Landscape 05

Air Pollution

Air Pollution has become a hallmark of urban life, with more than 99 percent of the world's population living in the places breathing unsafe air.1

This issue category includes three indicators: Average Exposure to PM2.5 (fine particulate matter in micrograms per cubic meter (µg/m3); Average Exposure to Nitrogen Dioxide (NO2) (ppm); and PM2.5 Exceedance (average percentage of the population exposed to PM2.5 levels at 10 µg/m3, 15 µg/m3, 25 µg/m3, and 35 µg/m3, which represent the World Health Organization’s (WHO) interim I, II, III, and IV targets, respectively according to its air quality guidelines).2

Description 

Risks and Sources

Air pollution is the leading environmental risk factor for death worldwide and the second leading cause of early death. It claimed 8.1 million deaths in 2021 alone, and more than 99 percent of the global population breathes unsafe air.9 The primary culprit for air pollution-related deaths is fine particulate pollution, made up of particles smaller than 2.5 microns in diameter (PM₂.₅). The small diameter allows particles to lodge deep into human lung and blood tissue. Particulate matter, a mixture of toxic particles, places exposed populations at risk of cardiovascular and lung disease, ranging from stroke to chronic obstructive pulmonary disease, asthma, and lung cancer.10 Elderly populations and young children are particularly vulnerable to health effects of PM₂.₅. The leading cause of mortality for children between the ages of one to five is pneumonia, and half of these cases are due to air pollution. 11

Airborne particulates originate from a variety of natural and anthropogenic sources. PM₂.₅ is primarily the product of combustion, whether from human activity, such as burning coal or car emissions, or natural processes, like volcanoes. In parts of Asia, where coal combustion is the primary source of electricity generation, PM₂.₅ pollution has led cities, like Beijing, to eliminate coal-fired power plants as part of their air pollution control plans.12 In other parts of the world, wildfires, such as those in California, are a major source of particulate matter.13 PM₂.₅ can also be generated through secondary formation, when other precursor gases, such as sulfates from power plants and industrial facilities and nitrates from mobile sources and power plants, react in the atmosphere. Secondary PM formation during winter months can be particularly intense due to temperature inversions that trap warm air and its pollutants below a layer of cold air. Temperature inversions and the secondary formation of PM₂.₅ during cold months are the primary cause of high levels of air pollution observed in cities like New Delhi. India’s capital city experienced air pollution levels 30 times higher than WHO recommended levels in November 2017. Its Chief Minister equated the city to a gas chamber.14 

Nitrogen dioxide (NO₂) is produced from combustion processes similar to particulate matter formation. It forms from road traffic and power plants, and is a precursor to particulate matter and ozone. Ground-level ozone at high concentrations, particularly during summer months, is a major component of urban smog. It can have acute respiratory health effects and has been responsible for a million premature deaths each year. 15

NO₂ pollution has increased in many European countries where diesel fuel is subsidized by as much as 15% more than other less-polluting fuels.16 Diesel cars are also not subject to the same strict emissions testing as other vehicles, such as heavy trucks and buses, meaning modern diesel cars produce 10 times more air pollution than other kinds of vehicles.17 This problem came under international scrutiny when “Dieselgate” revealed that Volkswagen had been circumventing nitrous oxides emissions controls on its vehicles, resulting in emissions higher than what lab tests suggested.18 A European Environment Agency report found that Italy had the highest number of NO₂-related deaths in 2013 at 20,000, London hosted the continent’s worst NO₂ hotspot, at double the allowable EU limit.19

