case study on air pollution in world

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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Air Pollution: Everything You Need to Know

How smog, soot, greenhouse gases, and other top air pollutants are affecting the planet—and your health.

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What is air pollution?

What causes air pollution, effects of air pollution, air pollution in the united states, air pollution and environmental justice, controlling air pollution, how to help reduce air pollution, how to protect your health.

Air pollution  refers to the release of pollutants into the air—pollutants that are detrimental to human health and the planet as a whole. According to the  World Health Organization (WHO) , each year, indoor and outdoor air pollution is responsible for nearly seven million deaths around the globe. Ninety-nine percent of human beings currently breathe air that exceeds the WHO’s guideline limits for pollutants, with those living in low- and middle-income countries suffering the most. In the United States, the  Clean Air Act , established in 1970, authorizes the U.S. Environmental Protection Agency (EPA) to safeguard public health by regulating the emissions of these harmful air pollutants.

“Most air pollution comes from energy use and production,” says  John Walke , director of the Clean Air team at NRDC. Driving a car on gasoline, heating a home with oil, running a power plant on  fracked gas : In each case, a fossil fuel is burned and harmful chemicals and gases are released into the air.

“We’ve made progress over the last 50 years in improving air quality in the United States, thanks to the Clean Air Act. But climate change will make it harder in the future to meet pollution standards, which are designed to  protect health ,” says Walke.

Air pollution is now the world’s fourth-largest risk factor for early death. According to the 2020  State of Global Air  report —which summarizes the latest scientific understanding of air pollution around the world—4.5 million deaths were linked to outdoor air pollution exposures in 2019, and another 2.2 million deaths were caused by indoor air pollution. The world’s most populous countries, China and India, continue to bear the highest burdens of disease.

“Despite improvements in reducing global average mortality rates from air pollution, this report also serves as a sobering reminder that the climate crisis threatens to worsen air pollution problems significantly,” explains  Vijay Limaye , senior scientist in NRDC’s Science Office. Smog, for instance, is intensified by increased heat, forming when the weather is warmer and there’s more ultraviolet radiation. In addition, climate change increases the production of allergenic air pollutants, including mold (thanks to damp conditions caused by extreme weather and increased flooding) and pollen (due to a longer pollen season). “Climate change–fueled droughts and dry conditions are also setting the stage for dangerous wildfires,” adds Limaye. “ Wildfire smoke can linger for days and pollute the air with particulate matter hundreds of miles downwind.”

The effects of air pollution on the human body vary, depending on the type of pollutant, the length and level of exposure, and other factors, including a person’s individual health risks and the cumulative impacts of multiple pollutants or stressors.

Smog and soot

These are the two most prevalent types of air pollution. Smog (sometimes referred to as ground-level ozone) occurs when emissions from combusting fossil fuels react with sunlight. Soot—a type of  particulate matter —is made up of tiny particles of chemicals, soil, smoke, dust, or allergens that are carried in the air. The sources of smog and soot are similar. “Both come from cars and trucks, factories, power plants, incinerators, engines, generally anything that combusts fossil fuels such as coal, gasoline, or natural gas,” Walke says.

Smog can irritate the eyes and throat and also damage the lungs, especially those of children, senior citizens, and people who work or exercise outdoors. It’s even worse for people who have asthma or allergies; these extra pollutants can intensify their symptoms and trigger asthma attacks. The tiniest airborne particles in soot are especially dangerous because they can penetrate the lungs and bloodstream and worsen bronchitis, lead to heart attacks, and even hasten death. In  2020, a report from Harvard’s T.H. Chan School of Public Health showed that COVID-19 mortality rates were higher in areas with more particulate matter pollution than in areas with even slightly less, showing a correlation between the virus’s deadliness and long-term exposure to air pollution. 

These findings also illuminate an important  environmental justice issue . Because highways and polluting facilities have historically been sited in or next to low-income neighborhoods and communities of color, the negative effects of this pollution have been  disproportionately experienced by the people who live in these communities.

Hazardous air pollutants

A number of air pollutants pose severe health risks and can sometimes be fatal, even in small amounts. Almost 200 of them are regulated by law; some of the most common are mercury,  lead , dioxins, and benzene. “These are also most often emitted during gas or coal combustion, incineration, or—in the case of benzene—found in gasoline,” Walke says. Benzene, classified as a carcinogen by the EPA, can cause eye, skin, and lung irritation in the short term and blood disorders in the long term. Dioxins, more typically found in food but also present in small amounts in the air, is another carcinogen that can affect the liver in the short term and harm the immune, nervous, and endocrine systems, as well as reproductive functions.  Mercury  attacks the central nervous system. In large amounts, lead can damage children’s brains and kidneys, and even minimal exposure can affect children’s IQ and ability to learn.

Another category of toxic compounds, polycyclic aromatic hydrocarbons (PAHs), are by-products of traffic exhaust and wildfire smoke. In large amounts, they have been linked to eye and lung irritation, blood and liver issues, and even cancer.  In one study , the children of mothers exposed to PAHs during pregnancy showed slower brain-processing speeds and more pronounced symptoms of ADHD.

Greenhouse gases

While these climate pollutants don’t have the direct or immediate impacts on the human body associated with other air pollutants, like smog or hazardous chemicals, they are still harmful to our health. By trapping the earth’s heat in the atmosphere, greenhouse gases lead to warmer temperatures, which in turn lead to the hallmarks of climate change: rising sea levels, more extreme weather, heat-related deaths, and the increased transmission of infectious diseases. In 2021, carbon dioxide accounted for roughly 79 percent of the country’s total greenhouse gas emissions, and methane made up more than 11 percent. “Carbon dioxide comes from combusting fossil fuels, and methane comes from natural and industrial sources, including large amounts that are released during oil and gas drilling,” Walke says. “We emit far larger amounts of carbon dioxide, but methane is significantly more potent, so it’s also very destructive.” 

Another class of greenhouse gases,  hydrofluorocarbons (HFCs) , are thousands of times more powerful than carbon dioxide in their ability to trap heat. In October 2016, more than 140 countries signed the Kigali Agreement to reduce the use of these chemicals—which are found in air conditioners and refrigerators—and develop greener alternatives over time. (The United States officially signed onto the  Kigali Agreement in 2022.)

Pollen and mold

Mold and allergens from trees, weeds, and grass are also carried in the air, are exacerbated by climate change, and can be hazardous to health. Though they aren’t regulated, they can be considered a form of air pollution. “When homes, schools, or businesses get water damage, mold can grow and produce allergenic airborne pollutants,” says Kim Knowlton, professor of environmental health sciences at Columbia University and a former NRDC scientist. “ Mold exposure can precipitate asthma attacks  or an allergic response, and some molds can even produce toxins that would be dangerous for anyone to inhale.”

Pollen allergies are worsening  because of climate change . “Lab and field studies are showing that pollen-producing plants—especially ragweed—grow larger and produce more pollen when you increase the amount of carbon dioxide that they grow in,” Knowlton says. “Climate change also extends the pollen production season, and some studies are beginning to suggest that ragweed pollen itself might be becoming a more potent allergen.” If so, more people will suffer runny noses, fevers, itchy eyes, and other symptoms. “And for people with allergies and asthma, pollen peaks can precipitate asthma attacks, which are far more serious and can be life-threatening.”

More than one in three U.S. residents—120 million people—live in counties with unhealthy levels of air pollution, according to the  2023  State of the Air  report by the American Lung Association (ALA). Since the annual report was first published, in 2000, its findings have shown how the Clean Air Act has been able to reduce harmful emissions from transportation, power plants, and manufacturing.

Recent findings, however, reflect how climate change–fueled wildfires and extreme heat are adding to the challenges of protecting public health. The latest report—which focuses on ozone, year-round particle pollution, and short-term particle pollution—also finds that people of color are 61 percent more likely than white people to live in a county with a failing grade in at least one of those categories, and three times more likely to live in a county that fails in all three.

In rankings for each of the three pollution categories covered by the ALA report, California cities occupy the top three slots (i.e., were highest in pollution), despite progress that the Golden State has made in reducing air pollution emissions in the past half century. At the other end of the spectrum, these cities consistently rank among the country’s best for air quality: Burlington, Vermont; Honolulu; and Wilmington, North Carolina. 

No one wants to live next door to an incinerator, oil refinery, port, toxic waste dump, or other polluting site. Yet millions of people around the world do, and this puts them at a much higher risk for respiratory disease, cardiovascular disease, neurological damage, cancer, and death. In the United States, people of color are 1.5 times more likely than whites to live in areas with poor air quality, according to the ALA.

Historically, racist zoning policies and discriminatory lending practices known as  redlining  have combined to keep polluting industries and car-choked highways away from white neighborhoods and have turned communities of color—especially low-income and working-class communities of color—into sacrifice zones, where residents are forced to breathe dirty air and suffer the many health problems associated with it. In addition to the increased health risks that come from living in such places, the polluted air can economically harm residents in the form of missed workdays and higher medical costs.

Environmental racism isn't limited to cities and industrial areas. Outdoor laborers, including the estimated three million migrant and seasonal farmworkers in the United States, are among the most vulnerable to air pollution—and they’re also among the least equipped, politically, to pressure employers and lawmakers to affirm their right to breathe clean air.

Recently,  cumulative impact mapping , which uses data on environmental conditions and demographics, has been able to show how some communities are overburdened with layers of issues, like high levels of poverty, unemployment, and pollution. Tools like the  Environmental Justice Screening Method  and the EPA’s  EJScreen  provide evidence of what many environmental justice communities have been explaining for decades: that we need land use and public health reforms to ensure that vulnerable areas are not overburdened and that the people who need resources the most are receiving them.

In the United States, the  Clean Air Act  has been a crucial tool for reducing air pollution since its passage in 1970, although fossil fuel interests aided by industry-friendly lawmakers have frequently attempted to  weaken its many protections. Ensuring that this bedrock environmental law remains intact and properly enforced will always be key to maintaining and improving our air quality.

But the best, most effective way to control air pollution is to speed up our transition to cleaner fuels and industrial processes. By switching over to renewable energy sources (such as wind and solar power), maximizing fuel efficiency in our vehicles, and replacing more and more of our gasoline-powered cars and trucks with electric versions, we'll be limiting air pollution at its source while also curbing the global warming that heightens so many of its worst health impacts.

And what about the economic costs of controlling air pollution? According to a report on the Clean Air Act commissioned by NRDC, the annual  benefits of cleaner air  are up to 32 times greater than the cost of clean air regulations. Those benefits include up to 370,000 avoided premature deaths, 189,000 fewer hospital admissions for cardiac and respiratory illnesses, and net economic benefits of up to $3.8 trillion for the U.S. economy every year.

“The less gasoline we burn, the better we’re doing to reduce air pollution and the harmful effects of climate change,” Walke explains. “Make good choices about transportation. When you can, ride a bike, walk, or take public transportation. For driving, choose a car that gets better miles per gallon of gas or  buy an electric car .” You can also investigate your power provider options—you may be able to request that your electricity be supplied by wind or solar. Buying your food locally cuts down on the fossil fuels burned in trucking or flying food in from across the world. And most important: “Support leaders who push for clean air and water and responsible steps on climate change,” Walke says.

• “When you see in the news or hear on the weather report that pollution levels are high, it may be useful to limit the time when children go outside or you go for a jog,” Walke says. Generally, ozone levels tend to be lower in the morning.
• If you exercise outside, stay as far as you can from heavily trafficked roads. Then shower and wash your clothes to remove fine particles.
• The air may look clear, but that doesn’t mean it’s pollution free. Utilize tools like the EPA’s air pollution monitor,  AirNow , to get the latest conditions. If the air quality is bad, stay inside with the windows closed.
• If you live or work in an area that’s prone to wildfires,  stay away from the harmful smoke  as much as you’re able. Consider keeping a small stock of masks to wear when conditions are poor. The most ideal masks for smoke particles will be labelled “NIOSH” (which stands for National Institute for Occupational Safety and Health) and have either “N95” or “P100” printed on it.
• If you’re using an air conditioner while outdoor pollution conditions are bad, use the recirculating setting to limit the amount of polluted air that gets inside. 

This story was originally published on November 1, 2016, and has been updated with new information and links.

This NRDC.org story is available for online republication by news media outlets or nonprofits under these conditions: The writer(s) must be credited with a byline; you must note prominently that the story was originally published by NRDC.org and link to the original; the story cannot be edited (beyond simple things such as grammar); you can’t resell the story in any form or grant republishing rights to other outlets; you can’t republish our material wholesale or automatically—you need to select stories individually; you can’t republish the photos or graphics on our site without specific permission; you should drop us a note to let us know when you’ve used one of our stories.

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• Open access
• Published: 03 May 2024

Burden of cardiovascular disease attributed to air pollution: a systematic review

• Amir Hossein Khoshakhlagh 1 ,
• Mahdiyeh Mohammadzadeh   ORCID: orcid.org/0000-0002-8288-8511 2 , 3 ,
• Agnieszka Gruszecka-Kosowska 4 &
• Evangelos Oikonomou 5  

Globalization and Health volume  20 , Article number:  37 ( 2024 ) Cite this article

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Cardiovascular diseases (CVDs) are estimated to be the leading cause of global death. Air pollution is the biggest environmental threat to public health worldwide. It is considered a potentially modifiable environmental risk factor for CVDs because it can be prevented by adopting the right national and international policies. The present study was conducted to synthesize the results of existing studies on the burden of CVDs attributed to air pollution, namely prevalence, hospitalization, disability, mortality, and cost characteristics.

A systematic search was performed in the Scopus, PubMed, and Web of Science databases to identify studies, without time limitations, up to June 13, 2023. Exclusion criteria included prenatal exposure, exposure to indoor air pollution, review studies, conferences, books, letters to editors, and animal and laboratory studies. The quality of the articles was evaluated based on the Agency for Healthcare Research and Quality Assessment Form, the Newcastle–Ottawa Scale, and Drummond Criteria using a self-established scale. The articles that achieved categories A and B were included in the study.

