Predicting formation of haloacetic acids by chlorination of organic compounds using machine-learning-assisted quantitative structure-activity relationships.

Cordero, José Andrés; He, Kai; Janya, Kanjira; Echigo, Shinya; Itoh, Sadahiko

Cordero, José Andrés; He, Kai; Janya, Kanjira; Echigo, Shinya; Itoh, Sadahiko.

Afiliação

Cordero JA; Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan.
He K; Research Center for Environmental Quality Management, Kyoto University, 1-2 Yumihama, Otsu, Shiga 5200811, Japan. Electronic address: he.kai.7e@kyoto-u.ac.jp.
Janya K; Department of Chemical Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom 73170, Thailand.
Echigo S; Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan.
Itoh S; Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Nishikyo, Kyoto 6158540, Japan.

J Hazard Mater ; 408: 124466, 2021 04 15.

Article em En | MEDLINE | ID: mdl-33191030

Palavras-chave

Anthropogenic compounds; Haloacetic acids; Machine-learning; Pollutant Release and Transfer Register; QSAR

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Hazard Mater Assunto da revista: SAUDE AMBIENTAL Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Japão País de publicação: Holanda

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