Bioaccumulation Factors and Pollution Indices of Heavy Metals in Selected Fruits and Vegetables From a Derelict Mine and Their Associated Health Implications
Bibliographic record
Abstract
Concentration of heavy metals in the top and sub soils and in selected vegetables and fruits grown in Enyigba lead-zinc mine derelict was investigated using the X-ray Fluorescence (XRF) spectrometric method. Samples of fruits and leaves of the studied plants, over a period of two years (2008-2010), were analyzed for arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), lead (Pb), and zinc (Zn) contents, and their corresponding Pollution Indices (PI) and Bioaccumulation Factors (BAF) were evaluated. The mean pH of the soil was found to be 6.5 and the mean concentrations (mg/Kg) of metals in the studied plants were of the range: Pb (0.22 – 6.72); As (0.10 – 10.6); Cd (0.10 – 12.4); Cu (12.6 – 82.1); Cr (0.01 – 1.02); Zn (34.2 – 162.1); Mn (412.1 – 42.6); and Ni (12.8 – 72.8). High Pollution Indices of 22.4, 12.37, 8.67, 7.27, and 6.13 were observed in Nauclea latifolia (African Peach), Sesamum indicum (Beni seed), Lactuca Sativa (Lettuce), Psidium Guajava (Guava), and C. Annum (Pepper) respectively; and as a result, they were not considered fit for human consumption. Bioaccumulation Factors (BAF > 1) were observed in some of the studied plants which suggested that they could be good phytoremediation agents. Statistical analysis of variance (ANOVA) at p< 0.05 showed variations in the heavy metal levels between and within groups while Fisher’s Least Significant Difference (LSD) Correlation analysis identified a strong relationship between the investigated plant and soil samples.
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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 | 0.000 | 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".