Novel Fluoroalkylated Surfactants in Soils Following Firefighting Foam Deployment During the Lac-Mégantic Railway Accident
Bibliographic record
Abstract
The derailment of an unmanned train carrying crude oil and subsequent fire in the town of Lac-Mégantic, Quebec, led to the use of 33 000 L of aqueous film forming foam (AFFF) concentrate. While it is known that per- and polyfluoroalkyl substances (PFASs) contained in AFFFs pose a potential environmental and health risk, critical knowledge gaps remain as regards to their environmental fate after release. The accident in Lac-Mégantic provided valuable information regarding the identity and concentration of PFASs present in the soil after the AFFF deployment, as well as their possible transformation over time. The current study analyzed four sets of samples from Lac-Mégantic: soil collected days after the accident from a heavily impacted area, soil sampled two years later from the treatment biopiles, soil collected two years after the accident from downtown Lac-Mégantic, and nonimpacted soil from a nearby area. A total of 33 PFASs were quantified in the soils. The highest observed concentrations correspond to those of 6:2 fluorotelomer sulfonamidoalkyl betaine, 6:2 and 8:2 fluorotelomer sulfonates, and short chain perfluorocarboxylic acids. The soils collected in Lac-Mégantic two years after the accident show a total PFAS concentration that is ∼50 times lower than soils collected in 2013, while the proportion of perfluoroalkyl acids in those samples shows an increase. Qualitative analysis revealed the presence in soil of 55 additional PFASs that had been previously identified in AFFF formulations. The present study highlights the need to perform detailed analysis of AFFF impacted sites, instead of focusing solely on perfluoroalkyl acids.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".