@article{ WOS:000413313700049,
Author = {Qiao, Xue and Ying, Qi and Li, Xinghua and Zhang, Hongliang and Hu,
   Jianlin and Tang, Ya and Chen, Xue},
Title = {{Source apportionment of PM2.5 for 25 Chinese provincial capitals and
   municipalities using a source-oriented Community Multiscale Air Quality
   model}},
Journal = {{SCIENCE OF THE TOTAL ENVIRONMENT}},
Year = {{2018}},
Volume = {{612}},
Pages = {{462-471}},
Month = {{JAN 15}},
Abstract = {{Source contributions to fine airborne particulate matter with
   aerodynamic diameters < 2.5 mu m(PM2.5) during 2013 were determined for
   25 Chinese provincial capitals and municipalities using a
   source-oriented version of the Community Multiscale Air Quality (CMAQ)
   model. Based on the hierarchical clustering analysis of the observed
   PM2.5 concentrations, the 25 cities were categorized into nine groups.
   Generally, annual PM2.5 concentrations were highest in the cities in the
   north (81-154 mu g m(-3)) and lowest in the cities close to seas in the
   south and east (27-57 mu g m(-3)). Seasonal PM2.5 observations in the
   cities were generally higher in winter than in the other seasons.
   Industrial or residential sources were predicted to be the largest
   contributor to PM2.5 for all the city groups, with annually fractional
   contributions of 25.0\%-38.6\% and 9.6\%-27\%, respectively. The annual
   contributions from power plants, agriculture NH3, windblown dust, and
   secondary organic aerosol (SOA) for the city groups were 8.7\%-12.7\%,
   9.5\%-12\%, 6.1\%-12.5\%, and 5.4\%-15.5\%, respectively. Meanwhile, the
   annual contributions from transportation, sea salt, and open burning
   were relatively low (< 8\%, < 2\%, and < 6\%, respectively). Secondary
   PM2.5 accounted for 47\%-63\% of total annual PM2.5 concentrations in
   the cities and contributed to as much as 70\% of daily PM2.5
   concentrations on PM2.5 pollution days ( daily concentrations > 75 mu g
   m(-3)). Industrial or residential sources were generally the largest
   contributor on PM2.5 pollution days for all the city groups in each
   season, except that open burning, SOA, and windblown dust could be more
   important on some days, particularly in spring. The results of this
   study would be helpful to develop measures to reduce annual PM2.5
   concentrations and the number of PM2.5 pollution days for different
   regions of China. (C) 2017 Elsevier B.V. All rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Ying, Q (Corresponding Author), Nanjing Univ Informat Sci \& Technol, Jiangsu Key Lab Atmospher Environm Monitoring \& P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm \& Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China.
   Qiao, Xue, Sichuan Univ, Inst New Energy \& Low Carbon Technol, Chengdu 610065, Sichuan, Peoples R China.
   Ying, Qi; Hu, Jianlin, Nanjing Univ Informat Sci \& Technol, Jiangsu Key Lab Atmospher Environm Monitoring \& P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm \& Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China.
   Ying, Qi, Texas A\&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843 USA.
   Li, Xinghua, Beihang Univ, Sch Space \& Environm, Beijing 100191, Peoples R China.
   Zhang, Hongliang, Louisiana State Univ, Dept Civil \& Environm Engn, Baton Rouge, LA 70803 USA.
