Accounting for spatial effects in land use regression for urban air pollution modelling

Publication date: Available online 2 July 2015 Source:Spatial and Spatio-temporal Epidemiology Author(s): Stefania Bertazzon , Markey Johnson , Kristin Eccles , Gilaad G. Kaplan 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 R2 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.

Source: Spatial and Spatio-temporal Epidemiology - July 2, 2015 Category: Epidemiology Source Type: research