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Physicochemical Heterogeneity Controls on Uranium Bioreduction Rates at the Field Scale Li Li,*,^,‡,§ Nitin Gawande,^ Michael B. Kowalsky,† Carl I. Steefel,† and Susan S. Hubbard† ^
John and Willie Leone Family Department of Energy and Mineral Engineering, Penn State University, University Park, Pennsylvania 16802, United States ‡ EMS Energy Institute, Penn State University, University Park, Pennsylvania 16802, United States § Earth and Environmental Systems Institute, Penn State University, University Park, Pennsylvania 16802, United States † Lawrence Berkeley National Laboratory, Earth Sciences Division, Berkeley, California 94720, United States
bS Supporting Information ABSTRACT: It has been demonstrated in laboratory systems that U(VI) can be reduced to immobile U(IV) by bacteria in natural environments. The ultimate efficacy of bioreduction at the field scale, however, is often challenging to quantify and depends on site characteristics. In this work, uranium bioreduction rates at the field scale are quantified, for the first time, using an integrated approach. The approach combines field data, inverse and forward hydrological and reactive transport modeling, and quantification of reduction rates at different spatial scales. The approach is used to explore the impact of local scale (tens of centimeters) parameters and processes on field scale (tens of meters) system responses to biostimulation treatments and the controls of physicochemical heterogeneity on bioreduction rates. Using the biostimulation experiments at the Department of Energy Old Rifle site, our results show that the spatial distribution of hydraulic conductivity and solid phase mineral (Fe(III)) play a critical role in determining the field-scale bioreduction rates. Due to the dependence on Fe-reducing bacteria, field-scale U(VI) bioreduction rates were found to be largely controlled by the abundance of Fe(III) minerals at the vicinity of the injection wells and by the presence of preferential flow paths connecting injection wells to down gradient Fe(III) abundant areas.
’ INTRODUCTION The goal of microbe-mediated bioremediation is to stimulate indigenous bacteria in situ to reduce and immobilize redox sensitive contaminants, such as uranium, by injecting organic carbon into the subsurface (e.g., refs 13). Although the idea has been demonstrated in laboratory systems successfully (e.g., ref 4), the ultimate efficacy of bioreduction at the field scale is controlled by many factors, including, for example, bioreduction kinetics, substrates bioavailability, competition between coexisting electron acceptors, and characteristics of contaminated sites (e.g., refs 57). Among these factors, some have been studied extensively, while others, including the effects of site characteristics and spatial heterogeneity, are largely unexplored. Subsurface systems commonly exhibit significant spatial variation in physical and chemical properties.810 For example, it has been widely recognized that spatial variability of hydraulic parameters control contaminant spreading in groundwater11,12 and the injectate distribution.3 In addition, the spatial distribution of mineral phases may control where, how much, and the rate at which biogeochemical reactions occur (e.g., refs 10 and 12). Therefore, it is important to understand and quantify the role of physical and geochemical heterogeneity in determining the ultimate efficacy of uranium bioreduction at the field scale. r 2011 American Chemical Society
Assessing and quantifying the effects of subsurface variability on remediation efficacy remains a challenge (e.g., refs 13 and 14). Detailed and extensive characterization of subsurface formations is often prohibitively expensive, if not impossible. As such, there is always a lack of data for the spatial characterization of site heterogeneity.8,13,15,16 Although field data from monitoring wells are often reported as evidence of bioreduction effectiveness and provide “local” transient reaction rates in the vicinity of the wells, quantification of bioreduction rates at the field scale is often lacking. The objective of this work is to quantify field-scale rates of uranium bioreduction using an integrated approach and understand the control of physicochemical heterogeneity on these rates. The modeling portion of the integrated approach is based on previously developed methods,17 while a more detailed calculation has been developed to quantify field scale (∼10 s of m) bioreduction rates from the rates at the local grid block scale (∼10 s of cm). Here we consider the 20072009 biostimulation field experiments at Rifle, Colorado, which were conducted in a different Received: April 2, 2011 Accepted: October 11, 2011 Revised: September 17, 2011 Published: October 11, 2011 9959
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Environmental Science & Technology location from the previously reported 20022003 experiments, with no prior injection of acetate.2 The uranium-contaminated Rifle site was formerly a uranium ore processing facility and is now a U.S. Department of Energy (DOE) Integrated Field Research Challenge (IFRC) site. It has been the subject of several biostimulation experiments2,18,19 that involved the injection of acetate as an electron donor to stimulate bacteria in situ and to mediate biogeochemical reactions with three competing electron acceptors: U(VI), Fe(III), and sulfate. The Fe-reducing bacteria (FeRB dominated by members of the family Geobacteraceae) at the site reduce mobile U(VI) to immobile U(IV), therefore preventing its further migration.2 Studies have found that the FeRB are responsible for uranium bioreduction, while sulfate reducers are not.2,19 Reaction products from acetate-induced biostimulation lead to the precipitation of secondary minerals, which can potentially alter porosity and permeability.1,20 Spatial variation in the physical and geochemical properties of the sediments, including hydraulic conductivity and mineral abundance, has been shown to affect patterns of biomass and precipitates accumulation.17 Studies have also used geophysical methods to track biogeochemical changes during the biostimulation.21,22 To the best of our knowledge, this is the first study that systematically quantifies field-scale uranium bioreduction rates and assesses the role of physical and geochemical heterogeneities in determining field-scale rates.
