Major Source Categories for PM2.5 in Pittsburgh using PMF and UNMIX |
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| Authors: | Natalie J Pekney Cliff I Davidson Allen Robinson Liming Zhou Philip Hopke Delbert Eatough |
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| Affiliation: | 1. Department of Civil and Environmental Engineering , Carnegie Mellon University , Pittsburgh, Pennsylvania, USA;2. Department of Civil and Environmental Engineering and Engineering and Public Policy , Carnegie Mellon University , Pittsburgh, Pennsylvania, USA;3. Department of Mechanical Engineering and Engineering and Public Policy , Carnegie Mellon University , Pittsburgh, Pennsylvania, USA;4. Center for Air Resources Engineering and Science, and Department of Chemical Engineering , Clarkson University , New York, Potsdam, USA;5. Department of Chemistry and Biochemistry , Brigham Young University , Provo, Utah, USA |
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| Abstract: | An objective of the Pittsburgh Air Quality Study was to determine the major sources of PM2.5 in the Pittsburgh region. Daily 24-hour averaged filter-based data were collected for 13 months, starting in July 2001, including sulfate and nitrate data from IC analysis, trace element data from ICP-MS analysis, and organic and elemental carbon from the thermal optical transmittance (TOT) method and the NIOSH thermal evolution protocol. These data were used in two source-receptor models, Unmix and PMF. Unmix, which is limited to a maximum number of seven factors, resolved six source factors, including crustal material, a regional transport factor, secondary nitrate, an iron, zinc and manganese factor, specialty steel production and processing, and cadmium. PMF, which has no limit to the number of factors, apportioned the PM2.5 mass into ten factors, including crustal material, secondary sulfate, primary OC and EC, secondary nitrate, an iron, zinc and manganese factor, specialty steel production and processing, cadmium, selenium, lead, and a gallium-rich factor. The Unmix and PMF common factors agree reasonably well, both in composition and contributions to PM2.5. To further identify and apportion the sources of PM2.5, specific OC compounds that are known markers of some sources were added to the PMF analysis. The results were similar to the original solution, except that the primary OC and EC factor split into two factors. One factor was associated with vehicles as identified by the hopanes, PAH's, and other OC compounds. The other factor had strong correlations with the OC and EC ambient data as well as wood smoke markers such as levoglucosan, syringols, and resin acids. |
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