Distribution mapping and risk assessment of lead in topsoil across the Tibetan Plateau.

Journal: Ecotoxicology and environmental safety
Published Date:

Abstract

Lead exposure poses substantial long-term health risks, accounting for over 900,000 annual deaths worldwide and impairing cognitive development in more than 800 million children. Recent studies have indicated elevated soil lead contamination levels on the Tibetan Plateau (TP), yet critical knowledge gaps remain in understanding its spatial risk distribution patterns, environmental drivers, and the magnitude of vulnerable populations. To address these uncertainties, this study systematically analyzed 733 topsoil samples across the TP and developed a machine learning framework to generate a 250-meter resolution predictive map of lead concentrations exceeding the regional background value (28.9 mg/kg). The derived spatial distribution model was subsequently integrated with the Seventh National Census in China conducted in 2020 to quantify the at-risk population and identify dominant environmental predictors. Results revealed a geometric mean soil lead concentration of 31.22 mg/kg, with 25.24 % of sampling sites surpassing the background threshold. Hazard hotspots exhibited pronounced spatial clustering in southeastern TP and sporadic distribution across the northern plateau region. Estimates from at-risk population modeling indicate that approximately 4.11 million residents, including 250,000 children aged 0-4, face lead exposure risks, predominantly concentrated in the northeastern and south-central TP. Multivariate analysis identified soil pH and terrain slope as the primary environmental determinants on lead accumulation. These findings underscore the critical role of environmental variables in shaping the spatial distribution of soil lead levels across the TP. The study provides a methodological framework for developing targeted risk mitigation strategies and evidence-based zoning policies to safeguard vulnerable populations, particularly children and rural communities in high-risk regions.

Authors

  • Linglong Chen
    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Ruxia Li
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Ru Zhang
    School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yonghua Li
    College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China.