Research Progress and Challenges of Machine Learning in Environmental Risk Identification and Control of Emerging Contaminants

As technology advances, an increasing number of emerging contaminants (ECs) are being generated and released into the environment. These ECs exhibit biological toxicity and environmental persistence, posing risks to ecological systems and human health. Consequently, many studies have focused on preventing and managing the risks associated with ECs. However, the detection and control of EC risks in natural environments through traditional analysis methods are complex, time-consuming, and labor-intensive. Machine learning (ML) is a data-driven research method that utilizes existing data to train models and forecast the trends of interest. Recently, ML has been widely adopted in the identification and control of risks associated with ECs owing to its ability to comprehend intricate parameter relationships within datasets. The integration of traditional technology and ML can reduce computational costs and save experimental time and energy consumption by optimizing and reducing the number of experiments required. The application of ML in addressing ECs can be categorized into several categories, such as toxicity prediction, identification/classification, property assessment, and assisted removal. This review outlines the application of ML in addressing various types of ECs, such as nanomaterials, microplastics, antibiotic-resistant genes, perfluoroalkyl and polyfluoroalkyl substances, endocrine-disrupting chemicals, and persistent organic pollutants. ML can reduce the need for animal experiments in toxicity studies. Nonetheless, there is limited data available in this field, and certain models rely on small datasets with low data quality that limit the ML application scope. However, not all environmental problems are suitable for ML. Hence, the over-reliance on this approach is not recommended. Complex ML models often lack interpretability, making it challenging to understand how ML generates specific predictive outcomes. The absence of interpretability in complex ML poses a challenge in explaining and understanding the environmental behavior and risks associated with ECs. In addition to the intricacies within ML models, the accuracy of ML models heavily relies on the quality of the input data. Therefore, the focus should be on enhancing the quality of EC data, promoting data sharing, establishing a comprehensive and unified database, and developing suitable frameworks for managing ECs.