Статья

Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Kumuduni Niroshika PalansooriyaKorea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South KoreaJie LiDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, SingaporePavani Dulanja DissanayakeKorea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South KoreaManu SuvarnaDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, SingaporeLanyu LiDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, SingaporeXiangzhou YuanKorea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South KoreaBinoy SarkarLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United KingdomDaniel C.W. TsangDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, ChinaJörg RinklebeDepartment of Environment, Energy and Geoinformatics, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 05006, Republic of KoreaXiaonan WangDepartment of Chemical Engineering, Tsinghua University, Beijing 100084, ChinaYong Sik OkKorea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea

2022en

ABI

Аннотация

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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Идентификаторы

DOI: 10.1021/acs.est.1c08302

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar