# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
* Preprocessing, data may need to be pre-processed to ensure that the input is free of missing values and anomalies before it is included in a model [496, 552, 553]. * Conceptual design, any analytical or numerical modelling should start from a concept. The conceptual model (or models) is based on the generalisation of the site-specific conditions and the physico-chemical processes involved in solute transport. The main problem of a deterministic approach arises precisely at this stage of modelling when trying to represent reality, as it is well known that data rarely represent complete phenomena and trends [554]. Conceptual uncertainties, for example, those related to model structure or inappropriate simplifications, have been identified as one of the main causes of uncertainty in deterministic modelling of fluxes and reactive solute transport, and this problem is receiving increasing attention [555, 556, 557, 558]. Ultimately, much is left to the creativity and experience of the model. This dependence on the human factor can be both a strength and a weakness of the model. Predictive or fate models are prone to immeasurable conceptual errors because the future is largely unknown and based on projections. However, predictive models can provide feedback and data that was not previously known [559] and can subsequently be used to create different (optimistic and pessimistic) scenarios [560]. In addition, predictive models contribute to continuous learning and adaptation, as models can be updated and improved over time as more data become available [561]. * When selecting or developing one or more numerical models, it is difficult to give guidelines due to the subjective nature of the conceptual model, but it should be possible to put the concept into a solvable, usually numerical, form. Once the expert has an overview of the processes and the desired level of accuracy or, as the modelers would say, model complexity, they need to decide which numerical tool is best suited to their needs. Model complexity is again a term for which there is no clear definition [562]. However, the study by Baartman et al. [563] agrees that model complexity takes into account the number of explicitly included processes and feedback loops. The size and completeness of the data set should also determine the level of complexity. It is also important to adhere to the principle of model parsimony, i.e., reflecting variability but not leading to overfitting, which is often the case with complex models. It is also recommended to start with a simple model and carefully increase the complexity [564, 565, 566]. However, users must be aware that oversimplification can also be a problem, as a very simple model can also have detrimental consequences for the prediction of future system behaviour [567]. Furthermore, adapting the cross-checking approach, i.e., constructing simple (surrogate, emulators) and complex models, could support future decisions [567, 568, 569, 570, 571, 572];
* Discretisation, when choosing a numerical model, the user must consider the issue of discretisation of the domain. Numerical deterministic models of reactive transport must be discretised due to the principle of conservation of mass. For one-dimensional models, this is comparable to dividing the domain into a chain of tank reactors. There are two common methods of spatial discretisation: finite differences, and finite elements. Unstructured geometry (finite element method) should be used where the consideration of heterogeneity has the greatest impact on flow and mass transfer, as it allows the user to adjust the resolution if necessary without having to refine the entire model. However, the first choice for modelling reactive transport is usually structured, as it is easier to implement biogeochemical processes in the model and mass-conservative calculations are available [573].