Published April 3, 2025
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Spatial-machine learning framework for rapid identification of soil cadmium risk in high geochemical background areas.

Creators

  • 1. Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/ International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi 541004, China; Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, Guangxi 531406, China.
  • 2. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China.
  • 3. China University of Geosciences (Beijing)
  • 4. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Key Laboratory of Environmental Remediation and Ecosystem Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
  • 5. Zhejiang University
  • 6. School of Science, China University of Geosciences, Beijing 100083, China.
  • 7. Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/ International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi 541004, China; Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, Guangxi 531406, China. Electronic address: jzhongcheng@mail.cgs.gov.cn.
  • 8. Mineral Resource Reservoir Evaluation Center of Guangxi, Nanning 530023, China.
  • 9. School of Environmental and Chemical Engineering, Foshan University, Guangdong, Foshan 528000, China.
  • 10. Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/ International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi 541004, China; Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, Guangxi 531406, China. Electronic address: zhaoliangjie@mail.cgs.gov.cn.
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