Understanding of ambient particle types and mixing state with improved identification and quantification by single particle mass spectrometry
Xiaoli Shenaa, Harald Saathoffa, Wei Huangaa, Claudia Mohra,b ,Ramakrishna Ramisettya,c, and Thomas Leisnera,d
aInstitute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
bNow at: Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, 11418, Sweden
cNow at: TSI Incorporated, Bangalore, 560102, India
dInstitute of Environmental Physics, University Heidelberg, In Neuenheimer Feld 229, 69120 Heidelberg, Germany
Single particle mass spectrometry (SPMS) is a useful tool to determine chemical composition and mixing state of aerosol particles in the atmosphere. However, quantification of aerosol particles on a single particle basis is still quite challenging [1‒3]. During a field campaign in summer 2016 at a rural site in the upper Rhine valley near Karlsruhe city in southwest Germany, ~3.7 × 105 particles were analysed by the laser ablation aerosol particles time-of-flight mass spectrometer (LAAPTOF). Combining fuzzy classification, marker peaks, signature ratios, and laboratory-based reference spectra, seven major particle classes were identified. With the precise identification and well characterized overall detection efficiency (ODE) for this instrument, particle similarity can be transferred into corrected number fractions and further transferred into mass fractions. Analysing the whole measurement period, “Potassium rich and aromatics coated dust” (class 5) dominate the particle number (46.5% number fraction) and mass (36% mass fraction); “Sodium salts like particles” (class 3), are the second lowest in number (3.5%) but the second dominating class in terms of particle mass (25.3%). This difference demonstrates the crucial role of particle quantification for SPMS data. Our semi-quantification approach could partially be validated by comparison with data from an aerosol mass spectrometer (AMS) for non-refractory species. Furthermore, our approach allows for the first time to assign the non-refractory species measured by AMS to different particle classes and it turns out that the dominating donor particles varied for different periods of the field campaign, e.g., overall AMS-nitrate was mainly arising from class 3, while class 5 was dominant during the organic burst event. Taken together, this study might provide fresh insights into the mixing state and sources of atmospheric aerosol particles.
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