Yilin Fang

Variably saturated flow and multicomponent biogeochemical reactive transport modeling of a uranium bioremediation field experiment.

Three-dimensional, coupled variably saturated flow and biogeochemical reactive transport modeling of a 2008 in situ uranium bioremediation field experiment is used to better understand the interplay of transport and biogeochemical reactions controlling uranium behavior under pulsed acetate amendment, seasonal water table variation, spatially variable physical (hydraulic conductivity, porosity) and geochemical (reactive surface area) material properties. While the simulation of the 2008 Big Rusty acetate biostimulation field experiment in Rifle, Colorado was generally consistent with behaviors identified in previous field experiments at the Rifle IFRC site, the additional process and property detail provided several new insights. A principal conclusion from this work is that uranium bioreduction is most effective when acetate, in excess of the sulfate-reducing bacteria demand, is available to the metal-reducing bacteria. The inclusion of an initially small population of slow growing sulfate-reducing bacteria identified in proteomic analyses led to an additional source of Fe(II) from the dissolution of Fe(III) minerals promoted by biogenic sulfide. The falling water table during the experiment significantly reduced the saturated thickness of the aquifer and resulted in reactants and products, as well as unmitigated uranium, in the newly unsaturated vadose zone. High permeability sandy gravel structures resulted in locally high flow rates in the vicinity of injection wells that increased acetate dilution. In downgradient locations, these structures created preferential flow paths for acetate delivery that enhanced local zones of TEAP reactivity and subsidiary reactions. Conversely, smaller transport rates associated with the lower permeability lithofacies (e.g., fine) and vadose zone were shown to limit acetate access and reaction. Once accessed by acetate, however, these same zones limited subsequent acetate dilution and provided longer residence times that resulted in higher concentrations of TEAP reaction products when terminal electron donors and acceptors were not limiting. Finally, facies-based porosity and reactive surface area variations were shown to affect aqueous uranium concentration distributions with localized effects of the fine lithofacies having the largest impact on U(VI) surface complexation. The ability to model the comprehensive biogeochemical reaction network, and spatially and temporally variable processes, properties, and conditions controlling uranium behavior during engineered bioremediation in the naturally complex Rifle IFRC subsurface system required a subsurface simulator that could use the large memory and computational performance of a massively parallel computer. In this case, the eSTOMP simulator, operating on 128 processor cores for 12h, was used to simulate the 110-day field experiment and 50 days of post-biostimulation behavior.

Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation.

The increasing availability of the genome sequences of microorganisms involved in important bioremediation processes makes it feasible to consider developing genome‐scale models that can aid in predicting the likely outcome of potential subsurface bioremediation strategies. Previous studies of the in situ bioremediation of uranium‐contaminated groundwater have demonstrated that Geobacter species are often the dominant members of the groundwater community during active bioremediation and the primary organisms catalysing U(VI) reduction. Therefore, a genome‐scale, constraint‐based model of the metabolism of Geobacter sulfurreducens was coupled with the reactive transport model HYDROGEOCHEM in an attempt to model in situ uranium bioremediation. In order to simplify the modelling, the influence of only three growth factors was considered: acetate, the electron donor added to stimulate U(VI) reduction; Fe(III), the electron acceptor primarily supporting growth of Geobacter; and ammonium, a key nutrient. The constraint‐based model predicted that growth yields of Geobacter varied significantly based on the availability of these three growth factors and that there are minimum thresholds of acetate and Fe(III) below which growth and activity are not possible. This contrasts with typical, empirical microbial models that assume fixed growth yields and the possibility for complete metabolism of the substrates. The coupled genome‐scale and reactive transport model predicted acetate concentrations and U(VI) reduction rates in a field trial of in situ uranium bioremediation that were comparable to the predictions of a calibrated conventional model, but without the need for empirical calibration, other than specifying the initial biomass of Geobacter. These results suggest that coupling genome‐scale metabolic models with reactive transport models may be a good approach to developing models that can be truly predictive, without empirical calibration, for evaluating the probable response of subsurface microorganisms to possible bioremediation approaches prior to implementation.

