HEAVY METAL CONTENT OF SOIL AND EDIBLE PLANTS IN THE VICINITY OF AN AUTOMOBILE SERVICE AND REPAIR AREA IN OKIGWE, NIGERIA
Bibliographic record
Abstract
The study assessed heavy metal content and physiochemical parameters of soil, as well as heavy metal content of plants in the study area. The study also examined the implications of automobile workshop wastes on soil, plant and human health. Soil samples and four edible plants were randomly collected from a farmland in the study area and control 1km away. Soil samples were collected at a depth of 20cm, while stem and leaf samples were collected at a depth of 20cm, while stem and leaf samples were collected for four edible plants namely; maize (Zea mays), bitter leaf (Vernoniaamygdalina), water leaf (Talinumtriangulare), spinach leaf (Spinaciaoleracea) in the study area and control. Soil and plan samples were analysed for chromium, copper, iron, lead and zinc. The concentration of these heavy metals were higher in all samples at the study area than at the control. Accumulation of heavy metals by plants in the study area followed the order Talinumtriangulare> Spinaciaoleracea> Zea mays> Vernoniaamygdalina. Significant difference was found between heavy metal content of soil at the study area and the control (t =0.98 tcal for all plants at P=0.05. The results obtained in this study of the soil. The concentrations of the individual metals in the soil in the study area wastes all above the permissible limits for agricultural soil. Crop farming in the study area raises serious environmental health concerns since use and consumption of these plants invariably means the ingestion of toxic metals. The high concentration of heavy metals in the soil at the study area implies a high risk of surface and ground water pollution from run off and percolation as well as increased level of metal laden particular matter in the ambient atmosphere. It is very important that waste management in automobile service and repair areas is given high and urgent priority. This should involve proper waste handling, reduction and recycling of wastes where necessary, as well as sensitization.
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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 | 0.000 |
| 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) | 0.000 | 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".