Biomass burning is a dominant source of ultrafine particulate matter in the atmosphere. Particulate matter is a leading health risk factor on a global scale, causing millions of premature deaths annually. Biomass burning also emits short-term climate forcers which contribute to the warming of the Earth’s atmosphere. Wood and animal dung are widely employed in the developing world as the primary sources of household energy. While wood burning is well studied, emissions from dung remain largely uncharacterized. Emissions from a given burn are highly complex chemical mixtures. While specific biomass tracerssuch as levoglucosanare employed to track burns, a fundamental chemical understanding of biomass emissions is required to predict their impacts. Herein, we conducted comprehensive sets of chemical analyses for particles emitted from biomass burning. Samples were generated using a tube furnace allowing reproducible, precise control of conditions. Emission factor data for levoglucosan and its isomers were measured from extracted particulate matter. We found that the levoglucosan emission factors from two distinct types of cow dung were consistently lower than that from wood. The water-extractable fraction of dung emissions exhibited light-absorptive properties greater than wood. Nontargeted chemical characterization was achieved through deconvolution of high-resolution mass spectrometry data. Overall, we present that the key differences between wood and dung emissions mirror the differences in their fuel compositions. The complexity of the extracted spectra and the unique characteristics of dung emissions accentuate the need for further study on biomass types less common within the Western context.

Alignment of fire debris data from GC-MS for chemometric analysis is challenged by highly variable, uncontrolled sample and matrix composition. The total ion spectrum (TIS) obviates the need for alignment but loses all separation information. We introduce the segmented total ion spectrum (STIS), which retains the advantages of TIS while retaining some retention information. We compare the performance of STIS with TIS for the classification of casework fire debris samples. TIS and STIS achieve good model prediction accuracies of 96% and 98%, respectively. Baseline removal improved model prediction accuracies for both TIS and STIS to 97% and 99%, respectively. The importance of maintaining some chromatographic information to aid in deciphering the underlying chemistry of the results and reasons for false positive/negative results was also examined.

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Forensic fire debris analysis is an important part of fire investigation, and gas chromatographymass spectrometry (GC-MS) is the accepted standard for detection of ignitable liquids in fire debris. While GC-MS is the dominant technique, comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) is gaining popularity. Despite the broad use of these techniques, their sensitivities are poorly characterized for petroleum-based ignitable liquids. Accordingly, we explored the limit of identification (LOI) using the protocols currently applied in accredited forensic labs for two 75% evaporated gasolines and a 25% evaporated diesel as both neat samples and in the presence of interfering pyrolysate typical of fire debris. GC-MSD (mass selective detector (MS)), GC-TOF (time-of-flight (MS)), and GC×GC-TOF were evaluated under matched conditions to determine the volume of ignitable liquid required on-column for correct identification by three experienced forensic examiners performing chromatographic interpretation in accordance with ASTM E1618-14. GC-MSD provided LOIs of~0.6 pL on-column for both neat gasolines, and~12.5 pL on-column for neat diesel. In the presence of pyrolysate, the gasoline LOIs increased to~6.2 pL on-column, while diesel could not be correctly identified at the concentrations tested. For the neat dilutions, GC-TOF generally provided 2× better sensitivity over GC-MSD, while GC×GC-TOF generally resulted in 10× better sensitivity over GC-MSD. In the presence of pyrolysate, GC-TOF was generally equivalent to GC-MSD, while GC×GC-TOF continued to show 10× greater sensitivity relative to GC-MSD. Our findings demonstrate the superior sensitivity of GC×GC-TOF and provide an important approach for interlaboratory benchmarking of modern instrumental performance in fire debris analysis.

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Fire debris analysis is focused on the recovery and identification of ignitable liquids to provide context for fire investigation. Investigators use a variety of methods to select suspicious debris for analysis, with ignitable liquid detection canines being one of the most popular. When properly trained and certified, ignitable liquid detection canines offer continuous sampling with high sensitivity and the ability to discriminate between irrelevant and suspicious odours to rapidly locate debris which may contain ignitable liquid residues. However, canine indications are presumptive as they cannot be sufficiently scrutinised by the legal process without confirmatory laboratory analysis. Standard debris analysis methods detect very small amounts of ignitable liquid residue (∼1-0.1 μL) without maximising sensitivity which minimises the risk from false positives and from detection of background petroleum which is ubiquitous in our environment. For canine-selected debris, the goal of the laboratory analysis should be to provide data to confirm or refute the validity of the canine indication. For such confirmatory analysis to be useful, analytical sensitivity should approximate the sensitivity of the canine. The sensitivity of fire debris analysis is most influenced by the selection of the extraction device and tuning of extraction conditions. Non-destructive extractions are preferred for forensic analyses, and solid phase microextraction (SPME) offers an excellent option. However, the original SPME fibres are fragile and tend to skew the chromatographic profile which can lead to high costs and a risk of ignitable liquid misclassification. Herein, we present an optimised SPME extraction method suited to confirmatory analysis of canine-selected exhibits. The method is non-destructive and non-exhaustive, is easily applied to cans of debris, and yields chromatographic profiles equivalent to those obtained by the gold-standard passive headspace sampling (PHS) methods based on activated carbon. Fibre selection, debris temperature, fibre temperature, and extraction time were optimised to yield chromatographic profiles with maximum comparability to reference samples collected as neat liquids or standard PHS extracts. The optimised method is applied to samples recovered from another study which estimated the threshold of the canine’s sensitivity, with the laboratory result compared to the canine result for each sample.

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