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Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach

Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach
Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach

Quantifying traffic contribution to air pollution in urban settings is required to inform traffic management strategies and environmental policies that aim at improving air quality. Assessments and comparative analyses across multiple urban areas are challenged by the lack of datasets and methods available for global applications. In this study, we quantify the traffic contribution to particulate matter concentration in multiple cities worldwide by synthesising 155 previous studies reported in the World Health Organization (WHO)’s air pollution source apportionment data for PM 10 and PM 2.5. We employed a Bayesian multilevel meta-regression that accounts for uncertainties and captures both within- and between-study variations (in estimation methods, study protocols, etc.) through study-specific and location-specific explanatory variables. The final sample analysed in this paper covers 169 cities worldwide. Based on our analysis, traffic contribution to air pollution (particulate matter) varies from 5% to 61% in cities worldwide, with an average of 27%. We found that variability in the traffic contribution estimates reported worldwide can be explained by the region of study, publication year, PM size fraction, and population. Specifically, traffic contribution to air pollution in cities located in Europe, North America, or Oceania is on average 36% lower relative to the rest of the world. Traffic contribution is 28% lower among studies published after 2005 than those published on or before 2005. Traffic contribution is on average 24% lower among cities with less than 500,000 inhabitants and 19% higher when estimated based on PM 10 relative to PM 2.5. This quantitative summary overcomes challenges in the data and provides useful information for health impact modellers and decision-makers to assess impacts of traffic reduction policies.

Air Quality, Meta-analysis, Particulate Matter, Source apportionment, Traffic, Uncertainty
Heydari, Shahram
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Tainio, M.
d6caa01f-5009-4ebc-9203-49c08512a169
Woodcock, James
5f2edb7e-7a42-4080-9379-7090bcc33234
de Nazelle, A.
4ed97c22-af6d-4a65-bc5f-8994a5512f8e
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Tainio, M.
d6caa01f-5009-4ebc-9203-49c08512a169
Woodcock, James
5f2edb7e-7a42-4080-9379-7090bcc33234
de Nazelle, A.
4ed97c22-af6d-4a65-bc5f-8994a5512f8e

Heydari, Shahram, Tainio, M., Woodcock, James and de Nazelle, A. (2020) Estimating traffic contribution to particulate matter concentration in urban areas using a multilevel Bayesian meta-regression approach. Environmental International, 141, [105800]. (doi:10.1016/j.envint.2020.105800).

Record type: Article

Abstract

Quantifying traffic contribution to air pollution in urban settings is required to inform traffic management strategies and environmental policies that aim at improving air quality. Assessments and comparative analyses across multiple urban areas are challenged by the lack of datasets and methods available for global applications. In this study, we quantify the traffic contribution to particulate matter concentration in multiple cities worldwide by synthesising 155 previous studies reported in the World Health Organization (WHO)’s air pollution source apportionment data for PM 10 and PM 2.5. We employed a Bayesian multilevel meta-regression that accounts for uncertainties and captures both within- and between-study variations (in estimation methods, study protocols, etc.) through study-specific and location-specific explanatory variables. The final sample analysed in this paper covers 169 cities worldwide. Based on our analysis, traffic contribution to air pollution (particulate matter) varies from 5% to 61% in cities worldwide, with an average of 27%. We found that variability in the traffic contribution estimates reported worldwide can be explained by the region of study, publication year, PM size fraction, and population. Specifically, traffic contribution to air pollution in cities located in Europe, North America, or Oceania is on average 36% lower relative to the rest of the world. Traffic contribution is 28% lower among studies published after 2005 than those published on or before 2005. Traffic contribution is on average 24% lower among cities with less than 500,000 inhabitants and 19% higher when estimated based on PM 10 relative to PM 2.5. This quantitative summary overcomes challenges in the data and provides useful information for health impact modellers and decision-makers to assess impacts of traffic reduction policies.

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Accepted/In Press date: 10 May 2020
Published date: 1 August 2020
Keywords: Air Quality, Meta-analysis, Particulate Matter, Source apportionment, Traffic, Uncertainty

Identifiers

Local EPrints ID: 441252
URI: http://eprints.soton.ac.uk/id/eprint/441252
PURE UUID: 18b7cad5-f225-463d-b1ed-04b65ebbaff3

Catalogue record

Date deposited: 08 Jun 2020 16:30
Last modified: 25 Nov 2021 19:33

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Contributors

Author: Shahram Heydari
Author: M. Tainio
Author: James Woodcock
Author: A. de Nazelle

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