Fine Particulate Air Pollution from Electricity Generation in the US: Health Impacts by Race, Income, and Geography

Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors

There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration-response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2-55.9%) to 49.9% (95% confidence interval 47.8-52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.

Adopting electric school buses in the United States: Health and climate benefits

Electric school buses have been proposed as an alternative to reduce the health and climate impacts of the current U.S. school bus fleet, of which a substantial share are highly polluting old diesel vehicles. However, the climate and health benefits of electric school buses are not well known. As they are substantially more costly than diesel buses, assessing their benefits is needed to inform policy decisions. We assess the health benefits of electric school buses in the United States from reduced adult mortality and childhood asthma onset risks due to exposure to ambient fine particulate matter (PM2.5). We also evaluate climate benefits from reduced greenhouse-gas emissions. We find that replacing the average diesel bus in the U.S. fleet in 2017 with an electric bus yields $84,200 in total benefits. Climate benefits amount to $40,400/bus, whereas health benefits amount to $43,800/bus due to 4.42*10-3 fewer PM2.5-attributable deaths ($40,000 of total) and 7.42*10-3 fewer PM2.5-attributable new childhood asthma cases ($3,700 of total). However, health benefits of electric buses vary substantially by driving location and model year (MY) of the diesel buses they replace. Replacing old, MY 2005 diesel buses in large cities yields $207,200/bus in health benefits and is likely cost-beneficial, although other policies that accelerate fleet turnover in these areas deserve consideration. Electric school buses driven in rural areas achieve small health benefits from reduced exposure to ambient PM2.5. Further research assessing benefits of reduced exposure to in-cabin air pollution among children riding buses would be valuable to inform policy decisions.

Sociodemographic and geographic variation in mortality attributable to air pollution in the United States

There are large differences in premature mortality in the USA by racial/ethnic, education, rurality, and social vulnerability index groups. Using existing concentration-response functions, particulate matter (PM2.5) air pollution, population estimates at the tract level, and county-level mortality data, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in mortality attributable to PM2.5 were consistently more pronounced between racial/ethnic groups than by education, rurality, or social vulnerability index, with the Black American population having by far the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic White population was attributable to PM2.5 in the years 2000 to 2011.

Formaldehyde Exposure Racial Disparities in Southeast Texas

Formaldehyde (HCHO) exposures during a full year were calculated for different race/ethnicity groups living in Southeast Texas using a chemical transport model tagged to track nine emission categories. Petroleum and industrial emissions were the largest anthropogenic sources of HCHO exposure in Southeast Texas, accounting for 44% of the total HCHO population exposure. Approximately 50% of the HCHO exposures associated with petroleum and industrial sources were directly emitted (primary), while the other 50% formed in the atmosphere (secondary) from precursor emissions of reactive compounds such as ethylene and propylene. Biogenic emissions also formed secondary HCHO that accounted for 11% of the total population-weighted exposure across the study domain. Off-road equipment contributed 3.7% to total population-weighted exposure in Houston, while natural gas combustion contributed 5% in Beaumont. Mobile sources accounted for 3.7% of the total HCHO population exposure, with less than 10% secondary contribution. Exposure disparity patterns changed with the location. Hispanic and Latino residents were exposed to HCHO concentrations +1.75% above average in Houston due to petroleum and industrial sources and natural gas sources. Black and African American residents in Beaumont were exposed to HCHO concentrations +7% above average due to petroleum and industrial sources, off-road equipment, and food cooking. Asian residents in Beaumont were exposed to HCHO concentrations that were +2.5% above average due to HCHO associated with petroleum and industrial sources, off-road vehicles, and food cooking. White residents were exposed to below average HCHO concentrations in all domains because their homes were located further from primary HCHO emission sources. Given the unique features of the exposure disparities in each region, tailored solutions should be developed by local stakeholders. Potential options to consider in the development of those solutions include modifying processes to reduce emissions, installing control equipment to capture emissions, or increasing the distance between industrial sources and residential neighborhoods.

