Advanced

A national fine spatial scale land-use regression model for ozone.

Kerckhoffs, Jules; Wang, Meng; Meliefste, Kees; Malmqvist, Ebba LU ; Fischer, Paul; Janssen, Nicole A H; Beelen, Rob and Hoek, Gerard (2015) In Environmental Research 140. p.440-448
Abstract
Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR model for ozone at a fine spatial scale. Ozone concentrations were measured with passive samplers at 90 locations across the Netherlands (19 regional background, 36 urban background, 35 traffic). All sites were measured simultaneously during four 2-weekly campaigns spread over the seasons. LUR models were developed for the summer average as the primary exposure and annual average using predictor variables obtained with Geographic Information... (More)
Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR model for ozone at a fine spatial scale. Ozone concentrations were measured with passive samplers at 90 locations across the Netherlands (19 regional background, 36 urban background, 35 traffic). All sites were measured simultaneously during four 2-weekly campaigns spread over the seasons. LUR models were developed for the summer average as the primary exposure and annual average using predictor variables obtained with Geographic Information Systems. Summer average ozone concentrations varied between 32 and 61µg/m(3). Ozone concentrations at traffic sites were on average 9µg/m(3) lower compared to regional background sites. Ozone correlated highly negatively with nitrogen dioxide and moderately with fine particles. A LUR model including small-scale traffic, large-scale address density, urban green and a region indicator explained 71% of the spatial variation in summer average ozone concentrations. Land use regression modeling is a promising method to assess ozone spatial variation, but the high correlation with NO2 limits application in epidemiology. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Research
volume
140
pages
440 - 448
publisher
Elsevier
external identifiers
  • pmid:25978345
  • wos:000357904100049
  • scopus:84930941093
ISSN
1096-0953
DOI
10.1016/j.envres.2015.04.014
language
English
LU publication?
yes
id
9364139e-7b76-41d1-988d-438b15b26977 (old id 5453167)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/25978345?dopt=Abstract
date added to LUP
2015-06-04 20:46:52
date last changed
2017-09-10 03:01:48
@article{9364139e-7b76-41d1-988d-438b15b26977,
  abstract     = {Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR model for ozone at a fine spatial scale. Ozone concentrations were measured with passive samplers at 90 locations across the Netherlands (19 regional background, 36 urban background, 35 traffic). All sites were measured simultaneously during four 2-weekly campaigns spread over the seasons. LUR models were developed for the summer average as the primary exposure and annual average using predictor variables obtained with Geographic Information Systems. Summer average ozone concentrations varied between 32 and 61µg/m(3). Ozone concentrations at traffic sites were on average 9µg/m(3) lower compared to regional background sites. Ozone correlated highly negatively with nitrogen dioxide and moderately with fine particles. A LUR model including small-scale traffic, large-scale address density, urban green and a region indicator explained 71% of the spatial variation in summer average ozone concentrations. Land use regression modeling is a promising method to assess ozone spatial variation, but the high correlation with NO2 limits application in epidemiology.},
  author       = {Kerckhoffs, Jules and Wang, Meng and Meliefste, Kees and Malmqvist, Ebba and Fischer, Paul and Janssen, Nicole A H and Beelen, Rob and Hoek, Gerard},
  issn         = {1096-0953},
  language     = {eng},
  pages        = {440--448},
  publisher    = {Elsevier},
  series       = {Environmental Research},
  title        = {A national fine spatial scale land-use regression model for ozone.},
  url          = {http://dx.doi.org/10.1016/j.envres.2015.04.014},
  volume       = {140},
  year         = {2015},
}