Ecological Risk Assessment of Heavy Metals in Sediments from the Soubeira Reservoir, a Small-Scale Reservoir in North Central Burkina Faso, West Africa
Bibliographic record
Abstract
Small-scale reservoirs play a central role in socio-economic development of Burkina Faso. In the absence of a best environmental management plan, these reservoirs can be potential factors of water pollution and ecological deterioration. In the present study, we investigated ecological status of sediments from the Soubeira reservoir, using concentrations of a series of heavy metals. Concentrations of the metals ranked as follows: Fe> Mn> Cr> Zn> Cu> Pb> As ~ Co> Hg ~Mo> Cd. Based on the correlation analysis, Fe, with weaker relationships with other metals, may be derived from the local ferruginous soil, whereas Cd, Cu and Cr could be mainly originated from anthropogenic sources and carried by clay minerals into the reservoir. In contrast, Hg and As abundance could be related to artisanal gold mining in the surrounding environment. Negative correlations between heavy metals (except As) with pH were consistent with desorption and mobility of the majority of heavy metals under low pH values. The significant negative correlations were also observed between CEC and As (r = - 0.75) and between clay and As (r = -0.64). This could be an indication of As mobility under the physico-chemical conditions of the reservoir. Both potential ecological risk and adverse effect indices suggested that the reservoir sediments were highly polluted. Five heavy metals (As, Cd, Cu, Cr and Hg) could cause adverse effect to biota, whilst only Hg and Cd appeared to show high and moderate potential ecological risk indices, respectively. The study demonstrated that the Soubeira reservoir requires a heavy metal pollution control program.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".