’ METHODOLOGY The Rifle Site and Uranium Biostimulation Experiments. The shallow aquifer at Rifle, with the thickness of 23 m, is contaminated with uranium (0.20.4 ppm). Detailed information about the site, including the initial mineral phases, aqueous geochemistry, the type of involved reactions, and microbial composition are provided in the Supporting Information. Several field bioremediation experiments in various locations at the site occurred over time.2,18 During the three consecutive experiments that took place in 2007 to 2009, acetate was injected together with the nonreactive bromide tracer. The experiments were carried out at a different location from that of the 2002 and 2003 experiments, for which our modeling studies were previously published.17 The plan view of the wells is shown in Figure S1. The injection conditions varied during the experiments, as is detailed in the Supporting Information. Aqueous samples were collected from monitoring wells periodically, and concentrations of various species were analyzed, including Br, acetate, U(VI), and SO42‑. Overview of the Integrated Approach for Quantification of Field-Scale U(VI) Bioreduction. The integrated approach involves multiple modeling schemes developed in a previous study 17 and an approach developed in this work for the calculation of field scale bioreduction rates. This section briefly outlines the steps, with more details in later sections and in the Supporting Information. Step one uses inverse hydrological modeling to obtain the spatial distributions of hydraulic conductivity based on tracer breakthrough data from monitoring wells. Step two generates spatial distributions of solid phase Fe(III) content based on “local” hydraulic conductivity values from step one and a probability model that inversely correlates hydraulic conductivity with solid phase Fe(III) content. With the same mean and variance for the entire domain, the inverse correlation in general results in high Fe(III) content in low conductivity regions and vice versa. Step three involves the determination of kinetic parameters that best fit geochemical field data
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from monitoring wells using inverse reactive transport modeling. Step four uses forward reactive transport modeling and the kinetic parameters from step three to simulate the coupled flow and biogeochemical processes and to predict the time evolution of U(IV) precipitation at the local scale. Step five sums up “local” precipitated U(IV) concentrations to calculate field scale rates over the entire domain. To address the problem of data scarcity and uncertainty, 30 realizations of hydraulic conductivity fields were generated using different random seed numbers. The final result is an assessment of the controls of hydraulic conductivity and Fe(III) distribution on field scale U(VI) bioreduction rates. Spatial Distribution of Hydraulic Conductivity and Fe(III) Content. Inverse hydrological modeling of the bromide breakthrough data has been carried out using the code iTOUGH2.23 As was detailed previously17 and in the Supporting Information, a pilot point approach was used to obtain two-dimensional (depth averaged) spatial distribution of hydraulic conductivity at the site with a resolution of 25 by 25 cm. Based on the analysis of slug test data from the site, we assume that the logarithm of the hydraulic conductivity can be described adequately using an exponential semivariogram model with a variance of 0.26 and an integral scale of 3.3 m. Thirty hydraulic conductivity fields were obtained through inversion, each honoring the spatial correlation (semivariogram model) and the bromide breakthrough data, as shown in Figure S2. For each hydraulic conductivity field, a corresponding 2D distribution of bioavailable solid Fe(III) was obtained using an inverse correlation between these two properties. This inverse correlation is based on the fact that there is much more Fe(III) associated with fine grained materials than is present in coarsegrained materials at the Rifle site.17 This is consistent with observations for other types of soils and sediments.9,24,25 The probability model is as follows μFe, i ¼ μFe þ F
σ Fe ðlog Ki μlog K Þ σ log K
σ 2Fe, i ¼ σ 2Fe ð1:0 F2 Þ
ð1Þ ð2Þ
Here μFe,i is the local mean of bioavailable Fe(III) content for a specific grid block i, μFe is the global mean of Fe(III) content for the entire domain, σFe is the global standard deviation of the Fe(III) content, log Ki is the logarithm of conductivity (m/day) at the grid block i, and μlog K is the global mean of log K, σlog K is the global standard deviation of log Ki, and F is the correlation coefficient between Fe(III) content and log K. As indicated by these two equations, the mean of the Fe(III) content at specific locations (μFe,i) depends on the global means of Fe(III) content and log K, the correlation between the global standard deviation of the two, and the local value log Ki. The local variance of Fe(III) content σFe,i2 is determined by the global variance of Fe(III) content σFe2 and the correlation coefficient. In this work we use the probability parameters as used before, namely, with a μFe value of 2.5 μmol/g, σFe value of 2.5 μmol/g, μlogK value of 0.37 m/day, σlogK value of 0.60 m/day, and a correlation coefficient F of 0.8. Justification of using values is provided in the Supporting Information. Reactive Transport Modeling. Mass conservation equations were solved for the spatial and temporal evolution of aqueous species using the process-based reactive transport code CrunchFlow.2628 CrunchFlow partitions aqueous species into primary and secondary species29 and solves the equations for primary species. The following is a representative equation for a 9960
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specific primary species i ∂ðϕCi Þ ¼ ∇ðϕDi ∇Ci Þ ∇ðϕuCi Þ ∂t
Nr
Nm
∑ νir Rr m∑¼ 1 νim Rm r¼1 ð3Þ
