Quantitative structure–property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software-Reference-Cited by-同舟云学术

Quantitative structure–property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software

Published:2021-05 Issue: Volume:196 Page:110363
ISSN:0013-9351
Container-title:Environmental Research
language:en
Short-container-title:Environmental Research

Author:

Kobayashi Yoshiyuki^ORCID,Yoshida Kenichi

Funder

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

General Environmental Science,Biochemistry

Reference58 articles.

1. NanoBRIDGES" software: open access tools to perform QSAR and nano-QSAR modeling;Ambure;Chemometr. Intell. Lab. Syst.,2015

2. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms;Arnot;Environ. Rev.,2006

3. Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes;Berthod;Sci. Total Environ.,2017

4. Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals;Bhhatarai;Environ. Sci. Technol.,2011

5. Ecological recovery and resilience in environmental risk assessments at the European Food Safety Authority. Integr;Brock;Environ. Assess. Manag.,2018

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