Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM _2.5 Exposure Assessment in Australia

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM _2.5 ). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM _2.5 , derived by relating satellite-observed aerosol optical depth to ground-level PM _2.5 ("SAT-PM _2.5 "). We aimed to determine the validity of such satellite-based LUR models for PM _2.5 in Australia. We used global SAT-PM _2.5 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-PM _2.5 predictor variable (and six others) explained the most spatial variability in PM _2.5 (adjusted R ² = 0.63, RMSE (μg/m ³ [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R ² = 0.52, RMSE: 1.15 [16%]). The evaluation R ² of the SAT-PM _2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM _2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM _2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM _2.5 exposure assessment in Australia.