Here ϕ is porosity, Ci is the aqueous concentration of i, t is time, Di is the diffusion/dispersion coefficient, u is the flow velocity, Nr is the total number of kinetic aqueous reactions that involve species i, νir is the stoichiometric coefficient of the species i associated with the reaction r, Rr is the rate of aqueous reaction r, Nm is the total number of mineral reactions that involve species i, νim is the stoichiometric coefficient of the species i associated with the reaction m, and Rm is the rate of mineral reaction m. This equation implies that the mass change rate of species i depends on the diffusion/dispersion, advection, and reaction processes. Complex biogeochemical reactions occur during the biostimulation experiments.1 These include microbe-mediated reactions, mineral dissolution and precipitation, aqueous speciation, and surface complexation, as detailed in Tables S2 and S3. In total, 53 species and 46 reactions are included. The driving forces are the three microbe-mediated reactions, Fe(III) reduction, U(VI) reduction, and sulfate reduction. Using bacterial energetics as outlined in Rittmann and McCarthy,30 these reactions are written as follows
The differences in kinetic parameters in these two cases were then compared to determine the need for inverse modeling for each realization. As it turned out, the kinetic parameters that best fit the data for these cases are similar. As shown in Figure S3 and S4, for example, in both cases the parameters for iron reduction rate constant was 4.53 1017 mol/L/s/cell. This value also generated the best fit to U(VI) aqueous geochemistry data, as indicated in Figures S5 and S6. Therefore, the same set of kinetic parameters was used for all 30 fields for forward reactive transport modeling and the prediction of U(IV) spatial distribution. Two additional simulations were also run for comparison. One is a hypothetical homogeneous case that uses the average hydraulic conductivity and Fe(III) values in all grid blocks. The other is the average case with hydraulic conductivity and Fe(III) values in each grid block calculated by averaging local values of 30 realizations in the same grid block. This average case is constrained by the field data and approaches the conditional mean field. Calculation of Field-Scale Rates from Local Scale Uranium Bioreduction Rates. The forward reactive transport modeling provided temporally and spatially resolved reduction rates of U(VI) at the local grid block scale. The local U(IV) precipitation rates, which are the negative values of the U(VI) reduction rates, follow the Monod rate law30,31 with a rate dependence on microbial cell populations as shown below
FeOOHðsÞ þ 1:925Hþ þ 0:033NHþ 4 þ 0:208CH3 COO f
Fe
2þ
þ 0:033C5 H7 O2 NðFeRBÞ þ
0:250HCO-3
rUðVIÞ, i ¼ rUðIV Þ, i
þ 1:600H2 O
¼ kmax XFeRB, i
ð4Þ þ þ SO24 þ 1:082CH3 COO þ 0:052H þ 0:035NH4 f
0:035C5 H7 O2 NðSRBÞ þ 0:104H2 O þ 2HCO-3 þ HSð5Þ þ UO2þ 2 þ 0:067NH4 þ 0:417CH3 COO þ 0:8H2 O f
UO2 ðsÞ þ 0:0667C5 H7 O2 NðFeRBÞ þ 0:5HCO-3 þ 2:15Hþ
ð6Þ In these equations C5H7O2N(FeRB) and C5H7O2N(SRB) represent biomass of Fe-reducing and sulfate-reducing bacteria, respectively. Details of the kinetic rate laws for various reactions are documented in the Supporting Information. Mineral dissolution and precipitation follow the rate law derived from Transition State Theory (TST).32 Details of the reactions and parameters are listed in Table S2 in the Supporting Information. Each depth averaged, two-dimensional reactive transport simulation was carried out using 68 64 grid blocks (a total of 4352), with a resolution of 25 cm by 25 cm. Inverse reactive transport modeling was used to generate kinetic parameters that could adequately fit the field data. Because the microbe-mediated reactions and mineral dissolution and precipitation are the key controls of the system, we obtain the rate constants of these 8 reactions from the inverse modeling. Because the number of kinetic parameters is large, it is computationally prohibitive at the present time to consider their range in as many as 30 different realizations. In order to circumvent this, we chose two cases (cases 8 and 21) and obtained the best set of fitting parameters for each of them. These two cases represent the extreme U(VI) bioreduction rates, with a least and a maximum amount of U(IV) precipitates, respectively, at the end of 2009, as shown in Figure 3.
CUðVIÞ, i Cacetate, i KM, acetate þ Cacetate, i KM, UðVIÞ þ CUðVIÞ, i
ð7Þ Here the uranium bioreduction rate in the grid block i, rU(IV),i, depends on the rate constant kmax (mol/L-s-cells), the space and time dependent value of Fe-reducing bacteria XFeRB (cells), the concentration of the electron donor acetate Cacetate,i (mol/L) and the electron acceptor U(VI),i through the dual Monod terms with their respective half-saturation constants KM,acetate and KM,U(VI) (mol/L). This equation indicates the dependence of uranium bioreduction on local densities of Fe-reducing bacteria, which again depends on local concentrations of solid Fe(III) and acetate. For each realization, the amount of reduced U(IV) (in the form of UO2(s) as the representative U(IV) form) was then summed over all grid blocks in the domain for each time step. Essentially, the following equations were applied n
∑ CUðIV Þ, i, t msediment, i i¼1 j
CUðIV Þ, tj ¼
RUðIV Þ, tj ¼
n
∑ msediment, i i¼1 CUðIV Þ, tjþ1 CUðIV Þ, tj tjþ1 tj
,
ð8Þ
where n is the total number of grid blocks, tj is certain specific time, CU(IV),i,tj is the solid concentration of precipitated U(IV) in the grid block i at time tj (mol/g of sediment), and msediment,i is the sediment mass in grid block i (g of sediment). As such, CU(IV),tj is the field scale averaged solid U(IV) concentration at time tj. The value of RU(IV),tj is the averaged field-scale U(VI) bioreduction rate or U(IV) precipitation rate between tj+1 and tj (mol of U(IV)/g sediment/day). Equation 8 permitted the estimation of 9961
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Figure 1. Plan view map of the initial spatial distribution of hydraulic conductivity and Fe(III) content for the cases with the least (A and D, Case 8) and the most U(VI) bioreduction (B and E, Case 21, and C and F, Case 23). Also shown in the figures are the location of background wells (black diamonds), injection wells (black circles), and monitoring wells (black squares). The white labels from DS01 to DS12 are the names of the monitoring wells. The main groundwater direction is from left to right. The white dashed boxes indicate the “high conductivity zones” that are referred to in the text. The red dashed boxes highlight regions directly down gradient of the injection wells.
