Our paper was just published in Environmental Science & Technology: “Positive matrix factorization of PM2.5 – comparison and implications of using different speciation datasets.” In this paper we explored how different datasets (bulk species such as sulfate and organic carbon, water soluble elements, or organic molecular markers) impacted identifying specific fine (PM2.5) particulate pollution sources. We found that both water soluble elements and organic molecular markers offer distinct advantages. The major factors we identified in Denver fine particulate matter included n-alkane, nitrate/PAH, winter/methoxyphenol, low molecular weight (LMW) PAH, summer/selective aliphatic, EC/sterane and inorganic ion. These translate into sources such as motor vehicle emissions, soil, road and processed dust, and summertime biogenic emissions.
Xie, M., Hannigan, M.P., Dutton, S.J., Milford, J.B., Hemann, J.G., Miller, S.L., Schauer, J.J., Peel, J.L., Vedal, S. Positive matrix factorization of PM2.5 – comparison and implications of using different speciation datasets. Environmental Science & Technology, 46(21):11962-11970, 2012.