Wastewater sources of per- and polyfluorinated alkyl substances (PFAS) and pharmaceuticals in four Canadian Arctic communities
Bibliographic record
Abstract
Effective removal of organic contaminants in wastewater effluent poses a challenge to small communities worldwide, particularly in the Arctic due to infrastructure challenges and harsh climates. To understand better the efficacy of current treatment options and risks posed by pharmaceuticals and pesticides on receiving waters in the Arctic, four representative human communities in Nunavut, Canada were evaluated. Per- and polyfluorinated alkyl substances (PFASs) were also investigated in one community. These communities have treatment ranging from primary lagoons, engineered wetlands, and natural lakes. Pharmaceuticals and pesticides were measured using the organic diffusive gradients in thin film (o-DGT) passive sampler in summer 2018. Of the 34 compounds studied, seven pharmaceuticals were found at least once: atenolol, carbamazepine, metoprolol, naproxen, sulfapyridine, sulfamethoxazole, and trimethoprim. With the exception of 5210 ng naproxen/L in Iqaluit, most receiving waters showed negligible amounts of contamination. Iqaluit had the poorest overall system performance while Baker Lake had the best. Measured pharmaceutical concentrations do not appear to pose a significant acute hazard to receiving waters at this time, based on known toxicological endpoints. PFAS concentrations were found to be over 100-fold greater in Cambridge Bay wastewater than previously reported Arctic seawater. Results suggest that wastewater may be an important point source of PFASs in Arctic communities. The o-DGT passive samplers performed well in marine Arctic settings. We recommend further testing of wastewater efficiencies in Arctic communities along with evaluations of seasonal variations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".