the time evolution of the total bioreduced uranium for the entire domain, thus providing a direct measure of uranium reduction rate at the field scale.
’ RESULTS AND DISCUSSION Figure 1 shows the spatial distributions of hydraulic conductivity and Fe(III) content for three cases (cases 8, 21, and 23) out of the 30 realizations. Case 8 is the realization with the least U(VI) bioreduction, while cases 21 and 23 show the most U(VI) bioreduction at the end of 2009. The three cases have similar averaged conductivity values, the same average Fe(III) content, and similar agreement with bromide breakthrough field data, as shown in Figure S2 in the Supporting Information. However, significant differences exist in the spatial patterns shown in Figure 1. As expected, due to the negative correlation between hydraulic conductivity and Fe(III) content, areas with high conductivity values have correspondingly low Fe(III) values. Cases 21 and 23 have high conductivity paths that connect injection wells to down gradient regions, as indicated with the white dashed boxes in Figure 1B and 1C. These high conductivity paths correspond to low Fe(III) zones in Figure 1E and 1F. However, at the edge of the conductive paths, abundant Fe(III) exists. In addition, comparison of Figure 1D, 1E, and 1F indicates that for case 8, Fe(III)-rich zones occur mostly in locations outside the direct down gradient region of the injection wells, while in cases 21 and 23, there is more Fe(III) in these regions. These characteristics will have significant implications for the overall rates of U(VI) bioreduction, as will be discussed later. Figure 2 compares the field data of aqueous U(VI) concentrations with the modeling output. The simulation has included the effects of clogging caused by the accumulation of minerals and biomass with updated porosity and permeability over time. The injected acetate concentrations in the groundwater are typically at the level of millimolar. Our simulation output shows that the clogging does affect the flow of acetate into the down gradient. However, this does not impact U(VI) bioreduction significantly,
primarily because U(VI) concentration is so low (at the micromolar level) that it only needs a micromolar level of acetate. As a result, uranium reduction rates are not sensitive to the change of acetate concentration caused by the clogging effect. Surprisingly, despite the large differences between the realizations, the simulation results are similar. The comparison shows that in some wells, for example, well DS01 and DS05, the model did not capture the prolonged period of U(VI) reduction in 2008. However, in general, the model captures the trend of U(VI) evolution. Each dip represents the time period of acetate injection. The times at which U(VI) started to be reduced and at which it bounced back have been accurately matched with field data, indicating that the model has captured the dynamics of the system. An expanded view of the response during the 2007 injection period is shown in Figure S8. Simulation results were also compared with field data for other species such as Br and acetate in the Supporting Information (Figures S2 and S7). With the level of complexity (injection scheme, heterogeneous porous media, and complex reaction network that involves 53 species and 46 reactions), the fit to the data is reasonably good for all species shown here. This indicates that the depth-averaged 2D model is sufficient to capture the system dynamics. The average case represents the average behavior of all 30 realizations. Interestingly, the hypothetical homogeneous case over predicts the amount of U(VI) bioreduction in some wells (e.g., DS04 and DS08). Figure 3A shows the time evolution of U(IV) concentration (CU(IV),tj ) at the field scale (eq 8), which increased over time during each acetate injection period. Figure 3B shows the corresponding time evolution of field scale reduction rates. As the acetate injection continues in each period, the rates of uranium bioreduction increases with increasing amount of accumulated FeRB. The rates drop abruptly at the end of each acetate injection period. As indicated by both figures, the relative bioreduction rates vary for different cases at different times. In early times, the homogeneous case and case 8 have relatively high bioreduction rates, while in later years, bioreduction rates in case 9962
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Figure 2. Comparison of aqueous U(VI) field data from the 12 monitoring wells (blue squares) and modeling output from all 30 realizations (grey lines) for the three periods of acetate injection in 20072009. The red line represents the simulation results from the average case with local conductivity and Fe(III) content values averaged over the 30 realizations. The green line represents the simulation results for the homogeneous case.
Figure 3. Time evolution of averaged U(IV) concentration (3A) and U(VI) bioreduction rates (3B) in the form of UO2(s) for all 30 realizations (grey dots), the averaged field (dark blue dots), the homogeneous case (pink dots), and the two extreme cases (red for case 8 and green for case 21). The three increasing period corresponds to the injection period of 2007, 2008, and 2009.
