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 (NO(2)), fine particulate matter (PM(2.5)), and ozone (O(3)) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO(2) and PM(2.5) to that of O(3). We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO(2) and PM(2.5) reductions and the advantaged neighborhoods experienced greatest rising O(3) concentrations. Further, day-to-day exposure variations decreased for NO(2) and O(3). The disparity in NO(2) exposure decreased, while it persisted for O(3). 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.