| 1. |
- Buchholz, Angela, et al.
(författare)
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Deconvolution of FIGAERO-CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation
- 2020
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Ingår i: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 20:13, s. 7693-7716
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Tidskriftsartikel (refereegranskat)abstract
- 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, the thermal desorption profiles obtained for each detected ion composition contain information about the volatility of the detected compounds, which is an important property for understanding 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 alpha-pinene secondary organic aerosol (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 keys to this analysis were 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 data set of multiple thermograms from different experiments to enable a direct comparison of the individual factors between separate measurements. The 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 other signals with the same composition that stem from the thermal decomposition of thermally instable compounds with 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 factors with the lowest desorption temperatures were observed with increasing isothermal evaporation time. Thus, the factors identified by 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, T-max). When aqueous-phase reactions took place, PMF was able to identify a new factor that directly identified the ions affected by the chemical processes. We conducted a PMF analysis of the FIGAERO-CIMS thermal desorption data for the first 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.
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| 2. |
- Ylisirniö, Arttu, et al.
(författare)
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Composition and volatility of secondary organic aerosol (SOA) formed from oxidation of real tree emissions compared to simplified volatile organic compound (VOC) systems
- 2020
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Ingår i: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 20:9, s. 5629-5644
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Tidskriftsartikel (refereegranskat)abstract
- Secondary organic aerosol (SOA) is an important constituent of the atmosphere where SOA particles are formed chiefly by the condensation or reactive uptake of oxidation products of volatile organic compounds (VOCs). The mass yield in SOA particle formation, as well as the chemical composition and volatility of the particles, is determined by the identity of the VOC precursor(s) and the oxidation conditions they experience. In this study, we used an oxidation flow reactor to generate biogenic SOA from the oxidation of Scots pine emissions. Mass yields, chemical composition and volatility of the SOA particles were characterized and compared with SOA particles formed from oxidation of alpha-pinene and from a mixture of acyclic-monocyclic sesquiterpenes (farnesenes and bisabolenes), which are significant components of the Scots pine emissions. SOA mass yields for Scots pine emissions dominated by farnesenes were lower than for a-pinene but higher than for the artificial mixture of farne-senes and bisabolenes. The reduction in the SOA yield in the farnesene- and bisabolene-dominated mixtures is due to exocyclic C =C bond scission in these acyclic-monocyclic sesquiterpenes during ozonolysis leading to smaller and generally more volatile products. SOA particles from the oxi- dation of Scots pine emissions had similar or lower volatility than SOA particles formed from either a single precursor or a simple mixture of VOCs. Applying physical stress to the Scots pine plants increased their monoterpene, especially monocyclic beta-phellandrene, emissions, which further decreased SOA particle volatility and increased SOA mass yield. Our results highlight the need to account for the chemical complexity and structure of real-world biogenic VOC emissions and stress-induced changes to plant emissions when modelling SOA production and properties in the atmosphere. These results emphasize that a simple increase or decrease in relative monoterpene and sesquiterpene emissions should not be used as an indicator of SOA particle volatility.
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