10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
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Ambient PM2.5 Source Apportionment: a Case Study of Tehran
SINA TAGHVAEE, Mohammad Sowlat, Amirhosein Mousavi, Mohammad Sadegh Hassanvand, Masud Yunesian, Kazem Naddafi, Constantinos Sioutas, University of Southern California
Abstract Number: 892 Working Group: Source Apportionment
Abstract In this study, PM2.5 source apportionment is performed using Positive matrix factorization (PMF) for two sampling site in the central part of Tehran (i.e."Tohid retirement home" and "school dormitory") to identify the main contributing sources to PM2.5 mass concentration. To achieve such goal, PM2.5 mass, water-soluble ions, and metals concentrations were used along with other auxiliary variables such as elemental and organic carbon (EC/OC), and meteorological data for identification and quantification of PM2.5 sources. A 7-factor solution was selected as the most acceptable and plausible one for both locations based on the evaluation of the resulting source profiles, temporal trends of each factor in cold phase (i.e. fall and winter) and warm phase (i.e. spring and summer), correlation analysis between EC/OC data and PMF resolved factor contributions, analysis of bootstrap runs, and R2 values of predicted vs. measured PM2.5 concentration . The factors included vehicular emissions, secondary aerosol, industrial emissions (i.e. industrial emissions 1 and 2), biomass burning, soil, and road dust in "Tohid retirement home". On the other hand, vehicular emissions, secondary aerosol, industrial emissions, biomass burning, soil, brake wear particles, and tire dust were the 7 factors resolved by PMF for another sampling site. Results indicate that most of the contribution belongs to vehicular emissions, with slightly higher contribution in "School dormitory" (49.3%) compared to "Tohid retirement home" (48.8%). Secondary aerosol has also the second highest percentage of contribution in both locations, with higher contribution in "Tohid retirement home" rather than "school dormitory" which might be reasonable due to its more distance from major traffic flows. In addition, while two industrial factors were identified in "Tohid retirement home" (with totally more than 17% contribution), only one industrial factor (less than 2 % contribution) is recognized in another sampling site which might be due to the fact that the retirement home is more impacted by industrial activities. The other resolved factor profiles for "Tohid retirement home" were biomass burning, soil, and road dust with relative contributions of 3%, 2.8% and less than 1% respectively. Biomass burning, soil, and the remaining non-tailpipe road emissions (including brake wear particle and tire dust) were also accounting for 16%, 8.2%, and less than 1% of total PM2.5 mass concentration in "school dormitory". Results of this study can be used as a beneficial tool for policy making purposes regarding air quality improvement and addressing adverse health effects of PM2.5.