# A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
## 1 Introduction
Laboratory tests and field demonstrations of ISCO technology began in the 1990s. The results were published in numerous articles, reports, and books (e.g., [5; 12; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]). In parallel to the experiments and based on their results, numerical simulations were developed. Most modelling studies dealt with the application of ozone in the vadose zone [36; 39; 40; 41; 42; 43] and permanganate in the saturated zone [23; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. Studies on the application of other oxidants are currently underway [55; 56; 57; 58; 59], but they are still quite experimental and difficult to model due to the complexity of the oxidation reactions and the lack of sufficient data that would be required for modelling. Laboratory experiments, followed by field experiments, provide information on the stoichiometry of ISCO reactions, their pathways, and rates. It is known that degradation processes generate intermediates and follow second-order kinetics [5]. However, practitioners and experts are aware of the discrepancy between the efficiency of laboratory-scale experiments and field experiments caused by various accompanying processes and phenomena such as heterogeneity, reactions with the aquifer matrix, sorption and rebound of COECs, etc. [19; 20; 60; 61]. It is known that the ISCO method is best suited for a homogeneous environment (sands, gravels, etc. ), but practitioners know that there is no such thing as a truly homogeneous site. Variability at the micro level leads to heterogeneity at the macro level, and the parameters that determine water flow can vary considerably within the same soil layer. Modelling should, therefore, incorporate soil heterogeneity as much as possible. In summary, understanding the reaction dynamics is a key factor in building an accurate control tool based on numerical modelling. The ISCO model should include the reactive transport of both pollutants and an oxidising agent, but also additional features that may include sorption/desorption processes responsible for the rebound of pollutants. Finally, the ISCO model should also take into account the competitive reaction between natural oxidant consumers and pollutants [62]. In the available literature, natural consumers are referred to as NOD (natural oxygen demand), and their chemical nature corresponds to complex organic compounds, e.g., phthalic acid (C\({}_{8}\)H\({}_{6}\)O\({}_{4}\)) [63], carbohydrate (CH\({}_{2}\)O) [64], or butenolides (C\({}_{7}\)H\({}_{8}\)O\({}_{4}\)) [65]. Although the models simulating permanganate oxidation are the most advanced, having been developed over decades, their source codes and conceptual models are often not accessible to potential users. This can be an advantage as we acquire new knowledge and tools over time and are not locked into existing solutions. ISCO models include the following processes: advective-dispersive transport, non-aqueous phase liquid (NAPL) dissolution, sorption/desorption of COECs, degradation of COECs using second-order reaction kinetics, and NOD oxidation using different approaches depending on reaction rates [65; 66]. As already mentioned, there are only a few ISCO models that can be tested, such as, for example, the 1D model CDISCO [66] and the 3D models CORT3D [67] and ISCO3D [45]. CDISCO is based on an Excel spreadsheet in which the user enters information about the aquifer properties (porosity, hydraulic conductivity, injection interval, NOD, contaminant concentrations, etc. ), the injection conditions (permanganate injection concentration, flow rate, and duration), and the target conditions (minimum oxidant concentration and duration to calculate the radius of influence--ROI). Based on the input data, the spatial distribution of permanganate, NOD, and contaminant concentration are calculated as a function of the radial distance at different points in time.