Urbanization and Air Quality

Now that more than half of the global population lives in cities, air pollution has become a hallmark of urban life. Industrialization has concentrated economic activity in cities, which are responsible for some 41.7% of global gross domestic product (GDP) by 2023.20 This aggregation has increased population density in urban areas relative to rural surroundings, subsequently increasing demand on energy and natural resources while generating more pollution and waste. The growth of personal car usage, building stock, and energy demand has led to severe air pollution crises in many cities around the world. London, for instance, had reported air pollution levels worse than Beijing in January 2017, reaching 197 on the Air Quality Index (AQI) (see Box 1, Communicating Air Quality).21 Paris became so polluted in 2015 that the city government enacted emergency measures that restricted vehicles and subsidized public transportation.22 Los Angeles has notoriously struggled with air pollution for decades, with smog levels persisting despite decreasing emissions in recent years.23Globally, about 99% of the world’s population breathes outdoor particulate matter concentrations higher than the WHO's most stringent Air Quality guideline (annual mean concentration less than 5 µg/m³). 

On the positive side, countries have made strides in improving air quality. By 2021, 81% of countries had met WHO air quality Interim Target 1(35 µg/m³).24 With worsening air pollution in urban areas, city managers have implemented a range of policies to address it. These efforts include tackling emissions sources and encouraging efficiency and cleaner fuel standards for motor vehicles. Since transportation is the top contributor to air pollution in many urban areas, many cities have made reducing transit-related emissions a top priority. For nearly two decades, New Delhi, for instance, has mandated that all public transportation use compressed natural gas (CNG), which generates less emissions than diesel. Now that the number of private vehicles has far outpaced other transportation modes in the capital city, New Delhi’s government is looking to expand public transit options to address its ever-growing air pollution problem.25 Other cities, like Singapore, have restricted the total number of vehicles altogether since 2018 by maintaining a zero growth rate per annum for cars and motorcycles until January 2025.26

Globally, growing policy attention has focused on air quality as an urban issue, with air pollution inserted as a target in Sustainable Development Goal (SDG) 11 for cities. Goal 11, to “Make cities inclusive, safe, resilient and sustainable,” sets a target to “reduce the adverse per capita environmental impact of cities” with particular attention to air quality.”27 Air is also included in the opening text of the SDGs, cementing the issue as central to both sustainable development and human health.

Towards Improved Monitoring

Despite its known health impacts, global monitoring of air pollution is lagging, usually because of lack of capacity, resources, technology, or public demand. Monitoring gaps primarily occur in developing countries outside of North America and Western Europe, where air pollution is more severe and the number of air-pollution related early deaths has increased dramatically over the last 15 years (see Figure 1).28 Given the sparseness of ground-based monitors, satellite-derived estimates have been utilized for global comparability and applied in epidemiological studies.29 Satellites develop “wall-to-wall” measures of aerosols and pollutants in the earth’s atmosphere, enabling consistent side-by-side comparisons between entities. While a potent proxy, this satellite data fails to measure ambient air conditions at the ground, where people breathe. It can also miss short-term spikes in air pollution that can occur episodically, particularly if the sensor fails to pass through an area during these events. Satellite estimates are also averaged over long periods of time, which smooths and presents lower pollution concentration values than what a city might experience on an hourly basis. 

A growing number of bottom-up, citizen-driven and private efforts show promise to improve the landscape of global air quality monitoring (see Box 2, Air quality data getting bigger). Improvements in technology, including low-cost air sensors, are critical to help fill air quality data gaps and allow for the real-time monitoring of health risks. This new data helps citizens and governments understand new sources of air pollution, identify personal health exposure risks, and forecast future air pollution events. Combined with other sources of near real-time data, citizen-generated information is providing new, previously unexplored insights into the economic and social costs of air pollution (see Box 3).

