Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method
To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were...
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Veröffentlicht in: | Chemosphere (Oxford) 2016-03, Vol.147, p.256-263 |
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creator | Tian, Ying-Ze Chen, Gang Wang, Hai-Ting Huang-Fu, Yan-Qi Shi, Guo-Liang Han, Bo Feng, Yin-Chang |
description | To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.
•A novel SRA (Source regional apportionment) method for PM2.5 sources was developed.•Contributions of each source category from diverse regions to ambient PM2.5 were quantified.•Seasonal variations were observed among source contributions from diverse regions. |
doi_str_mv | 10.1016/j.chemosphere.2015.12.132 |
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•A novel SRA (Source regional apportionment) method for PM2.5 sources was developed.•Contributions of each source category from diverse regions to ambient PM2.5 were quantified.•Seasonal variations were observed among source contributions from diverse regions.</description><identifier>ISSN: 0045-6535</identifier><identifier>EISSN: 1879-1298</identifier><identifier>DOI: 10.1016/j.chemosphere.2015.12.132</identifier><identifier>PMID: 26766363</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Air Pollutants - analysis ; Carbon - analysis ; China ; Cities ; Coal ; Dust ; Environmental Monitoring - methods ; ME2 ; Models, Theoretical ; Nitrates - analysis ; Particulate matter ; Particulate Matter - analysis ; Seasonal variations ; Seasons ; Source regional apportionment ; Sulfates - analysis ; Vehicle Emissions</subject><ispartof>Chemosphere (Oxford), 2016-03, Vol.147, p.256-263</ispartof><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-24f2cfc55a939bde698bcd3b354e29048abb037459a6aa6d03d3426d5a28eea83</citedby><cites>FETCH-LOGICAL-c410t-24f2cfc55a939bde698bcd3b354e29048abb037459a6aa6d03d3426d5a28eea83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0045653515305865$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26766363$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tian, Ying-Ze</creatorcontrib><creatorcontrib>Chen, Gang</creatorcontrib><creatorcontrib>Wang, Hai-Ting</creatorcontrib><creatorcontrib>Huang-Fu, Yan-Qi</creatorcontrib><creatorcontrib>Shi, Guo-Liang</creatorcontrib><creatorcontrib>Han, Bo</creatorcontrib><creatorcontrib>Feng, Yin-Chang</creatorcontrib><title>Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method</title><title>Chemosphere (Oxford)</title><addtitle>Chemosphere</addtitle><description>To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.
•A novel SRA (Source regional apportionment) method for PM2.5 sources was developed.•Contributions of each source category from diverse regions to ambient PM2.5 were quantified.•Seasonal variations were observed among source contributions from diverse regions.</description><subject>Air Pollutants - analysis</subject><subject>Carbon - analysis</subject><subject>China</subject><subject>Cities</subject><subject>Coal</subject><subject>Dust</subject><subject>Environmental Monitoring - methods</subject><subject>ME2</subject><subject>Models, Theoretical</subject><subject>Nitrates - analysis</subject><subject>Particulate matter</subject><subject>Particulate Matter - analysis</subject><subject>Seasonal variations</subject><subject>Seasons</subject><subject>Source regional apportionment</subject><subject>Sulfates - analysis</subject><subject>Vehicle Emissions</subject><issn>0045-6535</issn><issn>1879-1298</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9r3DAQxUVJaLZJv0JRbrnY1R9Lto5hadJCSgNJzkKWZne1rC1XkgP59pHZtJSechqGee8NvB9Cl5TUlFD5dV_bHQwhTTuIUDNCRU1ZTTn7gFa0a1VFmepO0IqQRlRScHGGPqW0J6SYhfqIzphspeSSr1B-CHO0gCNsfRjNAdsw5uj7OZc14Rzw_U9WC-xHbPAAW2N9flm29c6PBs_Jj1tsytE9m9GCw-m_PDNNIS5hA4y5JORdcBfodGMOCT6_zXP0dPPtcf29uvt1-2N9fVfZhpJcsWbD7MYKYRRXvQOput463nPRAFOk6UzfE942QhlpjHSEO94w6YRhHYDp-Dm6OuZOMfyeIWU9-GThcDAjhDlp2pY6Gq4UfY-UKSnadklVR6mNIaUIGz1FP5j4oinRCx-91__w0QsfTZkufIr3y9ubuR_A_XX-AVIE66MASi_PHqJO1sPSrI9gs3bBv-PNK4gQqLI</recordid><startdate>201603</startdate><enddate>201603</enddate><creator>Tian, Ying-Ze</creator><creator>Chen, Gang</creator><creator>Wang, Hai-Ting</creator><creator>Huang-Fu, Yan-Qi</creator><creator>Shi, Guo-Liang</creator><creator>Han, Bo</creator><creator>Feng, Yin-Chang</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7ST</scope><scope>7TV</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>201603</creationdate><title>Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method</title><author>Tian, Ying-Ze ; Chen, Gang ; Wang, Hai-Ting ; Huang-Fu, Yan-Qi ; Shi, Guo-Liang ; Han, Bo ; Feng, Yin-Chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-24f2cfc55a939bde698bcd3b354e29048abb037459a6aa6d03d3426d5a28eea83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Air Pollutants - analysis</topic><topic>Carbon - analysis</topic><topic>China</topic><topic>Cities</topic><topic>Coal</topic><topic>Dust</topic><topic>Environmental Monitoring - methods</topic><topic>ME2</topic><topic>Models, Theoretical</topic><topic>Nitrates - analysis</topic><topic>Particulate matter</topic><topic>Particulate Matter - analysis</topic><topic>Seasonal variations</topic><topic>Seasons</topic><topic>Source regional apportionment</topic><topic>Sulfates - analysis</topic><topic>Vehicle Emissions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tian, Ying-Ze</creatorcontrib><creatorcontrib>Chen, Gang</creatorcontrib><creatorcontrib>Wang, Hai-Ting</creatorcontrib><creatorcontrib>Huang-Fu, Yan-Qi</creatorcontrib><creatorcontrib>Shi, Guo-Liang</creatorcontrib><creatorcontrib>Han, Bo</creatorcontrib><creatorcontrib>Feng, Yin-Chang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Environment Abstracts</collection><collection>Pollution Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Chemosphere (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Ying-Ze</au><au>Chen, Gang</au><au>Wang, Hai-Ting</au><au>Huang-Fu, Yan-Qi</au><au>Shi, Guo-Liang</au><au>Han, Bo</au><au>Feng, Yin-Chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method</atitle><jtitle>Chemosphere (Oxford)</jtitle><addtitle>Chemosphere</addtitle><date>2016-03</date><risdate>2016</risdate><volume>147</volume><spage>256</spage><epage>263</epage><pages>256-263</pages><issn>0045-6535</issn><eissn>1879-1298</eissn><abstract>To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.
•A novel SRA (Source regional apportionment) method for PM2.5 sources was developed.•Contributions of each source category from diverse regions to ambient PM2.5 were quantified.•Seasonal variations were observed among source contributions from diverse regions.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>26766363</pmid><doi>10.1016/j.chemosphere.2015.12.132</doi><tpages>8</tpages></addata></record> |
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subjects | Air Pollutants - analysis Carbon - analysis China Cities Coal Dust Environmental Monitoring - methods ME2 Models, Theoretical Nitrates - analysis Particulate matter Particulate Matter - analysis Seasonal variations Seasons Source regional apportionment Sulfates - analysis Vehicle Emissions |
title | Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method |
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