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 PM2.5 emissions. Our approach combines a Gaussian plume model for near-source impacts of primary PM2.5 with a previously developed reduced-complexity model, EASIUR, to predict primary PM2.5 concentrations at a spatial resolution of 300 m across the contiguous United States. We find that lowresolution models underpredict important local spatial variation of air pollution exposure to primary PM2.5 emissions, potentially underestimating the contribution of these emissions to national inequality in PM2.5 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 PM2.5 emissions (EASIUR-HR) is a new, publicly available tool to assess inequality in air pollution exposure across the United States.

Automated summary

People of color bear disproportionate health impacts of air pollution, making air quality a critical issue of environmental justice. A high-resolution reduced-complexity model (EASIUR-HR) is developed to evaluate the disproportionate impacts of ground-level primary PM2.5 emissions. The model reveals that low-resolution models underestimate the local spatial variation of air pollution exposure and the contribution of these emissions to national inequality.