Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Kumuduni N. Palansooriya, Jie Li, Pavani D. Dissanayake, Manu Suvarna, Lanyu Li, Xiangzhou Yuan, Binoy Sarkar, Daniel C.W. Tsang, Jörg Rinklebe, Xiaonan Wang, Yong Sik Ok

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)

Abstract

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 significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.

Original languageEnglish
Pages (from-to)4187-4198
Number of pages12
JournalEnvironmental Science and Technology
Volume56
Issue number7
DOIs
Publication statusPublished - 2022 Apr 5

Bibliographical note

Funding Information:
This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475801) from Rural Development Administration, the Republic of Korea. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2011734). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A10045235). The authors acknowledge the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

Keywords

  • biochar
  • graphical user interface
  • heavy metal
  • machine learning models
  • soil remediation

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry

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