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Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 2820469
Author(s) Aguilera, Inmaculada; Foraster, Maria; Basagaña, Xavier; Corradi, Elisabetta; Deltell, Alexandre; Morelli, Xavier; Phuleria, Harish C; Ragettli, Martina S; Rivera, Marcela; Thomasson, Alexandre; Slama, Rémy; Künzli, Nino
Author(s) at UniBasel Phuleria, Harish Chandra
Ragettli, Martina
Künzli, Nino
Year 2015
Title Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities
Journal Journal of exposure science and environmental epidemiology
Volume 25
Number 1
Pages / Article-Number 97-105
Keywords traffic noise, noise propagation models, land use regression models, noise exposure

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a "GIS-only" model, which considered only predictor variables derived with Geographic Information Systems; and (b) a "Best" model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R(2)) was 0.66-0.87 for "GIS-only" models, and 0.70-0.89 for "Best" models. Short-term noise measurements showed a high correlation (r=0.62-0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.

Publisher Nature Publishing Group
ISSN/ISBN 1559-0631
Full Text on edoc No
Digital Object Identifier DOI 10.1038/jes.2014.61
PubMed ID
ISI-Number WOS:000346431500015
Document type (ISI) Journal Article

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