title: Improving source apportionment of fine particles in the eastern United States utilizing temperature-resolved carbon fractions.
abstract: The objectives of this study were to examine the use of carbon fractions to identify particulate matter (PM) sources, especially traffic-related carbonaceous particle sources, and to estimate their contributions to the particle mass concentrations. In recent studies, positive matrix factorization (PMF) was applied to ambient fine PM (PM2.5) compositional data sets of 24-hr integrated samples including eight individual carbon fractions collected at three monitoring sites in the eastern United States: Atlanta, GA, Washington, DC, and Brigantine, NJ. Particulate carbon was analyzed using the Interagency Monitoring of Protected Visual Environments/Thermal Optical Reflectance method that divides carbon into four organic carbons (OC): pyrolized OC and three elemental carbon (EC) fractions. In contrast to earlier PMF studies that included only the total OC and EC concentrations, gasoline emissions could be distinguished from diesel emissions based on the differences in the abundances of the carbon fractions between the two sources. The compositional profiles for these two major source types show similarities among the three sites. Temperature-resolved carbon fractions also enhanced separations of carbon-rich secondary sulfate aerosols. Potential source contribution function analyses show the potential source areas and pathways of sulfate-rich secondary aerosols, especially the regional influences of the biogenic, as well as anthropogenic secondary aerosol. This study indicates that temperature-resolved carbon fractions can be used to enhance the source apportionment of ambient PM2.5.
authors.affiliation: Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699, USA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Kim
authors.forename: Philip K
authors.initials: PK
authors.lastname: Hopke
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis*; D000465:Algorithms; D002244:Carbon / Q000032:analysis*; D015233:Models, Statistical; D015999:Multivariate Analysis; D009511:New England; D015144:Southeastern United States; D013431:Sulfates / Q000032:analysis; D013696:Temperature; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000395:Air Pollutants, Occupational; D013431:Sulfates; D001335:Vehicle Emissions; D002244:Carbon