Sources of ambient PM2.5 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 (PM2.5), the largest environmental cause of mortality, is caused by emissions within the...
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Veröffentlicht in: | Atmospheric environment (1994) 2022-10, Vol.286, p.119234-119234, Article 119234 |
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creator | Tessum, Mei W. Anenberg, Susan C. Chafe, Zoe A. Henze, Daven K. Kleiman, Gary Kheirbek, Iyad Marshall, Julian D. Tessum, Christopher W. |
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 (PM2.5), 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 PM2.5 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.
•The contributions of different air pollution sources vary widely among cities.•The contributions of within-city vs. external emission sources to a city's air pollution also varies widely among cities.•The contributions above cannot be accurately predicted without air quality modeling.•An opportunity exists to improve global emission inventories in urban areas. |
doi_str_mv | 10.1016/j.atmosenv.2022.119234 |
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•The contributions of different air pollution sources vary widely among cities.•The contributions of within-city vs. external emission sources to a city's air pollution also varies widely among cities.•The contributions above cannot be accurately predicted without air quality modeling.•An opportunity exists to improve global emission inventories in urban areas.</description><identifier>ISSN: 1352-2310</identifier><identifier>EISSN: 1873-2844</identifier><identifier>EISSN: 1352-2310</identifier><identifier>DOI: 10.1016/j.atmosenv.2022.119234</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>air ; air pollutants ; air pollution ; Air quality ; Air quality modeling ; Chemical transport modeling ; environment ; Environmental policy ; Fine particulate matter ; inventories ; issues and policy ; Metropolitan ; mortality ; particulates ; Pollution ; population density ; public health</subject><ispartof>Atmospheric environment (1994), 2022-10, Vol.286, p.119234-119234, Article 119234</ispartof><rights>2022 The Authors</rights><rights>2022 The Authors 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c481t-41f59d9b9d8ea4c8a8befeee3e76bdacd9a0e687056080bd4f9306bb8fce26733</citedby><cites>FETCH-LOGICAL-c481t-41f59d9b9d8ea4c8a8befeee3e76bdacd9a0e687056080bd4f9306bb8fce26733</cites><orcidid>0000-0002-8864-7436 ; 0000-0002-1690-1431 ; 0000-0001-6670-6979 ; 0000-0003-0298-8529 ; 0000-0003-4087-1209</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1352231022002990$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Tessum, Mei W.</creatorcontrib><creatorcontrib>Anenberg, Susan C.</creatorcontrib><creatorcontrib>Chafe, Zoe A.</creatorcontrib><creatorcontrib>Henze, Daven K.</creatorcontrib><creatorcontrib>Kleiman, Gary</creatorcontrib><creatorcontrib>Kheirbek, Iyad</creatorcontrib><creatorcontrib>Marshall, Julian D.</creatorcontrib><creatorcontrib>Tessum, Christopher W.</creatorcontrib><title>Sources of ambient PM2.5 exposure in 96 global cities</title><title>Atmospheric environment (1994)</title><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 (PM2.5), 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 PM2.5 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.
•The contributions of different air pollution sources vary widely among cities.•The contributions of within-city vs. external emission sources to a city's air pollution also varies widely among cities.•The contributions above cannot be accurately predicted without air quality modeling.•An opportunity exists to improve global emission inventories in urban areas.</description><subject>air</subject><subject>air pollutants</subject><subject>air pollution</subject><subject>Air quality</subject><subject>Air quality modeling</subject><subject>Chemical transport modeling</subject><subject>environment</subject><subject>Environmental policy</subject><subject>Fine particulate matter</subject><subject>inventories</subject><subject>issues and policy</subject><subject>Metropolitan</subject><subject>mortality</subject><subject>particulates</subject><subject>Pollution</subject><subject>population density</subject><subject>public health</subject><issn>1352-2310</issn><issn>1873-2844</issn><issn>1352-2310</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqNkUtLxDAUhYsoOI7-BenSTWsebZpsRBl8wYiCug5JejtmaJsxaQf997Z0FFzp6l6453wc7omiU4xSjDA7X6eqa1yAdpsSREiKsSA024tmmBc0ITzL9oed5iQhFKPD6CiENUKIFqKYRfmz672BELsqVo220Hbx0wNJ8xg-Ni70HmLbxoLFq9ppVcfGdhbCcXRQqTrAyW7Oo9eb65fFXbJ8vL1fXC0Tk3HcJRmuclEKLUoOKjNccQ0VAFAomC6VKYVCwHiBcoY40mVWCYqY1rwyQFhB6Ty6mLibXjdQmiGdV7XceNso_ymdsvL3pbVvcuW2UhBREDECznYA7957CJ1sbDBQ16oF1wdJCoJJzgUl_5BiTmjO8Ehlk9R4F4KH6icRRnLsRK7ldydy7EROnQzGy8kIw9O2FrwMZni5gdJ6MJ0snf0L8QU1KZjo</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Tessum, Mei W.