Health benefits of vehicle electrification through air pollution in Shanghai, China

Vehicle electrification has been recognized for its potential to reduce emissions of air pollutants and greenhouse gases in China. Several studies have estimated how national-level policies of electric vehicle (EV) adoption might bring very large environmental and public health benefits from improved air quality to China. However, large-scale adoption is very costly, some regions derive more benefits from large-scale EV adoption than others, and the benefits of replacing internal combustion engines in specific cities are less known. Therefore, it is important for policymakers to design incentives based on regional characteristics - especially for megacities like Shanghai - which typically suffer from worse air quality and where a larger population is exposed to emissions from vehicles. Over the past five years, Shanghai has offered substantial personal subsidies for passenger EVs to accelerate its electrification efforts. Still, it remains uncertain whether EV benefits justify the strength of incentives. The purpose of our study is to evaluate the health and climate benefits of replacing light-duty gasoline vehicles (ICEVs) with battery EVs in the city of Shanghai. We assess health impacts due to ICEV emissions of primary fine particulate matter, NOx, and volatile organic compounds, and to powerplant emissions of NOx and SO2 due to EV charging. We incorporate climate benefits from reduced greenhouse gas emissions based on existing research. We find that the benefit of replacing the average ICEV with an EV in Shanghai is US$6400 (2400-14,700), with health impacts of EVs about 20 times lower than the average ICEV. Larger benefits ensue if older ICEVs are replaced, but replacing newer China ICEVs also achieves positive health benefits. As Shanghai plans to stop providing personal subsidies for EV purchases in 2024, our results show that EVs achieve public health and climate benefits and can help inform policymaking strategies in Shanghai and other megacities.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity

BACKGROUND

Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question.

OBJECTIVE

The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales.

DISCUSSION

The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/10.1289/EHP13063.

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.

Fine particulate matter composition in American Indian vs. Non-American Indian communities

BACKGROUND

Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US.

METHODS

We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time.

RESULTS

Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties.

CONCLUSIONS

This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Methods in Public Health Environmental Justice Research: a Scoping Review from 2018 to 2021

PURPOSE OF REVIEW

The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods.

RECENT FINDINGS

We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

Exploring Regional Fine Particulate Matter (PM2.5) Exposure Reduction Pathways Using an Optimal Power Flow Model: The Case of the Illinois Power Grid

This work develops an exposure-based optimal power flow model (OPF) that accounts for fine particulate matter (PM_2.5) exposure from electricity generation unit (EGU) emissions. Advancing health-based dispatch models to an OPF with transmission constraints and reactive power flow is an essential development given its utility for short- and long-term planning by system operators. The model enables the assessment of the exposure mitigation potential and the feasibility of intervention strategies while still prioritizing system costs and network stability. A representation of the Illinois power grid is developed to demonstrate how the model can inform decision making. Three scenarios minimizing dispatch costs and/or exposure damages are simulated. Other interventions assessed include adopting best-available EGU emission control technologies, having higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints fails to account for 4% of exposure damages ($60 M/y) and dispatch costs ($240 M/y). Accounting for exposure in the OPF reduces damages by 70%, a reduction on the order of that achieved by high renewable integration. About 80% of all exposure is attributed to EGUs fulfilling only 25% of electricity demand. Siting these EGUs in low-exposure zones avoids 43% of all exposure. Operation and cost advantages inherent to each strategy beyond exposure reduction suggest their collective adoption for maximum benefits.

Combining Satellite-Derived PM2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: -48%, pNO3: 8%, pNH4: 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R 2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).

Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with Batteries

Falling costs of lithium-ion (Li-ion) batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants' load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.

