Source Apportionment of Potentially Toxic Elements in Topsoils Across Some Mining-Impacted Communities in Ghana Using the Positive Matrix Factorization Model
Bibliographic record
Abstract
In this study, we employed the positive matrix factorization receptor model to ascertain whether the prevalence of potentially toxic elements in surface soils of two mining communities in Ghana is exclusive to mining. At the two mining communities (Kenyasi and Obuasi), 49 samples were taken at each location, whereas 90 soil samples were collected from Sunyani, the non-mining community. All the soil samples were screened for elemental concentrations using the Niton XL3t GOLDD+ field portable X-ray fluorescence spectrometer. The positive matrix factorization model was set to three factors in the base model for the analyses, and the receptor model revealed three major contributing factors to the metal pollution in the study areas. Although mining activities contributed to As, Cd, Cu, Ni, and Pb in Kenyasi, a typical mining community, farming activities also contributed significantly to the presence of Zn and Cd in the surface soils. At Obuasi, construction works and geogenic factors were responsible for the enrichment of Cr and Cu in the surface soils, farming activities accounted for the Mn, Pb, and Zn contamination, and As, Cd, and Ni contamination by mining activities. In Sunyani, the non-mining community, the model revealed that the contamination of the soil by Cd, Mn, Ni, and Pb was primarily from multiple sources, such as the mass burning of wastes, farming, and construction works within the municipality. The prevalence of Zn in the municipality was attributed to natural origin since the pollution indices showed low contamination of the metal. This study will serve as a scientific reference for the holistic mitigation of soil heavy metal contamination in the study areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".