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Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: The ESCAPE project.
Environ. Sci. Technol. 47, 4357-4364 (2013)
Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
AIR-POLLUTION; MORTALITY; VEHICLES; EXPOSURE
Language
english
Publication Year
2013
HGF-reported in Year
2013
ISSN (print) / ISBN
0013-936X
e-ISSN
1520-5851
Quellenangaben
Volume: 47,
Issue: 9,
Pages: 4357-4364
Publisher
ACS
Publishing Place
Washington, DC
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)
POF-Topic(s)
30503 - Chronic Diseases of the Lung and Allergies
30202 - Environmental Health
30202 - Environmental Health
Research field(s)
Genetics and Epidemiology
PSP Element(s)
G-503900-001
G-504000-001
G-504000-004
G-504000-001
G-504000-004
PubMed ID
23534892
WOS ID
WOS:000318756000052
Scopus ID
84877614952
Erfassungsdatum
2013-06-28