Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples.

This study estimated annual average ambient fine particulate matter (PM2.5) concentrations at 1 km resolution using satellite Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD), land use parameters, and meteorology in California for the year 2016 (cross-validation R2 = 0.73 (site-based) and 0.81 (observation-based)). Using these high-resolution PM2.5 estimates, regionally varying urban enhancements of PM2.5 concentrations, 1.43-2.77 μg/m3 (23.9-36.2%), were identified in the densely populated air basins of San Francisco Bay Area, San Joaquin Valley, and South Coast. On the other hand, within-urban PM2.5 variability was found to be 31.4-35.6% of between-urban variability across California. However, this pattern was not consistent from region to region, even showing higher within-urban variability (e.g., San Francisco Bay Area). In addition, satellite-based PM2.5 concentrations were statistically significantly associated with demographic factors (i.e., % people of color, % poverty, and % low education) with the strongest positive association with % people of color (1.05 and 2.72 μg/m3 increases per interquartile range (IQR) and range increases, respectively). The fine-scale PM2.5 estimates enabled the assessment of long-term PM2.5 exposures for all populations particularly benefiting rural populations and socially vulnerable populations widely distributed in each urban area. This study provided evidence of regionally varying exposure misclassification that would arise without accounting for rural and within-urban exposure variabilities.