21 become increasingly dominant. In year 2009, the peak of the rates in case 21 is around 10.40 107 μmol/g/d, while that in case 8 is 2.34 107 μmol/g/d, a factor of more than 4. At the end of 2009, U(IV) concentrations differ from one realization to another, although all realizations have the same statistical average Fe(III) content and conductivity values. The differences among the various cases increased over time. The coefficient of variance (standard deviation divided by mean) for the average U(IV) concentration is 0.095 at the end of 2007, 0.11 at the end of 2008, and 0.14 at the end of 2009. The average concentration of U(IV) at the end of 2009 is 1.425 104 in case 21 and 0.841 104 μmol/g in case 8, a factor of 1.7. The mean for all 30 cases is 1.02 104 μmol/g, with a standard deviation of 1.42 105 μmol/g.
The homogeneous case represents a case where Fe(III) and conductivity are spatially evenly distributed, while the average case represents the conditioned average behavior of all 30 realizations. Interestingly, the homogeneous case has a much higher bioreduction rate than that in most realizations, while the averaged case is in general close to case 8, the one with the least U(IV) accumulation. This is because Fe(III) negatively correlates with conductivity in all 30 realizations, with zones of high conductivity having low Fe(III) content. This negative correlation prevents the coexistence of high acetate and high Fe(III) concentrations and slows down bioreduction. This emphasizes the importance of having both Fe(III) and acetate at the same location for fast bioreduction to occur. 9963
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Figure 5. Correlation of the average precipitated U(IV) concentration at the end of 2009 for the whole domain with average Fe(III) content over the whole domain (pink dot) and with the average Fe(III) content in regions close to the injection wells (x from 5 to 10 m and y from 3 to 12 m) (blue squares) for all 30 realizations.
Figure 4. The spatial distribution of precipitated U(IV) (in the form of uraninite UO2(s)) at the end of the 2009 experiments associated with the homogeneous case and cases 8, 21, and 23 shown in Figure 1. The red dashed boxes highlight the regions just down gradient of the injection wells. The white dashed boxes indicate locations of “high conductivity paths”.
To understand the U(VI) bioreduction rates in different cases, Figure 4 shows the spatial distribution of precipitated U(IV) at the end of 2009 for four realizations, the homogeneous case, cases 8, 21, and 23. Although not shown here, the average case is similar to case 8. The uranium bioreduction depends on both acetate concentration and Fe(III) content. Acetate concentrations are highest in the vicinity of the injection wells. However, acetate concentration in down gradients regions is significantly influenced by the pattern of hydraulic conductivity. With preferential high conductivity paths that connect injection wells to high Fe(III) areas in down gradient regions, as in the case of 21 and 23 (Figure 1B and 1C), the injected acetate can access the down gradient high Fe(III) regions. Significant amounts of U(IV) precipitated at the edge of these high conductivity paths that directly interface with Fe(III)-rich zones, as shown in Figure 4B and 4C. This is because the edges are the locations with high concentrations of both Fe(III) and acetate, the two key components for the occurrence of bioreduction. The precipitated U(IV) along these conductive paths distinguishes case 21 and 23 from other cases. This suggests that the presence of Fe(III) in the vicinity of the injection wells and the existence of preferential high conductivity pathways in the direction of groundwater flow are two key controls on bioreduction rates. Detailed spatial profiles of U(IV) precipitates at different times are also shown in Supporting Information (Figure S9). These suggest that U(VI) bioreduction mainly occurred close to the injection wells in early times and migrated to down gradient areas in later times. As can be observed from the figures, the simulation region is the region where the bulk of the U(VI) bioreduction occurs. At the boundary of the simulation domain the spatial patterns of U(IV) show either no U(IV) precipitation or negligible precipitation. As such, the uranium precipitation potential of the substrate that leaves the model domain is negligible. Note that the tracer data were measured in the monitoring wells and only contain information in regions covered by the wells. Therefore,
the hydraulic conductivity values (and Fe(III) content) outside of the well regions are not as well constrained. However, given all other factors the same, the fact that the cases 21 and 23 have the greatest overall U(VI) bioreduction indicate the importance of preferential flow paths. To explore the correlation between Fe(III) content and the total amount of U(VI) bioreduction, the average concentration of U(IV) at the end of 2009 for each realization was plotted against its two corresponding Fe(III) contents. One is the averaged Fe(III) content in locations close to the injection wells (from 510 m along the flow direction and from 3 to 12 m perpendicular to the flow direction), the other is the average Fe(III) content for the entire domain. With the same average Fe(III) in all 30 realizations, if the U(VI) bioreduction depends only on the average Fe(III) content over the entire domain, the U(IV) concentration should be the same. However, this is not the case, as shown in Figure 5. The ultimate U(IV) concentrations correlate well with the average Fe(III) content close to the injection well, with a statistical R2 = 0.59. Beyond that the Fe(III) content is not statistically significant. With the same total amount of acetate injection, abundant Fe(III) content close to the injection wells leads to high bioreduction rates and large amounts of U(IV) precipitates. This suggests that Fe(III) abundance close to the injection wells is a critical parameter that controls the rates of U(VI) reduction. This correlation also indicates that the difference in the total amount of U(VI) bioreduction among the 30 realizations is caused by the fundamental differences in the spatial patterns of physical and geochemical properties.
