## 3 Results and Discussion  
Reproduction of Concentration of 1,4-Dioxane Groundwater Pollution in Monitoring Well and Distribution Prediction  
A comparison between the reproduced 1,4-dioxane concentrations in each monitoring well using a model that considered groundwater flow (Figure 1) and the field monitoring data is shown in Figure 2. Between 2013 and 2017 (4 years), the reproduced data and monitoring data for all monitoring wells generally exhibited good fitting results. In 2013, both reproduced data and observed data showed relatively higher 1,4-dioxane concentrations in monitoring wells 1, 2, and 3 for Source 1, and monitoring well 6 for Source 2 than in the other wells. Significant decreases in 1,4-dioxane concentration were observed from 2014, when the excavation was completed. The good fitting results suggest that predicting the 1,4-dioxane concentrations by considering groundwater flow is possible due to its water-soluble characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol in Figure 1** &
\begin{tabular}{c} **Pumping Rate** \\ **[m\({}^{3}\)/day]** \\ \end{tabular} & **Reference** \\ \hline Monitoring well 1 & 1 & 250 & \\ Monitoring well 2 & 2 & 250 & \\ Monitoring well 3 & 3 & 250 & \\ Monitoring well 4 & 4 & 250 & \\ Monitoring well 5 & 5 & 250 & \\ Monitoring well 6 & 6 & 250 & \\ Monitoring well 7 & 7 & 250 & \\ Monitoring well 8 & 8 & 250 & \\ Monitoring well 9 & 9 & 250 & \\ Monitoring well a & a & 250 & \\ Monitoring well b & b & 250 & \\ Monitoring well c & c & 250 & \\ \hline Pumping well 1 & P1 & 10 & \\ Pumping well 2 & P2 & 31 & [35] \\ Pumping well 3 & P3 & 23 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pump specifications by active treatment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Pollution Area [m\({}^{2}\)]** & **Amount of Source [kg]** \\ \hline Source 1 & 2500 & 600 \\ Source 2 & 1500 & 20 \\ Source 3 & 2500 & 30 \\ Source 4 & 4500 & 15 \\ Source 5 & 400 & 30 \\ \hline \hline \end{tabular} These values were assumed based on fitting by the model.  
\end{table} 
Table 3: Assumed sources of contamination at the site. It should be noted that in terms of short periods of time such as each year, the reproduced 1,4-dioxane concentration did not fit well with the monitoring data. All monitoring wells, especially monitoring wells 1, 2, and 3, demonstrated highly fluctuating  
Figure 2: Comparison between the simulation results and observed data of 1,4-dioxane [26]. Red line means the excavation removal completed at the site (2014). 1,4-dioxane concentrations each year. These fluctuations may be related to the complex geological structure of the site and dynamic environment such as seasonal rainfall or artificial activities, for example, pumping wells, impermeable walls, and excavation work, which may have increased the complexity of the groundwater environments. However, the difference between the monitoring data and reproduced data over short periods does not influence their consistency in long-term trends; therefore, this model can be used to predict 1,4-dioxane concentrations and distribution. Using the reproduced 1,4-dioxane concentrations shown in Figure 2, the distribution of 1,4-dioxane in the groundwater of the study area over 25 years (1995-2020) is shown in Figure 3. The GL changes over time are also illustrated in the figure (light blue). In 1995 and 2000, the flow of groundwater containing pollutants was generally divided into two directions depending on the groundwater divide (northwest to southeast) of this dumping site area. From 2005, an impermeable wall was constructed along the border between the two prefectures to stop the movement of groundwater as well as the pollutants within it. As a result, the groundwater contours shown in Figure 4 overlapped along the impermeable wall, and the flow direction near this area changed from north to south. As expected, 1,4-dioxane was trapped on the east side of the impermeable wall to some extent.