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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning.
Palansooriya, Kumuduni N; Li, Jie; Dissanayake, Pavani D; Suvarna, Manu; Li, Lanyu; Yuan, Xiangzhou; Sarkar, Binoy; Tsang, Daniel C W; Rinklebe, Jörg; Wang, Xiaonan; Ok, Yong Sik.
Afiliação
  • Palansooriya KN; Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea.
  • Li J; Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore.
  • Dissanayake PD; Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea.
  • Suvarna M; Soils and Plant Nutrition Division, Coconut Research Institute, Lunuwila 61150, Sri Lanka.
  • Li L; Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore.
  • Yuan X; Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore.
  • Sarkar B; Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea.
  • Tsang DCW; Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom.
  • Rinklebe J; Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
  • Wang X; School of Architecture and Civil Engineering, Institute of Foundation Engineering, Water and Waste Management, Laboratory of Soil and Groundwater Management, University of Wuppertal, Pauluskirchstraße 7, 42285 Wuppertal, Germany.
  • Ok YS; Department of Environment, Energy and Geoinformatics, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 05006, Republic of Korea.
Environ Sci Technol ; 56(7): 4187-4198, 2022 04 05.
Article em En | MEDLINE | ID: mdl-35289167
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes do Solo / Metais Pesados / Recuperação e Remediação Ambiental Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes do Solo / Metais Pesados / Recuperação e Remediação Ambiental Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul