# A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
## 2 Model Concept
### Visualisation of Data (A3)  
The visualisation of groundwater flows and concentration changes over time play an undeniable role in better understanding the fate of pollutants. Plots, diagrams, graphs, and maps help to make an optimal decision in the treatment plan by showing the spatio-temporal changes in important parameters [76; 77; 78]. They can enable important discoveries, help with clean reporting, and reduce risks [79]. In the described tool, the following types of data can be used to show the efficiency of the remediation:  
* Parameters measured continuously with data loggers and CT2X(tm) (TempHion(tm) (Seametrics, Seattle, WA, USA) have been tested, but others can also be connected to the system). These sensors provide real-time information on temperature, Eh, pH, and conductivity;
* Parameters that are regularly measured in the field and linked to the modelling tool in the form of a separate Excel file. This dataset can contain a wide range of parameters such as pH, pE, temperature, electrical conductivity, the concentrations of COECs and background substances, the concentration of oxidising agents, etc. These data can be used to visualise the status of the remediation process; \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range [Source]** \\ \hline Temperature & Above 15 \({}^{\circ}\)C (10–15 \({}^{\circ}\)C is a typical for ambient water [5]) \\ pH & 2.5–12.5 [5] \\ Eh & +200 to +600 mV [75] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranges of the basic parameters expected during the ISCO process. * Parameters calculated with the hydrogeochemical model, such as the values of pH, pE, and temperature, as well as the concentrations of COECs and the oxidising agent, in the investigated case permanganate. All of the modelling results can be visualised as follows:
* Changes in parameters over time for a given distance between the injection site and the monitoring well;
* Changes in the parameters along the oxidant flow path at a given time;
* Comparison of the measured and calculated basic physico-chemical parameters such as pH, Eh, and temperature. Therefore, while the vertical axis represents the concentrations or values of the selected parameter(s), the horizontal axis can represent the following:  
* Distance to the injection point for the time specified by the user;
* The travel time from the start of the injection to the current time (or to the last data recorded by the loggers) for each point on the path of the injected fluid flow;
* Travel time from the start of the injection to the current time (or the last recorded data) for an observation well. This option allows two types of data to be compared in one graph: values calculated with the PHREQC model and the values measured by data loggers. Only three parameters (temperature, pE, and pH) can be displayed with this option as they are provided by both the model and the loggers. Once the user has specified the visualisation options, a diagram (or a series of diagrams) is generated and saved in a separate worksheet. Together with the generated charts, a table with the displayed data is also created. In addition to the XY diagrams, the tool also has a function for displaying the spatial distribution of the modelled and measured parameters. The imported data are visualised using an IDW method (Inverse Distance Weighting) [80], which is based on Python code and the associated libraries [81; 82]. The IDW method is suitable for multivariate interpolation. The concept is based on the assumption that the attribute value of an unsampled point is the weighted average of the known values in the neighbourhood [83]. Therefore, values are assigned to the unknown points by using values from a scattered set of known points. The value at the unknown point is a weighted sum of the values of N known points. This method is often used when analysing the distribution of different spatial data [84; 85; 86].