Polybrominated diphenyl ethers in an advanced wastewater treatment plant. Part 2: Potential effects on a unique aquatic system
Bibliographic record
Abstract
Concentrations of the mono- through deca-substituted polybrominated diphenyl ether (PBDE) flame retardants were determined in the aqueous effluent from a tertiary-level wastewater treatment plant (WWTP) that uses post-filtration ultraviolet light disinfection. The WWTP is located in a semi-arid region of British Columbia. Subsequent limnological modeling of receiving waters examined the potential long-term effects of various PBDE-loading scenarios on this unique aquatic system. Over the three decades from 2002 to 2031, total PBDE concentrations in the water column and in suspended and surficial sediments are expected to increase to >120 pg·L–1 and ~1 ng·g–1 wet weight, respectively. Following implementation of a hypothetic halt on PBDE releases into the aquatic system, individual PBDE congener concentrations in the water column and sediments declined by <35% over the ensuing 17-year modeling period after the ban, illustrating the potential long-term problem arising from continued PBDE inputs into aquatic systems worldwide. The results also suggest that PBDEs represent one of the single largest halogenated aromatic loadings to Canadian lakes, rivers, and streams from wastewaters, and their worldwide use continues to increase exponentially. Key words: polybrominated diphenyl ethers (PBDEs), flame retardants, municipal wastewater treatment effluent, contaminant fluxes, limnologic modeling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".