# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 3 Results and Discussion
### Comparison of Measured and Estimated Linear \(K_{\text{d}}\) Values of Soil Samples  
The distribution of PCE between the solid and liquid phases was characterized using adsorption isotherms. Table 2 provides the comparison of measured and estimated linear \(K_{\text{d}}\) values for all samples, along with their respective coefficients of determination (\(R^{2}\)) and least squares values. The linear sorption model (\(K_{\text{d}}=K_{\text{OC}}\times f_{\text{OC}}\)) was employed to estimate the \(K_{\text{d}}\) values in soil samples [45; 46]. The measured \(K_{\text{d}}\) values for PCE ranged from 0.38 to 2.27 L kg\({}^{-1}\) across different soil samples. Lower \(K_{\text{d}}\) values were observed in sand-dominated samples, while higher values were associated with clay-rich materials. These findings are consistent with previously reported \(K_{\text{d}}\) values for PCE in similar studies [47], highlighting the influence of soil composition on sorption behavior. Although the PCE sorption isotherms were slightly non-linear, a common occurrence in soils with low OC content (<1%) and high clay content, the differences in sum of squares and \(R^{2}\) were minimal. The measured \(K_{\text{d}}\) values were generally higher than their corresponding estimated values. This trend suggests that empirical sorption models may underestimate  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{\begin{tabular}{c} **Site of** \\ **Origin** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Sample.** \\ **Depth** \\ **(m)** \\ \end{tabular} } & \multirow{3}{*}{**Soil Texture**} & \multicolumn{6}{c}{**Distribution of Soil**} & \multicolumn{6}{c}{**Elemental**} & \multirow{3}{*}{
\begin{tabular}{c} **SSA** \\ **(\%)** \\ \end{tabular} } \\  & & & & **Particles \(\leq\) 2 mm** & & & & & **CO** & **Si** & **Al** & \\ \cline{4-13}  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\(\mathbf{m}^{2}\) g\({}^{-1}\))** \\ \hline
32 & Holesov & 6.50 & sandy loam & 15.5 & 24.2 & 60.3 & 2.43 & 1.33 & 45.2 & 0.10 & 35.62 & 7.86 \\
33 & Holesov & 3.00 & silly clay & 40.0 & 40.0 & 20.0 & 2.45 & 1.25 & 49.2 & 0.22 & 34.20 & 11.86 & 35.81 \\
34 & [PERSON] & 6.00 & sandy loam & 11.7 & 35.1 & 53.2 & 2.60 & 1.25 & 51.8 & \textless{}0.05 & 36.94 & 7.28 & \\ \hline \hline \end{tabular} Note: Clay, Slit, Sand mean clay, silt, sand particles content in samples (\(w/w\%\)). \(\rho_{w}\), \(\rho_{w}\), \(\epsilon\) is soil particle density, dry bulk density and porosity. Si and Al mean elemental silicon and aluminum content in samples (\(w/w\%\)).  
\end{table}
Table 1: _Cont._\(K_{\rm d}\) values [48; 49]. This indicates that factors other than OC, such as clay content or SSA, might also significantly influence sorption in these soils. A paired sample t-test showed no significant difference between them (t(33) = 1.056, \(p\) = 0.299) [50]. While this indicates that the linear predictive model maintains reliability in estimating \(K_{\rm d}\) values, the result from MAPE (45.6) suggests that empirically estimated \(K_{\rm d}\) values may underestimate the sorptive behavior of soils.