graphical representation of air pollution

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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JSmol Viewer

A visualization approach to air pollution data exploration—a case study of air quality index (pm 2.5 ) in beijing, china.

1. Introduction

2. method and data, 2.1. preliminary analysis, 2.2. hypotheses, 2.3. verification of the hypotheses, 2.4. application layer, 2.5. experimental data, 3. results and discussion, 3.1. general analysis and hypotheses, 3.2. multi-perspective and various visual analysis, 3.2.1. relationship between multiple factors, 3.2.2. temporal characteristics, 3.2.3. spatial characteristics, 4. conclusions, acknowledgments, author contributions, conflicts of interest, appendix a. open source visualization tools, pivottable.js.

Click here to enlarge figure

Appendix B. Data quality check of the U-Air data

Appendix c. data processing.

• Mao, F.; Duan, M.; Min, Q.; Gong, W.; Pan, Z.; Liu, G. Investigating the impact of haze on modis cloud detection. J. Geophys. Res. Atmos. 2015 , 120 , 12237–12247. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.L.; Cao, F. Fine particulate matter (PM 2.5 ) in China at a city level. Sci. Rep. 2015 , 5 , 14884. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Matejicek, L.; Engst, P.; Jaňour, Z. A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague’s environment extended by LiDAR data. Ecol. Model. 2006 , 199 , 261–277. [ Google Scholar ] [ CrossRef ]
• Retalis, A.; Sifakis, N. Urban aerosol mapping over Athens using the differential textural analysis (dta) algorithm on meris-envisat data. ISPRS J. Photogramm. Remote Sens. 2010 , 65 , 17–25. [ Google Scholar ] [ CrossRef ]
• Chen, W.; Tang, H.; Zhao, H. Diurnal, weekly and monthly spatial variations of air pollutants and air quality of beijing. Atmos. Environ. 2015 , 119 , 21–34. [ Google Scholar ] [ CrossRef ]
• Carslaw, D.C.; Ropkins, K. Openair—An R package for air quality data analysis. Environ. Model. Softw. 2012 , 27–28 , 52–61. [ Google Scholar ] [ CrossRef ]
• Janssen, N.A.H.; Fischer, P.; Marra, M.; Ameling, C.; Cassee, F.R. Short-term effects of PM 2.5 , PM 10 and PM 2.5–10 on daily mortality in the Netherlands. Sci. Total Environ. 2013 , 463 , 20–26. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shi, W.; Wong, M.S.; Wang, J.; Zhao, Y. Analysis of airborne particulate matter (PM 2.5 ) over Hong Kong using remote sensing and GIS. Sensors 2012 , 12 , 6825–6836. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van Wijk, J.J. The value of visualization. Proc. IEEE 2005 . [ Google Scholar ] [ CrossRef ]
• Cai, Z.; Wang, Q.; Weng, M.; Jiang, S.; Du, Q. Information organization and visualization mechanism of electronic map. Geo-Spat. Inf. Sci. 2008 , 11 , 262–268. [ Google Scholar ] [ CrossRef ]
• Andrienko, G.; Andrienko, N.; Demsar, U.; Dransch, D.; Dykes, J.; Fabrikant, S.I.; Jern, M.; Kraak, M.J.; Schumann, H.; Tominski, C. Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 2010 , 24 , 1577–1600. [ Google Scholar ] [ CrossRef ]
• Kraak, M.J. Geovisualization illustrated. ISPRS J. Photogramm. Remote Sens. 2003 , 57 , 390–399. [ Google Scholar ] [ CrossRef ]
• Keim, D.; Andrienko, G.; Fekete, J.-D.; Görg, C.; Kohlhammer, J.; Melançon, G. Visual analytics: Definition, process, and challenges. In Information Visualization ; Springer: Berlin/Heidelberg, Germany, 2008. [ Google Scholar ]
• Li, X.; Coltekin, A.; Kraak, M.-J. Visual exploration of eye movement data using the space-time-cube. In Geographic Information Science ; Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C., Eds.; Springer: Heidelberg, Germany, 2010; Volume 6292, pp. 295–309. [ Google Scholar ]
• Zirui, L.; Bo, H.; Dongsheng, J.; Yonghong, W.; Mingxing, W.; Yuesi, W. Diurnal and seasonal variation of the PM 2.5 apparent particle density in Beijing, China. Atmos. Environ. 2015 , 120 , 328–338. [ Google Scholar ]
• Pope, C.A.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston, G.D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Jama-J. Am. Med. Assoc. 2002 , 287 , 1132–1141. [ Google Scholar ] [ CrossRef ]
• Revised Air Quality Standards for Particle Pollution and Updates to the Air Quality Index (AQI). Available online: http://www.epa.gov/airquality/particlepollution/2012/decfsstandards.pdf (accessed on 25 February 2016).
• Technical Regulation on Ambient Air Quality Index (on Trial). Available online: http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/jcgfffbz/201203/W020120410332725219541.pdf (accessed on 25 February 2016).
• Jeon, S.J.; Meuzelaar, H.L.C.; Sheya, S.A.N.; Lighty, J.S.; Jarman, W.M.; Kasteler, C.; Sarofim, A.F.; Simoneit, B.R.T. Exploratory studies of PM 10 receptor and source profiling by GC/MS and principal component analysis of temporally and spatially resolved ambient samples. J. Air Waste Manag. Assoc. 2001 , 51 , 766–784. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chu, D.A.; Kaufman, Y.J.; Zibordi, G.; Chern, J.D.; Mao, J.; Li, C.C.; Holben, B.N. Global monitoring of air pollution over land from the earth observing system-terra moderate resolution imaging spectroradiometer (MODIS). J. Geophys. Res.-Atmos. 2003 , 108 . [ Google Scholar ] [ CrossRef ]
• Wang, J.; Christopher, S.A. Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: Implications for air quality studies. Geophys. Res. Lett. 2003 , 30 . [ Google Scholar ] [ CrossRef ]
• Othman, N.; MatJafri, M.Z.; Lim, H.S.; Abdullah, K. Retrieval of aerosol optical thickness (AOT) and its relationship to air pollution particulate matter (PM 10 ). In Proceedings of the Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009 CGIV ‘09, Tianjin, China, 11–14 August 2009; pp. 516–519.
• Zheng, Y.; Liu, F.; Hsieh, H.-P. U-air: When urban air quality inference meets big data. In 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; ACM: Chicago, IL, USA,, 2013; pp. 1436–1444. [ Google Scholar ]
• Bostock, M.; Ogievetsky, V.; Heer, J. D-3: Data-driven documents. IEEE Trans. Vis. Comput. Graph. 2011 , 17 , 2301–2309. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Matejicek, L. Spatio-temporal analysis of environmental pollution in urban areas: A case study of the environment in the city of Prague. In Proceedings of the 19th International Congress on Modelling and Simulation (Modsim2011), Perth, Australia, 12–16 December 2011; pp. 1909–1915.
• Guo, D.S.; Chen, J.; MacEachren, A.M.; Liao, K. A visualization system for space-time and multivariate patterns (Vis-Stamp). IEEE Trans. Vis. Comput. Graphics 2006 , 12 , 1461–1474. [ Google Scholar ]
• Andrienko, G.; Andrienko, N. A general framework for using aggregation in visual exploration of movement data. Cartogr. J. 2010 , 47 , 22–40. [ Google Scholar ] [ CrossRef ]
• Kveladze, I.; Kraak, M.J.; van Elzakker, C.P.J.M. A methodological framework for researching the usability of the space-time cube. Cartogr. J. 2013 , 50 , 201–210. [ Google Scholar ] [ CrossRef ]
• Li, H.; Fan, H.; Wu, H.; Feng, H.; Li, P. Resdap: A real-time data provision system architecture for sensor webs. In Web and Wireless Geographical Information Systems ; Springer: Heidelberg, Germany; pp. 85–99.
• Fan, H.; Li, H. An on-Demand provision model for geospatial multisource information with active self-adaption services. Proc. SPIE 2015 , 9815 . [ Google Scholar ] [ CrossRef ]
• Fan, M.; Fan, H.; Chen, N.; Chen, Z.; Du, W. Active on-demand service method based on event-driven architecture for geospatial data retrieval. Comput. Geosci. 2013 , 56 , 1–11. [ Google Scholar ] [ CrossRef ]
• Li, L.; Losser, T.; Yorke, C.; Piltner, R. Fast inverse distance weighting-based spatiotemporal interpolation: A web-based application of interpolating daily fine particulate matter PM 2.5 in the contiguous U.S. Using parallel programming and k-d tree. Int. J. Environ. Res. Public Health 2014 , 11 , 9101–9141. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van Wijk, J.J.; Van Selow, E.R. Cluster and calendar based visualization of time series data. IEEE Symp. Inf. Vis. 1999 , 140 . [ Google Scholar ] [ CrossRef ]
• Department of State Air Quality Monitoring Program. Available online: http://www.stateair.net/web/mission/1/ (accessed on 10 December 2015).
• Zheng, Y. Urban Air. Microsoft. Available online: http://research.microsoft.com/en-us/projects/urbanair/default.aspx (accessed on 15 October 2015).
• World Weather. Available online: http://en.tutiempo.net/ (accessed on 20 January 2016).
• Qiao, Z.; Tian, G.; Xiao, L. Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data. ISPRS J. Photogramm. Remote Sens. 2013 , 85 , 93–101. [ Google Scholar ] [ CrossRef ]
• Gong, W.; Zhang, T.; Zhu, Z.; Ma, Y.; Ma, X.; Wang, W. Characteristics of PM 1.0 , PM 2.5 , and PM 10 , and their relation to black carbon in Wuhan, central China. Atmosphere 2015 , 6 , 1377–1387. [ Google Scholar ] [ CrossRef ]
• Logan, T.; Xi, B.; Dong, X. A comparison of the mineral dust absorptive properties between two Asian dust events. Atmosphere 2013 , 4 , 1–16. [ Google Scholar ] [ CrossRef ]
• Cressie, N. The origins of kriging. Math. Geol. 1990 , 22 , 239–252. [ Google Scholar ] [ CrossRef ]
• Sanabria, L.; Qin, X.; Li, J.; Cechet, R.; Lucas, C. Spatial interpolation of McArthur’s forest fire danger index across Australia: Observational study. Environ. Model. Softw. 2013 , 50 , 37–50. [ Google Scholar ] [ CrossRef ]
• Clark, I. Practical Geostatistics ; Applied Science Publishers: London, UK, 1979; Volume 3. [ Google Scholar ]
• Huang, Y.; Nian, P.; Zhang, W. The prediction of interregional land use differences in Beijing: A markov model. Environ. Earth Sci. 2015 , 73 , 4077–4090. [ Google Scholar ] [ CrossRef ]
• Wang, W.; Gong, W.; Mao, F.; Zhang, J. Long-term measurement for low-tropospheric water vapor and aerosol by Raman LiDAR in Wuhan. Atmosphere 2015 , 6 , 521–533. [ Google Scholar ] [ CrossRef ]
• PivotTable.js. Available online: https://github.com/nicolaskruchten/pivottable (accessed on 24 October 2015).
• D3.js. Available online: http://d3js.org/ (accessed on 14 November 2015).

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Share and Cite

Li, H.; Fan, H.; Mao, F. A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM 2.5 ) in Beijing, China. Atmosphere 2016 , 7 , 35. https://doi.org/10.3390/atmos7030035

Li H, Fan H, Mao F. A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM 2.5 ) in Beijing, China. Atmosphere . 2016; 7(3):35. https://doi.org/10.3390/atmos7030035

Li, Huan, Hong Fan, and Feiyue Mao. 2016. "A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM 2.5 ) in Beijing, China" Atmosphere 7, no. 3: 35. https://doi.org/10.3390/atmos7030035

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Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021

1 Zhou Enlai School of Government, Nankai University, Tianjin 300071, China

2 College of Management and Economy, Tianjin University, Tianjin 300072, China

Kevin Huang

3 School of Accounting, Economics and Finance, University of Wollongong, Sydney, NSW 2522, Australia

4 School of Marxism, Hangzhou Medical College, Hangzhou 310053, China

Tiantong Xu

5 School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China

Zhenni Chen

6 School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China

7 School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing 102617, China

Associated Data

The original contributions presented in the study are included in the supplementary material . Further inquiries can be directed to the corresponding author.

Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: “Air pollutant source”, “Exposure-Response relationship”, “Public & Occupational Health”, and so on. Future research hotspots are analyzed through characteristics of the cluster groups, including the advancement of health risk assessment techniques, an interdisciplinary approach to quantifying human exposure to air pollution, and strategies in health risk assessment.

1. Introduction

The negative effects of air pollution on human health are well documented [ 1 , 2 ]. Harmful air pollutants, such as PM 2.5 , PM 10 , SO 2, and NO X , escaping into the environment through natural and human activities may adversely affect human health [ 3 ]. Air pollution has acute and chronic effects on various systems and organs. These range from upper respiratory tract irritation to chronic respiratory and heart diseases [ 4 ], lung cancer [ 5 ], childhood acute respiratory infections [ 6 ] and adult chronic bronchitis, exacerbating existing cardiopulmonary diseases or asthma attacks [ 7 ], and even causing serious mental illness [ 8 ]. According to the official statistics of the World Health Organization, the number of people killed by air pollution is as high as 7 million every year, and 9 out of every 10 people in the world still breathe air containing high levels of pollutant concentration [ 9 ].

Due to the growing public concern about the adverse health consequences caused by air pollution, an increasing number of publications have examined the correlation between exposure to air pollutants and the incidence and mortality of various diseases [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. For example, some studies have shown that exposure to particulate matter and ozone in the air is associated with increased mortality and hospitalization due to respiratory and cardiovascular disease [ 17 , 18 ]. Particulate matter is likely to penetrate the lungs and cardiovascular system, leading to diseases such as stroke, heart disease, lung cancer, and chronic obstructive pulmonary disease [ 19 , 20 ]. Nevertheless, most of the publications focusing on specific fields are scattered, and the interrelationship between them is not yet clear. Although many experts have summarized the results of research in this field [ 21 , 22 , 23 , 24 ], there are still few studies on the application of bibliometric methods for analysis. So far, there are only two articles that review air pollution and human health with a bibliometric method. Han et al. [ 25 ] used bibliometric methods to analyze the relevant literature in the field of health effects caused by PM 2.5 since 2000. Dhital et al. [ 26 ] carried out a bibliometric analysis of 2179 documents published during the last two decades. The limitation of the above two studies is that they mainly focus on the performance of literature from the perspectives of international collaboration, journal distribution, authorship cooperation, etc., which belong to the basic descriptive results. Therefore, in this paper, to further examine the hot research topics, we combine machine learning with bibliometric methods to quantitatively review the literature in this field. The quantitative methods are more conducive to helping us better understand the air pollution and health research landscape.

This paper explores the development of this field through statistical methods from a macro perspective. The primary objective of this study is to give a comprehensive overview of the research publications on air pollution and human health published during the last two decades (2001–2021). The main contributions of this paper are as follows: (1) This study aims to provide a qualitative and quantitative evaluation of the current research progress and trends in air pollution and health research. (2) Some publication characteristics, including countries, funding agencies, journals, and international collaborations, are presented. (3) The study applies the principle of machine learning to divide 10,212 records into 96 clusters and identifies the positional relationship between them. (4) Based on the clustering results, we develop co-occurrence networks and obtain more specific keyword classification results by keyword frequency, relationship, and semantic analysis to obtain future research hotspots.

2. Materials and Methods

2.1. data collection.

The Web of Science Core Collection provides a variety of records for each publication, including author information, journals, citations, and institutional affiliations. We mainly search for articles from SCI and SSCI, published in English from 2001 to 2021. The study used keywords (i.e., “air pollution*” or PM 2.5 or ozone or “particular matter”) and (health or mortality or fatality or death or epidemiology or fitness or morbidity) to search and collect research articles. A total of 46,934 pieces of literature were identified through database searches. After excluding 11,963 pieces of literature of the non-article type and an additional 2566 via screening of the titles and abstracts, finally, 14,955 articles were full-text screened for eligibility (See Figure 1 , Figures S1 and Table S1 ).

Framework of literature search, analysis, and interpretation.

In addition, OmniViz (BioWisdom Ltd., Cambridge, UK) was used to extract and cluster keywords from the articles. OmniViz is an advanced visual informatics software package that is designed to provide visualization of digital data, categorical data, genomic sequences, chemical structures, and text documents [ 27 ]. It can analyze large data sources through different clustering methods. We used OmniViz to identify important topics and hot research areas. In addition, the clustering method was used to measure the similarity of two records in a high-dimensional space. To achieve data visualization, we used Galaxy and Thememap. Galaxy provides relationships between lots of records, and Thememap identifies the most important topics in the field. Please see Figure S2 in the supplementary file for details on methodology and software.

2.2. Impact Factors

The impact factor (IF) and h-index are well-recognized indicators that are closely related to the bibliometric analysis. The IF is a useful indicator to quantify the rank and quality of a journal. The IF of a journal is calculated by dividing the citation count of the current year by the number of published articles in the journal during the previous two years [ 28 ]. It is created by the Institute of Scientific Information (ISI). A higher IF usually reflects a journal’s higher quality in various research fields. The h-index means that ‘h’ of one’s total articles are cited at least ‘h’ times. It is a popular indicator to measure the performance of a scientist and has been widely used to evaluate the academic performance of a journal or a country.

2.3. Social Network Analysis (SNA)

Social network analysis (SNA) refers to a computable analysis method based on multidisciplinary fusion theories and methods to understand the formation of various human social relationships, behavioral characteristics analysis, and the laws of information transmission. It aims to quantify the network’s structure features and the dynamic interactions among network vertices. Due to the development of social network theory, SNA has been widely used to analyze academic collaboration in different fields [ 29 ]. By using a variety of measurement metrics, the contributions from different countries, institutions, and scientists can be evaluated.

In this study, SNA is used to evaluate collaboration among different countries and institutions [ 30 ], which includes two steps. The first step is information extraction. The country and institutional information for each author was extracted using BibExcel so that the visualization effect of academic cooperation among different countries can be presented. The second step is to draw a cooperation diagram with the input data from BibExcel using Pajek to visualize their cooperation patterns.

3. Results and Discussions

3.1. the performance of related publications.

In recent years, the issues of air pollution have gradually drawn public attention. Our study focuses on the effect of air pollution on health from the perspective of bibliometrics so as to understand the current research status and future research trends. Figure 2 shows the annual number (NO) of articles published between 2001 and 2021, the total number of citations (TC) for the articles, and the average citation count (ACPP) for each article. It can be observed that the NO has grown slowly in the first 12 years and has increased rapidly with a growth rate of more than 9% since 2008. The number of articles published after 2009 accounted for nearly 85% of the total number of published articles. In addition, the TC grew steadily during the first 11 years, peaked in 2008 and 2018, and then gradually declined. Due to the increasing number of published articles, the ACPP showed a downward trend as a whole.

