Organic contaminants formed during fire extinguishing using different firefighting methods assessed by nontarget analysis
Bibliographic record
Abstract
During a fire event, potentially hazardous chemicals are formed from the combustion of burning materials and are released to the surrounding environment, both via gas and soot particles. The aim of this investigation was to study if firefighting techniques influence the emission of chemicals in gas phase and soot particles. Five full-scale fire tests were extinguished using four different firefighting techniques. A nontarget chemical analysis approach showed that important contaminants in gas and soot separating the different tests were brominated flame retardants (BFRs), organophosphate flame retardants (OPFR), polycyclic aromatic hydrocarbons (PAHs) and linear hydrocarbons. Reproducibility was evaluated by a field replicate test and it was determined that the temperature curve during the event had a bigger impact on the released chemicals than the firefighting technique used. However, despite fire intensity being a confounding factor, multivariate statistics concluded that water mist with additive resulted in less BFR emissions compared to foam extinguishing. The analysis also showed that the conventional spray nozzle method released more PAHs compared with the water mist method. The comprehensive chemical analysis of gas and soot released during fire events was able to show that different firefighting techniques influenced the release of chemicals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".