</creator><creator>Anenberg, Susan C.</creator><creator>Chafe, Zoe A.</creator><creator>Henze, Daven K.</creator><creator>Kleiman, Gary</creator><creator>Kheirbek, Iyad</creator><creator>Marshall, Julian D.</creator><creator>Tessum, Christopher W.</creator><general>Elsevier Ltd</general><general>Pergamon</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8864-7436</orcidid><orcidid>https://orcid.org/0000-0002-1690-1431</orcidid><orcidid>https://orcid.org/0000-0001-6670-6979</orcidid><orcidid>https://orcid.org/0000-0003-0298-8529</orcidid><orcidid>https://orcid.org/0000-0003-4087-1209</orcidid></search><sort><creationdate>20221001</creationdate><title>Sources of ambient PM2.5 exposure in 96 global cities</title><author>Tessum, Mei W. ; Anenberg, Susan C. ; Chafe, Zoe A. ; Henze, Daven K. ; Kleiman, Gary ; Kheirbek, Iyad ; Marshall, Julian D. ; Tessum, Christopher W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c481t-41f59d9b9d8ea4c8a8befeee3e76bdacd9a0e687056080bd4f9306bb8fce26733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>air</topic><topic>air pollutants</topic><topic>air pollution</topic><topic>Air quality</topic><topic>Air quality modeling</topic><topic>Chemical transport modeling</topic><topic>environment</topic><topic>Environmental policy</topic><topic>Fine particulate matter</topic><topic>inventories</topic><topic>issues and policy</topic><topic>Metropolitan</topic><topic>mortality</topic><topic>particulates</topic><topic>Pollution</topic><topic>population density</topic><topic>public health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tessum, Mei W.</creatorcontrib><creatorcontrib>Anenberg, Susan C.</creatorcontrib><creatorcontrib>Chafe, Zoe A.</creatorcontrib><creatorcontrib>Henze, Daven K.</creatorcontrib><creatorcontrib>Kleiman, Gary</creatorcontrib><creatorcontrib>Kheirbek, Iyad</creatorcontrib><creatorcontrib>Marshall, Julian D.</creatorcontrib><creatorcontrib>Tessum, Christopher W.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Atmospheric environment (1994)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tessum, Mei W.</au><au>Anenberg, Susan C.</au><au>Chafe, Zoe A.</au><au>Henze, Daven K.</au><au>Kleiman, Gary</au><au>Kheirbek, Iyad</au><au>Marshall, Julian D.</au><au>Tessum, Christopher W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sources of ambient PM2.5 exposure in 96 global cities</atitle><jtitle>Atmospheric environment (1994)</jtitle><date>2022-10-01</date><risdate>2022</risdate><volume>286</volume><spage>119234</spage><epage>119234</epage><pages>119234-119234</pages><artnum>119234</artnum><issn>1352-2310</issn><eissn>1873-2844</eissn><eissn>1352-2310</eissn><abstract>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 (PM2.5), 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 PM2.5 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.
•The contributions of different air pollution sources vary widely among cities.•The contributions of within-city vs. external emission sources to a city's air pollution also varies widely among cities.•The contributions above cannot be accurately predicted without air quality modeling.•An opportunity exists to improve global emission inventories in urban areas.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.atmosenv.2022.119234</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-8864-7436</orcidid><orcidid>https://orcid.org/0000-0002-1690-1431</orcidid><orcidid>https://orcid.org/0000-0001-6670-6979</orcidid><orcidid>https://orcid.org/0000-0003-0298-8529</orcidid><orcidid>https://orcid.org/0000-0003-4087-1209</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | air air pollutants air pollution Air quality Air quality modeling Chemical transport modeling environment Environmental policy Fine particulate matter inventories issues and policy Metropolitan mortality particulates Pollution population density public health |
title | Sources of ambient PM2.5 exposure in 96 global cities |
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