Distribution of acidic and neutral drugs in surface waters near sewage treatment plants in the lower Great Lakes, Canada
Bibliographic record
Abstract
Prescription and nonprescription drugs have been detected in rivers and streams in Europe and the United States. Sewage treatment plants (STPs) are an important source of these contaminants, but few data exist on the spatial distribution of drugs in surface waters near STPs. Samples of surface water were collected in the summer and fall of 2000 at open-water sites in the lower Great Lakes (Lake Ontario and Lake Erie), at sites near the two STPs for the city of Windsor (ON, Canada), and at sites in Hamilton Harbour (ON, Canada), an embayment of western Lake Ontario that receives discharges from several STPs. In a follow-up study in the summer of 2002, samples of surface water and final effluent from adjacent STPs were collected from sites in Hamilton Harbour and Windsor. In addition, surface water and STP effluent samples were collected in Peterborough (ON, Canada). All samples of surface water and STP effluents were analyzed for selected acidic and neutral drugs. In the survey of Hamilton Harbour and Windsor conducted in 2000, acidic drugs and the antiepileptic drug carbamazepine were detected at ng/L concentrations at sites that were up to 500 m away from the STP, but the hydrological conditions of the receiving waters strongly influenced the spatial distribution of these compounds. Drugs were not detected at open-water locations in western Lake Erie or in the Niagara River near the municipality of Niagara-on-the-Lake (ON, Canada). However, clofibric acid, ketoprofen, fenoprofen, and carbamazepine were detected in samples collected in the summer of 2000 at sites in Lake Ontario and at a site in the Niagara River (Fort Erie, ON, Canada) that were relatively remote from STP discharges. Follow-up studies in the summer of 2002 indicated that concentrations of acidic and neutral drugs in surface waters near the point of sewage discharge into the Little River (ON, Canada) STP were approximately equal to the concentrations in the final effluent from the STP. Caffeine and cotinine, a metabolite of nicotine, were generally present in STP effluents and surface waters contaminated by drugs. The antidepressant fluoxetine and the antibiotic trimethoprom were also detected in most STP effluents and some surface water samples. For the first time, the lipid regulating drug atorvastatin was detected in samples of STP effluent and surface water.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".