Figure 1. A map compares the number of ground-based PM₂.₅ monitoring stations with the potential gain in life expectancy if countries reduced fine particulate matter (PM₂.₅) pollution to meet the WHO guideline (5µg/m³). Size of the blue circles indicates the number of monitoring stations in a country. The potential gain in life expectancy in each country is shaded from yellow to black. A shortage of ground-based monitoring sites are found in countries where air-pollution related early deaths are highest. 30

Results 

The distribution of air pollution, particularly PM₂.₅ and NO₂, shows markedly different patterns across regions (Figure 2). For PM₂.₅, many cities in Sub-Saharan Africa, South Asia, Middle East & North Africa, and Latin America & the Caribbean manage to adhere to the 2005 WHO guideline, which recommends an annual average concentration below 10 µg/m³. Conversely, PM₂.₅ concentrations remain strikingly high in other parts of the world. Cities in East Asia & the Pacific and North America, in particular, demonstrate some of the most extreme exposure levels. For instance, Delhi, IND, registers an exceptionally high PM₂.₅ concentration of 125.5 µg/m³, followed by Shijiazhuang, CHN(70.5 µg/m³), and Kolkata, IND (56.2 µg/m³), making them the three cities with the most severe levels of PM₂.₅ pollution.

When considering NO₂ exposure, the majority of cities except meet the 2005 WHO guideline, which stipulates an annual concentration below 40 µg/m³, but not our UESI standard of 0 µg/m³. In contrast, some cities still exhibit significantly elevated NO₂ levels, with many far exceeding the recommended limit. Tehran, IRN, has the highest annual NO₂ concentration at 88.4 µg/m³, followed by Seoul, KOR (50.5 µg/m³), Suwon, KOR (49.8 µg/m³), Shanghai, CHN (47.3 µg/m³), and Mexico City, MEX (41.3 µg/m³). These five cities show exposure levels that surpass the WHO standard and UESI standard, underscoring serious air quality concerns and the urgent need for pollution reduction strategies.

The stark disparities in PM₂.₅ and NO₂ concentrations across regions emphasize the uneven progress in managing air pollution globally. While some areas have successfully kept pollutants in check, others continue to face critical challenges in curbing harmful emissions, particularly in densely populated urban centers. 

Figure 2. Distribution of PM₂.₅ and NO₂ Concentration (µg/m³) in UESI Cities Across Regions with WHO 2021 Air Quality Guidelines (Orange - PM₂.₅: 5 µg/m³ Annually, Purple - NO₂: 10 µg/m³ Annually).

UESI cities show a wide range of proximity-to-target scores. A high score indicates better performance on air pollution metrics, while a low score reflects comparatively worse outcomes. In Figure 3, we present the top and bottom 10 performing cities based on their air pollution exposure levels. Generally, cities in developed countries perform well in terms of PM₂.₅ pollution, with examples like Vancouver (CAN), Halifax (CAN), and Brisbane (AUS). However, seventeen UESI cities scored 0 for PM₂.₅, including 9 cities from China, such as Beijing, and 8 cities from other parts of the Global South, including Delhi (IND), Johannesburg (ZAF), and Hanoi (VNM), which reflect the highest average annual concentrations of PM₂.₅. For instance, the average concentration of PM₂.₅ in Delhi is approximately 125 µg/m³, compared to the acceptable level of 60 µg/m³ set by Delhi’s Department of the Environment. The department attributes these pollution levels primarily to airborne road dust, soil, and ash as well as combustion-related carbons from energy generation, automobiles, and the burning of municipal waste.31 Climatic conditions exacerbate the air pollution problems experienced in India. 

In contrast to PM₂.₅, NO₂ performance appears worse in more developed cities or countries. Primarily cities like Paris (FRA), Hangzhou (CHN), Shanghai CHN), Milan (ITA), and Seoul (KOR) are in the bottom 10 performing cities. These low scores likely reflect the use of diesel fuel in motorized vehicles as described above. Although regulations against polluting vehicles have strengthened in many of these cities over the past decades, pollution levels have continued to rise. Following the Volkswagen emissions scandal, studies by the German, French, and British governments found that vehicle manufacturers routinely take advantage of loopholes in European Union regulations to produce vehicles that do not meet emissions standards.32 These revelations have led many cities to propose new regulations regarding diesel-burning vehicles. In London, where approximately 40% of the city’s air pollution is due to diesel vehicles, a new regulation applies a daily monetary fine to vehicles that do not meet European Union emissions standards.33 The UESI results demonstrate the importance of tighter and more effective NO₂ regulations. 

