Predicting the Rate Coefficients of Attachment and Detachment of Colloids in Saturated Porous Media

Abstract

The transport behavior of pathogenic microorganisms and nanoparticles (NPs) in the subsurface is usually studied by performing laboratory soil column experiments. Parameters describing colloid deposition on grain surfaces are estimated by fitting observed breakthrough curves with an appropriate one-dimensional model. However, predictive tools to estimate colloid deposition parameters, knowing the system properties such as soil type, colloid type, solution chemistry, and flow velocity, are useful in estimating the travel distances of pathogenic microorganisms in the subsurface. Such predictive models are rare, except the colloid attachment rate coefficient predicted by colloid filtration theory (CFT) under favorable conditions. Although a couple of theoretical and empirical predictive models are available for estimating the deposition parameters under unfavorable conditions, they were developed for a small set of data, and their applicability to a wide range of conditions is unexplored. In this study, several sets of column-experimental data from literature, covering a wide range of experimental conditions, were analyzed to understand key factors that control colloid deposition. Empirical relationships were developed for deposition rate coefficients and sticking efficiency of various colloidal types [viruses, bacteria, graphene oxide (GO) NPs, silver (Ag) NPs, titanium dioxide (TiO2) NPs, and carboxylate-modified latex (CML) colloids] vis-à-vis 11 physicochemical parameters such as porosity, mean pore-water velocity, median grain size, colloid radius, solution ionic strength, surface potentials of colloids and grains, Hamaker constant, temperature, viscosity of water, and dielectric constant. While deposition of viruses and CML colloids on grain surfaces was found to be reversible, deposition of bacteria, GO NPs, Ag NPs, and TiO2 NPs was found to be irreversible. The empirical equations proposed in this study can predict deposition rate coefficients more closely (p