IMPACT OF LEACHATES ON THE QUALITY OF GROUNDWATER IN SHAGAMU SOUTHWESTERN, NIGERIA IN GROUNDWATER – SOME URBAN CITIES OF SOUTHWESTERN, NIGERIA
Bibliographic record
Abstract
The overwhelming environmental significance of solid waste has attracted a lot of attention because of its leaching into groundwater through waste deposit. The study determines impact of leachate from a landfill site on the quality of groundwater sources in Shagamu, Southwestern, Nigeria. Twelve groundwater samples, (hand dug wells (7) and boreholes (5)) were analyzed for their major ionic components. Groundwater qualities for cation were determined at Acme Laboratories, Canada and anion was determined at the University of Ibadan, Nigeria. Mean concentration of pH (5.89) was found to be outside the WHO 2004 and USEPA 2001 permissible limits, and was due to the effect of leachate on the groundwater. Total dissolved solid, Electrical conductivity and Alkalinity were found to be within the permissible limits. Mean concentration of cations and anions for all the samples were -also found to be within the permissible limits with the exception of Mn (0.50 mg/L), Cl (83.33 mg/L), -and NO (26.44 mg/L) respectively which could be as a result of abattoir found around the dumpsite. 3 Leachate has had significant impact on groundwater quality. Groundwater quality improves with increase in depth and distance of the well and boreholes from the pollution source (landfill). The present study demands for proper management of waste and suggests some remedial measures to reduce future groundwater contamination via leachate percolation,
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".