Figure 3. Top and bottom 10 cities’  NO₂ concentration across cities. A score of 100 means a city has achieved the target, while 0 represents the low-performance benchmark.

Figure 3. Top and bottom 10 cities’ PM₂.₅ concentration across cities. A score of 100 means a city has achieved the target, while 0 represents the low-performance benchmark.

Inequity in Air Pollution Exposure

Recent policy attention has also focused on the unequal distribution of air pollution within an urban area (see Box 4: Environmental Injustice and Exposure to Air Pollution). Wang et al. (2023) found major differences in different demographic groups’ exposure to NO₂ pollution within the United States. Nationally, non-white communities are exposed to NO₂ concentrations ranging from 43% to 70% higher than white communities within major metropolitan areas. In urban areas, NO₂ pollution is higher for low-income groups than for high income groups.34 In some North American cities, however, the opposite has been observed. Hajat et al. (2015) reviewed 37 studies from around the world (22 in North America, 10 in Europe, and 5 studies from Africa and the Asia Pacific) investigating air pollution exposure and socioeconomic status. This review found that larger cities, including New York, Toronto and Montreal, had opposite associations – areas with higher socioeconomic status had higher concentrations of ambient air pollution.35 The authors suggested that these surprising findings could be the result of clustering around busy roadways, where high income groups may have better access to urban amenities.

Governments are calling attention to environmental inequities through visual mapping tools. The U.S. Environmental Protection Agency (EPA) has developed an environmental justice mapping tool (EJScreen) to highlight which census blocks are more or less unequal with respect to the distribution of ozone, particulate pollution, and proximity to hazardous waste in major American cities, including Chicago, Boston and New York.36 The state of California has developed a more granular tool, CalEnviroScreen,37 to pinpoint communities disproportionately burdened by environmental pollution, including their exposure to pollution sources such as traffic, diesel exhaust and toxic releases. 

Performance results show similar PM₂.₅ and NO₂ distributions with respect to socioeconomic status (Figures 4 -7). This result is likely due to the satellite-derived air pollution data lacking the spatial resolution to distinguish small-scale differences between neighborhoods. In most cases, as illustrated in Figures 4 and 6, the air pollution curves are more equitably distributed (i.e., closer to the 45-degree line representing equitable distribution) than income. Bangkok (THA), Berlin (DEU), and Johannesburg (ZAF) are examples where the air pollution and income distribution curves are nearly identical and on top of the line of perfect equity - suggesting that the distribution of income is not exacerbating air pollution exposure. In other cities, such as Atlanta (USA), Istanbul (TUR), Los Angeles (USA), New York City (USA), and Sao Paulo (BRA), income is much more unequally distributed than air pollution.

To better distinguish subtle differences in air pollution and income distribution in the UESI cities, the equity typology quadrant plots (Figures 5 and 7) mathematically summarize the relationship between air pollution and income distribution.

In the top left quadrant (e.g., low Gini and negative Environmental Concentration Index, or ECI), Kyoto City (JPN) (ECI = -0.175, not shown in the plot) is located farthest to the left, indicating the greatest inequality in NO₂ exposure for the lowest income earners. Meanwhile, Guatemala City (Ciudad de Guatemala, GTM) has the most negative ECI for PM₂.₅ distribution. Cities like Beijing (CHN), Florence (ITA), Zhenjiang (CHN), and Chengdu (CHN) appear furthest to the right in the upper right quadrant (low Gini, positive ECI), suggesting that low-income inequality has not exacerbated the distribution of air pollution. However, Delhi (IND) and Tehran (IRN) rank among the highest in terms of absolute exposure to PM₂.₅ and NO₂, respectively, indicating that while the distribution may not disproportionately burden the poor, everyone, regardless of income, is exposed to poor air quality.

