The estimated change in the level and distribution of PM2.5-attributable health impacts in the United States: 2005-2014

Combined effect of changes in NO2, O3, PM2.5, SO2 and CO concentrations on small airway dysfunction

BACKGROUND AND OBJECTIVE

When multiple complex air pollutants are combined in real-world settings, the reliability of estimating the effect of a single pollutant is questionable. This study aimed to investigate the combined effects of changes in air pollutants on small airway dysfunction (SAD).

METHODS

We analysed Korea National Health and Nutrition Examination Survey (KNHANES) V-VIII database from 2010 to 2018 to elucidate the associations between annual changes in air pollutants over a previous 5-year period and small airway function. We estimated the annual concentrations of five air pollutants: NO2, O3, PM2.5, SO2 and CO. Forced expiratory flow between 25% and 75% of vital capacity (FEF25%-75%) <65% was defined as SAD. Using the quantile generalized-Computation (g-Computation) model, the combined effect of the annual changes in different air pollutants was estimated.

RESULTS

A total of 29,115 individuals were included. We found significant associations between SAD and the quartiles of annual changes in NO2 (OR = 1.10, 95% CI = 1.08-1.12), O3 (OR = 1.03, 95% CI = 1.00-1.05), PM2.5 (OR = 1.03, 95% CI = 1.00-1.05), SO2 (OR = 1.04, 95% CI = 1.02-1.08) and CO (OR = 1.16, 95% CI = 1.12-1.19). The combined effect of the air pollutant changes was significantly associated with SAD independent of smoking (OR = 1.31, 95% CI = 1.26-1.35, p-value <0.001), and this trend was consistently observed across the entire study population and various subgroup populations. As the estimated risk of SAD, determined by individual-specific combined effect models, increased and the log odds for SAD increased linearly.

CONCLUSION

The combined effect of annual changes in multiple air pollutant concentrations were associated with an increased risk of SAD.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

All-Cause NO2-Attributable Mortality Burden and Associated Racial and Ethnic Disparities in the United States

Nitrogen dioxide (NO2) is a regulated pollutant that is associated with numerous health impacts. Recent advances in epidemiology indicate high confidence linking NO2 exposure with increased mortality, an association that recent studies suggest persists even at concentrations below regulatory thresholds. While large disparities in NO2 exposure among population subgroups have been reported, U.S. NO2-attributable mortality rates and their disparities remain unquantified. Here we provide the first estimate of NO2-attributable all-cause mortality across the contiguous U.S. (CONUS) at the census tract-level. We leverage fine-scale, satellite-informed, land use regression model NO2 concentrations and census tract-level baseline mortality data to characterize the associated disparities among different racial/ethnic subgroups. Across CONUS, we estimate that the NO2-attributable all-cause mortality is ∼170,850 (95% confidence interval: 43,970, 251,330) premature deaths yr-1 with large variability across census tracts and within individual cities. Additionally, we find that higher NO2 concentrations and underlying susceptibilities for predominately Black communities lead to NO2-attributable mortality rates that are ∼47% higher compared to CONUS-wide average rates. Our results highlight the substantial U.S. NO2 mortality burden, particularly in marginalized communities, and motivate adoption of more stringent standards to protect public health.

Racial/ethnic disparities in PM2.5-attributable cardiovascular mortality burden in the United States

Average ambient fine particulate matter (PM2.5) concentrations have decreased in the US in recent years, but the health benefits of this improvement among different racial/ethnic groups are unknown. We estimate the associations between long-term exposure to ambient PM2.5 and cause-specific cardiovascular disease (CVD) mortality rate and assess the PM2.5-attributable CVD deaths by race/ethnicity across 3,103 US counties during 2001-2016 (n = 595,776 county-months). A 1 µg m-3 increase in PM2.5 concentration was associated with increases of 7.16 (95% confidence interval (CI): 3.81, 10.51) CVD deaths per 1,000,000 Black people per month, significantly higher than the estimates for non-Hispanic white people (1.76 (95% CI: 1.37, 2.15); difference in coefficients: 5.40 (95% CI: 2.03, 8.77), P = 0.001). No significant difference in this association was observed between Hispanic (2.66 (95% CI: -0.03, 5.35)) and non-Hispanic white people (difference in coefficients: 0.90 (95% CI: -1.81, 3.61), P = 0.523). From 2001 to 2016, the absolute disparity in PM2.5-attributable CVD mortality burden was reduced by 44.04% between non-Hispanic Black and white people and by 2.61% between Hispanic and non-Hispanic white people. However, in 2016, the burden remained 3.47 times higher for non-Hispanic Black people and 0.45 times higher for Hispanic people than for non-Hispanic white people. We call for policies that aim to reduce both exposure and vulnerability to PM2.5 for racial/ethnic minorities.

Benefits of Short-term Premature Mortality Reduction Attributed to PM2.5 Pollution: A Case Study in Long an Province, Vietnam

PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI - 0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI - 0.2; 3.1), and 3.6 (95% CI - 1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI - 2.7; 89.6) billion VND, equivalent to 8.3 (95% CI - 0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.