Of the 566 studies obtained, based on the inclusion/exclusion criteria, 92 studies were defined as eligible in the present systematic review. The results of these investigations supported that chronic exposure to various concentrations of air pollutants, increased the prevalence, hospitalization, disability, mortality, and costs of CVDs attributed to air pollution, even at relatively low levels. According to the results, the main pollutant investigated closely associated with hypertension was PM 2.5 . Furthermore, the global DALY related to stroke during 2016–2019 has increased by 1.8 times and hospitalization related to CVDs in 2023 has increased by 8.5 times compared to 2014.

Ambient air pollution is an underestimated but significant and modifiable contributor to CVDs burden and public health costs. This should not only be considered an environmental problem but also as an important risk factor for a significant increase in CVD cases and mortality. The findings of the systematic review highlighted the opportunity to apply more preventive measures in the public health sector to reduce the footprint of CVDs in human society.

Introduction

Cardiovascular diseases (CVDs) are responsible for most of the deaths and disabilities worldwide [ 1 , 2 ]. In 2017, CVDs resulted in more than 360 million disability-adjusted life years (DALYs) (Table  1 ) worldwide, making it a significant health concern in both developed and developing countries [ 3 , 4 ]. The World Health Organization (WHO) reports that CVDs, including ischemic heart disease (IHD), atrial fibrillation (AF), stroke, heart failure (HF), and other cardiovascular disorders account for 43% of all deaths from non-communicable diseases (NCDs) [ 5 ].

Currently, research revealed that more than 80% of CVD cases can be prevented by addressing risk factors such as smoking, arterial hypertension, diabetes mellitus, hypercholesterolemia, overweight, lack of physical activity, unhealthy diet, and exposure to air pollution [ 6 , 7 ]. Despite the significant impact of environmental factors, especially air pollution, on health outcomes, they are often overlooked in the assessment of the global burden of disease (GBD) [ 8 ].

Air pollution is a major environmental concern in terms of the occurrence of adverse health effects and the negative impact on public health [ 9 ]. Fossil fuel consumption, especially in industries and transportation, is considered one of the most important sources of air pollution after the industrial revolution. In addition to being the main perpetrator of hazardous pollutant emissions, industry also plays an undeniable role in the increase in the average temperature of the Earth [ 10 ]. The expotential increase in industrialization results in a devastating impact on the environment. In some countries with a high Human Development Index (HDI), this leads to their largest share in the world’s greenhouse gases and hazardous pollutant emissions.

As a consequence, preventive policies and tax measures were introduced, particularly for these activities with high emission levels. Unfortunately, the existing global disparities caused an enormous difference in the rate of use of clean fuels. Modern renewable energy sources supply only 2.3% of electricity in low HDI countries, whereas this figure is 11% in countries with very high HDI. The dependance on the use of biomass fuel as an energy source was equal to 92% of households in countries with low HDI compared to 7.5% in countries with very high HDI. This led to the failure to limit the consumption of fossil fuels despite the efforts made [ 11 , 12 , 13 ]. Biomass fuel is used for heating, cooking, and providing lighting inside the house, as well as an energy source for occupational, industry, and transportation purposes, which can cause the release of high levels of air pollutants. According to studies, air pollution is considered as a consequence of population growth and urbanization, which is considered an important factor in premature mortality. This in turn, increases the costs of many NCDs, especially among local populations [ 14 , 15 , 16 ].

According to the lancet commission on pollution and health, harmful environmental conditions are responsible for approximately 9 million excess deaths worldwide, half of them attributed to air pollution [ 8 ]. The monetary costs of premature deaths attributed to air pollution in 2020 were estimated at 2.2 trillion dollars, which was equivalent to 2.4% of the gross world product (GWP) [ 12 ]. Additionally, two-thirds of the health effects caused by exposure to air pollutants were found to be related to cardiovascular mortality and other health complications [ 17 ]. Specifically, acute myocardial infarction (AMI) and stroke contributed to almost 50% of these adverse effects, resulting in a significant burden on healthcare costs worldwide [ 17 , 18 ].

The published data report of the WHO indicated that almost 99% of the global population is exposed to inhalation of air pollutants that exceed the air quality threshold values recommended by this institution [ 19 ]. This alarming statistic revealed how much impact air pollution could have on the increase in CVDs, hospitalizations, disability, cases of mortality, and increase in economic costs.

The increase in the publication of related studies during the last 10 years can effectively draw a risk perspective of the growth in the burden of CVDs caused by exposure to air pollution. The results of these investigations may provide important evidence for implementing air pollution control measures based on maintaining the input-output balance (policy cost-benefit), especially in developing countries.

However, the existence of some limitations and gaps restricts the generalization of the results of these researches, including conducting each study in a limited number of countries [ 20 , 21 , 22 ], investigating the effect of air pollution on only one type of CVDs [ 23 , 24 , 25 ], examining a limited number of air pollutants [ 25 , 26 , 27 ], participants of only one gender (male or female) [ 28 , 29 , 30 ] and subjects with a relatively high socio-economic status [ 30 ]. In addition, considering that no review study has been published in this field, it seems necessary to conduct a systematic review to retrieve related studies and cover the gaps mentioned above to achieve more comprehensive results.

This systematic review gathers and summarizes up-to-date studies on the burden of CVDs, including prevalence, disability, hospitalization, mortality, and cost, caused by exposure to air pollution.

Research protocol

This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42023434702) and adhered to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analyses) statement.

Search strategy and data screening

The search was carried out without timeframe, up to June 13, 2023. A systematic search of the databases Scopus, PubMed, and Web of Science was conducted using the following keywords:

Disease: “Cardiovascular Disease*”, “Myocardial Infarction”, “Heart Failure”, Hypertension, Myocarditis, Arrhythmia, “Coronary Heart Disease*”, “Cerebrovascular Disease*”, “Abnormal Heart Rhythms”, “Aorta Disease*”, “Heart Attack”, “Coronary Artery Disease*”, Cardiomyopathy, “Heart Muscle Disease*”, “Pericardial Disease*”, “Peripheral Vascular Disease*”, Stroke, “Vascular disease*”, Angina, “Rheumatic Heart Disease*”;

Disease burden : “Illness Cost*”, “Sickness Cost*”, “Illness Burden*”, “Disease Burden*”, “Disease Cost*”, “Economic Burden of Disease”, “Disability-Adjusted Living Years”, DALY, Mortality, Morbidity, “Years of Life Lost”, YLL, “Years Lost due to Disability”, YLD;

Exposure: “Air pollution”.

Two researchers, M.M. and A.H.Kh., extracted keywords and conducted a systematic search for Title/Abstract and Mesh (if any). Studies obtained from databases were integrated using EndNote X20 software. After removing duplicates, M.M. and A.H.Kh. independently screened and extracted the studies. The third author (E.O.) resolved any ambiguities or contradictions during the review process. To ensure that no eligible studies were missed, the reference list of the selected studies was systematically searched. Additionally, a hand search was also conducted in parallel.

Entry and exit criteria of the study

In this systematic review, studies focused on prenatal exposure to air pollution and the impact of indoor air pollution on the burden of CVDs were excluded. Furthermore, review studies, conference studies, books, letters to the editors, and animal/laboratory studies were omitted and only original articles published in English and peer reviewed were examined.

Extracting the data

After reviewing and selecting eligible studies, their results were summarized in an electronic form in the Excel 2016 software. The data sheet encompassed various details such as author names, year of publication, title, country of investigation, number of participants, age range, gender, and type of pollutant. Variables related to disease burden included prevalence, hospitalization, disability (measured in disability-adjusted life years DALY), years lost to disability (YLD), years of life lost (YLL), mortality (mortality rate and death), and cost (total cost, economic loss due to missed work days, and overall economic losses).

Quality control

Two researchers, M.M. and A.H.Kh., assessed the quality of selected studies using a self-established scale. This scale was based on the Agency for Healthcare Research and Quality Assessment Form, the Newcastle–Ottawa Scale [ 31 ], and the Drummond Criteria [ 32 ]. Based on this method, the quality of studies is determined by answering 9 questions, that are presented in Table  2 . Questions 1–8 can only be answered as “yes” (1 score) or “no” (0 score). Question 9 can be answered as: “yes” (2 scores), “likely” (1 score), and “no” (0 score). The scores obtained were combined after confirmation, and each study was classified based on its quality score (between 0 and 10 points) into one of three categories: A, B, or C. A study with a quality score equal to or above 8 received category A. If the quality score was between 4 and 7, the study received category B. If the quality score was less than 4 a study received category C [ 33 ]. Only articles classified in categories A and B were included in the systematic review analysis.

Study selection

A systematic search was carried out in the PubMed, Scopus, and Web of Science databases as presented in Fig.  1 . The 566 articles found were screened using EndNote X20 software based on title and abstract. Then 122 articles remained, and due to the inaccessibility of the full text of 7 studies, 115 full texts were thoroughly examined based on inclusion/exclusion criteria and quality assessment. Finally, 92 studies were defined eligible in this systematic review.

PRISMA flow diagram of the literature search on CVDs related to air pollution

Synthesis of results

In this study, we found various study designs that caused differences in methodology and context that made it unsuitable to perform a quantitative synthesis or meta-analysis. Therefore, we combined the study results in a narrative format, which included information about the type of pollutant, its mean concentration, and the disease burden variables, including prevalence (Table  3 ), hospitalization (Table  4 ), disability (disability-adjusted living years DALY, years lost due to disability YLD, and years of life lost YLL) (Supplementary Material 1 ), mortality (mortality rate, death) (Supplementary Material 2 ), and costs (total cost, economic loss from loss of the work day, and economic losses) (Table  5 ).

We followed a two-step process for narrative synthesis. In the first step, we classified the information into five separate tables according to the type of disease burden. In the second step, we evaluated the severity of the consequences by examining the relationship between exposure levels and disease burden. The brief visual scheme of the seven steps that comprises the complete systematic review framework performed in the study is presented in Fig.  2 .

Visual representation of the complete framework of the systematic review guiding process

Prevalence of CVDs attributed to air pollution

Hypertension is the most common disease associated with chronic exposure to air pollutants. As presented in Table  3 in this section, the main pollutants examined were PM 2.5 (72.2%) and NO 2 (50%), and were found to be closely associated with the incidence of hypertension according to the results obtained. The prevalence of CVDs related to air pollution was investigated in 18 studies from 10 countries around the world (Table  3 ) [ 7 , 14 , 15 , 20 , 21 , 22 , 25 , 26 , 27 , 28 , 29 , 30 , 35 , 36 , 37 , 38 , 39 , 40 ]. Generally, 818,316 subjects of different age groups were evaluated during 2017–2023. A review of the listed studies showed that China was the most active in this field, publishing 6 studies [ 21 , 26 , 27 , 28 , 35 , 40 ].

Based on the available information on the types of areas in the studies included in this systematic review, it was revealed that most of the investigations were carried out on a national and/or regional scale depending on the size of the country, for example, the United States [ 29 , 30 , 53 , 57 ], Canada [ 58 ], Brazil [ 59 ], China [ 26 , 35 , 40 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ], and Europe [ 67 , 68 , 69 ]. There was also some research conducted worldwide [ 9 , 37 , 70 , 71 , 72 , 73 ], including several countries in international cohort studies.

Based on these studies, the prevalence of CVDs varied greatly, ranging from 0.5% for developed coronary heart disease to 74.5% for hypertension. Specifically, prevalence percentages for hypertension ranged from 5.3% to 74.5%, for coronary artery disease from 0.5% to 13.9%, for stroke from 1.2% to 3.2%, and for other CVDs from 2.00% to 1.46%. Reports of carotid plaque and arrhythmia were reported by a single study each. Consequently, the prevalence of carotid plaque related to air pollution was reported to be equal to 22.3% [ 15 , 30 , 38 , 39 ], and the prevalence of arrhythmia was estimated to be 3.2% [ 36 ].

Hospitalization due to CVDs attributed to air pollution

Table 4 presents a summary of the results of 15 studies [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ] related to the hospitalization rate due to CVDs episodes as an adverse consequence of exposure to air pollution. Among the investigated pollutants, PM 2.5 (86.6%) and PM 10 (40%) were found to be the most common in the analyzed studies. According to the results obtained, 103,899,123 subjects from the following 7 countries were examined: China (6 studies) [ 45 , 46 , 48 , 51 , 52 , 55 ], USA (4 studies) [ 41 , 42 , 49 , 53 ], Taiwan (1 study) [ 47 ], Brazil (1 study) [ 50 ], Mexico (1 study) [ 54 ], Thailand (1 study) [ 44 ], and Republic of Macedonia (1 study) [ 43 ]. The publication years of these studies were from 2014 to 2023 and included all age groups.

Studies indicated that arrhythmias had the lowest hospital admission rate, with only 7 cases (0.001%) out of the 445,216 patients examined in China [ 55 ]. Also, Liu et al. [ 51 ] found that the hospitalization rate for ischemic stroke attributed to PM 1 was 81.92% that was the highest rate among similar studies. This studies also demonstrated that an increase of 10 µg/m 3 PM 1 resulted in an increase of 0.53% (95% CI, 0.39%, 0.67%) in the hospital admission rate due to stroke [ 51 ].

Disability due to CVDs attributed to air pollution

In the course of the investigations, 33 studies from 11 countries [ 7 , 14 , 23 , 24 , 38 , 43 , 59 , 60 , 62 , 63 , 64 , 67 , 68 , 69 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] and 6 studies based on worldwide data focused on the impact of air pollutants on global disability (DALY, YLD, YLL) in patients with CVDs [ 9 , 70 , 71 , 72 , 73 , 87 ]. China was the most active in this field by publishing 12 related studies. Among these, 21 studies specifically examined the role of PM 2.5 in causing CVDs related disability during 2015–2023 (Supplementary Material 1 ). Generally, 2,338,344,120 subjects from different age groups were evaluated.

The results showed that over time, as industrial activity expanded and pollutant concentrations increased, the incidence of CVD-related disabilities, particularly stroke and IHD, also increased. The results of a global study showed that the DALY rate caused by exposure to PM 2.5 in 1990 was 10 million years, which increased to 20 million years in 2019 (a two-fold increase). In addition, similar results were also observed in stroke [ 73 ]. Rueda et al. [ 85 ] also concluded in a national study in the Kingdom of Saudi Arabia that exposure to a concentration of 87.9 μg/m 3 during 1990–2017 caused the increase of DALY rate 4 times due to IHD and 2.5 times due to stroke.

