efficiency-related parameters 24 .

Since the clean air policies and relevant regulations mainly focus on emission concentrations, vast quantities of other monitoring observations (especially flue gas rates) are missing from the CEMS database. Therefore, the application of theoretical flue gas rates in our estimation can significantly prevent serious underestimation of the actual flue gas volume due to these missing data 20 , 26 , 29 and flue gas leakage 26 , 53 . Such theoretical values are estimated according to the systematic field measurements and analogy method conducted by the CSG 17 , 21 and MEE 54 , 55 , with values determining by detailed products, process, scale, raw material, technologies, and fuel types. Accordingly, the actual flue gas rate can be obtained by multiplying the theoretical flue gas rate with the real industrial production or fuel consumption. Furthermore, we examine the theoretical flue gas rates based on the actual flue gas volume from CEMS monitoring samples for thermal power industry, iron and steel industry and cement industry (covering 1516, 210 and 919 facilities, respectively). Our estimates indicate that the actual values of flue gas rates generally approach their corresponding theoretical ones, within the uncertainty range (defined as the lower and upper bounds of a 95% confidence interval around the central estimates 42 ) of ±10.1%, ±12.1% and ±6.7% respectively, at the 95% confidence level (deposited at figshare 46 ). The results are consistent with the finding of existing studies 20 , 26 , 29 and confirm the application of theoretical flue gas rates.

Then, we estimate the total emissions of PM, SO 2 and NO X for Chinese industrial sectors by multiplying the emission factors by the activity data, on a source and monthly basis 19 :

where E f, i, y, m indicates the absolute atmospheric pollutant emissions (g); A i, y, m means the activity level, defined as the total amount of production (e.g., kg for crude steel in iron and steel industry) or fossil fuel consumption (kg for solid or liquid fuels and m 3 for gas fuels).

In the CIED dataset, we calculate the total emissions on a monthly scale, in which emission factors are aggregated from hourly values to monthly values. Notably, the comprehensive annual, facility-level activity data is only available for three industrial subsectors (i.e., thermal power industry 20 , 26 , iron and steel industry 29 and cement industry 35 ) for 2015–2018. Therefore, we need to use the production data on a monthly basis and a provincial scale as the weights to assign the yearly facility-level activity data 26 :

where the subscript s i indicates the industrial subcategory s to which facility i belongs; p i means the province p where facility i is located; \({A}_{{p}_{i},{s}_{i},y,m}\) denotes the monthly provincial production of industrial subcategory s in province p , which is derived from the official statistical yearbook (i.e., Chinese Energy Statistics Yearbooks 56 and China Statistical Yearbooks 57 ) and reports (available at http://www.cementchina.net/ ). Given that the lack of comprehensive facility-level activity data for other 30 subsectors (covering 74 types of industrial products), we directly use monthly province-level activity data (derived from China Statistical Yearbooks 57 ), i.e., \({A}_{i,y,m}={A}_{{p}_{i},{s}_{i},y,m}\) , or scale annual data (from China Statistical Yearbooks 57 , China Mineral Resources 58 , China’s Building Materials Industry Yearbook 59 and the association of China refractories industry) to monthly levels using the proxies of monthly production of counterpart products.

Uncertainties

We consider the uncertainties stemming from the volatility in the CEMS-monitored observations, theoretical flue gas rates and estimated monthly activity data, assuming that uncertainties in these three parameters are independent. Using Monte Carlo approach, we perform the systematic uncertainty analyses to examine the reliability and robustness of our estimated results introducing actual CEMS measurements. The detailed analysis steps are as follows: (a) assume the probability distributions for each tested model variable (CEMS-based smokestack concentrations, theoretical flue gas rates or activity levels) and obtain the related distribution parameters (e.g., mean and the standard deviation) as inputs to the Monte Carlo simulation; (b) produce random values following their respective probability distributions through Monte Carlo approach; (c) input random values to Eqs. ( 3 – 5 ) to generate a new group of emission factors and absolute emissions; and (d) run steps (b) and (c) for 10,000 times to obtain the range of uncertainty in our estimations in terms of 2 standard deviations (s.d.) of the above 10,000 sets of results 19 , 42 , 60 . Our results indicate that the estimates are relatively stable, with 2 s.d. compared with the associated mean (in %; reflecting the uncertainty ranges of our estimates) within ±7.2% for emission factors and ±4.0% for absolute emissions (Table  1 ). In particular, based on the detailed source-level activity data, uncertainty ranges in our estimates for three subsectors (i.e., thermal power industry, iron and steel industry and cement industry) are relatively small (±6.8% for emission factors and ±0.2% for emissions), compared to that for other subsectors (±7.2% and ±4.8%, respectively).

Uncertainties in CEMS data

To examine the volatilities in the high frequency CEMS data, we assume probability distributions (in a uniform distribution) for source-specific and monthly concentrations of each atmospheric pollutant, according to the tolerance ranges issued by the official regulation (HJ/T75-2007) 24 . In detail, a set of legal CEMS measures are mandated to control the systematic errors within ±15%, ±5% and ±5% for PM, SO 2 and NO X concentrations, respectively. Regarding the emission sources without CEMS, we use bootstrap simulation to randomly select samples from facilities with CEMS that in the same region, over the same period, and of similar emission source, fuel type and production process. Then, a Monte Carlo method is employed to generate random samples of pollutant concentrations following the associated distributions, and the simulations are conducted for 10,000 times to calculate the uncertainty ranges for emissions factors and absolute emissions (in terms of 2 s.d.). Our estimates indicate that the uncertainties ranges for emission factors and total emissions are within ±5.8% and ±3.2%, respectively (Table  1 ).

