# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models  
Deterministic approaches to the flow and transport of solutes assume that the parameters responsible for the movement of water and solutes are known and identifiable. If the parameters are known, their spatial distribution can be outlined and their heterogeneity can be captured. This is a classical extrapolation method that assumes that the heterogeneity can be deterministically captured by interpreting all field data. The main work that precedes deterministic modelling is therefore the identification of the parameters and the collection of all information about them from available sources and from experts in the field. The collaboration of many experts from different disciplines and the approach that everything can be defined give the nice aspects of multidisciplinary and certainty. And it is only natural that people demand certainty. For example, probability predictions of rainfall were received with scepticism because people wanted to know if it was going to rain, not how likely it was to rain [541]. This could be one explanation for the widespread use of deterministic approaches. Our society and science in general rely on deterministic technical systems. For example, the emails we receive are the emails that were sent, the files on our computers are the files we put there, and we can be sure it is nothing else. This is because we expect correct behaviour for a certain input pattern [542, 543]. This certainty can be a blessing in a highly mechanised world where we rely on accurate responses. Surprisingly, determinism even exists in quantum physics [544] and in AI algorithms [545, 546]. The deterministic paradigm of ordinary differential equations (ODEs) is undoubtedly highly valuable, as it adheres to the law of mass action and serves as a mathematical foundation for solute transport models. Employing a deterministic solution offers numerous benefits, foremost among them predictability and repeatability [543]. Advocates of deterministic models cite their user-friendly nature, richness, and the availability of graphical user interfaces, technical support, and detailed manuals as key advantages. Moreover, deterministic models are generally easier to comprehend and interpret, they boast efficiency, and have a proven track record in numerous environmental studies. Notably, the relationship between input and output parameters does not require governing equations [547]. The visualisation of results, often in the form of animations--a common feature in deterministic software--can also be more engaging for decision-makers and stakeholders compared to outcomes of probabilistic representations [548]. The use of deterministic models is not always possible or advisable, especially when unknown or unknowable parameters are crucial for the model [542]. Indeed, the main disadvantage of determinism is that it cannot deal with uncertainty, and additive uncertainty is analysed using probabilistic methods. Many deterministic codes are equipped with intuitive user interfaces, and the untrained modeler starts with the construction of the numerical form rather than the assumptions of the conceptual model. Furthermore, the mathematical core is hidden in the computer code, which is inaccessible or difficult to access for the average user. There is no strict and fast scheme for building an accurate deterministic solute transport model. However, most should meet the following requirements and rules:  
1. When collecting all available data, pay particular attention to the patterns of spatial variability of the controlling parameters (e.g., hydraulic conductivity and/or permeability) and the scarcity of the data, as this determines the complexity of the model. It is also important to assess the representativeness of the available samples. High-quality input data play a key role in ensuring the accuracy and reliability of predictions and effective decision-making [549, 550, 551].