Pore-Scale Modeling of Microplastic Transport in Saturated Porous Media Using an Optimized Eulerian–Lagrangian Framework

Microplastics (MPs; plastic particles ≤ 5 mm) are emerging contaminants of global concern due to their persistence and tendency to accumulate across ecological systems.The frequent presence of small MPs in subsurface environments is attributed to their ability to migrate through saturated porous media, governed by complex interactions between particle properties, and porous structure. Traditional advection–dispersion–reaction models struggle to represent these processes, as they rely on simplified assumptions and require extensive parameter tuning to account for the particles-pore interactions.These limitations underscore the need for physics-based, particle-resolving approaches that directly simulate multiphase interactions at the pore scale.

We present an optimized Eulerian–Lagrangian multiphase CFD model to simulate MP transport through saturated porous media. The fluid phase is resolved by solving the filtered Navier–Stokes equations on a staggered grid, while the porous matrix is represented as a fixed array of spherical grains with known porosity. No-slip boundary conditions on the grains are enforced using a computationally efficient smooth-transition volume penalization method, validated against experimental velocity profiles in packed beds of uniform glass beads.

MPs are tracked as Lagrangian particles governed by Newtonian dynamics. Inter-particle and particle–grain collisions are resolved using a soft-sphere spring–dashpot contact model incorporating friction and restitution. Simulations investigate MPs of varying size and density (positively, neutrally, and negatively buoyant) across different porous media packings. The model captures key transport phenomena, including gravitational settling, mechanical filtration, and physical clogging. Results reveal that particle-to-pore size ratio and density contrast strongly influence MP retention and breakthrough. This physics-based framework provides a scalable and extensible tool for advancing our understanding of MP fate in porous media and bridging the gap between pore-scale transport and environmental-scale prediction.