Transport and biogeochemical reaction of metals in a physically and chemically heterogeneous aquifer

Biologically mediated reductive dissolution and precipitation of metals and radionuclides play key roles in their subsurface transport. Physical and chemical properties of natural aquifer systems, such as reactive iron-oxide surface area and hydraulic conductivity, are often highly heterogeneous in complex ways that can exert significant control on transport, natural attenuation, and active remediation processes. Typically, however, few data on the detailed distribution of these properties are available for incorporation into predictive models. In this study, we integrate field-scale geophysical, hydrologic, and geochemical data from a well-characterized site with the results of laboratory batch-reaction studies to formulate two-dimensional numerical models of reactive transport in a heterogeneous granular aquifer. The models incorporate several levels of coupling, including effects of ferrous iron sorption onto (and associated reduction of reactive surface area of) ferric iron surfaces, microbial growth and transport dynamics, and cross-correlation between hydraulic conductivity and initial ferric iron surface area. These models are then used to evaluate the impacts of physical and chemical heterogeneity on transport of trace levels of uranium under natural conditions, as well as the effectiveness of uranium reduction and immobilization upon introduction of a soluble electron donor (a potential biostimulation remedial strategy).

Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.

The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment.

Modeling and sensitivity analysis of electron capacitance for Geobacter in sedimentary environments.

In situ stimulation of the metabolic activity of Geobacter species through acetate amendment has been shown to be a promising bioremediation strategy to reduce and immobilize hexavalent uranium [U(VI)] as insoluble U(IV). Although Geobacter species are reducing U(VI), they primarily grow via Fe(III) reduction. Unfortunately, the biogeochemistry and the physiology of simultaneous reduction of multiple metals are still poorly understood. A detailed model is therefore required to better understand the pathways leading to U(VI) and Fe(III) reduction by Geobacter species. Based on recent experimental evidence of temporary electron capacitors in Geobacter we propose a novel kinetic model that physically distinguishes planktonic cells into electron-loaded and -unloaded states. Incorporation of an electron load-unload cycle into the model provides insight into U(VI) reduction efficiency, and elucidates the relationship between U(VI)- and Fe(III)-reducing activity and further explains the correlation of high U(VI) removal with high fractions of planktonic cells in subsurface environments. Global sensitivity analysis was used to determine the level of importance of geochemical and microbial processes controlling Geobacter growth and U(VI) reduction, suggesting that the electron load-unload cycle and the resulting repartition of the microbes between aqueous and attached phases are critical for U(VI) reduction. As compared with conventional Monod modeling approaches without inclusion of the electron capacitance, the new model attempts to incorporate a novel cellular mechanism that has a significant impact on the outcome of in situ bioremediation.

A fluid pressure and deformation analysis for geological sequestration of carbon dioxide

We present a fluid pressure and deformation analysis for geological sequestration of carbon dioxide based on a simplified hydro-mechanical model. This model includes the geomechanical part that relies on the theory of linear elasticity, while the fluid flow is based on the Darcys law. Two parts are coupled together using the linear poroelasticity theory. For a typical geological sequestration in a semi-infinite geometry with diminishing pressure and deformation fields at infinity, the Helmholtz decomposition can be applied to the displacement vector. Hence, the flow equation can be decoupled from the equation of linear elasticity. Solutions for fluid pressure were obtained for this typical scenario and solutions for ground deformation were obtained using the method of Greens function. Finally, solutions were compared against numerical results using a finite element method for aquifers with two different thicknesses. General agreement can be obtained between analytical and numerical solutions. The model is useful in estimating the temporal and spatial variation of fluid pressure and the mechanical deformation during the entire injection period.

