PM2.5 data inputs alter identification of disadvantaged communities

Abstract

Abstract Communities of color and lower income are often found to experience disproportionate levels of fine particulate matter (PM2.5) air pollution in the US (Pope and Dockery 2006 J. Air Waste Manage. Assoc. 56 709–42; Brook et al 2010 Circulation 121 2331–78; Tessum et al 2021 Sci. Adv. 7 eabf4491). The federal and several state governments use relatively coarsely resolved (12 km) PM2.5 concentration estimates to identify overburdened communities. Newly available PM2.5 datasets estimate concentrations at increasingly high spatial resolutions (50 m–1 km), with different magnitudes and spatial patterns, potentially affecting assessments of racial, ethnic, and socioeconomic exposure disparities. We show that two recently available high-resolution datasets from the scientific community and the 12 km dataset are consistent for national and regional average, but not intraurban, PM2.5 concentration disparities in 2019. The datasets consistently indicate that regional average PM2.5 concentrations are higher in the least White (by 3%–65%) and most Hispanic census tracts (2%–47%), compared with in the most Non-Hispanic White tracts. However, in nine of the ten most populous cities, the three datasets differ on the order of least-to-most exposed population subgroups. We identified 1029 tracts (representing ∼4.5 million people) as disadvantaged (⩾65th percentile for poverty and ⩾90th percentile PM2.5 as defined by the Climate and Economic Justice Screening Tool) in all three datasets, 335 tracts (∼1.5 million people) as disadvantaged using both high-resolution datasets but not the 12 km dataset, and 695 tracts (∼2.7 million people) as disadvantaged in the 12 km dataset but not the high-resolution datasets. The 12 km dataset does not capture intraurban disparities and may mischaracterize disproportionately exposed neighborhoods. The high-resolution PM2.5 datasets can be further improved by ground-truthing with observations from rapidly expanding ground and mobile monitoring and by integrating across available datasets.