Sorption and biodegradation of six pharmaceutically active compounds under four different redox conditions
Bibliographic record
Abstract
This study explored the removal of six pharmaceutically active compounds (PhACs) in lab-scale experiments with sediments under four redox conditions, namely aerobic, nitrate reducing, sulfate reducing, and methanogenic conditions using batch and column set-ups. Redox conditions were found to influence PhAC removal by sorption and biodegradation. The most optimal PhAC removal was observed at the outer ranges of the redox spectrum, i.e. either aerobic or deep anaerobic (sulfate reducing and methanogenic conditions), whereas nitrate reducing conditions were found least effective for PhACs biodegradation and sorption. For instance, sorption coefficient Kd values for metoprolol in column experiments were 90, 65, 42 and 11 L/kg for sulfate reducing, methanogenic, aerobic and nitrate reducing conditions, respectively. For the same conditions Kd values for propranolol were 101, 94, 55 and 55 L/kg, respectively. As expected, biodegradation efficiencies were highest under aerobic conditions, showing >99% removal of caffeine and naproxen, but no removal for propranolol and carbamazepine. The adaptive capacity of sediment was demonstrated by pre-exposure to PhACs leading to improved PhAC biodegradation. The results of this study indicate the necessity to combine diverse redox conditions, including aerobic conditions, for maximizing PhAC removal by sorption and biodegradation. Furthermore, our findings stress the need for additional treatment measures as recalcitrant PhACs are not effectively removed under any redox condition.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".