College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China.
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, PR China.
J Environ Manage. 2023 Oct 15;344:118756. doi: 10.1016/j.jenvman.2023.118756. Epub 2023 Aug 11.
Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.
微塑料（MPs）作为一种新兴污染物，对人类和生态系统构成了重大威胁。然而，传统的 MPs 特征化方法受到样品要求和特征化时间的限制。机器学习（ML）由于其准确性、广泛的应用和强大的特征提取能力，已成为分析 MPs 污染的重要技术。然而，环境科学家在使用 ML 之前需要具备一定的门槛知识，这限制了 ML 在 MPs 研究中的应用。此外，ML 在 MPs 研究中的不平衡发展也是一个紧迫的问题。为了在 MPs 研究中广泛应用 ML，在本综述中，我们全面讨论了相关文献中 MPs 数据集的大小和来源，以帮助环境科学家加深对 MPs 数据集构建的理解。从可解释性和计算机设施需求的角度分析了常用的 ML 算法。此外，还讨论了改进和评估 ML 模型性能的方法，如数据集预处理、模型优化和模型评估指标。根据数据集和特征化技术，本文将 MPs 的 ML 识别分为光谱识别、图像识别和光谱成像识别三类。最后，全面讨论了 ML 在 MPs 研究中的其他应用，包括毒性分析、污染物吸附和微生物定殖，揭示了 ML 的巨大应用潜力。基于上述讨论，本综述提出了一种算法选择策略，以帮助研究人员在不同情况下选择最合适的 ML 算法，提高效率，减少试错成本。我们相信，这项工作为 ML 在 MPs 研究中的应用提供了启示。
