AAAR 32nd Annual Conference
September 30 - October 4, 2013
Oregon Convention Center
Portland, Oregon, USA
Abstract View
Single Particle Characterization Using a Soot Particle Aerosol Mass Spectrometer (SP-AMS) with a Light Scattering Module in Dowtown Toronto
ALEX K. Y. LEE, Megan D. Willis, Robert Healy, Jonathan Abbatt, University of Toronto
Abstract Number: 161 Working Group: Carbonaceous Aerosols in the Atmosphere
Abstract Atmospheric black carbon (BC) particles play an important role influencing air quality and radiative forcing on both regional and global scales. However, BC particles can be coated by other aerosol components during their lifetime in the atmosphere, thus introducing a significant modification to their physical and chemical properties. Better understanding of the mixing state of BC and the characteristics of its associated coatings is therefore important. In this study, we demonstrate the use of the Aerodyne soot particle aerosol mass spectrometer (SP-AMS) with a light scattering module to determine the mixing state of BC and non-refractory aerosol components (i.e. organics, ammonium, sulfate and nitrate) in downtown Toronto during September 18-22, 2012 This is the first report of single particle mass spectra of BC-containing particles measured by the SP-AMS. The individual single particle mass spectra clearly show that atmospheric BC can be coated with different types of organics (e.g., hydrocarbon-like organic aerosol (HOA) and oxygenated organic aerosol (OOA)) and inorganic species (e.g., ammonium sulfate). Clustering analysis was performed to categorize a large single particle mass spectral dataset into a few clusters. The K-means algorithm extracts a cluster that is dominated by a BC fragmentation signature and separates the organic aerosol signal into three cluster types, including HOA, OOA, and cooking organic aerosol (COA). By examining the size distribution of each cluster, our results suggest that most of the BC likely coexists with the HOA component. The clustering analysis will also be compared to the results obtained from positive matrix factorization analysis of the ensemble data measured by the SP-AMS.