The impact of three recent coal-fired power plant closings on Pittsburgh air quality: A natural experiment

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.

Update on air pollution control strategies for coal-fired power plants

ABSTRACT

Coal is expected to remain a significant power supply source worldwide and shifting to carbon-neutral fuels will be challenging because of growing electricity demand and booming industrialization. At the same time, coal consumption results in severe air pollution and health concerns. Improvement in emission control technologies is a key to improving air quality in coal power plants. Many scientists reported removing air pollutants individually via conventional control methods. However, controlling multiple pollutants combinedly using the latest techniques is rarely examined. Therefore, this paper overviews the current and advanced physical technologies to control multi-air pollutants synergistically, including carbon control technologies. Also, the paper aims to examine how potential air pollutants (e.g., PM_2.5, SO₂, NOx, CO₂), including mercury from the coal-fired power plants, cause environmental impacts. The data synthesis shows that coal quality is the most significant factor for increasing air emissions, regardless of power plant capacity. It is found that selecting techniques is critical for new and retrofitted plants depending on the aging of a power plant and other socio-economic factors. Considering the future perspective, this paper discusses possible pathways to transform from linear to a circular economy in a coal power plant sector, such as utilizing energy losses through energy-efficient processes and reuse of syngas. The article provides an in-depth analysis of advanced cost-effective techniques that would help to control the air pollution level. Additionally, a life cycle assessment-based decision-making framework is proposed that would assist the stakeholders in achieving net-zero emissions and offset the financial burden for air pollution control in coal-fired power plants.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10098-022-02328-8.

Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population: time stratified, case crossover study

OBJECTIVE

To assess risks and costs of hospital admission associated with short term exposure to fine particulate matter with diameter less than 2.5 µm (PM2.5) for 214 mutually exclusive disease groups.

DESIGN

Time stratified, case crossover analyses with conditional logistic regressions adjusted for non-linear confounding effects of meteorological variables.

SETTING

Medicare inpatient hospital claims in the United States, 2000-12 (n=95 277 169).

PARTICIPANTS

All Medicare fee-for-service beneficiaries aged 65 or older admitted to hospital.

MAIN OUTCOME MEASURES

Risk of hospital admission, number of admissions, days in hospital, inpatient and post-acute care costs, and value of statistical life (that is, the economic value used to measure the cost of avoiding a death) due to the lives lost at discharge for 214 disease groups.

RESULTS

Positive associations between short term exposure to PM2.5 and risk of hospital admission were found for several prevalent but rarely studied diseases, such as septicemia, fluid and electrolyte disorders, and acute and unspecified renal failure. Positive associations were also found between risk of hospital admission and cardiovascular and respiratory diseases, Parkinson's disease, diabetes, phlebitis, thrombophlebitis, and thromboembolism, confirming previously published results. These associations remained consistent when restricted to days with a daily PM2.5 concentration below the WHO air quality guideline for the 24 hour average exposure to PM2.5. For the rarely studied diseases, each 1 µg/m3 increase in short term PM2.5 was associated with an annual increase of 2050 hospital admissions (95% confidence interval 1914 to 2187 admissions), 12 216 days in hospital (11 358 to 13 075), US$31m (£24m, €28m; $29m to $34m) in inpatient and post-acute care costs, and $2.5bn ($2.0bn to $2.9bn) in value of statistical life. For diseases with a previously known association, each 1 µg/m3 increase in short term exposure to PM2.5 was associated with an annual increase of 3642 hospital admissions (3434 to 3851), 20 098 days in hospital (18 950 to 21 247), $69m ($65m to $73m) in inpatient and post-acute care costs, and $4.1bn ($3.5bn to $4.7bn) in value of statistical life.

CONCLUSIONS

New causes and previously identified causes of hospital admission associated with short term exposure to PM2.5 were found. These associations remained even at a daily PM2.5 concentration below the WHO 24 hour guideline. Substantial economic costs were linked to a small increase in short term PM2.5.

Developing an Advanced PM2.5 Exposure Model in Lima, Peru

It is well recognized that exposure to fine particulate matter (PM_2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM_2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM_2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM_2.5 concentrations at a 1 km² spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R² (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m³). Mean PM_2.5 for ground measurements was 24.7 μg/m³ while mean estimated PM_2.5 was 24.9 μg/m³ in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m³ (Std.Dev. = 5.97 μg/m³), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM_2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM_2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM_2.5 measurements at 1 km² spatial resolution to support future epidemiological studies.

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies-termed accountability studies-contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed "counterfactual"-the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

IMPLICATIONS

The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.