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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learningopen access

Authors
Palansooriya, Kumuduni N.Li, JieDissanayake, Pavani D.Suvarna, ManuLi, LanyuYuan, XiangzhouSarkar, BinoyTsang, Daniel C. W.Rinklebe, JoergWang, XiaonanOk, Yong Sik
Issue Date
5-4월-2022
Publisher
AMER CHEMICAL SOC
Keywords
machine learning models; heavy metal; soil remediation; graphical user interface; biochar
Citation
ENVIRONMENTAL SCIENCE & TECHNOLOGY, v.56, no.7, pp.4187 - 4198
Indexed
SCIE
SCOPUS
Journal Title
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume
56
Number
7
Start Page
4187
End Page
4198
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143100
DOI
10.1021/acs.est.1c08302
ISSN
0013-936X
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.
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College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles

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