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Predicting traffic noise using land use regression – A scalable approach

Staab, Jeroen and Schady, Arthur and Weigand, Matthias and Lakes, Tobia and Taubenböck, Hannes (2021) Predicting traffic noise using land use regression – A scalable approach. Journal of Exposure Science and Environmental Epidemiology, pp. 1-12. Springer Nature. doi: 10.1038/s41370-021-00355-z. ISSN 1559-0631.

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Official URL: https://www.nature.com/jes/

Abstract

Background In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. Objective Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model. Methods Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day–evening–night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas. Results The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A). Significance This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Item URL in elib:https://elib.dlr.de/138872/
Document Type:Article
Title:Predicting traffic noise using land use regression – A scalable approach
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Staab, JeroenJeroen.Staab (at) dlr.dehttps://orcid.org/0000-0002-7342-4440
Schady, ArthurDLR, IPAhttps://orcid.org/0000-0002-3078-9546
Weigand, MatthiasMatthias.Weigand (at) dlr.deUNSPECIFIED
Lakes, TobiaHU BerlinUNSPECIFIED
Taubenböck, HannesHannes.Taubenboeck (at) dlr.deUNSPECIFIED
Date:2 July 2021
Journal or Publication Title:Journal of Exposure Science and Environmental Epidemiology
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1038/s41370-021-00355-z
Page Range:pp. 1-12
Publisher:Springer Nature
ISSN:1559-0631
Status:Published
Keywords:Urban; Traffic Noise; Land Use Regression; Linear Model; Cross Validation; Environmental Justice
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Staab, Jeroen
Deposited On:02 Dec 2020 13:42
Last Modified:07 Jul 2021 15:00

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