Beta-Blockers and Antidepressants: Contributions to Municipal Wastewaters from Hospitals and Residential Areas
Bibliographic record
Abstract
Emerging contaminants in wastewater are of increasing concerns due to identification of previously undetected chemicals now being identified in wastewater treatment plant (WWTP) effluents, and subsequently, in surface waters. This paper provides monitoring results for selected beta-blockers (atenolol, sotalol, metoprolol, and propranolol) and antidepressants (venlafaxine, o-desmethylvenlafaxine, citalopram, desmethyl citalopram and carbamazepine) hospitals and residential neighborhoods in three different cities of Ontario, Canada. The average concentrations of compounds studied were determined for atenolol, metoprolol, propranolol and sotalol from the hospitals were 1291 ng/L, 848 ng/L, 71 ng/L and 274 ng/L respectively. The average observed concentrations of venlafaxine, o-desmethyl venlafaxine, citalopram and desmethyl citalopram from the hospitals were 1756 ng/L, 2878 ng/L, 650 ng/L and 356 ng/L respectively. The results show significant variability in the concentrations of beta-blockers and antidepressants from hospital to hospital. Results comparing these hospital effluents to wastewater treatment plant influents show that hospitals, on average, contributed 0.87% of the total load for the indicated emerging contaminants, with a range from hospitals varying between 0.25% and 1.79%. The findings also include the effects of short hospital stays indicate patients taking pharmaceuticals at home, as being evident from the monitoring results. Ninety-five percent upper confidence limits for individual beta blockers and anti-depressants are provided, as computed from available technical literature and monitoring results from this research, as a means of providing reasonable upper bounds on the magnitudes of the individual compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".