Assessment of carbamazepine removal from hospital wastewater in a non conventional biofilter and the application of electro-oxidation as pre-treatment
Bibliographic record
Abstract
Hospital wastewater (HWW) is characterized by a high drug \nconcentration, which can cause endocrine effects and bacterial resistance, \namong others. For this study, carbamazepine (CBZ) was selected as a \ncontaminant model to evaluate the removal efficiency from HWW of \nrecalcitrant pharmaceuticals in a non-conventional biofilter (BF), packed \nwith a mixture of wood chips (Prosopis) and porous rock (pouzzolane). \nThe effect of electro-oxidation (EO) as pre-treatment was assessed as \nwell. A biofilm adapted to the HWW was developed in the BF. The addition \nof high concentrations of CBZ (1,000 and 10,000 µg/L) to the influent \nHWW did not affect the removal efficiency of the BF to remove organic \nmatter (73%) and ammonia nitrogen (99 %), proving that the biomass \nwas not inhibited by the CBZ’s concentration. The BF showed a significant \nremoval of CBZ by adsorption during the start-up. The bed filter showed \nan adsorption capacity of 19.84 µg/g (Co=10,000 µg/L). After the bed \nfilter saturation operated in steady state, the BF removed by \nbiotransformation 17.2 ± 7.4% of CBZ which, in terms of concentration \n(1,551 ± 664 µg/L), is bigger than the concentration in most of the \nreports for hospital, pharmaceutical and municipal WW effluents, which \nare between 0.1 and 890 µg/L. By applying electro-oxidation as a \npretreatment, the global removal efficiency of CBZ increased to 55 ± 5.96 \n%. In the hybrid system, the EO biotransformed the CBZ, and in the BF \nthe nitrogen and the COD were removed and showed CBZ desorption.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".