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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Nov 2019

Submitted as: research article | 20 Nov 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Deconvolution of FIGAERO-CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation

Angela Buchholz1, Arttu Ylisirniö1, Wei Huang2, Claudia Mohr3, Manjula Canagaratna4, Douglas R. Worsnop4, Siegfried Schobesberger1, and Annele Virtanen1 Angela Buchholz et al.
  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 3Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden
  • 4Aerodyne Research Inc., Billerica, MA 08121-3976, USA

Abstract. Measurements of aerosol particles with a filter inlet for gases and aerosols (FIGAERO) together with a chemical ionisation mass spectrometer (CIMS) yield the overall chemical composition of the particle phase. In addition, the thermal desorption profiles obtained for each detected ion composition contain information about the volatility of the detected compounds, an important property to understand many physical properties like gas/particle partitioning. We coupled this thermal desorption method with isothermal evaporation prior to the sample collection to investigate the chemical composition changes during isothermal particle evaporation and particulate water driven chemical reactions in a-pinene SOA of three different oxidative states. The thermal desorption profiles of all detected elemental compositions were then analysed with positive matrix factorisation (PMF) to identify the drivers of the chemical composition changes observed during isothermal evaporation. The key to this analysis was to use the error matrix as a tool to weight the parts of the data carrying most information (i.e., the peak area of each thermogram) and to run PMF on a combined dataset of multiple thermograms from different experiments to enable direct comparison of the individual factors between separate measurements. PMF was able to identify instrument background factors and separate them from the part of the data containing particle desorption information. Additionally, PMF allowed us to separate the direct desorption of compounds detected at a specific elemental composition from signals at the same composition stemming from thermal decomposition of thermally instable compounds of lower volatility. For each SOA type, 7–9 factors were needed to explain the observed thermogram behaviour. The contribution of the factors depended on the prior isothermal evaporation. Decreased contributions from the lowest desorption temperatures factors were observed with increasing isothermal evaporation time. Thus, the factors identified with PMF could be interpreted as volatility classes. The composition changes in the particles due to isothermal evaporation could be attributed to the removal of volatile factors with very little change in the desorption profiles of the individual factors (i.e., in the respective temperatures of peak desorption, Tmax). When aqueous phase reactions took place, PMF was able to identify a new factor which directly identified ions affected by the chemical processes.

We conducted PMF analysis of FIGAERO-CIMS thermal desorption data for the first time using laboratory generated SOA particles. But this method can be applied to e.g. ambient FIGAERO-CIMS measurements as well. In addition to the information about the physical sources of the organic aerosol particles (which could also be obtained by PMF analysis of the mass spectra data integrated for each thermogram scan), changes in particle volatility can be investigated.

Angela Buchholz et al.
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Status: open (until 15 Jan 2020)
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Angela Buchholz et al.
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Short summary
To understand the role of aerosol particles in the atmosphere, it is necessary to know their detailed chemical composition and physical properties, especially volatility. The thermal desorption data from FIGAERO-CIMS provides both, but is difficult to analyse. With Positive Matrix Factorisation we can separate instrument background from real signal. Compounds are classified by their apparent volatility and the contribution of thermal decomposition in the instrument can be identified.
To understand the role of aerosol particles in the atmosphere, it is necessary to know their...