文章基本信息

标题：Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models
本地全文：下载
作者：Ping Hou ; Olivier Jolliet ; Ji Zhu 等
期刊名称：Environment International
印刷版ISSN：0160-4120
电子版ISSN：1873-6750
出版年度：2020
卷号：135
页码：1-12
DOI：10.1016/j.envint.2019.105393
出版社：Pergamon
摘要：In life cycle assessment, characterization factors are used to convert the amount of the chemicals and other pollutants generated in a product’s life cycle to the standard unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chemicals due to the lack of laboratory experiment data. Here we develop machine learning models to estimate ecotoxicity hazardous concentrations 50% (HC50) in USEtox to calculate characterization factors for chemicals based on their physical-chemical properties in EPA’s CompTox Chemical Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The average root mean squared error of the estimated HC50 on the test sets is 0.761. The average coefficient of determination ( R 2 ) on the test set is 0.630, meaning 63% of the variability of HC50 in USEtox can be explained by the predicted HC50 from the random forest model. Our model outperforms a traditional quantitative structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide estimates of missing ecotoxicity characterization factors for 552 chemicals in USEtox using the validated random forest model.
关键词：Life cycle assessment ; Ecotoxicity ; Hazardous concentration ; Characterization factors ; Machine learning ; Quantitative structure-activity relationship (QSAR)