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Assessing ozone exposure for epidemiological studies in Malmo and Umea, Sweden

Malmqvist, Ebba LU ; Olsson, D.; Hagenbjork-Gustafsson, A.; Forsberg, B.; Mattisson, Kristoffer LU ; Stroh, Emilie LU ; Stromgren, M.; Swietlicki, Erik LU ; Rylander, Lars LU and Hoek, G., et al. (2014) In Atmospheric Environment 94. p.241-248
Abstract
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... (More)
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. (Less)
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keywords
Land use regression, Ozone, Air pollution modelling, Epidemiology, Risk, assessment
in
Atmospheric Environment
volume
94
pages
241 - 248
publisher
Elsevier
external identifiers
  • wos:000340316300027
  • scopus:84901305763
ISSN
1352-2310
DOI
10.1016/j.atmosenv.2014.05.038
project
MERGE
language
English
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yes
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0071e744-97b8-4a8f-ae46-2d2e349d3f80 (old id 4659451)
date added to LUP
2014-09-25 07:47:11
date last changed
2017-09-10 04:03:38
@article{0071e744-97b8-4a8f-ae46-2d2e349d3f80,
  abstract     = {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.},
  author       = {Malmqvist, Ebba and Olsson, D. and Hagenbjork-Gustafsson, A. and Forsberg, B. and Mattisson, Kristoffer and Stroh, Emilie and Stromgren, M. and Swietlicki, Erik and Rylander, Lars and Hoek, G. and Tinnerberg, Håkan and Modig, L.},
  issn         = {1352-2310},
  keyword      = {Land use regression,Ozone,Air pollution modelling,Epidemiology,Risk,assessment},
  language     = {eng},
  pages        = {241--248},
  publisher    = {Elsevier},
  series       = {Atmospheric Environment},
  title        = {Assessing ozone exposure for epidemiological studies in Malmo and Umea, Sweden},
  url          = {http://dx.doi.org/10.1016/j.atmosenv.2014.05.038},
  volume       = {94},
  year         = {2014},
}