es8b02328_si_001.pdf (1.73 MB)

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

Download (1.73 MB)
journal contribution
posted on 02.10.2018, 00:00 by Luke D. Knibbs, Aaron van Donkelaar, Randall V. Martin, Matthew J. Bechle, Michael Brauer, David D. Cohen, Christine T. Cowie, Mila Dirgawati, Yuming Guo, Ivan C. Hanigan, Fay H. Johnston, Guy B. Marks, Julian D. Marshall, Gavin Pereira, Bin Jalaludin, Jane S. Heyworth, Geoffrey G. Morgan, Adrian G. Barnett
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.