Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model

被引:133
|
作者
Taghvaee, Sina [1 ]
Sowlat, Mohammad H. [1 ]
Mousavi, Amirhosein [1 ]
Hassanvand, Mohammad Sadegh [2 ]
Yunesian, Masud [3 ,4 ]
Naddafi, Kazem [2 ,3 ]
Sioutas, Constantinos [1 ]
机构
[1] Univ Southern Calif, Dept Civil & Environm Engn, Los Angeles, CA USA
[2] Univ Tehran Med Sci, CAPR, IER, Tehran, Iran
[3] Univ Tehran Med Sci, Sch Publ Hlth, Dept Environm Hlth Engn, Tehran, Iran
[4] Univ Tehran Med Sci, Inst Environm Res, Dept Res Methodol & Data Anal, Tehran, Iran
关键词
Source apportionment; PM2.5; PMF; Tehran; PARTICULATE AIR-POLLUTION; LONG-TERM EXPOSURE; PARTICLE NUMBER CONCENTRATIONS; BACKGROUND SITE; MATTER PM2.5; LOS-ANGELES; CHEMICAL-CHARACTERIZATION; MASS CONCENTRATIONS; SIZE DISTRIBUTIONS; ORGANIC AEROSOLS;
D O I
10.1016/j.scitotenv.2018.02.096
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, the positive matrix factorization (PMF) model was used for source apportionment of ambient PM2.5 in two locations in the central Tehran from May 2012 through June 2013. The average PM2.5 mass concentrations were 30.9 and 33.2 mu g/m(3) in Tohid retirement home and the school dormitory, respectively. Metals and trace elements, water-soluble ions, and PM2.5 mass concentrations were used as inputs to the model. Concentrations of elemental and organic carbon (EC and OC), and meteorological data were also used as auxiliary variables to help with the factor identification and interpretation. A 7-factor solution was identified as the best solution for both sites. The identified source factors included vehicular emissions, secondary aerosol, industrial emissions, biomass burning, soil, and road dust (including tire and brake wear particles) in both sampling sites. Results indicated that almost half of PM2.5 mass can be attributed to vehicular emissions at both sites. Secondary aerosol was the second major contributor to PM2.5 mass concentrations at both sites, with contributions of around 25% on average for both sites. In addition, while two industrial factors were identified in Tohid retirement home (with an overall contribution of 17%), only one industrial factor (with a minimal contribution of <2%) was identified at Tohid retirement home, probably due to the fact that the retirement home is impacted to a higher degree by industry-related activities. The other factors included biomass burning, road dust, and soil, with overall contributions of around 20% in both sites. Results of this study clearly indicate the major role of traffic-related emissions (both tailpipe and non-tailpipe) on ambient PM2.5 concentrations, and can be used as a beneficial tool for air quality policy makers to mitigate adverse health effects of exposure to PM2.5. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:672 / 686
页数:15
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