Mortality due to CVDs attributed to air pollution

Supplementary Material 2 summarizes the results of 58 studies investigating the mortality rate of CVDs caused by air pollution [ 9 , 14 , 15 , 20 , 23 , 26 , 27 , 28 , 42 , 43 , 49 , 50 , 53 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 80 , 81 , 82 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 ]. These studies were carried out between 2015 and 2023 and have been published in 18 countries, including India, USA, Thailand, China, Canada, Republic of Macedonia, South Korea, Iran, Europe, Kazakhstan, Kingdom of Saudi Arabia, Brazil, Soviet Republics, Germany, Malaysia, Colombia, and the United Kingdom.

The researchers examined a total of 1,237,022,761 people in various age groups. In 48 different studies, researchers focused on PM 2.5 as the main pollutant and its impact on deaths related to CVDs. The findings of these research confirmed that there is a direct correlation between the concentration of PM 2.5 pollutants and the mortality rate associated with IHD and stroke.

Mazeli et al. [ 104 ] in a national study in Malaysia investigated the relationship between PM 2.5 levels and CVD mortality. In this study, it was found that with the increase in pollutant concentration from 2000 (22 μg/m 3 ) to 2013 (24 μg/m 3 ), the death rate due to stroke has increased approximately twice, but this statistic remained almost constant in IHD [ 104 ]. In addition, the results of a recent study in Germany showed that exposure to levels of 13.7–10.8 μg/m 3 PM 2.5 can cause the death of 6,977 patients with IHD and 1,871 deaths due to stroke [ 86 ].

Costs due to CVDs attributed to air pollution

The results obtained from 4 studies related to the economic burden caused by CVDs attributed to air pollution are shown in Table  5 [ 46 , 48 , 52 , 56 ]. These studies were performed between 2019 and 2022, a total of 95,624,779 people from China were evaluated and PM 2.5 was evaluated in all these four studies. Comparing the results of the studies showed that the greatest economic losses for CVD are related to PM 2.5 , as reported by Zhu et al. [ 46 ]. Furthermore, Yao et al. [ 56 ] reported the highest economic loss from lost workdays. This cost was calculated only for CVD attributed to PM 10 , SO 2 , NO 2 , CO, and O 3 . According to this study, the economic loss of CVD lost workdays attributed to NO 2 was calculated at 604.02 billion CNY (US$ 83.4 billion), which was the highest amount compared to other pollutants [ 56 ]. According to Table  5 , the total cost caused by CHD attributed to PM 2.5 was found to be 1.6 times higher than those attributed to PM 10 [ 52 ].

Hypertension was reported in studies to be the most prevalent among people who were chronically exposed to pollutants such as NO 2 , O 3 , PM 10 , PM 2.5 , and SO 2 . A 12-year follow-up study in the United Kingdom showed that exposure to air pollution was positively related to hypertension and its development in normotensive subjects [ 15 ]. This finding was consistent with a study by Prabhakaran et al. [ 7 ], which found an increasing trend in systolic blood pressure in Indian residents following an increase in pollutant concentration from 1990 to 2016. In the study of Karimi et al. [ 38 ] on the prevalence, burden, and economic costs of chronic diseases caused by air pollution in Tehran, Iran, the prevalence of hypertension was estimated at 5.3%. However, a German cohort study from 2020 found a prevalence of 53% for hypertension after exposure to PM 2.5 , PM 10 , and NO 2 [ 36 ].

The results of an international cohort of Chinese men showed that exposure to an average level of 43.7 μg/m 3 PM 2.5 can increase the prevalence of hypertension to 26.9% [ 28 ]. This prevalence was consistent with the results reported by Prabhakaran et al. [ 7 ] (21.1%). In addition, Yang et al. [ 26 ] also demonstrated in a national cohort in China that exposure to a concentration of 64.9 μg/m 3 of this pollutant can be associated with a prevalence of 31.8% of this health complication.

Although various studies revealed a positive correlation between exposure to air pollution and an increase in hypertension and subsequent consequences such as blindness, chest pain, pregnancy complications, heart attack, and stroke, the prevalence rate varied between different countries [ 15 , 30 , 38 , 39 ]. Interestingly, developed countries had higher prevalence rates, contradicting the results of some studies [ 9 , 60 , 105 ]. The analysis of the research indicated that the studies conducted on a regional scale were carried out in urban areas with large populations and intense traffic. In addition, these sites were also heavily industrialized for economic reasons related to the distance from work to home. The results of studies showed that the incidence of CVDs was higher in low- or middle-income countries (LMIC) and developing countries [ 106 ]. The increase in industrial activities, the use of fossil fuels, the use of old and obsolete technologies in the production process, and the lack of growth in mechanization have led to a significant increase in the amount of pollutants produced by these countries. In addition, the use of manpower in heavily polluted industrial environments instead of using industrial machines, the growth of marginalization and residence in industrial areas have increased levels of exposure to high concentrations of pollutants, being an important risk factor considered to cause CVDs [ 107 ].

Therefore, the explanation for this contradiction in results can be population growth, aging, and suffering from chronic diseases, such as kidney dysfunction, as well as the additive effect of several risk factors, such as high systolic blood pressure, high blood sugar, low physical activity, high body mass index (BMI), and alcohol consumption [ 70 ]. Furthermore, limited access to clinical care and a lack of advanced diagnostic methods in low- and middle-income countries led to misdiagnosis of some CVDs [ 108 ], negatively affecting patient registries and statistics published by their health systems. Most of the articles published on this topic investigated in developed countries, while only a few papers came from developing countries. Finally, climate variability, air humidity, green space per capita, as well as the rate of industrial growth and the development of the studied society were among the factors that affected air pollution levels in different countries [ 109 ], becoming an important factor in the development of CVDs.

Based on the results presented in Table  3 , the prevalence of CVDs attributed to air pollution has been investigated in a wide range of age groups. Researchers believe that the elderly are more susceptible to CVDs than other age groups due to physiological changes, smoking, sedentary lifestyle, and chronic exposure to air pollutants [ 110 , 111 ]. Studies revealed that CVD frequency increases significantly after the 60 years of age, so the factor includes at least 40% of deaths in this age group [ 112 ].

Regarding the proposed mechanisms that implicate the association of air pollution with the occurrence of CVDs, air pollution was found to alter cardiovascular physiology, including heart rate and blood pressure [ 113 ], leading to an increased risk of IHD and stroke [ 61 ]. Air pollutants, specifically PM 2.5 , can enter the bloodstream after inhalation, causing systemic inflammation in the lungs and other organs [ 114 , 115 ]. Furthermore, inhaled pollutants can activate lung sensory receptors, leading to an imbalance in the autonomic nervous system and increased catecholamine secretion [ 114 , 115 ]. These changes can also trigger thrombosis, atherosclerosis, endothelial dysfunction, vasoconstriction, and elevated blood pressure [ 116 , 117 ].

The results of the present systematic review showed that CVDs were one of the three main factors that led to hospital admissions as a result of exposure to air pollution. To date, numerous studies have explored the correlation between exposure to different levels of air pollutants and hospitalization [ 53 , 54 , 55 ]. In a time-series analysis of Xie et al. [ 48 ] investigated the relationship between short-term exposure to particulate matter (PM) and hospitalization costs of specific CVDs in China. The study concluded that exposure to PM 2.5 could significantly increase hospital admissions and total costs of lower respiratory infections (LRI), coronary heart disease (CHD), and stroke.

The results of a national study in the USA showed that long-term exposure to low levels of PM 2.5 (8.7 μg/m 3 ) can cause hospitalization of 208,113 patients with CVDs [ 53 ]. Also, Castillo et al. [ 49 ] in a case study estimated intra-urban inequalities of exposure to this pollutant using mathematical models and datasets derived from North American satellites. The results obtained by them showed that inhalation exposure to levels of 10–17.1 μg/m 3 PM 2.5 caused hospitalization of 840 patients with IHD and 89 patients with stroke [ 49 ]. The results of these studies were consistent with the findings reported in China [ 46 , 48 ] and Taiwan [ 47 ].

From the available evidence, it seems that PM was related to changes in hemodynamics and body homeostasis [ 118 ]. Exposure to PM was related to a decrease in heart rate variability and an increase in ventricular fibrillation, as well as higher plasma viscosity and heart rate acceleration, and even with myocardial infarction [ 119 , 120 , 121 ]. These effects may be clinically meaningful in patients with cardioverter defibrillators [ 122 ].

Studies have shown that exposure to inhalable pollutants can lead to increased hospital admissions and stays in the intensive care unit [ 52 , 123 , 124 ]. In a study conducted by Pothirat et al. [ 44 ], they examined the acute impact of air pollution on daily hospitalizations and mortality rates related to respiratory diseases and cardiovascular complications in Thailand. Their results suggested that various pollutants could contribute to various types of cardiovascular complications in patients. Specifically, the study revealed a correlation between O 3 content and emergency hospital visits due to HF, NO 2 content and hospital admissions due to myocardial infarction, and SO 2 content and hospitalizations due to cerebrovascular accidents (CVA) [ 44 ]. Another recent study showed that an increase of 10 μg/m 3 NO 2 resulted in a risk increase of 1.9% (RR: 1.019, 95% CI: 1.005 to 1.032) for hospital admissions for CVDs at lag 0–2 days. Specifically, the risk increased by 2.1% (1.021, 1.006 to 1.036) for hospitalization due to IHD, and by 2.1% (1.021, 1.006 to 1.035) for hospitalization due to ischemic stroke [ 55 ].

However, this study did not find any significant relationship between NO 2 and hospital admissions due to arrhythmias, HF, and hemorrhagic stroke [ 55 ]. Differences in the results of other studies might be due to the number of subjects, industrial development, and socioeconomic levels of the investigated populations.

The results of the studies included in this systematic review indicate that PM 2.5 causes a two-fold increase in DALYs associated with CVDs. The 2015 GBD study identified PM 2.5 as the cause of 4.2 million deaths and 103.1 million DALYs worldwide [ 9 ], which is consistent with the results of the study by Sang et al. [ 73 ]. Research carried out in 204 countries during the 1990–2019 period estimated that exposure to PM 2.5 led to a two-fold increase in DALYs related to stroke and IHD, with IHD, stroke, and COPD being the three main causes of death, and DALYs attributed to this pollutant [ 73 ]. Furthermore, the European Environment Agency (EEA) reported 63,100 deaths and 710,900 years of YLL attributed to PM 2.5 in Germany in 2018 [ 125 ]. Meanwhile, Lelieveld et al. [ 67 ] investigated the burden of CVDs attributed to PM 2.5 in 28 European countries and revealed 14 million YLL, which is 19.7 times more than reported in EEA statistics.

The contribution of non-renewable energy sources to PM 2.5 emission and pollution, especially in urban areas, is undeniable. According to the Lancet report (2023), Asia accounted for 77% of all deaths attributed to fuel-related particulate matter, with 1.3 million deaths. Asia, where 43% of its total energy is coal-fired, has the highest mortality rate from coal-derived PM 2.5 among other continents (11 deaths per 100,000 people) [ 12 ]. Europe, by adopting air quality control measures, saw a 5.2% reduction in the share of coal-derived energy during 2005, reducing mortality rates related to ambient PM 2.5 by 36%, 44% of this is a result of the reduction of pollution attributed to coal. However, Europe in 2020 had the highest death rates from outdoor PM 2.5 pollution (69 deaths per 100,000 people) and dirty energy sources, such as biomass and fossil fuels (38 deaths per 100,000 people) [ 12 ].

So far, many studies on a national and international scale have shown the increase in disability cases associated with CVDs in recent years. Feigin et al. [ 70 ] in their systematic analysis on the global, regional, and national burden of stroke during the years 1990–2019 revealed that exposure to levels higher than 8.8 μg/m 3 PM 2.5 caused 28.7 million DALYs worldwide [ 70 ], which was consistent with the results obtained by Sang et al. [ 73 ]. When examining the global burden of disease attributable to ambient PM 2.5 in 204 countries, Sang et al. [ 73 ] concluded that the DALY index for stroke increased from 18 million in 1990 to 35 million in 2019 (approximately a two-fold increase) [ 73 ], but on the other hand, some studies have produced contradictory results.

The study by Campos Caldeira Brant et al. [ 59 ] found that the DALY rate associated with exposure to PM 2.5 for Brazilian residents in 2019 was 336 years, reflecting a 75% decrease compared to the DALY rate in 1990. Similar results were found in the study of Rueda et al. [ 85 ] on the burden of diseases caused by PM 2.5 in the Kingdom of Saudi Arabia (KSA) [ 85 ], which showed that DALY and YLL caused by IHD increased by approximately 3 and 1.2 times, respectively, during the years 1990–2010 and 2010–2017. The YLD of IHD also increased markedly by 3294.35 times from the value of 201 in 1990 to the value of 662,167 in 2010 [ 85 ]. The extensive use of fossil fuels, the development of industry and refineries, and proximity to the Great Arabian desert, which is the main source of natural PM [ 126 ], resulted in the increased disability caused by CVDs as the consequence of exposure to PM.

However, significant technological advances and the implementation of global corrective measures were able to have a positive impact on improving the health of communities in these countries by increasing the employment rate in the clean energy sector, as well as green lending by the World Bank and regional development banks. Furthermore, the significant increase in investment in the renewable energy sector in recent years has led to an important step towards achieving a reduction in fossil fuel consumption. The increase in the investment rate in 2022 was 15% compared to 2021 and 51% compared to 2015. Reduction in the usage of non-renewable fuel sources caused the decrease in exposure to air pollutants and related adverse health effects [ 127 , 128 , 129 ].

The occurrence and development of CVDs is a complex health issue influenced by several factors, including difficult to control variables, such as traffic noise, daily stress, lifestyle, and regional customs [ 130 , 131 ]. According to what was said, although the increase in PM 2.5 levels has been associated with an increase in cases of disability caused by exposure to this pollutant, the reason for the decrease in the DALY rate reported in the Brazilian [ 59 ] and Saudi Arabia [ 85 ] studies can be attributed to the aforementioned factors.

