Modelling microplastic transport through porous media, such as soils and aquifers, is an emerging research topic, where existing hydrogeological models for (reactive) solute and colloid transport have shown limited effectiveness thus far. This perspective article draws upon recent literature to provide a brief overview of key microplastic transport processes, with emphases on less well-understood processes, to propose potential research directions for efficiently modeling microplastic transport through the porous environment. Microplastics are particulate matter with distinct physicochemical properties. Biogeochemical processes and physical interactions with the surrounding environment cause microplastic properties such as material density, geometry, chemical composition, and DLVO interaction parameters to change dynamically, through complex webs of interactions and feedbacks that dynamically affect transport behavior. Furthermore, microplastic material densities, which cluster around that of water, distinguish microplastics from other colloids, with impactful consequences that are often underappreciated. For example, (near-)neutral material densities cause microplastic transport behavior to be highly sensitive to spatio-temporally varying environmental conditions. The dynamic nature of microplastic properties implies that at environmentally relevant large spatio-temporal scales, the complex transport behavior may be effectively intractable to direct physical modeling. Therefore, efficient modeling may require integrating reduced-complexity physics-constrained models, with stochastic or statistical analyses, supported by extensive environmental data.

中文翻译：

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模拟微塑料通过多孔介质的传输：动态传输行为带来的挑战


模拟微塑料通过多孔介质（如土壤和含水层）的迁移是一个新兴的研究课题，到目前为止，现有的（反应性）溶质和胶体迁移的水文地质模型显示出的有效性有限。这篇前瞻性文章借鉴了最近的文献，简要概述了关键的微塑料运输过程，重点是不太为人所知的过程，以提出有效的微塑料通过多孔环境的运输的潜在研究方向。微塑料是具有不同物理化学性质的颗粒物。生物地球化学过程和与周围环境的物理相互作用导致微塑料特性（如材料密度、几何形状、化学成分和 DLVO 相互作用参数）通过动态影响运输行为的复杂相互作用和反馈网络动态变化。此外，聚集在水周围的微塑料材料密度将微塑料与其他胶体区分开来，其影响后果往往被低估。例如，（接近）中性材料密度导致微塑料传输行为对时空变化的环境条件高度敏感。微塑料特性的动态性质意味着，在与环境相关的大时空尺度上，复杂的传输行为可能有效地难以直接进行物理建模。因此，高效的建模可能需要将复杂度较低的物理约束模型与随机或统计分析集成，并得到大量环境数据的支持。