EASIUR-HR: A Model To Evaluate Exposure Inequality Caused by Ground-Level Sources of Primary Fine Particulate Matter

People of color disproportionately bear the health impacts of air pollution, making air quality a critical environmental justice issue. However, quantitative analysis of the disproportionate impacts of emissions is rarely done due to a lack of suitable models. Our work develops a high-resolution reduced-complexity model (EASIUR-HR) to evaluate the disproportionate impacts of ground-level primary PM_2.5 emissions. Our approach combines a Gaussian plume model for near-source impacts of primary PM_2.5 with a previously developed reduced-complexity model, EASIUR, to predict primary PM_2.5 concentrations at a spatial resolution of 300 m across the contiguous United States. We find that low-resolution models underpredict important local spatial variation of air pollution exposure to primary PM_2.5 emissions, potentially underestimating the contribution of these emissions to national inequality in PM_2.5 exposure by more than a factor of 2. We apply EASIUR-HR to analyze the impacts of vehicle electrification on exposure disparities. While such a policy has small aggregate air quality impacts nationally, it reduces exposure disparity for race/ethnic minorities. Our high-resolution RCM for primary PM_2.5 emissions (EASIUR-HR) is a new, publicly available tool to assess inequality in air pollution exposure across the United States.

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.

The Health and Climate Benefits of Economic Dispatch in China's Power System

China's power system is highly regulated and uses an "equal-share" dispatch approach. However, market mechanisms are being introduced to reduce generation costs and improve system reliability. Here, we quantify the climate and human health impacts brought about by this transition, modeling China's power system operations under economic dispatch. We find that significant reductions in mortality related to air pollution (11%) and CO₂ emissions (3%) from the power sector can be attained by economic dispatch, relative to the equal-share approach, through more efficient coal-powered generation. Additional health and climate benefits can be achieved by incorporating emission externalities in electricity generation costs. However, the benefits of the transition to economic dispatch will be unevenly distributed across China and may lead to increased health damage in some regions. Our results show the potential of dispatch decision-making in electricity generation to mitigate the negative impacts of power plant emissions with existing facilities in China.

Tracing toxic chemical releases embodied in U.S. interstate trade and their unequal distribution

Toxic chemicals have severe impacts on ecosystem, climate change and human health, and the current toxic releases are inequitably distributed across regions. Investigating the toxic release embodied in final demand by states and income groups can reveal the responsibility transfer of different entities. In this paper, we extended the U.S. multi-regional input-output (MRIO) model with toxic chemical release data in 2017 to conduct the production- and consumption-based accounting of toxic release by each state, and the inter-regional transfer of embodied toxic release between states. In addition, this paper analyzed how the toxic releases and inter-state transfer of embodied toxic release have been driven by income groups across states. The results showed that the toxic release from production was highly concentrated on the central states and the Great Lakes Region, while the consumption-based accounting of toxic release was more equally distributed across regions in the US. The non-metallic and metallic products manufacturing sectors were the most important sectors for most states from both production and consumption-based perspectives and were also the most essential sectors for interregional flows of embodied toxic release from Great Lake Region to Southeast, Mid-Atlantic and Northeast. Our results also showed that the largest portion (41.88%) of embodied toxic releases were triggered by households' final demand, and that the consumption of the richest 35% of households contributed to more than 50% of the total toxic chemical releases triggered by total final demand of all households.

Air pollution disparities and equality assessments of US national decarbonization strategies

Energy transitions and decarbonization require rapid changes to a nation's electricity generation mix. There are many feasible decarbonization pathways for the electricity sector, yet there is vast uncertainty about how these pathways will advance or derail the nation's energy equality goals. We present a framework for investigating how decarbonization pathways, driven by a least-cost paradigm, will impact air pollution inequality across vulnerable groups (e.g., low-income, minorities) in the US. We find that if no decarbonization policies are implemented, Black and high-poverty communities may be burdened with 0.19-0.22 μg/m³ higher PM_2.5 concentrations than the national average during the energy transition. National mandates requiring more than 80% deployment of renewable or low-carbon technologies achieve equality of air pollution concentrations across all demographic groups. Thus, if least-cost optimization capacity expansion models remain the dominant decision-making paradigm, strict low-carbon or renewable energy technology mandates will have the greatest likelihood of achieving national distributional energy equality. Decarbonization is essential to achieving climate goals, but myopic decarbonization policies that ignore co-pollutants may leave Black and high-poverty communities up to 26-34% higher PM_2.5 exposure than national averages over the energy transition.

Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Sources of ambient PM2.5 exposure in 96 global cities

To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM_2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM_2.5 exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.

Diverse Pathways for Power Sector Decarbonization in Texas Yield Health Cobenefits but Fail to Alleviate Air Pollution Exposure Inequities

Decarbonizing power systems is a critical component of climate change mitigation, which can have public health cobenefits by reducing air pollution. Many studies have examined strategies to decarbonize power grids and quantified their health cobenefits. However, few of them focus on near-term cobenefits at community levels, while comparing various decarbonization pathways. Here, we use a coupled power system and air quality modeling framework to quantify the costs and benefits of decarbonizing the Texas power grid through a carbon tax; replacing coal with natural gas, solar, or wind; and internalizing human health impacts into operations. Our results show that all decarbonization pathways can result in major reductions in CO₂ emissions and public health impacts from power sector emissions, leading to large net benefits when considering the costs to implement these strategies. Operational changes with existing infrastructure can serve as a transitional strategy during the process of replacing coal with renewable energy, which offers the largest benefits. However, we also find that Black and lower-income populations receive disproportionately higher air pollution damages and that none of the examined decarbonization strategies mitigate this disparity. These findings suggest that additional interventions are necessary to mitigate environmental inequity while decarbonizing power grids.

Adoption of low-carbon fuels reduces race/ethnicity disparities in air pollution exposure in California

An environmental justice (EJ) analysis shows that adoption of low-carbon energy sources in the year 2050 reduces the race/ethnicity disparity in air pollution exposure in California by as much as 20% for PM_2.5 mass and by as much as 40% for PM_0.1 mass. An ensemble of six different energy scenarios constructed using the energy-economic optimization model CA-TIMES were evaluated in future years. Criteria pollutant emissions were developed for each energy scenario using the CA-REMARQUE model using 4 km spatial resolution over four major geographic areas in California: the greater San Francisco Bay Area including Sacramento (SFBA&SAC), the San Joaquin Valley (SJV), Los Angeles (LA), and San Diego (SD). The Weather Research & Forecasting (WRF) model was used to predict future meteorology fields by downscaling two different climate scenario (RCP4.5 and RCP8.5) generated by two different GCMs (the Community Climate System Model and the Canadian Earth Systems Model). Simulations were performed over 32 weeks randomly selected during the 10 year window from the year 2046 to 2055 to build up a long-term average in the presence of ENSO variability. The trends associated with low-carbon energy adoption were relatively stable across the ensemble of locations and scenarios. Deeper reductions in the carbon intensity of energy sources progressively reduced exposure to PM_2.5 mass and PM_0.1 mass for all California residents. The greater adoption of low-carbon fuels also reduced the racial disparity in the PM exposure. The three energy scenarios that achieved an ~80% reduction in GHG emissions relative to 1990 levels simultaneously produced the greatest reduction in PM exposure for all California residents and the greatest reduction in the racial disparity of that exposure. These findings suggest that the adoption of low-carbon energy can improve public health and reduce racial disparities through an improvement in air quality.

Mortality burden due to long-term exposure to ambient PM2.5 above the new WHO air quality guideline based on 296 cities in China

OBJECTIVE

Quantifying the spatial and socioeconomic variation of mortality burden attributable to particulate matters with aerodynamic diameter ≤ 2.5 µm (PM2.5) has important implications for pollution control policy. This study aims to examine the regional and socioeconomic disparities in the mortality burden attributable to long-term exposure to ambient PM2.5 in China.

