Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

Abstract

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 µm, PM2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM2.5 ("SAT-PM2.5"). We aimed to determine the validity of such satellite-based LUR models for PM2.5 in Australia. We used global SAT-PM2.5 estimates (~10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM2.5 predictor variable (and six others) explained the most spatial variability in PM2.5 (adjusted R2 = 0.63, RMSE (µg/m3 [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R2 = 0.52, RMSE: 1.15 [16%]). The evaluation R2 of the SAT-PM2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM2.5 exposure assessment in Australia.