Mixing state of carbonaceous aerosol in an urban environment: single particle characterization using the soot particle aerosol mass spectrometer (SP-AMS)
- 1Department of Chemistry, University of Toronto, Toronto, Canada
- 2Department of Chemistry and Environmental Research Institute, University College Cork, Cork, Ireland
- 3Southern Ontario Centre for Atmospheric Aerosol Research, University of Toronto, Toronto, Canada
- 4Aerodyne Research Inc., Billerica, Massachusetts, USA
Abstract. Understanding the impact of atmospheric black carbon (BC)-containing particles on human health and radiative forcing requires knowledge of the mixing state of BC, including the characteristics of the materials with which it is internally mixed. In this study, we examine the mixing state of refractory BC (rBC) and other aerosol components in an urban environment (downtown Toronto) utilizing the Aerodyne soot particle aerosol mass spectrometer equipped with a light scattering module (LS-SP-AMS). k-means cluster analysis was used to classify single particle mass spectra into chemically distinct groups. One resultant particle class is dominated by rBC mass spectral signals (C1+ to C5+) while the organic signals fall into a few major particle classes identified as hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and cooking emission organic aerosol (COA). A gradual mixing is observed with small rBC particles only thinly coated by HOA (~ 28% by mass on average), while over 90% of the HOA-rich particles did not contain detectable amounts of rBC. Most of the particles classified into other inorganic and organic particle classes were not significantly associated with rBC. The single particle results also suggest that HOA and COA emitted from anthropogenic sources were likely major contributors to organic-rich particles with vacuum aerodynamic diameter (dva) ranging from ~ 200 to 400 nm. The similar temporal profiles and mass spectral features of the organic classes identified by cluster analysis and the factors from a positive matrix factorization (PMF) analysis of the ensemble aerosol data set validate the interpretation of the PMF results.