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Description
Groundwater contamination remains a significant environmental challenge, necessitating the development of advanced remediation strategies. One promising approach involves the injection of nanomaterials, such as nano-sized zero-valent iron (nZVI) or colloidal activated carbon, to degrade or immobilize contaminants in situ. The success of nanoremediation hinges on quantitative understanding of nanoparticle transport under geochemical conditions which may promote coagulation by accident or design. Within porous media, nanoparticles tend to undergo complex interactions, including coagulation after particle–particle collisions, leading to aggregation and deposition onto the solid–fluid interface. These interactions directly influence their mobility and retention, with potential implications for permeability alterations caused by pore clogging. A comprehensive understanding of these coupled mechanisms is essential for improving the design and implementation of nanoremediation strategies.
This study aims to develop a pore network modeling (PNM) framework to simulate the transport and aggregation of unstable nanoparticles within a computer-generated porous medium. By incorporating the Smoluchowski coagulation model, the framework captures particle–particle interactions governing aggregation, while also considering particle–collector interactions that govern attachment and deposition on solid surfaces. The effects of ionic strength on both aggregation and deposition processes are explicitly examined. To capture the influence of aggregation on deposition, the collector contact efficiency is determined as a function of aggregate size and local pore-scale hydrodynamic conditions, using a neural-network model trained on pore-scale numerical simulations (Lin et al., 2022). Ionic strength regulates particle–particle collision efficiency, such that higher ionic strength enhances aggregation and promotes deposition. Furthermore, differences in the transport and retardation of dissolved salts and nanoparticles cause their concentration fronts to propagate at different velocities within the porous medium, leading to spatially heterogeneous aggregation and deposition zones.
The insights gained from this research will contribute to the advancement of pore-scale modeling techniques for nanoparticle transport and retention. By refining predictive capabilities, this study will support the optimization of nanoremediation strategies, ensuring the effective delivery and dispersion of reactive nanoparticles in contaminated groundwater systems. The results will provide valuable guidance for environmental engineers and researchers working to develop more efficient and sustainable remediation technologies.
| References | Lin, D., Hu, L., Bradford, S.A., Zhang, X., & Lo, I.M.C. (2022). Prediction of collector contact efficiency for colloid transport in porous media using pore-network and neural-network models. Separation and Purification Technology, 290, 120846. |
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| Country | Canada |
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