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Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM 2.5 " as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM 2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM 2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM 2.5 was associated with 2.1 times greater mortality risk than exposure to PM 2.5 from all sources. A total of 460,000 deaths were attributable to coal PM 2.5 , representing 25% of all PM 2.5 -related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

Background: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups., Objectives: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤ 2.5 μ m ( PM 2.5 ) associated with coal power plant SO 2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions., Methods: We developed a data set of annual PM 2.5 source impacts ("coal PM 2.5 ") associated with SO 2 emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups., Results: Nationwide population-weighted coal PM 2.5 declined from 1. 96 μ g / m 3 in 1999 to 0.06   μ g / m 3 in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO 2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West., Discussion: We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM 2.5 . Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM 2.5 associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

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.

NO 2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO 2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO 2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R 2 of 0.788 overall, a spatial R 2 of 0.844, and a temporal R 2 of 0.729. The relationship between daily monitored and predicted NO 2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO 2 levels. This NO 2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO 2 in unmonitored areas. We found the highest NO 2 levels along highways and in cities. We also observed that nationwide NO 2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.

Various approaches have been proposed to model PM 2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM 2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM 2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R 2 of 0.86 for daily PM 2.5 predictions. For annual PM 2.5 estimates, the cross-validated R 2 was 0.89. Our model demonstrated good performance up to 60 μg/m 3 . Using trained PM 2.5 model and predictor variables, we predicted daily PM 2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM 2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM 2.5 for every 1 km × 1 km grid cell. This PM 2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM 2.5 . Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance., (Copyright © 2019. Published by Elsevier Ltd.)

Background: National, state, and local policies contributed to a 65% reduction in sulfur dioxide emissions from coal-fired power plants between 2005 and 2012 in the United States, providing an opportunity to directly quantify public health benefits attributable to these reductions under an air pollution accountability framework., Methods: We estimate ZIP code-level changes in two different-but related-exposure metrics: total PM2.5 concentrations and exposure to coal-fired power plant emissions. We associate changes in 10 health outcome rates among approximately 30 million US Medicare beneficiaries with exposure changes between 2005 and 2012 using two difference-in-difference regression approaches designed to mitigate observed and unobserved confounding., Results: Rates per 10,000 person-years of six cardiac and respiratory health outcomes-all cardiovascular disease, chronic obstructive pulmonary disorder, cardiovascular stroke, heart failure, ischemic heart disease, and respiratory tract infections-decreased by between 7.89 and 1.95 per (Equation is included in full-text article.)decrease in PM2.5, with comparable decreases in coal exposure leading to slightly larger rate decreases. Results for acute myocardial infarction, heart rhythm disorders, and peripheral vascular disease were near zero and/or mixed between the various exposure metrics and analyses. A secondary analysis found that nonlinearities in relationships between changing health outcome rates and coal exposure may explain differences in their associations., Conclusions: The direct analyses of emissions reductions estimate substantial health benefits via coal power plant emission and PM2.5 concentration reductions. Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.

Background: Despite dramatic air quality improvement in the United States over the past decades, recent years have brought renewed scrutiny and uncertainty surrounding the effectiveness of specific regulatory programs for continuing to improve air quality and public health outcomes., Methods: We employ causal inference methods and a spatial hierarchical regression model to characterize the extent to which a designation of "nonattainment" with the 1997 National Ambient Air Quality Standard for ambient fine particulate matter (PM2.5) in 2005 causally affected ambient PM2.5 and health outcomes among over 10 million Medicare beneficiaries in the Eastern United States in 2009-2012., Results: We found that, on average across all retained study locations, reductions in ambient PM2.5 and Medicare health outcomes could not be conclusively attributed to the nonattainment designations against the backdrop of other regional strategies that impacted the entire Eastern United States. A more targeted principal stratification analysis indicates substantial health impacts of the nonattainment designations among the subset of areas where the designations are estimated to have actually reduced ambient PM2.5 beyond levels achieved by regional measures, with noteworthy reductions in all-cause mortality, chronic obstructive pulmonary disorder, heart failure, ischemic heart disease, and respiratory tract infections., Discussion: These findings provide targeted evidence of the effectiveness of local control measures after nonattainment designations for the 1997 PM2.5 air quality standard.

Importance: The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups., Objective: To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States., Design, Setting, and Participants: Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes., Exposures: Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year., Main Outcomes and Measures: All-cause mortality in the entire Medicare population from 2000 to 2012., Results: During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship., Conclusions and Relevance: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated.

Background: Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups., Methods: We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 μm [PM 2.5 ]) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 μg per cubic meter for PM 2.5 and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates., Results: Increases of 10 μg per cubic meter in PM 2.5 and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval [CI], 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM 2.5 of less than 12 μg per cubic meter and ozone of less than 50 ppb, the same increases in PM 2.5 and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM 2.5 , the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population., Conclusions: In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM 2.5 and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.).

