Source Identification of Atlanta Aerosol by Positive Matrix Factorization

Abstract Data characterizing daily integrated particulate matter (PM) samples collected at the Jefferson Street monitoring site in Atlanta, GA, were analyzed through the application of a bilinear positive matrix factorization (PMF) model. A total of 662 samples and 26 variables were used for fine particle (particles ≤2.5 µm in aerodynamic diameter) samples (PM2.5 ), and 685 samples and 15 variables were used for coarse particle (particles between 2.5 and 10 µm in aerodynamic diameter) samples (PM10–2.5 ). Measured PM mass concentrations and compositional data were used as independent variables. To obtain the quantitative contributions for each source, the factors were normalized using PMF-apportioned mass concentrations. For fine particle data, eight sources were identified: SO4 2−-rich secondary aerosol (56%), motor vehicle (22%), wood smoke (11%), NO3 −-rich secondary aerosol (7%), mixed source of cement kiln and organic carbon (OC) (2%), airborne soil (1%), metal recycling facility (0.5%), and mixed source of bus station and metal processing (0.3%). The SO4 2−-rich and NO3 −-rich secondary aerosols were associated with NH4 +. The SO4 2−-rich secondary aerosols also included OC. For the coarse particle data, five sources contributed to the observed mass: airborne soil (60%), NO3 −-rich secondary aerosol (16%), SO4 2−-rich secondary aerosol (12%), cement kiln (11%), and metal recycling facility (1%). Conditional probability functions were computed using surface wind data and identified mass contributions from each source. The results of this analysis agreed well with the locations of known local point sources.

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