Numbers of the NO, TC, and ACPP during the period of 2001–2021. Notes: NO represents the number of published articles, TC represents the total citations of articles, and ACPP represents the average citation counts of each article.

3.2. Publication Features of Different Countries

The number of publications reflects the academic strengths and attentions of each country in the field. The top five most productive countries are the United States (6932 publications), China (4350 publications), the United Kingdom (2206 publications), Canada (1035 publications), and Italy (974 publications). These top five countries published a total of over 10,000 articles, accounting for 78.55% of all publications. Among these productive countries, the United States outperforms others in the total number of published articles on air pollution during 2001–2021 [ 31 ]. It can be observed that China’s publications have grown rapidly since 2014, with an average growth rate of over 30%. Moreover, the amount of literature published by Chinese scholars gradually approached the USA’s productivity in 2021.

We applied SNA to analyze the international collaboration among the 20 most productive countries during the period 2001–2021 ( Figure 3 ). The lines connecting the countries represent their cooperation, and the line thickness indicates the degree of collaboration [ 32 , 33 ]. Collaboration was determined by the affiliations of the co-authors, and all countries or institutes stand to benefit if one publication is a collaborative study [ 34 , 35 ]. These 20 productive countries worked closely with each other, particularly the U.S.A., China, Canada, Germany, and the U.K. The U.S.A. was the center of this collaboration network and the leader of air pollution research in cooperation with the other productive countries. The U.S.A. and China had the closest collaboration, followed by the U.S.A.–U.K., U.S.A.–Canada, and the U.S.A.–Germany.

International collaboration according to social network analysis.

3.3. The Performances of Different Journals

The top 20 most productive journals are shown in Table 1 . These productive journals account for 63.4% of the total related publications. In particular, Atmospheric Environment is the most productive journal with a count of 1506 (10.07%) articles. Other dominating journals include Environmental Health Perspectives , Science of the Total Environment , and Environmental Research . The IF of the journal is not the only index to reflect the journal’s influence in the field. Therefore, we calculated the average citations to better reflect the journal’s influence. The results showed that Epidemiology and Environmental Health Perspectives have the highest average citations (86.25 and 81.30), followed by the American Journal of Epidemiology , Occupational and Environmental Medicine , and Environmental Science and Technology .

The top 20 most productive journals.

1 Note: IF2022 represents the published impact factor for 2022. The impact factor is a measure of the importance of a journal. The impact factor is calculated by dividing the number of times the articles are cited in the last two years by the total number of publications in those two years.

Furthermore, the field of air pollution and health is typically an interdisciplinary area. According to the statistics ( Figure 4 ), the largest proportion of research areas are in Environmental Sciences and Ecology; Public, Environmental, and Occupational Health; Meteorology and Atmospheric Sciences; and Toxicology, which account for 93.2% of the total number of publications. Among them, the Environmental Sciences and Ecology research area maintains an average growth rate of more than 15%, occupying the most important position.

The top 5 research areas of publications.

3.4. Institutions’ Performances

The performances of the top 20 most productive institutions are listed in Table 2 . Most institutions are from the productive countries shown in the previous section. Among them, twelve institutions are located in the U.S.A and three are from China. Harvard University is the most productive research organization with 1263 publications, followed by the University of California System, the United States Environmental Protection Agency, and the Chinese Academy of Sciences. In the U.S.A., universities and government research institutes, such as the United States Environmental Protection Agency, are the main forces in the field. Additionally, several European countries, such as England, the Netherlands, and Switzerland, have mature experience in air pollution prevention and reducing its negative impact on health. Therefore, it is not surprising to see that 4 institutions in European countries are listed among the top 20 most productive institutions.

The top 20 most productive institutions.

3.5. Research Hotspots Analysis

3.5.1. keyword clustering.

A machine learning-based cluster analysis was carried out on the keywords of 14,955 research articles using software named OmniViz. A total of 10,212 records were obtained and divided into 96 clusters. The article selected clusters with more than 50 records, for a total of 29 clusters, as shown in Table 3 . Moreover, we also give top 20 frequent keywords list ( Table S2 ). The cluster C22 (exposure, model, mortality) has the most publications with a total of 3629, which accounts for 37.1% of the total. In addition, C8 (health, environment, risk), C92 (health, environment, risk), C35 (exposure, person, particle), C6 (model, ozone, emit), and C64 (aerosol, source, dust) are recorded more than 300 times. These clusters were also usually selected as research topics. In addition, the distance of each cluster in the galaxy map can reflect their correlations. If their locations are closely related to their research relevance, their relevance is very high. On the contrary, these research themes are not strongly related. The clusters of publications recorded with more than 50 instances are shown in Figure 5 and marked as yellow. It can be concluded that C8, C47, C81, and C25 are closed to each other and concentrated at the top of the galaxy map. This indicates that keywords such as “environment, health, and risk” are often associated with “exposure, chronic, disease, heart,” and so on. Similarly, located in the middle and lower parts of the galaxy map, C89, C64, C35, C58, and C56 are also closely linked. This suggests that the relationships between the keywords of “particle, ozone, dust”, and other related pollutants and “aerosol, concentration, source” are very close. In addition, clusters C8 and C92 show the same term labels, both of which are “health, environment, risk”. The number of records is above 500. However, they are from different collections of publications at different positions on the galaxy map ( Figure 5 ). Each pollutant has different sources and measurement methods at the pollutant level [ 36 ]. The health level involves various diseases, and the related model methods are often employed in related research [ 37 ].

Keywords Galaxy map (records > 50).

The selected clusters (records > 50) by OmniViz analysis.

In addition, as shown in Figure 6 , a theme map can identify the main topics in the research field of air pollution and health, which is an effective complement to the Galaxy visualization. The height of the peak depends on the intensity of the topic and the concentration of information at that location. It can be observed that the four highest peaks are: “health, environment, risk”, “exposure, model, mortality”, “exposure, ozone, person”, and “model, ozone, emit”. The results are very similar to the cluster analysis; however, there are some differences in the height order. The main reason is that, although C22 has the most records, it is not closely related to the surrounding clusters. The cluster C8 is more intensive at this position, resulting in a stronger theme, which led to the highest peak. Compared with others, the peaks around “exposure, person, particle” are significantly denser. There are more valleys surrounding the rest of the peaks, which suggests that such research is highly relevant and often involves an interdisciplinary approach.

Theme map of keywords.

3.5.2. Relationship of Keywords among Different Groups

Through keyword clustering, 10,212 records were divided into 96 clusters. In the keywords Galaxy map ( Figure 7 ), we can roughly divide them into three groups, i.e., Group I, Group II, and Group III, based on the positional relationships of different clusters. Although the clusters located at the lower left of the Galaxy map are very dense, most of these clusters appear less than 50 times and, therefore, will not be analyzed further. Within each group, we develop co-occurrence networks and obtain more specific keyword classifications based on keyword frequency, relationship, and semantic analysis. The research hotspots are analyzed through the characteristics of three groups.

Group I research topics.

• (1) Group I

The impact of the deterioration of the ecological environment, especially of the air quality, on human health has received a growing level of attention. The damage to health caused by air pollution further increases the degree of health inequalities among groups of different income levels [ 38 , 39 ]. As shown in Figure 7 , “environment and health” are recognized as the central keywords of Group I because of their high frequencies and close relationships with other research topics. Focusing on two central keywords, we identified four relevant research areas, i.e., “Air pollutant source”, “Exposure–Response relationship”, “Health & Mortality”, and “Cost & Benefit”. In terms of air pollutant sources, outdoor sources often refer to the cluster C37 (Emit, vehicle, industry), mainly including industry and vehicle emissions. Household pollution sources, such as the keyword clusters C77 (Fuel, energy, household) and C20 (Standard, management, ventilation), are often combined with specific issues, such as fuel burning, use of building materials, chemicals, and ventilation in household activities [ 40 ]. Most air pollutants monitored by remote sensing technology are always assessed by the exposure-response function [ 41 ], such as the clusters C6 (Model, Ozone, emit) and C89 (Particle, aerosol, concentration), to reflect such research trends. In addition, some studies have stated that air pollution can cause increased morbidity, including heart disease and chronic diseases [ 42 ]. And in Table S3 , we also summarized highly co-cited documents among research clusters in Clusters I.

• (2) Group II

As shown in Figure 8 , “exposure” is recognized as a core keyword, and three keywords with high frequency are closely related (i.e., particle, aerosol, source). With these four keywords as the core, four relevant research areas have been formed, namely “Air pollution source”, “Air pollution monitoring”, “Particulate matter concentration”, and “Atmospheric aerosol”. The current methods of air pollution control focus on source management, so identifying air pollution sources is still an important research area [ 43 ].Moreover, dust in cities can also increase the short-term mortality of vulnerable populations, such as extreme dust episodes in high-density Asian cities [ 44 ]. In order to formulate reasonable air pollution control policies, real-time monitoring of air quality becomes more important. In addition to common air monitoring stations [ 45 ], some studies suggest that air quality monitoring can be performed in innovative ways, such as through social media [ 46 ] and mobile sensors [ 47 ]. Though PM and ozone are the main monitored pollutants, related studies have gradually evolved from single-pollutant to multi-pollutant collaborative studies, such as ozone, particle, carbon monoxide, PM 2.5 , PM 10 , etc. [ 48 ]. As shown in Figure 8 , there is a strong relationship between “particle” and “aerosol”. Studies have found that severe haze pollution incidents were mainly caused by the formation of secondary aerosols. Moreover, in Table S4 , we also summarized highly co-cited documents among research clusters in Clusters II.

Group II research topics.

• (3) Group III

As shown in Figure 9 , “aerosol” is the central word in this section. Around the central keyword, four related research areas can be identified, including “Atmospheric physics”, “Atmospheric chemistry”, “Health & Mortality”, and “Public & Occupational Health”. Atmospheric physics and atmospheric chemistry are the basic disciplines in related research. They can explain the formation and transmission mechanisms of atmospheric pollutants and provide a theoretical basis for controlling air pollution [ 49 ]. The research field “Health & Mortality” was once again emphasized in the clustering results. Compared with Group I, this part has more diseases mentioned. The measurement of health risks from the mortality index [ 50 ] gradually concentrated on the incidence of specific diseases, including cancer, heart disease, respiratory diseases, and so on. Further, based on the complexity of multi-pollutant collaborative research [ 51 ], studies must develop more targeted evaluation models and apply new model fusion methods, i.e., the air pollution mortality/incidence risk (Ri-MAP) model [ 52 ], comprehensive health risk index, exposure–response coefficient [ 53 ], CMAQ/GCAM evaluation model [ 54 ], and so on. We are pleasantly surprised to find that public and occupational health research is further valued in Group III. Some studies currently classify occupational characteristics and social status to assess the health effects of air pollution on different groups [ 55 ]. For example, the socioeconomic status of parents, including education, income, and living area, has an impact on children’s health, which suggests that health can play an important role in the intergenerational transmission of economic status [ 56 ]. In addition, the highly co-cited documents among research clusters in Cluster II have been summarized in Table S5 .

Group III research topics.

4. Conclusions

4.1. summary.

This study conducted a bibliometric analysis of 14,955 articles on air pollution and health from 2001 to 2021. In the past two decades, the most productive country, the most productive institution, and the most productive journal are the United States, Harvard University, and Atmospheric Environment , respectively. We used OmniViz to cluster the keywords of 10,212 records, and the results show that the clusters with the most occurrences were “Exposure, model, mortality”, “Health, environment, risk”, and “Model, Ozone, emit”. By observing the Thememap and Galaxy visualization results, additional popular topics in the study include “Health, environment, risk” and “Exposure, person, particle”. Based on the clustering results, we developed co-occurrence networks and obtained more specific keyword classification results according to keyword frequency, relationship, and semantic analysis. We have identified the most influential areas, such as “Air pollutant source”, “Exposure–Response relationship”, “Public & Occupational Health”, and so on. The research hotspots are analyzed through the characteristics of three groups of clusters. Indeed, this paper provides a qualitative and quantitative evaluation of the current research progress and trends in air pollution and health research.

4.2. Limitations and Future Research Directions

Nonetheless, although bibliometric analysis is an effective method for reviewing the literature, it is not without limitations. First, the bibliometric data from the Web of Science (including SCI-EXPAND and SSCI) are not produced exclusively for analysis, thus the data may contain errors, wherein the presence of errors is bound to influence any analysis performed using such data. Thus, to mitigate errors, we have carefully cleaned the bibliometric data that we searched. For example, we remove duplicates and erroneous entries. Second, the nature of the bibliometric method is in itself a limitation. We noticed that the qualitative assertions of bibliometrics can be subjective given that bibliometric analysis is quantitative in nature, whereas the relationship between qualitative and quantitative results is often unclear. To solve these problems, we tried to combine machine learning and the bibliometric method to make a more accurate results analysis, but there is still room for method improvement. Third, bibliometric studies can only offer a certain period forecast of the research field, and thus scholars should avoid making overly ambitious assertions in their research field. Notwithstanding these limitations, the bibliometric method can help us to overcome the fear of large bibliometric datasets and to pursue retrospectives of air pollution and human health. Indeed, the bibliometric methods can not only facilitate knowledge in this field but also help us to better understand the research trend. We take a short yet significant step in that direction.

Based on the bibliometric analysis, three main future directions of air pollution and health are identified in this study. The first future research direction is the advancement of health risk assessment techniques. At present, the air monitoring network does not take into account health factors such as changes in air pollution components, public medical data, and population activity patterns [ 57 ]. There is a lack of dynamic data to support the health risk assessment as the monitoring network of air pollution’s health impact is far from mature [ 58 ]. Health risk assessment techniques currently face the challenge of transformation from qualitative research to quantitative research. Future research can be conducted to examine the route and trajectory of pollutant exposure to determine the actual intake and intake coefficient of air pollutants [ 59 ]. Subsequently, sophisticated time-activity patterns and individual exposure monitoring techniques should be employed more widely to determine accurate exposure doses [ 60 ]. More suitable biological targets should be identified to establish a quantitative relationship between the growth in the concentration of pollutants and the increase in mortality and disease prevalence.

The second future research direction is an interdisciplinary approach to quantifying human exposure to air pollution. Quantifying human exposure to air pollutants is a challenging task [ 61 ]. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process [ 62 ]. For assessment of the health risks of air pollution, related studies might evolve from epidemiology research to toxicology research. From the perspective of environmental toxicology, future studies can be undertaken on the biological mechanism of the bioavailability and toxicity of particulate matter. In order to establish more accurate quantitative models, real-time air pollution health risks can be described using mobile air pollution monitoring techniques, meteorological information, and land use information [ 61 ]. Air quality changes and mobile monitoring allow relevant departments to respond to these changes quickly. Consequently, the threshold for the impact of different pollutants on human health can be determined more accurately.

The third future research direction is management strategies in health risk assessment [ 63 ]. Proven effective management strategies include the establishment and enforcement of air standards, reduction in emissions from coal-fired power plants and other stationary sources, banning of the use of polluting fuels in urban centers, improvements to access to public transportation, and so on [ 64 ]. Future air pollution prevention strategies will emphasize integration and cooperation. Air pollution control in some key areas should pay attention to regional and departmental cooperation. Policy boundaries will become increasingly blurred, and mandatory regulatory policies may also need to incorporate economic incentives. A simple economic incentive policy will have a small audience and need to be combined with other policies to innovate the current policy tool system. Risk management strategies will shift from simple restrictions and prohibitions to more flexible multi-policy coordination.

Acknowledgments

We express our gratitude for the Software support from Green Development Center at Tianjin University.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph191912723/s1 . Figure S1: Framework of literature search, analysis, and interpretation. Note: Science Citation Index (SCI); Social Science Citation Index (SSCI). Figure S2: Methodology and software support. Table S1: Keywords used in the search and results. Table S2: The trend of 15 most frequent keywords during 2001–2021. Table S3: Highly co-cited documents among research clusters in Cluster I. Table S4: Highly co-cited documents among research clusters in Cluster II. Table S5: Highly co-cited documents among research clusters in Cluster III.

Funding Statement

This study was funded by the Fundamental Research Funds for the Central Universities (Grant No. 63222036), China Postdoctoral Science Foundation (Grant No. 2022M710072; No. 2021M692568; No. 2020M670636), National Social Science Foundation (Grant No. 21CZZ007); and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 21YJC630014). And the APC was funded by China Postdoctoral Science Foundation (Grant No. 2022M710072).

Author Contributions

All authors contributed equally to this work. D.L. was responsible for analyzing the results with machine learning and bibliometric methods. K.C. made a substantial and meaningful contribution to the revision. K.H. was responsible for supervision. H.D. was responsible for reviewing, editing, and visualization with software. T.X. and Z.C. were responsible for searching, collecting, and screening related literature. Y.S. was responsible for writing, editing, and reviewing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

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Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Bibliometrics & citations.

• Yang L Li R Qiu X Bi C (2024) BLCM: a BP-LGBM-based atmospheric visibility forecasting model Journal of Visualization 10.1007/s12650-024-01009-6 Online publication date: 5-Jun-2024 https://doi.org/10.1007/s12650-024-01009-6
• Jiao C Bi C Yang L (2024) FFEINR: flow feature-enhanced implicit neural representation for spatiotemporal super-resolution Journal of Visualization 10.1007/s12650-024-00959-1 27 :2 (273-289) Online publication date: 19-Feb-2024 https://dl.acm.org/doi/10.1007/s12650-024-00959-1

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Human-centered computing

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Visual analytics

Information systems

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• Published: 18 November 2021

Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries

• Alen Juginović 1 ,
• Miro Vuković 2 ,
• Ivan Aranza 2 &
• Valentina Biloš 2  

Scientific Reports volume  11 , Article number:  22516 ( 2021 ) Cite this article

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• Cancer epidemiology
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• Risk factors

Air pollution is the fourth greatest overall risk factor for human health. Despite declining levels in Europe, air pollution still represents a major health and economic burden. We collected data from the Global Burden of Disease Study 2019 regarding overall, as well as ischemic heart disease (IHD), stroke, and tracheal, bronchus and lung cancer-specific disability adjusted life years (DALYs), years of life lost (YLL) and mortality attributable to air pollution for 43 European countries between 1990 and 2019. Concentrations of ambient particulate matter (aPM 2.5 ), ozone, and household air pollution from solid fuels were obtained from State of Global Air 2020. We analysed changes in air pollution parameters, as well as DALYs, YLL, and mortality related to air pollution, also taking into account gross national income (GNI) and socio-demographic index (SDI). Using a novel calculation, aPM 2.5 ratio (PMR) change and DALY rate ratio (DARR) change were used to assess each country’s ability to decrease its aPM 2.5 pollution and DALYs to at least the extent of the European median decrease within the analysed period. Finally, we created a multiple regression model for reliably predicting YLL using aPM 2.5 and household air pollution. The average annual population-weighted aPM 2.5 exposure in Europe in 1990 was 20.8 μg/m 3 (95% confidence interval (CI) 18.3–23.2), while in 2019 it was 33.7% lower at 13.8 μg/m 3 (95% CI 12.0–15.6). There were in total 368 006 estimated deaths in Europe in 2019 attributable to air pollution, a 42.4% decrease compared to 639 052 in 1990. The majority (90.4%) of all deaths were associated with aPM 2.5 . IHD was the primary cause of death making up 44.6% of all deaths attributable to air pollution. The age-standardised DALY rate and YLL rate attributable to air pollution were more than 60% lower in 2019 compared to 1990. There was a strong positive correlation (r = 0.911) between YLL rate and aPM 2.5 pollution in 2019 in Europe. Our multiple regression model predicts that for 10% increase in aPM 2.5 , YLL increases by 16.7%. Furthermore, 26 of 43 European countries had a positive DARR change. 31 of 43 European countries had a negative PMR change, thus not keeping up with the European median aPM 2.5 concentration decrease. When categorising countries by SDI and GNI, countries in the higher brackets had significantly lower aPM 2.5 concentration and DALY rate for IHD and stroke. Overall, air pollution levels, air pollution-related morbidity and mortality have decreased considerably in Europe in the last three decades. However, with the growing European population, air pollution remains an important public health and economic issue. Policies targeting air pollution reduction should continue to be strongly enforced to further reduce one of the greatest risk factors for human health.

