Pharmaceuticals and personal care products in Canadian municipal wastewater and biosolids: occurrence, fate, and time trends 2010–2013 to 2022
Bibliographic record
Abstract
The concentrations of 135 pharmaceuticals and personal care products (PPCPs) were determined in raw influent, final effluent, and treated biosolids at Canadian wastewater treatment plants (WWTPs) to evaluate the fate of PPCPs through liquid and solids trains of typical treatment types used in Canada and to assess changes in PPCP concentrations in wastewater matrices between 2010-2013 and 2022. PPCPs dominant in influent and effluent included the antidiabetic metformin, analgesics/anti-inflammatories (acetaminophen, ibuprofen, 2-hydroxy-ibuprofen), caffeine and its metabolite (1,7 - dimethylxanthine), theophylline (a bronchodilator and metabolite of caffeine), an insect repellent (N,N-diethyl-m-toluamide, DEET), and iopamidol (a contrast media for X-rays). PPCPs dominant in biosolids differed from those in influent/effluent and included antibiotics (fluoroquinolones and doxycycline), antidepressants (sertraline, citalopram, and amitriptyline), a preservative and antimicrobial agent (triclosan), an antihistamine (diphenhydramine), and an antifungal (clotrimazole). These elevated concentrations in influent/effluent and biosolids reflected their use in Canadian communities. PPCPs dominant in influent/effluent had relatively low hydrophobicity whereas those in biosolids tended to be more hydrophobic, or electrostatic forces governed their sorption. Higher removal of PPCPs was generally observed at WWTPs that used biological treatment compared to primary physical/chemical treatment. PPCP concentration changes in wastewater matrices between 2010-2013 and 2022 were influenced by risk management measures, warnings, the development of new pharmaceuticals, the COVID-19 pandemic, and other factors. These time trends reflected the limited information available on PPCP use in Canada. Continued periodic monitoring of PPCPs is recommended to fill data gaps on community use and release to the environment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 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 teacher head, 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".