Assessing ozone exposure for epidemiological studies in Malmِ and Umeه, Sweden

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, Umeه and Malmِ, as well as a temporal model for Malmِ 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 Malmِ, hourly ozone data was aggregated into daily means for two measurement stations in Malmِ and one in a rural area outside Malmِ. Using regression analyses we inserted meteorological variables into different temporal models and the one that performed best for all three stations was chosen. For Malmِ the LUR-model had an adjusted model R2 of 0.40 and cross validation R2 of 0.17. For Umeه the model had an adjusted model R2 of 0.67 and cross validation adjusted R2 of 0.48. When restricting the model to only including measuring sites from urban areas, the Malmِ model had adjusted model R2 of 0.51 (cross validation adjusted R2 0.33) and the Umeه model had adjusted model R2 of 0.81 (validation adjusted R2 of 0.73). The temporal model had adjusted model R2 0.54 and 0.61 for the two Malmِ sites, the cross validation adjusted R2 was 0.42. In conclusion, we can with moderate accuracy, at least for Umeه, predict the spatial variability, and in Malmِ the temporal variability in ozone variation.