Occurrence and removal of pharmaceutically active compounds in sewage treatment plants with different technologies
Bibliographic record
Abstract
Occurrence of eight selected pharmaceutically active compounds (PhACs; caffeine, carbamazepine, triclosan, gemfibrozil, diclofenac, ibuprofen, ketoprofen and naproxen) were investigated in effluents from fifteen sewage treatment plants (STPs) across South Australia. In addition, a detailed investigation into the removal of these compounds was also carried out in four STPs with different technologies (Plant A: conventional activated sludge; plant B: two oxidation ditches; plant C: three bioreactors; and plant D: ten lagoons in series). The concentrations of these compounds in the effluents from the fifteen STPs showed substantial variations among the STPs, with their median concentrations ranging from 26 ng/L for caffeine to 710 ng/L for carbamazepine. Risk assessment based on the "worst case scenario" of the monitoring data from the present study suggested potential toxic risks to aquatic organisms posed by carbamazepine, triclosan and diclofenac associated with such effluent discharge. With the exception of carbamazepine and gemfibrozil, significant concentration decreases between influent and effluent were observed in the four STPs studied in more detail. Biodegradation was found to be the main mechanism for removing concentrations from the liquid waste stream for the PhACs within the four STPs, while adsorption onto sludge appeared to be a minor process for all target PhACs except for triclosan. Some compounds (e.g. gemfibrozil) exhibited variable removal efficiencies within the four STPs. Plant D (10 lagoons in series) was least efficient in the removal of the target PhACs; significant biodegradation of these compounds only occurred from the sixth or seventh lagoon.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".