Figure 4. NO₂ and income distribution curves for selected UESI cities. These plots show the concentration distributions of NO₂ (e.g., the concentration curve) and income (e.g., the Lorenz curve) throughout neighborhoods in cities. Deviations from the dotted line (e.g., the line of perfect equity) illustrate cities that are less equitable in their distribution of NO₂. Concentration curves above the line of equity indicate the environmental burden is more heavily allocated to those with less income; concentration curves below the line of equity indicate that the environmental burden is more heavily allocated to those with greater income. (See the Equity and Social Inclusion Indicator Profile for a more detailed description of this plot).

Figure 5. NO₂ Equity typology quadrant plot. The plot considers the Income Gini and NO₂ Concentration Index to define four quadrants. The Income Gini Values represent the distribution of wealth across the population and range in value from 0 to 1. A Gini value of zero indicates a perfectly equal distribution of income across the population, while a high Gini value (out of a maximum of 1) suggests a highly unequal distribution of wealth. The Environmental Concentration Index (ECI) measures the variation of NO₂ in response to income. Positive ECI values indicate that the environmental burden is allocated on the poorest citizens, while a negative ECI indicates that the environmental burden is allocated on the wealthier citizens. The size of the dots represents the extent of a city’s NO₂ concentration (in ppb) (See the Equity and Social Inclusion Profile for a more detailed description of this plot).

Figure 6. PM₂.₅ and income distribution curves for selected UESI cities. These plots show the concentration distributions of PM₂.₅(e.g., the concentration curve) and income (e.g., the Lorenz curve) throughout neighborhoods in cities. Deviations from the dotted line (e.g., the line of perfect equity) illustrate cities that are less equitable in their distribution of PM2.5.

Figure 7. PM₂.₅ Equity typology quadrant plot. The plot considers the Income Gini and PM₂.₅ Concentration Index to define four quadrants. The size of the dots represents the extent of a city’s PM₂.₅ concentration (in µg/m³) (see the Equity and Social Inclusion Profile for a more detailed description of this plot).

Box 1. Communicating Air Quality

As a way of communicating the risks associated with various levels of air pollution, governments around the world have developed indices that translate different exposures to easily understandable health risks. Many use the Air Quality Index (AQI), which normalizes air pollution concentrations to a scale from 0 to 500, with 0 representing good air quality that poses little to no health risk, and a score of 500 indicating hazardous air pollution that likely affects the entire population (see Figure a). Within the United States, the AQI value for each major criteria air pollutant is determined from the station within a monitoring area that registers the highest concentration of that pollutant, among all monitors. The AQI communicated to the public is then the highest recorded AQI value among all of the pollutants. A color code, ranging from green (safe) to maroon (hazardous) communicates air quality and possibly health risks to the public (see Figure a).

Governments utilize a diverse array of methods for communicating the AQI. The European Environment Agency began providing short-term (from 6 to 48 hours) AQI information from 2,000 monitoring stations across Europe in November 2017 (see Figure 2). Other governments use social media and other forms of online media to share AQI data. Shanghai’s Environmental Protection Bureau, for example, has utilized Sina Weibo, a microblogging platform similar to Twitter, to share air quality information through a character whose hair color reflects the current AQI value. Some governments even issue warnings when AQI levels are high, shutting down schools and offices and cautioning people to stay indoors. Due to the high public health risks associated with air pollution, countries are experimenting with different forms of communication to inform residents when pollution levels may pose danger.

 

The Air Quality Index (AQI) utilized in many countries around the world, including the United States, ranges from 0 to 500 and communicates associated health risks. Image source: Santa Barbara County. Source:



Figure A. The Air Quality Index (AQI) utilized in many countries around the world, including the United States, ranges from 0 to 500 and communicates associated health risks. Image source: Santa Barbara County. Source: https://www.ourair.org/sbc/the-air-quality-index/?doing_wp_cron=1514486066.1177558898925781250000. 