Uncertainties in theoretical flue gas rates

In our estimation, the introduction of theoretical flue gas rates might arise uncertainty due to the large amount of missing data on real-monitoring flue gas rates from CEMS networks. Although this approach has the advantage of preventing severe underestimation and flue gas leakage, uncertainties may be attributed to the regardless of heterogeneities among individual facilities in production technologies, operational conditions and feedstocks, etc. Under such background, we calculate the uncertainty ranges based on the CEMS-monitored samples for 1,373, 210 and 919 facilities of thermal power industry, iron and steel industry and cement industry, respectively; and perform a single-sample two-tailed t -test (deposited at figshare 46 ) for each subcategory of these three industrial sectors. The results demonstrate that the actual CEMS monitoring flue gas rates mostly close to their theoretical values in our estimates, within a likely range of ±12.1% at a 95% confidence level. Then, the Monte Carlo technique is employed to generate random values of flue gas rates uniformly distributed in the relevant uncertainties ranges. In addition, we use the maximum ranges for the industrial sectors without uncertainty ranges (e.g., ±10.07% for thermal power facilities burning oil). With 10,000 simulations, our analysis indicate that uncertainty ranges, represented by 2 s.d., are quite small, within ±5.6% and ±2.5% for emission factors and emissions, respectively (Table  1 ).

Uncertainties in activity levels

To explore the uncertainty generated in the allocation of facility- and province-level activity data from yearly to monthly, we set a normal distribution with a 5% coefficient of variation (CV, the standard deviation divided by mean) for three subsectors of thermal power industry, iron and steel industry and cement industry with comprehensive facility-specific activity level 20 , 26 , 29 , 35 , and 10% CV for other industrial sectors according to existing literature 61 . Besides, the Monte Carlo method is conducted to produce random monthly activity values for estimated each facility or province. Relying on a total of 10,000 simulations, the uncertainty range in terms of 2 s.d. of all simulation results for absolute emissions is only ±0.9% from 2015 to 2018 (Table  1 ).

Data Records

The CIED datasets 46 are available at https://doi.org/10.6084/m9.figshare.c.6269295 . It is organized as a set of excel datasets according to indicator and date. The indicators including emission concentrations, emission factors, activity data, absolute emissions and additional descriptions (including subcategories description, flue gas rates, comparison of uncertainty, invalid data and CEMS ranges); the date covers four years from 2015 to 2018. After merging, there are 5 excel datasets with 22 sheets provided at Figshare. In particular, the CIED database provided the high-resolution data at a source and monthly basis.

The CIED dataset introduces actual systematic smokestack concentration measurements from China’s CEMS network and source-specific activity level from the MEE to directly estimate Chinese industrial emissions. In particular, the dataset presents systematic, dynamic, detailed emission factors and total emissions for PM, SO 2 and NOx from China’s industrial sources during 2015–2018, by region (including 26 provinces and 4 municipalities) and sector (33 subsectors and 170 subcategories; Fig.  1 ).

Estimated emissions of Chinese industrial sectors from 2015 to 2018. ( a – c ), Estimated monthly industrial emissions (Tg) of PM ( a ), SO 2 ( b ) and NO X ( c ). The error bars represent the uncertainty ranges.

Technical Validation

Independent verification.

The estimates drawn from the CEMS data need careful verification against other independent data, which can also provide insight as to how the large emission reductions in industrial sectors (based on CEMS data) translate to trends in regional atmospheric concentrations. Therefore, we conduct an independent verification against an atmospheric dataset (i.e., ground-level measurements by air-quality monitoring stations). In particular, we compare the changes in industrial emissions from 2015 to 2018 (based on CEMS data) with those in regional atmospheric concentrations, both at the national level and in the top 10 provinces with the largest atmospheric emissions as of 2018. This experimental design was also used in existing studies 62 , 63 . In each of the 10 provinces, the industrial sectors has already been shown to have a large contribution to air pollution 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 . These provinces include Anhui, Guangdong, Jiangsu, Zhejiang, Shandong, Hubei, Chongqing, Henan, Hebei and Inner Mongolia (ranked by atmospheric emissions).

The ground-level PM 10 , SO 2 and NO 2 concentrations measured by national air-quality monitoring stations are employed to verify the atmospheric impact of the changes in PM, SO 2 and NO X emissions, respectively; these data are obtained from China National Environmental Monitoring Center ( http://www.cnemc.cn/ ). The large reductions in PM, SO 2 and NO X emissions from China’s industrial sectors largely correlated with ground-level monitoring data. As shown in Fig.  2 , the changes in all PM, SO 2 and NO X associated atmospheric concentrations (yellow bars of Fig.  2 ) are generally similar to the changes in emissions from industrial sectors (blue bars).

Independent verification against atmospheric concentrations. a – c , Changes in industrial emissions (blue bars) for PM ( a ), SO 2 ( b ) and NOx ( c ) and the associated ambient concentrations from 2015 to 2018, at the national level and China’s top 10 provinces of the largest atmospheric emissions as of 2018. To verify the atmospheric impact of the emission changes for PM, SO 2 and NO X (based on the CEMS data), ground-level PM 10 , SO 2 and NO 2 concentration observations by national air-quality monitoring stations (yellow bars) are employed.

Comparisons with existing emission databases

For verification, we compare our estimates for Chinese industrial emissions to previous datasets, as illustrated in Fig.  3 . The results show that our estimates (based on the real measurements) are generally 85.15%, 20.32%, 23.21% below previous estimates. This is because existing studies resort to utilizing indirect average emission factors that were estimated up to 2017, overlooking the latest mitigation effects, especially associated with the upgraded pollution control technologies 5 , 6 , 7 , 8 , 9 for PM, SO 2 and NO X respectively. In addition, the uncertainty analysis shows that our estimation exhibits a relatively low uncertainty level (with 95% CI of [−0.2%, 0.1%]) compared to existing studies (with 95% CI of [−76.0%, 136.0%]; deposited at figshare 46 ) 10 , 13 , 38 , 39 , 40 , 41 , 42 , by using real, hourly and facility-level CEMS measurements.