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Introduction.

Clean air is considered one of the basic requirements of human health and well-being. However, more than 90% of the world population was exposed to air quality levels that exceeded the World Health Organization (WHO) Air Quality Guideline (AQG) limits in 2016 1 . Air pollution is the fourth greatest overall risk factor for human health globally, following high blood pressure, dietary risks, and smoking 2 . It has been associated with three of the leading causes of death in the world with significant shares of air pollution-related mortality: stroke (26%), ischemic heart disease (IHD) (20.2%), and primary cancer of the trachea, bronchus, and lung (TBL) (19%) 3 , 4 . Altogether, air pollution was linked to seven million deaths globally and in excess of 100 million disability-adjusted life years (DALYs) annually 5 , 6 . It also represents a major global annual economic impact of $5 trillion 7 .

Disparities in air quality have been observed based on a country's income. While air pollution in developed countries poses an important public health issue, it is even more pronounced in developing countries where fast-growing population along with widespread industrialization led to centers with poor air quality which became a serious threat to health 8 .

In Europe, emissions of air pollutants have been declining in the past decades 9 . Nevertheless, there were still more than 0.5 million deaths attributable to air pollution in 2013 while health-related external costs associated with air pollution reached close to €1 trillion annually in the European Union (EU) alone 10 , 11 .

In 2018, 73.6% of the EU urban population was exposed to excessive concentrations of particulate matter of diameter less than 2.5 microns (PM 2.5 ) which is considered the fifth leading mortality risk factor. The main contributors to the EU's PM 2.5 concentrations have been institutional, commercial, and household (55.5%), followed by road transport (10.7%) 12 . PM 2.5 pollution was associated with more than four million global deaths in 2016 with Europe counting for approximately 10% of that share 13 . Exposure to PM 2.5 pollution led to more than 1277 years of life lost (YLL) per 100,000 population in several European countries 14 . Long-term exposure to PM 2.5 pollution significantly increases both cardiopulmonary problems and lung cancer mortality, as well as risk for type two diabetes 15 , 16 . Conversely, one study showed that patients with lung cancer increased their life span by 0.35 years for every 10 μg/m 3 reduction of PM 2.5 concentration 17 . For short term exposure, every 10 μg/m 3 increase in PM 2.5 concentration was associated with 2.8% increase in PM-related mortality 18 .

Another major air pollutant is ozone, commonly found in urban areas which make up as much as 74.7% of the total EU population in 2019 19 . Long-term exposure to ozone has been linked to an increased risk of death from respiratory causes, as well as serious adverse pregnancy outcomes 20 , 21 .

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is a multinational collaborative research study of disease burden that assesses mortality and disability from major diseases, injuries and risk factors, including air pollution 22 . The study is an ongoing effort and is designed to systematically incorporate information over time, and its latest iteration includes data from 1990 to 2019, by age and sex, and across more than 200 countries and territories. The study contains standard epidemiological measures such as incidence, prevalence, and death rates, as well as summary measures of health, such as DALYs.

Using the most recent 2019 GBD data, we did a comprehensive analysis of temporal and spatial trends for PM 2.5 and ozone concentrations in Europe from 1990 to 2019 with a focus on ambient PM 2.5 (aPM 2.5 ) concentrations due to its highest health impact. We also evaluated each country's ability to decrease its aPM 2.5 and DALY values to at least the extent of the European aPM 2.5 and DALY median reduction. Then, we analysed mortality, DALYs, and YLL attributable to air pollution for stroke, IHD, and TBL cancer, also taking into account socio-demographic index (SDI) and gross national income (GNI). This analysis could be used to raise awareness among policymakers to take action on this important public health issue which originates mainly from anthropogenic sources, and as such can be undone by precise and determined measures.

Sources of data

Data for 43 European countries regarding overall mortality, YLL, and DALYs attributable to air pollution by age, year, and sex were collected using the Global Health Data Exchange GBD Results Tool between 1990 and 2019. We also collected data for stroke, IHD and TBL cancer attributable to aPM 2.5 , household air pollution from solid fuels, and ozone. Detailed description of metrics, data sources, and statistics in GBD 2019 has been reported elsewhere 22 .

Estimates for exposure levels to aPM 2.5 and ozone were obtained from Health Effects Institute–State of Global Air 2020 where methods for assessing their exposure levels are also described 23 . In brief, exposure to aPM 2.5 was measured as the average annual PM 2.5 concentration in the air at a spatial resolution of a 0.1° × 0.1° grid cell, which reflects to 11 × 11 km at the equator. Ozone concentration is measured in parts per billion (ppb). Exposure to ozone was defined as the highest seasonal (six-month) average daily eight-hour maximum ozone concentration.

We used GBD 2019 estimations of deaths, YLLs, and DALYs attributable to air pollution. Relative risks were estimated based on meta-regression and systematic reviews done for GBD 2019. DALYs, YLLs, and attributable deaths were estimated by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age, sex, location, and year. For continuous risk, PAFs are calculated using formula described in GBD 2019 study 22 . Inputs to estimation of PAFs for this study included continuous exposure distributions to air pollution, relative risk and the theoretical minimum risk exposure level (TMREL) for each group. TMREL was defined as the low point of the risk function and it represents the level of risk exposure that minimizes disease risk at the population level. Using PAF estimates, we calculated the number of deaths attributable to air pollution, DALYs and YLLs.

Data for SDI in 2019 for 43 European countries was obtained from the Global Burden of Disease Study 2019. For a robust analysis of disparities between groups in terms of aPM 2.5 pollution and disease burden, we divided these countries into 3 groups: high (> 0.850), medium (0.750–0.849), low (< 0.749) (Supplementary Table S4 ).

GNI per capita for 39 out of 43 European countries was obtained from the 2019 World Bank classification of world economies. Data for Andorra, Moldova, Monaco, and San Marino were not available 24 . The World Bank assigns the world’s economies into four income groups: low (< $1036), lower-middle ($1036–$4045), upper-middle ($4046–$12,535), and high-income (> $12,536) countries. Since 30 out of 39 analysed European countries are classified as high income, we divided those countries into an additional three groups: lower high income ($12,536–$36,766), moderate high income ($36,767–$60,997), very high income (> $60,997) for more robust analysis of disparities in aPM 2.5 pollution and disease burden between countries with different GNI (Supplementary Table S4 ). Also, since Ukraine is the only country in Europe classified as lower-middle income, we merged the lower-middle income and upper-middle income groups into one due to the same reasons.

Definitions

DALYs represent the overall number of years of potential life lost due to premature mortality and years of productive life lost due to disability and it is calculated as the sum of the aforementioned parameters. It summarizes the overall burden of disease and one DALY may be regarded as one year of healthy life lost 25 . DALYs can be expressed as the number of total DALYs or as DALY rate per 100,000 population. Additional methodologies for estimating DALYs have been described as part of GBD Study 2019 22 .

YLL is regarded as a summary measure of premature mortality. It estimates the years of potential life lost due to premature death, taking into account frequency of deaths and the age at which it occurs. YLLs were calculated by multiplying the estimated number of deaths by age with a standard life expectancy at that age. It can be expressed as a number of total YLLs or as YLL rate per 100,000 population. Additional methodologies for estimating YLLs are described in the GBD Study 2019 22 .

Death can be expressed as the rate per 100,000 population or as the total number of deaths. To calculate deaths attributable to air pollution, the total number of deaths is multiplied by the population attributable fraction (PAF), which may be interpreted as the proportion of deaths attributable to air pollution. Additional methodologies for estimating the number of deaths are described in the GBD Study 2019 22 .

SDI is a summary measure of socio‐demographic development status, strongly correlated with health outcomes. It is a geometric mean of the rankings of the lag-distributed income per capita, mean educational attainment for those age 15 or older, and fertility rate in those under 25 years old. It is expressed on a scale of 0 to 1, but for GBD 2019, values were multiplied by 100 for a scale of 0 to 100, where a location with an SDI of 0 has a theoretical minimum level of SDI, and a location with an SDI of 1 (prior to multiplying by 100 for reporting purposes) would have a theoretical maximum level of sociodemographic development relevant to health outcomes. Additional descriptions about SDI calculation can be found in the GBD Study 2019 22 .

GNI per capita represents the value produced by a country’s economy in a given year per one person, regardless of whether the value is produced domestically or abroad. Methodologies for calculation of GNI per capita in U.S. dollars are based on the Atlas method exchange rates described elsewhere 26 .

DALY rate ratio (DARR) represents the ratio between a country's DALY rate for a given year and a median DALY rate of all European countries for the same year. aPM 2.5 ratio (PMR) represents the ratio between a country's aPM 2.5 concentration for a given year and a median aPM 2.5 level of all European countries for the same year.

YLL rate ratio (YRR) represents the ratio between a country's YLL rate for a given year and the median YLL rate of all European countries for the same year.

Death rate ratio (DRR) represents the ratio between a country's death rate for a given year and the median death rate of all European countries for the same year.

Statistical analyses

Burden of disease (e.g. attributable DALYs or mortality) calculation requires a few factors to be taken into account: spatial and temporal estimates of population-weighted exposure, TMREL, estimation of relative risk from exposure, as well as estimates of DALYs and deaths for diseases linked causally to air pollution. First, the data for relative risk and estimates of exposure of the population are combined which allows for the calculation of PAF, a proportion of DALYs and deaths in a population that can be attributed to exposure (e.g. to air pollution) above TMREL. Finally, the number of DALYs and deaths for certain diseases are multiplied by PAF and the end value gives an estimation of the burden attributable to the exposure. Specifically, we used DALYs, mortality and YLL attributable to air pollution overall, as well as to aPM 2.5 , household air pollution and ozone. A more detailed description of these methods can be found in GBD Study 2015 and 2019 6 , 22 .

Since the primary and final time points of our study were 1990 and 2019, we determined DARR and PMR for each European country for both years. We then defined change in DARR and PMR between those two time points as a new variable (DARR change and PMR change) and used it to quantify each country's ability to decrease its aPM 2.5 and DALY values to at least the extent of the European aPM 2.5 concentration and DALY median decrease between those two temporal points. Furthermore, 1990 and 2019 also respectively represent the largest and smallest value of European median value for aPM 2.5 rate and DALY rate with other values following a linear decrease during that 29-year period, starting from 1990 (Supplementary Figure S1 ).

If DARR change or PMR change are positive, a country shows a reduction in its aPM 2.5 or DALY values minimally to the extent of the European aPM 2.5 concentration or DALY median decrease, but if DARR change or PMR change are negative, a country cannot follow the European median reduction. However, countries which improved their own DALY rate and aPM 2.5 concentration may still be represented with a negative DARR change or PMR change if that improvement is lower than the extent of the European DALY or aPM 2.5 concentration median decrease. Using this formula, we also calculated YRR and DRR, as well as their change between 1990 and 2019. Other variables in different time points can also be analysed in a similar manner. A visual representation of the calculation can be found in Supplementary Figure S6 .

For each country, all variables are represented as a numerical value along with a 95% uncertainty interval (95% UI). For descriptive analysis of subgroups and all countries together, median and interquartile range (IQR) are predominantly used due to significant dataset variability and deviation from Gaussian distribution, unless stated otherwise. On the other hand, the death number is the only variable presented as a cumulative death number of all countries within a certain subgroup.

Due to significant dataset variability and deviation from Gaussian distribution, dependence between variables (YLL and aPM 2.5 concentration, PMR and DARR) was established using Spearman correlation test. Furthermore, Kruskal–Wallis test by ranks with Dunn’s multiple comparisons test was used to determine statistical significance between subgroups of countries classified by their GNI and SDI.

Multiple linear regression was used to predict the outcome of YLL rate attributable to air pollution for 2019 using air pollution-related explanatory variables and to further establish relationship between air pollution parameters and health outcomes. Explanatory variables used in the model were aPM 2.5 and household air pollution (HAP) for 2019. Both explanatory and response variables were log-transformed (ln-transformed). Ozone was excluded from the model because it failed to meet the assumption of linear relationship with the response variable. Even after multiple data transformations, it seemed that ozone did not have any significant relationship with the response variable. Furthermore, forcing the ozone into the model did not produce any significant improvement in proportion of explained variance. Alpha value for all statistical tests was set at 0.05. Data was analysed using GraphPad Prism 9 and Statistica 13.5.

Overall change of air quality parameters and morbidity and mortality estimates attributable to air pollution in Europe from 1990 to 2019

The average aPM 2.5 exposure of European countries was 20.8 μg/m 3 (95% CI 18.3–23.2) in 1990, while in 2019 it was 33.7% lower at 13.8 μg/m 3 (95% CI 12.0–15.6). All European countries reduced their annual population-weighted aPM 2.5 concentration, except Monaco (Fig.  1 , Supplementary Table S1 ). Western, Nordic and Baltic countries showed the biggest improvement in general, whereas progress was smaller for Eastern and Southeastern countries. On the other hand, average seasonal population-weighted ozone concentrations did not reduce as much as aPM 2.5 with 44.7 ppb, a 6.5% decrease compared to 41.8 ppb in 2019 (Supplementary Figure S2 ).

The average annual population-weighted aPM 2.5 concentration in European countries for 1990 ( a ) and 2019 ( b ). Countries are categorised by parameters of the European Environment Agency for 2019 into groups based on aPM 2.5 concentrations [μg/m 3 ] in 2019: good (0–10.0), fair (10.0–19.9), moderate (20.0–24.9) and poor (≥ 25.0). Percent reduction in annual population-weighted aPM 2.5 concentration in 2019 compared to 1990 ( c ). The figure was made in Adobe Illustrator (version 24.1., URL: https://www.adobe.com/products/illustrator.html ).

We then analysed death number, death rates and DALY rates attributable to air pollution for 2019 in all 43 countries (Table 1 ). There were in total 368,006 deaths in Europe attributable to air pollution, a reduction of 271 046 (42.4%) compared to 1990 with 37 countries lowering their number of deaths (Fig.  2 ). In the same time period, an overall 43.9% decrease in total number of deaths attributable to air pollution was observed in the subset of EU countries. Estonia had the most significant decrease in mortality attributable to air pollution with a 82.3% reduction in 2019 when compared to 1990. It is followed by Norway and Sweden with 73.5% and 72.8% fewer deaths, respectively. During the 29-year time period, we also observed a decreasing trend of all-cause median death rate of all European countries, with a total reduction of 40.6% (Fig.  3 ). EU countries had a 1.3% lower death rate and 5.6% lower DALY rate attributable to air pollution (Supplementary Table S2 ) compared to all European countries. Furthermore, reduction of death rate attributable to air pollution was more pronounced when compared to overall death rate. It was 50.0 in 1990, whereas in 2019 it was 16.7, a 66.6% decrease.

Percent change in number of deaths attributable to air pollution in 2019 compared to 1990.

Median European death rate from 1990 to 2019.

IHD was the primary cause of death in Europe making up 44.6% of all deaths attributable to air pollution. Stroke and TBL cancer had a smaller contribution to the total death number with 25.2% and 10.7%, respectively. When analysing the air pollution parameters, the majority (90.4%) of all deaths were associated with aPM 2.5 pollution. Therefore, due to the high share in total number of deaths among all air pollutants, we primarily focused on analysing effects of aPM 2.5 on health.

Since we showed that aPM 2.5 concentration in Europe decreased, we aimed to determine if the contribution of death rate attributable to air pollution in all-cause death rate also decreased. In 1990, it was 6.3% while in 2019 it was 3.5%, a reduction of 44.4%. We then aimed to explore how air pollution impacts premature mortality. Our analysis showed that a total of 24,917 years of life were lost per 100,000 population in Europe in 2019 due to health conditions associated with air pollution exposure (Fig.  4 ). This is a 63% decrease compared to 1990 (YLL = 67 258). Among all European countries in 2019, Finland had both the lowest YLL rate (60.9) and aPM 2.5 concentration (5.6 μg/m 3 ), while North Macedonia had the highest YLL rate (2214.9) and aPM 2.5 concentration (30.3 μg/m 3 ). Furthermore, a strong positive correlation was observed between aPM 2.5 concentration and YLL rate (r = 0.911, p  < 0.0001) (Supplementary Figure S3 ).

Age-standardised YLL rate per 100,000 and average annual population-weighted aPM 2.5 in 2019.

With IHD being the primary cause of death attributable to air pollution in Europe, we analysed YLL rate among European countries and observed that IHD also contributed the most with 41.2% of total YLL rate attributable to air pollution in 2019. IHD contributed the most to the YLL rate in Belarus (63.6%), while in Denmark it had the lowest contribution (22.9%).

Progress of each country compared to Europe overall in terms of DALYs and aPM 2.5

To evaluate progress and the dynamics of reduction of air pollution parameters, we aimed to determine how efficient each European country was in reducing its DALY and aPM 2.5 values to at least the extent of the European median decrease from 1990 to 2019. Therefore, European countries were compared using the DARR change attributable to air pollution in the first and final year of the analysed period. This value represents a difference between each country's DALY rate attributable to air pollution for the two given years (1990 and 2019) divided by the median European DALY for 1990. Figure  5 shows that 26 of 43 European countries improved their DARR, i.e. had a positive DARR change. These countries decreased their DALY rate attributable to air pollution minimally to the extent of the European median reduction. Estonia and Finland had the greatest improvement by decreasing DARR from 1990 to 2019 by 64.5% and 51.2%, respectively. On the other hand, 17 of 43 countries increased their DARR and are represented with a negative DARR change. Therefore, they have not been able to reduce their DALY values to at least the extent of the European median decrease. Monaco and Montenegro had the most negative values with an increase of 146.0% and 79.5%, respectively.

