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Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models

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Title: Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models
Authors: Honey Dawn Alas, Almond Stöcker, Nikolaus Umlauf, Oshada Senaweera, Sascha Pfeifer, Sonja Greven, Alfred Wiedensohler
Source: J Expo Sci Environ Epidemiol
Publisher Information: Springer Science and Business Media LLC, 2021.
Publication Year: 2021
Subject Terms: PARTICLE NUMBER, SPATIAL VARIABILITY, 01 natural sciences, Article, Pedestrians [MeSH], New Approach Methodologies (NAMs), Personal Exposure, Particulate Matter, Humans [MeSH], Particulate Matter/analysis [MeSH], Bayes Theorem [MeSH], Criteria Pollutants, Air Pollution/analysis [MeSH], Air Pollution, Soot/analysis [MeSH], Environmental Monitoring, Carbon/analysis [MeSH], Air Pollutants/analysis [MeSH], Vehicle Emissions/analysis [MeSH], Environmental Exposure/analysis [MeSH], Environmental Monitoring/methods [MeSH], TERM EXPOSURE, POLLUTION, Soot, 11. Sustainability, QUALITY, Humans, Pedestrians, Vehicle Emissions, 0105 earth and related environmental sciences, ddc:610, ULTRAFINE PARTICLES, Air Pollutants, LAND-USE, Bayes Theorem, Environmental Exposure, Carbon, 13. Climate action, MASS CONCENTRATIONS, 610 Medizin und Gesundheit, METHODOLOGY, AIR-POLLUTANTS
Description: Background Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians’ exposure to particulate matter (black carbon (BC) and PM2.5 mass concentrations). Objective We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. Methods We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. Results The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM2.5. Significance Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.
Document Type: Article
Other literature type
File Description: application/pdf
Language: English
ISSN: 1559-064X
1559-0631
DOI: 10.1038/s41370-021-00379-5
DOI: 10.18452/27223
Access URL: https://www.nature.com/articles/s41370-021-00379-5.pdf
https://pubmed.ncbi.nlm.nih.gov/34455418
https://www.nature.com/articles/s41370-021-00379-5.pdf
https://www.nature.com/articles/s41370-021-00379-5
https://europepmc.org/article/MED/34455418
https://repository.publisso.de/resource/frl:6443870
http://edoc.hu-berlin.de/18452/27879
https://doi.org/10.18452/27223
Rights: CC BY
URL: http://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (http://creativecommons.org/licenses/by/4.0/) .
Accession Number: edsair.doi.dedup.....dda89618e05e4e9cb0d3881c75b26693
Database: OpenAIRE
Description
Abstract:Background Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians’ exposure to particulate matter (black carbon (BC) and PM2.5 mass concentrations). Objective We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. Methods We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. Results The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM2.5. Significance Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.
ISSN:1559064X
15590631
DOI:10.1038/s41370-021-00379-5