Comparison of estimated Chinese industrial emissions between 2015 and 2018. ( a – c ), The estimated industrial emissions in China (Tg) for PM ( a ), SO 2 ( b ) and NO X ( c ) in our dataset (yellow bars) and in existing datasets (MEIC ( www.meicmodel.org ); REAS ( https://www.nies.go.jp/REAS/ ); CEDS ( https://github.com/JGCRI/CEDS ); the Emissions Database for Global Atmospheric Research (EDGAR) ( https://edgar.jrc.ec.europa.eu/ ); non-yellow bars). The error bars denote the related uncertainties.

Usage Notes

The CIED dataset is subject to several limitations. First, China’s CEMS network has not yet covered all industrial emission sources, and these samples can be collected and incorporated to extend a complete CIED database in the future. Second, besides air pollutants, the CIED dataset can also introduces the real measurements of greenhouse gases (particularly CO 2 ) and water pollutants, to support a comprehensive analyse of climate change policies and clean air policies for Chinese industrial emissions. Third, to enhance the accuracy of the estimation, future work can incorporate comprehensive high-frequency operational data (including activity data and flue gas rates) for each facility. Fourth, although Chinese governments have issued a range of stringent regulations 30 to guarantee the reliability of the CEMS system, the careful verification, for example, comparing CEMS data with satellite data 27 or ground-level monitoring data 28 , is valuable for verifying the results drawn from the CEMS data. Given that, we would update our database in the future if data are available.

Code availability

The CIED datasets are available in the form of XLSX files. No custom code is used in the construction of the datasets.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (71971007, 72004144 and 72174125), the Beijing Natural Science Foundation (JQ21033), the Fundamental Research Funds for the Central Universities (buctrc202133).

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L.T., L.L., X.B. and Z.M. designed the research. M.J., J.Y., L.L. and X.B. coordinated the data processing. L.T. and M.J. performed the experimental work. L.T., M.J. J.Y., L.L. and Z.M. wrote the paper. All authors contributed to developing, writing and polishing the manuscript.

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SYSTEMATIC REVIEW article

Exposure to indoor air pollution and adverse pregnancy outcomes in low and middleincome countries: a systematic review and meta-analysis.

• 1 Wollo University, Dessie, Ethiopia
• 2 Amref Health Africa, Nairobi, Kenya

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Introduction: Exposure to indoor air pollution such as biomass fuel and particulate matter is a significant cause of adverse pregnancy outcomes. However, there is limited information about the association between indoor air pollution exposure and adverse pregnancy outcomes in low and middle-income countries. Therefore, this meta-analysis aimed to determine the association between indoor air pollution exposure and adverse pregnancy outcomes in low and middle-income countries. Methods: International electronic databases such as PubMed, Science Direct, Global Health, African Journals Online, HINARI, Semantic Scholar, and Google and Google Scholar were used to search for relevant articles. The study was conducted according to the updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A random effect model at a 95% confidence interval was used to determine the association between indoor air pollution exposure and adverse pregnancy outcomes using STATA version 14. Funnel plot and Higgs I 2 statistics were used to determine the publication bias and heterogeneity of the included studies, respectively. Results: A total of 30 articles with 2,120,228 study participants were included in this meta-analysis. The pooled association between indoor air pollution exposure and at least one adverse pregnancy outcome was 15.5% (95%CI: 12.6-18.5), with significant heterogeneity (I 2 =100%; p < 0.001). Exposure to indoor air pollution increased the risk of small for gestational age by 23.7% (95%CI: 8.2-39.3) followed by low birth weight (17.7%; 95%CI: 12.9-22.5). Exposure to biomass fuel (OR=1.16; 95%CI: 1.12-1.2), particulate matter (OR=1.28; 95%CI: 1.25-1.31), and kerosene (OR= 1.38; 95%CI: 1.09-1.66) were factors associated with developing at least one adverse pregnancy outcomes. We found that more than one in seven pregnant women exposed to indoor air pollution had at least one adverse pregnancy outcome. Specifically, exposure to particulate matter, biomass fuel, and kerosene were determinant factors for developing at least one adverse pregnancy outcome. Therefore, urgent comprehensive health intervention should be implemented in the area to reduce adverse pregnancy outcomes.

Keywords: Adverse pregnancy outcomes, low birth weight, Preterm Birth, small for gestational age, Stillbirth, biomass

Received: 22 Jan 2024; Accepted: 08 May 2024.

Copyright: © 2024 Yadate, Asmare, Demeke, Arefaynie, Mohammed, Tareke, Keleb, Kebede, TSEGA, Wndawkie, Kebede, Mesfin, Abeje and Bekele. 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: Chala D. Yadate, Wollo University, Dessie, Ethiopia

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ENCYCLOPEDIC ENTRY

Air pollution.

Air pollution consists of chemicals or particles in the air that can harm the health of humans, animals, and plants. It also damages buildings.

