Review of environmental, ecological, and public health impacts from municipal landfill leachates
Bibliographic record
Abstract
Exposure due to chemical toxicants emanating from landfill leachates is considered among major global concerns. This study aims to critically review the impact of selected non-threshold heavy metals mainly lead, cadmium, arsenic, mercury, and chromium. The study also reviews their effects on groundwater and surface water resources. It also reviews their effects on fauna and flora ecosystems. Health burdens from air pollutants and odours emitted by landfills/leachates on human populations are discussed. In addition, greenhouse gas (GHG) emissions associated with leachates/landfills and emerging sustainable mitigation measures are summarised. Among these heavy metals, lead consistently contaminates surface water and groundwater, while mercury levels often fall below World Health Organisation guidelines. Studies show DNA damage in aquatic fauna and growth inhibition in flora due to pollutant exposure. Common air pollutants from landfills include particulate matter, hydrogen sulfide, and airborne bacteria/fungi, linked to respiratory issues, cancer risks, and cellular disruptions in humans. Methane is the predominant GHG emitted from landfills. While nature-based solutions effectively remove organic matter and major cations/anions, their efficacy in heavy metal removal remains limited. Further research, including source apportionment and epidemiological studies, is needed to understand the public health and climate change impacts of landfills/leachates as sources of environmental pollution.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".