Impact of commercial cooking on urban PM 2.5 and O 3 with online data-assisted emission inventory
Commercial cooking (CC) is an intensive near-field source contributing to ambient PM and O concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated qua...
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| Veröffentlicht in: | The Science of the total environment 2023-05, Vol.873, p.162256 |
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| container_title | The Science of the total environment |
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| creator | Yuan, Yingzhi Zhu, Yun Lin, Che-Jen Wang, Shuxiao Xie, Yanghong Li, Haixian Xing, Jia Zhao, Bin Zhang, Mengmeng You, Zhiqiang |
| description | Commercial cooking (CC) is an intensive near-field source contributing to ambient PM
and O
concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM
and O
were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM
and O
concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m
of PM
, and accounted for 24±1 % of PM
concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM
was predicted in areas with elevated CC emissions, while the contribution to O
was insignificant. |
| format | Article |
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and O
concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM
and O
were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM
and O
concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m
of PM
, and accounted for 24±1 % of PM
concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM
was predicted in areas with elevated CC emissions, while the contribution to O
was insignificant.</description><identifier>EISSN: 1879-1026</identifier><identifier>PMID: 36805059</identifier><language>eng</language><publisher>Netherlands</publisher><ispartof>The Science of the total environment, 2023-05, Vol.873, p.162256</ispartof><rights>Copyright © 2023 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36805059$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yuan, Yingzhi</creatorcontrib><creatorcontrib>Zhu, Yun</creatorcontrib><creatorcontrib>Lin, Che-Jen</creatorcontrib><creatorcontrib>Wang, Shuxiao</creatorcontrib><creatorcontrib>Xie, Yanghong</creatorcontrib><creatorcontrib>Li, Haixian</creatorcontrib><creatorcontrib>Xing, Jia</creatorcontrib><creatorcontrib>Zhao, Bin</creatorcontrib><creatorcontrib>Zhang, Mengmeng</creatorcontrib><creatorcontrib>You, Zhiqiang</creatorcontrib><title>Impact of commercial cooking on urban PM 2.5 and O 3 with online data-assisted emission inventory</title><title>The Science of the total environment</title><addtitle>Sci Total Environ</addtitle><description>Commercial cooking (CC) is an intensive near-field source contributing to ambient PM
and O
concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM
and O
were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM
and O
concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m
of PM
, and accounted for 24±1 % of PM
concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM
was predicted in areas with elevated CC emissions, while the contribution to O
was insignificant.</description><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFjssKwjAURIMgtj5-Qe4PVPqgr7UouhBduC-3TarRJilJqvTvzULXzmYOzAzMhPhRkZdBFMaZR-bGPEKnvIhmxEuyIkzDtPQJHkWPjQXVQqOEYLrh2DlUTy5voCQMukYJlxPEmxRQUjhDAm9u7y7suGRA0WKAxnBjGQUmuEO34_LFpFV6XJJpi51hq68vyHq_u24PQT_UgtGq11ygHqvfp-Rv4QOTtkFR</recordid><startdate>20230515</startdate><enddate>20230515</enddate><creator>Yuan, Yingzhi</creator><creator>Zhu, Yun</creator><creator>Lin, Che-Jen</creator><creator>Wang, Shuxiao</creator><creator>Xie, Yanghong</creator><creator>Li, Haixian</creator><creator>Xing, Jia</creator><creator>Zhao, Bin</creator><creator>Zhang, Mengmeng</creator><creator>You, Zhiqiang</creator><scope>NPM</scope></search><sort><creationdate>20230515</creationdate><title>Impact of commercial cooking on urban PM 2.5 and O 3 with online data-assisted emission inventory</title><author>Yuan, Yingzhi ; Zhu, Yun ; Lin, Che-Jen ; Wang, Shuxiao ; Xie, Yanghong ; Li, Haixian ; Xing, Jia ; Zhao, Bin ; Zhang, Mengmeng ; You, Zhiqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-pubmed_primary_368050593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Yingzhi</creatorcontrib><creatorcontrib>Zhu, Yun</creatorcontrib><creatorcontrib>Lin, Che-Jen</creatorcontrib><creatorcontrib>Wang, Shuxiao</creatorcontrib><creatorcontrib>Xie, Yanghong</creatorcontrib><creatorcontrib>Li, Haixian</creatorcontrib><creatorcontrib>Xing, Jia</creatorcontrib><creatorcontrib>Zhao, Bin</creatorcontrib><creatorcontrib>Zhang, Mengmeng</creatorcontrib><creatorcontrib>You, Zhiqiang</creatorcontrib><collection>PubMed</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Yingzhi</au><au>Zhu, Yun</au><au>Lin, Che-Jen</au><au>Wang, Shuxiao</au><au>Xie, Yanghong</au><au>Li, Haixian</au><au>Xing, Jia</au><au>Zhao, Bin</au><au>Zhang, Mengmeng</au><au>You, Zhiqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of commercial cooking on urban PM 2.5 and O 3 with online data-assisted emission inventory</atitle><jtitle>The Science of the total environment</jtitle><addtitle>Sci Total Environ</addtitle><date>2023-05-15</date><risdate>2023</risdate><volume>873</volume><spage>162256</spage><pages>162256-</pages><eissn>1879-1026</eissn><abstract>Commercial cooking (CC) is an intensive near-field source contributing to ambient PM
and O
concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM
and O
were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM
and O
concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m
of PM
, and accounted for 24±1 % of PM
concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM
was predicted in areas with elevated CC emissions, while the contribution to O
was insignificant.</abstract><cop>Netherlands</cop><pmid>36805059</pmid></addata></record> |
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| source | Access via ScienceDirect (Elsevier) |
| title | Impact of commercial cooking on urban PM 2.5 and O 3 with online data-assisted emission inventory |
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