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

[Image: see text] 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.