Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models.

Journal: Marine pollution bulletin
Published Date:

Abstract

Revealing the key mechanisms influencing the behavior of potentially toxic elements (PTEs) in soil-plant systems is of great significance for environmental protection and grassland development in coastal areas. This study utilized redundancy analysis to assess the effects of soil environmental variables on the concentrations and enrichment of various PTEs in the advantageous forage species Phragmites australis. Advanced models like PLS-PM and RF-SHAP quantitatively assessed soil salinity impacts. The main findings are as follows: (1) P. australis exhibited enrichment capacity for Cd, Cr, and Cu. (2) Soil pH, exchangeable potassium (aK), and exchangeable calcium (aCa) were key determinants of PTE distribution, with Cu being highly sensitive to these variables. (3) Significant interactions between soil electronic conductivity (EC) and pH, as well as between soil EC and aCa (p < 0.01). (4) A pH value of 8.30 and an aCa concentration of 4.4 g/kg were identified as critical thresholds affecting the Cu uptake. These results provide insights into PTE migration and management strategies for coastal grasslands.

Authors

  • Mengge Zhou
    State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Molecular Drug Research, College of Pharmacy, Nankai University, Tianjin 300071, People's Republic of China.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.
  • Linglong Chen
    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Ziqiao Li
    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • XiaoYong Liao
    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
  • Yonghua Li
    College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China.