CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS IN SMOKE AT MUNICIPAL STRUCTURAL FIRES
Bibliographic record
Abstract
The objective of this study was to characterize volatile organic compounds (VOCs) found at municipal structural fires in order to identify sources of long-term health risks to firefighters, which may be contributing factors in heart disease and cancer. Firefighters collected air into evacuated Summa canisters inside burning buildings at nine municipal structural fires under conditions where they judged that at least some firefighters might remove their self-contained breathing apparatus masks. Volatile organic compounds were identified and quantified for 144 target compounds using cryogenic preconcentration and gas chromatography/mass spectral detection (GC/MSD) methodology operating in selected ion monitoring mode. Samples were also analyzed in SCAN mode and examined for the appearance of substances that were not present in the instrument standard calibration mixture. The spectra of municipal structural fires were surprisingly similar and remarkable for their simplicity, which was largely due to the dominating presence of benzene along with toluene and naphthalene. Propene and 1,3-butadiene were found in all of the fires, and styrene and other alkyl-substituted benzene compounds were frequently identified. Similar "fingerprints" of the same 14 substances (propene, benzene, xylenes, 1-butene/2-methylpropene, toluene, propane, 1,2-butadiene, 2-methylbutane, ethylbenzene, naphthalene, styrene, cyclopentene, 1-methylcyclopentene, isopropylbenzene) previously identified at experimental fires burning various solid combustible materials were also found at municipal structural fires, accounting for 76.8% of the total VOCs measured. Statistically significant positive correlations were found between increasing levels of benzene and levels of propene, the xylenes, toluene, 1-butene/2-methylpropene, 1,3-butadiene, and naphthalene. Given the toxicity/carcinogenicity of those VOCs that were found in the highest concentrations, particularly benzene, 1,3-butadiene, and styrene, further investigation of VOC exposures of firefighters is warranted. Benzene, or its metabolic product s-phenylmercapturic acid in urine, was identified as a suitable chemical marker for firefighter exposure to combustion products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".