Estimating PM2.5 in Southern California using satellite data: factors that affect model performance

Abstract

Abstract Background: Studies of PM2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM2.5 concentration for the study domain was 8.84 µg m−3. Linear regression between CV predicted PM2.5 concentrations and observations had an R 2 of 0.80 and RMSE 2.25 µg m−3. The ratio of PM2.5 to PM10 proved an important variable in modifying the AOD/PM2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R 2 and a 0.56 µg m−3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas.