# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 4 Discussion and Conclusions  
The decision-making process at the outset of modelling involves choosing between a deterministic or stochastic approach. Traditionally, these approaches were viewed as competing strategies [744], but contemporary practice recognizes their potential as complementary tools across various stages of a remediation project. This acknowledgment stems from the understanding that reality falls between complete determinism and total randomness [745]. Thus, selecting a modelling framework becomes an exercise in compromise, even among scientific circles. Deciding whether to adopt a simpler model, grounded in expert knowledge, or opting for a more intricate approach involving multiple models presents a challenge. Some experts advocate for simpler solutions due to their accessibility to end-users, who may lack hydrological or programming expertise [564], while others advocate for more complex models [746]. A relatively recent framework gaining traction is the hybrid approach, which combines surrogate emulators with more intricate models [572]. It is crucial for potential users to recognize that even simple screening models can be potent and suitable for practitioners [747]. Moreover, the pivotal step of conceptual modelling and prioritizing remediation objectives is often overlooked by newcomers, leading to potentially unrealistic outcomes. In the decision-making process regarding the choice of modelling approach, a boundary between science and practice undoubtedly emerges. The modeler must consider the preferences of decision-makers, who often favour simple and transparent tools. They may only resort to more complex and inherently costlier models when supported by experts capable of ensuring credibility and quality assurance. In theory, the expectations of policy makers and environmental managers may be lower compared to those of scientists. However, this dynamic could shift if science uncovers difficult problems or unexpected phenomena that are challenging to address [748]. This underscores the importance of collaborative efforts in assessing contaminated sites and their subsequent remediation using numerical tools. Dialogue between stakeholders and modelers is essential to establish a systematic framework supporting management decisions [749; 750]. An enhanced understanding of the processes underlying the conceptual model and the code is crucial for achieving better results. Therefore, users and beneficiaries of environmental modelling should be actively engaged throughout the modelling process from conception to validation. Selecting appropriate modelling tools demands experience, creativity, knowledge, and, at times, intuition. To meet the expectations of stakeholders, a modeler must possess not only a deep understanding of the model itself, but also the methodologies for its validation. Paradoxically, forecasting the future of modelling reactive transport presents challenges. Simple models are expected to remain essential due to their usefulness and the ease with which their elegant analytical solutions can describe extensive systems. Additionally, they can be readily adjusted for new tasks and integrated with more advanced tools, such as machine learning models [751]. Deterministic models, still predominant, are likely to persist, particularly in situations requiring process-based understanding and considerationof complexity [752]. However, stochastic approaches may offer greater reliability for long-term solute fate predictions, particularly in heterogeneous media with varying saturations. Consequently, stochastic models or their hybrid variants are anticipated to gain traction. In many instances, stakeholders versed in pollution risk assessment will prefer a range of probable values with an acceptable level of confidence. The accessibility of data and open-source solutions will undoubtedly facilitate the development of new models, with machine learning techniques and emulators increasingly applied in pollution transport studies at the operational and field levels.