   Tang, Ya; Chen, Xue, Sichuan Univ, Dept Environm, Coll Architecture \& Environm, Chengdu 610065, Sichuan, Peoples R China.}},
DOI = {{10.1016/j.scitotenv.2017.08.272}},
ISSN = {{0048-9697}},
EISSN = {{1879-1026}},
Keywords = {{Source apportionment; Airborne particulate matter; Secondary organic
   aerosol; Source-oriented CMAQ; Hierarchical cluster analysis}},
Keywords-Plus = {{SECONDARY ORGANIC AEROSOL; FINE PARTICULATE MATTER; CMAQ; EMISSIONS;
   POLLUTION; TRANSPORT; SULFATE; NITRATE; SIMULATIONS; SENSITIVITY}},
Research-Areas = {{Environmental Sciences \& Ecology}},
Web-of-Science-Categories  = {{Environmental Sciences}},
Author-Email = {{qying@civil.tamu.edu}},
ResearcherID-Numbers = {{Zhang, Hongliang/C-2499-2012
   }},
ORCID-Numbers = {{Zhang, Hongliang/0000-0002-1797-2311
   Qiao, Xue/0000-0001-7412-5090
   Ying, Qi/0000-0002-4560-433X}},
Funding-Acknowledgement = {{Science and Technology Department of Sichuan Province {[}2017HH048];
   National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China (NSFC) {[}21407110, 41575119, 41628102]; Program of
   Introducing Talents of Discipline to UniversitiesMinistry of Education,
   China - 111 Project {[}B08037]; Jiangsu Key Laboratory of Atmospheric
   Environment Monitoring and Pollution Control {[}KHK1511]; Priority
   Academic Program Development of Jiangsu Higher Education Institutions
   (PAPD)}},
Funding-Text = {{This study was partially sponsored by the Science and Technology
   Department of Sichuan Province (2017HH048), the National Natural Science
   Foundation of China (21407110, 41575119 and 41628102), the Program of
   Introducing Talents of Discipline to Universities (B08037) and the open
   fund provided by Jiangsu Key Laboratory of Atmospheric Environment
   Monitoring and Pollution Control (KHK1511), a project funded by the
   Priority Academic Program Development of Jiangsu Higher Education
   Institutions (PAPD). We thank the undergraduate students, Ruixin Zhang,
   Xiaoyang Tang, and Yi Yang, from the College of Architecture and
   Environment of Sichuan University for assisting with data processing.
   The authors also want to acknowledge the Texas A\&M Supercomputing
   Facility (http://sc.tamu.edu/) for providing computing resources useful
   in conducting the research reported in this paper.}},
Number-of-Cited-References = {{55}},
Times-Cited = {{53}},
Usage-Count-Last-180-days = {{8}},
Usage-Count-Since-2013 = {{187}},
Journal-ISO = {{Sci. Total Environ.}},
Doc-Delivery-Number = {{FK2LM}},
Unique-ID = {{WOS:000413313700049}},
DA = {{2021-12-02}},
}

{"_id":"JXDnhC4wYGgDakCHS","bibbaseid":"qiao-ying-li-zhang-hu-tang-chen-sourceapportionmentofpm25for25chineseprovincialcapitalsandmunicipalitiesusingasourceorientedcommunitymultiscaleairqualitymodel-2018","author_short":["Qiao, X.","Ying, Q.","Li, X.","Zhang, H.","Hu, J.","Tang, Y.","Chen, X."],"bibdata":{"bibtype":"article","type":"Article","author":[{"propositions":[],"lastnames":["Qiao"],"firstnames":["Xue"],"suffixes":[]},{"propositions":[],"lastnames":["Ying"],"firstnames":["Qi"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Xinghua"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Hongliang"],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["Jianlin"],"suffixes":[]},{"propositions":[],"lastnames":["Tang"],"firstnames":["Ya"],"suffixes":[]},{"propositions":[],"lastnames":["Chen"],"firstnames":["Xue"],"suffixes":[]}],"title":"Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model","journal":"SCIENCE OF THE TOTAL ENVIRONMENT","year":"2018","volume":"612","pages":"462-471","month":"JAN 15","abstract":"Source contributions to fine airborne particulate matter with aerodynamic diameters < 2.5 mu m(PM2.5) during 2013 were determined for 25 Chinese provincial capitals and municipalities using a source-oriented version of the Community Multiscale Air Quality (CMAQ) model. Based on the hierarchical clustering analysis of the observed PM2.5 concentrations, the 25 cities were