Biology, Ecology, Earth Science, Geography

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Air pollution consists of chemicals or particles in the air that can harm the health of humans, animals, and plants. It also damages buildings. Pollutants in the air take many forms. They can be gases , solid particles, or liquid droplets. Sources of Air Pollution Pollution enters the Earth's atmosphere in many different ways. Most air pollution is created by people, taking the form of emissions from factories, cars, planes, or aerosol cans . Second-hand cigarette smoke is also considered air pollution. These man-made sources of pollution are called anthropogenic sources . Some types of air pollution, such as smoke from wildfires or ash from volcanoes , occur naturally. These are called natural sources . Air pollution is most common in large cities where emissions from many different sources are concentrated . Sometimes, mountains or tall buildings prevent air pollution from spreading out. This air pollution often appears as a cloud making the air murky. It is called smog . The word "smog" comes from combining the words "smoke" and " fog ." Large cities in poor and developing nations tend to have more air pollution than cities in developed nations. According to the World Health Organization (WHO) , some of the worlds most polluted cities are Karachi, Pakistan; New Delhi, India; Beijing, China; Lima, Peru; and Cairo, Egypt. However, many developed nations also have air pollution problems. Los Angeles, California, is nicknamed Smog City. Indoor Air Pollution Air pollution is usually thought of as smoke from large factories or exhaust from vehicles. But there are many types of indoor air pollution as well. Heating a house by burning substances such as kerosene , wood, and coal can contaminate the air inside the house. Ash and smoke make breathing difficult, and they can stick to walls, food, and clothing. Naturally-occurring radon gas, a cancer -causing material, can also build up in homes. Radon is released through the surface of the Earth. Inexpensive systems installed by professionals can reduce radon levels. Some construction materials, including insulation , are also dangerous to people's health. In addition, ventilation , or air movement, in homes and rooms can lead to the spread of toxic mold . A single colony of mold may exist in a damp, cool place in a house, such as between walls. The mold's spores enter the air and spread throughout the house. People can become sick from breathing in the spores. Effects On Humans People experience a wide range of health effects from being exposed to air pollution. Effects can be broken down into short-term effects and long-term effects . Short-term effects, which are temporary , include illnesses such as pneumonia or bronchitis . They also include discomfort such as irritation to the nose, throat, eyes, or skin. Air pollution can also cause headaches, dizziness, and nausea . Bad smells made by factories, garbage , or sewer systems are considered air pollution, too. These odors are less serious but still unpleasant . Long-term effects of air pollution can last for years or for an entire lifetime. They can even lead to a person's death. Long-term health effects from air pollution include heart disease , lung cancer, and respiratory diseases such as emphysema . Air pollution can also cause long-term damage to people's nerves , brain, kidneys , liver , and other organs. Some scientists suspect air pollutants cause birth defects . Nearly 2.5 million people die worldwide each year from the effects of outdoor or indoor air pollution. People react differently to different types of air pollution. Young children and older adults, whose immune systems tend to be weaker, are often more sensitive to pollution. Conditions such as asthma , heart disease, and lung disease can be made worse by exposure to air pollution. The length of exposure and amount and type of pollutants are also factors. Effects On The Environment Like people, animals, and plants, entire ecosystems can suffer effects from air pollution. Haze , like smog, is a visible type of air pollution that obscures shapes and colors. Hazy air pollution can even muffle sounds. Air pollution particles eventually fall back to Earth. Air pollution can directly contaminate the surface of bodies of water and soil . This can kill crops or reduce their yield . It can kill young trees and other plants. Sulfur dioxide and nitrogen oxide particles in the air, can create acid rain when they mix with water and oxygen in the atmosphere. These air pollutants come mostly from coal-fired power plants and motor vehicles . When acid rain falls to Earth, it damages plants by changing soil composition ; degrades water quality in rivers, lakes and streams; damages crops; and can cause buildings and monuments to decay . Like humans, animals can suffer health effects from exposure to air pollution. Birth defects, diseases, and lower reproductive rates have all been attributed to air pollution. Global Warming Global warming is an environmental phenomenon caused by natural and anthropogenic air pollution. It refers to rising air and ocean temperatures around the world. This temperature rise is at least partially caused by an increase in the amount of greenhouse gases in the atmosphere. Greenhouse gases trap heat energy in the Earths atmosphere. (Usually, more of Earths heat escapes into space.) Carbon dioxide is a greenhouse gas that has had the biggest effect on global warming. Carbon dioxide is emitted into the atmosphere by burning fossil fuels (coal, gasoline , and natural gas ). Humans have come to rely on fossil fuels to power cars and planes, heat homes, and run factories. Doing these things pollutes the air with carbon dioxide. Other greenhouse gases emitted by natural and artificial sources also include methane , nitrous oxide , and fluorinated gases. Methane is a major emission from coal plants and agricultural processes. Nitrous oxide is a common emission from industrial factories, agriculture, and the burning of fossil fuels in cars. Fluorinated gases, such as hydrofluorocarbons , are emitted by industry. Fluorinated gases are often used instead of gases such as chlorofluorocarbons (CFCs). CFCs have been outlawed in many places because they deplete the ozone layer . Worldwide, many countries have taken steps to reduce or limit greenhouse gas emissions to combat global warming. The Kyoto Protocol , first adopted in Kyoto, Japan, in 1997, is an agreement between 183 countries that they will work to reduce their carbon dioxide emissions. The United States has not signed that treaty . Regulation In addition to the international Kyoto Protocol, most developed nations have adopted laws to regulate emissions and reduce air pollution. In the United States, debate is under way about a system called cap and trade to limit emissions. This system would cap, or place a limit, on the amount of pollution a company is allowed. Companies that exceeded their cap would have to pay. Companies that polluted less than their cap could trade or sell their remaining pollution allowance to other companies. Cap and trade would essentially pay companies to limit pollution. In 2006 the World Health Organization issued new Air Quality Guidelines. The WHOs guidelines are tougher than most individual countries existing guidelines. The WHO guidelines aim to reduce air pollution-related deaths by 15 percent a year. Reduction Anybody can take steps to reduce air pollution. Millions of people every day make simple changes in their lives to do this. Taking public transportation instead of driving a car, or riding a bike instead of traveling in carbon dioxide-emitting vehicles are a couple of ways to reduce air pollution. Avoiding aerosol cans, recycling yard trimmings instead of burning them, and not smoking cigarettes are others.

Downwinders The United States conducted tests of nuclear weapons at the Nevada Test Site in southern Nevada in the 1950s. These tests sent invisible radioactive particles into the atmosphere. These air pollution particles traveled with wind currents, eventually falling to Earth, sometimes hundreds of miles away in states including Idaho, Utah, Arizona, and Washington. These areas were considered to be "downwind" from the Nevada Test Site. Decades later, people living in those downwind areascalled "downwinders"began developing cancer at above-normal rates. In 1990, the U.S. government passed the Radiation Exposure Compensation Act. This law entitles some downwinders to payments of $50,000.

Greenhouse Gases There are five major greenhouse gases in Earth's atmosphere.

