Modelling the dynamics of the cancer risk due to potentially toxic elements in agricultural soils, in the upper Crocodile River catchment, North-West province, South Africa
Bibliographic record
Abstract
Contaminated agricultural soil with potentially toxic elements (PTEs) poses a threat to food safety and can adversely affect human health. This study evaluates the potential health risk caused by PTEs contamination in cultivated soils and the possible related health effect. Sixty-four (64) soil samples from cultivated soils before crop cultivation and post-crop were harvested and twenty-four (24) soil samples from a natural game reserve park were collected and. The PTEs were analysed using the inductively coupled plasma-mass spectrometry (ICP-MS) for the following elements As, Cd, Cr, Cu, Ni, Pb, Zn, Mn, Al, and Fe. The contamination factor (CF), pollution load index (PLI), hazard quotient (HQ), and hazard index (HI) was calculated to assess the human health risks for the different pathways to the exposed population. A carcinogenic probabilistic risk model based on the mathematical derivation and PTEs transmission pathways is presented. The mean concentrations of the PTEs in the cultivated soil varied in order of Al > Fe > Mn > Cr > Ni > Zn > Cu > Pb > As > Cd. Cr and Ni concentrations in all the cultivated soils were found to be higher than the Canadian guidelines and the Finnish permissible threshold guidelines for agricultural. The CF in the soil indicates extreme contamination suggestion an ecotoxicological effect. The PLI value also suggests that the soils have undergone some form of deterioration, particularly with Cr and Ni. The ingestion seems to be the major pathway followed by dermal to children. The HQ and HI values for the children were > 1 indicating an adverse health effect for the children residing around the cultivated fields. The simulated results indicate that the percentage of the affected humans that may be consuming crop derived from the contaminated soil increases by a factor of 2 as the contamination factor increases. The transmission compartments can be used as an effective measure in which mitigation can be effectively used.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".