Assessment Of Temporal Variations In Leachate Characteristics At An Active Landfill Site Of Delhi.
Bibliographic record
Abstract
Extraordinary population growth joined with commercial development, industrialization and rapid urbanization have led to the significant generation of municipal solid waste (MSW).Landfilling requires the lowest invest investment but it is the least favoured step in the integrated waste management order.The current effort was accomplished to measure the temporal variation of the leachate quality from the Okhla landfill site, operational since 1996 closed in 2022 and receiving approximately 2,000 tons of MSW daily in this period.Analysing leachate samples from 2018 to 2022 reveals significant temporal variations, Chemical Oxygen Demand (COD) levels consistently exceed 3,600 mg/L in pre-monsoon samples, decreasing to approximately 3,400 mg/L post-monsoon.Total Solids (TS) also decreased, indicating a dilution effect from rainfall.Notably, heavy metals, particularly lead, remain a concern, with concentrations persistently above 0.35 mg/L, highlighting ongoing contamination risks.The leachate, which lacks adequate treatment facilities, can migrate through nearby drainage systems, ultimately polluting the Yamuna River.This poses significant ecological risks, by impacting the water quality of River Yamuna.The findings emphasize the need for improved waste management practices and leachate treatment strategies to mitigate environmental risks and protect the Yamuna River's water quality and its aquatic life.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".