• water vapor
• carbon dioxide
• nitrous oxide

London Smog What has come to be known as the London Smog of 1952, or the Great Smog of 1952, was a four-day incident that sickened 100,000 people and caused as many as 12,000 deaths. Very cold weather in December 1952 led residents of London, England, to burn more coal to keep warm. Smoke and other pollutants became trapped by a thick fog that settled over the city. The polluted fog became so thick that people could only see a few meters in front of them.

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Related Resources

Home Essay Examples Environment Air Pollution

The Ongoing Severity of Air Pollution: Analytical Essay

• Category Environment
• Subcategory Human Impact
• Topic Air Pollution

The ensuing paper identifies the key effect of climate change, along with its origin and detrimental effects on mankind. Combustion of fossil fuels for transportation, heating and cooling, and industry are the main sources of air pollution (Abelsohn, 2011). The gradual expansion of industrialization over the years has triggered air pollution (AR), the most pressing result of climate change, which has now become a synthetic hazard. Advanced technology and factories used to produce life necessities is now at the expense of mankind’s health. This paper utilizes the skyrocketing statistics conducted by the World Health Organization (WHO) to further prove the health issues as a results of seven million premature deaths motivated by AR on a global scale (2014). In addition, provided are achievable steps and possible solutions individuals can take in an effort to mitigate this ongoing severity in order to fortify legislation primarily focused on AR.

Introduction

Within the extents of American Samoa’s borders, it is evident that air pollution is currently a major problem that is prevalent not only in our island, but also on a global scale. The frequent release of carbon emission poses a significant threat on air cleanliness, and will eventually impose on people’s health. Due to these emissions, a vast audience are at high risk and may contract certain health issues such as the following: chronic diseases, respiratory problems, cancer, and premature hospitalization. Therefore, matters regarding air pollution demand immediate legislative attention. Although industrialization provides many technological conveniences, air pollution poses a great threat to the public health and environment on a global and local scale; however, there are possible routes collective communities can take in an effort to tackle this synthetic hazard.

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Description of Sides of Issue

Air pollution is the mixture of the following chemicals: motor vehicle emissions, factories fossil-fuel combustions, power plants, industrial and electrical power-generation processes, wildfires, volcanic eruptions, and gasoline powered equipment with the air that forms a manifold of toxic, poisonous substances that harm a society, a community, or even worse corrupt an entire nation with its toxic fumes. Air pollution can contribute to many world-wide problems in specific areas of the world. It is important to stay alert because air pollution is able to ruin the most beautiful cities with its smog, rain, fog, snow or dry particles. Thomas Gale (2005) implores the world to recognize the effects of air pollution because it is not only deadly, but extremely dangerous when people breathe in its harmful chemicals. The chemical sulfur dioxide is created through the burning of sulfur, which comes from the fossil fuel combustion. It forms a substance called sulfur dioxides (Gale, 2005). According to the research of the World Health Organization, air pollution is the most threatening problem of the world because it affects the environment and the health of the public. The chemicals in that are released in the air are sulfur dioxide, nitrogen dioxide, and carbon monoxide. The sulfur dioxide originates from the industrial and electrical power-generating in the fossil fuel combustion process. It is emitted into the air and it is combined with the water, oxygen, and oxidants. It falls to the earth as either snow, fog, rain, or dry particles, and it is commonly known or referred to as acid rain. This chemical makes it very difficult to breathe for a person with a respiratory problem like asthma and it can corrupt your respiratory system in the process. It can also cause symptoms such as: fatigue, nausea, it can put you in a coma, and worse death.

Nitrogen dioxide is another harmful chemical that is emitted into the air from the motor vehicles, power plants, and fossil-fuel burning industries (Gerdes, 2009). It can cause a myriad symptoms to occur but it can damage a person’s respiratory system. According to Gale (2005), nitrogen dioxide can trigger the defense mechanisms of the lungs and it can worsen a person’s respiratory infections. In order to reduce the risks of these chemicals, people need to spend more time walking and less time driving vehicles to increase the quality of the air. It said that biking, carpooling, and walking can be helpful in decreasing the effect of air pollution. Carbon Monoxide is processed through the incomplete combustion of carbon-containing materials, such as: gasoline, natural gas, oil, coal, wood, and tobacco. The chemical is hazardous because it can cause an interference during the process of the blood being carried to the brain (Gerdes, 2011). It is mainly emitted by motor vehicles and it is highly effective in downtown urban areas where the density is severely high (Gale, 2005). Other chemicals can also contribute to air pollution, harming the ozone layer and causing climate change to occur. This outdoor pollutant can cause some fatal damage to certain areas of a city. With further study, people in communities can come together to combat this threat of air pollution. Separated or combined, these chemicals can seriously destroy and devastate the environment with its chemicals; it is significant to reduce air pollution.

The frequent emission of these harmful chemicals have a major effect on the ozone layer. The sole purpose of the ozone layer, the outer layer of the earth’s stratosphere, is to absorb the ultraviolet rays of the sun. According to the National Geographic magazine, the depletion of the ozone layer is primarily triggered by chlorofluorocarbons (2018). These are chemicals that contained in products such as refrigerants, aerosol sprays, and solvents (Elkins, 1999). The University Corporation for Atmospheric Research states that as these chemicals are released, it is broken down into chlorine due to exposure to ultraviolet rays (2018). In the form of chlorine, it contacts with oxygen atoms within the ozone layer producing a depletion of the ozone layer.

As a result of the deterioration of the ozone layer, the earth becomes more susceptible to the harmful rays of the sun. Air pollution is motivated by the additional chemicals such as carbon dioxide and methane, also commonly referred to as “greenhouse gases”. The Department of Environmental Protection of Massachusetts attests that the atmosphere begins to trap heat producing the greenhouse effect, causing a major increase in temperature (2016). As such, the earth then begins to experience severe weather, eventually triggering the global crisis of climate change. Severe weather has the ability to heighten natural disasters, making it more dangerous than expected. Examples would include heavy rains, drought, or unbearably hot weather. Despite the several, effects of climate change, air pollution is the most pressing casualty that influences not only the environment, but public health as well.

