Journal
SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY
Volume 14-15, Issue -, Pages 9-21Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.sste.2015.06.002
Keywords
Air pollution; Nitrogen dioxide; Land use regression; Spatial analysis; Environmental modeling; Wind
Categories
Funding
- Health Canada
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In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects - e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models - may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R-2 values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. (C) 2015 The Authors. Published by Elsevier Ltd.
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