DARR change (%) and PMR change (%) for European countries.

PMR was also determined for each European country. Similarly to DARR change, PMR change was used as a measure of each country's ability to decrease its aPM 2.5 value minimally to the extent of the European median reduction. Although all countries (except Monaco) decreased their aPM 2.5 level in 2019 compared to 1990, 31 of 43 European countries had a negative PMR change and an unfavorable trend of increasing PMR in 2019 compared to 1990, thus not reducing its aPM 2.5 concentration to at least the extent of decrease in the European median. Monaco had the most prominent PMR change with an increase in PMR of 92% while other countries showed an increase of less than 50%. Although Finland increased its PMR by 7%, it still remained the country with the lowest aPM 2.5 level among European countries in 2019 and second lowest in 1990. Italy and Netherlands had a neutral PMR change (0%) because they had equal PMRs both in 1990 and 2019. 10 of 43 countries had a positive PMR change and favorable trend of decreasing aPM 2.5 values to at least the extent of the European median decrease with Switzerland having the largest reduction in PMR of 8%, followed by Norway and Denmark with a decrease of 6.9% and 5.9%, respectively. Furthermore, using the Spearman test, positive correlation between DARR and PMR was established both for 1990 (r = 0.854) and 2019 (r = 0.921). Using this calculation, YRR and DRR were also analysed (Supplementary Table S3 ).

In order to explore whether aPM 2.5 and HAP can significantly predict YLL attributable to air pollution for 2019, a multiple regression was performed. Our model statistically significantly predicted YLL values and also explained a significant proportion of variance in YLL (R 2  = 0.885, F(2,40) = 154.116, p  < 0.0001). Both aPM 2.5 (b = 1.623, p  < 0.0001) and HAP (b = 0.150, p  < 0.0001) were statistically significant predictors. The final model was:

As a practical example of this model, it predicts that for a 10% increase in aPM 2.5 , YLL increases by 16.7%. Furthermore, for a 10% increase in household air pollution, YLL increases by 1.4%.

Impact of economic and social factors on air pollution-related disease burden

Then, we aimed to determine if there was a significant difference in aPM 2.5 concentration and DALY rate attributable to air pollution based on a country's SDI. Thus, countries were grouped by their SDI in three categories (Fig.  6 ). Kruskal–Wallis test showed significant differences between groups for all three diseases: IHD (H(2) = 22.344, p  < 0.0001), stroke (H(2) = 21.847, p  < 0.0001) and TBL cancer (H(2) = 8.258, p  = 0.016). When analysing differences between groups for IHD and stroke attributable to air pollution in 2019, low SDI countries showed more than a 11-fold higher DALY rate both for IHD and stroke when compared to high SDI countries. Furthermore, more than a fivefold higher DALY rate for IHD and nearly fourfold higher DALY rate for stroke was observed in medium SDI countries compared to high SDI countries. When looking at TBL cancer, DALY rate showed lower variability among groups, but a statistically significant difference ( p  = 0.028) in DALY rate was observed when comparing high SDI to medium SDI countries. Significant differences (H(2) = 16.844, p  = 0.0002) were also observed between groups of countries when comparing their aPM 2.5 concentrations (Supplementary Figure S4B ).

Comparison of countries by SDI and DALY rate attributable to air pollution for IHD, stroke and TBL cancer in 2019. Countries are categorised by SDI into three categories: high SDI, medium SDI and low SDI. Groups are represented as median with interquartile range. Statistical analysis by Kruskal–Wallis test by ranks with Dunn’s multiple comparisons test. Bonferroni correction for multiple tests was used to adjust significance values.

In addition, we also compared groups of countries based on their GNI to assess if a country’s income might be a differentiating factor in terms of DALY rate attributable to air pollution. There were significant differences between groups for all three diseases: IHD (H(3) = 28.038, p  < 0.0001), stroke (H(3) = 28.963, p  < 0.0001) and TBL cancer (H(3) = 15.550, p  = 0.001). We showed that groups of countries with lower GNI had significantly higher DALY compared to groups of countries with higher GNI (Supplementary Figure S4A ). This effect is most prominent when looking at countries in the lowest economic bracket which have more than a 11-fold higher DALY rate for IHD and nearly 25-fold higher DALY rate for stroke when compared to very high income (VHI) countries. Furthermore, significant differences (H(3) = 19.918, p  = 0.0002) were also observed between GNI groups when comparing their aPM 2.5 concentrations (Supplementary Figure S4B ). These findings align well with SDI comparisons, aided by a strong correlation between GNI and SDI (Spearman r = 0.893, p  < 0.0001). Taken together, these comparisons show that countries with higher GNI and SDI have lower DALY rates attributable to air pollution compared to countries in lower GNI and SDI brackets.

Air pollution is the most important environmental risk to human health 27 . It is also perceived among Europeans as the second biggest environmental concern 28 . A growing interest in the topic of air pollution has led to public and political actions ultimately successfully reducing air pollution levels.

We showed that all European countries excluding Monaco decreased the concentration of the leading air pollutant aPM 2.5 by up to 44% in 2019 compared to 1990. One of the reasons that Monaco is an exception to this positive trend might be the very high population density (more than 170-fold more than the average population density of the European Union) which influences the aPM 2.5 value since the aPM 2.5 exposure calculation accounts for population density. The overall decrease in Europe may partly be due to strong and proactive political legislations which have proved effective in curbing air pollution in many studies 29 , 30 , 31 . The Convention on Long-range Transboundary Air Pollution has successfully reduced air pollution in Europe, whereas the European Green Deal focuses on making Europe climate neutral by 2050 32 . The act empowers to prioritize a sustainable industry, energy efficiency by using clean energy, the importance of recycling, optimizing agriculture, and sustainable mobility 33 . Reductions in air pollution were closely followed by reduced DALYs and mortality attributable to air pollution improving the overall population health (Supplementary Figure S5 ). However, despite the progress made, air pollution remains an important pan-European public health issue with nearly ¾ of European countries still exceeding the annual WHO AQG for aPM 2.5 pollution of 10 μg/m 3 in 2019. This is especially important in the context of the ever-growing European population where urbanization is expected to increase from 74.7% to approximately 83.7% in 2050 19 , 34 .

Study from Boldo et al. with 23 European cities showed that life expectancy at age 30 would increase by a range between one month and more than two years if long-term exposure to aPM 2.5 level was reduced to 15 μg/m 3 35 . Also, a recent study from almost 1000 European cities calculated that 51,213 deaths per year could be avoided if PM 2.5 exposure was compliant with WHO air pollution guidelines 36 .

As a result of improved air quality, more than 85% of countries in Europe had a lower number of deaths attributable to air pollution in 2019 compared to 1990. Also, the overall number of deaths attributable to air pollution was lowered by more than 270,000 (42.4%). Interestingly, the share of death rate attributable to air pollution in the overall all-cause death rate in Europe decreased more than the overall death rate, thus outpacing it from an approximate 6% share in 1990 to nearly 3.5% in 2019. On the other hand, despite reducing air pollution, Southeast European countries Bosnia and Herzegovina and North Macedonia still had the highest aPM 2.5 levels in Europe in 2019, three times higher than the WHO AQG. Our findings align with Lelieveld et al. who also found that air pollution-related mortality per capita was high in Eastern European countries, especially concerning cardiovascular mortality 37 . Death rates in Bosnia and Herzegovina and North Macedonia were five-fold and seven-fold higher than the European median, respectively. Their DALY rates were also the highest in Europe, multiple times higher than both the EU and European median and up to 32 times higher than Iceland, which had the lowest DALY rate, thus indicating room for improvement. In another study, Lehtomäki et al. showed that Iceland had the lowest death rate among five Nordic countries, all of which have relatively low levels of air pollution, and generally meet the EU guideline values 38 . Furthermore, for a deeper understanding of time trends in burden estimates, a decomposition of total changes in DALY rates or mortality over time for each European country could be performed, taking into account the contribution of population size change, age, cause specific-mortality rates (excluding the effect of air pollution), and air pollution exposure. These analyses could provide a useful overview of how each factor contributes to changes in DALY rates or mortality, and thus inform policy makers, as well as health officials, about potentially implementing specific measures to address the factors contributing most to disease burden. Decomposition methods have been previously reported in GBD studies, as well as papers that focused primarily on this method, which due to its significant comprehensiveness, was not within the scope of our research 6 , 39 , 40 .

As a summary measure of premature mortality, YLL has been highly associated with air pollution and each year more than 200 million years of life are lost due to air pollution globally 41 . Our results align closely since we showed a strong positive association between aPM 2.5 concentration and YLL. One study using data from 72 Chinese cities estimated that for every 10 μg/m 3 increase in PM 2.5 an additional 0.43 years of life are lost, whereas PM 2.5 levels in accordance with WHO AQG would result in 0.14 years of gain in life expectancy 42 . To get a better and more comprehensive understanding of the relationship between disease burden metrics such as YLL and aPM 2.5 and household air pollution, the model we created came very useful. Due to its significant prediction of YLL attributable to air pollution using aPM 2.5 and household air pollution, we think this might be of good use to policymakers and researchers in the field to reliably predict YLL based on trends of air pollution components, especially during a period of years. Furthermore, since the model predicts that a 10% increase in aPM 2.5 would result in YLL increase of 16.7%, whereas a 10% increase in household air pollution would increase YLL by only 1.4%, it strongly supports that aPM 2.5 has the predominant impact on disease burden, thus pointing to the importance of curbing this air pollution parameter.

Comparing the results to the overall trend in the region is also a significant factor regarding not only a country’s environmental consciousness, but also the ability to leverage resources to combat air pollution. Both PMR change and DARR change are important to see if the country is reducing its aPM 2.5 concentration and DALY values at least at the pace of the European region between 1990 and 2019. Despite almost all countries decreasing their aPM 2.5 concentrations in 2019 , only 10 countries actually did that to at least the extent of the European median decrease. Sweden and Norway were among the countries with the lowest aPM 2.5 concentrations in 1990, yet still managed to decrease their aPM 2.5 values in 2019 more than the European median, thus having a positive PMR change. On the other hand, Bosnia and Herzegovina and Albania had among the most negative PMR changes in spite of reducing their aPM 2.5 concentrations. Belis et al. identified energy production in inefficient coal-fueled power plants as one of the main sources of PM 2.5 in the Western Balkans. Also, agriculture and residential combustion significantly affected PM 2.5 levels 43 . Since some Western Balkan countries had among the highest aPM 2.5 levels both in 1990 and 2019, more powerful ways of curbing air pollution are needed.

Similarly, we used DARR change to evaluate each country's progress in terms of reduction in DALY rates attributable to air pollution compared to the European median change. All 43 countries reduced their own DALY rate in 2019 compared to 1990, but less than 2/3 had a decrease to at least the extent of the European median reduction, thus having a positive DARR change. EU countries Finland, Sweden, and Estonia all had DARR change above 40%, while Monaco, Ukraine and Montenegro had the most negative DARR change, up to -146%. The wide disparity in terms of DARR change may be due to more strict environmental policies in EU countries, ambitious targets for emission reductions, and economic power and development 44 . Therefore, both aPM 2.5 concentration and DALY rate, as well as DARR change and PMR change are complementary methods which should be used when evaluating changes in aPM 2.5 concentration and DALY rate between countries. It is important to note that multiple diverse variables and geographical regions could used in this calculation, which gives it a breath of flexibility and applicability in various scenarios.

Taking into account social and economic factors using SDI and GNI in regard to air pollution-related health burden is an important metric. We showed that countries in the lowest SDI category in Europe had a higher aPM 2.5 concentration compared to those in the highest. This aligns well with another study showing that the country's lower development status might be associated with overall poorer air quality 8 . Developing countries undergoing the process of intense urbanization and industrialization became the countries with the largest air pollution-related burdens in recent years. Furthermore, indoor air pollution originating from coal and biomass in the form of wood, dung and crop residues for domestic energy represents a major environmental and public health challenge in developing economies, especially in rural areas 45 . Even though we expected more developed countries to have lower air pollution parameters, we were surprised by how large some of the differences were when comparing countries. Low SDI countries had more than a 11-fold higher DALY rate both for IHD and stroke when compared to high SDI countries. This difference has even greater meaning when taking into account that stroke and IHD are two of the leading causes of death in the world. Similar striking differences were found when comparing countries by GNI, thus excluding educational attainment and total fertility rate which are part of SDI. Upper middle income (UMI) countries had more than a 11-fold higher DALY rate for IHD and nearly 25-fold higher DALY rate for stroke when compared to VHI countries. This big difference emphasizes a larger than expected gap between more and less developed countries in Europe and shows how disparities in controlling air pollution can negatively affect population health. This is a strong call to action, especially in lower developed countries, to double down on curbing air pollution considering that stroke, IHD and TBL cancer made up more than 80% of all European deaths attributable to air pollution in 2019 alone. The difference in economic power and DALYs might also be due to other factors such as a more comprehensive and stronger healthcare system in wealthier countries. A study showed that better and more extensive quality of healthcare is needed to improve patient outcomes, especially since 63.8% of deaths in Eastern Europe occurred due to use of poor-quality services 46 . The somewhat smaller statistical significance for TBL cancer might be due to cancer being a complex heterogeneous disease which can be attributable to both genetics and lifestyle and thus various factors might be triggering its genesis in different and unequal manners 47 .

With cooperation at intra- and inter-national levels, strong policies could be implemented at curbing both air pollution and premature mortality and morbidity, while serving as a catalyst for economic development and promotion of healthy lifestyle 48 . Emissions from vehicles could be reduced by prioritizing green and sustainable forms of transport such as rapid and optimized urban and international transport, cycling, as well as implementing stricter vehicle emissions standards and working on more efficient engine technologies 48 , 49 . Making cities more compact and with energy efficient homes, optimizing urban transport and waste management will be of utmost importance to mitigate air pollution increase. Improving the management of agricultural waste and livestock manure, while reducing agriculture field burning and promoting healthy diets low in processed meat and rich in plant-based food will keep the food production environmentally sustainable 50 . Despite improvements in wastewater management and pollution abatement technology, industry is still a significant source of pollutant releases in Europe 51 . Further implementation of clean technologies, filters, and recovery of gas released during fossil fuel production are recommended to optimize soil, water and air quality management 52 . Public actions and national robust policies could have a long-lasting impact on bringing down air pollution, as well as growing human health and welfare.

Our study has several limitations. First, important causes of death like chronic obstructive pulmonary disease, dementias, diabetes and kidney disease were not individually addressed in the context of air pollution-related morbidity and mortality. Second, DARR change and PMR change might not give a clear indication of the country's progress in certain conditions. A negative DARR change and/or PMR change might underestimate the country's progress since it could already have low DALY rates or aPM 2.5 values. Third, estimates on levels of aPM 2.5 and ozone might be skewed given the smaller numbers and less well diversified locations of air quality monitoring stations, as well as the spread of air pollution from other countries through changes in wind pattern with temperature 53 , 54 . Also, GBD study estimates of household air pollution include solid fuels used for cooking, but not for heating. Fourth, availability of primary data is a major limitation of the GBD study and as such applies here for mortality and morbidity estimates, along with other general limitations described in the GBD Study 2019 22 . Fifth, decomposing the total changes in mortality or DALY rates over time for each European country, taking into account the contribution of population size change, age, cause specific-mortality rates (excluding the effect of air pollution), and air pollution exposure was not performed which could have given the paper a deeper understanding of the factors influencing them the most. Sixth, recommendations on how to reduce air pollution might not be feasible for every country in the same way due to different dominant industries and economic power. Finally, air pollution, as well as mortality and morbidity estimates for each country might not be representative for all the country's regions. Also, specific air pollution-related medical conditions might not be represented in an equal manner in the whole population and certain subpopulations might be more or less affected. Even with these limitations, our study provides a useful overview and analysis regarding the health effects of air pollution in Europe using the most recent data available.

In conclusion, Europe made significant progress in decreasing aPM 2.5 concentration, mortality and disease burden attributable to air pollution in the last three decades. However, nearly 75% of Europeans still live in areas where aPM 2.5 concentration do not meet WHO AQG. Even though implementing air pollution reduction measures may be a significant challenge for some countries, with population growth and increased urbanization in Europe, air quality should be prioritized for long term economic growth and improved overall population health.

Data availability

The dataset regarding overall, as well as ischemic heart disease (IHD), stroke, and tracheal, bronchus and lung cancer-specific disability adjusted life years (DALYs), years of life lost (YLL) and mortality attributable to air pollution for 43 European countries between 1990 and 2019 are available in the Global Burden of Disease 2019 repository which can be found on this web page: http://www.healthdata.org/gbd/2019 . Socio-demographic index for each country can be found in the same database. The dataset regarding concentrations of ambient particulate matter less than 2.5 microns in size, ozone, and household air pollution from solid fuels were obtained from State of Global Air 2020 which can be found on this web page: https://www.stateofglobalair.org/ . The dataset regarding the gross national income of each European country was obtained from the World Bank Classification and can be found on this web page: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD . Other data used in this manuscript can be found within the reference section.

Ambient (outdoor) air pollution. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (2018).

Rafaj, P. et al. Outlook for clean air in the context of sustainable development goals. Glob. Environ. Change 53 , 1–11 (2018).

Article   Google Scholar  

The top 10 causes of death. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death (2020).

Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Institute for Health Metrics and Evaluation. http://ghdx.healthdata.org/gbd-results-tool (2020).

Air Pollution. World Health Organization. https://www.who.int/health-topics/air-pollution#tab=tab_1 (2020).

Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389 , 10082. https://doi.org/10.1016/S0140-6736(17)30505-6 (2017).

World Bank; Institute for Health Metrics and Evaluation. The cost of air pollution: Strengthening the economic case for action. World Bank. https://openknowledge.worldbank.org/handle/10986/25013 (2016).

Mannucci, P. M. & Franchini, M. Health effects of ambient air pollution in developing countries. Int. J. Environ. Res. Public Health 14 , 1048. https://doi.org/10.3390/ijerph14091048 (2017).

Article   CAS   PubMed Central   Google Scholar  

Air pollution. European Environment Agency. https://www.eea.europa.eu/themes/air/intro (2020)

European Environment Agency. Air quality in Europe: 2016 Report. European Environment Agency. https://www.eea.europa.eu/publications/air-quality-in-europe-2016 (2016).

Commission Staff Working Document — Impact Assessment. European Commission. http://ec.europa.eu/environment/archives/air/pdf/Impact_assessment_en.pdf (2013).

Air Quality in Europe: 2018 Report. European Environment Agency. https://www.eea.europa.eu/publications/air-quality-in-europe-2018 (2018).

Anenberg, S. C. et al. Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide. Sci. Rep. 9 , 11552. https://doi.org/10.1038/s41598-019-48057-9 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

The European environment — state and outlook 2020: Knowledge for transition to a sustainable Europe. European Environment Agency. https://www.eea.europa.eu/publications/soer-2020 (2019).

