Application of mass defect filtering and statistical analysis for non-target data mining of gas and soot data from a study testing different firefighting methods

Due to the high temperatures during a fire event, a large variety of compounds are formed or released from burning materials, all of which have a varying degree of environmental effects. In an incidental fire there are several variables that are important for which and how much combustion products t...

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Bibliographic Details
Main Author: Ydstål, Danielle
Format: Others
Language:English
Published: Örebro universitet, Institutionen för naturvetenskap och teknik 2020
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-86568
Description
Summary:Due to the high temperatures during a fire event, a large variety of compounds are formed or released from burning materials, all of which have a varying degree of environmental effects. In an incidental fire there are several variables that are important for which and how much combustion products that are formed, including the burning material, ventilation (presence of air), and temperature. The aim of this project is to evaluate if there is a difference between formed fire residues in gas and soot using four different fire extinguishing techniques. A non-target approach is used with gas chromatography connected with ultrahigh-resolution mass spectrometry. Unlike target analysis, non-target analysis enables identification of not only known chemicals, but also previously unknown chemicals. However, one of the major challenges in non-target analysis is how to handle the large amount of data generated in order to identify important markers for the current research question. Mass defect filtering is used to interpret the complex mass spectral data. Plotting the mass defect against the measured m/z allows you to visualize a high number of mass spectral peaks, linking homologues and congeners. The plot is based on a specific mass scale and can be used to find m/z that belong to compounds of a specific compound group. Statistical methods such as Principle Component Analysis (PCA) are also useful as it extracts and displays systematic variation in a data set, which can be used to find interesting variables. Mass defect filtering proved to be useful for the detection of a number of different compound groups: Alkylated hydrocarbons, halogenated compounds and PAHs. There were several differences in the composition of the gas versus soot. Gas had little variation between the samples whereas soot varied more depending on firefighting method used. Despite the fact that the chemical composition of gas and soot does differ between the four firefighting techniques, the variations in wind conditions made it hard to draw any conclusions regarding how the different firefighting techniques affect the compound formation and to what extent.