CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS IN SMOKE AT EXPERIMENTAL FIRES
Why this work is in the frame
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Bibliographic record
Abstract
Significant associations between firefighting and cancer have been reported; however, studies finding toxic products of combustion at municipal fires have been limited by (1) technical difficulties encountered at the scene of working fires, (2) the lack of a coherent sampling strategy, and (3) the absence of verified sampling methods. The objective of the present study was to characterize the presence of volatile organic compound (VOC) combustion products in fire smoke. Air samples from experimental fires burning various materials commonly found at structural fires were collected into evacuated Summa canisters and analyzed for 144 target VOCs using cryogenic preconcentration and gas chromatography/mass spectroscopy (GC/MSD) methodology. The resulting chromatograms were characterized by a small number of predominant peaks, with 14 substances (propene, benzene, xylenes, 1-butene/2-methylpropene, toluene, propane, 1,2-butadiene, 2-methylbutane, ethylbenzene, naphthalene, styrene, cyclopentene, 1-methylcyclopentene, isopropylbenzene) being found in proportionately higher concentrations in all experimental fires and accounting for 65% (SD = +/-12%) by mass of total measured VOCs. Benzene, toluene, 1,3-butadiene, naphthalene, and styrene were found at higher concentrations than most other VOCs and increased with the time of combustion together with increasing levels of carbon monoxide. Benzene was found in the highest concentrations, with peak levels ranging from 0.6 ppm to 65 ppm, while the levels of 1,3-butadiene, styrene, and naphthalene peaked at 0.1, 0.4, and 3 ppm, respectively. This study revealed that there were no new or novel, toxic nonpolar VOCs resulting from the burning of common building materials. This is important in view of the studies that have found associations between firefighting and various forms of cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it