发表机构
University of Bayreuth; University of Cologne（拜罗伊特大学; 科隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出仅需几何信息的孔隙网络模型minD-PNM，高效预测饱和土壤中对流主导的微塑料颗粒输运穿透曲线，经模拟和实验验证，可扩展至宏观尺度。
AI 中文摘要
微塑料颗粒的环境输运因其潜在的生态和人类健康影响而引起了关注。在土壤中进行微塑料输运实验的一个特殊挑战是，土壤是一种高度复杂且不透明的多孔介质。因此，我们对微塑料在土壤中的输运机制及相关生态影响的认识仍然有限，而计算模拟为获得微塑料物理输运行为的重要见解提供了额外可能性。然而，在真实土壤结构中对宏观样品尺寸进行直接颗粒分辨模拟仍然是一个计算挑战。我们引入了一种高效的孔隙网络模型minD-PNM，该模型仅使用实验X射线微计算机断层扫描（$\mu$CT）孔隙几何作为输入，预测水饱和土壤中的颗粒穿透曲线。该模型将约$t^{-3}$的孔隙尺度输运时间分布与多入口多出口孔隙的最小耗散分配相结合，并通过孔隙网络传播这些分布以预测穿透曲线。我们将minD-PNM与针对小系统的格子玻尔兹曼和浸没边界模拟进行基准比较，并与针对宏观样品尺寸的石英沉积物中的微塑料柱实验进行比较。在两种情况下，预测的穿透曲线与相应的参考数据吻合良好。由于仅需要几何信息，minD-PNM可扩展到宏观样品体积，为颗粒污染物的污染物水文学研究提供了实用工具。
英文摘要
The environmental transport of microplastic particles has raised concerns because of their potential ecological and human-health impacts. A particular challenge for experiments on microplastic transport in soils is the fact that soil is a highly complex and non-transparent porous medium. Thus, our knowledge of transport mechanisms of microplastics in soils and related ecological implications is still limited and computational simulations offer an additional possibility to obtain important insights into the physical transport behaviour of microplastics. Yet, direct particle-resolved simulations in realistic soil structures for macroscopic sample sizes remain a computational challenge. We introduce an efficient pore-network model, minD-PNM, that predicts particle breakthrough curves in water-saturated soils using experimental X-ray micro-computed tomography ($μ$CT) pore geometries as the only input. The model combines an approximately $t^{-3}$ pore-scale transit time distribution with a minimum-dissipation partition of multi-inlet-multi-outlet pores, and propagates these distributions through the pore network to predict breakthrough curves. We benchmark minD-PNM against lattice Boltzmann and immersed boundary simulations for small systems, and against microplastic column experiments in quartz sediments for macroscopic sample sizes. In both cases, the predicted breakthrough curves agree well with the corresponding reference data. Because it requires only geometric information, minD-PNM scales to macroscopic sample volumes offering a practical tool for contaminant-hydrology studies of particulate pollutants.