’ ENVIRONMENTAL IMPLICATIONS This work assesses the control of physicochemical heterogeneity on field scale U(VI) bioreduction rates. To the best of our knowledge, this is the first time field-scale uranium bioreduction rates are quantified systematically. The general integration approach can be used to quantify field-scale rates at other bioremediation sites. Our results show that the spatial distribution of Fe(III) content and hydraulic conductivity can be critically important in determining the ultimate rates of bioreduction. In particular, bioreduction rates can be enhanced by the presence of Fe(III)-rich zones at the vicinity of injection wells and the presence of preferential conductivity zones that connect to down gradient Fe(III)-rich zones. The fact that the homogeneous case leads to a much higher U(IV) concentration than the average case emphasizes the importance of the coexistence of both reacting substrates in controlling bioreduction rates. 9964
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Environmental Science & Technology This study is at a spatial scale of tens of meters with a stimulation period of approximately a hundred days. If the domain is increased to the entire Rifle site with a length scale of hundreds of meters and a time scale of years, the conclusions may still hold. However, upscaling in both space and time can be critical in extrapolating these findings.26,33 In addition, this study is for the specific hydraulic and geochemical conditions at the Rifle site. Although the developed integrated approach can be used for other contaminated sites, our conclusions may not apply directly for other sites. Systematic studies are needed to understand controls of fieldscale processes at other sites. Nonetheless, the findings have important environmental implications for bioremediation efforts that involve the substrate injection for the stimulation of in situ bacteria. The conclusion on the importance of Fe(III) at the vicinity of the injection wells has significant implications. It is almost a universal observation at both column and field scales that the largest extent (and/or rate) of biogeochemical reactions occur close to the injection points where the concentrations of nutrients or electron donors are highest.1,17,34,35 This indicates that it may be beneficial to perform characterization specifically targeting the Fe(III) and the identification of conductive pathways before installment of injection wells. Other techniques, for example, injection of Fe(III) nano particles at the vicinity of injection wells, could potentially improve the remediation performance.36,37 This study also highlights the importance of subsurface characteristics in determining reaction rates in natural subsurfaces, which echoes the message in many other studies.9,10,12,3847 Although the rates of uranium bioreduction in well-mixed small scale laboratory systems have been studied extensively (e.g., refs 4 and 48), research on field scale rates and their key controls are still in its infancy. It is critically important to extrapolate understanding from small scales to larger spatial scales. This study emphasizes the fact that controls of rates at different spatial scales are significantly different. This calls for more studies on large scale systems, for which significant challenges exist. On the one hand, characterization of mineralogy and physical properties is conventionally laborious and destructive. On the other hand, obtaining extensive field scale data can be expensive and challenging. In that regard, methods such as geophysical characterization (e.g., ref 49) or correlation models between physiochemical properties (as was assumed here) may offer lower cost and less destructive alternatives. In addition, modeling and stochastic methods, such as that was done in this work, can be used as integration tools to combine field data and to quantitatively assess the importance of various processes and key controls.
’ ASSOCIATED CONTENT
bS
Supporting Information. Details of the Rifle site, simulation details, and results. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: 814-867-3547. Fax: 814-865-3248. E-mail: lili@eme. psu.edu.
’ ACKNOWLEDGMENT Funding for this study was provided by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental
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Research to the LBNL Sustainable Systems Scientific Focus Area under Award Number DE-AC02-05CH1123 and through a subcontract to Penn State University. We acknowledge Kenneth Williams (LBNL), Phil Long (PNNL), and the Rifle IFRC research team for facilitating collaboration and access to Rifle data. We acknowledge the associate editor Jorge Gardea-Torresdey and four anonymous reviewers for their thorough, insightful, and constructive comments that have significantly improved the manuscript.
