Speaker
Description
Modeling reactive transport in porous media is inherently affected by uncertainty.
Uncertainty in mechanistic models primarily stems from our limited knowledge of geochemical reaction pathways and (often site-specific) underlying processes, giving rise to structural model uncertainty, as well as from our limited understanding of how spatially heterogeneous hydrogeological properties of porous media (resulting in complex flow fields and heterogeneous redox conditions) control the spatiotemporal evolution of species concentrations and system reactivity. An additional challenge emerges from the difficulty in identifying appropriate values (or plausible ranges) for geochemical model parameters, whose variability is often site- and scale-dependent and may span several orders of magnitude, a situation that is typically exacerbated by the lack of sufficiently high quality/quantity experimental data.
In this work, we apply a suite of modern stochastic modeling tools to characterize reactive transport of pollutants and redox-active chemicals in porous media, while providing a comprehensive assessment of the role of uncertainty on predicted concentration fields and redox patterns. Our adopted stochastic modeling framework rests on ensemble-based Monte Carlo simulations to explicitly account for uncertainty in flow and reactive transport parameters, subsurface properties, and modeling assumptions. By exploring a broad range of plausible representations of the system, our approach moves beyond deterministic best-fit solutions and enables a probabilistic assessment of variability in flow and reactive transport processes in the subsurface.
Overall, our results emphasize the value of stochastic modelling approaches for uncertainty-aware interpretation and prediction of the fate of pollutants in porous media. Our methodology is general and transferable, and can be applied to a wide range of reactive solutes and experimental or environmental settings across multiple scales of analysis.
| Country | Spain |
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