Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning  

作　　者：Jin-Man Cao Yu-Qian Liu Yan-Qing Liu Shu-Dan Xue Hai-Hong Xiong Chong-Lin Xu Qi Xu Gui-Lan Duan 

机构地区：[1]State Key Lab of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China [2]Henan Institute of Advanced Technology,Zhengzhou University,Zhengzhou 450052,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Journal of Environmental Sciences》2025年第1期259-267,共9页环境科学学报(英文版)

基　　金：supported by the National Key Research and Development Program of China(No.2020YFC1808701).

摘　　要：Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar.However,the influence of a specific property on As immobilization varies among different studies,and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge.To enhance immobilization efficiency and reduce labor and time costs,a machine learning(ML)model was employed to predict As immobilization efficiency before biochar application.In this study,we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models.The results demonstrated that the random forest(RF)model outperformed gradient boost regression tree and support vector regression models in predictive performance.Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization.These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils.Furthermore,the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization.These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.

关 键 词：BIOCHAR Arsenic immobilization SOIL Machine learning 

分 类 号：S15[农业科学—土壤学]