METHODS

Using data of 296 cities across China from 2015 to 2019, we estimated all-cause mortality (people aged ≥ 16 years) attributable to the long-term exposure to ambient PM2.5 above the new WHO air quality guideline (5 µg/m3). Attributed fraction (AF), attributed deaths (AD), attributed mortality rate (AMR) and total value of statistical life lost (VSL) by regional and socioeconomic levels were reported.

RESULTS

Over the period of 2015-2019, 17.0% [95% confidence interval (CI): 7.4-25.2] of all-cause mortality were attributable to long-term exposure to ambient PM2.5, corresponding to 1,425.2 thousand deaths (95% CI: 622.4-2,099.6), 103.5/105 (95% CI: 44.9-153.3) AMR, and 1006.9 billion USD (95% CI: 439.8-1483.4) total VSL per year. The AMR decreased from 120.5/105 (95% CI: 52.9-176.6) to 92.7/105 (95% CI:39.9-138.5) from 2015 to 2019. The highest mortality burden was observed in the north region (annual average AF = 24.2%, 95% CI: 10.8-35.1; annual average AMR = 137.0/105, 95% CI: 60.9-198.5). The highest AD and economic loss were observed in the east region (annual average AD = 390.0 thousand persons, 95% CI: 170.3-574.6; annual total VSL = 275.6 billion USD, 95% CI: 120.3-406.0). Highest AMR was in the cities with middle level of GDP per capita (PGDP)/urbanization. The majority of the top ten cities of AF, AMR and VSL were in high and middle PGDP/urbanization regions.

CONCLUSION

There were significant regional and socioeconomic disparities in PM2.5 attributed mortality burden among Chinese cities, suggesting differential mitigation policies are required for different regions in China.

Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework

Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.

Calling Attention to the Role of Race-Driven Societal Determinants of Health on Aggressive Tumor Biology: A Focus on Black Americans

Blacks have the highest incidence and mortality from most cancers. The reasons for these disparities remain unclear. Blacks are exposed to adverse social determinants because of historic and contemporary racist polices; however, how these determinants affect the disparities that Blacks experience is understudied. As a result of discriminatory community policies, like redlining, Blacks have higher exposure to air pollution and neighborhood deprivation. Studies investigating how these factors affect tumor biology are emerging. We highlight the literature that connects racism-related community exposure to the tumor biology in breast, lung, prostate, and colorectal cancer. Further investigations that clarify the link between adverse social determinants that result from systemic racism and aggressive tumor biology are required if health equity is to be achieved. Without recognition that racism is a public health risk with carcinogenic impact, health care delivery and cancer care will never achieve excellence. In response, health systems ought to establish corrective actions to improve Black population health and bring medical justice to marginalized racialized groups.

Municipal Solid Waste Incineration Ash-Incorporated Concrete: One Step towards Environmental Justice

Municipal solid waste and cement manufacture are two sources of environmental justice issues in urban and suburban areas. Waste utilization is an attractive alternative to disposal for eliminating environmental injustice, reducing potential hazards, and improving urban sustainability. The re-use and recycling of municipal solid waste incineration (MSWI) ash in the construction industry has drawn significant attention. Incorporating MSWI ash in cement and concrete production is a potential path that mitigates the environmental justice issues in waste management and the construction industry. This paper presents a critical overview of the pretreatment methods that optimize MSWI ash utilization in cement/concrete and the influences of MSWI ash on the performance of cement/concrete. This review aims to elucidate the potential advantages and limitations associated with the use of MSWI ash for producing cement clinker, alternative binder (e.g., alkali-activated material), cement substitutes, and aggregates. A brief overview of the generation and characteristics of MSWI ash is reported, accompanied by identifying opportunities for the use of MSWI ash-incorporated products in industrial-scale applications and recognizing associated environmental justice implications.

