Removal of Pollutants in Different Landfill Leachate Treatment Processes on the Basis of Organic Matter Fractionation
Bibliographic record
Abstract
A combination of processes was required for the proper treatment of old landfill leachate, as it contained a high concentration of pollutants. Humic substances comprised half of the total organic carbon in the raw leachate. Mobility of di(2-ethylhexyl) phthalate (DEHP) and metals could depend on the fate of these substances. Characterization of carbon in raw leachate and effluent of the membrane bioreactor, biofiltration, electro-oxidation, electro-coagulation, and nanofiltration showed complete removal of suspended solids and colloids. Physical processes could not remove the hydrophilic fraction due to its lower molecular weight. However, high removal of the hydrophilic fraction with a molecular weight <500 Da was expected in the biological process. In comparison with fulvic acid, larger sized humic acid resulted in complete removal by physicochemical processes. Because of DEHP partitioning on dissolved organic matter, especially on humic substances, its removal could be correlated with total organic carbon removal. Metals such as iron, aluminum, magnesium, and lead showed removal efficiency >80% in biological processes. Electro-deposition on the surface of an electrode and precipitation by hydroxide resulted in removal efficiencies >90 and >50% in electro-coagulation and electro-oxidation, respectively. Rejection of metals by nanofiltration was >80% and depended on the size and charge of cation. All in all, a combination of membrane bioreactor and nanofiltration seems to be the optimal process configuration for efficient treatment of old landfill leachate.
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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) | 0.000 | 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) | 0.000 | 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".