’ REFERENCES (1) Li, L.; Steefel, C. I.; Williams, K. H.; Wilkins, M. J.; Hubbard, S. S., Mineral transformation and biomass accumulation during uranium bioremediation at Rifle, Colorado. Environ. Sci. Technol. 2009, 43, (14), 54295435. doi: 10.1021/es900016v. (2) Anderson, R. T.; Vrionis, H. A.; Ortiz-Bernad, I.; Resch, C. T.; Long, P. E.; Dayvault, R.; Karp, K.; Marutzky, S.; Metzler, D. R.; Peacock, A.; White, D. C.; Lowe, M.; Lovley, D. R. Stimulating the in situ activity of Geobacter species to remove uranium from the groundwater of a uraniumcontaminated aquifer. Appl. Environ. Microbiol. 2003, 69 (10), 5884–5891. (3) Hubbard, S. S.; Williams, K. H.; Conrad, M. E.; Faybishenko, B.; Peterson, J.; Chen, J. S.; Long, P. E.; Hazen, T. C. Gepphysical monitoring of hydrological and biogeochemical transformation associated with Cr(VI) bioremediation. Environ. Sci. Technol. 2008. (4) Lovley, D. R. Dissimilatory Metal Reduction. Annu. Rev. Microbiol. 1993, 47, 263–290. (5) Luo, J.; Weber, F.-A.; Cirpka, O. A.; Wu, W.-M.; Nyman, J. L.; Carley, J.; Jardine, P. M.; Criddle, C. S.; Kitanidis, P. K. Modeling in-situ uranium(VI) bioreduction by sulfate-reducing bacteria. J. Contam. Hydrol. 2007, 92 (12), 129–148. (6) Brusseau, M. L.; Sandrin, S. K.; Li, L.; Yolcubal, I.; Jordan, F. L.; Maier, R. M. Biodegradation during contaminant transport in porous media: 8. The influence of microbial system variability on transport behavior and parameter determination. Water Resour. Res. 2006, 42, 4. (7) Sturman, P. J.; Stewart, P. S.; Cunningham, A. B.; Bouwer, E. J.; Wolfram, J. H. Engineering Scale-up of in-Situ Bioremediation Processes a Review. J. Contam. Hydrol. 1995, 19 (3), 171–203. (8) de Marsily, G.; Delay, F.; Goncalves, J.; Renard, P.; Teles, V.; Violette, S. Dealing with spatial heterogeneity. Hydrogeol. J. 2005, 13 (1), 161–183. (9) Barber, L. B.; Thurman, E. M.; Runnells, D. D. Geochemical Heterogeneity in a Sand and Gravel Aquifer - Effect of Sediment Mineralogy and Particle-Size on the Sorption of Chlorobenzenes. J. Contam. Hydrol. 1992, 9 (12), 35–54. (10) Scheibe, T. D.; Fang, Y.; Murray, C. J.; Roden, E. E.; Chen, J.; Chien, Y.-J.; Brooks, S. C.; Hubbard, S. S., Transport and biogeochemical reaction of metals in a physically and chemically heterogeneous aquifer. Geosphere 2006, 2, (4), doi: 10.1130/GES00029.1. (11) Gelhar, L. W. Stochastic subsurface hydrology; Prentice-Hall: Englewood Cliffs, NJ, 1993; p 390. (12) Dentz, M.; Le Borgne, T.; Englert, A.; Bijeljic, B. Mixing, spreading and reaction in heterogeneous media: A brief review. J. Contam. Hydrol. 2011, 12021, 1–17. (13) Rubin, Y.; Hubbard, S. S. Hydrogeophysics; Springer: Netherlands, 2005; Vol. 50. (14) DiFilippo, E. L.; Carroll, K. C.; Brusseau, M. L. Impact of organic-liquid distribution and flow-field heterogeneity on reductions in mass flux. J. Contam. Hydrol. 2010, 115 (14), 14–25. (15) Chen, J. S.; Hubbard, S. S.; Williams, K. H.; Pride, S.; Li, L.; Steefel, C.; Slater, L. A state-space Bayesian framework for estimating biogeochemical transformations using time-lapse geophysical data. Water Resour. Res. 2009, 45. (16) Arrigo, J. A. S.; Salvucci, G. D. Investigation hydrologic scaling: Observed effects of heterogeneity and nonlocal processes across hillslope, watershed, and regional scales. Water Resour. Res. 2005, 41, 11. 9965
dx.doi.org/10.1021/es201111y |Environ. Sci. Technol. 2011, 45, 9959–9966
Environmental Science & Technology (17) Li, L.; Steefel, C. I.; Kowalsky, M. B.; Englert, A.; Hubbard, S. S. Effects of physical and geochemical heterogeneities on mineral transformation and biomass accumulation during a biostimulation experiment at Rifle, Colorado. J. Contam. Hydrol. 2010, 112 (14), 45–63. (18) Yabusaki, S. B.; Fang, Y.; Long, P. E.; Resch, C. T.; Peacock, A. D.; Komlos, J.; Jaffe, P. R.; Morrison, S. J.; Dayvault, R. D.; White, D. C.; Anderson, R. T. Uranium removal from groundwater via in situ biostimulation: Field-scale modeling of transport and biological processes. J. Contam. Hydrol. 2007, 93 (14), 216–235. (19) Vrionis, H. A.; Anderson, R. T.; Ortiz-Bernad, I.; O’Neill, K. R.; Resch, C. T.; Peacock, A. D.; Dayvault, R.; White, D. C.; Long, P. E.; Lovley, D. R. Microbiological and geochemical heterogeneity in an in situ uranium bioremediation field site. Appl. Environ. Microbiol. 2005, 71 (10), 6308–6318. (20) Englert, A.; Hubbard, S. S.; Williams, K. H.; Li, L.; Steefel, C. I. Feedbacks Between Hydrological Heterogeneity and Bioremediation Induced Biogeochemical Transformations. Environ. Sci. Technol. 2009, 43 (14), 5197–5204. (21) Williams, K. H.; Kemna, A.; Wilkins, M.; Druhan, J.; Arntzen, E.; N’Guessan, L.; Long, P. E.; Hubbard, S. S.; Banfield, J. F., Geophysical monitoring of microbial activity during stimulated subsurface bioremediation. Environ. Sci. Technol. 2009. (22) Williams, K. H.; N’Guessan, A. L.; Druhan, J.; Long, P. E.; Hubbard, S. S.; Lovley, D. R.; Banfield, J. F. Electrodic voltages accompanying stimulated bioremediation of a uranium-contaminated aquifer. J. Geophys. Res., [Biogeosci.] 