Assessing the Effect of Mining Waste on the Quality of Soil; A Case of Eprocomi Mining Company in Gakenke District, Rwanda
Bibliographic record
Abstract
The mining sector plays a crucial role in Rwanda’s economic growth, contributing significantly to employment, export revenues, and infrastructure growth. Even though, mining generates substantial economic benefits; the improper mining wastes management poses environmental challenges worldwide and in Rwanda as well. This study assesses the effect of mining wastes on soil quality focusing on Eprocomi Mining Company in Gakenke District from Northern Province of Rwanda. The research employed a combination of observations, field sampling, laboratory analysis, and analytical methods to evaluate key soil quality indicators of pH and heavy metals concentration. The tailings samples from mining wastes were collected in Eprocomi Mining concession for laboratory analysis to determine the extent of contamination and degradation. The collected soil samples needed to be pre-treated with air-drying, grinding, and sifting. A quantity of 3 g was weighed for each of the samples and pressed into the grinding machine under a 10 MPa pressure for 180 seconds and for testing of the samples, Niton XRF Analyzer instrument used. The findings indicate that mining activities have led to changes in soil pH and increase in concentrations of heavy metals such as lead (Pb), cadmium (Cd), and arsenic (As), which pose potential risks to soil fertility and agricultural productivity. According to Boyer and Pietrowiez's standards for interpreting soil pH, the pH of the soil varied from severely acidic to alkaline, with values ranging from 3.9 (the acid range) to 7.8 (the basic range). The concentration values for arsenic, lead and cadmium metals varies from 30 to 140 mg/kg, 92mg/kg to 200 mg/kg and 110 mg/kg to 321 mg/kg respectively which classify the area to have strongly polluted by Arsenic and moderately polluted by Chromium and Lead with reference to Germany soil regulation and Canadian standards measures. Based on the extent of environmental degradation with the influence of mining activities in the area, the study explored possible mitigation measures and recommendations to restore soil health and reduce the negative effects of mining operations on the environment and need to raise awareness about sustainable mining practices and foster collaboration between government, researchers and mining sector industry. Keywords: Environmental impact, Eprocomi Mining Company, GIS, Heavy metals, Mining wastes, Soil qualityTop of Form
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".