Antidepressants and their metabolites in municipal wastewater, and downstream exposure in an urban watershed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antidepressants are a widely prescribed group of pharmaceuticals that can be biotransformed in humans to biologically active metabolites. In the present study, the distribution of six antidepressants (venlafaxine, bupropion, fluoxetine, sertraline, citalopram, and paroxetine) and five of their metabolites was determined in a municipal wastewater treatment plant (WWTP) and at sites downstream of two WWTPs in the Grand River watershed in southern Ontario, Canada. Fathead minnows (Pimephales promelas) caged in the Grand River downstream of a WWTP were also evaluated for accumulated antidepressants. Finally, drinking water was analyzed from a treatment plant that takes its water from the Grand River 17 km downstream of a WWTP. In municipal wastewater, the antidepressant compounds present in the highest concentrations (i.e., >0.5 microg/L) were venlafaxine and its two demethylation products, O- and N-desmethyl venlafaxine. Removal rates of the target analytes in a WWTP were approximately 40%. These compounds persisted in river water samples collected at sites up to several kilometers downstream of discharges from WWTPs. Venlafaxine, citalopram, and sertraline, and demethylated metabolites were detected in fathead minnows caged 10 m below the discharge from a WWTP, but concentrations were all < microg/kg wet weight. Venlafaxine and bupropion were detected at very low (<0.005 microg/L) concentrations in untreated drinking water, but these compounds were not detected in treated drinking water. The present study illustrates that data are needed on the distribution in the aquatic environment of both the parent compound and the biologically active metabolites of pharmaceuticals.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it