A framework for spatial correlations between industrial pollution sources and groundwater vulnerabilities based on machine learning and spatial cluster analysis: Implications for risk control.

Journal: Journal of hazardous materials
Published Date: May 7, 2025

Abstract

The efficacy of groundwater pollution risk control is often limited by a lack of information on spatial correlations between industrial pollution sources and groundwater vulnerabilities. To overcome this limitation, a novel data-driven framework was established to reveal the spatial correlations between industrial pollution sources and groundwater vulnerabilities in Guangdong Province, China, mainly using a combination of genetic algorithm (GA), back propagation neural network (BPNN), kernel density estimation (KDE) and bivariate local Moran's I (BLMI). The subjectivity in the indicator weighting of a DRASTICL model was successfully reduced by GA-BPNN, and then the groundwater vulnerability map was created by the GA-BPNN-DRASTICL model. A smoother surface of the industrial pollution source distribution was produced by KDE. The spatial clustering map between industrial pollution sources and groundwater vulnerabilities was generated by BLMI, explicitly showing their distribution characteristics and implying that the specific measures should be taken for controlling risks of groundwater pollution in the different parts of the study area.

Authors

  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Jian Chen
    School of Pharmacy, Shanghai Jiaotong University, Shanghai, China.
  • Haobo Bian
    College of New Energy and Environment, Jilin University, Changchun 130021, China; Chinese Academy of Environmental Planning, Beijing 100041, China.
  • Lu Li
    State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Lei Liao
    Research Institute No. 290, CNNC, Shaoguan 512029, China.
  • Haobo Niu
    Chinese Academy of Environmental Planning, Beijing 100041, China.
  • Leyi Yin
    Chinese Academy of Environmental Planning, Beijing 100041, China.
  • Guoxin Huang
    Chinese Academy of Environmental Planning, Beijing 100041, China. Electronic address: huanggx@caep.org.cn.

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