Understanding the time-activity pattern to improve the measurement of personal exposure: An exploratory and experimental research

Although ambient fine particulate matter (PM2.5) concentrations and their components are commonly used as proxies for personal exposure monitoring, developing an accurate and cost-effective method to use these proxies for personal exposure measurement continues to pose a significant challenge. Herein, we propose a scenario-based exposure model to precisely estimate personal exposure level of heavy metal(loid)s (HMs) using scenario HMs concentrations and time-activity patterns. Personal exposure levels and ambient pollution levels for PM2.5 and HMs differed significantly with corresponding personal/ambient ratios of approximately 2, and exposure scenarios could narrow the assessment error gap by 26.1-45.4%. Using a scenario-based exposure model, we assessed the associated health risks of a large sample population and identified that the carcinogenic risk of As exceeded 1 × 10-6, while we observed non-carcinogenic risks from As, Cd, Ni, and Mn in personal exposure to PM2.5. We conclude that the scenario-based exposure model is a preferential alternative for monitoring personal exposure compared to ambient concentrations. This method ensures the feasibility of personal exposure monitoring and health risk assessments in large-scale studies.

Inequality in the Distribution of Air Pollution Attributable Mortality Within Canadian Cities

Recent studies have identified inequality in the distribution of air pollution attributable health impacts, but to our knowledge this has not been examined in Canadian cities. We evaluated the extent and sources of inequality in air pollution attributable mortality at the census tract (CT) level in seven of Canada's largest cities. We first regressed fine particulate matter (PM2.5) and nitrogen dioxide (NO2) attributable mortality against the neighborhood (CT) level prevalence of age 65 and older, low income, low educational attainment, and identification as an Indigenous (First Nations, Métis, Inuit) or Black person, accounting for spatial autocorrelation. We next examined the distribution of baseline mortality rates, PM2.5 and NO2 concentrations, and attributable mortality by neighborhood (CT) level prevalence of these characteristics, calculating the concentration index, Atkinson index, and Gini coefficient. Finally, we conducted a counterfactual analysis of the impact of reducing baseline mortality rates and air pollution concentrations on inequality in air pollution attributable mortality. Regression results indicated that CTs with a higher prevalence of low income and Indigenous identity had significantly higher air pollution attributable mortality. Concentration index, Atkinson index, and Gini coefficient values revealed different degrees of inequality among the cities. Counterfactual analysis indicated that inequality in air pollution attributable mortality tended to be driven more by baseline mortality inequalities than exposure inequalities. Reducing inequality in air pollution attributable mortality requires reducing disparities in both baseline mortality and air pollution exposure.

Global air pollution exposure and poverty

Air pollution is one of the leading causes of health complications and mortality worldwide, especially affecting lower-income groups, who tend to be more exposed and vulnerable. This study documents the relationship between ambient air pollution exposure and poverty in 211 countries and territories. Using the World Health Organization's (WHO) 2021 revised fine particulate matter (PM2.5) thresholds, we show that globally, 7.3 billion people are directly exposed to unsafe average annual PM2.5 concentrations, 80 percent of whom live in low- and middle-income countries. Moreover, 716 million of the world's lowest income people (living on less than $1.90 per day) live in areas with unsafe levels of air pollution, especially in Sub-Saharan Africa. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. These findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

Exposure to Air Pollution and Emergency Department Visits During the First Year of Life Among Preterm and Full-term Infants

IMPORTANCE

Previous studies have focused on exposure to fine particulate matter 2.5 μm or less in diameter (PM2.5) and on birth outcome risks; however, few studies have evaluated the health consequences of PM2.5 exposure on infants during their first year of life and whether prematurity could exacerbate such risks.

OBJECTIVE

To assess the association of PM2.5 exposure with emergency department (ED) visits during the first year of life and determine whether preterm birth status modifies the association.

DESIGN, SETTING, AND PARTICIPANTS

This individual-level cohort study used data from the Study of Outcomes in Mothers and Infants cohort, which includes all live-born, singleton deliveries in California. Data from infants' health records through their first birthday were included. Participants included 2 175 180 infants born between 2014 and 2018, and complete data were included for an analytic sample of 1 983 700 (91.2%). Analysis was conducted from October 2021 to September 2022.

EXPOSURES

Weekly PM2.5 exposure at the residential ZIP code at birth was estimated from an ensemble model combining multiple machine learning algorithms and several potentially associated variables.

MAIN OUTCOMES AND MEASURES

Main outcomes included the first all-cause ED visit and the first infection- and respiratory-related visits separately. Hypotheses were generated after data collection and prior to analysis. Pooled logistic regression models with a discrete time approach assessed PM2.5 exposure and time to ED visits during each week of the first year of life and across the entire year. Preterm birth status, sex, and payment type for delivery were assessed as effect modifiers.

RESULTS

Of the 1 983 700 infants, 979 038 (49.4%) were female, 966 349 (48.7%) were Hispanic, and 142 081 (7.2%) were preterm. Across the first year of life, the odds of an ED visit for any cause were greater among both preterm (AOR, 1.056; 95% CI, 1.048-1.064) and full-term (AOR, 1.051; 95% CI, 1.049-1.053) infants for each 5-μg/m3 increase in exposure to PM2.5. Elevated odds were also observed for infection-related ED visit (preterm: AOR, 1.035; 95% CI, 1.001-1.069; full-term: AOR, 1.053; 95% CI, 1.044-1.062) and first respiratory-related ED visit (preterm: AOR, 1.080; 95% CI, 1.067-1.093; full-term: AOR,1.065; 95% CI, 1.061-1.069). For both preterm and full-term infants, ages 18 to 23 weeks were associated with the greatest odds of all-cause ED visits (AORs ranged from 1.034; 95% CI, 0.976-1.094 to 1.077; 95% CI, 1.022-1.135).

