Low removal of acidic and hydrophilic pharmaceutical products by various types of municipal wastewater treatment plants
Bibliographic record
Abstract
Pharmaceutical substances represent a risk for aquatic environments and their potential impacts on the receiving environment are poorly understood. Municipal effluents are important sources of contaminants including common pharmaceuticals like anti-inflammatory and anti-convulsive substances. The removal of pharmaceuticals, particularly those highly soluble can represent a great challenge to conventional wastewater treatment processes. Hydrophilic drugs (e.g. acidic drugs) have properties that can highly influence removal efficiencies of treatment plants. The performance of different wastewater treatment processes for the removal of specific pharmaceutical products that are expected to be poorly removed was investigated. The obtained results were compared to inherent properties of the studied substances. Clofibric acid, carbamazepine, diclofenac, ibuprofen and naproxen were largely found in physicochemical primary-treated effluents at concentrations ranging from 77 to 2384 ng/L. This treatment type showed removal yields lower than 30%. On the other hand, biological treatments with activated sludge under aerobic conditions resulted in much better removal rates (>50% for 5 of the 8 studied substances). Interestingly, this latter type of process showed evidence of selectivity with respect to the size (R2=0.7388), solubility (R2=0.6812), and partitioning (R2=0.9999) of the removed substances; the smallest and least sorbed substances seemed to be removed at better rates, while the persistent carbamazepine (392 ng/L) and diclofenac (66 ng/L) were poorly removed (<10%) after biological treatment. In the case of treatment by aerated lagoons, the most abundant substances were the highly soluble hydroxy-ibuprofen (350-3321 ng/L), followed by naproxen (42-413 n/L) and carbamazepine (254-386 ng/L). In order to assess the impacts of all these contaminants of various properties 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".