Air pollution is known to have manifold outweighing effects on certain aspects of life that is often regarded with apathy. Despite its negative impacts, some substances that contribute to air pollution have been known to actually have a positive influence. When one takes a deep breath, they are bound to inhale tens of millions of solid particles and liquid droplets. These specks of matter are known as aerosols, and they can be found in the air over oceans, deserts, mountains, forests, ice, and every ecosystem in between. They drift in Earth’s atmosphere from the stratosphere to the surface and range in size. Despite their small size, they have major impacts on our climate and our health (Voiland, 2010).

Aerosols are also referred to as pollutants. In an article published by the National Public Radio, it said that aerosols could be good for the planet. Natalie Mahowald, a climate researcher at Cornell University, attests that scientists are currently trying to identify what activity is performed aerosols are released into the atmosphere. ‘There are so many different kinds of aerosols and they have many different sources,’ she says. ‘Some warm and some cool. But in the net, humans are emitting a lot of extra aerosols, and they tend to cool for the most part.’

Mahowald continues to describe its usage by stating that aerosols reflect sunlight back into space, or they stimulate clouds that keep us cool. But it turns out that’s not all they do. These aerosols also influence how much carbon dioxide gets drawn out of the air by plants on land and in the sea. ‘They can add nutrients, for example, to the oceans or to the land,’ Mahowald says. ‘But also while they’re in the atmosphere they can change the climate, and so that also can impact the amount of carbon the land or the ocean can take up. So there are quite a few different ways that aerosols can interact.’In addition, aerosols are not only helping reduce global warming by cooling the atmosphere, but they’re helping reduce the amount of carbon dioxide that stays in the air once we emit it (Harris, 2011). However, mankind persists in attempting to limit the emission of aerosols because it damages public health, but it greatly assists in terms of the climate.

Air pollution is a serious matter that must be paid heed to in order to provide a remedy. It is a synthetic hazard that is primarily started by major factories and industries. The effects of air pollution are known to negatively impact our environment, deplete the ozone layer, and spark skyrocketing statistics in immortality rates that are related to respiratory diseases. This is all achieved by the frequent emission of greenhouse gases that are released into the atmosphere. However, some of the substances released are proven to have a positive effect on our environment and climate. In spite of the opposing views, it is in my best interest that air pollution must be precluded in order to promote a sustainable future because our resources, land, water, health and agriculture is at stake. Although it is not entirely ominous, the bad still outweighs the good.

Discussion of Position

In order to keep our air free from chemicals, people must take the necessary steps to prevent infections and other deadly illnesses that are caused by air pollution. The United States Environmental Protection Agency urges people to use less equipment that is powered by gasoline due to the toxic chemicals that is released into the air (2018). The International Association for Medical Assistance to Travelers deems it preferable to engage in physical activity by walking to work instead of using motor vehicles or using public transportation to diminish the effect of the chemicals on the human health (2019). People need to avoid burning plastics, trash, leaves, and other materials to hinder air pollution from affecting countries, especially American Samoa. People should make sure that gasoline does not spill out of gas tanks because it can contribute lethal gases into the air that can cause air pollution. People should take this air pollution problem as a major crisis so that it can be addressed and solved for future and present generations. People should spend more time enjoying the fresh air; instead of polluting it, countries like American Samoa and India should do their absolute best to shape a better place and environment where people can live and breathe clean air.

According to the Science and Development Network report (2018), people have been suffering from air pollution and it has been creating chaos for some parts of the world, including American Samoa. Air pollution has proven to be the known killer then tobacco because it has been affecting other developed countries such as India. Additionally, the World Health Organization states that one third of the world’s seven million deaths in Western Pacific Region have happened because of air pollution (2019). It is also a life threatening situation for all human beings because ninety percent of people inhale these toxic gases around the world. In spite of this, young adult and children are more susceptible to the inhalation of these chemicals because of strenuous activities. It is significant to reduce the air people breathe for the sake of the future generations that will seek to form a better environment for others to grow, prosper, and nurture. Furthermore, President Emmanuel Macron of France chimes in to further support the high demand for immediate action to combat this pressing issue at the Seventy Second United Nations General Assembly (2017) :

We all know that the degradation of the environment is already causing hundreds of thousands of deaths – millions according to some calculations – due to global warming and air pollution. And those most affected are always the most vulnerable people in the most vulnerable countries: children, elderly people and women, particularly pregnant women, and unless it’s slowed down this change will cause the disappearance of entire territories. It will accentuate water wars, famines, the exhaustion of natural resources, exoduses and therefore all the geopolitical turmoil issues of which we’re perfectly aware and of which, much too often, we deal only with the ultimate consequences without tackling the root causes.

In researching this topic, I understood the casualties that it could produce; however, my most pressing question was what can we, as a collective community, do to address it? It is without a doubt that air pollution has atrocious effects on society, and it is conspicuous that certain steps must be taken to combat this issue at hand with an iron fist. As such, this can be achieved by humbly approaching the Fono to pass an additional regulation. Within this regulation, it is compulsory for customers to have their vehicle undergo an extensive emission inspection program, also referred to as the smog check. This must be carried out by the American Samoa Environmental Protection Agency in coordination with the Department of Public Safety to decrease the effect of air pollution. A smog check monitors a vehicle and its engine emission in an effort to ensure clean air quality and place a sense of supervision on carbon emission, specifically from vehicles (Moor, 2016). The smog check is currently effective and is currently used by thirty-three states and is conducted biennially; however, vehicles that are twenty-five years or older are exempt from emission testing.

The Bureau of Automotive Repair Smog Check Referee Program explains that prior to car renewal registrations, each vehicle that has been inspected must present a specific indication if the vehicle requires a smog check (2016). It is well understood that this implementation will require sufficient time to fully process each vehicle on island. In spite of this, the emission inspection program will commence specifically with heavy-duty motor vehicles such as the following: busses, colossal trucks, recreational vehicles, delivery trucks, etc. Vehicles that contribute the most towards air pollution can be processed by tackling the commercial vehicles. As time proceeds, the program will then extend to personal vehicles to help with this world crisis the communities face.