Investigation of the included studies showed that exposure to different levels of air pollutants, especially PMs, has a direct relationship with the increase in CVDs mortality. The findings of this systematic review are consistent with the results published by the WHO in 2016, which reported that 74% of global deaths (2,161,550 cases) attributed to air pollution were related to CVDs, particularly stroke and IHD [ 132 ]. In the study on the global burden of CVDs in India, air pollution was identified as the main cause of approximately one third of CVDs incidences, namely 31.1% (UI 29.0–33.4) during the years 1990–2016, resulting in a total mortality rate of 28.1% (95% UI 26.5–29.1) [ 7 ]. Furthermore, the study by Lelieveld et al. [ 67 ] showed that ambient air pollution in Europe was responsible for approximately 790,000 deaths per year (95% confidence interval [95% CI] 645,000–934,000), of which 40–80% occurred due to cardiovascular events. Furthermore, eliminating greenhouse gas emissions from fossil fuels could reduce annual death rates in Europe by 434,000 (95% CI 355,000–509,000) cases [ 67 ].

Anthropogenic activities cause emissions of man-made greenhouse gases (GHGs) such as hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF 6 ), as well as increases in natural GHGs such as carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ), and water vapour. The rate and the amount of GHGs emissions in recent decades has led to the global issue of climate change and the implementation of various measures to mitigate this environmental problem. One of the most important global actions is the Paris Agreement [ 11 ], which was ratified in 2015 at the United Nations Climate Change Conference (COP21) in Paris, France by 196 countries, representing 95% of the countries responsible for anthropogenic greenhouse gas emissions. The Paris Agreement priority goal is to keep the average global temperature increase below 2 ºC above preindustrial levels (the preferable limit of 1.5 ºC). This can be achieved only by significant reductions in all GHGs emissions. The success in achieving this objective depends on the reduction of industrial activities with high pollutant emissions, the use of Best Available Technologies (BATs) in the production of vehicles to reduce pollutant emissions, the encouragement of the production and usage of electric vehicles, the use of clean fuels instead of fossil ones, in combating deforestation and increasing the forest cover. The latter is considered to be a very effective solution in reducing air pollution and the related burden of diseases [ 11 ]. Furthermore, the analyzed studies indicated that the reduction in air pollution is estimated to prevent many of the current 3.3 million deaths resulting from exposure to anthropogenic PM 2.5 [ 12 ].

For example, the national cohort study from the USA with a six-year follow-up revealed that the increase of 1 μg/m 3 in the mean annual concentration of PM 2.5 was associated with an increase in the rate of cardiovascular events (hazard ratio HR, 1.02 [95% CI, 1.01–1.02]) and specific mortality (HR, 1.02 [95% CI, 1.02–1.03]) of associated CVDs [ 53 ]. These results suggest that chronic exposure to particulate matter, even at relatively low levels, has a potential positive association with CVDs and mortality, especially for chronic diseases.

Toxicological studies revealed that PM 10 and PM 2.5 can cause lung inflammation, oxidative stress, and cytotoxicity, leading to cardiovascular damage and even death [ 133 , 134 ]. Some researchers argue that exposure to PM 2.5 causes higher cytotoxicity than exposure to PM 10 [ 135 ]. Previous studies also reported a significant relationship between exposure to O 3 and cardiovascular morbidity and mortality [ 62 , 78 , 82 ]. Yin et al. [ 34 ] investigated 272 cities in southern China and found that the increase by 10 µg/m 3 in the maximum 8-h O 3 concentration led to a 0.66% (95% CI: 0.02%, 1.30%) increase in daily mortality due to hypertension in the general local population. Similar results were presented in the study by Li et al. [ 84 ] on the short-term effects of exposure to environmental NO 2 .

The increase in morbidity, disability, and death caused by CVDs attributed to air pollution imposes huge costs on governments involved in this environmental dilemma. Several studies have investigated the economic losses associated with chronic exposure to ambient air pollution [ 136 , 137 ], but only a few examined the related economic burden (Table  5 ) [ 46 , 48 , 52 , 56 ]. The results of the surveys showed that exposure to different levels of pollutants could increase health costs, reduce labor supply, and cause job losses. Short-term exposure to air pollutants was found to increase hospital admissions due to cardiorespiratory diseases, causing the government to significantly increase the costs spent on public health [ 123 , 138 , 139 ]. According to Xie et al. [ 48 ], the estimated costs associated with the most common CVDs related to short-term exposure to PM 2.5 (49.7 μg/m 3 ) were 220 million CNY (US$ 30.4 million) for LRI, 458 million CNY (US$ 63.2 million) for CHD, and 410 million CNY (US$ 56.6 million) for stroke. These numbers represented 1.45–2.05% of all hospital admission costs [ 48 ]. Workday loss related to CVDs due to exposure to air pollution calculated by Yao et al. [ 56 ] revealed that NO 2 with a concentration of 30.23 μg/m 3 caused the highest economic burden (604.02 billion CNY or US$ 83.04 billion), while SO 2 with a level of 18.14 μg/m 3 caused the lowest (195.28 billion CNY or US$ 27.9 billion). Yip et al. [ 140 ] revealed in their studies a four-fold increase in government health expenditures for health care from 2008 to 2017, which is consistent with the study of Dobkin et al. [ 141 ]. Hospital admissions can significantly increase out-of-pocket medical expenses, unpaid medical bills, reduced income, and even bankruptcy [ 141 ]. Direct costs will be much higher considering also outpatient visits.

From the available evidence, it appears that air pollution plays a very significant role in increasing the economic costs of the health system. The monetary costs of premature deaths attributed to air pollution in 2020 were estimated at 2.2 trillion US dollars, which was equivalent to 2.4% of the gross world product [ 12 ].

Although many efforts have been made to solve this global environmental issue, attempts to maintain people’s health and safety have so far been insufficient and unfair [ 142 ]. Obviously, some actions during recent years also played an important role in neutralizing the corrective measures. The demand for economic recovery after the COVID-19 pandemic crisis, the war outbreak in Ukraine in 2021, the subsequent imposition of economic sanctions and the disruption of oil and gas supplies, and extreme weather events after the El Niño phenomenon in 2023, has affected energy production and caused dramatic price increases. Unfortunately, it also caused the return to fossil fuels in many anthropogenic activities and new sources of oil and gas prospecting [ 143 , 144 ]. The increase in energy prices caused significant profits for oil and gas companies ($4 trillion in 2022 versus an average of $1.5 trillion in the previous years), resulting in a further decrease in the company’s adherence to the implementation of the Paris Agreement [ 145 , 146 , 147 ].

Furthermore, the gradual elimination of fossil fuels and the transition to clean and renewable energy have become a significant challenge due to several reasons. Among these reasons are a 10% increase in global investment in fossil fuels in 2022, direct net subsidies provided by governments, and an increase in bank lending to the fossil fuel sector by the top 55% of private banks [ 148 , 149 ]. These conditions were associated with imposing a high economic and health burden and a high death rate attributed to air pollution, especially for local populations, making countries with abundant natural renewable energy resources, such as Africa, Asia, and South and Central America, lag in the transition to clean energy. Therefore, it is crucial to achieve equality in access to clean fuel technologies, to support sustainable development, to reduce global inequalities, and as a result, to achieve global health goals.

Providing green energy transfer subsidies and increasing lending to the renewable energy sector are required and undertaken to reduce air pollution and greenhouse gases in low-HDI countries. Among these, the following efforts are vitally important to achieve the goals of reducing air pollutants and also reducing the costs imposed on the health system: Efforts to 1) improve sustainable city design and spatial management focusing on health issues; 2) reduce pollutant emissions from buildings, and increase the flexibility of communities in the face of climate risks; and 3) encourage governments to develop electric public transportation and impose strict tax laws for companies in case of violation of emission laws.

In addition, it can be very useful to take advantage of artificial intelligence-based long-term estimators and policymakers that have recently been developed to address challenging health problems [ 150 ]. The use of this tool can help to estimate future losses, determine and prioritize effective interventions and determine the most optimal conditions for applying interventions.

Our study revealed multiple strengths. First, to our knowledge, it was the first systematic review exploring the global burden of CVDs related to air pollution. Second, we conducted a systematic search without restrictions on publication date, study type, or countries under review, except for the language of the studies (only English). This approach allowed us to examine more studies, to analyze ample data, and to conclude on the air pollution in the burden of CVDs worldwide. However, since we did not have access to the full text of certain studies that met the eligibility criteria (7 studies), we are aware that it could affect the global picture of the conclusions presented in this systematic review.

Ambient air pollutants, especially PM 2.5 , are known to trigger the occurrence of CVDs. Hypertension was revealed to have the highest prevalence, while coronary heart disease was documented to have the lowest prevalence among other types of CVDs caused by air pollution. Based on the reviewed studies, CVDs were shown to be one of the three main factors that lead to hospital admissions as a result of exposure to air pollution. Furthermore, disabilities such as DALY, YLD, and YLL caused by CVDs, particularly stroke and IHD, increased significantly as a consequence of the ambition of the countries to improve the degree of industrialization. Thus, related air pollution is higher for obvious reasons in low- and middle-income and developing countries. Moreover, the consequence is not only environmental pollution itself, but also the significant number of CVD cases and deaths in the global population. In terms of economic burden, there was a lack of comprehensive research on the economic impact of CVDs due to air pollution. This indicates either an underestimation of the impact of this risk factor or a gap in research efforts. Although it is evident that CVDs linked to air pollutants impose a substantial constraint on public health, delve into this aspect could potentially offer a strategic vantage point for mitigating the burden of CVDs.

Availability of data and materials

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

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Amir Hossein Khoshakhlagh

Department of Health in Emergencies and Disasters, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

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Khoshakhlagh, A.H., Mohammadzadeh, M., Gruszecka-Kosowska, A. et al. Burden of cardiovascular disease attributed to air pollution: a systematic review. Global Health 20 , 37 (2024). https://doi.org/10.1186/s12992-024-01040-0

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Globalization and Health

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Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province

• Published: 09 May 2024
• Volume 196 , article number  525 , ( 2024 )

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• Yiğitalp Kara   ORCID: orcid.org/0000-0002-1527-6064 1 , 2 ,
• Sena Ecem Yakut Şevik   ORCID: orcid.org/0000-0001-6126-7567 2 &
• Hüseyin Toros   ORCID: orcid.org/0000-0002-3028-6308 2  

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Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM 10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM 10 and SO 2 , indicating moderate positive correlations under pressure conditions ( r  = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed ( r  = -0.35, -0.50), and temperature also exhibits a negative correlation ( r  = -0.39, -0.54). During atmospheric conditions with high pressure, PM 10 and SO 2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

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Data availability

The data used in this study are available from the following sources:

HYSPLIT data can be accessed at the National Oceanic and Atmospheric Administration (NOAA) website: https://www.ready.noaa.gov/HYSPLIT.php . These data were utilized to analyze atmospheric dispersion patterns and trajectories.

Air pollutants data can be gathered from the website of the General Directorate of Environmental Management of the Republic of Türkiye: https://www.havaizleme.gov.tr . These data were employed to assess the levels and distribution of air pollutants.

Meteorological data utilized in this study were licensed to us by the Turkish State Meteorological Service. As such, the meteorological data cannot be shared openly or accessed directly from a public source.

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Acknowledgements

The authors extend their heartfelt appreciation to the Turkish State Meteorological Service for their generous provision of essential meteorological data. Gratitude is also expressed to the Ministry of Environment, Urbanisation, and Climate Change for their invaluable contribution of pollutant observation data. Special thanks are extended to Thanks to Abdulgani Adıyaman, Deputy Director of the Branch Responsible for Environmental Management and Inspection, for their support and collaboration, which greatly enriched this study. Additionally, the authors sincerely acknowledge and thank the reviewers for their constructive feedback, contributing significantly to the enhancement of the study's significance and quality.

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Yiğitalp KARA contributed to the study design, data analysis, interpretation of results, and co-wrote the manuscript, Sena Ecem YAKUT ŞEVİK contributed to data collection, literature review, interpretation of results, and participated in manuscript writing and editing, while Hüseyin TOROS provided guidance, supervision, expertise, and valuable feedback throughout the research process as the supervisor.

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Kara, Y., Şevik, S.E.Y. & Toros, H. Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province. Environ Monit Assess 196 , 525 (2024). https://doi.org/10.1007/s10661-024-12649-4

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• Published: 30 June 2022

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland

• Tomasz Danek 1   na1 ,
• Elzbieta Weglinska 1   na1 &
• Mateusz Zareba 1   na1  

Scientific Reports volume  12 , Article number:  11050 ( 2022 ) Cite this article

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Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran’s I spatial autocorrelation analysis, and Getis–Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow’s unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors’ influence on air pollution inflow and outflow from the city.

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Spatio-temporal variation of noise pollution in South Paris during and outside the COVID-19 lockdowns

Introduction.

Air pollution has an impact on human health 1 . It has been proven that elevated concentrations of PM1, PM2.5 and PM10 may contribute to the development of diseases such as lung cancer 2 , asthma 3 , pneumonia 4 , high blood pressure 5 , Alzheimer’s and Parkinson’s disease 6 . 7% of global deaths are caused by overexposure to air pollution 7 . It is estimated that air pollution in Poland shortens life expectancy by almost 3 years, which is more than the European Union (EU) average 8 . To protect citizens from overexposure, the EU issued directive 2008/50/EC on ambient air quality and cleaner air for Europe (AAQD) 9 . Member States, including Poland, should adjust their laws to EU regulations. The air quality standards in Poland (in line with EU standards) are 40 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) (1-year averaged) and 50 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) (24-h averaged) for PM10 and 25 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) for PM2.5 (1-year averaged). Reference measurements can be divided into gravimetric manual measurements (norm PN-EN 12341) and automatic measurements (norm PN-EN 16450). The advantages of these measurements are their high accuracy and low uncertainties, but they are very expensive and characterized by very low spatial density (only 10 stations in the almost 15,000 \(\mathrm{km}^{2}\) area around Krakow). There are also low-cost sensors (LCS) that are less accurate than reference measuring stations, have greater uncertainties, and are significantly impacted by external meteorological conditions. In contrast, LCS are characterized by a very dense spatial network 10 , which allows them to be used for advanced spatial analyses after proper data preparation. Bulot et al. 11 confirmed that they can be applied in spatial studies in urban areas. In this study, Airly LCS were used. Their measurement correctness was very high in the examined period and was close to the reference measurements 12 . LCS uncertainties are higher than gravimetric measurements. It is also not easy to calculate them, as measurements based on light scattering can be affected by many meteorological factors 13 .

