Fate, Partitioning, and Mass Loading of Polybrominated Diphenyl Ethers (PBDEs) during the Treatment Processing of Municipal Sewage
Bibliographic record
Abstract
Sewage treatment plant (STP) effluents are likely a major source of contamination for PBDEs, especially in the receiving water bodies of local aquatic environments surrounding the location of these discharges. Congeners of the pentaBDE mixture, 2,2,',4,4'-tetrabromodiphenyl ether (BDE47), 2,2,',4,4',5-pentabromodiphenyl ether (BDE99), 2,2,',4,4',6-pentabromodiphenyl ether (BDE100), 2,2,',4,4',5,5'-hexabromodiphenyl ether (BDE153), and 2,2,',4,4',5,6'-hexabromodiphenyl ether (BDE154), are of great environmental concern in North America due to their persistence, potential for bioaccumulation, and >97% use of the global production of the mixture in the region. Detailed characterization of the distribution of eight PBDE congeners (2,4,4'-tribromoDE (BDE28) and BDE47, 99, 100, 138, 153, 154, and 183) was carried out at five sites along the treatment process at an activated sludge-type secondary treatment municipal STP facility. PentaBDE mixture congeners, sigma5PBDE (sum of BDE47, 99, 100, 153, and 154) accounted for >98% of the total (sigma 8) PBDE concentration at all sites, with over 80% of the composition being BDE47 and BDE99. Presence of dissolved organic matter affected the mobility of PBDEs during the initial stages of the treatment process. About 9% of the influent mass of sigma5PBDE to the facility is estimated to be discharged into the Little River (leading to the Detroit River) with the final effluent, resulting in an estimated mass loading of approximately 0.7 kg/year. The total mass loading of sigma5PBDE to the Detroit River is expected to be much larger as effluent from this facility accounts for <10% of the total STP discharges to the river.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".