Assessing Soil and Water Pollution: A Case Study of an Abandoned Coal Mine for Remediation and Repurposing in Mpumalanga Province, South Africa
Bibliographic record
Abstract
Despite South Africa’s robust environmental legislation governing the mining industry, abandoned coal mines persist as a significant environmental concern, largely due to some companies evading accountability. This study assesses the level of contamination at an abandoned coal mine site in Mpumalanga, South Africa, and proposes preliminary remediation strategies and potential site repurposing options. The analysis included measuring parameters such as pH, electrical conductivity (EC), sulphates (SO4), calcium (Ca), iron (Fe), manganese (Mn), magnesium (Mg), and lead (Pb) in both soil and water samples. Additionally, soil samples were analyzed for ammonia (NH3), while water samples were analyzed to determine total suspended solids (TSSs) and total dissolved solids (TDSs). The results revealed that soil samples exceeded prescribed thresholds for SO4 and Pb, according to Soil Screening Values 1 (SSV1) for protection of land and resources. Water samples also showed exceedances for several parameters, except for Mg and Pb, as per South African National Standards and guidelines. Water quality assessment using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) yielded scores of 43.33 and 15.56, indicating poor quality for livestock watering and unsuitability for domestic use, respectively. These results suggest threatened water conditions, highlighting significant implications for human health and ecosystem. The study recommends a circular economy-driven approach to environmental remediation, where acid mine drainage is treated using passive systems like constructed wetlands, and phytomining is used to extract valuable metals or minerals. Invasive alien species are harvested and converted into compost, reducing waste and promoting sustainable land use. This approach not only restores the site but also generates economic opportunities through resource recovery, paving the way for sustainable post-mining land uses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".