Levels of Heavy Metals Contamination (As, Cd, Hg, Pb) in Some Human Consumption Water Sources in Agbangnizoun and Za-Kpota Town Halls, Southern Benin
Bibliographic record
Abstract
In the current decades, the increasing presence of metallic contaminants in water for human consumption has become a major public health concern. This concern is even more pronounced in rural areas such as in the Town Halls of Agbangnizoun and Za-Kpota where the majority of households use surface water, wells and tanks to satisfy their daily drinking water needs, without any prior treatment, due to the low level of access to drinking water supplied by the State. This study aims at assessing the levels of contamination of these resources in mercury (Hg), cadmium (Cd), lead (Pb) and arsenic (As). The mercury was determined using the cold vapor technique by the Direct Mercury Analyzer (DMA-80) while lead and cadmium were analyzed by molecular absorption spectrophotometry by the DR 3900. The Arsenic was extracted by distillation using the silver diethyldithiocarbamate method then measured by molecular spectrophotometry technique. The results show that surface waters contain great quantities of metals than well and cistern waters. Lead (220.97 ± 9.45 µg/L) and cadmium (20.13 ± 0.17 µg/L) in surface waters have levels above WHO guidelines and Bénin standards. On the other hand, there is no significant difference between the metal concentrations of well and cistern waters at the 5% threshold compared to the borehole water (witness sample). Significant correlations are established between toxic metals (Pb and Cd) and physical parameters (turbidity and suspended matters) at the threshold of 1 %. As for mercury (Hg) and arsenic (As), the concentrations are very lower than these of Cd and Pb and below the quantification limit of the device. These results confirm that the surface waters consumed by the populations of the Town Halls of Agbangnizoun and Za-Kpota do not respect drinking water standards.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".