Comparison of land-use regression models for predicting spatial NO_x contrasts over a three year period in Oslo, Norway

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• 作者：Christian Madsen^a ; ^{christian.madsen@fhi.no"" rel=""nofollow} ; Ulrike Gehring^b ; Siri Eldevik H&#xe5 ; berg^a ; Per Nafstad^a ; ^c ; Kees Meliefste^b ; Wenche Nystad^a ; Karin C. Lø ; drup Carlsen^d ; Bert Brunekreef^b ; ^e
• 关键词：Exposure ; Air pollution ; Geographic information system ; Traffic ; Land-use regression modelling ; Spatial variability
• 刊名：Atmospheric Environment
• 出版年：2011
• 出版时间：July 2011
• 年：2011
• 卷：45
• 期：21
• 页码：3576-3583
• 全文大小：694 K

文摘

Background

Spatial modelling of traffic-related air pollution through land-use regression (LUR) is increasingly applied in epidemiological studies. These models provide highly spatially resolved data, but assume that the spatial contrasts are stable over long periods of time. It is not known to which extent these models can be used to predict concentrations in earlier or later periods. We aimed at testing the stability of measured and modelled spatial contrasts over a three year period in order to assess the relevance for future assessments of individual exposure to traffic-related air pollutants in epidemiological studies.

Methods

A land-use regression model was previously developed to estimate address-level outdoor concentrations of traffic-related air pollution based on samples of nitrogen oxides (NO_x) at 80 locations during the winter of 2005. In the winter of 2008, we measured NO_x again at 69 of these 80 locations and developed a new LUR model. This enabled us to compare short-term measurements and model predictions with three years apart in the same area.

Results

Measurements conducted in 2008 agreed well with measurements sampled in 2005 at the same locations (r = 0.91–0.95). The LUR models from 2005 and 2008 explained 66–77 % and 60–74 % of the variability of the measured concentrations, respectively. The 2008 LUR models explained 55–68 % of the spatial variability of the 2005 measurements, while the 2005 LUR models explained 53–66 % of the spatial variability of the 2008 measurements. The models performed better for NO_x and NO₂ compared to NO, and were shown to be equally valid when using leave-one-out cross-validation and validation of models based on independent training sets.

Conclusion

We found a good agreement between short-term measured spatial contrasts in outdoor NO_x over a three year period. LUR models for this area performed equally well using two different validation methods. These models predicted the spatial variation well for this area both forward and backward in time.