Transport and Attenuation of an Artificial Sweetener and Six Pharmaceutical Compounds in a Sequenced Wetland-Steel Slag Wastewater Treatment System
Bibliographic record
Abstract
The occurrence of pharmaceutically active compounds (PhACs), nutrients, and an artificial sweetener acesulfame in wastewater, and subsequent removal in an engineered system comprising aerobic wetland, anaerobic wetland, and steel slag cells, were investigated. The PhACs evaluated in this study covered a range of octanol–water partition coefficients (log Kow = 0.07–2.45) and acid dissociation constants (pKa = 1.7–13.9) and included carbamazepine, caffeine, sulfamethoxazole, ibuprofen, and naproxen. The mean flow rate in the system was 0.89 m3 day−1 (0.02 to 4.27 m3 day−1), representing a hydraulic retention time of 5 days. The removal efficiencies of PO4-P, NH3-N, and cBOD5 in the treatment system were >99, 82, and 98%. The removal efficiencies for the PhACs and acesulfame were classified into four groups, including those that were (a) efficiently removed (caffeine by >75%); (b) moderately removed (ibuprofen by 50–75%); (c) poorly removed (sulfamethoxazole and naproxen by 25–50%); and (d) recalcitrant (carbamazepine and acesulfame by <25%). Variability in concentrations and treatment efficiencies was observed in different sampling events, which may be due to variations in input concentrations or changes in the flow rate. The addition of a steel slag cell increased the overall removal efficiency of the studied compounds, except for carbamazepine.
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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 | 0.000 |
| 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 | 0.000 | 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".