Environmental models and software are essential tools for understanding the complex interactions of the natural world. They empower us to foresee potential futures, unravel intricate trends and expand our scientific knowledge, ensuring we make informed decisions for a sustainable future. This includes environmental emissions and exposure models, which tell us how chemicals and other potential pollutants enter, move around and behave in the environment. To achieve accurate, efficient, collaborative and integrative insights into this pollution and its sources, it is imperative that our models and software keep pace with scientific and technological advances, the increasing availability of data and a heightened importance on assessing complex, interconnected systems under a changing climate. But often our models follow outmoded programming paradigms and technological setups that makes this difficult. They are often monolithic codebases rather than flexible, standalone modules, making them difficult to adapt to emerging risks or integrate with other models to predict, e.g., the societal drivers and One Health impacts of pollution. Furthermore, modelling efforts often overlook ethical and sustainability issues, like the carbon footprint of running complex simulations or the societal impacts of using model predictions to inform policy. These considerations framed a workshop that took place in October 2024, bringing together interdisciplinary environmental modellers from around the world to discuss the question: what does the next generation of environmental models look like?
The focus was interactive sessions, where participants discussed this question by referering to six pillars: Software engineering and collaborative platforms; Interdisciplinary learning; Cloud-based and exascale computing; Citizen science; Artificial intelligence, and; Big data and better monitoring. In this presentation, we reflect on the outcomes, placing them in the context of emissions and exposure modelling.
The workshop was part of broader efforts to build an international community of practice around environmental modelling. A priority identified is that training, education and knowledge transfer are vital to ensuring that we empower the next generation of environmental modellers, as well as the models themselves, and we hope this community will provide a space to enable this.

This study presents a 3D process-based morphodynamic model that uses orthogonal unstructured grids. It is designed for coastal applications involving complex bathymetry and varying spatial scales. The model extends the Delft3D-FM framework by incorporating full 3D representation of wave, wind, and density-driven effects in the short-wave-averaged, non-linear shallow water equations. The framework includes expressions for wave and roller effects on flow forcing, turbulence, and bed shear stress, and integrates sediment transport and morphodynamic feedback. Multi-fraction sediment transport is supported, and the model tracks stratigraphy through a layered bed composition framework. Features such as infragravity wave dynamics, sediment mass slumping, swash zone slope nudging and morphological acceleration techniques are incorporated to better capture long-term morphological trends as well as storm erosion. The framework supports in-memory model coupling and is fully parallelized, enabling efficient, large-scale simulations. Model verifications presented here include analytical benchmarks and comparisons with laboratory and field observations, demonstrating reliable reproduction of wave–current interaction, sediment transport rates, and bed level changes. The model has the potential to bridge the gap between high-resolution event-scale modelling and long-term morphodynamic prediction, offering a flexible framework to study coastal sedimentary dynamics.

This paper presents a fast consolidation model suitable for long-term morphodynamic simulations. This model is applicable for muddy systems where sedimentation rates are smaller than consolidation rates, assuming quasi-equilibrium of the consolidating bed. It compares to the consolidation model developed by Sanford (2008). However, in that model, a heuristic, exponential density profile was used. Instead, the current model is derived from the full consolidation (Gibson) equation. The model’s material parameters (hydraulic conductivity, consolidation coefficient and strength) can therefore be derived from soil mechanical experiments in the laboratory.