Electro-Oxidation in Combination with Biological Processes for Removal of Persistent Pollutants in Wastewater: A Review
Bibliographic record
Abstract
Persistent organic pollutants (POPs) and emerging pollutants (EP) are characterized by their difficulty to be removed through biological oxidation processes (BOPs); they persist in the environment and could have adverse effects on the aquatic ecosystem and human health. The electro-oxidation (EO) process has been successfully used as an alternative technique to oxidize many kinds of the aforementioned pollutants in wastewater. However, the EO process has been criticized for its high energy consumption cost and its potential generation of by-products. In order to decrease these drawbacks, its combination with biological oxidation processes has been reported as a solution to reduce costs and to reach high rates of recalcitrant pollutants removal from wastewaters. Thus, the location of EO in the treatment line is an important decision to make, since this decision affects the formation of by-products and biodegradability enhancement. This paper reviews the advantages and disadvantages of EO as a pre and post-treatment in combination with BOPs. A perspective of the EO scale-up is also presented, where hydrodynamics and the relationship of A/V (area of the electrode/working volume of the electrochemical cell) experiments are examined and discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".