Stochastic modeling of contaminant reactions requires the definition of prior distributions for the respective rate constants. We use data from several experiments reported in the literature to better understand the distribution of pseudo-first-order rate constants of abiotic TCE reduction in different sediments. These distributions can be used to choose informed priors for these parameters in reactive-transport models.

Groundwater contamination with trichloroethylene (TCE) persists at many hazardous waste sites due to back diffusion from low-permeability zones such as clay lenses. In recent years, the abiotic reduction of TCE by reduced iron minerals has gained attention as a natural attenuation process, but there is uncertainty as to whether the process is fast enough to be effective. Pseudo-first-order rate constants have been determined in laboratory experiments and are reported in the literature for various sediments and rocks, as well as for individual reactive minerals. However, rate constants can vary between sites and aquifer materials. Reported values range over several orders of magnitude.

To assess the uncertainty and variability of pseudo-first-order rate constants, we compiled data reported in several studies. We built a statistical model based on a hierarchical Bayesian approach to predict probability distributions of rate constants at new sites based on this data set. We then investigated whether additional information about the sediment composition at a site could reduce the uncertainty. We tested two sets of predictors: reactive mineral content or the extractable Fe(II) content. Knowing the reactive mineral content reduced the uncertainty only slightly. In contrast, knowing the Fe(II) content greatly reduced the uncertainty because the relationship between Fe(II) content and rate constants is approximately log-log-linear. Using a simple example of diffusion-controlled transport in a contaminated aquitard, we show how the uncertainty in the predicted rate constants affects the predicted remediation times.@en

Fe(II) minerals can mediate the abiotic reduction of trichloroethylene (TCE), a widespread groundwater contaminant. If reaction rates are sufficiently fast for natural attenuation, the process holds potential for mitigating TCE pollution in groundwater. To assess the variability of abiotic TCE reduction rate constants, we collected pseudo-first-order rate constants for natural sediments and rocks from the literature, as well as intrinsic (surface-area-normalized) rate constants of individual minerals. Using a Bayesian hierarchical modeling approach, we were able to differentiate the contributions of natural variability and experimental error to the total variance. Applying the model, we also predicted rate constants at new sites, revealing a considerable uncertainty of several orders of magnitude. We investigated whether incorporating additional information about sediment composition could reduce this uncertainty. We tested two sets of predictors: reactive mineral content (measured by X-ray diffraction) combined with surface areas and intrinsic rate constants, or the extractable Fe(II) content. Knowledge of the mineral composition only marginally reduced the uncertainty of predicted rate constants. We attribute the low information gain to the inability to measure the (reactive) surface areas of individual minerals in sediments or rocks, which are subject to environmental factors like aqueous geochemistry and redox potential. In contrast, knowing the Fe(II) content reduced the uncertainty about the first-order rate constant by nearly two orders of magnitude, because the relationship between Fe(II) content and rate constants is approximately log–log-linear. We demonstrate how our approach provides estimates for the range of cleanup times for a simple example of diffusion-controlled transport in a contaminated aquitard.@en

Multiphase flows in porous media are important in many natural and industrial processes. Pore-scale models for multiphase flows have seen rapid development in recent years and are becoming increasingly useful as predictive tools in both academic and industrial applications. However, quantitative comparisons between different pore-scale models, and between these models and experimental data, are lacking. Here, we perform an objective comparison of a variety of state-of-the-art pore-scale models, including lattice Boltzmann, stochastic rotation dynamics, volume-of-fluid, level-set, phase-field, and pore-network models. As the basis for this comparison, we use a dataset from recent microfluidic experiments with precisely controlled pore geometry and wettability conditions, which offers an unprecedented benchmarking opportunity. We compare the results of the 14 participating teams both qualitatively and quantitatively using several standard metrics, such as fractal dimension, finger width, and displacement efficiency. We find that no single method excels across all conditions and that thin films and corner flow present substantial modeling and computational challenges.

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