Incentives and Barriers to Adopting Fluorine-Free Foams (FFFs) in Fire Training Facilities: Results of the First North American Survey
Bibliographic record
Abstract
Fluorine-free foams (FFFs) have been introduced as alternatives to aqueous film-forming foams (AFFFs), which are based on per- and polyfluoroalkyl substances (PFASs). However, adoption of FFFs remains limited due to the lack of universal drop-in replacements and limited data on their health and environmental impacts. This study examined incentives and barriers to implementing FFFs in Fire Training Facilities (FTFs) to support the transition away from PFAS-based products. A survey was conducted from September 2022 to December 2023 across the U.S. and Canadian FTFs, including state-funded facilities, metropolitan fire departments, airports, military, and industrial brigades. Developed in partnership with fire service organizations, the survey assessed current foam use, motivations for transition, and associated challenges. Of all FTF training with Class B foams, 38% reported using FFF products. Primary incentives included environmental and health concerns, safety, and regulatory pressures. Key challenges were transition costs, training requirements, and uncertainties around disposal of foams. These findings highlight that while momentum toward FFF adoption is evident, ensuring products are genuinely PFAS-free and providing comprehensive training will be critical for effective, large-scale implementation. Fire training facilities can play a pivotal role in guiding this transition.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".