CONCLUSIONS AND RELEVANCE

Increasing PM2.5 exposure was associated with an increased ED visit risk for both preterm and full-term infants during the first year of life, which may have implications for interventions aimed at minimizing air pollution.

Chemical Characteristics and Cytotoxicity to GC-2spd(ts) Cells of PM2.5 in Nanjing Jiangbei New Area from 2015 to 2019

PM_2.5 is an air pollutant with complex components. After entering the body through respiration, PM_2.5 can not only cause respiratory diseases, but also break through the blood-testis barrier and influence the reproductive system. PM_2.5 with different components may result in different toxic effects. In the first five years of Nanjing Jiangbei New Area, industrial transformation would change the concentration and chemical fraction of PM_2.5 in the local environment to a certain extent. In this study, PM_2.5 collected in Nanjing Jiangbei New Area every autumn and winter from 2015 to 2019 was analyzed. PM_2.5 concentration generally decreased year by year. The large proportion of secondary inorganic ions indicated the presence of secondary pollution at the sampling site. PM_2.5 was mainly emitted from fossil fuel combustion and vehicle exhaust. The cytotoxicity of PM_2.5 samples was evaluated by PM_2.5 exposure to mouse spermatocytes (GC-2spd(ts) cells). Cell viability was relatively low in 2016 and 2018, and relatively high in 2017 and 2019. Reactive oxygen species levels and DNA damage levels followed similar trends, with an overall annual decrease. The cytotoxicity of PM_2.5 on GC-2spd(ts) cells was significantly correlated with water-soluble ions, water-soluble organic carbon, heavy metals and polycyclic aromatic hydrocarbons (p < 0.01). According to principal component analysis and multiple linear regression, fossil fuel combustion, secondary transformation of pollutants and construction dust were identified as the major contributors to cytotoxic effects, contributing more than 50%.

The dynamics of cardiovascular and respiratory deaths attributed to long-term PM2.5 exposures in global megacities

Exposure to ambient fine particulate matter (PM_2.5) air pollution is a significant driver of premature deaths. We estimate the number of cardiovascular and respiratory (CR) premature deaths attributed to long-term exposure to PM_2.5 in 33 global megacities based on long-term remotely sensed observations from 2000 to 2019. Our analysis uses high-resolution (0.01 degree) PM_2.5 concentration data and cause-specific integrated exposure-response (IER) functions developed for the Global Burden of Disease Project. From 2000 to 2019, PM_2.5-related CR death rates per 1000 people increased in 6 of 33 megacities, decreased in 9, and remained constant in 18 megacities. The increase in PM_2.5-related CR mortality in 11 megacities located in South and East Asia during the period 2000-2019 can be attributed to the increases in PM_2.5 concentrations. All 33 megacities could avoid 30,248 (9 %), 62,989 (20 %), 128,457 (40 %), 198,462 (62 %) and all of the estimated 322,515 CR deaths attributed to PM_2.5 pollution in 2019 if they were to attain the World Health Organization's four interim PM_2.5 targets (IT-1, IT-2, IT-3, and IT-4) and the new air quality guideline (AQG), respectively. Major improvements in air quality are needed to reduce the number of CR deaths attributed to PM_2.5 in South and East Asia, in addition to ny reductions that would likely follow shifts in the population structures of these megacities moving forward.

Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999-2012

BACKGROUND

Ambient fine particulate matter (PM_2.5) pollution has been associated with mortality from various diseases, however, its association with under-five mortality rate (U5MR) has remained largely unknown.

METHODS

Based on the U5MR data across 2851 counties in Mainland China from 1999 to 2012, we employed approximate Bayesian latent Gaussian models to assess the association between ambient PM_2.5 and U5MR at the county level for the whole nation and sub-regions. GDP growth rate, normalized difference vegetation index (NDVI), temperature, and night-time light were included as covariates using a smoothing function. We further implemented an empirical dynamic model (EDM) to explore the potential causal relationship between PM_2.5 and U5MR.

RESULTS

We observed a declining trend in U5MR in most counties throughout the study period. Spatial heterogeneity in U5MR was observed. Nationwide analysis suggested that each 10 µg/m³ increase in annual concentration of PM_2.5 was associated with an increase of 1.2 (95% CI: 1.0 - 1.3) per 1000 live births in U5MR. Regional analyses showed that the strongest positive association was located in the Northeastern part of China [1.8 (95% CI: 1.4 - 2.1)]. The EDM showed a significant causal association between PM_2.5 and U5MR, with an embedding dimension of 5 and 7, and nonlinear values θ of 4 and 6, respectively.

