Groundwater Contamination by Leachate from Landfill and Open Dumpsite of Solid Waste: A Review
Bibliographic record
Abstract
This research attempts to review most of the studies of groundwater contamination near landfills, mainly arising from un-engineered landfills or open dumps overseas, and its influence on human health. Leachate water from landfills contains various types of municipal toxic wastes as well as heavy metals, which eventually seep into the ground and infiltrate the groundwater table. Consumption of such water causes severe health risks and can occasionally be lethal if consumed over lengthy periods of time. Several investigations have demonstrated indications of significant quantities of heavy metals in both leachate water and surrounding groundwater sources. In addition, the environmental impacts of leachate on groundwater quality are critically analyzed, focusing on the Leachate Pollution Index (LPI) and the Canadian Water Quality Index (CCME WQI) as tools for assessing contamination levels. Finally, It is recommended that all open waste dumps be removed and engineered waste dumps be established in accordance with approved environmental specifications. Furthermore, the generated leachate should be collected by constructing wells from which the leachate should be diverted to a basin (with a suitable lining system) for treatment.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".