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. The 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, thethermal desorption profiles obtained for each detected ion compositioncontain information about the volatility of the detected compounds, which is animportant property for understanding many physical properties like gas–particlepartitioning. We coupled this thermal desorption method with isothermalevaporation prior to the sample collection to investigate the chemicalcomposition changes during isothermal particle evaporation and particulate-water-driven chemical reactions in α-pinene secondary organic aerosol (SOA) of three differentoxidative states. The thermal desorption profiles of all detected elementalcompositions were then analysed with positive matrix factorisation (PMF) toidentify the drivers of the chemical composition changes observed duringisothermal evaporation. The keys to this analysis were to use the error matrixas 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 data set ofmultiple thermograms from different experiments to enable a direct comparisonof the individual factors between separate measurements. The PMF was able to identify instrument background factors and separate themfrom the part of the data containing particle desorption information.Additionally, PMF allowed us to separate the direct desorption of compoundsdetected at a specific elemental composition from other signals with the samecomposition that stem from the thermal decomposition of thermally instablecompounds with lower volatility. For each SOA type, 7–9 factors were neededto explain the observed thermogram behaviour. The contribution of thefactors depended on the prior isothermal evaporation. Decreasedcontributions from the factors with the lowest desorption temperatures were observed with increasing isothermal evaporation time. Thus, the factors identifiedby PMF could be interpreted as volatility classes. The composition changesin the particles due to isothermal evaporation could be attributed to theremoval of volatile factors with very little change in the desorptionprofiles of the individual factors (i.e. in the respective temperatures ofpeak desorption, Tmax). When aqueous-phase reactions took place, PMFwas able to identify a new factor that directly identified the ions affected bythe chemical processes. We conducted a PMF analysis of the FIGAERO–CIMS thermal desorption data for thefirst time using laboratory-generated SOA particles. But this method can be applied to, for example, ambient FIGAERO–CIMS measurements as well. There, the PMF analysis of the thermal desorption data identifies organic aerosol (OA) sources (such as biomass burning or oxidation of different precursors) and types, e.g. hydrocarbon-like (HOA) or oxygenated organic aerosol (OOA). This information could also be obtained with the traditional approach, namely the PMF analysis of the mass spectra data integrated for each thermogram. But only our method can also obtain the volatility information for each OA source and type. Additionally, we can identify the contribution of thermal decomposition to the overall signal.

atmospheric chemistry and physics

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