Occupational Exposure of On-Shift Ottawa Firefighters to Flame Retardants and Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
Firefighters can be exposed to complex mixtures of airborne substances, including hazardous substances released during structural fires. This study employed silicone wristbands (SWBs) as passive samplers to investigate potential exposure to polycyclic aromatic hydrocarbons (PAHs) and flame retardants (FRs). SWBs were deployed at different areas of four fire stations, in four truck cabins, and at an office control location; they were also donned outside the jackets of 18 firefighters who responded to fire calls. Overall, office areas had significantly lower PAHs than fire station areas. Vehicle bays and truck cabins had significantly higher concentrations of low molecular weight (LMW) PAHs than sleeping and living room areas. For organophosphate ester flame retardants (OPFRs), tri-n-butyl phosphate (TnBP) and tris(1-chloro-2-propyl) phosphate (TCPP) were detected in all the samples; 2-ethylhexyl diphenyl phosphate (EHDPP) was more frequently detected in the fire station areas. Triphenyl phosphate (TPP) concentrations were highest in the truck cabin and office areas, and tris(1,3-dichloro-2-propyl)phosphate (TDCPP) was highest in truck cabins. Thirteen of 16 PAHs and nine of 36 OPFRs were detected in all the SWBs worn by firefighters, and tris (2-butoxyethyl) phosphate (TBEP) was the predominant OPFR. Levels of LMW PAHs were significantly lower when firefighters did not enter the fire. LMW PAHs, HMW (high molecular weight) PAHs, and EHDPP were significantly elevated when heavy smoke was reported. This work highlights the potential for occupational exposure to PAHs and flame retardants in some fire station areas; moreover, factors that may influence exposure during fire suppression. Whilst firefighters' occupational exposure to PAHs is likely related to fire suppression and exposure to contaminated gear and trucks, exposure to OPFRs may be more related to their presence in truck interiors and electronics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".