Abstract
Thallium (Tl) pollution from natural and anthropogenic sources is increasingly recognized for its environmental and health risks, with localized threats in polluted areas despite low global background levels. Utilizing over 20,000 topsoil Tl measurements with 21 related environmental variables, a CatBoost classification model (AUC = 0.89, recall = 0.80, balanced accuracy = 0.84) was applied to predict whether global topsoil Tl concentrations exceeding 1 mg/kg, identifying both known and unreported hotspots. A CatBoost regression model (R2 = 0.62) further predicted Tl concentration distributions, highlighting regional variations. This study reveals that high-risk areas are highly overlapped with anthropogenic factors (mining activities and land cover) and geological conditions (mineralized zones, lithology, and geological structures), collectively influencing 14.81% of the model outputs. By integrating cropland cover maps with our predictions, we found that approximately 9.9% of the world’s cropland has a greater than 47% probability of Tl concentrations exceeding 1 mg/kg, particularly in South America (34.7%), Asia (12.3%), and Africa (10.8%). These findings underscore the need for heightened attention to soil Tl testing in high-risk croplands to ensure agricultural safety.
| Original language | English |
|---|---|
| Pages (from-to) | 8777-8789 |
| Number of pages | 13 |
| Journal | Environmental Science and Technology |
| Volume | 59 |
| Issue number | 17 |
| DOIs | |
| State | Published - 6 May 2025 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- cropland
- distribution
- machine learning
- soil contamination
- thallium
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