Zhang Jiaqi, Choi Clarence Edward
The Department of Civil Engineering, The University of Hong Kong, HKSAR, PR China.
The Department of Civil Engineering, The University of Hong Kong, HKSAR, PR China.
Water Res. 2025 Mar 15;272:122961. doi: 10.1016/j.watres.2024.122961. Epub 2024 Dec 12.
Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, filmed, and fibrous morphologies, respectively. However, none of the existing models demonstrates universal applicability across all three morphologies. Scientists now have to rely on the predominate microplastic morphology extracted from filed samples to determine the appropriate settling model used for transport modeling. Given the spatiotemporal variability in morphologies and the coexistence of diverse morphologies of microplastics in natural aquatic environments, the extracted morphological information poses significant challenges in reliably determining the appropriate model. Evidently, to reliably model the transport of microplastics in aquatic environments, a universal settling model for microplastics with diverse shapes is warranted. To develop such a universal model, a unique shape factor, which can explicitly distinguish between the distinct morphologies of microplastics, was first proposed in this study by using a specifically-modified machine learning method. Using this newly-proposed shape factor, a universal model for predicting the settling velocity of microplastics with distinct morphologies was developed by using a physics-informed machine learning algorithm and then systematically evaluated against independent data sets. The newly-developed model enables reasonable predictions of the settling velocity of microplastic fragments, films, and fibers. In contrast to purely data-driven models, the newly-developed model is characterized by its transparent formulaic structure and physical interpretability, which is conducive to further expansion and improvement. This study can serve as a paradigm for future studies, inspiring the adoption of machine learning techniques in the development of physically-based models to investigate the transport of microplastics in aquatic environments.
准确预测微塑料在水生环境中的沉降速度是可靠模拟其输运过程的前提条件。针对具有碎片状、薄膜状和纤维状形态的微塑料，分别提出了越来越多的沉降模型。然而，现有的模型均未表现出对所有这三种形态都具有普遍适用性。科学家现在不得不依赖从实地样本中提取的主要微塑料形态来确定用于输运模拟的合适沉降模型。鉴于形态的时空变异性以及天然水生环境中微塑料多种形态的共存，提取的形态信息在可靠确定合适模型方面带来了重大挑战。显然，为了可靠地模拟微塑料在水生环境中的输运，需要一个适用于各种形状微塑料的通用沉降模型。为了开发这样一个通用模型，本研究首先通过使用一种经过特殊修改的机器学习方法提出了一个独特的形状因子，该因子可以明确区分微塑料的不同形态。利用这个新提出的形状因子，通过使用物理信息机器学习算法开发了一个用于预测具有不同形态微塑料沉降速度的通用模型，然后针对独立数据集进行了系统评估。新开发的模型能够合理预测微塑料碎片、薄膜和纤维的沉降速度。与纯数据驱动模型不同，新开发的模型具有透明的公式结构和物理可解释性，这有利于进一步扩展和改进。本研究可以作为未来研究的范例，激励在基于物理的模型开发中采用机器学习技术来研究微塑料在水生环境中的输运。
