Protecting Firefighters from Carcinogenic Exposure: Emerging Tools for PAH Detection and Decontamination
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are increasingly recognized as a major contributor to the occupational cancer risk among firefighters. In response, the National Fire Protection Association (NFPA) and other regulatory bodies have recommended rigorous decontamination protocols to minimize PAH exposure. Despite these efforts, a critical gap persists: the absence of real-time, field-deployable devices capable of detecting these invisible and toxic compounds during firefighting operations or within fire stations. Additionally, the lack of effective and optimized methods for the removal of these hazardous substances from the immediate environments of firefighters continues to pose a serious occupational health challenge. Although numerous studies have investigated PAH detection in environmental contexts, current technologies are still largely confined to laboratory settings and are unsuitable for field use. This review critically examines recent advances in PAH decontamination strategies for firefighting and explores alternative sensing solutions. We evaluate both conventional analytical methods, such as gas chromatography, high-performance liquid chromatography, and mass spectrometry, and emerging portable PAH detection technologies. By highlighting the limitations of existing systems and presenting novel sensing approaches, this paper aims to catalyze innovation in sensor development. Our ultimate goal is to inspire the creation of robust, field-deployable tools that enhance decontamination practices and significantly improve the health and safety of firefighters by reducing their long-term risks 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".