# A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
## 3 Results
### Testing the ISCO Modelling Tool
#### 3.2.4 Calibration of the Geochemical Model  
The uncertainties of the model arise in the design phase, which is when the entire remediation plan must be encapsulated in a mathematical formulation and the response of the system to the input of the oxidising agent must be taken into account. In addition, there are two main sources of uncertainty in geochemical modelling: the choice of thermodynamic database or kinetic data and the choice of secondary minerals used in the model [87; 88; 89]. In the elaborated model, the definition of mineral composition was not the case as the dissolution and precipitation of minerals was not simulated. However, an initial uncertainty in the data was present because the oxidation reaction was kinetically controlled and the rates for each component subjected to oxidation must be specified. The uncertainties in the conceptual work phase were reduced by the knowledge of experts and practitioners, while the calibration of the model helped to reduce the uncertainties in the kinetic data. The PHRREQ model could be calibrated/fitted to the following: (i) field data obtained during periodic monitoring, (ii) the laboratory test results and the literature data at, for example, the ISCO design stage, and (iii) real-time monitoring data. The model presented here combines all types of model validation. First, the model was developed using the available literature data, and the kinetic data controlling the dynamics of the oxidation process of both NOD and COECs were necessary and extremely important for building the reactive model. The model was then validated using real-time measurements and periodic chemical analyses at the ISCO injection site. The real-time measurements included several parameters: pH, temperature, and the oxidation-reduction potential. As ISCO is very dynamic and, in some cases, unpredictable, the fitting of these parameters to the modelled values was based on a similar response to the arrival of the oxidant rather than on accuracy measures. Plots generated by the tool were used to visually check the observed and measured values. For example, Figures 11 and 12 show a comparison of the redox potential and pH measured by loggers in monitoring wells, and it was simulated by the hydrogeochemical model. The general patterns of chemical processes were reproduced with satisfactory accuracy. In some cases, due to soil heterogeneity, the propagation of oxidants in a real injection was delayed compared to the simulated one. However, on long time scales (i.e., weeks, months) such shifts were of minor importance. Of course, in some wells, due to very invasive conditions, the sensors showed abnormal values of some parameters, as described in Section 3.2.2. This was reported to the operators, and the equipment was subsequently checked. In addition, and more importantly, the model reflected with good accuracy the concentrations of the COECs immediately after injection and during the post-injection phase, when the rebound of contaminants can occur. The modelled values of the three BTEXs, toluene, benzene, and ethylbenzene, were compared with observations made over a few days, at one month, and at two months after injection. The measured values were successfully matched to the modelled values in a calibration that included changes in the contaminant concentrations in the micropores and the reaction rates of the BTEX oxidation. The latter was performed using the available literature sources. It should be noted that the injection conditions deviated from the ideal and controllable conditions of the laboratory experiment. During the recognition of the geological conditions, it turned out that, within the aquifer, there were layers of more impervious soils, which can be the secondary sources of contaminants when desorption occurs, and it was also noted that the heterogeneity of the matrix could affect the movement of the oxidant.