Mobile surveys and machine learning can improve urban noise mapping: beyond A-weighted measurements of exposure
Mobile surveys and machine learning can improve urban noise mapping: beyond A-weighted measurements of exposure
Urban noise pollution is a major environmental issue, second only to fine particulate matter in its impacts on physical and mental health. To identify who is affected and where to prioritise actions, noise maps derived from traffic flows and propagation algorithms are widely used. These may not reflect true levels of exposure because they fail to consider noise from all sources and may leave gaps where roads or traffic data are absent. We present an improved approach to overcome these limitations. Using walking surveys, we recorded 52,366 audio clips of 10 s each along 733 km of routes throughout the port city of Southampton. We extracted power levels in low (11 to 177 Hz), mid (177 Hz to 5.68 kHz), high (5.68 to 22.72 kHz) and A-weighted frequencies and then built machine-learning (ML) models to predict noise levels at 30 m resolution across the entire city, driven by urban form. Model performance (r
2) ranged from 0.41 (low frequencies) to 0.61 (mid frequencies) with mean absolute errors of 4.05 to 4.75 dB. The main predictors of noise were related to modes of transport (road, air, rail and water) but for low frequencies, port activities were also important. When mapped to the city scale, A-weighted frequencies produced a similar spatial pattern to mid-frequencies, but did not capture the major sources of low frequency noise from the port or scattered hotspots of high frequencies. We question whether A-weighted noise mapping is adequate for health and wellbeing impact assessments. We conclude that mobile surveys combined with ML offer an alternative way to map noise from all sources and at fine resolution across entire cities that may more accurately reflect true exposures. Our approach is suitable for noise data gathered by citizen scientists, or from a network of sensors, as well as from structured surveys.
Noise modelling, Noise pollution, Personal exposure, Port city, Spatial modelling, Urban environment
Alvares-sanches, Tatiana
1b9cb890-d1ad-4955-a876-fd62811620c6
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
25 June 2021
Alvares-sanches, Tatiana
1b9cb890-d1ad-4955-a876-fd62811620c6
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Alvares-sanches, Tatiana, Osborne, Patrick E. and White, Paul R.
(2021)
Mobile surveys and machine learning can improve urban noise mapping: beyond A-weighted measurements of exposure.
Science of the Total Environment, 775, [145600].
(doi:10.1016/j.scitotenv.2021.145600).
Abstract
Urban noise pollution is a major environmental issue, second only to fine particulate matter in its impacts on physical and mental health. To identify who is affected and where to prioritise actions, noise maps derived from traffic flows and propagation algorithms are widely used. These may not reflect true levels of exposure because they fail to consider noise from all sources and may leave gaps where roads or traffic data are absent. We present an improved approach to overcome these limitations. Using walking surveys, we recorded 52,366 audio clips of 10 s each along 733 km of routes throughout the port city of Southampton. We extracted power levels in low (11 to 177 Hz), mid (177 Hz to 5.68 kHz), high (5.68 to 22.72 kHz) and A-weighted frequencies and then built machine-learning (ML) models to predict noise levels at 30 m resolution across the entire city, driven by urban form. Model performance (r
2) ranged from 0.41 (low frequencies) to 0.61 (mid frequencies) with mean absolute errors of 4.05 to 4.75 dB. The main predictors of noise were related to modes of transport (road, air, rail and water) but for low frequencies, port activities were also important. When mapped to the city scale, A-weighted frequencies produced a similar spatial pattern to mid-frequencies, but did not capture the major sources of low frequency noise from the port or scattered hotspots of high frequencies. We question whether A-weighted noise mapping is adequate for health and wellbeing impact assessments. We conclude that mobile surveys combined with ML offer an alternative way to map noise from all sources and at fine resolution across entire cities that may more accurately reflect true exposures. Our approach is suitable for noise data gathered by citizen scientists, or from a network of sensors, as well as from structured surveys.
Text
Accepted manuscript for STOTEN 145600
- Accepted Manuscript
More information
Accepted/In Press date: 29 January 2021
e-pub ahead of print date: 8 February 2021
Published date: 25 June 2021
Additional Information:
Funding Information:
The authors thank Jerome Kreule, Dominika Murienova and Rodrigo Batistela for help with the field surveys, funded by the Excel and University of Southampton Placement Schemes. Ordnance Survey® data (see appendices) were used under licence from EDINA Digimap® ( http://digimap.edina.ac.uk/ ) as part of an agreement for UK Higher Education establishments. Two anonymous reviewers provided helpful comments on the manuscript.
Funding Information:
This research formed part of Tatiana Alvares-Sanches's PhD project funded by the Engineering and Physical Sciences Research Council [ EP/J017698/1 , Transforming the Engineering of Cities to Deliver Societal and Planetary Wellbeing].
Publisher Copyright:
© 2021 Elsevier B.V.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
Noise modelling, Noise pollution, Personal exposure, Port city, Spatial modelling, Urban environment
Identifiers
Local EPrints ID: 448448
URI: http://eprints.soton.ac.uk/id/eprint/448448
ISSN: 0048-9697
PURE UUID: 1b8f9d70-89bb-4080-8017-fc352d032c4b
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Date deposited: 22 Apr 2021 16:46
Last modified: 17 Mar 2024 06:26
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Author:
Tatiana Alvares-sanches
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