An overview of potentially toxic element pollution in soil around lead–zinc mining areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Metal mining activities have been major contributors of potentially toxic elements (PTEs) in the environment, leading to substantial soil pollution. One such example is lead–zinc mining around the world. Elevated concentrations of PTEs are commonly detected in nearby areas of both active and abandoned mines. This is primarily attributed to the release and dispersal of untreated waste materials from these mines into the surrounding environment. Mining-related soil pollution with PTEs can pose many different kinds of risks in a variety of contexts such as eco-toxicity, phytotoxicity, human health risk, as well as soil and water pollution. This review summarizes available data in the literature (2000–2023) on PTEs polluted soils originating from lead–zinc mining areas across the world. In this study, an attempt has been taken to evaluate the pollution level of PTEs in soils using collected data. The study shows the most polluted world regions are reported in Asia, followed by Europe and Africa, and only a few studies are reported in north, central and south America. The elements commonly analyzed in conjunction with Pb and Zn were Cd and Cu, whereas those responsible for increased pollution were Cd > As > Cu > Hg–Mn–Tl. Assessment of the pollution and health hazards has shifted to include a variety of quality indexes, including multivariate statistical analyses and microbial diversity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it