We compare the ability of various continuum-scale models to reproduce the key features of a transport setting associated with a bimolecular reaction taking place in the fluid phase and numerically simulated at the pore-scale level in a disordered porous medium. We start by considering a continuum-scale formulation which results from formal upscaling of this reactive transport process by means of volume averaging. The resulting (upscaled) continuum-scale system of equations includes nonlocal integro-differential terms and the effective parameters embedded in the model are quantified directly through computed pore-scale fluid velocity and pore space geometry attributes. The results obtained through this predictive model formulation are then compared against those provided by available effective continuum models which require calibration through parameter estimation. Our analysis considers two models recently proposed in the literature which are designed to embed incomplete mixing arising from the presence of fast reactions under advection-dominated transport conditions. We show that best estimates of the parameters of these two models heavily depend on the type of data employed for model calibration. Our upscaled nonlocal formulation enables us to reproduce most of the critical features observed through pore-scale simulation without any model calibration. As such, our results clearly show that embedding into a continuum-scale model the information content associated with pore-scale geometrical features and fluid velocity yields improved interpretation of typically available continuum-scale transport observations.

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Bacterial metabolisms using electron acceptors other than oxygen (e.g., methanogenesis and fermentation) largely contribute to element cycling and natural contaminant attenuation/mobilization, even in well-oxygenated porous environments, such as shallow aquifers. This paradox is commonly explained by the occurrence of small-scale anoxic microenvironments generated by the coupling of bacterial respiration and dissolved oxygen (O 2 ) transport by pore water. Such microenvironments allow facultative anaerobic bacteria to proliferate in oxic environments. Microenvironment dynamics are still poorly understood due to the challenge of directly observing biomass and O 2 distributions at the microscale within an opaque sediment matrix. To overcome these limitations, we integrated a microfluidic device with transparent O 2 planar optical sensors to measure the temporal behavior of dissolved O 2 concentrations and biomass distributions with time-lapse videomicroscopy. Our results reveal that bacterial colony morphology, which is highly variable in flowing porous systems, controls the formation of anoxic microenvironments. We rationalize our observations through a colony-scale Damkoḧler number comparing dissolved O 2 diffusion and a bacterial O 2 uptake rate. Our Damkoḧler number enables us to predict the pore space fraction occupied by anoxic microenvironments in our system for a given bacterial organization.

We focus on the joint application of local and global sensitivity analyses to characterize propagation of model parameter uncertainties to outputs of subsurface water geochemical models. The latter typically involve uncertain inputs, including environmental conditions, mineral rock composition, and flow/transport features. In this context, implementation of sensitivity analysis techniques enables us to grasp the relative role of each model input. Here we focus on the application of several sensitivity approaches to the assessment of Cr (VI) geogenic leakage due to water‐rock interactions. We specifically target the impact of uncertain environmental conditions on the chemical composition of spring waters following water transfer through a host rock system with given mineral composition. We employ a reaction path modeling approach and represent uncertainties of environmental conditions through three parameters, that is, oxygen fugacity (fO2), CO2 fugacity (fCO2), and temperature, which we consider as random quantities. We consider three diverse methodologies, that is, (a) the Scatter Plots sensitivity analysis, (b) the Distributed Evaluation of Local Sensitivity Analysis, and (c) a moment‐based global sensitivity analysis. Our results suggest that (a) the relative importance of a given model parameter in driving the uncertainty of the spring water composition may display remarkable variations across the sampled parameter space and (b) parameter ranking through sensitivity metrics for geochemical applications in subsurface water resources requires a joint assessment of local and global sensitivity.

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