Xu, X. et al. Long-term exposure to ambient fine particulate pollution induces insulin resistance and mitochondrial alteration in adipose tissue. Toxicol. Sci. 124 , 99–98 (2011).

Xing, Y. F., Xu, Y. H., Shi, M. H. & Lian, Y. X. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 8 , E69–E74 (2016).

Google Scholar  

Correia, A. W. et al. Effect of air pollution control on life expectancy in the United States: An analysis of 545 U.S. counties for the period from 2000 to 2007. Epidemiology 24 , 23–31 (2013).

Kloog, I., Ridgway, B., Koutrakis, P., Coull, B. A. & Schwartz, J. D. Long- and short-term exposure to PM2.5 and mortality: Using novel exposure models. Epidemiology 24 , 555–561 (2013).

Urban population (% of total population) — European Union. World Bank. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?locations=EU (2018).

Jerrett, M. et al. Long-term ozone exposure and mortality. N. Engl. J. Med. 360 , 1085–1095 (2009).

Article   CAS   Google Scholar  

Bekkar, B., Pacheco, S., Basu, R. & DeNicola, N. Association of air pollution and heat exposure with preterm birth, low birth weight, and stillbirth in the US: A systematic review. JAMA Netw. Open. 3 , e208243. https://doi.org/10.1001/jamanetworkopen.2020.8243 (2020).

Article   PubMed   PubMed Central   Google Scholar  

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet . 396, 1204–1222 (2020).

Health Effects Institute. State of Global Air 2020. Health Effects Institute. www.stateofglobalair.org (2020).

World Development Indicators (Series: GNI per capita, Atlas method (current US$). The World Bank. http://data.worldbank.org/indicator (2020).

Global Burden of Disease Cancer Collaboration. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: A systematic analysis for the global burden of disease study. JAMA Oncol. 3 , 524–548 (2017).

The World Bank Atlas method — detailed methodology. World Bank. https://datahelpdesk.worldbank.org/knowledgebase/articles/378832-what-is-the-world-bank-atlas-method (2020).

Chau, T. T. & Wang, K. Y. An association between air pollution and daily most frequently visits of eighteen outpatient diseases in an industrial city. Sci. Rep. 10 , 2321. https://doi.org/10.1038/s41598-020-58721-0 (2020).

Special Eurobarometer 468: Attitudes of European citizens towards the environment. EU Open Data Portal . https://data.europa.eu/euodp/en/data/dataset/S2156_88_1_468_ENG (2021).

Turnock, S. T. et al. The impact of European legislative and technology measures to reduce air pollutants on air quality, human health and climate. Environ. Res. Lett. 11 , 024010. https://doi.org/10.1088/1748-9326/11/2/024010 (2016).

Article   ADS   CAS   Google Scholar  

Guerreiro, C. B. B., Foltescu, V. & de Leeuw, F. Air quality status and trends in Europe. Atmos. Environ. 98 , 376–384 (2014).

Kuklinska, K., Wolska, L. & Namiesnik, J. Air quality policy in the U.S. and the EU—A review. Atmos. Pollut. Res. 6 , 129–137 (2015).

Byrne, A. The 1979 convention on long-range transboundary air pollution: Assessing its effectiveness as a multilateral environmental regime after 35 years. Trans. Environ. Law 4 , 37–67 (2015).

Communication from the Commission to the European Parliament, the European Council, The Council, the European Economic and Social Committee and the Committee of the Regions: The European Green Deal. European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2019%3A640%3AFIN (2019).

Developments and Forecasts on Continuing Urbanisation. European Commission. https://knowledge4policy.ec.europa.eu/foresight/topic/continuing-urbanisation/developments-and-forecasts-on-continuing-urbanisation_en (2021).

Boldo, E. et al. Apheis: Health impact assessment of long-term exposure to PM 2.5 in 23 European cities. Eur. J. Epidemiol. 21 , 449–458 (2006).

Khomenko, S. et al. Premature mortality due to air pollution in European cities: A health impact assessment. Lancet Planet. Health 5 , e121–e134 (2021).

Lelieveld, J., Klingmüller, K. & Pozzer, A. Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions. Eur. Heart J. 40 , 1590–1596 (2019).

Lehtomäki, H., Geels, C. & Brandt, J. Deaths attributable to air pollution in nordic countries: Disparities in the estimates. Atmosphere 11 , 467 (2020).

Article   ADS   Google Scholar  

Cheng, X. Population ageing and mortality during 1990–2017: A global decomposition analysis. PLoS Med. 17 , 6. https://doi.org/10.1371/journal.pmed.1003138 (2020).

Ščasný, M., Ang, B. W. & Rečkaa, L. Decomposition analysis of air pollutants during the transition and post-transition periods in the Czech Republic. Renew. Sustain. Energy Rev. 145 , 111137. https://doi.org/10.1016/j.rser.2021.111137 (2021).

Lelieveld, J. et al. Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovasc. Res. 116 , 1910–1917 (2020).

Qi, J. et al. Potential gains in life expectancy by attaining daily ambient fine particulate matter pollution standards in mainland China: A modeling study based on nationwide data. PLoS Med. 17 , e1003027. https://doi.org/10.1371/journal.pmed.1003027 (2020).

Belis, C. A. et al. Urban pollution in the Danube and Western Balkans regions: The impact of major PM2.5 sources. Environ. Int. 133 , 105158. https://doi.org/10.1016/j.envint.2019.105158 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Koolen, C. D. & Rothenberg, G. Air pollution in Europe. Chemsuschem 12 , 164–172 (2019).

Bruce, N., Perez-Padilla, R. & Albalak, R. Indoor air pollution in developing countries: A major environmental and public health challenge. World Health Organ. 78 , 1078–1092 (2000).

CAS   Google Scholar  

Kruk, M. E. et al. Mortality due to low-quality health systems in the universal health coverage era: A systematic analysis of amenable deaths in 137 countries. Lancet 392 , 2146–2147 (2018).

Chen, Z., Fillmore, C. M., Hammerman, P. S., Kim, C. F. & Wong, K.-K. Non-small-cell lung cancers: A heterogeneous set of diseases. Nat. Rev. Cancer 14 , 535–546 (2014).

Scovronick, N. Reducing Global Health Risks Through Mitigation of Short-Lived Climate Pollutants . (World Health Organization, 2015).

Overview of Air Pollution from Transportation. United States Environmental Protection Agency. https://www.epa.gov/transportation-air-pollution-and-climate-change/learn-about-air-pollution-transportation (2021).

Interventions: Agriculture. World Health Organization. https://www.who.int/airpollution/ambient/interventions/agriculture/en/ (2021).

Air Quality in Europe: 2020 Report. European Environment Agency. https://www.eea.europa.eu//publications/air-quality-in-europe-2020-report (2020).

Industrial pollution in Europe. European Environment Agency . https://www.eea.europa.eu/data-and-maps/indicators/industrial-pollution-in-europe-3/assessment (2021).

De Sario, M., Katsouyanni, K. & Michelozzi, P. Climate change, extreme weather events, air pollution and respiratory health in Europe. Eur. Respir. J. 42 , 826–843 (2013).

Kracht, O. et al. Spatial representativeness of air quality monitoring sites — Outcomes of the FAIRMODE/AQUILA intercomparison exercise. European Commission - Joint Research Centre. https://publications.jrc.ec.europa.eu/repository/handle/JRC108791 (2017).

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Juginović, A., Vuković, M., Aranza, I. et al. Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries. Sci Rep 11 , 22516 (2021). https://doi.org/10.1038/s41598-021-01802-5

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● Air pollution accounted for 8.1 million deaths globally in 2021, becoming the second leading risk factor for death, including for children under five years. Of the total deaths, noncommunicable diseases including heart disease, stroke, diabetes, lung cancer, and chronic obstructive pulmonary disease (COPD) account for nearly 90% of the disease burden from air pollution.

● In 2021, more than 700,000 deaths in children under 5 years were linked to air pollution; this represents 15% of all global deaths in children under five. As in previous years, the State of Global Air 2024 report and accompanying website provides comprehensive data on the levels and trends in air quality and health for every country in the world. This State of Global Air report was produced in partnership with UNICEF. In the State of Global Air 2024 interactive app , you can explore, compare, and download data and graphics reflecting the latest air pollution levels and associated burden of disease for over 200 individual countries, territories, and regions, as well as track trends from 1990 to 2021.

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Visualizing Air Quality Data - Know through Graph of Air Pollution

Air quality and health are intricately linked. Understanding the real-time status of urban and industrial air quality – and communicating this well – is important for ambient air quality control.

Air quality visualization – the graphical display of data – helps us understand the distribution of air pollutants in the atmosphere. This is hard to do just by looking at a modern air monitor equipment with its digital display.

By combining real-time monitoring data with python programming, one can easily visualize air monitoring data. Interactive graphs can be created which makes it easier to check air quality, and increasingly diverse colors can visually highlight the air quality level. Visualization of data has a resilient expression (more images and more insightful) than the original data table, which is favorable for further analysis of data.

In this article, I will share examples of data visualization that have helped Devic Earth to convey more clearly the message on air quality.

The first example is a heatmap – a plot comparing the data of particulate matter levels (PM2.5) during, the last week of March in 2019 and 2020 - Also here is the judgment of cities with best air quality in india during this period. The purpose of the visualization shown here is to compare the air quality during the lockdown with the same time period in the previous year.

Particulate matter less than 2.5 microns. This pollutant causes cardio-respiratory diseases and is emitted by vehicular pollution and combustion activities

In this graph of air pollution, I have plotted PM2.5 levels in 5 cities during March 22-31 2019 and 2020 using python programming and plotly package.

Observation:

In Figure 1, the legend on the right side indicates safe levels of PM2.5 through the color-coding – green indicating safe levels, and purple and brown indicating unsafe levels.

At first glance, the graph of air pollution makes it clear that PM2.5 in 2019 was higher than PM2.5 in 2020 in those 5 cities for the same week in March. For example, the 2019 graph shows PM2.5 levels in Delhi are consistently above 60 µg/m3 and even crosses 160 µg/m3 for a few days, whereas in 2020, the PM2.5 levels have been in the safe zone for most of the days. The CPCB ambient air quality standard in India for 24-hour average PM2.5 is less than 60 µg/m3.

What did I do:

Getting CPCB air quality data for each city for the year 2019 and 2020: CPCB air quality data is easily downloaded from the CPCB website (https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing)

Data cleaning and processing it into data frames: Here the missing values and outliers are deleted from the dataset and the date & time values which are in one column are separated into 2 different columns

Creating two subplots and combining them into a single plot in the figures: Two subplots are created for each year setting the x-axis to dates, y-axis to cities, and z-axis i.e. the color to PM2.5 values.

Setting up the layout: This involves setting the color scale to that of Air Quality Index, formatting the text for both the axis in each plot to make it readable in the layouts

Plotting the figures along with the layout : Taking the combined subplots and setting it to the layout created in Step 4, plot the entire graph to get the above visual.

Scatter plot

This is the second graph of air pollution where I have plotted PM2.5 reduction levels against the number of days that Pure Skies has been installed at different industry sectors. Using NumPy, I defined a function to calculate the trend line using OLSM (Ordinary Least Square Method) to show the trend of PM2.5 reductions over time.

PM2.5 reductions improve over time, especially in the heavy machine industries where the baseline graph of air pollution is higher. The trend line which shows a steady increase also indicates the same. The trend line indicates the PM2.5 reductions of 40-60% in the first 150 days. After that, there is an 80% reduction in PM2.5.

Divide the dataset based on the type of industries.

Define a function to calculate the trendline (y = mx + c, where m is the slope and c is the intercept) using Ordinary Least Square Methods (source:https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)

For the plot, using each divided dataset as a separate trace for scatter plot, with the x-axis being the number of days and the y-axis being the PM2.5 reduction, combine all the traces as one array for the figure.

Set the format for the axis and texts in the layout to make it readable.

Run the function created in step 2 giving the values of x and y in the graph, then add a trace for line plot keeping the x-axis the same and y as m*x + c.

Plot the entire figure with the layout.

“A picture speaks a thousand words”

Data visualizations make big and small data simpler for the human brain to comprehend. Visualization also makes it easier to identify patterns, trends, and outliers in sets of data. Good data visualizations should place meaning into complex datasets so that their significance is well-defined and brief.

In terms of business, data visualization speeds up the process of decision making and analytics where instead of calculating for trends or patterns one can easily see it on a graph, saving a measurable amount of time. Since visualization is something everyone can understand it enhances communication across departments in the business, thus making it more efficient.

In terms of air quality – which often cannot be seen – data visualization eliminates that disadvantage, hence displaying the quality of air a person is breathing. It even displays the efficacy of air control equipment, which will help businesses when it comes to investing in air control equipment.

Looking for a Devic Earth recommended and internally used Indoor Air Quality Monitor?

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(This article is written by Vidhya Sreenivasan with updates from Shashank Aggarwal)

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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.