Krakow is a city with a long history of air pollution problems and has a significant history in the fight to reduce it. Kobus et al. 14 indicated the importance of providing air quality information to cities’ residents to help make them aware of this problem. Danek and Zareba 12 presented similar conclusions on the basis of an analysis of long-term trends and seasonality of PM10 indications in Krakow. In particular, they showed the effectiveness of social and informational campaigns, but also specific legal actions. The main sources of pollution have changed over the years. In the early 1970s, the metallurgy industry was the main source. As the city’s population grew, the share of fossil fuel heating as a source of pollution began to increase 15 and it is now the dominant source in the winter months 16 . Surprisingly there is a total ban on solid fuel use for heating in Krakow, so the main sources of pollution are located outside the city. The official government research on PM10 composition showed that the carbon fraction has a 50% share, secondary aerosols (inorganic) have a 20% share, 10% is related to remaining ions, and the metal fraction is no more than 4%. Isotopic studies have proved that the burning of coal causes the greatest impact on the carbon fraction, but this changes depending on the time of year. In the cold period (late autumn, winter, and early spring), the main source of the carbon fraction is solid fuel heating, while in the warm period (late spring, summer, and early autumn) this is only about 20%. The second main factor is car transportation, which also varies depending on the time of year. Its concentration in the annual distribution is inversely proportional to the share of the fraction coming from solid fuel heating, which varies from 11% in winter up to 42% in summer. Natural emissions have a 30% share in the carbon fraction, and this remains constant throughout the year 17 .

The current air pollution problem in Krakow is related to this city’s geographical location 18 , but there are no detailed studies regarding this factor in combination with meteorological variables and pollutant source differentiation. This city is situated in a valley that is crossed latitudinally by the Vistula river valley. The specific morphology of the Krakow area makes vertical and horizontal natural air ventilation very difficult 19 . The Vistula river enters the city from the west, where the Oswiecimska basin and Krakow Gate are situated. It is part of fault-block hills. The river leaves the city from the east (lowland Sandomierz basin). The north upheaval is related to the occurrence of Jurassic limestones (known as the Polish Jurassic Highland). The southern upheaval is part of the Wielickie foothills and consists mostly of limestone 20 . The Tatra Mountains and the Carpathian inner-mountain basin are less than 100 km in a straight line to the south of the city, which causes the occurrence of strong, warm halny (foehn-type) winds in Krakow 21 . The air pollution problem in Krakow is critical. Despite many regulations prohibiting the use of fossil fuels for heating, pollutants still migrate to the city from external locations, making it one of the most polluted cities in the world 22 . The research shows the indisputable influence of meteorological factors on PM concentrations in the air. The impact of these factors on air pollution and its prediction 23 varies according to many characteristic local climate variables and human activity and energy consumption 24 . Depending on the studied area, the dominant factors vary, e.g., temperature in the USA 25 , humidity with temperature in Bangladesh 26 , and air pressure in China 27 . Depending on elevation, atmospheric properties vary and can also influence PM concentrations and long-distance pollution migration 28 . The COVID-19 pandemic period provided unique conditions for geospatial observations 29 . In this case, the effects of solid fuel heating on PM concentrations with very limited background noise caused by car transportation were investigated. The typical approach that is based on reference sensors does not provide sufficient density for quantitative analyses of the influence of meteorological factors and the influence of topography. There is also no unambiguous indicator determining the origin of pollutants that can be used in time and space directly from concentration measurements without the need to perform complex radiometric analyses. This research allows this gap to be filled. The aim of this work is detailed investigation of the influence of meteorological factors and morphology on air pollution in Krakow and the migration of these pollutants using geostatistical methods, including standardized Geographically Weighted Regression (GWR), local Moran’s I spatial autocorrelation analysis, and Getis–Ord Gi* statistic for hot-spot detection. Each of the geostatistical methods used has some limitations, so multiple methods were integrated to minimize these uncertainties. Kernel density estimate (KDE) maps with box and swarm plots were analyzed to determine patterns of meteorological factors and facilitate the distribution study.

It was specifically hypothesized that it is possible (1) to investigate how the influence of meteorological factors on pollution concentration changes spatially; (2) to quantify the temporal variability of the influence of these factors; (3) to connect these changes with topography; (4) to track the sources of pollutants from solid fuel heating. The conclusions are extended with an analysis of the results based on the topography of the research area and the analysis of pollution sources using the PM2.5/PM10 ratio. This indicator was chosen because research shows that it is a good indicator of whether PM pollution is anthropogenic-related 30 or not 31 . The presented research is unique because it uses accurate, high-resolution, short-time measurements, sampled in a regular grid in a very specific area. Most of the studies conducted so far try to show the dominant meteorological factor based on many years of measurements at a single point or a few points. In this study, the impact of all factors at many points in a short period was analyzed. In the examined period, the high variability of parameters that are indicated in the literature as dominant (temperature, pressure) is not expected. Thanks to the use of a high-resolution terrain model, it was possible to accurately determine the impact of topography on the migration of pollutants in a relatively small area of complicated morphology. This is not possible when sparse or one-point observation is conducted. Until now, this has been difficult due to the lack of a dense network of sensors or the low resolution of satellite air pollution analyses (especially in the case of large relative elevation changes within a short distance). The impact of background noise was significantly reduced due to limited car traffic during the COVID-19 pandemic. An unusual approach to the analysis of the PM2.5/PM10 ratio is also presented to distinguish different anthropogenic dust sources (typically used to analyze the origin of PMs from natural and anthropogenic sources). In a spatio-temporal sense, this is one of the most detailed studies conducted so far on air pollutants generated by solid fuel heating.

Data source and validation

1-hour averaged measurements from 90 LCS stations located in Krakow and its surroundings were used. Figure  1 shows the locations of these stations and the digital terrain model map (source: European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA)). Sensors were divided into 5 groups (due to their geographic location):

K group—sensors located in Krakow urban area (Table 1 );

NW group—sensors located in the north-west section outside the Krakow urban area (Table 2 );

SW group—sensors located in the south-west section outside the Krakow urban area (Table 3 );

NE group—sensors located in the north-east section outside the Krakow urban area (Table 4 );

SE group—sensors located in the south-east section outside the Krakow urban area (Table 5 ).

Krakow topography map (digital terrain model) with Airly sensor locations and their IDs (white rectangle), together with the borders of Krakow districts (grey lines and the main rivers (blue lines). Digital terrain model source: European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

The data comes from the Airly sensors network ( https://map.airly.org/ ) and were downloaded using free data access from the Airly API. Sensors used in this study are optical-type detectors that use the light scattering phenomena for PM measurement, but this can be influenced by many factors 32 . The measured quantities from the Airly API are already verified and calibrated with the use of machine learning algorithms and indications from reference stations. It should be borne in mind that despite the proven high accuracy of Airly LCS measurements, it is important to compare them with reference measurements as these are characterized by the lowest measurement uncertainties and the highest accuracy of indications 13 . Validation of the indications of these sensors (including the intervals studied in this paper) has been the subject of many studies 12 . Research shows that the Plantower 5003 sensor (used by Airly) provides a measurement accuracy that may be lower at high relative humidity. Despite the greater accuracy of LCS measurements for PM10, the indications of this parameter may differ significantly from the reference indications (this is not the case for PM2.5). This is related to the dominant particle-type changes within the PM10 fraction. The greatest discrepancies occur for sensors affected by street dust resuspension and in areas where building construction and demolition are occurring. In this study, the COVID-19 pandemic and partial lockdown significantly reduced the impact of these factors 33 .

Bartyzel et al. 34 performed an analysis of the compliance of Airly sensors with a reference station according to the PN-EN 16450:2017-05 standard. These authors showed that proper calibration significantly reduces measurement uncertainty and that the accuracy increases with the average daily concentration. Adamiec et al. 35 showed that thanks to the use of dedicated calibration techniques and appropriate validation of manufacturers’ indications, Airly sensors can complement reference measurements. Research carried out in Krakow with the use of these sensors also shows their high accuracy and compliance with the indications of a reference station, both on windless days 12 and during periods with strong winds 22 .

For the analysis, in order to focus on solid fuel heating-related pollution, we wanted to select days during the COVID-19 pandemic period when car traffic density was up to 40% lower compared to previous years. The second motivation was the selection of days on which the wind was relatively light to limit the effect of wind as a factor in the models. These requirements were met on March 11th and 18th, 2021. On March 11th, analyzing the hours of 00:00, 04:00, and 08:00, the process of pollutants moving away from the city is visible, while on March 18th at 12:00, 18:00, and 24:00, an inflow of pollutants can be observed (compare Danek and Zareba’s study 12 ).

Geostatistical methods

Statistical analyses were performed with the use of ArcGIS Pro 36 and Python 37 (including libraries such as seaborn 38 and scikit-learn 39 ). GWR, Moran’s I spatial autocorrelation analysis, and Getis–Ord \(G_{i}^{*}\) hot-spot detection were performed in ArcGIS Pro software 36 for all sensors presented in Tables 1 , 2 , 3 , 4 and 5 . To be able to assess the importance of individual meteorological factors, the data were standardized before performing GWR 40 using z-score according to Eq. ( 1 ):

where x is original sample value, \(\mu\) is simple mean of all observations, \(\sigma\) is standard deviation.

The multivariate kernel density estimations were calculated for each meteorological factor to study the average relationship between them and the PM concentrations. Analyses of descriptive statistics and patterns were also performed. Exploratory data analysis (EDA) includes box and swarm plots. Box plots provide very similar information to KDEs but in a simplified form, while swarm plots help in understanding data structure.

Geographically Weighted Regression

Geographically weighted regression (GWR) 41 is a local form of regression that is used to model spatially varying relationships. In ArcGIS Pro software 36 , this function is realized using a Geographically Weighted Regression tool that provides three types of regression models: continuous (Gaussian), binary (Logistic) and count (Poisson). The type of model to analyze should be chosen based on how the dependent variable is measured. The next important assumption to make is choosing the neighborhood type and the neighbor selection method. In the GWR method, N local linear regression equations are calculated using a certain distance-based weighting scheme. To determine the area from which the model should investigate spatial variation, crossvalidation is performed 42 .

The GWR model can be expressed by Eq. ( 2 ):

for \(i=1,\ldots ,N\) and \(j=0,\ldots ,M\) , where \((u_{i},v_{i})\) gives the point of the coordinates’ region i , \(y_{i}\) is the dependent variable, \(\beta _{j}\) are regression coefficients, \(x_{ij}\) is the j th variable at observation i , and \(\varepsilon _{i}\) is the residual variable. Parameter estimation of local regression models is performed using explanatory variables derived mainly from neighboring observations. GWR detects spatial variation in model dependencies, which allows the creation of maps to explore spatial nonstationarity 41 . The GWR method has an advantage over other regression methods (e.g., Ordinary Least Square) because it does not assume a constant variance and allows for more accurate analysis when nonstationarity is present 43 . The limitations of this method are considered to be multicollinearity in local coefficients 44 and computationally demanding cross-validation for large datasets 42 . Despite some limitations, GWR is a valuable technique for studying spatial nonstationarity 45 .

Local Moran’s I

Local Moran’s I is a local spatial autocorrelation statistic proposed by Anselin 46 as a way to identify local clusters and outliers. In ArcGIS Pro software 36 , this function is realized by the Cluster and Outlier Analysis (Anselin Local Moran’s I ) tool. The local Moran’s I is given as Eq. ( 3 ):

where \(x_{i}\) is the value of the attribute x at location i, \(\bar{X}\) is the mean of the attribute at each of n points, \(x_{j}\) is the attribute value at all other locations ( \(j\ne i\) ), and \(S_{i}^{2}\) is the variance of the variable x . The matrix of weights \(w_{ij}\) defines the distance between objects. The matrix was calculated using the inverse distance method.

A positive index I value indicates that the study location has similar high or low values to its neighbors. This tool allows for the determination of high-high clusters (high values in a high-value neighborhood) and low-low clusters (low values in a low-value neighborhood). A negative index I value indicates that the study location is a spatial outlier. This tool determines high-low (a high value in a low-value neighborhood) and low-high (a low value in a high-value neighborhood) outliers. The obvious advantage of Moran’s method is its simplicity, but like any statistical method it has some limitations. The results of using local Moran’s I statistic to detect PM hot-spots are affected by the choice of the weight matrix. There is no general rule regarding when the different types of weights should be used 47

Getis–Ord \(G_{i}^{*}\)

The Getis–Ord \(G_{i}^{*}\) local statistic 48 allows detection of local concentrations of high and low values in neighboring sites, and it tests the statistical significance of this relationship. This statistic can identify hot-spots (clusters of high attribute levels) and cold-spots (clusters of low attribute levels) with varying levels of significance. In ArcGIS Pro software 36 , this function is realized by Getis–Ord \(G_{i}^{*}\) (High/Low Clustering). The Getis–Ord local statistic is given as Eq. ( 4 ):

High values of the \(G_{i}^{*}\) index indicate objects with high PM concentration values, while low values indicate objects with low values. When the values are close to the expected value, the distribution of the analyzed attribute is random in space. \(G_{i}^{*}\) statistics, similar to Moran’s technique, requires establishing a conceptualization of spatial relationships, which can result in ambiguous solutions. The Getis–Ord \(G_{i}^{*}\) method is widely used and favored over other available statistics (e.g., \(G_{i}\) 48 , Geary’s C 49 , Moran’s I ). One of the many advantages of the method mentioned by 50 is the possibility to identify areas with increased values of the examined parameter, even if the values do not differ from the global average.

Exploratory data analysis

Figures  2 and 3 show the multivariate kernel density estimations of PM10 and different meteorological factors on 11th and 18th March. It is clearly visible that higher PM10 values are related to lower temperatures. PM10 concentration above 50 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) occurred when the temperature dropped below \(0\,^{\circ }\mathrm{C}\) degrees. A rapid PM10 increase is observed for the range of − 3 to −  \(5\,^{\circ }\mathrm{C}\) degrees (up to 150 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) ). It is important to note that on 18th March the temperature (Fig. 3 a) was above \(0\,^{\circ }\mathrm{C}\) degrees for some time, and the PM10 concentration was low—about 25 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) for the temperature range 2–4 \(^{\circ }\mathrm{C}\) .

