Quantitative Analysis of Mixed Halogen Dioxins and Furans in Fire Debris Utilizing Atmospheric Pressure Ionization Gas Chromatography-Triple Quadrupole Mass Spectrometry
Bibliographic record
Abstract
Residential and commercial fires generate a complex mixture of volatile, semivolatile, and nonvolatile compounds. This study focused on the semi/nonvolatile components of fire debris to better understand firefighter exposure risks. Using the enhanced sensitivity of gas chromatography coupled to atmospheric pressure ionization-tandem mass spectrometry (APGC-MS/MS), complex fire debris samples collected from simulation fires were analyzed for the presence of potentially toxic polyhalogenated dibenzo-p-dioxins and dibenzofurans (PXDD/Fs and PBDD/Fs). Extensive method development was performed to create multiple reaction monitoring (MRM) methods for a wide range of PXDD/Fs from dihalogenated through hexa-halogenated homologue groups. Higher halogenated compounds were not observed due to difficulty eluting them off the long column used for analysis. This methodology was able to identify both polyhalogenated (mixed bromo-/chloro- and polybromo-) dibenzo-p-dioxins and dibenzofurans in the simulated burn study samples collected, with the dibenzofuran species being the dominant compounds in the samples. Levels of these compounds were quantified as total homologue groups due to the limitations of commercial congener availability. Concentration ranges in household simulation debris were observed at 0.01-5.32 ppb (PXDFs) and 0.18-82.11 ppb (PBDFs). Concentration ranges in electronics simulation debris were observed at 0.10-175.26 ppb (PXDFs) and 0.33-9254.41 ppb (PBDFs). Samples taken from the particulate matter coating the firefighters' helmets contained some of the highest levels of dibenzofurans, ranging from 4.10 ppb to 2.35 ppm. The data suggest that firefighters and first responders at fire scenes are exposed to a complex mixture of potentially hundreds to thousands of different polyhalogenated dibenzo-p-dioxins and dibenzofurans that could negatively impact their health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".