The fate of pharmaceuticals and personal care products (PPCPs), endocrine disrupting contaminants (EDCs), metabolites and illicit drugs in a WWTW and environmental waters
Bibliographic record
Abstract
A large number of emerging contaminants (ECs) are known to persist in surface waters, and create pressure on wastewater treatment works (WWTW) for their effective removal. Although a large database for the levels of these pollutants in water systems exist globally, there is still a lack in the correlation of the levels of these pollutants with possible long-term adverse health effects in wildlife and humans, such as endocrine disruption. The current study detected a total of 55 ECs in WWTW influent surface water, 41 ECs in effluent, and 40 ECs in environmental waters located upstream and downstream of the plant. A list of ECs persisted through the WWTW process, with 28% of all detected ECs removed by less than 50%, and 18% of all ECs were removed by less than 25%. Negative mass balances of some pharmaceuticals and metabolites were observed within the WWTW, suggesting possible back-transformation of ECs during wastewater treatment. Three parental illicit drug compounds were detected within the influent of the WWTW, with concentrations ranging between 27.6 and 147.0 ng L −1 for cocaine, 35.6–120.6 ng L −1 for mephedrone, and 270.9–450.2 ng L −1 for methamphetamine. The related environmental risks are also discussed for some ECs, with particular reference to their ability to disrupt endocrine systems. The current study propose the potential of the pharmaceuticals carbamazepine, naproxen, diclofenac and ibuprofen to be regarded as priority ECs for environmental monitoring due to their regular detection and persistence in environmental waters and their possible contribution towards adverse health effects in humans and wildlife.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".