edoc

International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

Morley, D. W. and de Hoogh, K. and Fecht, D. and Fabbri, F. and Bell, M. and Goodman, P. S. and Elliott, P. and Hodgson, S. and Hansell, A. and Gulliver, J.. (2015) International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies. Environmental pollution, Vol. 206. pp. 332-341.

Full text not available from this repository.

Official URL: http://edoc.unibas.ch/dok/A6419776

Downloads: Statistics Overview

Abstract

The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p > 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)).
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Environmental Exposures and Health > Physical Hazards and Health (Röösli)
UniBasel Contributors:de Hoogh, Kees
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Elsevier ; [online:] Amsterdam
ISSN:0269-7491
Note:Publication type according to Uni Basel Research Database: Journal article
Related URLs:
Identification Number:
Last Modified:02 Oct 2015 10:00
Deposited On:02 Oct 2015 10:00

Repository Staff Only: item control page