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

    Access Status
    Fulltext not available
    Authors
    Knibbs, L.
    Van Donkelaar, A.
    Martin, R.
    Bechle, M.
    Brauer, M.
    Cohen, D.
    Cowie, C.
    Dirgawati, M.
    Guo, Y.
    Hanigan, I.
    Johnston, F.
    Marks, G.
    Marshall, J.
    Pereira, Gavin
    Jalaludin, B.
    Heyworth, J.
    Morgan, G.
    Barnett, A.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Knibbs, L. and Van Donkelaar, A. and Martin, R. and Bechle, M. and Brauer, M. and Cohen, D. and Cowie, C. et al. 2018. 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.
    Source Title
    Environmental Science and Technology
    DOI
    10.1021/acs.est.8b02328
    ISSN
    0013-936X
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/73137
    Collection
    • Curtin Research Publications
    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.

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