Partitioning of pharmaceuticals and personal care products in secondary wastewater treatment and estimated loadings and potential effects to the receiving environment
Bibliographic record
Abstract
Thirty pharmaceuticals and personal care products (PPCPs) were measured in the liquid (influent and effluent) and solid (influent solids and sludge) streams in a conventional activated sludge secondary wastewater treatment plant, and their loadings to the receiving environment and potential toxicity levels were assessed. Most compound loadings were reduced by treatment, although there were none that were completely degraded by the process, and five were higher in the output than input (carbamazepine, clarithromycin, diltiazem, oxytetracycline, and warfarin). The treatment process did result in the partitioning of some influent liquid phase compounds to the sludge, although the highest potential loadings to the receiving environment were via the final effluent liquid phase. Concentrations of all compounds were well below predicted and known toxicity levels for aquatic and terrestrial receiving environments, suggesting they are not likely to be toxic in the marine environment around the treatment plant outfall. The treatment plant did have a net positive impact on the reduction of PPCPs in wastewater, resulting in lower concentrations and loadings being discharged to the receiving environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".