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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...

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Autores principales: Tessum, Mei W., Anenberg, Susan C., Chafe, Zoe A., Henze, Daven K., Kleiman, Gary, Kheirbek, Iyad, Marshall, Julian D., Tessum, Christopher W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pergamon 2022
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.
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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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