Ground level ozone [ozone] is considered a harmful air pollutant but there is a knowledge gap regarding its long term health effects. The main aim of this study is to develop local Land Use Regression [LUR] models that can be used to study long term health effects of ozone. The specific aim is to develop spatial LUR models for two Swedish cities, Umea and Malmo, as well as a temporal model for Malmo in order to assess ozone exposure for long term epidemiological studies. For the spatial model we measured ozone, using Ogawa passive samplers, as weekly averages at 40 sites in each study area, during three seasons. This data was then inserted in the LUR-model with data on traffic, land use, population density and altitude to develop explanatory models of ozone variation. To develop the temporal model for Malmo, hourly ozone data was aggregated into daily means for two measurement stations in Malmo and one in a rural area outside Malmo. Using regression analyses we inserted meteorological variables into different temporal models and the one that performed best for all three stations was chosen. For Malmo the LUR-model had an adjusted model R-2 of 0.40 and cross validation R-2 of 0.17. For Umea the model had an adjusted model R-2 of 0.67 and cross validation adjusted R-2 of 0.48. When restricting the model to only including measuring sites from urban areas, the Malmo model had adjusted model R-2 of 0.51 (cross validation adjusted R-2 0.33) and the Umea model had adjusted model R-2 of 0.81 (validation adjusted R-2 of 0.73). The temporal model had adjusted model R-2 0.54 and 0.61 for the two Malmo sites, the cross validation adjusted R-2 was 0.42. In conclusion, we can with moderate accuracy, at least for Umea, predict the spatial variability, and in Malmo the temporal variability in ozone variation. (C) 2014 The Authors. Published by Elsevier Ltd.
Subject classification (UKÄ)
- Meteorology and Atmospheric Sciences
- Land use regression
- Air pollution modelling