A province-wide mapping of per- and polyfluoroalkyl substances (PFAS) in surface waters of the St. Lawrence River watershed, Québec, Canada
Bibliographic record
Abstract
This study investigates the spatial distribution of per - and polyfluoroalkyl substances (PFAS) in surface waters of the St. Lawrence watershed, Québec, Canada. Water samples (232 sites, overall n = 447) were collected along a ~ 700-km longitudinal gradient of the St. Lawrence as well as in tributary rivers, streams, creeks, and lakes with different anthropogenic pressures. St. Lawrence samples had a mean and median Σ 77 PFAS both at ~12 ng/L (the maximum at 26 ng/L) and PFAS composition profiles dominated by perfluorooctane sulfonate (PFOS: 19 % of the summed PFAS), perfluorobutanoic acid (PFBA: 16 %), and perfluorooctanoic acid (PFOA: 14 %). Samples were also characterized by frequent detections of perfluorobutane sulfonamide and perfluoroethylcyclohexane sulfonate. PFAS concentrations slightly decreased from west to east along the St. Lawrence River and cross-sections revealed higher levels in the Great Lakes/Central water mass than in the Ottawa River/Northern water mass. Tributary rivers displayed a much wider span of PFAS levels and were influenced by local point sources, with common PFAS signatures revealed by a Kohonen mapping (artificial neural networks). Top hotspots (Σ 77 PFAS = ~100–3600 ng/L) included watersheds downstream three airports, where positive ion mode fluorotelomer betaines 6:2 FTAB and 5:1:2 FTB also occurred at the highest levels (max = 156 and 570 ng/L, respectively). A mass balance analysis indicated, however, that tributaries contributed marginally (~10 %) to the ΣPFAS flux transiting in the St. Lawrence River, and that the bulk of the contamination mostly originates from upstream sources in the Laurentian Great Lakes. While PFOS and PFOA concentrations observed in the St. Lawrence River remained within Canadian and USEPA guidelines for surface water, exceedances would be observed under stricter thresholds. These findings underscore the importance of upstream sources in PFAS contamination and highlight the need for stricter monitoring and regulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".