CONCLUSION

China exhibited a downward trend in U5MR from 1999 to 2012, with spatial heterogeneity observed across the country. Our analysis reveals a positive association between PM_2.5 and U5MR, which may support a causal relationship.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM_2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM_2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM_2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM_2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM_2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM_2.5 values, which exhibit similar distribution trends to those of the observed PM_2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM_2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.

Sources of cellular oxidative potential of water-soluble fine ambient particulate matter in the Midwestern United States

We investigated the spatiotemporal distribution and sources of cellular oxidative potential (OP) in the Midwest US. Weekly samples were collected from three urban [Chicago (IL), Indianapolis (IN), and St. Louis (MO)], one rural [Bondville (IL], and one roadside site [Champaign (IL)] for a year (May 2018 to May 2019), and analyzed for water-soluble cellular OP using a macrophage reactive oxygen species (ROS) assay. Chemical composition of the samples including several carbonaceous components, inorganic ions, and water-soluble elementals, were also analyzed. The emission sources contributing to water-soluble cellular OP and PM_2.5 mass were analyzed using positive matrix factorization. The secondary organic aerosols contributed substantially (≥54%) to PM_2.5 cellular OP at urban sites, while the roadside and rural OP were dominated by road dust (54%) and agricultural activities (62%), respectively. However, none of these sources contributed substantially to the PM_2.5 mass (≤21%). Other sources contributing significantly to the PM_2.5 mass, i.e., secondary sulfate and nitrate, biomass burning and coal combustion (14-26%) contributed minimally to the cellular OP (≤13%). Such divergent profiles of the emission sources contributing to cellular OP vs. PM_2.5 mass demonstrate the need of considering more health-relevant metrics such as OP in the design of air pollution control strategies.

Non-negligible contributions to human health from increased household air pollution exposure during the COVID-19 lockdown in China

BACKGROUND

Ambient and household air pollution are found to lead to premature deaths from all-cause or cause-specific death. The national lockdown measures in China during COVID-19 were found to lead to abrupt changes in ambient surface air quality, but indoor air quality changes were neglected. In this study, we aim to investigate the impacts of lockdown measures on both ambient and household air pollution as well as the short-term health effects of air pollution changes.

METHODS

In this study, an up-to-date emission inventory from January to March 2020 in China was developed based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. These emission inventories, together with the emissions data from 2017 to 2019, were fed into the state-of-the-art regional chemistry transport model to simulate the air quality in the North China Plain. A hypothetical scenario assuming no lockdown effects in 2020 was also performed to determine the effects of the lockdown on air quality in 2020. A difference-to-difference approach was adopted to isolate the effects on air quality due to meteorological conditions and long-term decreasing emission trends by comparing the PM2.5 changes during lockdown to those before lockdown in 2020 and in previous years (2017-2019). The short-term premature mortality changes from both ambient and household PM2.5 changes were quantified based on two recent epidemiological studies, with uncertainty of urban and rural population migration considerations.

FINDINGS

The national lockdown measures during COVID-19 led to a reduction of 5.1 µg m-3 in ambient PM2.5 across the North China Plain (NCP) from January 25th to March 5th compared with the hypothetical simulation with no lockdown measures. However, a difference-to-difference method showed that the daily domain average PM2.5 in the NCP decreased by 9.7 µg m-3 between lockdown periods before lockdown in 2020, while it decreased by 7.9 µg m-3 during the same periods for the previous three-year average from 2017 to 2019, demonstrating that lockdown measures may only have caused a 1.8 µg m-3 decrease in the NCP. We then found that the integrated population-weighted PM2.5, including both ambient and indoor PM2.5 exposure, increased by 5.1 µg m-3 during the lockdown periods compared to the hypothetical scenario, leading to additional premature deaths of 609 (95% CI: 415-775) to 2,860 (95% CI: 1,436-4,273) in the short term, depending on the relative risk chosen from the epidemiological studies.

INTERPRETATION

Our study indicates that lockdown measures in China led to abrupt reductions in ambient PM2.5 concentration but also led to significant increases in indoor PM2.5 exposure due to confined indoor activities and increased usages of household fuel for cooking and heating. We estimated that hundreds of premature deaths were added as a combination of decreased ambient PM2.5 and increased household PM2.5. Our findings suggest that the reduction in ambient PM2.5 was negated by increased exposure to household air pollution, resulting in an overall increase in integrated population weighted exposure. Although lockdown measures were instrumental in reducing the exposure to pollution concentration in cities, rural areas bore the brunt, mainly due to the use of dirty solid fuels, increased population density due to the large-scale migration of people from urban to rural areas during the Chinese New Year and long exposure time to HAP due to restrictions in outdoor movement.

Air pollution exposure disparities across US population and income groups

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 μm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

Estimating Intra-Urban Inequities in PM2.5-Attributable Health Impacts: A Case Study for Washington, DC

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM2.5)- attributable mortality and morbidity. We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite-derived annual mean surface PM2.5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5-attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.

Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

BACKGROUND

We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies.

METHODS

The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies.

RESULTS

We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM_2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM_2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM₁₀.

