Risk assessment of heavy metal and trace elements contamination in groundwater in some parts of Ogun state
Bibliographic record
Abstract
The present study was carried out to investigate the risk of heavy metal contamination in groundwater in Ota, Ogun state. Water samples were taken from seven (7) major groundwater sources popularly consumed by the population in the study area. The samples were analyzed for heavy metals and trace elements, which include K, Mn, Cr, and Cu at the ACME laboratory in Canada using ICP-MS. The average concentrations obtained for each metal are as follows: 0.30, 7.30, 0.85, 25.34 μg l−1. This could be represented in this order, Cu > Mn > Cr > K. Furthermore, the average daily dose was determined for the heavy metal and Trace Elements in each sample, samples 1 and 7 reported higher results in Cu for adult male and female while samples 1, 2, 3 and 4 reported 3.878, 1.653, 1.980 and 4.467 μg (kg.day)−1 for Cu in children. The concentration of these elements detected in the water samples could be as a result of the geology of the area of study or due to human actions. Further study revealed the values of hazard quotient to be less than the recommended safe limit of 1 for all the samples. The average hazard quotient also reported values lower than 1 for all the age group, but Cu was noticed to be prominent across all the estimation. Therefore, regular monitoring must be considered for groundwater samples in the study area in order to avoid possible health risk that may occur as a result of the increase in the concentration of these heavy metals and Trace Elements over a long period if their sources are not eliminated.
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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".