Nanotechnologies have been proposed for a variety of environmental applications, including subsurface characterization, enhanced oil recovery, and in situ contaminant remediation. For such applications, quantitative predictive models will be of great utility for system design and implementation. Electrolyte chemistry, which can vary substantially within subsurface pore waters, has been shown to strongly influence nanoparticle aggregation and deposition in porous media. Thus, it is essential that mathematical models be capable of tracking changes in electrolyte chemistry and predicting its influence on nanoparticle mobility. In this work, a modified version of a multi-dimensional multispecies transport simulator (SEAWAT) was employed to model nanoparticle transport under transient electrolyte conditions. The modeling effort was supported by experimental measurements of paramagnetic magnetite (Fe₃O₄) nanoparticle, coated with polyacrylamide-methylpropane sulfonic acid – lauryl acrylate (nMag-PAMPS), mobility in columns packed with 40–50 mesh Ottawa sand. Column effluent analyses and magnetic resonance imaging (MRI) were used to quantify nanoparticle breakthrough and in situ aqueous phase concentrations, respectively. Experimental observations revealed that introduction of de-ionized water into the brine saturated column (80 g L⁻¹ NaCl + 20 g L⁻¹ CaCl₂) promoted release and remobilization of deposited nanoparticles along a diagonal front, coincident with the variable density flow field. This behavior was accurately captured by the simulation results, which indicated that a two-site deposition-release model provided the best fit to experimental observations, suggesting that heterogeneous nanoparticle–surface interactions governed nanoparticle attachment. These findings illustrate the importance of accounting for both physical and chemical processes associated with changes in electrolyte chemistry when predicting nanoparticle transport behavior in subsurface formations.