In opposition to this inspection program, one may raise the concerns of those who are not equipped with the financial means to support themselves, if their vehicle requires service when it fails to meet the emission standards. It is imperative that the Fono implements a government transportation system to those who may be affected by these issues. Although there are busses that exist, they are owned by private businesses. The government transportation line is anticipated to be comprised of electric busses, in order to eliminate car exhaust and promote clean transportation. The Environment America Research and Policy Center proves that electric busses can preclude 5.3 million tons of greenhouse gas emissions per year (2018). In addition, it would be highly appreciated if island-wide bicycle lanes were added to our road infrastructure to provide another alternative in order to reduce the frequent usage of cars and enrich the air quality of American Samoa.

People need to realize the dangers of air pollution and its effect on communities. It is able to devastate countries and corrupt nations, such as India and American Samoa with its toxic chemicals. Air pollution is damaging the ozone layer, and it is causing the change of climate. The ozone layer seems to be getting thinner and thinner by the day because of air pollution. In spite of the manifold casualties, people with respiratory problems and other pressing conditions will continue to suffer and eventually face immortality if air pollution continues to be regarded with complacency. Individually, one option people have, but are not limited to, is to spend more time walking and less time in their vehicles polluting the air. In order to increase the chance of human survival, immediate action must be taken to decrease the emission of harmful chemicals into the air. As a collective community, we must disregard the minimal positive impact parts of air pollution is said to have. It is highly imperative that these measures be taken and that air pollution is approached with a seriousness because our means of living and public health are jeopardized.

• Abelsohn, A. (2011). Health effects of outdoor air pollution. Canadian Family Physician, 57(8): 881–887.
• Aerosols Absorb; Aerosols Reflect. (2009). National Aeronautics and Space Administration. Retrieved from https://svs.gsfc.nasa.gov/10389.
• Air Pollution. (2019). World Health Organization. Retrieved from https://www.who.int/airpollution/en/.
• Air pollution, causes, effects and solutions. (2017). National Geographic. Retrieved from https://www.nationalgeographic.com/environment/global-warming/pollution/.
• Air Pollution Challenges:Common Pollutants. (2018). United States Environmental Protection Agency. Retrieved from https:/www.epa.gov/clean-air-act-overview/air-pollution-current-and-future-challenges.
• American Samoa General Health Risks: Air Pollution. (2019). International Association for Medical Assistance to Travelers. Retrieved from https://www.iamat.org/country/american-samoa/risk/air-pollution.
• Gale, T. (2005). Is air pollution a serious threat to health? Farmington Hills, Michigan: Green Haven Press.
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• Gerdes, L. I. (Ed.). (2011). Opposing Viewpoints: Pollution. Farmington Hills, MI: Christine Nasso.
• Harris, R. (2011). Air pollution: bad for health but good for planet? National Public Radio. Retrieved from https://www.npr.org/2011/11/11/142218650/air-pollution-bad-for-health-but-good-for-planet.
• India’s air pollution deadlier than tobacco – study. (2018). Science and Development Network Retrieved from https://www.scidev.net/asia-pacific/pollution/news/india-s-air-that-tobacco- study.html pollution-deadlier-.com.
• Nordstrom, L. (2014). Paris pollution: ‘like a room with eight smokers’. The Local. Retrieved from https://www.thelocal.fr/20141125/paris-pollution-like-a-tiny-room-with-8-smokers.
• Rowell, M. (2016). What it’s like to live in the world’s most polluted city. National Geographic. Retrieved from https://news.nationalgeographic.com/2016/04/160425-new-delhi-most-most-polluted-city-matthieu-paley/.
• Voiland, A. (2010). Aerosols: tiny particles, big impact. Earth Observatory National Aeronautics and Space Administration. Retrieved from https://earthobservatory.nasa.gov/features/Aerosols.
• Woodward, J. (2004). Conserving the Environment: Current Controversies. Farmington Hills, Michigan: GreenHaven Press.

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• Biology Article
• Essay On Air Pollution 200 Words 500 Words

Essay on Air Pollution

Essay on air pollution is a crucial topic for students from an academic perspective. Moreover, an essay is one of the most effective ways to educate students about the plight of nature and the repercussions of human activities. Creating awareness for future generations is important if we have to undo decades of ignorance and neglect.

Furthermore, air pollution essay helps students to realize the gravity of the scenario and enable them to take action. Some as simple as using public transport or even carpooling will help reduce a significant amount of air pollution. Read on to discover how to write an engaging essay on air pollution.

Essay on Air Pollution – Important Points to Note

Please consider adopting the following points when writing an essay on air pollution. These tips are also helpful for other essay topics as well:

• Always begin with an introductory paragraph about the topic, preferably detailing its origin.
• Unless the topic is technical, try to avoid jargons.
• Present content in bulleted points wherever possible
• Insert factual data, such as important dates, places or name wherever possible.
• Avoid writing the content in a large monotonous block of text. Remember to break up the content into digestible chunks
• Always conclude the essay with a closing paragraph.

Essay on Air Pollution – Sample 1 (200 Words)

Air pollution is a serious issue and a cause for major concern in today’s world. A report published in 2014  by the World Health Organisation states that 4.21 million individuals died prematurely in 2012 as a result of air pollution. Air pollution existed much before humans, in the form of volcanic eruptions and forest fires. However, it became much more prevalent after the Industrial Revolution.

Rapid industrial growth, unregulated emissions and a host of other issues significantly contributed to the rise in air pollution. In some cases, the severity of air pollution reached an extent where government intervention was necessary. The Great Smog of London, 1952, was an extreme case of air pollution where visibility was severely hampered. It also caused a host of illnesses and the consequent deaths of countless civilians. In November 2017, the levels of air pollution in Delhi were ten times above the safe limits. For reference, the healthy air quality index is between 0 to 50, but during that particular time period, the air quality index hit 500+. This event is now called the Great Smog of Delhi.

