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Identifying the acute toxicity of contaminated sediments using machine learning models.
Min Jeong Ban1, Dong Hoon Lee1, Sang Wook Shin1
1Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
Environmental Pollution (Barking, Essex : 1987)
|September 5, 2022
Summary
A new machine learning approach effectively predicts sediment toxicity, outperforming traditional methods. This ecological risk assessment tool identifies key contaminants like chromium, copper, lead, and zinc for better water quality management.
Area of Science:
- Environmental Science
- Ecotoxicology
- Data Science
Background:
- Ecological risk assessment of contaminated sediment is crucial for water quality management.
- Existing index-based methods and toxicity testing can be costly and time-consuming.
Purpose of the Study:
- To develop and evaluate a machine learning (ML)-based approach for assessing sediment toxicity.
- To compare the performance of ML algorithms against traditional index-based methods.
Main Methods:
- Utilized 327 Korean datasets with 14 sediment quality parameters and toxicity data.
- Compared three ML algorithms (Random Forest, Support Vector Machine, XGBoost) as both classifiers and regressors.
- Evaluated three index-based methods: Pollution Load Index, Potential Ecological Risk Index, and Mean Probable Effect Concentration.
Main Results:
- ML regressors, particularly XGBoost, outperformed classifiers and traditional methods in predicting sediment toxicity.
- Permutation feature importance identified Chromium (Cr), Copper (Cu), Lead (Pb), and Zinc (Zn) as key predictors of toxicity.
- Classifiers struggled due to limited data and sediment composition information.
Conclusions:
- The ML-based approach offers a promising, cost-effective alternative for ecological risk assessment of contaminated sediments.
- XGBoost regressor demonstrates superior performance for sediment toxicity prediction.
- Future increases in sediment data will enhance the utility of ML-based ecological risk assessments.