CONCLUSION

253 premature deaths per million population are associated with exposure to ambient PM_2.5. We observed an unstable estimate for PM₁₀, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM₁₀ and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM_2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions

Expanded use of reduced complexity approaches in epidemiology and environmental justice investigations motivates detailed evaluation of these modeling approaches. Chemical transport models (CTMs) remain the most complete representation of atmospheric processes but are limited in applications that require large numbers of runs, such as those that evaluate individual impacts from large numbers of sources. This limitation motivates comparisons between modern CTM-derived techniques and intentionally simpler alternatives. We model population-weighted PM_2.5 source impacts from each of greater than 1100 coal power plants operating in the United States in 2006 and 2011 using three approaches: (1) adjoint PM_2.5 sensitivities calculated by the GEOS-Chem CTM; (2) a wind field-based Lagrangian model called HyADS; and (3) a simple calculation based on emissions and inverse source-receptor distance. Annual individual power plants' nationwide population-weighted PM_2.5 source impacts calculated by HyADS and the inverse distance approach have normalized mean errors between 20 and 28% and root mean square error ranges between 0.0003 and 0.0005 µg m^-3 compared with adjoint sensitivities. Reduced complexity approaches are most similar to the GEOS-Chem adjoint sensitivities nearby and downwind of sources, with degrading performance farther from and upwind of sources particularly when wind fields are not accounted for.

Near-Source Risk Functions for Particulate Matter Are Critical When Assessing the Health Benefits of Local Abatement Strategies

When mortality or other health outcomes attributable to fine particulate matter (PM2.5) are estimated, the same exposure-response function (ERF) is usually assumed regardless of the source and composition of the particles, and independently of the spatial resolution applied in the exposure model. While several recent publications indicate that ERFs based on exposure models resolving within-city gradients are steeper per concentration unit (μgm-3), the ERF for PM2.5 recommended by the World Health Organization does not reflect this observation and is heavily influenced by studies based on between-city exposure estimates. We evaluated the potential health benefits of three air pollution abatement strategies: electrification of light vehicles, reduced use of studded tires, and introduction of congestion charges in Stockholm and Gothenburg, using different ERFs. We demonstrated that using a single ERF for PM2.5 likely results in an underestimation of the effect of local measures and may be misleading when evaluating abatement strategies. We also suggest applying ERFs that distinguish between near-source and regional contributions of exposure to PM2.5. If separate ERFs are applied for near-source and regional PM2.5, congestion charges as well as a reduction of studded tire use are estimated to be associated with a significant reduction in the mortality burden in both Gothenburg and Stockholm. In some scenarios the number of premature deaths is more than 10 times higher using separate ERFs in comparison to using a single ERF irrespective of sources as recommended by the WHO. For electrification, the net change in attributable deaths is small or within the uncertainty range depending on the choice of ERF.

Recent trends in premature mortality and health disparities attributable to ambient PM2.5 exposure in China: 2005-2017

In the past decade, particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) has reached unprecedented levels in China and posed a significant threat to public health. Exploring the long-term trajectory of the PM_2.5 attributable health burden and corresponding disparities across populations in China yields insights for policymakers regarding the effectiveness of efforts to reduce air pollution exposure. Therefore, we examine how the magnitude and equity of the PM_2.5-related public health burden has changed nationally, and between provinces, as economic growth and pollution levels varied during 2005-2017. We derive long-term PM_2.5 exposures in China from satellite-based observations and chemical transport models, and estimate attributable premature mortality using the Global Exposure Mortality Model (GEMM). We characterize national and interprovincial inequality in health outcomes using environmental Lorenz curves and Gini coefficients over the study period. PM_2.5 exposure is linked to 1.8 (95% CI: 1.6, 2.0) million premature deaths over China in 2017, increasing by 31% from 2005. Approximately 70% of PM_2.5 attributable deaths were caused by stroke and IHD (ischemic heart disease), though COPD (chronic obstructive pulmonary disease) and LRI (lower respiratory infection) disproportionately affected poorer provinces. While most economic gains and PM_2.5-related deaths were concentrated in a few provinces, both gains and deaths became more equitably distributed across provinces over time. As a nation, however, trends toward equality were more recent and less clear cut across causes of death. The rise in premature mortality is due primarily to population growth and baseline risks of stroke and IHD. This rising health burden could be alleviated through policies to prevent pollution, exposure, and disease. More targeted programs may be warranted for poorer provinces with a disproportionate share of PM_2.5-related premature deaths due to COPD and LRI.

Do fine particulate air pollution (PM2.5) exposure and its attributable premature mortality differ for immigrants compared to those born in the United States?

In the United States (US), immigrants constitute a considerable and growing proportion of the general population. Compared to the US-born, immigrants have differential health risks, and it is unclear if environmental exposures contribute. In this work, we estimated disparities between immigrants and the US-born in fine particulate matter (PM2.5) exposure and attributable premature mortality, including by region of origin and time since immigration. With PM2.5 estimates from a validated model at ~1 km2 spatial resolution and residential Census tract population data, we calculated the annual area-weighted average PM2.5 exposure for immigrants overall, the US-born, and immigrants separately by geographic region of origin and time since immigration. We then calculated the premature mortality attributed to PM2.5 for each population group, assessing disparities by immigrant status in PM2.5 exposure and attributable premature mortality in the US as a whole and in each US county to evevaluate spatial heterogeneity. Overall, immigrants were exposed to slightly higher PM2.5 (0.36 μg/m3, 3.8%) than the US-born. This exposure difference translates to 2.11 more premature deaths attributable to PM2.5 per 100,000 in population for immigrants compared to the US-born in 2010. Immigrant - US-born disparities in PM2.5 and attributable premature mortality were more severe among immigrants originating from Asia, Africa, and Latin America than those from Europe, Oceania, and North America. Disparities between immigrant groups by time since immigration were comparatively small. Sensitivity analyses using 2000 data and a non-linear set of PM2.5 attributable mortality coefficients identified similar patterns. Our findings suggest that environmental exposure disparities, such as in PM2.5, may contribute to immigrant health disparities in the US.