In the case of relative humidity, it is clearly noticeable that values above 80% are associated with higher PM10 values. On March 18th (Fig. 3 b), when the relative humidity was in the 60–70% range, the PM10 values were significantly lower than those of the 80% range. On March 11th (Fig. 2 b), the humidity remained at the level of 75–85% and was positively correlated with increased concentrations of PM10. Atmospheric pressure does not display an unequivocal relationship. On March 11th (Fig. 2 c) it was also possible to observe an increase in pollutants along with an increase in atmospheric pressure from 1016 to 1022 hPa, while on March 18th (Fig. 3 c) this relationship was reversed. For the value of 1017–1020 hPa, PM10 pollution was negligible, but when the pressure dropped to 1014 hPa, an increase in air pollution was visible. The wind speed shows the expected trend. As wind speed decreases, air pollution increases. The winds on March 11th and 18th were relatively weak. On March 11th (Fig. 2 d), the average speed was 2 m/s, while on 18th March (Fig. 3 d) the two dominant speed values were 1 m/s and 4 m/s. Wind speed as low as 4 m/s is associated with nearly zero PM10 values. As for the wind azimuth, on March 11th (Fig. 2 e), NW, NNW, and N winds prevailed. The highest concentration is observed for around \(310\,^ {\circ }\) . On March 18th (Fig. 3 e), the main wind azimuths are SSW, S and SSE. The lowest concentration values are observed for the SE wind azimuth.

The Fig. 4 a–f show swarm and box plots for individual meteorological factors and PM10 indicators on March 11th at midnight, 4, and 8 am. The temperature (Fig. 4 a) distribution changes with time. At midnight, a one-modal distribution is visible, while for 08:00 a three-modal distribution is present, with one mode being close to maximum. The most compact and symmetrical distribution is for midnight with the median at around \(-\,3\,^{\circ }\mathrm{C}\) degrees, for 04:00 the median ( \(-\,4.5\,^{\circ }\mathrm{C}\) degrees) is closer to the 1st quartile. The most asymmetric distribution is for hour 08:00. The Median is around \(-\,2\,^{\circ }\mathrm{C}\) degrees, but there is a very large percentage of indications where the temperature was above zero. The distributions for humidity (Fig. 4 b) are asymmetrical for all hours and are multi-modal. The Median changes from 80% at midnight to 83% at 04:00 and then decreases to 70% at 08:00, The pressure (Fig. 4 c) is characterized by the symmetrical and constant nature of the distribution, despite the clearly decreasing trend with successive hours. The width of the boxes and the location of the median at their centers are similar. Wind azimuth and speed distributions (Fig. 4 d,e) are asymmetric. Most of the outliers are located near the maximum values, and the median is close to the first quartile. The distribution for box 3 in Fig. 4 d is different. In roughly half of the boxes there is a clear multi-modal pattern with a wide box. The distributions of PM10 concentrations (Fig. 4 f) are interesting. In the following hours, the width of the boxes decreases significantly. At 08:00, a very compact, basically one-modal distribution is visible with values strongly clustered around the median that amount to just over 50 \(\upmu \mathrm{g}/\mathrm{m}^{3}\) .

Figure  5 a–f show swarm and box plots for individual meteorological factors and PM10 indicators on March 18th at 12:00, 18:00, and 24:00. The temperature distribution is three-modal for 12:00 and 18:00, and two-modal for 24:00 (Fig. 5 a). The most compact and symmetrical distribution is for midnight, with the median around \(-2\,^{\circ }\mathrm{C}\) ; for 12:00, the median ( \(3\,^{\circ }\mathrm{C}\) ) is closer to the 1st quartile. In general, the temperature decreases with time. The distributions for humidity (Fig. 5 b) are asymmetrical for all hours and are multi-modal (similar to those from 11th March). The Median changes from 67% at 12:00 to 63% at 18:00, and then increases up to 80% at midnight. The pressure (Fig. 5 c) is characterized by quite symmetrical distributions. In contrast to 11th March, the boxes are wide and the distributions are not so compact. The distributions for wind azimuth and speed do not show a trend. It can be noticed that the wind azimuth (Fig. 5 d) at 12:00 and 18:00 was the same for almost all observation points, while at midnight an extremely wide box with a multi-modal pattern is present. The highest values for wind speed (Fig. 5 e) are observed for 12:00, with a compact and symmetrical distribution. For hours 18:00 and 24:00, the distributions are still compact but asymmetry is visible. At 18:00, the median is closer to the 3rd quartile with a long 1st whisker, while at midnight it is the opposite. The distribution of PM10 concentration (Fig. 5 f) at 12:00 is very compact with an extremely narrow box. This situation changes over time: the distributions at each subsequent hour become less consistent with the observed shifts of the medians towards higher concentrations and with numerous outliers towards the maximum values.

Multivariate kernel density estimations of PM10 and temperature ( a ), humidity ( b ), pressure ( c ), wind speed ( d ), wind azimuth ( e ) on the 11th of March.

Multivariate kernel density estimations of PM10 and temperature ( a ), humidity ( b ), pressure ( c ), wind speed ( d ), wind azimuth ( e ) on the 18th of March.

Box and swarm plots for temperature ( a ), humidity ( b ), pressure ( c ), wind azimuth ( d ), wind speed ( e ), and PM10 concentration ( f ) on the 11th of March.

Box and swarm plots for temperature ( a ), humidity ( b ), pressure ( c ), wind azimuth ( d ), wind speed ( e ), and PM10 concentration ( f ) on the 18th of March.

Spatial autocorrelation

To estimate the spatial autocorrelation for PM2.5 and PM10 indicators at all sensors, the local Moran’s I and Getis–Ord \(Gi^{*}\) were calculated. Statistical significance was assumed at the 95 percent confidence level. The inverse distance method was used for the conceptualization of the spatial relationship.

Figure  6 a–c show local Moran’s I cluster maps for PM2.5 concentration on March 11th at 0:00, 4:00, and 8:00; Fig. 6 d–f show local Moran’s I cluster maps for PM2.5 concentration on March 18th, at 12:00, 18:00, and 24:00. The local Moran’s II analyses identified areas of positive autocorrelation (high-high and low-low clusters) as well as areas of negative autocorrelation (high-low and low-high outliers). For PM2.5 concentration, high-high clusters were identified on March 11th at 0:00 in the north-east section (Slomniki, Waganowice, Smrokow), at 4:00 in Krakow (Wzgorza Krzeslawickie, Nowa Huta) and in the north-east (Pietrzejowice and Proszowice). At 8:00 that same day, high-high clusters of PM2.5 were identified in the south-west section (Brzeznica, Stanislaw Dolny, Brody and Zarzyce Male). On March 11th, low-low clusters were identified at 0:00 at one sensor in the S-W section (Harbutowice) and in the north-west section (Czubrowice, Gotkowice, Skala, Bedkowice, Bialy Kosciol, Wieckowice and Tomaszowice), at 4:00 south of Krakow (Wiatowice, Harbutowice, Czaslaw, Wisniowa) and at the same sensors from the N-W section for 0:00, at 8:00 south of Krakow (Rzeszotary, Raciborsko, Wiatowice, Czechowka, Winiary, Zakliczyn, Czaslaw, Kwapinka, Myslenice, Myslenice II, Trzemesnia and Wisniowa) and at three sensors located in the north-west part (Czubrowice, Gotkowice, Bedkowice). Sensors which detected anomalously high PM2.5 concentrations were Swoszowice and Nawojowa Gora at 0:00, and Krzywaczka at 4:00. Areas with anomalously low PM2.5 values in relation to neighboring areas were identified at 0:00 and 4:00 in Prandocin and Luborzyca II. At 8:00, a low-high outlier was identified only in Prandocin.

On March 18th, high-high clusters were recognized at 12:00 in the N–W (Golyszyn, Grzegorzowice Wielkie, Wilczkowice) and N–E (Skala, Przybyslawice, Iwanowice, Zagorzyce Stare,Luborzyca, Luborzyca II, Pietrzejowice, Prawda) sections, at 18:00 in Gotkowice, Proszowice and Myslenice, at 24:00 in Jeziorzany and Zarzyce Male. Low-low clusters of PM2.5 concentration were recognized at 12:00 in Krakow (Pradnik Bialy, Zwierzyniec, Debniki, Swoszowice, Podgorze Duchackie) and west of Krakow (Szczyglice, Aleksandrowice, Liszki, Czernichow, Jeziorzany, Rzozow, Skawina, Mogilany, Radziszow, Krzywaczka, Zarzyce Male, Harbutowice and Brody), at 18:00 in Krakow (Pradnik Czerwony, Wzgorza Krzeslawickie, Podgorze, Podgorze Duchackie, Swoszowice and Debniki), at 24:00 in Krakow (Swoszowice, Wzgorza Krzeslawickie and Nowa Huta) and in sensors north of Krakow (Tropiszow, Karniow, Pietrzejowice, Luborzyca, Prawda, Zagorzyce Stare, Wilczkowice and Tomaszowice). PM2.5 indications with increased values relative to neighboring sensors were detected by the sensors at Tenczynek (at 12:00), in Szczyglice and Wieliczka (at 18:00), and in Wieckowice at 24:00. Low-high outliers for PM2.5 concentration were detected at 12:00 in Brzozowka and Prandocin, and at 24:00 in Stanislaw Dolny, Radziszow and Skawina.

Figure  7 a–c show local Moran’s I cluster maps for PM10 concentration on March 11th at 0:00, 4:00, and 8:00. Figure  7 d–f show local Moran’s I cluster maps for PM10 concentration on March 18th at 12:00, 18:00, and 24:00. This tool allowed the selection of sensors that had high PM10 values against which there were also high PM10 indications. High-high clusters of PM10 indications were identified on March 11th at 0:00 at the same sensors as for PM2.5. On the same day at 4:00, high-value clusters of PM10 were determined in Wzgorza Krzeslawickie, Pietrzejowice, and Proszowice. On March 11th at 8:00, high-high clusters were identified in the south-west section (Brzeznica, Stanislaw Dolny, Brody, Zarzyce Male) and at one sensor to the north of Krakow (Prandocin). Local Moran’s I statistic made it possible to determine clusters of low values on March 11th at 0:00 at sensors located to the north-west of Krakow (Czubrowice, Gotkowice, Skala, Bedkowice, Bialy Kosciol, Wieckowice, Tomaszowice). Spatial autocorrelation analysis of PM10 at 4:00 made it possible to identify clusters including the same sensors as at 0:00, except Tomaszowice, and also to identify low-low clusters in Harbutowice, Czaslaw, and Wisniowa. On the same day at 8:00, clusters of low values of PM10 were identified southeast of Krakow (Raciborsko, Wiatowice, Winiary, Czechowka, Zakliczyn, Czaslaw, Kwapinka, Trzemesnia and Wisniowa), in the south-west section (Rzeszotary, Myslenice, Myslenice II), and northwest of Krakow (Czubrowice, Gotkowice, Bedkowice). High-low outliers were detected on March 11th only at 0:00 (Nawojowa Gora) and at 4:00 (Krzywaczka). Low-high outliers were identified at 0:00 (Prandocin and Luborzyca II), at 4:00 (Prandocin, Luborzyca II and Nowa Huta), at 8:00 in Iwanowice.

On March 18th high-high clusters were recognized at 12:00 in the north-east section (Smrokow, Iwanowice, Zagorzyce Stare, Prawda, Luborzyca II and Pietrzejowice) as well as in the north-west (Golyszyn, Grzegorzowice Wielkie, Skala, Przybyslawice), at 18:00 in Gotkowice, and at 24:00 in Jeziorzany and Zarzyce Male. Low-low clusters were identified at 12:00 in Krakow (Pradnik Bialy, Zwierzyniec, Debniki,Swoszowice, Podgorze Duchackie), in the N-W (Szczyglice, Aleksandrowice, Liszki), and in the S–W (Jeziorzany, Czernichow, Rzozow, Skawina, Radziszow, Mogilany, Brody, Zarzyce Male, Krzywaczka and Harbutowice) sections. At 18:00, low-low clusters of PM10 were detected in Krakow (Pradnik Czerwony, Wzgorza Krzeslawickie, Podgorze, Debniki, Swoszowice and Podgorze Duchackie). At 24:00, clusters of low values were identified in Krakow (Swoszowice, Wzgorza Krzeslawickie, Nowa Huta) and north of Krakow (Tropiszow, Karniow, Pietrzejowice, Luborzyca, Prawda, Zagorzyce Stare and Tomaszowice). High-low outliers were recognized at 12 in Tenczynek, at 18:00 in Szczyglice and Wieliczka, at 24:00 in Wieckowice and Niepolomice. Low-high outliers of PM10 were recognized at 12 in Brzozowka and Prandocin, at 18:00 in Myslenice, at 24:00 in Skawina, Radziszow, Stanislaw Dolny.

Local Moran’s I cluster maps showing high-high, low-low, low-high, and high-low spatial associations for PM2.5 concentration on March 11th and March 18th.

Local Moran’s I cluster maps showing high-high, low-low, low-high, and high-low spatial associations for PM10 concentration on March 11th and March 18th.

