Examining the Effect of Spatial Proximity of Geo-located Dumpsites on Groundwater Quality in Samaru-Nigeria
Bibliographic record
Abstract
The effect of improper waste disposal on man’s health and environment due to the closeness of solid waste dumpsites to underground water sources in some parts of the world has raised issues of serious concern. This study thus sought to examine groundwater quality dependence on the spatial proximity of dumpsites in Samaru, Kaduna state-Nigeria. The coordinates of 10 solid waste dumpsites in proximity to groundwater sources (boreholes) in the study area were acquired for spatial analyses with a GPS-enabled smartphone. Ten groundwater samples from boreholes in relation to dumpsites were collected for testing and analyses of 11 physical and chemical parameters of water quality based on the Canadian Council of Ministers of the Environment (CCME) and World Health Organisation (WHO) standard limits. Thereafter, the water quality index (WQI) for all the locations was calculated. The results of the spatial proximity analyses carried out revealed that the requirement for locating dumpsites was not met as specified by the Environmental Protection Agency (EPA) regarding the minimum safe distance from groundwater sources as a majority (about 80%) of the dumpsites were located too close to the boreholes. The results of the study, however, revealed that the majority (about 80%) of the groundwater samples met the conditions for good drinking water (suitable for drinking water) even with their closeness to the dumpsites based on the computed WQI values and ratings. Meanwhile, only Calcium, Dissolved Oxygen, and Biochemical Oxygen Demand concentrations were significantly affected (p < 0.05 at the 95% significance level) by the closeness of the solid waste dumpsites to the boreholes with very strong (R2 = 86%) and strong (R2 = 79%) relationships, respectively. Suggestions were nonetheless made for the monitoring of land use activities in the areas surrounding groundwater sources to prevent groundwater contamination.
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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 | 0.001 |
| 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 | 0.001 |
| 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".