Abstract
Batch and column experiments were conducted to investigate sorption and transport of uranium (U) in
the presence of saprolite derived from interbedded shale, limestone, and sandstone sequences. Sorption
kinetics were measured at two initial concentrations (C0; 1, 10 mM) and three soil:solution ratios (Rs/w;
0.005, 0.25, 2 kg/L) at pH 4.5 (pH of the saprolite). The rate of U loss from solution (mmole/L/h) increased
with increasing Rs/w. Uranium sorption exhibited a fast phase with 80% sorption in the first eight hours
for all C0 and Rs/w values and a slow phase during which the reaction slowly approached (pseudo)
equilibrium over the next seven days. The pH-dependency of U sorption was apparent in pH sorption
edges. U(VI) sorption increased over the pH range 4e6, then decreased sharply at pH > 7.5. U(VI) sorption
edges were well described by a surface complexation model using calibrated parameters and the reaction
network proposed by Waite et al. (1994). Sorption isotherms measured using the same Rs/w and pH
values showed a solids concentration effect where U(VI) sorption capacity and affinity decreased with
increasing solids concentration. This effect may have been due to either particle aggregation or
competition between U(VI) and exchangeable cations for sorption sites. The surface complexation model
with calibrated parameters was able to predict the general sorption behavior relatively well, but failed to
reproduce solid concentration effects, implying the importance of appropriate design if batch experiments
are to be utilized for dynamic systems. Transport of U(VI) through the packed column was
significantly retarded. Transport simulations were conducted using the reactive transport model
HydroGeoChem (HGC) v5.0 that incorporated the surface complexation reaction network used to model
the batch data. Model parameters reported by Waite et al. (1994) provided a better prediction of U
transport than optimized parameters derived from our sorption edges. The results presented in this
study highlight the challenges in defining appropriate conditions for batch-type experiments used to
extrapolate parameters for transport models, and also underline a gap in our ability to transfer batch
results to transport simulations.
