Mercury and Lead Contamination in Three Fish Species and Sediments from Lake Rukwa and Catchment Areas in Tanzania
Bibliographic record
Abstract
Background. Mining activity in the catchment area of Tanzania’s Lake Rukwa is suspected of adding to the lake and connected rivers’ heavy metal load. There has been no study done, however, on the levels of mercury (Hg) and lead (Pb) in lake sediment and fish muscle, and what the results could mean for human health. Objectives. This study investigated the concentration of Hg and Pb in lake sediment and in the muscles of African sharptooth catfish (Clarias gariepinus), Lake Rukwa tilapia (Oreochromis rukwaensis) and Singida tilapia (Oreochromis esculentus) from Tanzania’s Lake Rukwa and connected rivers. Methods. Concentrations of Hg and Pb in fish muscle and lake sediment were measured using inductively coupled plasma atomic emission spectroscopy (ICP-AES) and mercury analyzers, respectively. Results. Levels of Pb and Hg from C. gariepinus ranged between 0.01 to 1.9 μg/g and 0.03 to 0.33 μg/g, respectively. Pb and Hg in O. esculentus varied between 0.02 to 1.4 μg/g and <0.01 to 0.29 μg/g, respectively. Pb and Hg levels in O. rukwaensis ranged from 0.12 to 0.88 μg/g and 0.12 to 0.88 μg/g, respectively. On the other hand, concentrations of Pb and Hg in the sediment samples ranged between 0.02 to 16.23 μg/g and from 0.01 to 1.43 μg/g, respectively. Concentrations of Hg in the muscles of C. gariepinus and O. esculentus were above World Health Organization (WHO) permissible limits, indicating that they are not safe for human consumption. Concentrations of Pb in fish muscle samples were below WHO permissible limits and United States Environmental Protection Agency (USAEPA) provisional tolerable weekly intake (PTWI) standards. Furthermore, Hg and Pb in sediment were below the threshold value of Environment Canada and Florida’s ‘No effect level’. Conclusions. Although levels of Pb in fish samples and Hg and Pb levels in sediment were below international standards, it is important to consider that fish forms an important source of animal protein for local inhabitants, who are likely to consume more fish than considered by these standards. The study recommends further research on the levels of mercury and lead in humans, especially children and pregnant women. Competing Interests. The authors declare no competing financial interests.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".