## 4 Discussion  
The fine micro-fractures and pores within intact granite were analyzed using a micro-CT analysis. This is an important issue for radionuclide migration in a safety assessment due to the long disposal period. Furthermore, the ADE experimental results indicated that the dispersion coefficients (\(D\)) and retardation factor (\(R\)) (depending on the porosity and dominant minerals, such as biotite) are responsible for the sorption of Se(IV) and Cs. In fact, it was found that the experimental data exhibited a poor fit using the equilibrium model, where all of the non-equilibrium models showed a better fitting result than the equilibrium model. On the other hand, the dispersion coefficients (\(D\)) tended to increase with column length because of higher dispersivity (\(\alpha\)), with a fixed flow rate or Darcy velocity. The increase in \(\alpha\) with increases in \(D\) may have been caused by an increase in the amount of immobile water, and hence by an increase in the diffusion length for the radionuclides from the mobile to immobile center [22]. Among all of the results, the retardation factor (\(R\)) of Cs showed a higher value than Se by the two-region and two-site transport models, which was in agreement with the findings of several other reports, because of more adsorption sites or frayed edge sites (FES) in biotite [29]. Scale-dependent dispersivity is very important for engineering designs in terms of providing appropriate dispersivity associated with the design scale. In this work, it should be noted that our laboratory study deals with relatively small column scales, with path lengths ranging from 2 to 8 cm and diameters of 5 cm, that are within an order of magnitude in centimeters. This small-scale heterogeneity may be observed in engineering studies due to the non-uniform packing and micro-structure of the materials. However, this is not very clear, and more experimental results should be provided indicating whether the relation of scale-dependent dispersivity can be extended to small-scale data such as the testing columns used in this study. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RN**} & \multirow{2}{*}{**No.**} & \(D\) & \(\theta\) & \(\alpha\) & \(R\) & \(\beta\) & \(\omega\) & \(SSQ\) & \(RMSE\) \\  & & **(cm2/min)** & **(-)** & **(cm)** & & & & & & \\ \hline \multirow{3}{*}{HTO} & Fit 1-2 & 0.0584 & 0.28 & 0.16 & 1.00 & 0.60 & 0.05 & \(6.89\times 10^{-3}\) & \(2.22\times 10^{-2}\) \\  & Fit 2-2 & 0.1454 & 0.24 & 0.35 & 1.00 & 0.52 & 0.08 & \(3.34\times 10^{-1}\) & \(9.77\times 10^{-2}\) \\  & Fit 3-2 & 0.2319 & 0.28 & 0.64 & 1.00 & 0.60 & 0.02 & \(2.77\times 10^{-2}\) & \(3.26\times 10^{-2}\) \\ \hline \multirow{3}{*}{Se(IV)} & Fit 4-2 & 0.0584 & 0.28 & 0.16 & 3.53 & 0.37 & 0.32 & \(3.47\times 10^{-2}\) & \(2.66\times 10^{-2}\) \\  & Fit 5-2 & 0.1449 & 0.24 & 0.35 & 2.44 & 0.58 & 0.29 & \(2.36\times 10^{-2}\) & \(2.19\times 10^{-2}\) \\  & Fit 6-2 & 0.2319 & 0.28 & 0.64 & 2.21 & 0.45 & 0.66 & \(6.44\times 10^{-2}\) & \(3.28\times 10^{-2}\) \\ \hline \multirow{3}{*}{Cs} & Fit 7-2 & 0.1958 & 0.28 & 0.54 & 14.30 & 0.07 & 0.40 & \(2.41\times 10^{-2}\) & \(2.22\times 10^{-2}\) \\  & Fit 8-2 & 0.3876 & 0.24 & 0.93 & 15.50 & 0.06 & 0.45 & \(1.34\times 10^{-2}\) & \(1.58\times 10^{-2}\) \\ \cline{1-1}  & Fit 9-2 & 0.5469 & 0.28 & 1.51 & 13.00 & 0.03 & 0.84 & \(3.01\times 10^{-2}\) & \(2.43\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fitting parameters for HTO, Se(IV), and Cs for the chemical non-equilibrium model.