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Satellite-based land-use regression for continental-scale long-term ambient PM2.5 exposure assessment in Australia


Knibbs, LD and van Donkelaar, A and Martin, RV and Bechle, MJ and Brauer, M and Cohen, DD and Cowie, CT and Dirgawati, M and Guo, Y and Hanigan, IC and Johnston, FH and Marks, GB and Marshall, JD and Pereira, G and Jalaludin, B and Heyworth, JS and Morgan, GG and Barnett, AG, Satellite-based land-use regression for continental-scale long-term ambient PM2.5 exposure assessment in Australia, Environmental Science and Technology, 52, (21) pp. 12445-12455. ISSN 0013-936X (2018) [Refereed Article]

Copyright Statement

Copyright 2018 American Chemical Society

DOI: doi:10.1021/acs.est.8b02328


Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 m, 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 non-satellite-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 (g/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.

Item Details

Item Type:Refereed Article
Keywords:air pollution, particulate matter, regression LUR
Research Division:Environmental Sciences
Research Group:Environmental management
Research Field:Environmental assessment and monitoring
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Public health (excl. specific population health) not elsewhere classified
UTAS Author:Johnston, FH (Associate Professor Fay Johnston)
ID Code:128859
Year Published:2018
Deposited By:Menzies Institute for Medical Research
Deposited On:2018-10-18
Last Modified:2019-03-18

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