Optimizing Solar-Plus-Storage Deployment on Public Buildings for Climate, Health, Resilience, and Energy Bill Benefits

Climate change, public health, and resilience to power outages are of critical concern to local governments and are increasingly motivating investments in on-site solar and storage. However, designing a solar-plus-storage system to co-optimize for climate, health, resilience, and energy bill benefits requires complex trade-offs that are not captured in current analyses. To fill this gap, we integrate the climate and health impacts of grid-purchased electricity into the REopt Lite optimization model using forward-looking marginal emissions costs. Using this new model, we quantify the impact of including energy bill, climate, health, and/or power outage cost savings on the optimal sizing, battery dispatch, and economic returns of solar-plus-storage on three public building types (a hospital, school, and warehouse) across 14 U.S. cities. We find that monetizing and co-optimizing for climate and health benefits, as compared to only energy bill savings and resilience, increases the net present value of the solar-plus-storage systems by $200k to $5.2M and makes larger systems financially attractive. Our results illustrate significant differences across geographies and building types, highlighting the need for site-specific analyses and associated policies regarding the costs and benefits of solar-plus-storage.

Is It All about Efficiency? Exploring Students’ Conceptualizations of Sustainability in an Introductory Energy Course

Engineers are increasingly called on to develop sustainable solutions to complex problems. Within engineering, however, economic and environmental aspects of sustainability are often prioritized over social ones. This paper describes how efficiency and sustainability were conceptualized and interrelated by students in a newly developed second-year undergraduate engineering course, An Integrated Approach to Energy. This course took a sociotechnical approach and emphasized modern energy concepts (e.g., renewable energy), current issues (e.g., climate change), and local and personal contexts (e.g., connecting to students’ lived experiences). Analyses of student work and semi-structured interview data were used to explore how students conceptualized sustainability and efficiency. We found that in this cohort (n = 17) students often approached sustainability through a lens of efficiency, believing that if economic and environmental resources were prioritized and optimized, sustainability would be achieved. By exploring sustainability and efficiency together, we examined how dominant discourses that privilege technical over social aspects in engineering can be replicated within an energy context.

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.

Adopting a "Compound" Exposome Approach in Environmental Aging Biomarker Research: A Call to Action for Advancing Racial Health Equity

BACKGROUND

In June 2020, the National Academies of Sciences, Engineering, and Medicine hosted a virtual workshop focused on integrating the science of aging and environmental health research. The concurrent COVID-19 pandemic and national attention on racism exposed shortcomings in the environmental research field's conceptualization and methodological use of race, which have subsequently hindered the ability of research to address racial health disparities. By the workshop's conclusion, the authors deduced that the utility of environmental aging biomarkers-aging biomarkers shown to be specifically influenced by environmental exposures-would be greatly diminished if these biomarkers are developed absent of considerations of broader societal factors-like structural racism-that impinge on racial health equity.

OBJECTIVES

The authors reached a post-workshop consensus recommendation: To advance racial health equity, a "compound" exposome approach should be widely adopted in environmental aging biomarker research. We present this recommendation here.

DISCUSSION

The authors believe that without explicit considerations of racial health equity, people in most need of the benefits afforded by a better understanding of the relationships between exposures and aging will be the least likely to receive them because biomarkers may not encompass cumulative impacts from their unique social and environmental stressors. Employing an exposome approach that allows for more comprehensive exposure-disease pathway characterization across broad domains, including the social exposome and neighborhood factors, is the first step. Exposome-centered study designs must then be supported with efforts aimed at increasing the recruitment and retention of racially diverse study populations and researchers and further "compounded" with strategies directed at improving the use and interpretation of race throughout the publication and dissemination process. This compound exposome approach maximizes the ability of our science to identify environmental aging biomarkers that explicate racial disparities in health and best positions the environmental research community to contribute to the elimination of racial health disparities. https://doi.org/10.1289/EHP8392.