The European Environment Agency and the European Commission launched a new Air Quality Index communication platform in November 2017. This online dashboard reflects short-term air quality from more than 2,000 monitoring stations across Europe

Figure B. The European Environment Agency and the European Commission launched a new Air Quality Index communication platform in November 2017. This online dashboard reflects short-term air quality from more than 2,000 monitoring stations across Europe. Source: https://airindex.eea.europa.eu/AQI/index.html.

Box 2. Air quality data is getting bigger

Mobile information communication technologies (ICTs) and growing concern over global air quality has led to a bloom of big data platforms boasting real-time data, often collected from a variety of low-cost sensors and crowdsourcing techniques. A proliferation of start-ups have capitalized on this new space, developing proprietary algorithms to forecast air pollution and fill gaps in existing public monitoring systems. 

These organizations often integrate data collection with air pollution forecasting. Plume Labs, a France-based start-up, has designed its own algorithms to predict air pollution using 12,000 environmental monitoring stations across 60 countries. It has also designed a mobile sensor called The Flow to track indoor and outdoor air pollution for PM₂.₅ , NOx, Ozone, and Volatile Organic Compounds (VOCs). Another start-up, AirVisual, provides air pollution forecasts for 6,000 cities and has developed a map to display global air pollution from 8,000 monitoring stations in real time. Like Plume, AirVisual has developed a consumer-based sensor called The Node that allows users to send the data they collect back to the company’s air pollution models in a citizen science-like feedback mechanism. 

Some of these air pollution data start-ups have strategic clientele for their data products. Breezometer, for instance, is an air quality analytics provider that targets cosmetics companies, who utilize its air pollution data to develop pollution-fighting skin-care products. Other platforms draw on the power of crowdsourcing to make air pollution data more transparent, centralized and easily accessible. For example, OpenAQ is an online community of scientists, researchers, and activists that has collected more than 76 million air quality measurements from 5,830 locations in 50 countries, drawn primarily from government and research-grade resources. Many of these companies develop their own proprietary Application Programmer Interfaces (APIs) to allow clients real-time access to air pollution data. 

This convergence of technological innovation presents a prime opportunity for private companies and citizens to contribute to improve global monitoring of air pollution, particularly in areas that suffer from lack of monitoring capacity. 

Box 3. Exploring the relationship between city air quality relative performance and electricity consumption.

Urban air pollution emissions are intrinsically linked to public health, contributing to an estimated 9 million premature deaths per year.41 Mainstream air pollution research has focused on the spatial and temporal dynamics of air pollutants, their drivers, and associated health impacts.42 However, the equity and policy components related to pollution emissions and management across different cities are often neglected. To address this gap, we developed a comprehensive composite index – the City Clean Air Index – to evaluate the relative air quality performance of approximately 300 cities worldwide. This index encompasses various dimensions of air quality, social equity, and policy effectiveness, serving as a pivotal resource for policymakers to evaluate their urban air quality landscapes, understand the spatial distribution of air pollutants, and identify effective pollution mitigation strategies. Encompassing ambient air quality, air quality trends over more than 10 years, equitable distribution of air pollution, and the efficacy of policy measures, the Clean Air Index aims to offer a holistic analysis that resonates with SDG11’s vision for sustainable and inclusive cities.

Existing literature highlights a complex interplay between air pollution and electricity consumption, where increased power demand can beget air pollution and vice versa.43 44 On one hand, fossil-fuel-powered systems may emit large amounts of air pollutants during the electricity supply phase, constituting a major stationary source of air pollution. On the other hand, individuals may choose to stay indoors to reduce pollution exposure if their area has heavy pollution-induced smog, thus contributing to demand-driven electricity consumption. Utilizing the composite air quality index, we selected a subset of global cities to analyze and visualize the relationship between electricity consumption and relative air quality performance, with electricity consumption data obtained from a peer-reviewed study.45