Figure 8 a–c show hot-spot and cold-spot maps for PM2.5 concentration using Getis–Ord \(G_{i}^{*}\) with significance on March 11th at 0:00, 4:00, and 8:00 and Fig. 8 d–f on March 18th at 12:00, 18:00, and 24:00. These local statistics did not determine any cold-spots of PM2.5 with 90, 95 or 99% confidence levels. On March 11th at 0:00, hot-spots were identified with 99% confidence in Slomniki, Waganowice, Cikowice, with 95% in Nowe Brzesko, and with 90% in Tropiszow and Rzozow. Four hours later, values with increased concentration of PM2.5 were recorded in Nowe Brzesko (99% confidence), in Slomniki, Waganowice, Luborzyca, Karniow and Cikowice (95%), and in Smrokow (90%). At 8:00, hot-spots were identified at fewer sensors: in Brody (with 99% confidence), in Smrokow (95%), and in Stanislaw Dolny (90%). On March 18th at 12:00, hot-spots of PM2.5 were determined at 4 sensors located north of Krakow: Tenczynek, Skala, Zagorzyce Stare (99%), and in Proszowice (90%). The use of local Getis–Ord \(G_{i}^{*}\) statistics enabled the determination of hot-spots with 99% confidence in Myslenice and Wisniowa, with 95% in Rzozow and Czubrowice, and with 90% in Nowe Brzesko. On March 18th at 24:00, hot-spots were identified in Rzozow (99%), in Krzywaczka (95%), and in Brody and Waganowice (90%). Figure 9 a–c show hot-spot and cold- spot maps for PM10 concentration using Getis–Ord \(G_{i}^{*}\) with significance on March 11th at 0:00, 4:00, and 8:00 and Fig. 9 d–f on March 18th at 12:00, 18:00, and 24:00. This tool did not determine any cold-spots of PM10 with 90, 95 or 99% confidence level, as in the case of PM2.5. On March 11th at 0:00, hot-spots of PM10 were found with 99% confidence in Slomniki, Waganowice and Cikowice, with 95% in Rzozow, and with 90% in Nowe Brzesko. At 4:00, hot-spots were identified in Slomniki, Nowe Brzesko and Podgorze (99%), in Waganowice, Luborzyca, Karniow, Cikowice (95%), and in Smrokow (90%). Four hours later, only a few hot-spots were found : in Brody (99%), in Smrokow, Podgorze (95%), in Slomniki and Stanislaw Dolny (90%). On March 18th at 12:00, hot-spots were identified at 3 sensors north of Krakow : in Tenczynek, Skala, and Zagorzyce Stare (99%). At 18:00, hot-spots of PM10 were identified south of Krakow in Rzozow, Myslenice II, Wisniowa (99%), and northwest of Krakow in Czubrowice and Szczyglice (95%). That same day at 24:00, one hot-spot in Rzozow (99%), one hot-spot in Brody (95%), and three hot-spots in Skala, Waganowice and Krzywaczka (90%) were identified.

Hot-spots and cold-spots maps for PM2.5 concentration on March 11th and March 18th using Getis–Ord \(G_{i}^{*}\) .

Hot-spots and cold-spots maps for PM10 con centration on March 11th and March 18th using Getis–Ord \(G_{i}^{*}\) .

Geographically weighted regression

GWR of PM10 was performed on standardized data to assess the importance of individual meteorological factors. Figures 10 and 11 show GWR coefficients for different meteorological factors on the 11th and 18th of March: temperature, pressure, humidity, wind speed and wind azimuth. Tables 6 and 7 show the summary of the GWR models for 11th and 18th of March. The minimum and maximum values of GWR coefficients and standard errors for each factor as well as local R \(^{2}\) is shown. The analysis shows that the factors which influenced PM10 concentration the most on 11th of March were temperature and wind speed and direction. The influence of temperature was most noticeable at 0:00, when the coefficient changed in the north-west-southeast directions in zones. In each of the three analyzed hours, the pressure and humidity effects were the least significant. Wind speed was most significant at 0:00 and 8:00. Wind azimuth had a very significant effect on PM10 values at each of the 3 h. At midnight, the highest positive coefficient values occurred northwest of Krakow. At 4:00, two zones of influence of wind azimuth on PM10 readings could be distinguished : low significance in the western part and high significance in the eastern part of the investigated area.

When the coefficients of the standardized GWR model on PM10 concentrations on March 18th are analyzed, a greater variation of values in the study area can be observed. The influence of these coefficients is also characterized by greater variability between observation hours. The influence of temperature was most significant at 12:00, when the highest positive values of the standardized coefficient occurred northeast of Krakow and the lowest negative values were northwest of Krakow. At 18:00, the influence of temperature was constant in the area, and at 24:00 the coefficient was negative and constant except for Nowe Brzesko and Proszowice in the north-east section, where the value of the coefficient was around 0. The pressure coefficients at 18:00 and 24:00 were constant in the whole area, whereas at 18:00 it was about 0, and at midnight it was high and positive (0.4). The greatest variation in pressure influence occurred at 12:00, with the largest positive values in the northeast and south, the most negative values in the west, and around zero in other regions. Humidity did not have a large effect on PM10 readings at 18:00 and 24:00, while it had a large effect on readings at 12:00. In the northeast, northwest, and south, the coefficient values were negative. At the easternmost and westernmost points, the values were positive, while in the rest of the area the coefficient was around 0. Wind speed and direction were significant in the model. At 12:00, the wind speed coefficients were the largest and positive in the east and smallest and negative in the west. At 18:00, the coefficient was negative across the area and had less spatial variation, while at 24:00 in the eastern part it was about 0.1, and in the rest of the region it was about 0. The wind azimuth coefficients at 12:00 show 2 anomalous areas of increased value. They cover places where sensors are located in Zreczyce, Jaroszowka, Winiary, Wiatowice and Slomniki, Prandocin. At 18:00, the values at the coefficient are zoned from positive values in the southwest to negative values in the northeast. At 24:00, there is the least spatial variation in the wind azimuth influence on the PM10 value.

Figures 12 and 13 show PM10 and PM2.5 GWR models with their ratios on the 11th and 18th of March. Previous research shows that PM10 and PM2.5 ratios are a good indicator of whether PM pollution is natural 51 (mineral) or of anthropogenic origin 31 . The PM2.5/PM10 ratio can also be a useful tool for characterization of local atmospheric processes 30 . For both analyzed days, the ratios’ values are higher and start from 0.75. This allows the conclusion that, in the analyzed hours, the pollution in Krakow is of anthropogenic origin. The relative difference in individual hours seems to be interesting. Receivers located at a significant elevations in relation to the vicinity (Rzeszotary - SW12, Mogilany - SW13, Raciborsko - SE13) are characterized by almost constant ratio values (11th March - 0.75, 18th March - 0.8). The absolute values of PM10 and PM2.5 are also significantly lower there than at other LCS. The significantly higher values of the coefficients in the hours when pollutants are produced from combustion and during their migration coincide with the course of the main river valleys. PM outflow from Krakow on 11th March in the GWR models show the latitudinal system and the transport route of pollutants. PMs accumulate in the natural depression of the Vistula river valley, in which the city of Krakow is situated. Their outflow from the city is blocked from the north by the slope of the Ojcow and Krzeszowice plateaus, and from the south by the hills of the Krakow Upland and the Wieliczka Foothills. On March 18th, it is visible how the pollution bypasses the hills in the south of Krakow and is transported to the city through the valleys from the southwest.

GWR coefficients for different meteorological factors on the 11th of March (outflow).

GWR coefficients for different meteorological factors on the 18th of March (inflow).

PM10 and PM2.5 GWR models with their ratio on the 11th of March (inflow).

PM10 and PM2.5 GWR models with their ratio on the 18th of March (outflow).

EDA (Figs. 2 , 3 , 4 and 5 ) showed that the influence of particular meteorological factors on PM measurements is slightly different on 11th and 18th March. There are also some similarities. It is clearly visible that temperature had a direct impact on PM concentrations, especially the relative temperature perception below \(0\,^{\circ }\mathrm{C}\) degrees. This is in line with the results of long-term analyses in the US 52 and Poland 53 . This is also true for short-term temperature anomalies 12 . When people start feeling relative cold, the rapid emission of PMs from fossil fuel heating can be noticed. It is important to note that the humidity above 70% on both days is related to higher pollution concentrations. This local dependence may be related to the tendency for mists to form in Krakow during this period, which keeps pollution at the surface. Multi-annual research conducted in China shows similar conclusions: in urban areas increased pollution is related to fog 54 . Long-term observation of this factor for PM concentrations is recommended, including local climate of Krakow with warm and cold seasons.

The ambiguous correlation for pressure does not allow for a clear statement of whether its change has a positive or negative effect. Some studies show that this is the dominant factor 27 . In the short term, it may not vary significantly and will not dominate the solution. Wind speed is of great importance, as even a slight 1–2 m/s increase in speed strongly correlated with a decrease in pollution concentrations. This is because when particles start to move, the migration accelerates along the Vistula river valley. The south winds are associated with a lower concentration of pollutants. Less pollution in the case of southern winds is related to the presence of a natural terrain barrier on the southern side in the form of the Wieliczka Hills. The migration of pollutants in the case of the dominance of the west wind will be greater due to the Vistula valley. This is clearly visible in Figs. 12 and 13 . Southern winds in this area may be associated with warm fen winds 55 , which is also important for pollution generated by household heating. Nevertheless, with such a low speed and relatively short observation time, it is difficult to talk about a noticeable trend for this factor. The multi-modal distributions show that, despite the small spatial area of the research that focus on Krakow and the nearest towns, both individual meteorological factors and concentration values vary greatly.

Spatial autocorrelation analysis of PM2.5 and PM10 values using local Moran’s I allowed us to separate clusters with high and low values as well as areas with anomalously high and low values in relation to neighboring areas. Clusters of high PM2.5 concentrations on both 11th and 18th March occurred mainly to the northeast and southwest of Krakow. Sensors with anomalously high PM2.5 concentrations on March 11th at 0:00 were Swoszowice and Nawojowa Gora, and Krzywaczka at 4:00. PM2.5 indications with increased values relative to neighboring sensors on March 18th were detected in Tenczynek (at 12:00), in Szczyglice and Wieliczka (at 18:00), and in Wieckowice (at 24:00). High-high clusters of PM10 indications were identified on March 11th at 0:00 at the same sensors as for PM2.5. At 4:00, high-value clusters of PM10 were determined in Wzgorza Krzeslawickie, Pietrzejowice, and Proszowice. On March 11th at 8:00, high-high clusters were identified in the south-west section in Brzeznica, Stanislaw Dolny, Brody, Zarzyce Male, and at one sensor to the north of Krakow in Prandocin. High-low outliers were detected on March 11th only at 0:00 (Nawojowa Gora) and at 4:00 (Krzywaczka). On March 18th, high-high clusters were recognized at 12:00 northeast of Krakow, at 18:00 in Gotkowice, and at 24:00 in Jeziorzany and Zarzyce Male. High-low outliers of PM10 were recognized at 12:00 in Tenczynek, at 18:00 in Szczyglice and Wieliczka, and at 24:00 in Wieckowice and Niepolomice. The locations of the high-low, high-high, low-high, and low-low PM10 and PM2.5 clusters are similar, but differences can be observed. High concentrations of PM2.5 in relation to the surroundings were observed in some places in the south of the city, where there are no significant clusters of PM10 concentrations. Two of the sensors are located in the vicinity of the main access roads to Krakow. The K9 Swoszowice sensor is located near the S7 expressway, while the SE17 Wieliczka sensor is located near the 94 expressway. Both are located near the A4 highway exits. Research shows that driving with studded tires in spring significantly increases the concentration of PMs 56 . The occurrence of abnormally high concentrations of PM2.5 in relation to neighboring clusters in the morning and evening hours may be associated with increased car traffic in these areas. This is in line with observations in Opole (Poland) 57 . The low-high clusters are mainly associated with land elevations, except for the K4 sensor located in the eastern part of Nowa Huta. This receiver is located in the wet area of Przylasek Rusiecki, near the eastern border of the city. It is also a part of strategic urban project called Krakow - Nowa Huta of the Future 58 . The low density of buildings and the proximity of forest complexes (the Niepolomicka Forest on one side of the river and the Przylasek Rusiecki complex on the other side) have a positive effect on air quality in this area. It was proved that urban composition has an impact on average PMs concentrations. Greater city fragmentation without densely built-up areas is positively correlated with lower PMs values 59 .

The hot-spots and cold- spots analysis using Getis–Ord \(G_{i}^{*}\) statistics made it possible to identify a few sensors with increased PM2.5 and PM10 concentrations. Increased PM values were identified at single sensors located mainly outside Krakow. On March 11th, hot-spots were located in the northwest and west. This is in line with the highest values of pollutant concentrations and the dominant values of the GWR coefficients for temperature and humidity. Interestingly, it also clearly coincides with the river valley there and a significant terrain dip to the east of the Ojcowski and Krzeszowice plateaus. The two largest hot-spots on March 11 were at the NE17 and NE16 receivers (in Slomniki and Waganowice), which are located almost next to the river in the greatest depression and parallel to the river valley, where small elevations are present. This limits the possibility of the migration of pollutants accumulated in this depression. A completely different hot-spot location occured on March 18th. In the initial stage of observation (12:00), the hot-spots were located on hills, which is the opposite of what was observed on March 11th. The Ojcow Plateau region is also clearly distinguished when it comes to the values of temperature, humidity, and pressure coefficients for GWR. The combination of these meteorological factors probably made it necessary to start heating houses at 12 o’clock. This effect may not be always visible on distribution maps, because gridding may average the values of individual sensors based on the values at neighboring sensors. Hot-spot analysis is a useful tool to help make more accurate inferences than just studying pollutant distributions on maps. In general, the identified clusters provide good insight into the occurrence of local infrastructure and terrain. It can be seen that the clusters determined by local Moran’s index indicate highways, access roads and proximity to forests. Clusters determined by Getis–Ord \(G_{i}^{*}\) statistics are mainly related to morphology, i.e., the occurrence of rivers and Ojcowski and Krzeszowice plateaus.