Examining PM2.5 concentrations and exposure using multiple models

Epidemiologic studies have found associations between fine particulate matter (PM_2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM_2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM_2.5 concentrations in 2011, ii) potential changes in PM_2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM_2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM_2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m^-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM_2.5 concentrations were estimated to decrease by about 1 μg m^-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m^-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM_2.5 concentrations less than 10 μg m^-3 in 2011 and PM_2.5 improvements of about 2 μg m^-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM_2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM_2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM_2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM_2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Estimation of excess mortality due to long-term exposure to PM2.5 in continental United States using a high-spatiotemporal resolution model

BACKGROUND

Exposure to fine particulate matter (<2.5 mm in aerodynamic diameter, PM_2.5) pollution, even at low concentrations is associated with increased mortality. Estimates of the magnitude of the effect of particulate air pollution on mortality are generally done on a coarse spatial scale, such as 0.5°, and may fail to capture small spatial differences in exposure and baseline rates, which can bias results and impede the ability to consider environmental justice. We estimated the burden of mortality attributable to long-term exposure to ambient PM_2.5 among adults in the Continental United States on a 1 km scale, in order to provide information for decision makers setting health priorities.

METHODS

We conducted a health impact assessment for 2015 using a model predicting U.S. PM_2.5 concentrations at a spatial resolution of 1 km cells. We applied a concentration-response curve from a recently published meta-analysis of long-term PM_2.5 mortality association which incorporates new findings at high and low PM_2.5 concentrations. We computed the change in deaths in each grid cell, based on its grid cell population, Zip code level baseline mortality rates, and exposure under two scenarios; a decrease of PM_2.5 exposure levels by 40% and a decrease of PM_2.5 exposure levels to the county minimum PM_2.5 concentrations.

RESULTS

We estimated the deaths would fall by 104,786 (95% CI 57,016-135,726) and 112,040 (95% CI 63,261-159,116) attributable to 40% reduction and reduction to the county minimum PM_2.5 concentrations_, respectively. The greatest mortality impact due to 40% reduction in PM_2.5 was observed in California with; 11,621 (95% CI; 7156-15,989) and Texas with; 9616 (95% CI; 5798-13,352) excess deaths attributable to annual mean PM_2.5 concentrations of 9.54 and 9.12 μg m^-3, respectively. Within city analyses showed substantial heterogeneity in risk. The estimated Attributable fraction (AF %) in locations with high PM_2.5 levels was 8.6% (95% CI 5.4-11.7) compared to the overall AF% of 4.9% (95% CI; 2.9-6.8). In comparison, results using county average PM_2.5 were smaller than the estimates from the 1 km PM_2.5 datasets. Similarly, estimates using county-level mortality rates were smaller than estimates based on Zip-code level mortality rates.

CONCLUSIONS

Our study provides evidence of major health benefits expected from reducing PM_2.5 exposure, even in regions with relatively low PM_2.5 concentrations. Spatial characteristics of exposure and baseline mortality (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results.

The Effects of Fireworks Discharge on Atmospheric PM2.5 Concentration in the Chinese Lunar New Year

Discharging fireworks during the Chinese Lunar New Year celebrations is a deep-rooted custom in China. In this paper, we analyze the effect of this cultural activity on PM2.5 concentration using both ground observations and satellite data. By combining remote sensing data, the problem of uneven spatial distribution of ground monitoring has been compensated, and the research time span has been expanded. The results show that the extensive firework displays on New Year's Eve lead to a remarkable increase in nationwide PM2.5 concentration, which were 159~223% of the average level, indicating the instantaneous effect far exceeds that of any other factor over the whole year. However, the averaged PM2.5 concentrations of the celebration period were 0.99~16.32 μg/m3 lower compared to the average values of the corresponding pre-celebration period and post-celebration period, indicating the sustained effect is not very significant. The implementation of firework prohibition policies can greatly reduce the instantaneous PM2.5 increase, but no obvious air quality improvement is observed over the entire celebration period. Combining these findings and the cultural significance of this activity, we recommend that this custom is actively maintained, using new technologies and scientific governance programs to minimize the negative effects.