Introduction: The regulatory and policy environment surrounding air quality management warrants new types of epidemiological evidence. Whereas air pollution epidemiology has typically informed previous policies with estimates of exposure-response relationships between pollution and health outcomes, new types of evidence can inform current debates about the actual health impacts of air quality regulations. Directly evaluating specific regulatory strategies is distinct from and complements estimating exposure-response relationships; increased emphasis on assessing the effectiveness of well-defined regulatory interventions will enhance the evidence supporting policy decisions. The goal of this report is to provide new analytic perspectives and statistical methods for what we refer to as "direct"-accountability assessment of the effectiveness of specific air quality regulatory interventions. Toward this end, we sharpened many of the distinctions surrounding accountability assessment initially raised by the HEI Accountability Working Group (2003) through discussion, development, and deployment of statistical methods for drawing causal inferences from observational data. The methods and analyses presented here are unified in their focus on anchoring accountability assessment to the estimation of the causal consequences of well-defined actions or interventions. These analytic perspectives are discussed in the context of two direct-accountability case studies pertaining to four different links in the so-called chain of accountability, the related series of events leading from the intervention to the expected outcomes (see Preface; HEI Accountability Working Group 2003)., Methods: The statistical methods described in this report consist of both established methods for drawing causal inferences from observational data and newly developed methods for assessing causal accountability. We have sharpened the analytic distinctions between studies that directly evaluated the effectiveness of specific policies and those that estimated exposure-response relationships between pollution and health. We emphasized how a potential-outcomes paradigm for causal inference can elevate policy debates by means of more direct evidence of the extent to which complex regulatory interventions affect pollution and health outcomes. We also outlined the potential-outcomes perspective and promoted its use as a means to frame observational studies as approximate randomized experiments. Our newly developed methods for assessing causal accountability draw on propensity scores, principal stratification, causal mediation analysis, spatial hierarchical models, and Bayesian estimation. The first case study made use of health outcomes among approximately four million Medicare beneficiaries living in the Western United States to estimate the causal health impacts of areas designated as being in nonattainment for particulate matter ≤10 μm in aerodynamic diameter (PM10*) according to the 1987 National Ambient Air Quality Standards (NAAQS). The second case study focused on developing and testing our new, advanced methodology for multipollutant accountability assessment by examining the extent to which sulfur dioxide (SO2) scrubbers on coal-fired power plants causally affect emissions of SO2, nitrogen oxides (NO(x)), and carbon dioxide (CO2) as well as the extent to which emissions reductions mediate the causal effect of a scrubber on ambient concentrations of PM2.5. Both case studies were anchored in our compilation of national, linked data on ambient air quality monitoring, weather, population demographics, Medicare hospitalization and mortality outcomes, continuous-emissions monitoring for electricity-generating units (EGUs) in power plants, and a variety of regulatory control interventions. The resulting database has unprecedented accuracy and granularity for conducting the types of accountability assessments presented in this report. A key component of our work was the creation of tools to help distribute our linked database and to facilitate reproducible research., Results: In the first case study, we focused on illustrating the most fundamental features of a causal-inference perspective on direct-accountability assessment. The results indicated that all-cause Medicare mortality and respiratory-related hospitalization rates were causally reduced in areas designated as nonattainment for PM10 during 1990 to 1995 compared with the rates that would have occurred without the designation. In the second case study, which examined power-plant emissions and illustrated our newly developed statistical methods, the results indicated that the presence of an SO2 scrubber causally reduced ambient PM2.5 and that this reduction was mediated almost entirely through causal reductions in SO2 emissions. The results were interpreted in light of the well-documented relationships between scrubbers, power-plant emissions, and PM2.5., Conclusion: By grounding accountability research in a potential-outcomes framework and applying our new methods to our collection of national data sets, we were able to provide additional sound evidence of the health effects of long-term, large-scale air quality regulations. This additional, rigorous evidence of the causal effects of well-defined actions augments the existing body of research and ensures that the highest-level epidemiological evidence will continue to support regulatory policies. Ultimately, our research contributed to the evidence available to support to the U.S. Environmental Protection Agency (U.S. EPA) and other stakeholders for incorporating health outcomes research into policy development.

Summary: Coal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified. We offer a novel statistical evaluation of some of these presumed causal relationships, integrating the formality of causal inference methods with repurposed tools from atmospheric science to accommodate the central challenge of long‐range pollution transport of emissions from power plants to exposed populations. The statistical approach follows recent work on Bayesian methods for deploying principal stratification and causal mediation analysis in tandem to characterize the extent to which decreased sulphur dioxide emissions from 410 power plants across the USA impact mortality and hospitalization outcomes across Medicare beneficiaries residing across 12370 locations in a manner that is mediated through reductions of ambient fine particulate pollution. The result is the first epidemiological investigation integrating causal inference methodology with direct measurements of coal emissions, pollution transport, ambient pollution and human health in a single analysis, indicating the potential for data science at the intersection of statistics, epidemiology and atmospheric science. [ABSTRACT FROM AUTHOR]

Importance: The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups.Objective: To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States.Design, Setting, and Participants: Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes.Exposures: Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year.Main Outcomes and Measures: All-cause mortality in the entire Medicare population from 2000 to 2012.Results: During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship.Conclusions and Relevance: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups.Methods: We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 μm [PM2.5]) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 μg per cubic meter for PM2.5 and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates.Results: Increases of 10 μg per cubic meter in PM2.5 and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval [CI], 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM2.5 of less than 12 μg per cubic meter and ozone of less than 50 ppb, the same increases in PM2.5 and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM2.5, the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population.Conclusions: In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM2.5 and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.). [ABSTRACT FROM AUTHOR]