Assessment of Heavy Metals Contamination in Surface Soil of Ishiagu, Southeastern Nigeria.
Bibliographic record
Abstract
Identification and quantification of heavy metals contamination in soil is very important as far as human health and environmental quality protection are concerned. Heavy metals enter the soil through natural and anthropogenic sources but contamination concern is mainly associated with anthropogenic input which is capable of increasing the natural concentration to contamination and toxic levels. Heavy metals can migrate from contaminated soil to other components of the environment with the possibility of human exposure and severe health implication. This study investigated heavy metals contamination of soils in Ishiagu area using pollution indices. Soil samples were collected at various points within the study area at depths of 0-20cm. Collected soil samples were analyzed for total metal concentration using ICP-OES after Aqua regia digestion in Bureau Veritas laboratory, Canada. The result of the analysis showed that heavy metals content in Ishiagu soil varies with location and is in the order: Zn (5 – 1450), Ni(1 – 73 ) Cu (3 – 37 )Cr(6 – 64 )Fe (2800 – 80800 )Al (2200 – 27400 ). The average concentration of the metals in the soil of the study area is in the order: Fe>Al > Zn>Cr > Ni> Cu. Based on the calculated average contamination Factors (CFs), there is moderate contamination of Zn in the investigated soil while the contamination level of other heavy metals that include Cr, Ni, Cu, Al and Fe are low; mean values less than 1(CF<1).The Igeo values calculated for each location range from practically uncontaminated with Cr, Ni, Al, Fe and Cu in all the locations to moderately contaminated with Zn in mining areas. However, the average igeo values for all the metals indicate no contamination in the study area soil. The knowledge of soil contamination status is relevant for environmental management decision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".