• Apte, J.S., Pant, P. (2019). Toward cleaner air for a billion Indians. PNAS 116, 10614–10616.  https://doi.org/10.1073/pnas.1905458116
• Avnery, S., Mauzerall, D.L., Fiore, A.M. (2013). Increasing global agricultural production by reducing ozone damages via methane emission controls and ozone‐resistant cultivar selection. Global Change Biol. 19, 1285–1299.  https://doi.org/10.1111/gcb.12118
• Avnery, S., Mauzerall, D.L., Liu, J., Horowitz, L.W. (2011). Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses and economic damage under two scenarios of O 3   pollution. Atmos. Environ. 45, 2297–2309.   https://doi.org/10.1016/j.atmosenv.2011.01.002
• Balakrishnan, K., Cohen, A., Smith, K.R. (2014). Addressing the burden of disease attributable to air pollution in India: The need to integrate across household and ambient air pollution exposures. Environ. Health Perspect. 122.  https://doi.org/10.1289/ehp.1307822
• Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R.S., Brauer, M., Cohen, A.J., Stanaway, J.D., Beig, G., Joshi, T.K., Aggarwal, A.N., Sabde, Y., Sadhu, H., Frostad, J., Causey, K., Godwin, W., Shukla, D.K., Kumar, G.A., Varghese, C.M., Muraleedharan, P., Agrawal, A.,   et al.   (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017. Lancet Planet. Heath 3, e26–e39.  https://doi.org/10.1016/S2542-5196(18)30261-4
• Brauer, M., Guttikunda, S.K., Nishad, K.A., Dey, S., Tripathi, S.N., Weagle, C., Martin, R.V. (2019). Examination of monitoring approaches for ambient air pollution: A case study for India. Atmos. Environ. 216, 116940.  https://doi.org/10.1016/j.atmosenv.2019.116940
• Chen, Y., Wild, O., Conibear, L., Ran, L., He, J., Wang, L., Wang, Y. (2020). Local characteristics of and exposure to fine particulate matter (PM 2.5 ) in four Indian megacities. Atmos. Environ. 5, 100052.  https://doi.org/10.1016/j.aeaoa.2019.100052
• Chin, M. (2012). Dirtier air from a weaker monsoon. Nat. Geosci. 5, 449–450.   https://doi.org/10.1038/ngeo1513
• Chowdhury, S., Dey, S. (2016). Cause-specific premature death from ambient PM 2.5   exposure in India: Estimate adjusted for baseline mortality. Environ. Int. 91, 283–290.   https://doi.org/10.1016/j.envint.2016.03.004
• Chowdhury, S., Dey, S., Di Girolamo, L., Smith, K.R., Pillarisetti, A., Lyapustin, A. (2019). Tracking ambient PM 2.5   build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmos. Environ. 204, 142–150.   https://doi.org/10.1016/j.atmosenv.2019.02.029
• Chowdhury, S., Pozzer, A., Dey, S., Klingmueller, K., Lelieveld, J. (2020). Changing risk factors that contribute to premature mortality from ambient air pollution between 2000 and 2015. Environ. Res. Lett. 15, 074010.  https://doi.org/10.1088/1748 9326/ab8334
• Cusworth, D.H., Mickley, L.J., Sulprizio, M.P., Liu, T., Marlier, M.E., DeFries, R.S., Guttikunda, S.K., Gupta, P. (2018). Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India. Environ. Res. Lett. 13, 044018.   https://doi.org/10.1088/1748-9326/aab303
• Gao, M., Gao, J., Zhu, B., Kumar, R., Lu, X., Song, S., Zhang, Y., Jia, B., Wang, P., Beig, G., Hu, J., Ying, Q., Zhang, H., Sherman, P., McElroy, M.B. (2020). Ozone pollution over China and India: Seasonality and sources. Atmos. Chem. Phys. 20, 4399–4414.   https://doi.org/10.5194/acp-20-4399-2020
• GDB Maps Working Group (2018). Burden of disease attributable to major air pollution sources in India.
• Ghude, S.D., Jena, C., Chate, D.M., Beig, G., Pfister, G.G., Kumar, R., Ramanathan, V. (2014). Reductions in India's crop yield due to ozone. Geophys. Res. Lett. 41, 5685–5691.   https://doi.org/10.1002/2014GL060930
• Gurjar, B.R., Ravindra, K., Nagpure, A.S. (2016). Air pollution trends over Indian megacities and their local-to-global implications. Atmos. Environ. 142, 475–495.   https://doi.org/10.1016/j.atmosenv.2016.06.030
• Guttikunda, S.K., Gurjar, B.R. (2012). Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ. Monit. Assess. 184, 3199–3211.   https://doi.org/10.1007/s10661-011-2182-8
• Guttikunda, S.K., Calori, G. (2013). A GIS based emissions inventory at 1 km× 1 km spatial resolution for air pollution analysis in Delhi, India. Atmos. Environ. 67, 101–111.   https://doi.org/10.1016/j.atmosenv.2012.10.040
• Guttikunda, S.K., Nishadh, K.A., Jawahar, P. (2019). Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim. 27, 124–141.   https://doi.org/10.1016/j.uclim.2018.11.005
• Hama, S.M.L., Kumar, P., Harrison, R.M., Bloss, W.J., Khare, M., Mishra, S., Namdeo, A., Sokhi, R., Goodman, P., Sharma, C. (2020). Four-year assessment of ambient particulate matter and trace gases in the Delhi-NCR region of India. Sustainable Cities Soc. 54, 102003.   https://doi.org/10.1016/j.scs.2019.102003
• Hammer, M.S., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A.M., Hsu, N.C., Levy, R.C., Garay, M.J., Kalashnikova, O.V., Kahn, R.A., Brauer, M., Apte, J.S., Henze, D.K., Zhang, L., Zhang, Q., Ford, B., Pierce, J.R., Martin, R.V. (2020). Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 54, 7879–7890.   https://doi.org/10.1021/acs.est.0c01764
• Jain, S., Sharma, S.K., Vijayan, N., Mandal, T.K. (2020). Seasonal characteristics of aerosols (PM 2.5   and PM 10 ) and their source apportionment using PMF: A four year study over Delhi, India. Environ. Pollut. 262, 114337.  https://doi.org/10.1016/j.envpol.2020.114337
• Jat, R., Gurjar, B.R., Lowe, D. (2021). Regional pollution loading in winter months over India using high resolution WRF-Chem simulation. Atmos. Res. 249, 105326.   https://doi.org/10.1016/j.atmosres.2020.105326
• Kota, S.H., Guo, H., Myllyvirta, L., Hu, J., Sahu, S.K., Garaga, R., Ying, Q., Gao, A., Dahiya, S., Wang, Y., Zhang, H. (2018). Year-long simulation of gaseous and particulate air pollutants in India. Atmos. Environ. 180, 244–255.  https://doi.org/10.1016/j.atmosenv.2018.03.003
• Kulshrestha, M.J., Kulshrestha, U.C., Parashar, D.C., Vairamani, M. (2003). Estimation of SO 4   contribution by dry deposition of SO 2   onto the dust particles in India. Atmos. Environ. 37, 3057–3063.   https://doi.org/10.1016/S1352-2310(03)00290-5
• Lu, X., Zhang, L., Liu, X., Gao, M., Zhao, Y., Shao, J. (2018). Lower tropospheric ozone over India and its linkage to the South Asian monsoon. Atmos. Chem. Phys. 18, 3101–3118.   https://doi.org/10.5194/acp-18-3101-2018
• Mahesh, B., Rama, B.V., Spandana, B., Sarma, M.S.S.R.K.N., Niranjan, K., Sreekanth, V. (2019). Evaluation of MERRAero PM 2.5   over Indian cities. Adv. Space Res. 64, 328–334.   https://doi.org/10.1016/j.asr.2019.04.026
• Martin, R.V., Brauer, M., van Donkelaar, A., Shaddick, G., Narain, U., Dey, S. (2019). No one knows which city has the highest concentration of fine particulate matter. Atmos. Environ. 3, 100040.  https://doi.org/10.1016/j.aeaoa.2019.100040
• Ministry of Environment, Forests and Climate Change (MoEF) (2019). National Clean Air Program (NCAP). Government of India.   http://moef.gov.in/wp-content/uploads/2019/05/NCAP_Report.pdf
• National Ambient Air Quality Monitoring Program (NAMP) (2020). Data Year wise.   https://cpcb.nic.in/namp-data/   (accessed 15 December 2020).
• Navinya, C.D., Vinoj, V., Pandey, S.K. (2020). Evaluation of PM 2.5   surface concentrations simulated by NASA’s MERRA Version 2 aerosol reanalysis over India and its relation to the air quality index. Aerosol Air Qual. Res. 20, 1329–1339.   https://doi.org/10.4209/aaqr.2019.12.0615
• Pande, P., Dey, S., Chowdhury, S., Choudhary, P., Ghosh, S., Srivastava, P., Sengupta, B. (2018). Seasonal transition in PM 10   exposure and associated all-cause mortality risks in India. Environ. Sci. Technol. 52, 8756–8763.   https://doi.org/10.1021/acs.est.8b00318
• Pandey, A., Brauer, M., Cropper, M.L., Balakrishnan, K., Mathur, P., Dey, S., Turkgulu, B., Kumar, G.A., Khare, M., Beig, G., Gupta, T., Krishnankutty, R.P., Causey, K., Cohen, A.J., Bhargava, S., Aggarwal, A.N., Agrawal, A., Awasthi, S., Bennitt, F., Bhagwat, S.,   et al.   (2021). Health and economic impact of air pollution in the states of India: The Global Burden of Disease Study 2019. Lancet Planet. Heath 5, e25–e38.   https://doi.org/10.1016/S2542-5196(20)30298-9
• Pant, P., Lal, R.M., Guttikunda, S.K., Russell, A.G., Nagpure, A.S., Ramaswami, A., Peltier, R.E. (2019). Monitoring particulate matter in India: recent trends and future outlook. Air Qual. Atmos. Health 12, 45–58.  https://doi.org/10.1007/s11869-018-0629-6
• Ravindra, K., Singh, T., Mor, Sahil, Singh, V., Mandal, T.K., Bhatti, M.S., Gahlawat, S.K., Dhankhar, R., Mor, Suman, Beig, G. (2019). Real-time monitoring of air pollutants in seven cities of North India during crop residue burning and their relationship with meteorology and transboundary movement of air. Sci. Total Environ. 690, 717–729.  https://doi.org/10.1016/j.scitotenv.2019.06.216
• Sahu, S.K., Kota, S.H. (2017). Significance of PM 2.5   air quality at the Indian capital. Aerosol Air Qual. Res. 17, 588–597.   https://doi.org/10.4209/aaqr.2016.06.0262
• Sharma, M., Dikshit, O. (2016). Comprehensive study on air pollution and green house gases (GHGs) in Delhi. A report submitted to Government of NCT Delhi and DPCC Delhi, 1–334. (accessed 20 December 2020).
• Sharma, S.K., Mandal, T.K., Sharma, A., Jain, S. (2018). Carbonaceous species of PM 2.5   in megacity Delhi, India during 2012–2016. Bull. Environ. Contam. Toxicol. 100, 6952.5701.   https://doi.org/10.1007/s00128-018-2313-9
• Silva, R.A., West, J.J., Lamarque, J.-F., Shindell, D.T., Collins, W.J., Faluvegi, G., Folberth, G.A., Horowitz, L.W., Nagashima, T., Naik, V., Rumbold, S.T., Sudo, K., Takemura, T., Bergmann, D., Cameron-Smith, P., Doherty, R.M., Josse, B., MacKenzie, I.A., Stevenson, D.S., Zeng, G. (2017). Future global mortality from changes in air pollution attributable to climate change. Nature Clim. Change 7, 647–651.   https://doi.org/10.1038/nclimate3354
• Sreekanth, V., Mahesh, B., Niranjan, K. (2018). Gradients in PM 2.5   over India: Five city study. Urban Clim. 25, 992.5108.   https://doi.org/10.1016/j.uclim.2018.06.001
• Tyagi, B., Singh, J., Beig, G. (2020). Seasonal progression of surface ozone and NO x   concentrations over three tropical stations in North-East India. Environ. Pollut. 258, 113662.   https://doi.org/10.1016/j.envpol.2019.113662
• Venkataraman, C., Brauer, M., Tibrewal, K., Sadavarte, P., Ma, Q., Cohen, A., Chaliyakunnel, S., Frostad, J., Klimont, Z., Martin, R.V., Millet, D.B., Philip, S., Walker, K., Wang, S. (2018). Source influence on emission pathways and ambient PM 2.5   pollution over India (2015–2050). Atmos. Chem. Phys. 18, 8017–8039.   https://doi.org/10.5194/acp-18-8017-2018
• Wang, Y., Chen, Y. (2019). Significant climate impact of highly hygroscopic atmospheric aerosols in Delhi, India. Geophys. Res. Lett. 46, 55352.55545.   https://doi.org/10.1029/2019GL082339
• Yang, X., Jiang, L., Zhao, W., Xiong, Q., Zhao, W., Yan, X. (2018). Comparison of ground-based PM 2.5   and PM 10   concentrations in China, India, and the US. Int. J. Environ. Res. Public Health 15, 1382.   https://doi.org/10.3390/ijerph15071382
• Zhu, J., Liao, H., Li, J. (2012). Increases in aerosol concentrations over eastern China due to the decadal‐scale weakening of the East Asian summer monsoon. Geophy. Res. Lett. 39, L09809.   https://doi.org/10.1029/2012GL051428

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Investigating the association between air pollutants’ concentration and meteorological parameters in a rapidly growing urban center of West Bengal, India: a statistical modeling-based approach

• Original Article
• Published: 05 January 2023
• Volume 9 , pages 2877–2892, ( 2023 )

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• Arghadeep Bose   ORCID: orcid.org/0000-0002-3284-4633 1 &
• Indrajit Roy Chowdhury   ORCID: orcid.org/0000-0001-9491-2999 1  

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The ambient air quality in a city is heavily influenced by meteorological conditions. The city of Siliguri, known as the “Gateway of Northeast India”, is a major hotspot of air pollution in the Indian state of West Bengal. Yet almost no research has been done on the possible impacts of meteorological factors on criterion air pollutants in this rapidly growing urban area. From March 2018 to September 2022, the present study aimed to determine the correlations between meteorological factors, including daily mean temperature (℃), relative humidity (%), rainfall (mm), wind speed (m/s) with the concentration of criterion air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO, O 3 , and NH 3 ). For this research, the trend of all air pollutants over time was also investigated. The Spearman correlation approach was used to correlate the concentration of air pollutants with the effect of meteorological variables on these pollutants. Comparing the multiple linear regression (MLR) and non-linear regression (MLNR) models permitted to examine the potential influence of meteorological factors on concentrations of air pollutants. According to the trend analysis, the concentration of NH3 in the air of Siliguri is rising, while the concentration of other pollutants is declining. Most pollutants showed a negative correlation with meteorological variables; however, the seasons impacted on how they responded. The comparative regression research results showed that although the linear and non-linear models performed well in predicting particulate matter concentrations, they performed poorly in predicting gaseous contaminants. When considering seasonal fluctuations and meteorological parameters, the results of this research will definitely help to increase the accuracy of air pollution forecasting near future.

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Introduction

Urban air pollution is a severe and escalating environmental issue, particularly in developing nations. It also has a significant impact on global public health, notably on respiratory and cardiovascular diseases (Mage et al. 1996 ; Bernard et al. 2001 ; Shi et al. 2020 ). Despite the significant progress achieved in the prevention and management of air pollution over the past several years, the majority of inhabitants continue to be quite concerned about such crucial ambient exposure (WHO 2021 ; Leung et al. 2020 ; Molina 2021 ). Many nations in the developing world have air pollution levels that are dangerously close to, or even over, the threshold recommended by the World Health Organization (Tiwari et al. 2014 ; Doreswamy et al. 2020 ; Srivastava 2022 ). Since the industrial revolution and widespread urbanization, air pollution has risen to the top of the environmental concerns list in both developed and developing nations (Anwar et al. 2021 ; Wei et al. 2021 ; Zhang et al. 2022 ). The key source of pollutants that contribute to the degradation of air quality are various human activities, such as fossil fuel combustion to drive production processes, motor vehicles, and industrial plants (Pachón et al. 2018 ; Rajput et al. 2021 ; Munsif et al. 2021 ; Molina 2021 ). In addition, the primary factors contributing to the degradation of air quality in developing nations are the tremendous expansion of the urban population and the changes in land use carried on by urban development (Liang et al. 2019 ; Surya et al. 2020 ). Because of this, the quality of the air, both indoors and outdoors, varies from what is considered normal in urban settings. This implies that a large number of urban inhabitants are continually exposed to an unhealthy amount of air pollution (Chen et al. 2020 ). In metropolitan areas, more than 80% of people are exposed to pollutants at levels exceeding WHO standards, and 98% of metropolitan areas in middle as well as low-income countries fail to fulfill air quality standards (Manju et al. 2018 ; Glazener and Khreis 2019 ; Afghan et al. 2022 ). In 2012, there were almost 7 million air pollution-related deaths, with 4.2 million of such mortalities being directly linked to exposure to outdoor air pollution (Sharma et al. 2013 ; Kayes et al. 2019 ; Piracha and Chaudhary 2022 ).

India is experiencing problems pertaining to a growing population and poor air quality. India ranked third worldwide for PM pollution-related deaths in 2017 (Mitoma et al. 2021 ). There were about 1.1 million avoidable fatalities in India in 2017 due to air pollution (Jat and Gurjar 2021 ), with 56% attributable to outdoor PM2.5 and 44% to indoor air pollution (Pal et al. 2022 ). According to WHO ( 2016 ), 10 of the 20 most populous cities are in India. After analyzing PM2.5 emissions from all countries, WHO ( 2019 ) categorize India as the fifth most polluting country, with 21 of the top 30 most polluted cities being situated in India. Considering 2016 statistics, at least 140 million Indians breathe air ten times or more above the WHO acceptable limit (Chatterji 2021 ), while 13 of the world's top 20 cities with the greatest yearly air pollution are already in India (Agarwal et al. 2020 ; Roy and Singha 2021 ). Due to an increase in industry, population concentration, anthropogenic influences, and automobile usage, India's air quality has been worse over time (Gurjar et al. 2008 ; Jain et al. 2018 ; Jat and Gurjar 2021 ). In recent decades, greenhouse gas emissions (GHGs) and other pollutants have increased in both megacities and small to medium-sized urban centers (Kulkarni et al. 2018 ; Kumar and Gurjar 2019 ; Wen et al. 2020 ). Siliguri isn't an exception in this case (Roy and Singha 2020 ; Biswas et al. 2020 ; Halder and Bandyopadhyay ( 2022 ). Constant population growth has resulted in excessive energy consumption, which has a negative influence on the environment and the air quality in big as well as medium-sized cities like Siliguri (Bose and Chowdhury 2020 ; Roy and Singha 2020 ; Biswas et al. 2020 ).

Urban air quality is affected by meteorological factors (Gualtieri et al. 2015 ; Manju et al. 2018 ; Seo et al. 2018 ; Suhaimi et al. 2020 ; Peng et al. 2020 ; Haddad and Vizakos 2020 ). Atmospheric pollutants are affected by a variety of factors, including temperature, humidity, wind speed, and direction, including the processes of generation and diffusion (Whiteman et al. 2014 ; Hoek et al. 2015 ; Seo et al. 2018 ; Jain et al. 2018 ). They are very important for controlling pollution levels (Peng et al. 2020 ; Haddad and Vizakos 2020 ). The chemical interactions in the atmosphere between precipitation and air pollutants may remove gaseous pollutants and deposit particles, although rain's impact on air quality varies greatly (Dayan and Levy 2005 ; Elperin et al. 2011 ; Ouyang et al. 2015 ; Kayes et al. 2019 ). Evidence from numerous research indicate that air quality may indeed be altered by meteorological conditions (Jhun et al. 2015 ; Manju et al. 2018 ; Borge et al. 2019 ; Castelhano et al. 2022 ). West Bengal has seasonal changes in temperature, precipitation, and humidity due to its subtropical monsoon climate (Bhunia et al. 2019 ; Kundu 2020 ). Seasonal changes in air quality may be seen across the state (Biswas et al. 2020 ). PM2.5 and PM10 levels are higher than acceptable during the dry season but are lower during the monsoon, according to national regulations (Jain et al. 2020 ; Roy and Singha 2020 ). Numerous studies on the effects of weather have been conducted in China (Kan et al. 2012 ; Li et al. 2014 , 2020 ; He et al. 2017 ; Song et al. 2017 ; Lin et al. 2020 ; Liu and Wang 2020 ; Gao et al. 2021 ) and India especially at the national level and large cities (Ramanathan and Feng 2009 ; Bhaskar and Mehta 2010 ; Jayamurugan et al. 2013 ), but it is understudied in medium-sized urban areas like Siliguri. This research aimed to examine the links between air pollutant concentration and meteorological factors, assess seasonal fluctuations in air pollutant concentration and perform a time series assessment of air pollutant concentration in Siliguri city.

Siliguri is a rapidly expanding city with a solid foundation of trade as well as commerce and substantial economic activity. At an elevation of approximately 122 m (400 feet), it is situated in the Himalayan foothills on the banks of the river Mahananda. It has had substantial population growth in recent years, and the Siliguri Municipal Corporation's geographic area is about 41.9 km2 and is bounded by 47 wards that have grown by five times since 1931. Siliguri, in West Bengal, is indeed the third-largest urban agglomeration after Kolkata and Asansol. By luring a sizable number of migrants over the course of time, Siliguri has transformed from a small village into a financially advanced city. This amazing expansion of the city is a result of the city's tremendous population expansion (Bose and Chowdhury 2020 ). Evidence from the results of the 2011 Census showed that the city's population increased at a quicker pace, from 4.72 lakh in 2001 to more than seven lakhs in the 2011 census report (District Census Handbook 2011 ). The research area was chosen because of its advantageous position as a center for trade and business, tourist activity, population expansion, the hub of employment, and essential supply to the entire northeast region (Roy et al. 2022a , b ). Because of this, the city's air quality suffers as traffic levels increase, which in turn creates more air pollution (The Statesman 2018b ; Roy and Singha 2020 ). Being the connecting point between the north-eastern states and the rest of India, Siliguri has earned the titles like "Gateway of North-east India" and "The chicken's neck" (Bose and Chowdhury 2020 ; Roy et al. 2022b ). This rapidly urbanized city extends over the Jalpaiguri and Darjeeling districts of West Bengal. The city of Siliguri is located between the coordinates of 26°39′57.88" and 26°46′19.03"N and 88°25′16.47" and 88°26′53.62"E. In Siliguri, air pollution is a serious problem that is mostly caused by the city's fast urbanization and population expansion, which is also related to enormous spikes in the vehicle population plying on the road (CDP Report Siliguri 2015 ; CRCAP Report 2018 ; The Telegraph 2018 ; The Statesman 2018a ; Roy and Singha 2020 ). According to experts, the vast majority of diesel-powered vehicles operating in the city are the prime cause of ambient air pollution in the city (CDP Report Siliguri 2015 ; The Telegraph 2018 ; The Statesman 2018b ; Roy and Singha 2020 ). Along with it, extensive road construction and poor road dust management procedures are causing particle pollution levels in the city to climb (Fig.  1 ).

The geographic location of Siliguri city showing the continuous ambient air quality monitoring (CAAQM) station situated in Babupara (Ward 32)

To ascertain the connection between the air pollutants and meteorological factors of Siliguri city, all the data were collected and analyzed based on the data and information accessible from the Central Pollution Control Board (CPCB) and West Bengal Pollution Control Board (WBPCB). There is just one continuous ambient air quality monitoring station in Siliguri at present, and it is situated in Babupara (Ward 32), near Tinbatti more. The daily mean (24 h average) concentration of seven air pollutants, i.e., Particulate Matter (PM 2.5 , PM 10 ), Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Carbon Monoxide (CO), Ozone (O 3 ), and Ammonia (NH 3 ), as well as four meteorological factors, i.e., Temperature, Relative humidity, Rainfall as well as Wind speed, have been obtained from the CPCB online portal for data acquisition from March 2018 to September 2022. CPCB implements stringent processes for sampling, analysis, and calibration in order to deliver data quality assurance with quality control programs.

Methodology

Initially, descriptive statistics were computed for both meteorological parameters and air pollutants. To find patterns in the concentration of air pollutants during the same time period, the Mann–Kendall trend analysis has been used. Tukey's HSD multiple comparisons were used to test for seasonal changes in air pollution concentration throughout the years at a 5% threshold of significance. The Spearman correlation analysis was evaluated to identify any associations between the air pollutants and the meteorological factors. Seasonal classifications used in this study, i.e., pre-monsoon (March–May), monsoon (June–September), post-monsoon (October–November), and winter (December–February), were taken from the study conducted by Sivaprasad and Babu ( 2014 ) and Dutta and Gupta ( 2021 ). Finally, in order to investigate the possible effects of temp, RH, RF, and WS on air pollutants' concentration, multiple linear as well as non-linear models were used. Yin et al. ( 2016 ) also considered only two parameters, i.e., Temp and RH as predictive factors to determine the daily PM concentration in Beijing, China.

