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.

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.

Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait.

Many epidemiologic studies concerned with acute exposure to ambient PM2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM2.5 were modeled using 0-5-day cumulative distributed lags. For total PM2.5, we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 µg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM2.5 was positive (0.11%) but not statistically significant (95% CI = -0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM2.5 source and population affected.

A retrospective assessment of COVID-19 model performance in the USA.

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that-(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.

Disparities in Activity and Traffic Fatalities by Race/Ethnicity.

INTRODUCTION: Traffic fatalities remain a major public health challenge despite progress made during recent decades. This study develops exposure-based estimates of fatalities per mile traveled for pedestrians, cyclists, and light-duty vehicle occupants and describes disparities by race/ethnicity, including a subanalysis of fatality rates during darkness and in urban areas. METHODS: Estimates of person-miles traveled by mode and race/ethnicity group were derived from the 2017 National Household Travel Survey using replicate weights. Three-year average (2016‒2018) traffic fatalities were measured by mode and race/ethnicity group with the U.S. Fatality Analysis Reporting System. Fatality rates per mile traveled and CIs were calculated for each subgroup as well as separately for trips occurring during darkness and in urban areas. Analysis was conducted in 2021‒2022. RESULTS: Exposure to traffic fatality differs by race/ethnicity group and by mode, indicating that adjustment for differential exposure is needed when estimating disparities. The authors find that fatality rates per 100 million miles traveled are systematically higher for Black and Hispanic Americans for all modes and notably higher for vulnerable modes (e.g., Black Americans died at more than 4 times the rate for White Americans while cycling, 33.71 [95% CI: 21.84, 73.83] compared with 7.53 [95% CI: 6.64, 8.69], and more than 2 times the rate while walking, 40.92 [95% CI: 36.58, 46.44] compared with 18.77 [95% CI: 17.30, 20.51]). Previous estimates that do not adjust for differential exposure may underestimate disparities by race/ethnicity. Observed disparities remained when considering only urban areas and appear to be exacerbated during darkness. CONCLUSIONS: Traffic fatalities are a substantial and preventable public health challenge in America. Black and Hispanic Americans have higher traffic fatality rates per mile traveled than White Americans across the transportation system, requiring urgent attention.

Health benefits of decreases in on-road transportation emissions in the United States from 2008 to 2017.

Decades of air pollution regulation have yielded enormous benefits in the United States, but vehicle emissions remain a climate and public health issue. Studies have quantified the vehicle-related fine particulate matter (PM2.5)-attributable mortality but lack the combination of proper counterfactual scenarios, latest epidemiological evidence, and detailed spatial resolution; all needed to assess the benefits of recent emission reductions. We use this combination to assess PM2.5-attributable health benefits and also assess the climate benefits of on-road emission reductions between 2008 and 2017. We estimate total benefits of $270 (190 to 480) billion in 2017. Vehicle-related PM2.5-attributable deaths decreased from 27,700 in 2008 to 19,800 in 2017; however, had per-mile emission factors remained at 2008 levels, 48,200 deaths would have occurred in 2017. The 74% increase from 27,700 to 48,200 PM2.5-attributable deaths with the same emission factors is due to lower baseline PM2.5 concentrations (+26%), more vehicle miles and fleet composition changes (+22%), higher baseline mortality (+13%), and interactions among these (+12%). Climate benefits were small (3 to 19% of the total). The percent reductions in emissions and PM2.5-attributable deaths were similar despite an opportunity to achieve disproportionately large health benefits by reducing high-impact emissions of passenger light-duty vehicles in urban areas. Increasingly large vehicles and an aging population, increasing mortality, suggest large health benefits in urban areas require more stringent policies. Local policies can be effective because high-impact primary PM2.5 and NH3 emissions disperse little outside metropolitan areas. Complementary national-level policies for NOx are merited because of its substantial impacts-with little spatial variability-and dispersion across states and metropolitan areas.

Health benefits of on-road transportation pollution control programs in China.

China started to implement comprehensive measures to mitigate traffic pollution at the end of 1990s, but the comprehensive effects, especially on ambient air quality and public health, have not yet been systematically evaluated. In this study, we analyze the effects of vehicle emission control measures on ambient air pollution and associated deaths attributable to long-term exposures of fine particulate matter (PM2.5) and O3 based on an integrated research framework that combines scenario analysis, air quality modeling, and population health risk assessment. We find that the total impact of these control measures was substantial. Vehicular emissions during 1998-2015 would have been 2-3 times as large as they actually were, had those measures not been implemented. The national population-weighted annual average concentrations of PM2.5 and O3 in 2015 would have been higher by 11.7 µg/m3 and 8.3 parts per billion, respectively, and the number of deaths attributable to 2015 air pollution would have been higher by 510 thousand (95% confidence interval: 360 thousand to 730 thousand) without these controls. Our analysis shows a concentration of mortality impacts in densely populated urban areas, motivating local policymakers to design stringent vehicle emission control policies. The results imply that vehicle emission control will require policy designs that are more multifaceted than traditional controls, primarily represented by the strict emission standards, with careful consideration of the challenges in coordinated mitigation of both PM2.5 and O3 in different regions, to sustain improvement in air quality and public health given continuing swift growth in China's vehicle population.

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 (PM2.5), 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 PM2.5, SO2, NOx, NH3, and volatile organic compounds, and power plant emissions of PM2.5, SO2, and NOx. 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 PM2.5-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.

Global Effect Factors for Exposure to Fine Particulate Matter.

We evaluate fine particulate matter (PM2.5) exposure-response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure-response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure-response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure-response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.