Assessment of contamination and potential ecological risks of heavy metals in riverine sediments from gold mining and pristine areas in Ghana
Bibliographic record
Abstract
This study explore the characteristics of heavy metal pollution, contamination levels, and potential ecological risks in riverine sediments found in both gold mining and pristine areas. Such investigations are vital for the ongoing monitoring and preservation of water bodies and overall ecosystem health. In total, 44 composite sediment samples were collected from seven pristine environments and four mining sites. These samples underwent preparation, digestion, and analysis for heavy metal content, utilizing the inductively coupled plasma emission mass spectrometer (ICP-MS). Various tools and models, including the geo-accumulation index, enrichment factor, and degree of contamination, were employed to assess the impact of pollution on the environment. Modified ecological risk index were also used to evaluate potential ecological risks. The average concentrations of heavy metals in pristine sites spanned from 0.01±0.01 (Cd) to 73,753.64 ± 388.15 mgkg−1 (Fe), while in mining sites, they ranged from 0.04±0.02 (Cd) to 56,394.25±400.66 mgkg−1 (Fe). Comparing these concentrations against the USEPA Ecological Screening Values, Mean Shales levels, and the Canadian ISQG, it became evident that Pb, Cd, Zn, Ni, Co, and Mn concentrations generally remained below the recommended guideline values in both pristine and mining areas. However, concentrations of Cr, As, Cu, and Hg exceeded the prescribed threshold limits, particularly in the mining regions. Through Principal Component Analysis (PCA) and Geometrical Analysis for Interactive Aid (GAIA) modeling, two primary sources of heavy metals were identified: anthropogenic-related and geogenic-related. GAIA and PCA together explained 78.53 % and 79.40 % of the total variability in heavy metal concentrations, respectively. Overall, the pollution and ecological risk assessment indicated low to moderate contamination levels, with a notable exception of high arsenic contamination in the Nyam river. The findings of this study hold significance for assessing sediment conditions and river quality in mining communities within Ghana and globally. They also provide empirical data to recommend measures for mitigating water contamination in such communities.
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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".