Pharmaceutical concentrations in screened municipal wastewaters in Victoria, British Columbia: A comparison with prescription rates and predicted concentrations
Bibliographic record
Abstract
Pharmaceuticals and personal care products (PPCPs) are emerging chemicals of concern detected in surface waters globally. Recent reviews advocate that PPCP occurrence, fate, and exposure need to be better predicted and characterized. The use of pharmaceutical prescription rates to estimate PPCP concentrations in the environment has been suggested. Concentrations of 7 pharmaceuticals (acetylsalicylic acid, diclofenac, fenoprofen, gemfibrozil, ibuprofen, ketoprofen, and naproxen) were measured in municipal wastewater using gas chromatography/ion trap-tandem mass spectroscopy (GC/IT-MS/MS). Subregional pharmaceutical prescription data were investigated to determine whether they could predict measured effluent concentrations (MECs) in wastewaters. Predicted effluent concentrations (PECs) for 5 of the 7 pharmaceuticals were within 2-fold agreement of the MECs when the fraction of parent pharmaceutical excreted was not considered. When the fraction of parent pharmaceutical excreted was considered, the respective PECs decreased, and most were within an order of magnitude of the MECs. Regression relationships of monthly PECs versus MECs were statistically significant (p < 0.05) but weak (R(2) = 0.18-0.56) for all pharmaceuticals except ketoprofen. This suggests high variability in the data and may be the result of factors influencing MECs such as the analytical methods used, wastewater sampling frequency, and methodology. The PECs were based solely on prescription rates and did not account for inputs of pharmaceuticals that had a significant over-the-counter component or were from other sources (e.g., hospitals).
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".