Fate and mass balances of triclosan (TCS), tetrabromobisphenol A (TBBPA) and tribromobisphenol A (tri-BBPA) during the municipal wastewater treatment process
Bibliographic record
Abstract
Municipal wastewater treatment plants (MWTPs) are impacted by down-the-drain influents of anthropogenic chemicals. These chemicals are in consumer products and include the flame retardant tetrabromobisphenol A (TBBPA) and antimicrobial triclosan (TCS). Characterization of the distribution of TBBPA, TCS and the TBBPA product tribromobisphenol A (tri-BBPA) was determined at five stages along the treatment process of a typical Canadian MWTP facility. Overall, the TCS concentrations for both liquid (influents, primary effluents and final effluents (FEs)) and solid samples (primary and waste activated sludges) were similar to reported ranges in the literature. In contrast to TCS, both TBBPA and tri-BBPA concentrations were scarcely available in the literature. The TBBPA concentrations were within literature ranges for both influents and sludges, while the tri-BBPA sludge concentrations were markedly higher than a single available previous study. Mass balances for TCS, TBBPA and tri-BBPA indicated 7, 9 and 42%, respectively, of each chemical remaining in the FEs. The resultant annual mass loadings into the Detroit River were estimated to be 3.3 kg, 6.57 g, and 21.5 g for TCS, TBBPA and tri-BBPA, 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 | 0.000 |
| 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".