categorized into nine groups. Generally, annual PM2.5 concentrations were highest in the cities in the north (81-154 mu g m(-3)) and lowest in the cities close to seas in the south and east (27-57 mu g m(-3)). Seasonal PM2.5 observations in the cities were generally higher in winter than in the other seasons. Industrial or residential sources were predicted to be the largest contributor to PM2.5 for all the city groups, with annually fractional contributions of 25.0%-38.6% and 9.6%-27%, respectively. The annual contributions from power plants, agriculture NH3, windblown dust, and secondary organic aerosol (SOA) for the city groups were 8.7%-12.7%, 9.5%-12%, 6.1%-12.5%, and 5.4%-15.5%, respectively. Meanwhile, the annual contributions from transportation, sea salt, and open burning were relatively low (< 8%, < 2%, and < 6%, respectively). Secondary PM2.5 accounted for 47%-63% of total annual PM2.5 concentrations in the cities and contributed to as much as 70% of daily PM2.5 concentrations on PM2.5 pollution days ( daily concentrations > 75 mu g m(-3)). Industrial or residential sources were generally the largest contributor on PM2.5 pollution days for all the city groups in each season, except that open burning, SOA, and windblown dust could be more important on some days, particularly in spring. The results of this study would be helpful to develop measures to reduce annual PM2.5 concentrations and the number of PM2.5 pollution days for different regions of China. (C) 2017 Elsevier B.V. All rights reserved.","publisher":"ELSEVIER SCIENCE BV","address":"PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS","language":"English","affiliation":"Ying, Q (Corresponding Author), Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring & P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm & Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China. Qiao, Xue, Sichuan Univ, Inst New Energy & Low Carbon Technol, Chengdu 610065, Sichuan, Peoples R China. Ying, Qi; Hu, Jianlin, Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring & P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm & Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China. Ying, Qi, Texas A&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843 USA. Li, Xinghua, Beihang Univ, Sch Space & Environm, Beijing 100191, Peoples R China. Zhang, Hongliang, Louisiana State Univ, Dept Civil & Environm Engn, Baton Rouge, LA 70803 USA. Tang, Ya; Chen, Xue, Sichuan Univ, Dept Environm, Coll Architecture & Environm, Chengdu 610065, Sichuan, Peoples R China.","doi":"10.1016/j.scitotenv.2017.08.272","issn":"0048-9697","eissn":"1879-1026","keywords":"Source apportionment; Airborne particulate matter; Secondary organic aerosol; Source-oriented CMAQ; Hierarchical cluster analysis","keywords-plus":"SECONDARY ORGANIC AEROSOL; FINE PARTICULATE MATTER; CMAQ; EMISSIONS; POLLUTION; TRANSPORT; SULFATE; NITRATE; SIMULATIONS; SENSITIVITY","research-areas":"Environmental Sciences & Ecology","web-of-science-categories":"Environmental Sciences","author-email":"qying@civil.tamu.edu","researcherid-numbers":"Zhang, Hongliang/C-2499-2012 ","orcid-numbers":"Zhang, Hongliang/0000-0002-1797-2311 Qiao, Xue/0000-0001-7412-5090 Ying, Qi/0000-0002-4560-433X","funding-acknowledgement":"Science and Technology Department of Sichuan Province [2017HH048]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [21407110, 41575119, 41628102]; Program of Introducing Talents of Discipline to UniversitiesMinistry of Education, China - 111 Project [B08037]; Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control [KHK1511]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","funding-text":"This study was partially sponsored by the Science and Technology Department of Sichuan Province (2017HH048), the National Natural Science Foundation of China (21407110, 41575119 and 41628102), the Program of Introducing Talents of Discipline to Universities (B08037) and the open fund provided by Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (KHK1511), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We thank the undergraduate students, Ruixin Zhang, Xiaoyang Tang, and Yi Yang, from the College of Architecture and Environment of Sichuan University for assisting with data processing. The authors also want to acknowledge the Texas A&M Supercomputing Facility (http://sc.tamu.edu/) for providing computing resources useful in conducting the research reported in this paper.","number-of-cited-references":"55","times-cited":"53","usage-count-last-180-days":"8","usage-count-since-2013":"187","journal-iso":"Sci. Total Environ.","doc-delivery-number":"FK2LM","unique-id":"WOS:000413313700049","da":"2021-12-02","bibtex":"@article{ WOS:000413313700049,\nAuthor = {Qiao, Xue and Ying, Qi and Li, Xinghua and Zhang, Hongliang and Hu,\n Jianlin and Tang, Ya and Chen, Xue},\nTitle = {{Source apportionment of PM2.5 for 25 Chinese provincial capitals and\n municipalities using a source-oriented Community Multiscale Air Quality\n model}},\nJournal = {{SCIENCE OF THE TOTAL ENVIRONMENT}},\nYear = {{2018}},\nVolume = {{612}},\nPages = {{462-471}},\nMonth = {{JAN 15}},\nAbstract = {{Source contributions to fine airborne particulate matter with\n aerodynamic diameters < 2.5 mu m(PM2.5) during 2013 were determined for\n 25 Chinese provincial capitals and municipalities using a\n source-oriented version of the Community Multiscale Air Quality (CMAQ)\n model. Based on the hierarchical clustering analysis of the observed\n PM2.5 concentrations, the 25 cities were categorized into nine groups.\n Generally, annual PM2.5 concentrations were highest in the cities in the\n north (81-154 mu g m(-3)) and lowest in the cities close to seas in the\n south and east (27-57 mu g m(-3)). Seasonal PM2.5 observations in the\n cities were generally higher in winter than in the other seasons.\n Industrial or residential sources were predicted to be the largest\n contributor to PM2.5 for all the city groups, with annually fractional\n contributions of 25.0\\%-38.6\\% and 9.6\\%-27\\%, respectively. The annual\n contributions from power plants, agriculture NH3, windblown dust, and\n secondary organic aerosol (SOA) for the city groups were 8.7\\%-12.7\\%,\n 9.5\\%-12\\%, 6.1\\%-12.5\\%, and 5.4\\%-15.5\\%, respectively. Meanwhile, the\n annual contributions from transportation, sea salt, and open burning\n were relatively low (< 8\\%, < 2\\%, and < 6\\%, respectively). Secondary\n PM2.5 accounted for 47\\%-63\\% of total annual PM2.5 concentrations in\n the cities and contributed to as much as 70\\% of daily PM2.5\n concentrations on PM2.5 pollution days ( daily concentrations > 75 mu g\n m(-3)). Industrial or residential sources were generally the largest\n contributor on PM2.5 pollution days for all the city groups in each\n season, except that open burning, SOA, and windblown dust could be more\n important on some days, particularly in spring. The results of this\n study would be helpful to develop measures to reduce annual PM2.5\n concentrations and the number of PM2.5 pollution days for different\n regions of China. (C) 2017 Elsevier B.V. All rights reserved.}},\nPublisher = {{ELSEVIER SCIENCE BV}},\nAddress = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},\nType = {{Article}},\nLanguage = {{English}},\nAffiliation = {{Ying, Q (Corresponding Author), Nanjing Univ Informat Sci \\& Technol, Jiangsu Key Lab Atmospher Environm Monitoring \\& P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm \\& Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China.\n Qiao, Xue, Sichuan Univ, Inst New Energy \\& Low Carbon Technol, Chengdu 610065, Sichuan, Peoples R China.\n Ying, Qi; Hu, Jianlin, Nanjing Univ Informat Sci \\& Technol, Jiangsu Key Lab Atmospher Environm Monitoring \\& P, Jiangsu Engn Technol Res Ctr Environm Cleaning Ma, Collaborat Innovat Ctr Atmospher Environm \\& Equip, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China.