Prospects on coupling UV/H2O2 with activated sludge or a fungal treatment for the removal of pharmaceutically active compounds in real hospital wastewater
Bibliographic record
Abstract
Conventional active sludge (AS) process at municipal centralized wastewater treatment facilities may exhibit little pharmaceuticals (PhACs) removal efficiencies when treating hospital wastewater (HWW). Therefore, a dedicated efficient wastewater treatment at the source point is recommended. In this sense, advanced oxidation processes (AOPs) and fungal treatment (FG) have evidenced promising results in degrading PhACs. The coupling of the AOP based on UV/H2O2 treatment with biological treatment (AS or FG) treating a real non-sterile HWW, was evaluated in this work. In addition, a coagulation-flocculation pretreatment was applied to improve the efficiency of all approaches. Twenty-two PhACs were detected in raw HWW, which were effectively removed (93–95%) with the combination of any of the biological treatment followed by UV/H2O2 treatment. Similar removal results (94%) were obtained when placing UV/H2O2 treatment before FG, while a lower removal (83%) was obtained in the combination of UV/H2O2 followed by AS. However, the latest was the only treatment combination that achieved a decrease in the toxicity of water. Moreover, deconjugation of conjugated PhACs has been suggested for ofloxacin and lorazepam after AS treatment, and for ketoprofen after fungal treatment. Monitoring of carbamazepine and its transformation products along the treatment allowed to identify the same carbamazepine degradation pathway in UV/H2O2 and AS treatments, unlike fungal treatment, which followed another degradation route.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".