Application of advanced oxidation methods for landfill leachate treatment – A review
Bibliographic record
Abstract
Landfill leachate is a complex wastewater generated when the moisture content or the water content of the landfilled solid waste is larger than its field capacity. The major fraction of old or biologically treated landfill leachate is large recalcitrant organic molecules that are not easily removed during biological treatment. Advanced oxidation using ozone (O3), ozone with hydrogen peroxide (O3/H2O2), ozone with ultraviolet light (O3/UV), hydrogen peroxide with ultraviolet light (H2O2/UV), Fenton process (H2O2/Fe2+), and photo-Fenton process (H2O2/Fe2+/UV) for the treatment of old or biologically treated landfill leachate has been intensively studied in the past decade to improve the removal of these large recalcitrant organic molecules or to transform them into more easily biodegradable substances. The characteristics of landfill leachate and the mechanisms of O3, O3/H2O2, O3/UV, H2O2/UV, H2O2/Fe2+, and H2O2/Fe2+/UV oxidation processes and their applications for landfill leachate treatment are reviewed in this paper. In addition, the influences of ammonia nitrogen (NH3-N), pH, and alkalinity on the advanced oxidation processes applied for treating landfill leachates as well as the methods for the removal of the oxidants residues are also discussed in this paper. Key words: landfill leachate, ozone, hydrogen peroxide, UV, Fenton processes, photo-Fenton processes, refractory organics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".