A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Knibbs, Luke D, Hewson, Michael G, Bechle, Matthew J., Marshall, Julian D. and Barnett, Adrian G. (2014) A national satellite-based land-use regression model for air pollution exposure assessment in Australia. Environmental Research, 135 204-211. doi:10.1016/j.envres.2014.09.011


Author Knibbs, Luke D
Hewson, Michael G
Bechle, Matthew J.
Marshall, Julian D.
Barnett, Adrian G.
Title A national satellite-based land-use regression model for air pollution exposure assessment in Australia
Journal name Environmental Research   Check publisher's open access policy
ISSN 0013-9351
1096-0953
Publication date 2014-10-03
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.envres.2014.09.011
Volume 135
Start page 204
End page 211
Total pages 8
Place of publication Maryland Heights, MO United States
Publisher Academic Press
Collection year 2015
Language eng
Formatted abstract
Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.
Keyword Nitrogen dioxide
Land use regression
Exposure
Epidemiology
Australia
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Fri, 03 Oct 2014, 19:11:53 EST by Michael Hewson on behalf of School of Geography, Planning & Env Management