\n Ying, Qi, Texas A\\&M Univ, Zachry Dept Civil Engn, College Stn, TX 77843 USA.\n Li, Xinghua, Beihang Univ, Sch Space \\& Environm, Beijing 100191, Peoples R China.\n Zhang, Hongliang, Louisiana State Univ, Dept Civil \\& Environm Engn, Baton Rouge, LA 70803 USA.\n Tang, Ya; Chen, Xue, Sichuan Univ, Dept Environm, Coll Architecture \\& Environm, Chengdu 610065, Sichuan, Peoples R China.}},\nDOI = {{10.1016/j.scitotenv.2017.08.272}},\nISSN = {{0048-9697}},\nEISSN = {{1879-1026}},\nKeywords = {{Source apportionment; Airborne particulate matter; Secondary organic\n aerosol; Source-oriented CMAQ; Hierarchical cluster analysis}},\nKeywords-Plus = {{SECONDARY ORGANIC AEROSOL; FINE PARTICULATE MATTER; CMAQ; EMISSIONS;\n POLLUTION; TRANSPORT; SULFATE; NITRATE; SIMULATIONS; SENSITIVITY}},\nResearch-Areas = {{Environmental Sciences \\& Ecology}},\nWeb-of-Science-Categories = {{Environmental Sciences}},\nAuthor-Email = {{qying@civil.tamu.edu}},\nResearcherID-Numbers = {{Zhang, Hongliang/C-2499-2012\n }},\nORCID-Numbers = {{Zhang, Hongliang/0000-0002-1797-2311\n Qiao, Xue/0000-0001-7412-5090\n Ying, Qi/0000-0002-4560-433X}},\nFunding-Acknowledgement = {{Science and Technology Department of Sichuan Province {[}2017HH048];\n National Natural Science Foundation of ChinaNational Natural Science\n Foundation of China (NSFC) {[}21407110, 41575119, 41628102]; Program of\n Introducing Talents of Discipline to UniversitiesMinistry of Education,\n China - 111 Project {[}B08037]; Jiangsu Key Laboratory of Atmospheric\n Environment Monitoring and Pollution Control {[}KHK1511]; Priority\n Academic Program Development of Jiangsu Higher Education Institutions\n (PAPD)}},\nFunding-Text = {{This study was partially sponsored by the Science and Technology\n Department of Sichuan Province (2017HH048), the National Natural Science\n Foundation of China (21407110, 41575119 and 41628102), the Program of\n Introducing Talents of Discipline to Universities (B08037) and the open\n fund provided by Jiangsu Key Laboratory of Atmospheric Environment\n Monitoring and Pollution Control (KHK1511), a project funded by the\n Priority Academic Program Development of Jiangsu Higher Education\n Institutions (PAPD). We thank the undergraduate students, Ruixin Zhang,\n Xiaoyang Tang, and Yi Yang, from the College of Architecture and\n Environment of Sichuan University for assisting with data processing.\n The authors also want to acknowledge the Texas A\\&M Supercomputing\n Facility (http://sc.tamu.edu/) for providing computing resources useful\n in conducting the research reported in this paper.}},\nNumber-of-Cited-References = {{55}},\nTimes-Cited = {{53}},\nUsage-Count-Last-180-days = {{8}},\nUsage-Count-Since-2013 = {{187}},\nJournal-ISO = {{Sci. Total Environ.}},\nDoc-Delivery-Number = {{FK2LM}},\nUnique-ID = {{WOS:000413313700049}},\nDA = {{2021-12-02}},\n}\n\n","author_short":["Qiao, X.","Ying, Q.","Li, X.","Zhang, H.","Hu, J.","Tang, Y.","Chen, X."],"key":"WOS:000413313700049","id":"WOS:000413313700049","bibbaseid":"qiao-ying-li-zhang-hu-tang-chen-sourceapportionmentofpm25for25chineseprovincialcapitalsandmunicipalitiesusingasourceorientedcommunitymultiscaleairqualitymodel-2018","role":"author","urls":{},"keyword":["Source apportionment; Airborne particulate matter; Secondary organic aerosol; Source-oriented CMAQ; Hierarchical cluster analysis"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://yingqi95616.ddns.net:8001/publicationlist.bib","dataSources":["kTLQ96xxQwQovcx6r","LT3gToj3w22mutXHY","MjJL6KgnAM64Por3d","SN9t6exrr8GS3PxiX"],"keywords":["source apportionment; airborne particulate matter; secondary organic aerosol; source-oriented cmaq; hierarchical cluster analysis"],"search_terms":["source","apportionment","pm2","chinese","provincial","capitals","municipalities","using","source","oriented","community","multiscale","air","quality","model","qiao","ying","li","zhang","hu","tang","chen"],"title":"Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model","year":2018}