학술논문
Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5Exposure Assessment in Australia
Document Type
Article
Author
Knibbs, Luke D.; van Donkelaar, Aaron; Martin, Randall V.; Bechle, Matthew J.; Brauer, Michael; Cohen, David D.; Cowie, Christine T.; Dirgawati, Mila; Guo, Yuming; Hanigan, Ivan C.; Johnston, Fay H.; Marks, Guy B.; Marshall, Julian D.; Pereira, Gavin; Jalaludin, Bin; Heyworth, Jane S.; Morgan, Geoffrey G.; Barnett, Adrian G.
Source
Environmental Science & Technology; September 2018, Vol. 52 Issue: 21 p12445-12455, 11p
Subject
Language
ISSN
0013936X; 15205851
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 PM2.5in Australia. We used global SAT-PM2.5estimates (∼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-PM2.5predictor 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 R2of the SAT-PM2.5estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM2.5estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM2.5estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM2.5exposure assessment in Australia.