Plasticizers and related toxic degradation products in wastewater sludges
Bibliographic record
Abstract
Plasticizers can persist during the treatment of wastewaters in sewage treatment plants (STPs) and can be discharged in effluents and/or accumulated in sewage sludges. For example, di-2-ethylhexyl phthalate (DEHP) is a common plasticizer that is now considered a priority pollutant and is known to accumulate in sludges. This may add constraints to the exploitation of the beneficial uses of sludges that contain significant quantities of plasticizers. Recently, it was demonstrated in studies with pure cultures that the biodegradation of plasticizers including DEHP and di-ethylhexyl adipate (DEHA) generates toxic metabolites including 2-ethylhexanoic acid, 2-ethylhexanol, and 2-ethylhexanal. However, the environmental impacts and fate of the degradation products arising from plasticizers are unknown. Therefore, this work investigated the concentrations of DEHP and DEHA and their metabolites in the sludges from several STPs in Quebec, Canada. DEHP and DEHA were found in concentrations ranging from 15 to 346 mg kg(-1) and 4 to 743 mg kg(-1), respectively, in primary, secondary, digested, dewatered or dried sludges. Metabolites were detected in almost all sludges, except those that had undergone a drying process at high temperature. It is concluded that sludges can represent significant sources of plasticizers and their toxic metabolites in the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".