Evaluation of Heavy Metal in Soils From Enyimba Dumpsite in Aba, Southeastern Nigeria Using Contamination Factor and Geo-Accumulation Index
Bibliographic record
Abstract
The manner in which municipal wastes generated are disposed in most urban areas in Nigeria is worrisome. The upsurge in population density and its resultant increase in urbanization and industrialization and the amount of waste generated in Aba, are of great concern. The objective of this research is to evaluate the concentration of some heavy metals in soils in the vicinity of Enyimba dumpsite in Aba, Nigeria. Thirty soil samples were collected and analyzed in the laboratory for some heavy metals by atomic absorption spectrophotometric method and multivariate statistical techniques. Twenty-five of the samples were obtained from the vicinity of the dumpsite while five samples are collected far away from the dumpsite to serve as control samples. The overall decreasing metal concentration in the dumpsite soil is: Cd > Co > Cu > Zn > As > Pb > Mn > Ni > Cr. A positive correlation exists between Cd and organic matter (r = 0.598). Geo-accumulation index and contamination factor showed a moderate contaminated with Cd only while the other metals are in their uncontaminated level. Factor analysis revealed four major components accounting for 78.82% of cumulative variance of the contamination: Cd, Cu, Co and organic matter; Pb, Zn and pH; Mn, As, clay + silt and finally Cr and Ni. From the above observations, it is evident that only Cd showed more pronounced level of pollution than any other metal. The need to replace open dumpsites with well designed sanitary landfills is advocated.
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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 | 0.000 |
| 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".