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

2019 ◽  
Author(s):  
Angela Buchholz ◽  
Arttu Ylisirniö ◽  
Wei Huang ◽  
Claudia Mohr ◽  
Manjula Canagaratna ◽  
...  

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.

2020 ◽  
Vol 20 (13) ◽  
pp. 7693-7716 ◽  
Author(s):  
Angela Buchholz ◽  
Arttu Ylisirniö ◽  
Wei Huang ◽  
Claudia Mohr ◽  
Manjula Canagaratna ◽  
...  

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.


1992 ◽  
Vol 75 (2) ◽  
pp. 272-279 ◽  
Author(s):  
J Matthew Rodewald ◽  
John W Moran ◽  
Alvin L Donoho ◽  
Mark R Coleman

Abstract A method Is described for the detection and quantitation of monensin In raw material, premix, and feeds by liquid chromatography (LC) with postcolumn derlvatlzatlon with vanillin. Monensin was mixed with vanillin under acidic conditions and heated, and the resulting products were measured by a variable wavelength visible detector operating at 520 nm. The LC response of monensin and monensln-llke factors was determined and correlated to the microbiological response of each factor as determined with Streptococcus faeclum. Monensin reference standard was characterized In the same manner as the individual factors. The chemical composition of the reference standard and the relative microbiological potency values were used In combination to calculate the biopotency contribution of each of the monensin factors. A formula was used to transform chemical composition values of the reference standard to total microbiological activity as obtained directly from a microbiological assay. The formula was tested by comparing samples assayed by LC using the formula to report microbiological potency with samples assayed by the Autoturb method. Finally, the LC method was validated with raw material, premix, cattle rations (including liquid supplements), and poultry rations.


2008 ◽  
Vol 8 (3) ◽  
pp. 9347-9404 ◽  
Author(s):  
B. Winterholler ◽  
P. Hoppe ◽  
J. Huth ◽  
S. Foley ◽  
M. O. Andreae

Abstract. Sulfur isotope analysis of atmospheric aerosols is a well established tool for identifying sources of sulfur in the atmosphere, estimating emission factors, and tracing the spread of sulfur from anthropogenic sources through ecosystems. Conventional gas mass spectrometry averages the isotopic compositions of several different types of sulfur aerosol particles, and therefore masks the individual isotopic signatures. In contrast, the new single particle technique presented here determines the isotopic signature of the individual particles. Primary aerosol particles retain the original isotopic signature of their source. The isotopic composition of secondary sulfates depends on the isotopic composition of precursor SO2 and the oxidation process. The fractionation with respect to the source SO2 is −9‰ for homogeneous and +16.5‰ for heterogeneous oxidation. The sulfur isotope ratio of secondary sulfate particles can therefore be used to identify the oxidation pathway by which this sulfate was formed. With the new single particle technique, different types of primary and secondary sulfates were first identified based on their chemical composition, and then their individual isotopic signature was measured separately. Our samples were collected in Mainz, Germany, in an urban environment. Secondary sulfates (ammonium sulfate, gypsum, mixed sulfates) and coatings on silicates or organic aerosol dominated sulfate loadings in our samples. Comparison of the chemical and isotopic composition of secondary sulfates showed that the isotopic composition was homogeneous, independent of the chemical composition. This is typical for particles that derive from in-cloud processing. The isotopic composition of the source SO2 of secondary sulfates was calculated based on the isotopic composition of particles with known oxidation pathway and showed a strong dependence on wind direction. The contribution of heterogeneous oxidation to the formation of secondary sulfate was highly variable (35–75%) on day-to-day basis and depended on meteorological conditions.


2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
HV Thakkar ◽  
L Hollingsworth ◽  
JA Enright ◽  
S Sanderson ◽  
RJ Macfadyen ◽  
...  

Abstract Funding Acknowledgements Type of funding sources: None. Background Factors influencing return to remunerated work following an acute cardiac illness are poorly defined. We wished to compare the factors in our cohorts following first presentation of acute coronary syndrome(ACS) and decompensated heart failure(HF). Methods Prospectively identified subjects, aged 18-65years, from a rehabilitation population for ACS and HF during 2018-2019 underwent a survey. Results Of 133cases meeting inclusion criteria, 84 completed the survey(41 HF, 80% male, mean age 55years; 43 ACS, 86% male, mean age 57years). Socio-economic indexes for Areas(SIEFA) index were similar for HF(900) & ACS(909) groups, which represents 11th and 14th percentile for Australia respectively. Cardiovascular risk factors were similar except hypercholesterolemia(37% v 60%; p = 0.029) was more common in ACS. Many subjects did not continue beyond Yr12, (54% HF v 30% ACS; p = 0.029). A majority of ACS cases returned to work as compared with HF(70% v 44%; p = 0.017)(Figure). On multivariate analysis, male gender[p = 0.031;OR 13.71 (1.28-147.36)]; access to financial benefits[p < 0.001;OR 22.75 (4.31-119.99)] and a desire to return to work [p = 0.014;OR 12.1 (1.67-87.82)] were associated with successful return to work (Table). Limitations Our study has small numbers so will be difficult to generalise to a wider population. We do show a signal towards the complex interplay of the social and individual factors in determining return to work. Further larger studies are required to tease out the differences between the individual factors to help predict return to work in the Australian context. Conclusion Successful return to work for patients with first presentation of ACS or HF could not be reliably predicted. Patients with ACS returned to work more often than HF. In HF patients who do n to return to work, recurrent symptoms, individual motivation, social support and access to financial benefits have a complex interplay. Predictors of return to work Predictor P value OR (95% CI) Diagnosis (heart failure) 0.095 0.29 (0.07, 1.24) Gender (male) 0.031 13.71 (1.28, 147.36) Access to benefit (none) <0.001 22.75 (4.31, 119.99) Desire to RTW (yes) 0.014 12.1 (1.67, 87.82) Abstract Figure. Rates of return to work in the 2 groups


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