Review of Bucholz et al. ACPD 2019, “Deconvolution of FIGAERO-CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation”:

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.

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-7693-2020 ◽

2020 ◽

Vol 20 (13) ◽

pp. 7693-7716 ◽

Cited By ~ 2

Author(s):

Angela Buchholz ◽

Arttu Ylisirniö ◽

Wei Huang ◽

Claudia Mohr ◽

Manjula Canagaratna ◽

...

Keyword(s):

Thermal Decomposition ◽

Chemical Composition ◽

Thermal Desorption ◽

Organic Aerosol ◽

Individual Factors ◽

Positive Matrix ◽

Isothermal Evaporation ◽

The Individual ◽

Composition Changes ◽

Matrix Factorisation

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 α-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, Tmax). 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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Volatile organic compounds sources in Paris in spring 2007. Part II: source apportionment using positive matrix factorisation

Environmental Chemistry ◽

10.1071/en10067 ◽

2011 ◽

Vol 8 (1) ◽

pp. 91 ◽

Cited By ~ 42

Author(s):

Cécile Gaimoz ◽

Stéphane Sauvage ◽

Valérie Gros ◽

Frank Herrmann ◽

Jonathan Williams ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Organic Compound ◽

Organic Compounds ◽

Emission Inventory ◽

Positive Matrix ◽

Air Mass ◽

Volatile Organic ◽

Industrial Sources ◽

Fuel Evaporation ◽

Matrix Factorisation

Environmental context Volatile organic compounds are key compounds in atmospheric chemistry as precursors of ozone and secondary organic aerosols. To determine their impact at a megacity scale, a first important step is to characterise their sources. We present an estimate of volatile organic compound sources in Paris based on a combination of measurements and model results. The data suggest that the current emission inventory strongly overestimates the volatile organic compounds emitted from solvent industries, and thus needs to be corrected. Abstract A positive matrix factorisation model has been used for the determination of volatile organic compound (VOC) source contributions in Paris during an intensive campaign (May–June 2007). The major sources were traffic-related emissions (vehicle exhaust, 22% of the total mixing ratio of the measured VOCs, and fuel evaporation, 17%), with the remaining emissions from remote industrial sources (35%), natural gas and background (13%), local sources (7%), biogenic and fuel evaporation (5%) and wood-burning (2%). It was noted that the remote industrial contribution was highly dependent on the air-mass origin. During the period of oceanic influences (when only local and regional pollution was observed), this source made a relatively low contribution (<15%), whereas the source contribution linked to traffic was high (54%). During the period of continental influences (when additional continental pollution was observed), remote industrial sources played a dominant role, contributing up to 50% of measured VOCs. Finally, the positive matrix factorisation results obtained during the oceanic air mass-influenced period were compared with the local emission inventory. This comparison suggests that the VOC emission from solvent industries might be overestimated in the inventory, consistent with findings in other European cities.

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An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx

Atmospheric Environment ◽

10.1016/j.atmosenv.2014.05.039 ◽

2014 ◽

Vol 94 ◽

pp. 274-286 ◽

Cited By ~ 81

Author(s):

M.C. Bove ◽

P. Brotto ◽

F. Cassola ◽

E. Cuccia ◽

D. Massabò ◽

...

Keyword(s):

Source Apportionment ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Positive Matrix ◽

Matrix Factorisation

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Comparative source apportionment of PM10 in Switzerland for 2008/2009 and 1998/1999 by Positive Matrix Factorisation

Atmospheric Environment ◽

10.1016/j.atmosenv.2012.02.036 ◽

2012 ◽

Vol 54 ◽

pp. 149-158 ◽

Cited By ~ 31

Author(s):

M.F.D. Gianini ◽

A. Fischer ◽

R. Gehrig ◽

A. Ulrich ◽

A. Wichser ◽

...

Keyword(s):

Source Apportionment ◽

Positive Matrix ◽

Matrix Factorisation

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Technical note: A new approach to discriminate different black carbon sources by utilising fullerene and metals in positive matrix factorisation analysis of high-resolution soot particle aerosol mass spectrometer data

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-10763-2021 ◽

2021 ◽

Vol 21 (13) ◽

pp. 10763-10777

Author(s):

Zainab Bibi ◽

Hugh Coe ◽

James Brooks ◽

Paul I. Williams ◽

Ernesto Reyes-Villegas ◽

...

Keyword(s):

High Resolution ◽

Black Carbon ◽

Source Apportionment ◽

Atmospheric Aerosol ◽

Soot Particle ◽

Organic Aerosol ◽

Positive Matrix ◽

Aerosol Mass Spectrometer ◽

Aerosol Mass ◽

Matrix Factorisation

Abstract. Atmospheric aerosol particles are known to have detrimental effects on human health and climate. Black carbon is an important constituent of atmospheric aerosol particulate matter (PM), emitted from incomplete combustion. Source apportionment of BC is very important, to evaluate the influence of different sources. The high-resolution soot particle aerosol mass spectrometer (HR-SP-AMS) instrument uses a laser vaporiser, which allows the real-time detection and characterisation of refractory black carbon (rBC) and its internally mixed particles such as metals, coating species, and rBC subcomponents in the form of HOA + fullerene. In this case study, the soot data were collected by using HR-SP-AMS during Guy Fawkes Night on 5 November 2014. Positive matrix factorisation was applied to positively discriminate between different wood-burning and bonfire sources for the first time, which no existing black carbon source apportionment technique is currently able to do. Along with this, the use of the fullerene signals in differentiating between soot sources and the use of metals as a tracer for fireworks has also been investigated, which did not significantly contribute to the rBC concentrations. The addition of fullerene signals and successful positive matrix factorisation (PMF) application to HR-SP-AMS data apportioned rBC into more than two sources. These bonfire sources are HOA + fullerene, biomass burning organic aerosol, more oxidised oxygenated organic aerosol (MO-OOA), and non-bonfire sources such as hydrocarbon-like OA and domestic burning. The result of correlation analysis between HR-SP-AMS data and previously published Aethalometer, MAAP, and CIMS data provides an effective way of gaining insights into the relationships between the variables and provide a quantitative estimate of the source contributions to the BC budget during this period. This research study is an important demonstration of using HR-SP-AMS for the purpose of BC source apportionment.

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