Aqueous potentially ecotoxic metal(loid)s in a tropical mining-affected river system: sources and environmental and human health risks
Bibliographic record
Abstract
Water contamination poses threats not only to aquatic life but also to human health. Understanding mining-related potentially ecotoxic metal(loid) (PEM) contamination in a tropical environment is vital for regulation and management. The study aimed to examine the concentrations, sources, and environmental and human health risks of PEMs in tropical river systems. Using the mining-impacted River Nyamwamba in Southwestern Uganda as an exemplar case study, 19 water samples along the River Nyamwamba and its tributaries, and Lake George were collected. The samples were analysed with ICP-MS. Concentrations decreased in the order Co > Mn > Fe > Cu > Ni > Al > Zn > Mo > As > Pb > Cd = Cr. Cobalt, Mn, and Ni concentrations exceeded safe drinking water standards. Multivariate statistical analysis revealed that mining contributed to the presence of As, Co, Cu, Mn, Mo, Ni, and Zn. Pollution load index and potential ecological risk index indicated severe ecological risks. Health risk assessment showed that both carcinogenic and non-carcinogenic risks were posed to human health, with children being the most vulnerable. Up to 2 in 10 children and 3 in 100 adults were at risk of developing excess cancer from PEMs exposure in the river water. The study highlights the importance of preventing untreated aqueous and mine waste discharge into tropical river systems, and recommends that the local government sensitise the community and restrict the use of River Nyamwamba water in favour of other sources (shallow wells, boreholes, springs, and rainwater harvesting), while regularly monitoring water quality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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 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".