PM2.5 polluters disproportionately and systemically affect people of color in the United States

Racial-ethnic minorities in the United States are exposed to disproportionately high levels of ambient fine particulate air pollution (PM_2.5), the largest environmental cause of human mortality. However, it is unknown which emission sources drive this disparity and whether differences exist by emission sector, geography, or demographics. Quantifying the PM_2.5 exposure caused by each emitter type, we show that nearly all major emission categories-consistently across states, urban and rural areas, income levels, and exposure levels-contribute to the systemic PM_2.5 exposure disparity experienced by people of color. We identify the most inequitable emission source types by state and city, thereby highlighting potential opportunities for addressing this persistent environmental inequity.

Positive Externalities of Climate Change Mitigation and Adaptation for Human Health: A Review and Conceptual Framework for Public Health Research

Anthropogenic climate change is adversely impacting people and contributing to suffering and increased costs from climate-related diseases and injuries. In responding to this urgent and growing public health crisis, mitigation strategies are in place to reduce future greenhouse gas emissions (GHGE) while adaptation strategies exist to reduce and/or alleviate the adverse effects of climate change by increasing systems' resilience to future impacts. While these strategies have numerous positive benefits on climate change itself, they also often have other positive externalities or health co-benefits. This knowledge can be harnessed to promote and improve global public health, particularly for the most vulnerable populations. Previous conceptual models in mitigation and adaptation studies such as the shared socioeconomic pathways (SSPs) considered health in the thinking, but health outcomes were not their primary intention. Additionally, existing guidance documents such as the World Health Organization (WHO) Guidance for Climate Resilient and Environmentally Sustainable Health Care Facilities is designed primarily for public health professionals or healthcare managers in hospital settings with a primary focus on resilience. However, a detailed cross sectoral and multidisciplinary conceptual framework, which links mitigation and adaptation strategies with health outcomes as a primary end point, has not yet been developed to guide research in this area. In this paper, we briefly summarize the burden of climate change on global public health, describe important mitigation and adaptation strategies, and present key health benefits by giving context specific examples from high, middle, and low-income settings. We then provide a conceptual framework to inform future global public health research and preparedness across sectors and disciplines and outline key stakeholders recommendations in promoting climate resilient systems and advancing health equity.

Assessing the health impacts of electric vehicles through air pollution in the United States

The environmental consequences of electric vehicles (EV) have been extensively studied, but the literature on their health impacts is scant. At the same time, fine particulate matter (PM_2.5), for which transportation is a major source, remains an important public health issue in the United States. Motivated by recent developments in epidemiology and reduced-form air pollution modeling, as well as reductions in power plant emissions, we conduct an updated assessment of health benefits of light-duty vehicle electrification in large metropolitan areas (MSAs) in the United States. We first calculate MSA-specific mortality impacts per mile attributable to fine particles from internal combustion engine vehicle (ICEV) tailpipe emissions of PM_2.5, SO₂, NO_x, NH₃, and volatile organic compounds, and power plant emissions of PM_2.5, SO₂, and NO_x. We complement these with changes in greenhouse-gas emissions associated with vehicle electrification. We find that electrification leads to large benefits, even with EVs powered exclusively by fossil fuel plants. VMT-weighted mean benefits in the 53 MSAs are 6.9 ¢/mile ($10,400 per 150,000 miles), 83% of which (5.7 ¢/mile or $8600 per 150,000 miles) comes from reductions in PM_2.5-attributable mortality. Variability among the MSAs is large, with benefits ranging from 3.4 ¢/mile ($5100 per 150,000 miles) in Rochester, NY, to 11.5 ¢/mile ($17,200 per 150,000 miles) in New York, NY. This large variability suggests incentives should vary by MSA and presents an opportunity to target areas for EV deployment aimed at maximizing public health benefits. Impacts are smaller when EVs disproportionately replace newer ICEV models but EVs still lead to positive benefits in all MSAs. Vehicle electrification in urban areas is an opportunity to achieve large public health benefits in the United States in the short term.

Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits

Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO₂ reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO₂ by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.