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

Statistical inference for complete and incomplete mobility trajectories under the flight-pause model

We formulate a statistical flight-pause model (FPM) for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the FPM, representing an understudied missing data phenomenon. We demonstrate the consequences of missing data and our proposed adjustments in both simulations and real data, outlining implications for MPT data collection and design.

Mortality risk from United States coal electricity generation

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 PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

Source-Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts

Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.

Public health benefits of zero-emission electric power generation in Virginia

Curbing the worst impacts of global climate change will require rapidly transitioning away from fossil fuel across all sectors of the economy. This transition will also yield substantial co-benefits, as fossil fuel combustion releases harmful pollutants into the air. In this article, we present an analysis of the co-benefits to health and health-care costs related from decarbonization of the power sector, using the Virginia Clean Economy Act (VCEA) as a case study. Using a model that combines a source-response matrix approach to pollutant concentration modelling tied to health impact functions, our analysis shows that, by 2045, the VCEA will save up to 32 lives per year across the state, and avoid up to $355 million per year in health-related costs. Fossil-fuel free generation will also help the most disadvantaged communities, as counties in the highest poverty rate quintile also avoid the most pollutant-related deaths.

Site-specific Atg13 methylation-mediated autophagy regulates epithelial inflammation in PM2.5-induced pulmonary fibrosis

Fine particulate matters (PM2.5) increased the risk of pulmonary fibrosis. However, the regulatory mechanisms of lung epithelium in pulmonary fibrosis remained elusive. Here we developed PM2.5-exposure lung epithelial cells and mice models to investigate the role of autophagy in lung epithelia mediating inflammation and pulmonary fibrosis. PM2.5 exposure induced autophagy in lung epithelial cells and then drove pulmonary fibrosis by activation of NF-κB/NLRP3 signaling pathway. PM2.5-downregulated ALKBH5 protein expression promotes m6A modification of Atg13 mRNA at site 767 in lung epithelial cells. Atg13-mediated ULK complex positively regulated autophagy and inflammation in epithelial cells with PM2.5 treatment. Knockout of ALKBH5 in mice further accelerated ULK complex-regulated autophagy, inflammation and pulmonary fibrosis. Thus, our results highlighted that site-specific m6A methylation on Atg13 mRNA regulated epithelial inflammation-driven pulmonary fibrosis in an autophagy-dependent manner upon PM2.5 exposure, and it provided target intervention strategies towards PM2.5-induced pulmonary fibrosis.

The local impacts of coal and oil power plant retirements on air pollution and cardiorespiratory health in California: An application of generalized synthetic control method

BACKGROUND

This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method.

METHODS

We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record.

RESULTS

The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses.

CONCLUSIONS

We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

Inequitable Exposures to U.S. Coal Power Plant-Related PM2.5: 22 Years and Counting

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 ≤2.5μm (PM2.5) associated with coal power plant SO2 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 PM2.5 source impacts ("coal PM2.5") associated with SO2 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 PM2.5 declined from 1.96μg/m3 in 1999 to 0.06 μg/m3 in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO2 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 PM2.5. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM2.5 associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States

Understanding impacts of renewable energy on air quality and associated human exposures is essential for informing future policy. We estimate the impacts of U.S. wind power on air quality and pollution exposure disparities using hourly data from 2011 to 2017 and detailed atmospheric chemistry modeling. Wind power associated with renewable portfolio standards in 2014 resulted in $2.0 billion in health benefits from improved air quality. A total of 29% and 32% of these health benefits accrued to racial/ethnic minority and low-income populations respectively, below a 2021 target by the Biden administration that 40% of the overall benefits of future federal investments flow to disadvantaged communities. Wind power worsened exposure disparities among racial and income groups in some states but improved them in others. Health benefits could be up to $8.4 billion if displacement of fossil fuel generators prioritized those with higher health damages. However, strategies that maximize total health benefits would not mitigate pollution disparities, suggesting that more targeted measures are needed.