An air quality index of 500 and above indicates that the air is heavily polluted and will cause irreversible lung damage and a host of other illnesses to everyone who is exposed to it. Therefore, to avoid such situations in the future, relevant actions must be implemented.

Essay on Air Pollution – Sample 2 (500 Words)

Air pollution may seem like the result of anthropological activities, however, it has been around even before humans evolved. Places which are naturally arid and have minimal vegetation are prone to dust storms. When this particulate matter is added to the air, it can cause health issues in animals exposed to the dust storms.

Furthermore, active volcanoes pump extremely large amounts of toxic plumes and particulate matter into the atmosphere. Wildfires also pump large amounts of carbon monoxide into the atmosphere and hamper photosynthesis for plants. Even animals, especially ruminants such as cows contribute to global warming by producing large quantities of methane, a greenhouse gas.

However, air pollution was never a major concern until the industrial revolution. Industries grew rapidly, untreated emissions were pumped into the atmosphere, and the rise of automobiles significantly contributed to air pollution. Such activities continued without any restrictions until they started to cause a wide range of repercussions.

In humans, air polluted with contaminants can cause a wide array of illnesses ranging from asthma and bronchitis the various forms of cancer. Air pollution is not only present outdoors; interior air pollution is also a great concern. Recent research has actually found credible evidence that room fresheners have the many compounds within them, some of which are classified carcinogens. This means some of those compounds present in the aerosol has the potential to cause some forms of cancer. Other sources of air pollution can include gases such as carbon monoxide and radon.

Radon, in particular, is quite alarming. It is an odourless, colourless gas that occurs naturally. It is found in the soil as Uranium, which breaks down and eventually turns into radon gas. Radon has limited repercussions on health if exposed to low concentrations, however, when this gas gets trapped indoor, the higher levels of concentration can have wreak havoc or ultimately be lethal. Radon is also reported to be released from building materials such as granite. Exposure to radon causes no immediate health effects, but long term exposure has the potential to cause lung cancer.

Air pollution not only affects the lungs but the central nervous system too. It has been linked to a lot of diseases such as schizophrenia and autism. A study also implied that it can cause short-term memory losses or distortion of memory.

Historically, air pollution has caused many crises with the worst ever being the Bhopal Disaster in 1984. Fatalities were estimated at 3,800, with at least 600,000 injured. Next in severity was the Great Smog of 1952 which formed over London, killing an estimated 4,000 civilians over the course of four days.

Though measures have been taken to reduce the effects of air pollution, a lot of irreversible damage has been done. For instance, the effects of global warming have drastically increased; this is very apparent with the rise in sea levels and melting glaciers. If the ice caps continue to melt, then we will have to face drastic repercussions. Scientists have proposed a hypothetical scenario where the greenhouse effect becomes “uncontrolled.” Here, greenhouse gases build up and temperatures continue to rise steeply. Oceans will start to evaporate, adding more water vapour into the earth’s atmosphere. This intensifies the effect, reaching a point where temperatures are sufficiently high for rocks start sublimating. Though this scenario is hypothetical, some speculate that this phenomenon already occurred on Venus. The supporters of this theory back this up by claiming Venus has an atmosphere composed primarily of carbon dioxide. The theory also explains why Venus has an extremely high surface temperature of 462 degrees Celcius; which is in fact, the hottest planet in the solar system.

Hence, we need to reduce our impact on the planet and make a conscious effort to reduce air pollution. Explore more essay topics or other fascinating concepts by registering at BYJU’S

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Home / Essay Samples / Environment / Environment Problems / Air Pollution

Air Pollution Essay Examples

How usa and china nations destroy humans respiratory system.

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Mitigating Air Pollution: a Comprehensive Look at Solutions

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Humanity's Impact on the Environment: before the Flood

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Pollution: a Case Study in Environmental Harm

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Comprehensive Strategies for the Solution of Air Pollution

Air pollution is a major environmental issue that affects the health of millions of people worldwide. It is caused by a variety of factors, including industrial emissions, vehicle exhaust, and the burning of fossil fuels. In order to combat this problem, it is essential to...

Unveiling the Main Causes of Air Pollution

Air pollution, a grave environmental concern, has far-reaching consequences for both human health and the well-being of the planet. This essay delves into the primary causes of air pollution, examining the human activities and natural processes that contribute to the degradation of air quality and...

The Relationship Between Edward I and Scotland

The point that achieved a longer period of stability between England and Scotland in the Middle Ages was thanks to the Anglo-Scottish agreement of 1217, establishing peace between the two kingdoms that would last for 80 years. In 1290, Scotland had a huge succession crisis...

The Environmental Challenges of the 21st Century

In recent years, certainly, environmental challenges like climate change, air pollution, and urbanization affect every human and species. In an article, Smith (2008) mentioned that all across the world, people are facing a wealth of new and challenging environmental problems every day. This shows how...

Energy Consumption and Co2 Emissions in the Uae

The revelation of oil in 1958 in Abu Dhabi and 1966 in Dubai changed the economy significantly, empowering the nation to push far from a subsistence economy toward an advanced, modern base. In a few regards, be that as it may, it appears, the energy...

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About Air Pollution

Air pollution can be defined as the presence of toxic chemicals or compounds (including those of biological origin) in the air, at levels that pose a health risk.

Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide.

Long-term health effects from air pollution include heart disease, lung cancer, and respiratory diseases such as emphysema. Air pollution can also cause long-term damage to people's nerves, brain, kidneys, liver, and other organs.

Supporting sustainable land use, cleaner household energy and transport, energy-efficient housing, power generation, industry, and better municipal waste management.

A child born today might not breathe clean air until they are 8. Children are most vulnerable to air pollution – but we are all affected Inhaling air pollution takes away at least 1-2 years of a typical human life. Pollutants that are released into the air, as opposed to land and water pollutants, are the most harmful. Air pollution is one of the UK’s (and the world’s) biggest killers

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