Wastewater Treatment Lagoons: Local Pathways of Perfluoroalkyl Acids and Brominated Flame Retardants to the Arctic Environment
Bibliographic record
Abstract
Abstract Concentrations of perfluoroalkyl acids (PFAAs), polybrominated diphenyl ethers (PBDEs), and “novel” brominated flame retardants (NBFRs) were determined in lagoons processing wastewater from two high-Arctic and two sub-Arctic of Canada communities to assess the importance of local anthropogenic sources. ∑PFAAs in influent and effluent of the Arctic lagoons were within the lower end of the range of concentrations previously observed in Canadian temperate wastewater treatment plants (WWTPs). In comparison, influent and effluent concentrations of ∑PBDEs and NBFRs were significantly greater (p < 0.05) in high-Arctic lagoons compared to sub-Arctic and temperate plants. The surprisingly elevated concentrations of PBDEs and NBFRs in high-Arctic lagoons were probably related to high organic matter found in Arctic wastewater due to lower consumption of potable water leading to less dilution compared to temperate regions. Although PFAAs also sorb to solids, the wastewater samples were filtered prior to analysis of PFAAs (but not PBDEs and NBFRs), which likely reduced the impacts of solids on the results for PFAAs. Based on an extrapolation of per capita mass effluent loadings of the four Arctic lagoons, mass loadings to the Arctic of Canada via WWTP effluent were estimated as 1405 g/year and 549 g/year for ∑PFAAs and ∑PBDEs, respectively.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".