数字农科院2.0

Underestimated distribution and health risks of arsenic in soils across the Tibetan Plateau

文献类型： 外文期刊

作者： Li, Yan;Shi, Rongguang;Ding, Shiyuan;Guo, Jinghao;Dai, Wenjing;Li, Yifan;Li, Xiaodong

作者机构：

关键词： Arsenic distribution map;Tibetan Plateau;Predictive framework;Machine learning;Health risk

期刊名称： JOURNAL OF HAZARDOUS MATERIALS

ISSN： 0304-3894

年卷期： 2025 年 502 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Arsenic (As) is a toxic metalloid with significant global environmental and health implications. Reliable, high resolution mapping of soil arsenic remains a major challenge, particularly in data limited, high altitude regions such as the Tibetan Plateau (TP). We assembled 676 surface soil samples across the TP and coupled them with multi-source environmental predictors to build a 1 km resolution Extreme Gradient Boosting (XGBoost) model. The response was field-measured topsoil As, and predictors covered lithology, terrain, climate, soil properties, vegetation metrics and a proxy for human activity. Model performance was robust with an independent R2 of 0.74, and an ensemble bootstrap produced pixel wise uncertainty estimates. Predictions reveal a clear west high to east low gradient, with three major high As zones aligned with the Himalayan, Lhasa and Qiangtang terranes. SHAP interpretation shows lithology, population density, precipitation, elevation and cation exchange capacity as the leading drivers, natural factors explained 84.7 % of spatial variance. We delineated agricultural soils using land cover data and field verification. Within these agricultural areas, As exceedance of screening thresholds is spatially heterogeneous, with hotspots concentrated in Ali, central-western Nagqu and western Shigatse. Health risk calculations indicate non-carcinogenic hazard indices below one, while carcinogenic risks for adults and children fall between 3.56 x 10-5 and 7.59 x 10-5, with children about 1.78 times higher than adults. Furthermore, a spatiotemporal comparison with a 2009 map highlights regional contrasts critical for guiding future monitoring efforts. This integrated framework provides an essential scientific foundation for soil risk assessment and land management in the TP.

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