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

Luke D. Knibbs, Michael G. Hewson, Matthew J. Bechle, Julian D. Marshall, Adrian G. Barnett

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


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=1.4ppb), while the best monthly model explained 76% (absolute RMS error=1.9ppb). 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 7.3ppb (2006) to 6.3ppb (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.

Original languageEnglish (US)
Pages (from-to)204-211
Number of pages8
JournalEnvironmental Research
StatePublished - Nov 1 2014

Bibliographical note

Funding Information:
Please contact the corresponding author if you would like to use the model predictions described in the paper. LDK acknowledges an NHMRC Early Career (Australian Public Health) Fellowship ( APP1036620 ). This material is partially based on work supported by the United States National Science Foundation under Grant No. 0853467 . Computational resources and services used in this study were provided by the High Performance Computer and Research Support Unit, Queensland University of Technology, and the Research Computing Centre, The University of Queensland. Analyses and visualisations used in this study were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. We acknowledge the Aura mission for the production of the data used in this research effort, and the Netherlands Agency for Aerospace Programs in collaboration with the Finnish Meteorological Institute through their contribution to the Aura mission via the Ozone Monitoring Instrument. We thank the state and territory authorities in Australia that freely provided NO 2 monitoring data. We also acknowledge the free use of data provided by the Global Land Cover Facility, NOAA National Geophysical Data Centre, Australian Bureau of Meteorology, Australian Bureau of Statistics, and Australian National Pollutant Inventory. Roads and airports data were purchased from the Public Sector Mapping Agencies (Australia). Elevation data were purchased from Geoscience Australia.

Publisher Copyright:
© 2014 Elsevier Inc.


  • Australia
  • Epidemiology
  • Exposure
  • Land use regression
  • Nitrogen dioxide


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