To further our assessment, we categorized cities based on physical and socio-economic characteristics to perform a clustering analysis. The comparative analysis revealed that highly developed and established cities tend to exhibit better relative air quality performance (Figure A). For instance, cities like Paris and Copenhagen, despite their high electricity consumption, maintain superior air quality, likely due to their reliance on low-carbon energy sources such as nuclear and renewable energy. Conversely, medium to low-developed cities, particularly those experiencing rapid growth—such as Jakarta and Medellin—often display worse air quality performance. These cities typically have electricity mixes that are heavily dependent on fossil fuels, which contributes to their lower rankings in relative air quality performance. Although electricity consumption in these cities remains low, without swift and decisive action to enhance air quality management and transition to cleaner energy systems, their use of unclean electricity is expected to rise as urbanization progresses, further reducing their expected air quality performance.

Relationship between air quality relative performance and electricity consumption.

Figure A. Relationship between air quality relative performance and electricity consumption.

 

Box. 4. Environmental injustice and exposure to air pollution

The global burden of environmental degradation is increasing, but not all people and populations share this burden equally. From air pollution to lead and polluted water, certain segments of the population are disproportionately exposed to environmental hazards. A recent report by the U.S. EPA, published in the American Journal of Public Health, quantified disparities in the location of particulate matter (PM)-emitting facilities based on the racial/ethnic composition and socio-demographic characteristics of the surrounding residential population. The study found that people living in poverty and non-White populations have higher exposure to PM, particularly PM₂.₅ . However, the most surprising finding was that disparities for Black populations are greater than the disparities observed on the basis of poverty status: considering PM₂.₅, those in poverty had a burden 1.35 times higher than the overall population and Black communities had a burden 1.54 times higher than the overall population (non-Whites, in general, had 1.28 times higher burden). 46

These results were consistent across national, state, and county scales. The researchers investigated whether individuals’ rural or urban status modified the relationship between race, socioeconomic status and PM exposure. They found that high emissions in metropolitan (population of at least 50,000) and “micropolitan” (population between 10,000 and 50,000) cities, coupled with high representation of non-White residents in these population-dense centers, drove the national trends (Figure d).47 In other words, urban centers tend to have both high levels of PM₂.₅ pollution and higher proportions of non-White residents. Those living above the poverty line experience lower burdens than those below it within these urban areas; however, disparities in emissions are significantly larger when comparing Black  and White populations (Figure A).48  The authors conclude that this finding reinforces “the overall finding that racial disparities appear to be markedly higher than are poverty-based disparities.”49

Absolute Burden of PM2.5 emissions from nearby facilities stratified by Rural-Urban Commuting Area (RUCA) code and sub-stratified by race/ethnicity and poverty (2009-2013). “High-commuting” is defined as greater than 30% flow to an urbanized area; “low-commuting” defined as 10 to 30% flow. Source: Mikati, et al., 2018.

Figure A. Absolute Burden of PM₂.₅ emissions from nearby facilities stratified by Rural-Urban Commuting Area (RUCA) code and sub-stratified by race/ethnicity and poverty (2009-2013). “High-commuting” is defined as greater than 30% flow to an urbanized area; “low-commuting” defined as 10 to 30% flow. Source: Mikati, et al., 2018. 

Exposure to PM is not the only example of inequitable distribution of environmental risks. Disposal of wastewater from hydraulic-fracturing (“fracking”) occurs disproportionately in non-White and poor communities, according to a study performed in southern Texas.50 A small study using personal air samples collected through the National Health and Nutrition Examination Survey (NHANES) found that the levels of total volatile organic compound (VOC) exposure were 52 percent and 37 percent higher for Mexican Americans and non-Hispanic blacks, respectively, than for non-Hispanic whites, even after adjusting for socioeconomic status.51 Black children have the highest prevalence of elevated blood lead levels in the U.S. A recent study found that the disproportionate burden of lead exposure is transmitted from mother-to-child, such that Black children have elevated blood lead levels before birth (in utero) and into early childhood.52