The effect of residential proximity to brownfields, highways, and heavy traffic on serum metal levels in the Detroit Neighborhood Health Study

Research in environmental sciences has demonstrated that land in close proximity to brownfields and heavily trafficked highways is contaminated with toxic metals. Despite this, little is known about the influence of brownfields and highways on metal levels in residents living nearby. We used data from 774 participants in the Detroit Neighborhood Health Study to estimate the effect of residential proximity to brownfields, highways, and present-day traffic on serum levels of lead, mercury, manganese, and copper using generalized estimating equations. We found that a 1 standard deviation increase in residential brownfield density within 200m was associated with increased serum lead levels (β: 0.04, 95% CI: -0.01, 0.09). The same modeled increase in a subset of historic industrial-use brownfields was associated with elevated serum mercury (β: 0.06, 95% CI: 0.03, 0.09). Increased highway and traffic density was positively associated with serum manganese (β: 0.02, 95% CI: 0.01, 0.04). Highway and traffic density was also positively associated with serum lead (β: 0.02, 95% CI: 0.01, 0.03) after restricting the analysis to participants who did not move during the study follow-up period. These findings draw attention to the importance of remediating polluted post-industrial sites in heavily populated areas, particularly as residents continue to move into densely populated cities around the globe.

Association between county-level coal-fired power plant pollution and racial disparities in preterm births from 2000 to 2018

Coal has historically been a primary energy source in the United States. The byproducts of coal combustion, such as fine particulate matter (PM_2.5), have increasingly been associated with adverse birth outcomes. The goal of this study was to leverage the current progressive transition away from coal in the United States (U.S.) to assess whether coal PM_2.5 is associated with preterm birth rates and whether this association differs by maternal Black/White race/ethnicity. Using a novel dispersion modeling approach, we estimated PM_2.5 pollution from coal-fired power plants nationwide at the county-level during the study period (2000-2018). We also obtained county-level preterm birth rates for non-Hispanic White and non-Hispanic Black mothers. We used a generalized additive mixed model to estimate the relationship between coal PM_2.5 and preterm birth rates, overall and stratified by maternal race. We included a natural spline to allow for non-linearity in the concentration-response curve. We observed a positive non-linear relationship between coal PM_2.5 and preterm birth rate, which plateaued at higher levels of pollution. We also observed differential associations by maternal race; the association was stronger for White women, especially at higher levels of coal PM_2.5 (> 2.0 μg/m³). Our findings suggest that the transition away from coal may reduce preterm birth rates in the U.S.

Improved asthma outcomes observed in the vicinity of coal power plant retirement, retrofit, and conversion to natural gas

Coal-fired power plants release substantial air pollution, including over 60% of U.S. sulfur dioxide (SO2) emissions in 2014. Such air pollution may exacerbate asthma however direct studies of health impacts linked to power plant air pollution are rare. Here, we take advantage of a natural experiment in Louisville, Kentucky, where one coal-fired power plant retired and converted to natural gas, and three others installed SO2 emission control systems between 2013 and 2016. Dispersion modeling indicated exposure to SO2 emissions from these power plants decreased after the energy transitions. We used several analysis strategies, including difference-in-differences, first-difference, and interrupted time-series modeling to show that the emissions control installations and plant retirements were associated with reduced asthma disease burden related to ZIP code-level hospitalizations and emergency room visits, and individual-level medication use as measured by digital medication sensors.

Air pollution accountability of energy transitions: the relative importance of point source emissions and wind fields in exposure changes

Recent studies have sought epidemiological evidence of the effectiveness of energy transitions. Such evidence often relies on so-called "natural experiments," wherein environmental and/or health outcomes are assessed before, during, and after the transition of interest. Often, these studies attribute air pollution exposure changes-either modeled or measured-directly to the transition. We formalize a framework for separating the fractions of a given exposure change attributable to meteorological variability and emissions changes. Using this framework, we quantify relative impacts of wind variability and emissions changes from coal-fired power plants on exposure to SO2 emissions across the United States under three unique combinations of spatial-temporal and source scales. We find that the large emissions reductions achieved by United States coal-fired power plants after 2005 dominated population exposure changes. In each of the three case studies, however, we identified periods and regions in which meteorology dampened or accentuated differences in total exposure relative to exposure change expected from emissions reductions alone. The results evidence a need for separating meteorology-induced variability in exposure when attributing health impacts to specific energy transitions.