Accounting for spatial effects in land use regression for urban air pollution modeling
Bertazzon, S; Johnson, M; Eccles, K; Kaplan, GG
HERO ID
3435666
Reference Type
Journal Article
Year
2015
Language
English
PMID
| HERO ID | 3435666 |
|---|---|
| In Press | No |
| Year | 2015 |
| Title | Accounting for spatial effects in land use regression for urban air pollution modeling |
| Authors | Bertazzon, S; Johnson, M; Eccles, K; Kaplan, GG |
| Journal | Spatial and Spatio-temporal Epidemiology |
| Volume | 14-15 |
| Page Numbers | 9-21 |
| Abstract | 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. |
| Doi | 10.1016/j.sste.2015.06.002 |
| Pmid | 26530819 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Language Text | English |