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Investigation of the relationship between chemical composition and size distribution of airborne particles by partial least squares and positive matrix factorization
Center for Air Resources Engineering and Science and Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA
Center for Air Resources Engineering and Science and Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, USA
Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, USA
Two multivariate data analysis methods, partial least square (PLS) and positive matrix factorization (PMF), were used to analyze aerosol size distribution data and composition data. The relationships between the size distribution data and composition data were investigated by PLS. Three latent variables summarized chemical composition data and most variations in size distribution data especially for large particles and proved the existence of the linearity between the two data sets. The three latent variables were associated with traffic and local combustion sources, secondary aerosol, and coal-fired power plants. The size distribution, particle composition, and gas composition data were combined and analyzed by PMF. Source information was obtained for each source using size distribution and chemical composition simultaneously. Eleven sources were identified: secondary nitrate 1 and 2, remote traffic, secondary sulfate, lead, diesel traffic, coal-fired power plant, steel mill, nucleation, local traffic, and coke plant.
Received 20 May 2004; accepted 11 October 2004; published 9 March 2005.
Citation: (2005), Investigation of the relationship between chemical composition and size distribution of airborne particles by partial least squares and positive matrix factorization, J. Geophys. Res., 110, D07S18, doi:10.1029/2004JD005050.
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