Imperial College London

ProfessorAnnaHansell

Faculty of MedicineSchool of Public Health

Visiting Professor
 
 
 
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Contact

 

a.hansell

 
 
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Location

 

UG42Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Gulliver:2015,
author = {Gulliver, J and Morley, D and Fecht, D and Fabbri, F and Elliott, P and Hansell, A and Hodgson, S and de, Hoogh K and Bell, M and Goodman, P},
pages = {481--486},
title = {Feasibility study for using the CNOSSOS-EU road traffic noise prediction model with low resolution inputs for exposure estimation on a Europe-wide scale},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - A noise model based on the CNOSSOS-EU method was developed to estimate exposures to road traffic noise at individual address locations for studies of noise and health in European cohorts in the EU FP7 BioSHaRE project. We assessed the loss in model performance from necessarily (i.e. at national scale) using low resolution data on traffic flows, road geography and land cover. To assess the feasibility of this approach in terms of the loss of model performance, we applied CNOSSOS-EU with different combinations of high- and low-resolution inputs (e.g. high resolution road geography with low resolution land cover) and compared noise level estimates with measurements of LAeq1hr from 38 locations in Leicester, a medium sized city in the UK. The lowest resolution model performed reasonably well in terms of correlation [rs = 0.75; p = 0.000)] but with relatively large model errors [RMSE = 4.46 dB(A)]. For a sample of postcode (zip code) locations (n=721) in Leicester, in comparing output from Model A (highest resolution) and Model F (lowest resolution), 81.8% and 72.8% of exposure estimates remained in the lowest and highest of three equal exposure categories, respectively.
AU - Gulliver,J
AU - Morley,D
AU - Fecht,D
AU - Fabbri,F
AU - Elliott,P
AU - Hansell,A
AU - Hodgson,S
AU - de,Hoogh K
AU - Bell,M
AU - Goodman,P
EP - 486
PY - 2015///
SP - 481
TI - Feasibility study for using the CNOSSOS-EU road traffic noise prediction model with low resolution inputs for exposure estimation on a Europe-wide scale
ER -