Here, β 0  = model constant β 1 , β 2 , β 3 , β 4 , β 5 , β 6 , β 7 and β 8  = model parameters, \(\hat{Y}\)  = dependent variables (pollutants), and \(X_1 ,X_2 , X_3 , X_4\)  = independent variables (Temp, RH, RF, and WS, respectively). The overall methodology is illustrated in Fig.  2 through a flow chart for clear and comprehensive understanding.

Methodological flow chart adopted for the present study

Results and discussion

The outcomes of this study, together with discussions of the criterion air pollutants as well as meteorological parameters, are presented in the following sub-sections.

Descriptive statistics

The data regarding air pollutants, as well as meteorological parameters in Siliguri city ranging from March 2018 to September 2022, are summarized in Table 1 , Fig.  3 . Overall, the mean intensity of air pollutants, i.e., PM 2.5 , PM 10, NO 2, SO 2, CO, O 3, and NH 3 ranged from 6.02 to 290.38 µg/m3, 14.76 to 393.80 µg/m3, 2.16 to 113.31 µg/m3, 1.35 to 60.89 µg/m3, 0.1 to 2.04 mg/m3, 6.10 to 75.61 µg/m3, 1.06 to 166.41 µg/m3, respectively, from 2018 to 2022. Furthermore, meteorological factors, i.e., temp, RH, RF, and WS, ranged from 11.55 to 33.38 °C, 36.29 to 97.77%, 0.00 to 2.19 mm, 0.21 to 12.55 m/s, respectively.

Trends of concentration and descriptive statistics of criterion air pollutants as well as meteorological parameters from March 2018 till September 2022

Trends of air pollution levels and meteorological parameters

The trends of all seven air pollutants, as well as meteorological variables considered for this study (Temp, RH, RF, and WS) from March 2018 to September 2022 in Siliguri, are illustrated in Fig.  3 . There was a noticeable tendency of fluctuations in the concentrations of air pollutants and in the meteorological parameters during the period of the research (Fig.  3 ). Mann–Kendall (MK) trend analysis was used to look at whether or not the concentration of air pollutants has a trend (increasing or decreasing) over time. The final findings of the MK trend test are shown in Table 2 . Here, the alpha and Z values denote the statistical significance of the rising or falling trend in air pollution, while the tau value indicates the direction of the trend. Sen's slope's value reveals how quickly the trend is rising or falling on a daily basis.

Since the estimated p -values were less than the significance threshold of alpha = 0.05 and the null hypothesis of the Mann–Kendall analysis is that there was no trend in the concentration of air pollutants during the relevant period of the study, the research rejected the null hypothesis (Except NO2) and accepted the alternative hypothesis. With the exception of NO 2 and O 3 , all air pollutants had statistically significant trends at a 99% confidence level. At a 95% confidence level, the trend of O 3 is significant, but the trend of NO 2 is not significant. It is evident from Table 2 , Fig.  4 , that every pollutant had a trend over time. All of the pollutants, with the exception of NH 3 , exhibit a declining trend over time. The trend of NH 3 has been rising over the sands of time.

Area trend diagram showing the concentration of air pollutants in Siliguri city

Comparison of regression models

Multiple linear regression (MLR) and non-linear regression (MNLR) were compared to identify the possible impacts of meteorological factors (Temp, RH, RF, and WS) on the concentrations of air pollutants (See Table 3 ). The Coefficient of determination ( R 2 ) of both models has been calculated in order to evaluate their efficacy. Table 3 compares the two models and demonstrates that the multiple non-linear regression (MNLR) model performed slightly better than the multiple linear regression (MLR) model. Considering all four meteorological factors as explanatory variables in the model, variations in PM 2.5 concentration are best explained by the meteorological parameters ( R 2  = 0.65 and 0.67, respectively), followed by PM 10 ( R 2  = 0.61 and 0.62, respectively), while variations in SO 2 concentration are least explained by temperature, relative humidity, rainfall, as well as wind speed ( R 2  = 0.09 and 0.11, respectively). Yin et al. ( 2016 ) demonstrated superior MNLR model performance in explaining PM 2.5 concentration in connection to meteorological factors in China. For CO, O 3 , and NH 3 , however, no model did well enough. However, NO 2 shows a significant link with meteorological factors than they do. As per Table 3 , R 2 values for MLR and MNLR vary from 0.09 to 0.65 and 0.11 to 0.67, respectively. Wise and Comrie (2005) showed coefficient of determination ( R 2 ) values between 0.1 and 0.5 when modeling the impacts of meteorological data on particulate matter concentration in the United States.

Seasonal fluctuations of air pollutants concentrations

A one-way between-groups ANOVA was carried out to ascertain if there is a variation in the concentrations of air pollutants between the seasons (pre-monsoon, monsoon, post-monsoon, as well as winter). The mean difference in the amounts of pollutants throughout the seasons was then shown using Tuckey's HSD post-hoc multiple comparisons (see Table 4 ).

The concentration of each and every pollutant varied across the four seasons, and this variation was statistically significant at the 0.05 level. The seasonal concentrations of PM 2.5 and PM 10 over the years showed significant maximum differences (mean score) according to Tukey's HSD multiple comparison tests (See Table 4 ). In contrast, NO 2 , CO, O 3 , and NH 3 show moderate variation across seasons, whereas SO 2 shows negligible variation. It is important to note that the difference in concentration between pre- and post-monsoon for PM 2.5 and SO 2 and between monsoon and post-monsoon for NH 3 is not statistically significant at all (see Table 4 ). Although the seasonal concentration varies depending on the various pollutants, the monsoon season depicts the minimum concentration of air pollutants, and the winter portrays the maximum (see Table 4 ). When evaluating seasonal fluctuations of particulate matter, it is obvious that, for all years, the mean concentrations of PM 2.5 and PM 10 were much greater in the winter than they were during the monsoon seasons (Table 4 ). When compared to monsoon, the amounts of fine particulate matter increased every year during post-monsoon season. Winter and pre-monsoon CO, NO 2 , SO 2 , and NH 3 concentrations were greater than the monsoon and post-monsoon seasons over the years, notwithstanding the little changes. O 3 levels during pre-monsoon were consistently greater than during other times of the year. In addition to seasonal changes, meteorological variables primarily contribute to the causes of the seasonal swings in the pollutants level (Manju et al. 2018 ). In addition, winter pollution levels may be higher due to road dust, vehicle exhaust during the dry winter, and urban construction activities. On the other hand, the moist deposition of particles during the monsoon season is linked to lower concentration levels in that season (Roy and Singha 2020 ; Biswas et al. 2020 ).

Association between changes in meteorological parameters and the levels of air pollutants concentration

The relationship between seasonal fluctuations in air pollution concentrations and meteorological factors, i.e., Temp, RH, RF, and WS, were examined using a 5% significant level of spearman correlation analysis. The values of the correlation coefficient are shown in Table 5 . Insignificant relationships are denoted by a cross ( X ) in Fig.  5 , which shows the season-wise correlation matrix between air pollution concentration and meteorological factors. However, Fig.  6 , 7 , 8 , and 9 show graphical presentations of the correlation between air pollutant levels and seasonal Temp, RH, RF, and WS.

Correlation matrix showing the season wise ( a,b,c,d represents pre-monsoon, monsoon, post-monsoon and winter respectively) Spearman correlation coefficient values in between air pollutants and meteorological parameters

Correlation plot between season wise air pollutants concentration and temperature, a pre‐monsoon (March–May), b monsoon (June–September), c post‐monsoon (October–November), d winter (December–February)

Correlation plot between season wise air pollutants concentration and relative humidity, a pre‐monsoon (March–May), b monsoon (June–September), c post‐monsoon (October–November), d winter (December–February)

Correlation plot between season wise air pollutants concentration and rainfall, a pre‐monsoon (March–May), b monsoon (June–September), c post‐monsoon (October–November), d winter (December–February)

Correlation plot between season wise air pollutants concentration and wind speed, a pre‐monsoon (March–May), b monsoon (June–September), c post‐monsoon (October–November), d winter (December–February)

Table 5 shows that during the post-monsoon and monsoon seasons, PM 2.5 and PM 10 had strong negative as well as moderate positive correlations associated with atmospheric temperature, whereas during the pre-monsoon as well as winter seasons, there was negligible or no correlation (Figs.  5 , 6 ). The meteorological features of the monsoon season and their interactions with the particulate matter may be used to explain the positive link between air temperature and particulate matter (PM 2.5 and PM 10 ) during the monsoon season. Giri et al. ( 2008 ) and Kayes et al. ( 2019 ) in their study made comparable observations. They said that since the particulate matter is mostly composed of soil or road dust, it quickly settles to the ground after absorbing water vapor from the atmosphere. Furthermore, this research shows that when compared to other times of the year, PM 2.5 and PM 10 concentrations are at their lowest during the monsoon season. The monsoon season of West Bengal is characterized by an intense downpours, gusty winds, and warm temperatures (Chatterjee et al. 2016 ). Although airborne particles may settle to the ground this time of year due to the abundance of precipitation, their concentration might be enhanced by drying up as a consequence of intense summer heat (Kayes et al. 2019 ). Another factor that may interact with the weather this time of year is the high frequency and intensity of winds. A significant link between fine particulate matter and temperature in the United States was also observed by Tai et al. ( 2010 ). It is noteworthy that among other pollutants, NO2, SO2, CO, O3 as well as NH3 resulted in a moderately negative correlation with temperature in post-monsoon season (Table 5 ).

Most air pollutants’ concentrations had a negative association with relative humidity throughout the seasons (Table 5 , Figs. 5 , 7 ). In particular, the concentration of fine and coarse particulate matter (PM 2.5 and PM 10 ) resulted in a negative association during the pre-monsoon, monsoon, and post-monsoon ( r  = − 0.71, − 0.20, − 0.14 and − 0.72, − 0.33, − 0.29, respectively); however, there was little positive to no association during the winter ( r  = 0.20, − 0.04). This is due to the fact that relative humidity affects the mobility of particles and might lead them to settle on the ground. As a result, the concentration of air contaminants decreases as relative humidity rises (Giri et al. 2008 ). Similarly, our research discovered that there was often a negative association between relative humidity and particulate matter concentration. However, a positive link between wintertime humidity and fine particulate matter (PM 2.5 ) suggests that the lack of precipitation and water vapor in the air may assist PM2.5's ventilation effects (Giri et al. 2008 ). Again, because of the favorable weather, the majority of construction work is done in the winter. O 3 and NH 3 were always negatively associated, with the exception of the monsoon season. This study's findings on the adverse relationship between O 3 and relative humidity and those of Swamy et al. ( 2012 ), Kumar et al. ( 2014 ), and Manju et al. ( 2018 ) are consistent. According to Ojha et al. ( 2016 ), the summer monsoon has greater CO and O 3 concentrations than other times of the year. Similar findings were also reported in China by Zhang et al. ( 2015 ), who made the case that the direction of the wind has a big impact on the amount of CO and O3 in the air. Ojha et al. ( 2016 ) said that these air pollutants might be carried from the African area to the Indian subcontinent by monsoon wind from the southwestern direction. As an outcome, it exhibits a positive link with humidity during the winter when it is feasible for vehicles to exhale more CO into the atmosphere.

The relationship between the concentration of pollutants and seasonal rainfall throughout time is shown in Figs. 5 , 8 , and Table 5 . Most air pollution concentrations had a negative link with rainfall throughout all seasons when it came to relative humidity (Table 5 ). For the pre-monsoon and post-monsoon seasons, a moderate to the low negative connection between rainfall and pollutants has been noted. However, research also demonstrates a practically weak negative association between pollutants and rainfall throughout the winter. PM2.5 and PM10 have the strongest reaction (in terms of negative correlation) with rainfall throughout the seasons among all the pollutants.

Table 5 and Figs. 5 , 9 show the relationship between wind speed and pollution concentration. Although NH 3 has a moderately positive correlation with wind speed during the monsoon and post-monsoon seasons ( r  = 0.47, 0.46) and particulate matter (PM 2.5 and PM 10 ) has a moderately negative correlation with wind speed during the winter ( r  = − 0.40, − 0.48), most air pollutants’ concentrations have a weak to negligible negative correlation with wind speed across all seasons. Since NH 3 is a thinner gas than air and tends to rise, it typically does not concentrate in low-lying locations and cannot be fully described by simple meteorological factors, according to research by Osada ( 2020 ). In their investigation on the level of particulate matter concentration in winter, Cichowicz et al. ( 2020 ) suggested that because of the low wind speed, particulate matter concentration rises throughout the winter. The wind speed slows throughout the winter months in Siliguri as well, increasing particle pollution.

It was necessary to look into the relationship between the concentration of seasonal ambient air pollutants and variations in meteorological parameters throughout time in the city of Siliguri due to the dearth of previous research on this particular area. As shown by other investigations, Siliguri city's air quality is a major problem. When looking at data from 2018 to 2022, the concentrations of all criterion air pollutants in Siliguri city have a decreasing trend; however, the concentration of NH 3 is rising. This could be attributable to COVID-19, the effects of which would be felt in reduced pollution levels in 2020 and 2021 as a result of the continuous lockdown associated with a substantial decrease in vehicle movement and other activities, such as construction works, which contributed to a significant decrease in criteria air pollutants. Although there were other air contaminants present, however, a negative correlation was shown between PM 2.5 and PM 10 and the four meteorological parameters. The study found that during the monsoon season, PM 2.5 , PM 10 , and temperature all have a positive relationship, in contrast to the negative relationships seen during the other seasons. This suggests that the high temperature and humidity experienced during this time contribute to the suspension of particulate matter. However, dry air with reduced humidity aggregates greater pollutants in urban environments; hence winter has the highest concentrations of air pollutants among the seasons. Although not the primary focus of this study, it is worth noting that particulate matters (PM 2.5 and PM 10 ) were found to be in infringement of the allowable limit established by the National Ambient Air Quality Standards in Siliguri city, posing a serious risk to the city's residents. The high quantities of PM 10 and PM 2.5 in the city's air are mostly attributable to air pollution, especially caused by diesel-powered automobiles. Compressed natural gas and electric cars are two examples of greener fuels that need to replace traditional options as quickly as feasible. There has to be an immediate increase in the number of air quality monitoring stations around the city. The renewal period for Pollution Under Control (PUC) certificates should be shortened to encourage more frequent renewal and reduce the number of cars owned by individual households. Meanwhile, construction sites must immediately adopt measures to cut down on dust-particle emissions. Enhancing the public transportation system should also be relevant to improving the efficiency of roads and services. The current study only used four meteorological parameters, including temperature, relative humidity, rainfall, and wind speed, to establish the associations, despite the fact that it is well-known that wind direction, solar radiations, and other factors can have an impact on the concentration of air pollutants. Future research should integrate more parameters to express this issue.

Data availability

The datasets used in the present study are publicly available from the official website of the Central Pollution Control Board (CPCB) and West Bengal Pollution Control Board (WBPCB).

Afghan FR, Habib H, Akhunzada NA, Wafa W, Shirzad MD, Sahak K, Hashimi SK, Mujeeb M, Wardak K, Ahmadzai MR (2022) Customization of GIS for spatial and temporal analyses of Air Quality Index trends in Kabul city. Model Earth Syst Environ 8(4):5097–5106. https://doi.org/10.1007/s40808-022-01396-5

Article   Google Scholar  

Agarwal A, Kaushik A, Kumar S, Mishra RK (2020) Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air Qual Atmos Health 13(10):1167–1178. https://doi.org/10.1007/s11869-020-00881-z

Anwar MN, Shabbir M, Tahir E, Iftikhar M, Saif H, Tahir A, Murtaza MA, Khokhar MF, Rehan M, Aghbashlo M, Tabatabaei M, Nizami AS (2021) Emerging challenges of air pollution and particulate matter in China, India, and Pakistan and mitigating solutions. J Hazardous Mater 416:125851. https://doi.org/10.1016/j.jhazmat.2021.125851

Bernard SM, Samet JM, Grambsch A, Ebi KL, Romieu I (2001) The potential impacts of climate variability and change on air pollution-related health effects in the United States. Environ Health Perspect 109(Suppl 2):199–209. https://doi.org/10.1289/ehp.109-1240667

Bhaskar BV, Mehta VM (2010) Atmospheric particulate pollutants and their relationship with meteorology in Ahmedabad. Aerosol Air Qual Res 10(4):301–315. https://doi.org/10.4209/aaqr.2009.10.0069

Bhunia P, Das P, Maiti R (2019) Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst Environ 4(1):43–55. https://doi.org/10.1007/s41748-019-00137-6

Biswas K, Chatterjee A, Chakraborty J (2020) Comparison of air pollutants between Kolkata and Siliguri, India, and its relationship to temperature change. J Geovisual Spat Anal. https://doi.org/10.1007/s41651-020-00065-4

Borge R, Requia WJ, Yagüe C, Jhun I, Koutrakis P (2019) Impact of weather changes on air quality and related mortality in Spain over a 25 year period [1993–2017]. Environ Int 133:105272. https://doi.org/10.1016/j.envint.2019.105272

Bose A, Chowdhury IR (2020) Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using markov chain model: a case study in Siliguri Metropolitan area, West Bengal, India. Model Earth Syst Environ 6(4):2235–2249. https://doi.org/10.1007/s40808-020-00842-6

Castelhano FJ, Pedroso AC, Cobelo I, Borge R, Roig HL, Adams M, Amini H, Koutrakis P, Réquia WJ (2022) The impact of long-term weather changes on air quality in Brazil. Atmos Environ 283:119182. https://doi.org/10.1016/j.atmosenv.2022.119182

CDP Report Siliguri (2015) City development plan for Siliguri 2041. Capacity building for urban development project, ministry of urban development. http://siligurismc.in/userfiles/file/siliguri-CDP-final-report-29April15.pdf

Chatterjee S, Khan A, Akbari H, Wang Y (2016) Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901–2002), West Bengal, India. Atmos Res 182:54–75. https://doi.org/10.1016/j.atmosres.2016.07.010

Chatterji A (2021) Air pollution in Delhi: filling the policy gaps. Massach Undergr J Econ 17:1

Google Scholar  

Chen Y, Wild O, Conibear L, Ran L, He J, Wang L, Wang Y (2020) Local characteristics of and exposure to fine particulate matter (PM2.5) in four Indian megacities. Atmos Environ 5:100052. https://doi.org/10.1016/j.aeaoa.2019.100052

Cichowicz R, Wielgosiński G, Fetter W (2020) Effect of wind speed on the level of particulate matter PM10 concentration in atmospheric air during winter season in vicinity of large combustion plant. J Atmos Chem 77(1–2):35–48. https://doi.org/10.1007/s10874-020-09401-w

