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

BRIAN GENTRY, Allen Robinson, Peter Adams, Carnegie Mellon University

     Abstract Number: 737
     Working Group: Meet the Job Seekers

Abstract
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.