Human health risk assessment of potentially toxic elements in soil and air particulate matter of automobile hub environments in Kumasi, Ghana
Bibliographic record
Abstract
Rapid urbanization and uncontrolled industrial activities in developing countries have raised concerns about potentially toxic metal contamination of the environment. This study assessed the levels of potentially toxic elements in soil and airborne particulate matter in the Suame and Asafo areas in the Kumasi metropolis, characterized by a high concentration of auto mechanic workshops and residential settlements. X-ray fluorescence analysis and inductively coupled plasma-mass spectrometry were used to determine the metal concentrations in the samples. The results showed high concentrations of potentially toxic elements in the soil and air samples, indicating contamination from automotive activities. Metals such as Co, Ni, Pb, and Zn were found to be present at concentrations (13.42-6101.58 mg/kg and 14.15-11.74 mg/kg for Suame and Asafo respectively) that pose potential health risks to exposed populations. Mathematical models such as pollution indices were used to assess the extent of contamination and determine the potential sources of the metals - the automotive repairs. The findings highlight the urgent need for environmental management and remediation strategies to mitigate the health risks of exposure to potentially toxic elements in the Kumasi metropolis automotive hub.
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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) | 0.001 | 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".