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 tracerssuch
as levoglucosanare 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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.