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Sources of ambient PM(2.5) exposure in 96 global cities
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM(2.5)), the largest environmental cause of mortality, is caused by emissions within t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Pergamon
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297293/ https://www.ncbi.nlm.nih.gov/pubmed/36193038 http://dx.doi.org/10.1016/j.atmosenv.2022.119234 |
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author | Tessum, Mei W. Anenberg, Susan C. Chafe, Zoe A. Henze, Daven K. Kleiman, Gary Kheirbek, Iyad Marshall, Julian D. Tessum, Christopher W. |
author_facet | Tessum, Mei W. Anenberg, Susan C. Chafe, Zoe A. Henze, Daven K. Kleiman, Gary Kheirbek, Iyad Marshall, Julian D. Tessum, Christopher W. |
author_sort | Tessum, Mei W. |
collection | PubMed |
description | To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM(2.5)), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM(2.5) exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health. |
format | Online Article Text |
id | pubmed-9297293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Pergamon |
record_format | MEDLINE/PubMed |
spelling | pubmed-92972932022-10-01 Sources of ambient PM(2.5) exposure in 96 global cities Tessum, Mei W. Anenberg, Susan C. Chafe, Zoe A. Henze, Daven K. Kleiman, Gary Kheirbek, Iyad Marshall, Julian D. Tessum, Christopher W. Atmos Environ (1994) Article To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM(2.5)), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM(2.5) exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health. Pergamon 2022-10-01 /pmc/articles/PMC9297293/ /pubmed/36193038 http://dx.doi.org/10.1016/j.atmosenv.2022.119234 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tessum, Mei W. Anenberg, Susan C. Chafe, Zoe A. Henze, Daven K. Kleiman, Gary Kheirbek, Iyad Marshall, Julian D. Tessum, Christopher W. Sources of ambient PM(2.5) exposure in 96 global cities |
title | Sources of ambient PM(2.5) exposure in 96 global cities |
title_full | Sources of ambient PM(2.5) exposure in 96 global cities |
title_fullStr | Sources of ambient PM(2.5) exposure in 96 global cities |
title_full_unstemmed | Sources of ambient PM(2.5) exposure in 96 global cities |
title_short | Sources of ambient PM(2.5) exposure in 96 global cities |
title_sort | sources of ambient pm(2.5) exposure in 96 global cities |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297293/ https://www.ncbi.nlm.nih.gov/pubmed/36193038 http://dx.doi.org/10.1016/j.atmosenv.2022.119234 |
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