Quantitative Characterization of Uncertainty in the Concentration-Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations

Extensive epidemiologic evidence supports a linear, no-threshold concentration-response (C-R) relationship between long-term exposure to fine particles (PM_2.5) and mortality in the United States. While examinations of the C-R relationship are designed to assess the shape of the C-R curve, they do not provide the information needed to quantitatively characterize uncertainty at specific PM_2.5 concentrations, which is often needed in the context of risk assessments and benefits analyses. We developed a novel approach, using information that is typically available in published epidemiologic studies, to quantitatively characterize uncertainty at different concentrations along the PM_2.5 concentration distribution. Our approach utilizes the annual mean PM_2.5 concentration and corresponding standard deviation from a published epidemiologic study to estimate the standard deviation of hypothetical PM_2.5 concentration distributions defined at 0.1 μg/m³ increments. The hypothetical distributions are then used to derive adjusted uncertainty estimates in the reported effect estimate at low concentrations (i.e., concentrations lower than the annual mean observed in the study). We demonstrate the application of this method in six individual epidemiologic studies that examined the relationship between long-term PM_2.5 exposure and mortality and were conducted in different geographic locations worldwide and at different PM_2.5 concentrations. This new method allows for a more comprehensive quantitative evaluation of uncertainty in the shape of the C-R relationship between long-term PM_2.5 exposure and mortality at concentrations below the mean annual concentrations observed in current studies.

Effects of ambient PM2.5 and particle-bound metals on the healthy residents living near an electric arc furnace: A community- based study

Fine particulate matter (PM_2.5) emitted from electric arc furnaces (EAFs) poses health concerns. However, little research has been done on the impact of EAF on the health of community residents. This cross-sectional study conducted a PM_2.5 exposure assessment and health examination of community residents living near an EAF. A total of 965 residents aged 40-90 years were recruited. The residents' exposure to PM_2.5 was categorized according to the distance of their residence from the EAFs (<500, 500-1000, 1000-1500, 1500-2000, and > 2000 m). Average ambient PM_2.5 concentrations were estimated using a hybrid kriging/land-use regression (LUR) model. In addition, we selected two air-sampling sites to monitor the 2-year levels of PM_2.5 and particle-bound metals. A spot urine sample and blood samples were collected and ten heavy metal concentrations in the blood were analyzed. Inflammation- and oxidative stress-related biomarkers were measured. The associations between environmental factors and a biochemical examination were estimated using a generalized linear model. Active air sampling and hybrid kriging/LUR model simulation indicated increased levels of PM_2.5 near the EAF. The metal concentrations in PM_2.5 included Fe, Pb, Mn, Ni, As, Cu, Ni, Zn, and Al, which also significantly increased near the EAF. PM_2.5 levels were significantly associated with an increased total cholesterol-high-density lipoprotein (TC/HDL) ratio. High levels of PM_2.5 and malondialdehyde were associated with a 1.72-fold increased risk of TC/HDL ratio ≥ 4 (95% CI: 1.12-2.65) after adjusting for potential confounding factors. Blood Pb levels were significantly associated with increased systolic and diastolic blood pressure and decreased estimated glomerular filtration rate but negatively associated with distance from the EAF. The results show that people living near EAFs should pay more attention to adverse health problems, including atherogenic dyslipidemia, hypertension, and chronic kidney disease associated with exposure to PM_2.5 and particle-bound metals.

The recent and future health burden of the U.S. mobile sector apportioned by source

Mobile sources emit particulate matter as well as precursors to particulate matter (PM_2.5) and ground-level ozone, pollutants known to adversely impact human health. This study uses source-apportionment photochemical air quality modeling to estimate the health burden (expressed as incidence) of an array of PM_2.5- and ozone-related adverse health impacts, including premature death, attributable to 17 mobile source sectors in the US in 2011 and 2025. Mobile sector-attributable air pollution contributes a substantial fraction of the overall pollution-related mortality burden in the U.S., accounting for about 20% of the PM_2.5 and ozone-attributable deaths in 2011 (between 21 000 and 55 000 deaths, depending on the study used to derive the effect estimate). This value falls to about 13% (between 13 000 and 37 000 deaths) by 2025 due to regulatory and voluntary programs reducing emissions from mobile sources. Similar trends across all morbidity health impacts can also be observed. Emissions from on-road sources are the largest contributor to premature deaths; this is true for both 2011 (between 12 000 and 31 000 deaths) and 2025 (between 6700 and 18 000 deaths). Non-road construction engines, C3 marine engines and emissions from rail also contribute to large portions of premature deaths. Across the 17 mobile sectors modeled, the PM_2.5-attributable mortality and morbidity burden falls between 2011 and 2025 for 12 sectors and increases for 5. Ozone-attributable mortality and morbidity burden increases between 2011 and 2025 for 10 sectors and falls for 7. These results extend the literature beyond generally aggregated mobile sector health burden toward a representation of highly-resolved source characterization of both current and future health burden. The quantified future mobile source health burden is a novel feature of this analysis and could prove useful for decisionmakers and affected stakeholders.

Premature mortality related to United States cross-state air pollution

Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States^1-3. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM_2.5)⁴. Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality². Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states^5,6. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM_2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state's emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM_2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.

