Spatial and ecological health impacts of potentially toxic elements in road dust from long-term mining activities: A case study of the Bayan Obo deposit
Bibliographic record
Abstract
The long-term impacts of mining activities at the Bayan Obo deposit on potentially toxic elements (PTEs) in road dust remain insufficiently understood. This study aims to enrich knowledge in this area by investigating the spatial and eco-health impacts of PTEs in both bulk road dust (BRD) and resuspended road dust (RRD) from mining. An integrated approach combining Monte Carlo simulations with multiple statistical and geostatistical methods was used to quantify mining-related impacts. The findings revealed that Cd was the most polluted element. Concentrations of Cd, Mo, Pb and Zn were notably higher near the mine and decreased with increasing distance, with mining activities directly contributing over 20 % to these PTEs. Moderate and considerable eco-risks were identified for BRD and RRD, respectively, primarily driven by Cd and Mo, with higher risks closer to the mine. While non-carcinogenic risks were negligible, carcinogenic risks for adults required attention. Mining-related sources accounted for over 30 % of eco-risks but less than 10 % of health risks. This research integrates multiple methods, providing a more comprehensive understanding of the spatial and eco-health impacts of mining activities on PTEs in road dust. These findings offer critical insights and guidance for managing similar environmental challenges in other mining regions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".