Identifying the acute toxicity of contaminated sediments using machine learning models. (1st November 2022)

• Record Type: Journal Article
• Authors: Ban, Min Jeong; Lee, Dong Hoon; Shin, Sang Wook; Kim, Keugtae; Kim, Sungpyo; Oa, Seong-Wook; Kim, Geon-Ha; Park, Yeon-Jeong; Jin, Dal Rae; Lee, Mikyung; Kang, Joo-Hyon
• Is Part Of: Environmental pollution. Volume 312(2022)
• Publication Date: 2022-11-01
• Subjects: Sediment toxicity -- Pollution index -- Heavy metals -- Ecological risk assessment -- Machine learning -- Feature importance; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.120086
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data. Graphical abstract: Image 1 Highlights: Machine learning models can support sediment ecological risk assessment. Widely used index-based methods poorly predicted sediment toxicity. An XGB regressor performed well in toxicity classification using limited input data. Cr, Cu, Pb, and Zn were the major contributors to the toxicity prediction.