A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.

Journal: Journal of hazardous materials
Published Date: May 3, 2025

Abstract

Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM-contaminated site and used amplicon sequencing to determine the differences in depth-resolved bacterial communities and to assess the thresholds and ecological impacts of HMs. Results revealed that HM levels reduced markedly with soil depth. The bacteria in upper soil exhibited higher community diversity and a more complex and stable ecological network structure. As depth increased, the proportion of negative interactions gradually elevated, indicating more competitive interspecies behavior. Threshold analyses based on machine learning revealed that arsenic (As) and copper (Cu) exhibited nonlinear impacts on ecosystems. Cu demonstrated a low-threshold effect, with its ecological consequences manifested at extremely low concentrations. Our results highlighted the utility of microbial monitoring in assessing the adverse effects of HMs on soil health to support environmental management and ecological restoration.

Authors

  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Yajing Qu
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
  • Wenhao Zhao
    Department of Spine Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xiaochen Wu
    Hainan Research Academy of Environmental Sciences, Haikou 570100, China.
  • Anfu Yang
    Hainan Research Academy of Environmental Sciences, Haikou 570100, China.
  • Yulin Hu
    Hainan Research Academy of Environmental Sciences, Haikou 570100, China.
  • Haiyan Chen
    Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Meiying Wang
    Department of Rheumatology and Immunology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
  • Yuxuan Cai
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
  • Jin Ma
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. Electronic address: majin@craes.org.cn.
  • Fengchang Wu
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China.

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