The contributions of socioeconomic indicators to global PM2.5 based on the hybrid method of spatial econometric model and geographical and temporal weighted regression

PM_2.5 pollution poses a negative effect on human health and economic growth. However, the major socioeconomic driving forces of global PM_2.5 pollution during a long-term period remained unclear. In this study, we explored the potential association between socioeconomic indicators and the PM_2.5 level worldwide using a spatial econometric model coupled with a geographical and temporal weighted regression (GTWR). The results suggested that renewable energy consumption ratio, per capita gross domestic production (GDP), per capita CO₂ emission, urban population ratio, and fossil fuel consumption ratio were major factors responsible for the global PM_2.5 pollution. The impacts of socioeconomic indicators on the PM_2.5 level varied with the income-level and time. Fossil fuel consumption ratio, per capita CO₂ emission, urban population ratio were major contributors for severe PM_2.5 pollution in the developing countries (e.g., China and India). Further, these impacts have become more remarkable in recent years. Per capita GDP still played a crucial role on the PM_2.5 pollution in India, indicating that energy-intensive industries were major contributors to its economic growth, thereby leading to the higher PM_2.5 concentration in India. However, China has strode across the inflection of Environmental Kuznets Curve (EKC) as a whole and decreased the reliance on the secondary industries. Compared with the developing countries, the impacts of socioeconomic indicators on PM_2.5 pollution in most of the developed countries remained relatively stable and weak, implicating that fossil fuel consumption and urbanization were not major contributors for local PM_2.5 level. The findings of this study clarified major contributors for PM_2.5 pollution, and provided scientific basis for mitigating the PM_2.5 pollution.

A database for evaluating the InMAP, APEEP, and EASIUR reduced complexity air-quality modeling tools

Policy analysts and researchers often use models to translate expected emissions changes from pollution control policies to estimates of air pollution changes and resulting changes in health impacts. These models can include both photochemical Eulerian grid models or reduced complexity models; these latter models make simplifying assumptions about the emissions-to-air quality relationship as a means of reducing the computational time needed to simulate air quality. This manuscript presents a new database of photochemical- and reduced complexity-modelled changes in annual average particulate matter with aerodynamic diameter less than 2.5 μm and associated health effects and economic values for five case studies representing different emissions control scenarios. The research community is developing an increasing number of reduced complexity models as lower-cost and more expeditious alternatives to full form Eulerian photochemical grid models such as the Comprehensive Air-Quality Model with eXtensions (CAMx) and the Community Multiscale Air Quality (CMAQ) model. A comprehensive evaluation of reduced complexity models can demonstrate the extent to which these tools capture complex chemical and physical processes when representing emission control options. Systematically comparing reduced complexity model predictions to benchmarks from photochemical grid models requires a consistent set of input parameters across all systems. Developing such inputs is resource intensive and consequently the data that we have developed and shared (https://github.com/epa-kpc/RFMEVAL) provide a valuable resource for others to evaluate reduced complexity models. The dataset includes inputs and outputs representing 5 emission control scenarios, including sector-based regulatory policy scenarios focused on on-road mobile sources and electrical generating units (EGUs) as well as hypothetical across-the-board reductions to emissions from cement kilns, refineries, and pulp and paper facilities. Model inputs, outputs, and run control files are provided for the Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 and 3, Intervention Model for Air Pollution (InMAP), Estimating Air pollution Social Impact Using Regression (EASIUR), and EPA's source apportionment benefit-per-ton reduced complexity models. For comparison, photochemical grid model annual average PM2.5 output is provided for each emission scenario. Further, inputs are also provided for the Environmental Benefits and Mapping Community Edition (BenMAP-CE) tool to generate county level health benefits and monetized health damages along with output files for benchmarking and intercomparison. Monetized health impacts are also provided from EASIUR and APEEP which can provide these outside the BenMAP-CE framework. The database will allow researchers to more easily compare reduced complexity model predictions against photochemical grid model predictions.

Health disparities attributable to air pollutant exposure in North Carolina: Influence of residential environmental and social factors

Understanding the environmental justice implications of the mortality impacts of air pollution exposure is a public health priority, as some subpopulations may face a disproportionate health burden. We examined which residential environmental and social factors may affect disparities in the air pollution-mortality relationship in North Carolina, US, using a time-stratified case-crossover design. Results indicate that air pollution poses a higher mortality risk for some persons (e.g., elderly) than others. Our findings have implications for environmental justice regarding protection of those who suffer the most from exposure to air pollution and policies to protect their health.

State-level drivers of future fine particulate matter mortality in the United States

Future fine particulate matter (PM₂.₅) concentrations and resulting health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographcs. In this study, a human-earth system model is used to estimate PM_2.5 mortality costs (PMMC) due to air pollutant emissions from each US state over the period 2015 to 2050, considering current major air quality and energy regulations. Contributions of various socioeconomic and energy factors to PMMC are quantified using the Logarithmic Mean Divisia Index. National PMMC are estimated to decrease 25% from 2015 to 2050, driven by decreases in energy intensity and PMMC per unit consumption of electric sector coal and transportation liquids. These factors together contribute 68% of the decrease, primarily from technology improvements and air quality regulations. States with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PMMC from their emissions. In contrast, states with larger projected decreases in PMMC have smaller increases in population and per capita GDP, and greater decreases in electric sector coal share and PMMC per unit fuel consumption.

A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards

PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

To estimate PM_2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM_2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R² value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m³, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM_2.5 measurements increase CV R² by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM_2.5 measurements and AOD values are among the most-important predictor variables for the training process.