2010, 115. (23) Finsterle, S. Multiphase inverse modeling: Review and iTOUGH2 applications. Vadose Zone J. 2005, 3 (3), 747–762. (24) Shelobolina, E. S.; Anderson, R. T.; Vodyanitskii, Y. N.; Sivtsov, A. V.; Yuretich, R.; Lovley, D. R. Importance of clay size minerals for Fe(III) respiration in a petroleum-contaminated aquifer. Geobiology 2004, 2, 67–76. (25) Zachara, J. M.; Gassman, P. L.; Smith, S. C.; Taylor, D. Oxidation and Adsorption of Co(Ii)Edta(2-) Complexes in Subsurface Materials with Iron and Manganese Oxide Grain Coatings. Geochim. Cosmochim. Acta 1995, 59 (21), 4449–4463. (26) Li, L.; Steefel, C. I.; Yang, L. Scale dependence of mineral dissolution rates within single pores and fractures. Geochim. Cosmochim. Acta 2008, 72 (2), 360–377. (27) Steefel, C. I.; Lasaga, A. C. A coupled model for transport of multiple chemical species and kinetic precipitation/dissolution reactions with application to reactive flow in single phase hydrothermal systems. Am. J. Sci. 1994, 294, 529–592. (28) Steefel, C. I.; Maher, K. Fluid-rock interaction: A reactive transport approach. In Reviews in Mineralogy and Geochemistry; 2009; Vol. 70 10.2138/rmg.2009.70.11 (29) Lichtner, P. C. Continuum Model for Simultaneous ChemicalReactions and Mass- Transport in Hydrothermal Systems. Geochim. Cosmochim. Acta 1985, 49 (3), 779–800. (30) Rittmann, B. E.; McCarty, P. L. Environmental Biotechnology: Principles and Applications; McGraw-Hill: New York, 2001. (31) Monod, J. The Growth of Bacterial Cultures. Ann. Rev. Microbiol. 1949, 3, 371–394. (32) Lasaga, A. C. Kinetic theory in the earth sciences; Princeton University Press: Princeton, 1998; p 811. (33) Li, L.; Peters, C. A.; Celia, M. A. Upscaling geochemical reaction rates using pore-scale network modeling. Adv. Water Resour. 2006, 29 (9), 1351–1370. (34) Seifert, D.; Engesgaard, P. Use of tracer tests to investigate changes in flow and transport properties due to bioclogging of porous media. J. Contam. Hydrol. 2007, 93 (14), 58–71. (35) Thullner, M. Comparison of bioclogging effects in saturated porous media within one- and two-dimensional flow systems. Ecol. Eng. 2010, 36 (2), 176–196. (36) Tratnyek, P. G.; Johnson, R. L. Nanotechnologies for environmental cleanup. Nano Today 2006, 1 (2), 44–48. (37) Mueller, N. C.; Nowack, B. Nanoparticles for Remediation: Solving Big Problems with Little Particles. Elements 2010, 6 (6), 395–400.
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(38) Li, L.; Peters, C. A.; Celia, M. A. Applicability of averaged concentrations in determining geochemical reaction rates in heterogeneous porous media. Am. J. Sci. 2007, 307, 1146–1166. (39) Li, L.; Peters, C. A.; Celia, M. A., Effects of mineral spatial distribution on reaction rates in porous media. Water Resour. Res. 2007, 43, (1), doi:10.1029/2005WR004848. (40) Scheibe, T.; Yabusaki, S. Scaling of flow and transport behavior in heterogeneous groundwater systems. Adv. Water Resour. 1998, 22 (3), 223–238. (41) Miller, A. W.; Rodriguez, D. R.; Honeyman, B. D. Upscaling Sorption/Desorption Processes in Reactive Transport Models To Describe Metal/Radionuclide Transport: A Critical Review. Environ. Sci. Technol. 2010, 44 (21), 7996–8007. (42) Allen-King, R. M.; Divine, D. P.; Robin, M. J. L.; Alldredge, J. R.; Gaylord, D. R. Spatial distributions of perchloroethylene reactive transport parameters in the Borden Aquifer. Water Resour. Res. 2006, 42, 1. (43) Brusseau, M. L.; Nelson, N. T.; Oostrom, M.; Zhang, Z. H.; Johnson, G. R.; Wietsma, T. W. Influence of heterogeneity and sampling method on aqueous concentrations associated with NAPL dissolution. Environ. Sci. Technol. 2000, 34 (17), 3657–3664. (44) Cirpka, O. A.; Schwede, R. L.; Luo, J.; Dentz, M. Concentration statistics for mixing-controlled reactive transport in random heterogeneous media. J. Contam. Hydrol. 2008, 98 (12), 61–74. (45) Luo, J.; Dentz, M.; Carrera, J.; Kitanidis, P. Effective reaction parameters for mixing controlled reactions in heterogeneous media. Water Resour. Res. 2008, 44, 2. (46) Werth, C. J.; Cirpka, O. A.; Grathwohl, P. Enhanced mixing and reaction through flow focusing in heterogeneous porous media. Water Resour. Res. 2006, 42, 12. (47) Zinn, B.; Meigs, L. C.; Harvey, C. F.; Haggerty, R.; Peplinski, W. J.; Von Schwerin, C. F. Experimental visualization of solute transport and mass transfer processes in two-dimensional conductivity fields with connected regions of high conductivity. Environ. Sci. Technol. 2004, 38 (14), 3916–3926. (48) Lovley, D. R.; Coates, J. D. Bioremediation of metal contamination. Curr. Opin. Biotechnol. 1997, 8 (3), 285–289. (49) Chen, J. S.; Hubbard, S.; Rubin, Y.; Murray, C.; Roden, E.; Majer, E. Geochemical characterization using geophysical data and Markov Chain Monte Carlo methods: A case study at the South Oyster bacterial transport site in Virginia. Water Resour. Res. 2004, 40, 12.
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