Occurrence of neutral and acidic drugs in the effluents of Canadian sewage treatment plants
Bibliographic record
Abstract
Samples of influent (untreated) and effluent (treated) from 18 sewage treatment plants (STPs) in 14 municipalities in Canada were analyzed for residues of selected prescription and nonprescription drugs. Several neutral and acidic drugs were detected in effluents, including analgesic/anti-inflammatory agents, lipid regulators, and an antiepileptic drug, carbamazepine. Residues were extracted from effluents by solid-phase extraction, followed by either methylation and analysis of acidic drugs by gas chromatography/mass spectrometry or direct analysis of neutral drugs by liquid chromatography/tandem mass spectrometry. Analgesic/anti-inflammatory drugs such as ibuprofen and naproxen, as well as the metabolite of acetylsalicyclic acid, salicylic acid, were often detected in final effluents at microg/L concentrations. The acidic lipid regulator, clofibric acid, and the analgesic/anti-inflammatory drug diclofenac were not detected in any final effluent samples, which is not consistent with data from Europe. The precursor to clofibric acid, clofibrate, is not widely prescribed as a lipid regulator in Canada. However, the lipid regulators bezafibrate and gemfibrozil were detected in some samples of influent and effluent. The chemotherapy drugs ifosfamide and cyclophosphamide and the anti-inflammatory phenazone were not detected in influent or effluent samples, but the vasodilator drug pentoxyfylline was detected at ng/L concentrations in some final effluents. The widespread occurrence of carbamazepine at concentrations as high as 2.3 microg/L may be explained by use of this drug for other therapeutic purposes besides treatment of epilepsy and its resistance to elimination in STPs. The rates of elimination of ibuprofen and naproxen appeared to be elevated in STPs with hydraulic retention times for sewage greater than 12 h.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".