Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
J Hazard Mater. 2022 Jun 15;432:128730. doi: 10.1016/j.jhazmat.2022.128730. Epub 2022 Mar 17.
Microplastics (MPs, sizes <5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.
微塑料（尺寸<5 毫米）已被广泛发现存在于各种环境中，如海洋、淡水、陆地和大气系统。机器学习为基于大数据评估 MPs 的生态风险提供了一种潜在的解决方案。与传统模型相比，数据驱动的机器学习可以加速实现对危险 MPs 的控制，并减少 MPs 在地方和全球范围内的影响。然而，仍存在一些亟待解决的问题。例如，缺乏微塑料数据库和不可比文献导致当前的微塑料数据无法充分支持大数据研究。因此，当务之急是制定一套标准且通用的微塑料收集和测试方案。对于机器学习来说，大规模微塑料分布的预测和相应的环境风险仍然缺乏。为了加速未来对微塑料的研究，可以参考其他颗粒污染物（如纳米材料和气溶胶）的方法和理论。除了预测之外，机器学习的因果关系和可解释性的提高在微塑料风险的研究中也值得关注。总的来说，本观点文章为机器学习方法在微塑料环境风险研究中的发展提供了一些见解。
