CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS IN SMOKE AT EXPERIMENTAL FIRES
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".