Assessment of Groundwater Quality Using Water Quality Indices in Illegal Mining Communities: A Case Study of the Atwima-kwanwoma District and Obuasi East Metropolis, Ghana
Bibliographic record
Abstract
This study investigated groundwater quality in illegal mining zones within the Atwima-Kwanwoma District and Obuasi East Metropolis of the Ashanti Region, Ghana, employing both the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) and Nemerow's Pollution Index (NPI). The analysis revealed severe contamination across multiple parameters, including heavy metals, microbial indicators, and physicochemical parameters. The CCME-WQI values for the five towns consistently indicated "Poor" water quality, ranging from 26.8 to 31.1, reflecting significant deviations from acceptable water quality standards. Notably, Town A exhibited a cyanide concentration of 11.25 mg/L, while Town B recorded lead levels at 118.73 μg/L, both far exceeding permissible limits set by health authorities. The presence of Escherichia coli further exacerbates health risks, underscoring the urgent need for improved water treatment and management practices. This study demonstrates that the integrated use of NPI and CCME-WQI provides a comprehensive assessment of groundwater quality, revealing significant environmental and public health challenges. Immediate intervention, including regulatory enforcement, sustainable mining practices, and remediation strategies, is crucial to safeguard groundwater resources. The findings contribute uniquely to the understanding of water quality dynamics in mining-affected regions and advocate for a coordinated approach to mitigate environmental degradation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".