The life-long health effects of these exposures are well established. Exposure to PM is associated with respiratory and cardiovascular disease, premature mortality, and adverse birth outcomes.53 The International Agency for Research on Cancer (IARC) has designated PM in outdoor air pollution as carcinogenic to humans.54 Quantifying the potential harm from fracking has proven difficult because fracking companies classify the chemicals—and their concentrations—in fracking wastewater as classified “confidential business information.”55 Though the exact composition of fracking fluids remains unknown, over 1,000 substances have been identified in fluids and wastewater, including solvents, heavy metals, aromatic hydrocarbons, VOCs, and naturally-occurring radioactive materials.56  A systematic evaluation of 1021 chemicals in fracking fluids and wastewater found significant potential for reproductive and developmental health risks.57 Lead is a neurotoxin that affects IQ and development.58 The health effects of disproportionate exposure to these environmental hazards suggest that poor communities and racial minorities are placed at greater risk for chronic and acute disease as well as developmental, neurologic and reproductive changes. Since the siting of polluting facilities and regulation of emissions is the result of a deliberate decision-making process, these disparities are hardly coincidental or benign; rather, they “may indicate underlying disparities in the power to influence that process.”59

Changes in mortality attributable to ambient PM pollution by country, 1990-2015. Source: Cohen, et al., 2017.

Figure B. Changes in mortality attributable to ambient PM pollution by country, 1990-2015. Source: Cohen, et al., 2017.

Environmental injustice is not limited to the United States. Between and within countries, different populations are exposed to a range of different hazards. Although these disparities are seen across a range of environmental measures, they are best illustrated by air pollution. Mean annual exposure to PM₂.₅ air pollution in the U.S. was 8.4 µg/m³; that same year exposure measured at 106.2 µg/m³ in Saudi Arabia, 74.3 µg/m³ in India, and 58.4 µg/m³ in China.60 The causes of these differences in air pollution include environmental conditions (e.g., dust storms in Saudi Arabia), pollution levels (e.g., in 2016, India had 22 of the 50 most polluted cities in the world) and industrial production (e.g., un-filtered coal burning power plants in China). In China, India, Bangladesh, and Japan, increases in exposure to PM combined with increases in population growth and an aging population have led to net increases in mortality attributable to air pollution exposure (Figure B).61 This means that many countries have a growing and increasingly vulnerable population (elderly people, along with children, pregnant women, and people with pre-existing asthma, cardiovascular disease, and lung disease) being exposed to hazardous and injurious levels of air pollution; as a result, a greater number of deaths associated with air pollution exposure are occurring in these countries. 

Within countries, disparities in environmental exposure by socioeconomic status (SES) have been observed in North America, Asia and Africa; studies conducted on European cities have had more mixed results.62 A meta-analysis of 37 studies regarding SES disparities and air pollution exposure included 22 North American studies, 10 European studies and 5 studies from New Zealand (3 studies), Asia (1 study in Hong Kong) and Africa (1 study in Ghana) (Hajat, et al., 2015); this distribution of studies itself suggests the need for greater geographic diversity in research on this topic. In New Zealand, low-income neighborhoods had higher concentrations of PM₂.₅ compared to higher SES areas.63 The Ghana air pollution inequality study found that community SES was inversely related to both PM2.5 and PM10.64  In Hong Kong, among those living in private housing, the lower SES population had higher exposure to PM10 and other air pollutants compared to the high SES population; among those living in public housing (low income families), no such inequalities were detected.65 The authors hypothesize that the location of public housing (compared to siting of private housing) was an important factor in reducing residents’ exposure to traffic related air pollution.66

Merely looking at overall air pollution trends globally, or even by country, belies the real burden that many populations face. Environmental exposures, whether by design or coincidence, disproportionately fall on already vulnerable populations. This inequitable burden should be a crucial piece of environmental conversations. 

 

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