Characterization of Per- and Polyfluoroalkyl Substances in Drinking Water Sources in the Greater Montreal Area, Quebec, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants that are subject to increasingly restrictive regulations. This study characterized the occurrence of 77 PFAS compounds in raw and treated water from 15 drinking water treatment plants (WTPs) in the Greater Montreal Area, including an urban creek receiving airport runoff. A total of 32 compounds were detected at least once, representing diverse classes and carbon chain lengths. This helped to identify trends and precursor impacts on the PFAS profiles. Perfluoroalkyl carboxylic acids (PFCA) and perfluoroalkyl sulfonic acids (PFSA) were the most frequently detected. The highest concentrations occurred in WTPs drawing from the St. Lawrence River, while the Ottawa and L'Assomption Rivers demonstrated the occurrence of localized contamination. Conventional treatment showed negligible PFAS removal. WTPs drawn from the same water source were generally correlated. Correlation analyses also demonstrated that some plants are influenced by both the Ottawa and St. Lawrence Rivers. Airport-related PFAS compounds, such as those from aqueous firefighting foam and hydraulic fluids, were detected in downstream WTPs. Seasonal trends suggest that temperature and flow variations might affect PFAS concentrations. These findings illustrate the challenges when protecting water sources against PFAS at a basin scale while offering insights into how their patterns can assist with the identification of local contamination sources.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 it