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. Core Ideas Humic substances comprise half of the organic carbon in landfill leachate. Given its size & interaction with sludge, fulvic acid is the most concerning type of carbon. Organic carbon with size 0.5 to 10 kDa is rarely removed by biological treatment. We study the fate of metal & hydrophobic emerging contaminants bound with humic substances. Combination of biological treatment & nanofiltration is the most efficient treatment option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.006 | 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".