Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most…

• NR
  National Research Foundation Singapore
• RD
  Rural Development Administration

  Award: PJ01475801
• NR
  National Research Foundation of Korea

  Awards: 2021R1A2C2011734, 2021R1A6A1A10045235