Assessment of Environmental Geochemistry of Lead-Zinc Mining at Ishiagu Area, Lower Benue Trough, Southeastern Nigeria
Bibliographic record
Abstract
Mining activities have long been recognized as a major source of environmental contamination associated with heavy metals and metalloids. This study evaluated the relationship between the occurrence and mining of lead-zinc sulphide ores at Ishiagu, Nigeria, and heavy metal and metalloid contamination. A comparative study of two zones in the area, with and without mining activities was also made Water, soil, stream sediment and ore samples were analyzed, after acid digestion, using atomic absorption spectrophotometer (AAS). The concentration levels of seven heavy metals and a metalloid namely Pb, Cu, Ni, Zn, Mn, Co, Cd and as were evaluated. While the highest concentration levels of As, Co and Pb (5.20 mg/l, 0.54 mg/l and 3.40 mg/l respectively) were found in water, those of Ni and Mn (2.26 mg/l and 5.48 mg/l respectively) occurred in soil. For Cu and Zn, highest levels of concentration (2.80 mg/l and 0.41 mg/l respectively) occurred in stream sediments. The variations in the concentration levels of these elements in varying geologic media (soil, water and sediment) indicate influence of rock types, human activities and media physiochemical characteristics. Geostatistical analyses using QQPlot, semivariogram and kriging showed normal distribution of these elements. Distribution and dispersion patterns of the heavy metals indicated increase in concentration levels in the local stream flow direction. Pb, Cu, As, Cd, Mn, and Ni concentrations had reached pollutant levels in water based on WHO standards, while Zn level is below. Since the local people use untreated surface water and groundwater for drinking and other domestic purposes, soil for farming and lead for cosmetics, long term exposure poses significant health risk for humans, animals and plants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".