Performing a standardized GWR allowed us to analyze the influence of individual meteorological factors on PM10 indications. It is noticeable that the values of each coefficient change depend on the day and time of measurement. On the 11th of March, the greatest spatial variation in the influence of the coefficients in the model was for temperature, wind speed, and wind azimuth. It can also be seen that humidity had a significant influence in the Northeast region at 00:00, when high concentrations of PMs were observed there. You can see this effect in the KDE analyses, where, firstly, the concentration value increases with humidity, but secondly, in the highest range, a significant increase in the concentration of pollutants can be seen. The distributions of the temperature and humidity isolines are similar and are elongated in the north-west and south-east direction, while the influence of these factors is opposite. In places where the temperature has a positive effect, humidity has a negative effect. The overall influence of temperature is on average twice as large as that of humidity when the absolute values of the coefficients are taken into consideration. The wind azimuth and speed on March 11th had a big impact on the PM values, even though the wind was rather weak. This is an important observation that allows us to conclude that even a small amount of air movement improves air quality over time. The wind azimuth had a very big influence on the Ojcow plateau (comparable to temperature). In the next hours on that day, the concentration values decreased, as did the influence of meteorological factors on their values. The impact of wind varied according to the location of receivers and were different for those located on slopes and for those located in depressions in the terrain. Results are in line with the observations of Yang et al. 60 , which showed a large variability of individual meteorological factors depending on the measurement period, with wind being the dominant factor. On March 18th, there were much larger differences in the values of all analyzed coefficients. An influx of pollutants to Krakow could be observed. The greatest spatial variation was observed at 12:00 for each of the meteorological factors: temperature, pressure, humidity, wind speed and wind azimuth. At that time, the pollutant concentrations were very low and were within European standards. The longitudinal distribution of meteorological factors such as temperature, pressure, wind speed, and direction is visible. It coincides with the division of the city at this hour into the eastern and western parts in terms of the value of the PM2.5/PM10 ratio. On this basis, it can be concluded that the meteorological factors favored the concentration of pollutants from the morning road traffic peak in the eastern part of the analyzed region, while in the western part of the city the factor derived from fuel combustion was not dominant, but only secondary anthropogenic dust was present (for more information on the PM2.5/PM10 analysis, see the last paragraph of this section). At 18:00, the most influential coefficients were wind speed and wind azimuth. Again, there is a strong coincidence between the coefficient values for wind parameters and the terrain. The region can be divided into the Ojcow plateau and its slope, which reaches as far as the Vistula valley, and the southern part below the Wieliczka Uplands. At 24:00 the highest standardized coefficient and constant for the whole area was the pressure coefficient. Temperature and wind azimuth were also important in that model. The distribution of isolines for temperature and wind speed is similar and again shows similarity to the PM2.5/PM10 ratio distribution map. These factors can affect the relative feeling of cold and act as triggers for household heating with solid fuels and, in consequence, the production of PMs from combustion. Temperature drop increases the need for the fossil fuels combustion 61 . For pollution outflow, in the absence of thermal inversion, the wind azimuth is the dominant factor. Relatively low wind speed is enough. The pressure did not change significantly and its influence was close to zero at each analyzed hour. The situation is different in the case of the inflow of pollutants. In the initial phase, the dominant factor is wind speed and direction. Pressure plays a dominant role in preventing the movement of pollution to the city. The share of temperature can be indirectly analyzed to indicate PM emission sources.

Some studies have shown that higher PM2.5/PM10 ratio values (about 0.9) are associated with anthropogenic processes such as fuel combustion (by heating houses or in car engines), and lower but still high values (about 0.7) are related to other anthropogenic factors like mining, secondary dust lifting by car or bicycle wheels, and agriculture 62 . This relationship is clearly visible in the analyzed hours on March 11th and 18th. In places where the increased emission of pollutants from the combustion of solid fuels occurred, an increase in the ratio was observed. It can be seen that the dominant factor for the migration of pollutants is related to the valleys that coincide with the main rivers in the analyzed region. In elevated regions, PM2.5/PM10 ratio values remain at levels characteristic of anthropogenic non-fuel emissions (around 0.7-0.8), even if combustion has occurred there. Based on the analysis of the PM2.5/PM10 ratio, the influence of the unfavorable geographical situation of the city is clearly visible. Pollution related to the combustion of solid fuels accumulates in the city even though household heating with solid fuels is forbidden there. Municipalities located on the northern and southern elevations are less exposed to long-term exposure, even if they are the main emitters of these pollutants. Of course, this situation may vary in scale depending on the meteorological situation, including phenomena such as temperature inversion 63 .

The problem of air pollution is important for public health. The impact of individual meteorological factors on the concentration of PMs and the impact of macro-geographical factors on their migration has been analyzed in many studies whose main focus was finding long-term relationships based on sparse-sensor grids. The subject of these studies is a short-term spatial analysis based on a dense and regularly sampled network of 100 LCS receivers whose measurements are characterized by relatively high uncertainty. The use of dedicated machine learning techniques by data providers allowed for compliance with the reference stations at the level of 99%. The research was conducted in the early spring during the COVID-19 pandemic. This allowed for the observation of pollutants mainly from the combustion of solid fuels without the additional background pollution resulting from car transportation.

The use of geostatistical methods made it possible to accurately trace places with increased or decreased PM emissions and the location of places that are anomalous in relation to their surroundings.To determine the share of individual meteorological factors, GWR was used based on standardized data. This allowed the performing of a quantitative time-space analysis of individual variables using a common scale while preserving differences in their ranges. The analysis of PM2.5/PM10 ratio values made it possible to distinguish between pollutants generated from combustion and other anthropogenic sources. The high usefulness of this indicator has been demonstrated for tracking solid fuel heating sources. These sources were located outside Krakow. Analysis of the influence of meteorological factors on the concentration of PM air pollutants is a difficult and ambiguous task. The influence of individual meteorological factors, depending on their combination with other factors, on one day gave the opposite dependence than on another study day. The roles of these factors depend on whether the outflow or inflow is analyzed. For the spring period in this terrain regime, the biggest impact on PM outflow was wind azimuth (west and north-west), while the least relevant was pressure. For inflow, the most important factors in the initial phase were wind speed and direction. Later, air pressure was the dominant factor in terms of trapping pollutants in the city. Terrain plays a very important role in the production and migration of pollutants. On the studied days, pollution accumulated along the river valleys. Krakow, which located in the Vistula valley and is limited to the north and south by hills, has a very unfavorable location which favors the accumulation of external pollution. Longitudinal winds have bigger impact on both the inflow and outflow of PMs than winds from perpendicular directions. This conclusion cannot be directly transferred to other cities without detailed investigation of local terrain.

Our study show how complicated it is to combine many factors into a single cause-effect sequence. Determining the general relationships is not as complicated as trying to describe them hour by hour, when significant PM concentration changes can occur. The presented statistical analysis and its results may, in the future, be used as a data source for continuous analysis of time series with the use of machine learning and artificial intelligence. This research shows that for air pollution management planning, a localized multi-factor impact study should be performed.

Data availability

Publicly available datasets from Airly sensors were analyzed in this study and can be found here: ( https://map.airly.org/ , accessed on 17 Feb 2022). API documentation from Airly is available here: ( https://developer.airly.org/en/docs , accessed on 17 Feb 2021). Publicly available datasets from the Chief Inspectorate For Environmental Protection database were analyzed in this study. This data can be found here: ( http://powietrze.gios.gov.pl/pjp/home , accessed on 17 Feb 2022). API documentation is available here: ( http://powietrze.gios.gov.pl/pjp/content/api , accessed on 17 Feb 2022).

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Acknowledgements

This research was supported as a part of the statutory project by AGH University of Science and Technology, Faculty of Geology, Geophysics and Environmental Protection.

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These authors contributed equally: Tomasz Danek, Elzbieta Weglinska and Mateusz Zareba.

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Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059, Kraków, Malopolska, Poland

Tomasz Danek, Elzbieta Weglinska & Mateusz Zareba

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All authors contributed equally to: conceptualization, investigation, methodology, data curation, formal analysis, and writing-review and editing. E.W. and M.Z.: writing—original draft preparation. T.D.: supervision.

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Danek, T., Weglinska, E. & Zareba, M. The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland. Sci Rep 12 , 11050 (2022). https://doi.org/10.1038/s41598-022-15160-3

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Big data reveals true climate impact of worldwide air travel

Global aviations emissions reporting requirements under the unfcc treaty don't show the real impact of air travel.

For the first time ever, researchers have harnessed the power of big data to calculate the per-country greenhouse gas emissions from aviation for 197 countries covered by an international treaty on climate change.

When countries signed the 1992 United Nations Framework Convention on Climate Change treaty, high-income countries were required to report their aviation-related emissions. But 151 middle and lower income countries, including China and India, were not required to report these emissions, although they could do so voluntarily.

This matters because the United Nations Framework Convention on Climate Change relies on country reports of emissions during negotiations on country-specific emissions cuts.

"Our work fills the reporting gaps, so that this can inform policy and hopefully improve future negotiations," says Jan Klenner, a PhD candidate at NTNU's Industrial Ecology Programme and the first author of the new article, which was recently published in Environmental Research Letters.

The new data show that countries such as China, for example, which did not report its 2019 aviation-related emissions, was second only to the United States when it came to total aviation-related emissions.

"Now we have a much clearer picture of aviation emissions per country, including previously unreported emissions, which tells you something about how we can go about reducing them," said Helene Muri, a research professor at the Norwegian University of Science and Technology's Industrial Ecology Programme. Muri was one of Klenner's supervisors and a co-author of the paper.

Big surprises -- or not

As might be expected, the United States is at the top of the list of emitters when it comes to the total sum of aviation emissions for both international and domestic flights.

"When we looked at how emissions are distributed per capita, we could see that economic well-being leads to more aviation activity," Klenner said.

That analysis also showed that wealthy Norway, with just 5.5 million people, was third place overall, just behind the US and Australia, when domestic emissions were calculated on a per-capita basis.

Klenner tested the model he developed for this analysis by using data from Norway. He published a paper reporting those results in 2022.

You might think that Norway's geography -- a long, narrow country with lots of mountains and a sparsely populated northern area -- would be the culprit behind the numbers. But Klenner's 2022 analysis showed that fully 50 per cent of Norway's domestic flights were between the country's major cities, Oslo, Trondheim, Stavanger, Bergen and Tromsø.

"The per person emissions in Norway were incredibly high," Muri, who also co-authored that paper, said. "With this data set we can confirm that from a Norwegian perspective we have a lot of work to do, because we are third in the world when it comes to emissions per person from domestic emissions."

A role for big data

Anders Hammer Strømman, a professor at NTNU's Industrial Ecology Programme and Klenner's co-supervisor, said one important aspect of the study is that it shows how big data can be used to help in regulating climate emissions. Strømman was also a co-author of the new paper.

"I think it very nicely illustrates the potential in this type of work, where we have previously relied on statistical offices and reporting loops that can take a year or more to get this kind of information," he said. "This model allows us to do instant emissions modeling -- we can calculate the emissions from global aviation as it happens."

The model, called AviTeam, is the first to provide information for the 45 lesser-developed countries that have never inventoried their greenhouse gas emissions from aviation. Strømman says the model provides these countries with information that might be otherwise difficult or impossible for them to collect.

The abillity to calculate nearly real-time aviation emissions could also provide an important tool as the industry makes changes to de-carbonize.

"In the transition where we're talking about the introduction of new fuels and new technologies, this type of big data allows us to identify those types of corridors or operations where it makes sense to test those strategies first," Strømman said.

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• Air pollution
• Polyethylene
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Materials provided by Norwegian University of Science and Technology . Original written by Nancy Bazilchuk. Note: Content may be edited for style and length.

Journal Reference :

• Jan Klenner, Helene Muri, Anders H Strømman. Domestic and international aviation emission inventories for the UNFCCC parties . Environmental Research Letters , 2024; 19 (5): 054019 DOI: 10.1088/1748-9326/ad3a7d

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China Air Pollution Data Center launched to combat evolving complexity of air quality challenges

by Chinese Academy of Sciences

While significant strides have been made in improving air quality in China through regulations like the Clean Air Act issued in 2013, air pollution has become increasingly complex. Despite notable improvements, the development of the economy and expansion of vehicular activity have given rise to new challenges, such as the emergence of ozone (O 3 ) pollution, complicating the landscape of air quality management.

In response, a dedicated air pollution data center has been launched, supported by a Major Research Plan of National Natural Science Foundation of China (NSFC) titled "Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China." This initiative aims to delve into the formation mechanisms of air pollution, crucial chemical and physical processes, and their interconnectedness.

This Major Research Plan, comprising 76 individual research projects, has yielded extensive and high-quality data. To consolidate and disseminate these findings for the benefit of scientific research on air pollution, a comprehensive data sharing platform was initiated in 2020.

Spearheaded by Peking University, in collaboration with Tsinghua University, the Institute of Atmospheric Physics of the Chinese Academy of Sciences, Beijing Normal University, and 3Clear Science & Technology Co., Ltd., this platform marks the inception of the China Air Pollution Data Center (CAPDC).

Accessible at www.capdatabase.cn , CAPDC represents the first-ever data sharing platform focused specifically on atmospheric pollution complexities in China. Designed to be inclusive, the platform welcomes both domestic and international scientists.

The introduction of CAPDC has been featured in the journal Advances in Atmospheric Sciences , categorizing the results from the Major Research Plan into eight distinct categories, encompassing both data and non-data types. The data categories include emission inventory, chemical reanalysis, field observation, satellite observation, laboratory measurement, and source profile, comprising a total of 258 datasets. Non-data type results are further divided into new technology and online source apportionment technology, totaling 15 reports.

Here's a brief overview of some key data categories available on CAPDC:

• Emissions Inventory: Providing nine datasets covering various anthropogenic and natural sources , including a 10-km resolution emission inventory for China in 2017.
• Chemical Reanalysis: Comprising three datasets, including high-resolution air quality reanalysis and PM 2.5 composition data, continuously updated on the platform.
• Field Observation: Offering 221 datasets from 2011 to 2021, capturing field measurements in 41 cities, focusing on parameters such as cloud characteristics and aerosol parameters.
• Satellite Observation: Collating high-resolution data for various atmospheric pollutants through the Major Research Plan and the ChinaHighAirPollutants (CHAP) dataset.
• Laboratory Measurement: Encompassing physicochemical property parameters and chemical reaction parameters across six datasets.

The CAPDC website provides bilingual access in Chinese and English, facilitating functions such as project information inquiries, data retrieval, and downloading after registration and agreement to the data use terms. Notably, emissions inventory, chemical reanalysis, and satellite observation data can be previewed prior to downloading.

"Looking ahead, CAPDC aims to expand its repository with additional data and resources, continually enhancing user experience and bolstering efforts in combating air pollution." Said the PI of CAPDC, Prof. Mei Zheng from Peking University.

Journal information: Advances in Atmospheric Sciences

Provided by Chinese Academy of Sciences

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