A general simulator for reaction-based biogeochemical processes

As more complex biogeochemical situations are being investigated (e.g., evolving reactivity, passivation of reactive surfaces, dissolution of sorbates), there is a growing need for biogeochemical simulators to flexibly and facilely address new reaction forms and rate laws. This paper presents an approach that accommodates this need to efficiently simulate general biogeochemical processes, while insulating the user from additional code development. The approach allows for the automatic extraction of fundamental reaction stoichiometry and thermodynamics from a standard chemistry database, and the symbolic entry of arbitrarily complex user-specified reaction forms, rate laws, and equilibria. The user-specified equilibria and kinetic rates (i.e., they are not defined in the format of the standardized database) are interpreted by the Maple V (Waterloo Maple) symbolic mathematical software package. FORTRAN 90 code is then generated by Maple for (1) the analytical Jacobian matrix (if preferred over the numerical Jacobian matrix) used in the Newton-Raphson solution procedure, and (2) the residual functions for governing equations, user-specified equilibrium expressions and rate laws. Matrix diagonalization eliminates the need to conceptualize the system of reactions as a tableau, which comprises a list of components, species, the stoichiometric matrix, and the formation equilibrium constant vector that forms the species from components (Morel and Hering, 1993), while identifying a minimum rank set of basis species with enhanced numerical convergence properties. The newly generated code, which is designed to operate in the BIOGEOCHEM biogeochemical simulator, is then compiled and linked into the BIOGEOCHEM executable. With these features, users can avoid recoding the simulator to accept new equilibrium expressions or kinetic rate laws, while still taking full advantage of the stoichiometry and thermodynamics provided by an existing chemical database. Thus, the approach introduces efficiencies in the specification of biogeochemical reaction networks and eliminates opportunities for mistakes in preparing input files and coding errors. Test problems are used to demonstrate the features of the procedure.

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

ABSTRACT Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

Water Table Dynamics and Biogeochemical Cycling in a Shallow, Variably-Saturated Floodplain

Three-dimensional variably saturated flow and multicomponent biogeochemical reactive transport modeling, based on published and newly generated data, is used to better understand the interplay of hydrology, geochemistry, and biology controlling the cycling of carbon, nitrogen, oxygen, iron, sulfur, and uranium in a shallow floodplain. In this system, aerobic respiration generally maintains anoxic groundwater below an oxic vadose zone until seasonal snowmelt-driven water table peaking transports dissolved oxygen (DO) and nitrate from the vadose zone into the alluvial aquifer. The response to this perturbation is localized due to distinct physico-biogeochemical environments and relatively long time scales for transport through the floodplain aquifer and vadose zone. Naturally reduced zones (NRZs) containing sediments higher in organic matter, iron sulfides, and non-crystalline U(IV) rapidly consume DO and nitrate to maintain anoxic conditions, yielding Fe(II) from FeS oxidative dissolution, nitrite from denitrification, and U(VI) from nitrite-promoted U(IV) oxidation. Redox cycling is a key factor for sustaining the observed aquifer behaviors despite continuous oxygen influx and the annual hydrologically induced oxidation event. Depth-dependent activity of fermenters, aerobes, nitrate reducers, sulfate reducers, and chemolithoautotrophs (e.g., oxidizing Fe(II), S compounds, and ammonium) is linked to the presence of DO, which has higher concentrations near the water table.

Snowmelt Induced Hydrologic Perturbations Drive Dynamic Microbiological and Geochemical Behaviors across a Shallow Riparian Aquifer

Shallow riparian aquifers represent hotspots of biogeochemical activity in the arid western US. While these environments provide extensive ecosystem services, little is known of how natural environmental perturbations influence subsurface microbial communities and associated biogeochemical processes. Over a six-month period we tracked the annual snowmelt-driven incursion of groundwater into the vadose zone of an aquifer adjacent to the Colorado River, leading to increased dissolved oxygen (DO) concentrations in the normally suboxic saturated zone. Strong biogeochemical heterogeneity was measured across the site, with abiotic reactions between DO and sulfide minerals driving rapid DO consumption and mobilization of redox active species in reduced aquifer regions. Conversely, extensive DO increases were detected in less reduced sediments. 16S rRNA gene surveys tracked microbial community composition within the aquifer, revealing strong correlations between increases in putative oxygen-utilizing chemolithoautotrophs and heterotrophs and rising DO concentrations. The gradual return to suboxic aquifer conditions favored increasing abundances of 16S rRNA sequences matching members of the Microgenomates (OP11) and Parcubacteria (OD1) that have been strongly implicated in fermentative processes. Microbial community stability measurements indicated that deeper aquifer locations were relatively less affected by geochemical perturbations, while communities in shallower locations exhibited the greatest change. Reactive transport modeling of the geochemical and microbiological results supported field observations, suggesting that a predictive framework can be applied to develop a greater understanding of such environments.