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Source Apportionment of Ambient Pm2.5 in Two Locations in Central Tehran Using the Positive Matrix Factorization (Pmf) Model Publisher Pubmed



Taghvaee S1 ; Sowlat MH1 ; Mousavi A1 ; Hassanvand MS2 ; Yunesian M3, 4 ; Naddafi K2, 3 ; Sioutas C1
Authors
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Authors Affiliations
  1. 1. University of Southern California, Department of Civil and Environmental Engineering, Los Angeles, CA, United States
  2. 2. Center for Air Pollution Research (CAPR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Department of Research Methodology and Data Analysis, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, Iran

Source: Science of the Total Environment Published:2018


Abstract

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 μg/m3 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. © 2018 Elsevier B.V.
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