Model-Based Interpretation of Groundwater Arsenic Mobility during in Situ Reductive Transformation of Ferrihydrite

Abstract

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Integrating Process‐Based Reactive Transport Modeling and Machine Learning for Electrokinetic Remediation of Contaminated Groundwater

“…An increasing use of surrogate models in the field of groundwater contamination and remediation would help lowering the technical barriers in the simulation and increase the trust in technologies that are currently considered very difficult to predict due to the complex interplay between several physical and biogeochemical processes. In fact, complexity is a key feature of subsurface systems where inherently coupled flow, mass transfer processes, chemical and biological reactions control the fate of contaminants (Battistel et al., 2021; Fakhreddine et al., 2016; Guo et al., 2020; Li, 2019; Prommer et al., 2019; Rathi et al., 2017; Steefel et al., 2015; Stolze et al., 2019a, 2019b) and the efficiency of in situ remediation technologies (Ni et al., 2015; Piscopo et al., 2013; Sookhak Lari et al., 2019; Sprocati et al., 2020).…”

Multicomponent Ionic Transport Modeling in Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM Coupling for Geochemical Reactions

“…Besides the investigation of charge‐induced processes that was the main focus of this study, the model offers extended capability to capture a series of chemical reactions, including aqueous speciation, mineral precipitation‐dissolution, degradation and kinetic reactions, mobilization of heavy metals and metalloids, acidic front propagation, and radionuclides displacement and isotope fractionation, utilizing PHREEQC as a reaction engine. Therefore, the developed MMIT‐Clay framework can also be applied to subsurface systems where, besides charge interactions, other physical, chemical, and biological processes are of interest (e.g., Druhan et al, ; Fakhreddine et al, ; McNeece & Hesse, ; Molins et al, ; Poonoosamy et al, ; Prigiobbe & Bryant, ; Stolze et al, , ). The multidimensional and flow‐through perspective on multicomponent ionic transport in heterogeneous clayey formations introduced in this study could also be extended to fully 3‐D setups where complex anisotropy and flow topology may play a major role (e.g., Chiogna et al, ; Cirpka et al, ; Ye et al, , ).…”

Unraveling biogeochemical complexity through better integration of experiments and modeling

“…16 As example for the application to geochemical transport, a few recent studies have successfully adopted the approach of iteratively advancing the conceptual model and calibrating it to observation data to gain new insights into the biogeochemical processes controlling arsenic partitioning and transport. [17][18][19][20][21][22][23][24][25] Rawson et al 19 performed a model-based analysis in which several conceptual models were tested for their ability to represent the release, transport and attenuation of arsenite in cm-scale column experiments. Each of the conceptual models was calibrated through an automatic parameter estimation process, before the most plausible model was selected.…”

“…However, in recent years heuristic algorithms, such as particle swarm optimization (PSO), 35 have shown great promise in being a good compromise between being able to obtain the global solution to highly nonlinear inverse problems but at a computational expense much less than MCMC methods. 18,19,[21][22][23]32,[36][37][38][39][40] However, it is important to note that, like the Levenberg-Marquardt-type methods, such heuristic methods do not necessarily provide an immediate nonlinear estimation of parameter uncertainty.…”