Aqueous pesticide degradation by hydrogen peroxide/ultraviolet irradiation and Fenton-type advanced oxidation processes: a review
Bibliographic record
Abstract
Pesticide pollution of surface water and groundwater has been recognized as a major problem in many countries because of the persistence of pollutants in aquatic environments and the consequent potential adverse health effects. Various hydrogen peroxide-based advanced oxidation processes, such as hydrogen peroxide/ultraviolet irradiation, Fenton, photo-Fenton, and electro-Fenton processes are likely key technologies for degrading and detoxifying these pollutants in water and wastewater. In this paper, the hydrogen peroxide-based advanced oxidation treatment of eight major groups of pesticides, namely aniline-based compounds, carbamates, chlorophenoxy compounds, organochlorines, organophosphates, pyridine and pyrimidine derivatives, triazines, and substituted ureas, as well as that of several miscellaneous pesticides, is reviewed. The degree of pesticide degradation, reaction kinetics, identity and characteristics of degradation by-products and intermediates, and possible degradation pathways are covered and discussed. Key words: advanced oxidation processes, degradation, Fenton, fungicide, herbicide, hydrogen peroxide/ultraviolet irradiation, insecticide, pesticide, photo Fenton, wastewater treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".