Examining air pollution exposure dynamics in disadvantaged communities through high-resolution mapping.

Abstract

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), and ozone (O₃) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO₂ and PM_2.5 to that of O₃. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO₂ and PM_2.5 reductions and the advantaged neighborhoods experienced greatest rising O₃ concentrations. Further, day-to-day exposure variations decreased for NO₂ and O₃. The disparity in NO₂ exposure decreased, while it persisted for O₃. In addition, PM_2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.