Article,

Search criteria and rules for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry analysis of airborne particulate matter

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Journal of Chromatography A, 1019 (1-2): 233--249 (November 2003)

Abstract

Direct thermal desorption-gas chromatography-time-of-flight mass spectrometry (DTD-GC-TOFMS) and comprehensive two-dimensional (2D) gas chromatography-time-of-flight mass spectrometry (GC x GC-TOFMS) was applied for characterisation of semi-volatile organic compounds (SVOC) in fine particulate matter (PM), with a diameter of up to 2.5 mum (PM2.5), from ambient air in Augsburg, Germany. DTD-GC-TOFMS measurements on the SVOC in PM2.5 are done on a daily basis (time series over several years). The data will be used in an epidemiological study questioning the influence of SVOC in PM2.5 on ambient aerosol related health effects. The outcome of the first measurements periods is that the organic inventory in the ambient aerosol can undergo drastic fluctuations, e.g. due to meteorological influences or specific emission sources. This includes also the large fraction of chromatographically not resolved peaks (unresolved carbonaceous matter (UCM)). The UCM fraction contains about 70% of the SVOC mass in PM2.5. GC x GC-TOFMS is a suited technique to study the nature of the yet unidentified compounds forming the UCM. The considerably increased chromatographic resolution in GC x GC allows separation of many UCM compounds while the TOFMS supplies mass spectral data of all separated compounds. However, the data sets are getting enormously complex. In a typical PM2.5 sample from Augsburg more than 15,000 peaks can be detected. Thus, it is important to classify the observed GC x GC peaks by rational means. A classification procedure based on GC x GC retention times and the fragmentation patterns is suggested. With a preliminary classification procedure it is already possible to group compounds with some certainty into substance classes. After some further development, this approach can be used for classifying GC x GC data, e.g. for environmental and epidemiological studies. (C) 2003 Elsevier B.V. All fights reserved.

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