CRCAP Report (2018) Climate resilient city action plan—Siliguri. Capacities. http://capacitiesindia.org/wp-content/uploads/2019/12/CRCAP_Siliguri_Sept2018.pdf

Dayan U, Levy I (2005) The influence of meteorological conditions and atmospheric circulation types on PM10 and visibility in Tel Aviv. J Appl Meteorol 44(5):606–619. https://doi.org/10.1175/jam2232.1

District Census Handbook, Darjiling (2011). Census of India—2011. West Bengal. https://censusindia.gov.in/2011census/dchb/1901_PART_B_DCHB_DARJILING.pdf

Doreswamy KSH, Yogesh KM, Gad I (2020) Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Proc Comput Sci 171:2057–2066. https://doi.org/10.1016/j.procs.2020.04.221

Dutta D, Gupta S (2021) Rising trend of air pollution and its decadal consequences on meteorology and thermal comfort over Gangetic West Bengal, India. In: Spatial Modeling and Assessment of Environmental Contaminants. Springer, Cham, pp 689–720

Elperin T, Fominykh A, Krasovitov B, Vikhansky A (2011) Effect of rain scavenging on altitudinal distribution of soluble gaseous pollutants in the atmosphere. Atmos Environ 45(14):2427–2433. https://doi.org/10.1016/j.atmosenv.2011.02.008

Gao C, Li S, Liu M, Zhang F, Achal V, Tu Y, Zhang S, Cai C (2021) Impact of the COVID-19 pandemic on air pollution in Chinese megacities from the perspective of traffic volume and meteorological factors. Sci Total Environ 773:145545. https://doi.org/10.1016/j.scitotenv.2021.145545

Giri D, Adhikary PR, Murthy VK (2008) The influence of meteorological conditions on PM10 concentrations in Kathmandu Valley

Glazener A, Khreis H (2019) Transforming our cities: best practices towards clean air and active transportation. Curr Environ Health Rep 6(1):22–37. https://doi.org/10.1007/s40572-019-0228-1

Gualtieri G, Toscano P, Crisci A, Di Lonardo S, Tartaglia M, Vagnoli C, Zaldei A, Gioli B (2015) Influence of road traffic, residential heating and meteorological conditions on PM10 concentrations during air pollution critical episodes. Environ Sci Pollut Res 22(23):19027–19038. https://doi.org/10.1007/s11356-015-5099-x

Gurjar B, Butler T, Lawrence M, Lelieveld J (2008) Evaluation of emissions and air quality in megacities. Atmos Environ 42(7):1593–1606. https://doi.org/10.1016/j.atmosenv.2007.10.048

Haddad K, Vizakos N (2020) Air quality pollutants and their relationship with meteorological variables in four suburbs of Greater Sydney, Australia. Air Qual Atmos Health 14(1):55–67. https://doi.org/10.1007/s11869-020-00913-8

Halder B, Bandyopadhyay J (2022) Air quality fluctuation monitoring and statistical data analysis during COVID-19 pandemic in Siliguri city, India. J Air Pollut Health. https://doi.org/10.18502/japh.v6i3.8232

He J, Gong S, Yu Y, Yu L, Wu L, Mao H, Song C, Zhao S, Liu H, Li X, Li R (2017) Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities. Environ Pollut 223:484–496. https://doi.org/10.1016/j.envpol.2017.01.050

Hoek G, Beelen R, Brunekreef B (2015) Land use regression models for outdoor air pollution. Exp Assess Environ Epidemiol. https://doi.org/10.1093/med/9780199378784.003.0013

Jain S, Sharma SK, Mandal TK, Saxena M (2018) Source apportionment of PM10 in Delhi, India using PCA/apcs, UNMIX and PMF. Particuology 37:107–118. https://doi.org/10.1016/j.partic.2017.05.009

Jain S, Sharma SK, Vijayan N, Mandal TK (2020) Seasonal characteristics of Aerosols (PM2.5 and PM10) and their source apportionment using PMF: A Four Year Study over Delhi, India. Environ Pollut 262:11437. https://doi.org/10.1016/j.envpol.2020.114337

Jat R, Gurjar BR (2021) Contribution of different source sectors and source regions of Indo-Gangetic Plain in India to PM2.5 pollution and its short-term health impacts during peak polluted winter. Atmos Pollut Res 12(4):89–100. https://doi.org/10.1016/j.apr.2021.02.016

Jayamurugan R, Kumaravel B, Palanivelraja S, Chockalingam MP (2013) Influence of temperature, relative humidity and seasonal variability on ambient air quality in a coastal urban area. Int J Atmos Sci 2013:1–7. https://doi.org/10.1155/2013/264046

Jhun I, Coull BA, Schwartz J, Hubbell B, Koutrakis P (2015) The impact of weather changes on air quality and health in the United States in 1994–2012. Environ Res Lett 10(8):084009. https://doi.org/10.1088/1748-9326/10/8/084009

Kan H, Chen R, Tong S (2012) Ambient air pollution, climate change, and population health in China. Environ Int 42:10–19. https://doi.org/10.1016/j.envint.2011.03.003

Kayes I, Shahriar SA, Hasan K, Akhter M, Kabir MM, Salam MA (2019) The relationships between meteorological parameters and air pollutants in an urban environment. Glob J Environ Sci Manag 5(3):265–278

Kulkarni GE, Muley AA, Deshmukh NK, Bhalchandra PU (2018) Autoregressive integrated moving average time series model for forecasting air pollution in Nanded City, Maharashtra, India. Model Earth Syst Environ 4(4):1435–1444. https://doi.org/10.1007/s40808-018-0493-2

Kumar A, Gurjar B (2019) Low-cost sensors for air quality monitoring in developing countries—a critical view. Asian J Water Environ Pollut 16(2):65–70. https://doi.org/10.3233/ajw190021

Kumar A, Singh BP, Punia M, Singh D, Kumar K, Jain VK (2014) Determination of volatile organic compounds and associated health risk assessment in residential homes and hostels within an academic institute. New Delhi Indoor Air 24(5):474–483. https://doi.org/10.1111/ina.12096

Kundu SK (2020) Rainfall trend analysis in the lower gangetic plain: a study on selected rainfall stations of Maldah District, West Bengal, India. Curr Sci 119(6):1031. https://doi.org/10.18520/cs/v119/i6/1031-1038

Leung KM, Yeung KW, You J, Choi K, Zhang X, Smith R, Zhou G, Yung MM, Arias-Barreiro C, An Y, Burket SR, Dwyer R, Goodkin N, Hii YS, Hoang T, Humphrey C, Iwai CB, Jeong S, Juhel G, Brooks BW (2020) Toward sustainable environmental quality: priority research questions for Asia. Environ Toxicol Chem 39(8):1485–1505. https://doi.org/10.1002/etc.4788

Li L, Qian J, Ou CQ, Zhou YX, Guo C, Guo Y (2014) Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011. Environ Pollut 190:75–81. https://doi.org/10.1016/j.envpol.2014.03.020

Li B, Shi XF, Liu YP, Lu L, Wang GL, Thapa S, Sun XZ, Fu DL, Wang K, Qi H (2020) Long-term characteristics of criteria air pollutants in megacities of Harbin-Changchun megalopolis, Northeast China: Spatiotemporal variations, source analysis, and meteorological effects. Environ Pollut 267:115441. https://doi.org/10.1016/j.envpol.2020.115441

Liang L, Wang Z, Li J (2019) The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. J Clean Prod 237:117649. https://doi.org/10.1016/j.jclepro.2019.117649

Lin S, Wei D, Sun Y, Chen K, Yang L, Liu B, Huang Q, Paoliello MMB, Li H, Wu S (2020) Region-specific air pollutants and meteorological parameters influence COVID-19: a study from mainland China. Ecotoxicol Environ Saf 204:111035. https://doi.org/10.1016/j.ecoenv.2020.111035

Liu Y, Wang T (2020) Worsening urban ozone pollution in China from 2013 to 2017—part 1: the complex and varying roles of meteorology. Atmos Chem Phys 20(11):6305–6321. https://doi.org/10.5194/acp-20-6305-2020

Mage D, Ozolins G, Peterson P, Webster A, Orthofer R, Vandeweerd V, Gwynne M (1996) Urban air pollution in megacities of the world. Atmos Environ 30(5):681–686. https://doi.org/10.1016/1352-2310(95)00219-7

Manju A, Kalaiselvi K, Dhananjayan V, Palanivel M, Banupriya GS, Vidhya MH, Panjakumar K, Ravichandran B (2018) Spatio-seasonal variation in ambient air pollutants and influence of meteorological factors in Coimbatore, Southern India. Air Qual Atmos Health 11(10):1179–1189. https://doi.org/10.1007/s11869-018-0617-x

Mitoma H, Nagashima F, Kagawa S, Nansai K (2021) Critical supply chains for mitigating PM2.5 emission-related mortalities in India. Sci Rep. https://doi.org/10.1038/s41598-021-91438-2

Molina LT (2021) Introductory lecture: air quality in megacities. Faraday Discuss 226:9–52. https://doi.org/10.1039/d0fd00123f

Munsif R, Zubair M, Aziz A, Nadeem Zafar M (2021) Industrial air emission pollution: potential sources and sustainable mitigation. Environ Emissions. https://doi.org/10.5772/intechopen.93104

Ojha N, Pozzer A, Rauthe-Schöch A, Baker AK, Yoon J, Brenninkmeijer CAM, Lelieveld J (2016) Ozone and carbon monoxide over India during the summer monsoon: regional emissions and transport. Atmos Chem Phys 16(5):3013–3032. https://doi.org/10.5194/acp-16-3013-2016

Osada K (2020) Measurement report: short-term variation in ammonia concentrations in an urban area increased by mist evaporation and emissions from a forest canopy with bird droppings. Atmos Chem Phys 20(20):11941–11954. https://doi.org/10.5194/acp-20-11941-2020

Ouyang W, Guo B, Cai G, Li Q, Han S, Liu B, Liu X (2015) The washing effect of precipitation on particulate matter and the pollution dynamics of rainwater in downtown Beijing. Sci Total Environ 505:306–314. https://doi.org/10.1016/j.scitotenv.2014.09.062

Pachón J, Galvis B, Lombana O, Carmona L, Fajardo S, Rincón A, Meneses S, Chaparro R, Nedbor-Gross R, Henderson B (2018) Development and evaluation of a comprehensive atmospheric emission inventory for air quality modeling in the megacity of Bogotá. Atmosphere 9(2):49. https://doi.org/10.3390/atmos9020049

Pal S, Mahato S, Mandal I, Das P, Joshi P (2022) Evaluating aerosols concentration and air quality of Indian urban agglomerations over nationwide and regional lockdown. Atmos Pollut Res 13(10):101567. https://doi.org/10.1016/j.apr.2022.101567

Peng L, Zhao X, Tao Y, Mi S, Huang J, Zhang Q (2020) The effects of air pollution and meteorological factors on measles cases in Lanzhou, China. Environ Sci Pollut Res 27(12):13524–13533. https://doi.org/10.1007/s11356-020-07903-4

Piracha A, Chaudhary MT (2022) Urban air pollution, urban heat island and human health: a review of the literature. Sustainability 14(15):9234. https://doi.org/10.3390/su14159234

Rajput S, Arora K, Mathur R (2021) Level and extent of air pollution in Delhi. Urban Green Space, Health Economics and Air Pollution in Delhi, pp 59–88. https://doi.org/10.4324/9780429340581-4

Ramanathan V, Feng Y (2009) Air pollution, greenhouse gases and climate change: global and regional perspectives. Atmos Environ 43(1):37–50. https://doi.org/10.1016/j.atmosenv.2008.09.063

Roy S, Singha N (2021) Reduction in concentration of PM2.5 in India’s top most polluted cities: with special reference to post-lockdown period. Air Qual Atmos Health 14(5):715–723. https://doi.org/10.1007/s11869-020-00974-9

Roy S, Basak D, Bose A, Chowdhury IR (2022a) Citizens’ perception towards landfill exposure and its associated health effects: a PLS-SEM based modeling approach. Environ Monit Assess. https://doi.org/10.1007/s10661-022-10722-4

Roy S, Bose A, Majumadar S, Roy Chowdhury I, Abdo HG, Almohamad H, Al Dughairi AA (2022b) Evaluating urban environment quality (UEQ) for class-I Indian city: an integrated RS-GIS based exploratory spatial analysis. Geocarto Int. https://doi.org/10.1080/10106049.2022.2153932

Roy S, Singha N (2020) Analysis of ambient air quality based on exceedance factor and air quality index for Siliguri City, West Bengal. Curr World Environ. https://doi.org/10.12944/cwe.15.2.11

Seo J, Park DSR, Kim JY, Youn D, Lim YB, Kim Y (2018) Effects of meteorology and emissions on urban air quality: a quantitative statistical approach to long-term records (1999–2016) in Seoul, South Korea. Atmos Chem Phys 18(21):16121–16137. https://doi.org/10.5194/acp-18-16121-2018

Sharma P, Sharma P, Jain S, Kumar P (2013) An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi. Atmos Environ 70:7–17. https://doi.org/10.1016/j.atmosenv.2013.01.004

Shi Y, Bilal M, Ho HC, Omar A (2020) Urbanization and regional air pollution across South Asian developing countries—a nationwide land use regression for ambient PM2.5 assessment in Pakistan. Environ Pollut 266:15145. https://doi.org/10.1016/j.envpol.2020.115145

Sivaprasad P, Babu CA (2014) Seasonal variation and classification of aerosols over an inland station in India. Meteorol Appl 21(2):241–248

Song C, Wu L, Xie Y, He J, Chen X, Wang T, Lin Y, Jin T, Wang A, Liu Y, Dai Q, Liu B, Wang Y-N, Mao H (2017) Air pollution in China: status and spatiotemporal variations. Environ Pollut 227:334–347. https://doi.org/10.1016/j.envpol.2017.04.075

Srivastava AK (2022) Air pollution: facts, causes, and impacts. Asian Atmos Pollut. https://doi.org/10.1016/b978-0-12-816693-2.00020-2

Suhaimi NF, Jalaludin J, Latif MT (2020) Demystifying a possible relationship between COVID-19, air quality and meteorological factors: evidence from Kuala Lumpur, Malaysia. Aerosol Air Qual Res 20(7):1520–1529. https://doi.org/10.4209/aaqr.2020.05.0218

Surya B, Ahmad DNA, Sakti HH, Sahban H (2020) Land use change, spatial interaction, and sustainable development in the metropolitan urban areas, South Sulawesi Province, Indonesia. Land 9(3):95. https://doi.org/10.3390/land9030095

Swamy Y, Venkanna R, Nikhil G, Chitanya D, Sinha P, Ramakrishna M, Rao A (2012) Impact of nitrogen oxides, volatile organic compounds and black carbon on atmospheric ozone levels at a semi arid urban Site in Hyderabad. Aerosol Air Qual Res 12(4):662–671. https://doi.org/10.4209/aaqr.2012.01.0019

Tai APK, Mickley LJ, Jacob DJ (2010) Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmos Environ 44(32):3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060

The Statesman (2018a) Call to solve Siliguri pollution problems. https://www.thestatesman.com/cities/call-solve-siliguri-pollution-problems-1502623872.html

The Statesman (2018b) Man seeks answers on Siliguri pollution. https://www.thestatesman.com/cities/man-seeks-answers-siliguri-pollution-1502606581.html

The Telegraph (2018) Siliguri smokes into pollution top heap. https://www.telegraphindia.com/west-bengal/siliguri-smokes-into-pollution-topheap/cid/1453963

Tiwari S, Bisht D, Srivastava A, Pipal A, Taneja A, Srivastava M, Attri S (2014) Variability in atmospheric particulates and meteorological effects on their mass concentrations over Delhi, India. Atmos Res 145–146:45–56. https://doi.org/10.1016/j.atmosres.2014.03.027

Wei G, Zhang Z, Ouyang X, Shen Y, Jiang S, Liu B, He BJ (2021) Delineating the spatial–temporal variation of air pollution with urbanization in the Belt and Road Initiative area. Environ Impact Assess Rev 91:106646. https://doi.org/10.1016/j.eiar.2021.106646

Wen Y, Zhang S, Zhang J, Bao S, Wu X, Yang D, Wu Y (2020) Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data. Appl Energy 260:114357. https://doi.org/10.1016/j.apenergy.2019.114357

Whiteman CD, Hoch SW, Horel JD, Charland A (2014) Relationship between particulate air pollution and meteorological variables in Utah’s Salt Lake Valley. Atmos Environ 94:742–753. https://doi.org/10.1016/j.atmosenv.2014.06.012

World Health Organization (WHO) (2016) Ambient air pollution: a global assessment of exposure and burden of disease

World Health Organization (WHO) (2019) World air quality report

World Health Organization (2021) WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization

Yin Q, Wang J, Hu M, Wong H (2016) Estimation of daily PM2. 5 concentration and its relationship with meteorological conditions in Beijing. J Environ Sci 48:161–168

Zhang H, Wang Y, Hu J, Ying Q, Hu XM (2015) Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ Res 140:242–254

Zhang Y, Wang L, Tang Z, Zhang K, Wang T (2022) Spatial effects of urban expansion on air pollution and eco-efficiency: evidence from multisource remote sensing and statistical data in China. J Clean Prod 367:132973. https://doi.org/10.1016/j.jclepro.2022.132973

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Acknowledgements

First, the authors would like to express their sincere gratitude to the Department of Geography and Applied Geography, University of North Bengal, for providing the opportunity to conduct the research work. Besides, the authors would like to express their sincere gratitude to the Central Pollution Control Board (CPCB) and West Bengal Pollution Control Board (WBPCB) for the data, without which it is not possible to find such a novel result. This research paper was completed during the tenure of the UGC-JRF period. Besides, the authors would also like to thank Mr. Subham Roy and Mr. Suranjan Majumder (co-researchers) for supporting the ideas of this research. Lastly, the author wants to express their sincere gratitude to the anonymous referees and the editor in chief Md. Nazrul Islam for their insightful suggestions and comments, which greatly helped in the improvement of the earlier version of the manuscript.

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Arghadeep Bose & Indrajit Roy Chowdhury

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AB: Writing—Original Draft, Review and Editing, Conceptualization, Methodology, Software, Validation, Supervision. IRC: Review and Editing.

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Bose, A., Roy Chowdhury, I. Investigating the association between air pollutants’ concentration and meteorological parameters in a rapidly growing urban center of West Bengal, India: a statistical modeling-based approach. Model. Earth Syst. Environ. 9 , 2877–2892 (2023). https://doi.org/10.1007/s40808-022-01670-6

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Received : 23 October 2022

Accepted : 28 December 2022

Published : 05 January 2023

Issue Date : June 2023

DOI : https://doi.org/10.1007/s40808-022-01670-6

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