Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

Journal: Journal of hazardous materials
PMID: 38972204

Abstract

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Authors

  • Zihan Bi
    Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
  • Jian Sun
    Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.
  • Yutong Xie
  • Yilu Gu
    Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
  • Hongzhen Zhang
    Center for Soil Protection and Landscape Design, The Innovation Center of Zero-Waste Society, Chinese Academy of Environmental Planning, Beijing, China.
  • Bowen Zheng
    Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Rongtao Ou
    Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
  • Gaoyuan Liu
    Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Xuya Peng
    Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
  • Xiaofeng Gao
    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. gao-xf@cs.sjtu.edu.cn.
  • Nan Wei
    Center for Soil Protection and Landscape Design, Chinese Academy of Environmental Planning, Beijing 100041, China. Electronic address: weinan@caep.org.cn.

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