Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

Journal: The Science of the total environment
PMID: 39137845

Abstract

Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging. In this study, integrating HM influx from atmospheric deposition, a boosted regression tree model with an average R > 0.8 was obtained to predict accumulation of Pb, As, and Cd in wheat grain across a smelting region. The atmospheric deposition serves as the dominant factor influencing the accumulation of Pb (28.2 %) and As (31.2 %) in wheat grain, but shows a weak influence on Cd accumulation (12.1 %). The contents of available HM in soil affect HM accumulation in wheat grain more significantly than their total contents in soil with relative importance rates of Pb (14.4 % > 8.2 %), As (30.9 % > 4.0 %), and Cd (55.0 % > 16.9 %), respectively. Marginal effect analysis illustrates that HM accumulation in wheat grain begins to intensify when Pb content in atmospheric dust reaches 5140 mg/kg and available Cd content in soil exceeds 1.15 mg/kg. The path analysis rationalizes the cascading effects of distances from study sites to smelting factories on HM accumulation in wheat grain via negatively influencing atmospheric HM deposition. The study provides data support and a theoretical basis for the sustainable development of non-ferrous metal smelting industry, as well as for the restoration and risk management of HM-contaminated soils.

Authors

  • Lingkun Meng
    School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Anxu Sheng
    School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address: anxusheng@hust.edu.cn.
  • Liu Cao
    Environmental Protection Agency of Jiyuan Production City Integration Demonstration Area, Jiyuan 459000, China.
  • Mingyue Li
    Department of Obstetrics and Gynecology, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, 224001 Jiangsu, China.
  • Gang Zheng
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
  • Sen Li
    Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Xiaohui Wu
    Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
  • Zhemin Shen
    School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Linling Wang
    College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.

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