Persistence and fate of highly soluble pharmaceutical products in various types of municipal wastewater treatment plants
Bibliographic record
Abstract
Municipal effluents are important source of contaminants including many socalled Pharmaceutical and Personal Care Products (PPCPs) substances, whose potential impacts on the receiving environment are poorly understood. New emerging substances, in the form of pharmaceutical drugs like antibiotics, antiinflammatory, and anti-convulsive, are now being frequently measured in these wastewaters. While PPCPs substances undergo major transformation at the treatment plant and again in the receiving waters, their bioavailability and toxicity may be modified considerably. The influence of different wastewater treatment processes on pharmaceutical products was investigated. Pharmaceutical substances such as clofibric acid, carbamazepine, diclofenac, ibuprofen and naproxen were found in the Montreal physicochemical primarytreated effluents at concentrations ranging from 13 to 3522 ng/L. Most of the substances were eliminated at a rate lower than 10%. Biological treatments (aerobic conditions) with activated sludge resulted in much better removal rates (> 50%) for those studied substances. Interestingly, this type of process showed some selectivity with respect to the size and polarity of the removed substances; the smallest and most polar substances were removed at better rates, while the persistent carbamazepine (273-483 ng/L) and diclofenac (52-68 ng/L) were poorly removed. In the case of treatment by aerated lagoons, the most abundant substances were hydroxy-ibuprofen (339-3938 ng/L), naproxen (16-763 n/L) and carbamazepine (164-425 ng/L). To assess the impacts of all these contaminants on the environment and human health, we need to better understand the chemical and physical transformations occurring at the treatment plant and in the receiving waters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".