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 m) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California from 2012 to 2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019 though the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced the 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. Additionally, PM2.5 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.

This dataset was collected and processed as detailed in our Science Advances paper: Jason G. Su, Vy Vuong, Eahsan Shahriary, Emma Yakutis, Emma Sage, Rebecca Haile, John Balmes, Michael Jerrett, Meredith Barrett. "Examining Air Pollution Exposure Dynamics in Disadvantaged Communities through High-Resolution Mapping." Science Advances, https://doi.org/10.1126/sciadv.adm9986.

Funding provided by: California Air Resources BoardROR ID: https://ror.org/021h56y19Award Number: 19RD004