Mining airborne particulate size distribution data by positive matrix factorization

Authors

  • Liming Zhou,

    1. Center for Air Resources Engineering and Science and Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA
    2. Now at Providence Engineering and Environmental Group LLC, Baton Rouge, Louisiana, USA.
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  • Eugene Kim,

    1. Center for Air Resources Engineering and Science and Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA
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  • Philip K. Hopke,

    1. Center for Air Resources Engineering and Science and Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA
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  • Charles Stanier,

    1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
    2. Now at Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, Iowa, USA.
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  • Spyros N. Pandis

    1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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Abstract

[1] Airborne particulate size distribution data acquired in Pittsburgh from July 2001 to June 2002 were analyzed as a bilinear receptor model solved by positive matrix factorization (PMF). The data were obtained from two scanning mobility particle spectrometers and an aerodynamic particle sampler with a temporal resolution of 15 min. Each sample contained 165 size bins from 0.003 to 2.5 μm. Particle growth periods in nucleation events were identified, and the data in these intervals were excluded from this study so that the size distribution profiles associated with the factors could be regarded as sufficiently constant to satisfy the assumptions of the receptor model. The values for each set of five consecutive size bins were averaged to produce 33 new size intervals. Analyses were made on monthly data sets to ensure that the changes in the size distributions from the source to the receptor site could be regarded as constant. The factors from PMF could be assigned to particle sources by examination of the number size distributions associated with the factors, the time frequency properties of the contribution of each source (Fourier analysis of source contribution values), and the correlations of the contribution values with simultaneous gas phase measurements (O3, NO, NO2, SO2, CO) and particle composition data (sulfate, nitrate, organic carbon/elemental carbon). Seasonal trends and weekday/weekend effects were investigated. Conditional probability function analyses were performed for each source to ascertain the likely directions in which the sources were located. Five factors were separated. Two factors, local traffic and nucleation, are clear sources, but each of the other factors appears to be a mixture of several sources that cannot be further separated.

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