Reactive Transport Modeling of Chromium Isotope Fractionation

Article pubs.acs.org/est

Reactive Transport Modeling of Chromium Isotope Fractionation during Cr(VI) Reduction Julia H. Jamieson-Hanes, Richard T. Amos,* and David W. Blowes Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, N2L 3G1, Canada S Supporting Information *

ABSTRACT: Chromium isotope fractionation is indicative of masstransfer processes, such as reduction of Cr(VI) to Cr(III) during groundwater remediation. Laboratory experiments comparing batch and column treatment of Cr(VI) using organic carbon suggest that the associated isotope fractionation may be influenced by solute-transport mechanisms. These batch and column experiments were simulated using the reactive transport model MIN3P to further evaluate the effects of Cr reduction and transport on isotope fractionation under saturated flow conditions. Simulation of the batch experiment provided a good fit to the experimental data, where a fractionation factor (α53) of 0.9965 was attributed to a single, dominant Cr(VI) removal mechanism. Calibration of the column simulations to the experimental results suggested the presence of a second, more rapid Cr(VI) removal mechanism with α53 = 0.9992. Results from this study demonstrate that the interpretation of Cr isotope fractionation during reduction can be complex, particularly where multiple removal mechanisms are evident. Reactive transport modeling of Cr isotope fractionation can provide a quantitative assessment of the contaminant removal mechanisms, thus improving the application of Cr isotope measurements as a tool to track Cr(VI) migration and attenuation in groundwater.

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INTRODUCTION

Laboratory batch experiments have demonstrated that the degree of δ53Cr enrichment is dependent on the reductant and the mechanism of Cr(VI) removal.13−20 Observations of enriched δ53Cr values in field samples have been reported in the literature;13,16,21−26 however, the influence of transport on Cr isotope fractionation is not yet fully understood. JamiesonHanes et al.27 measured Cr isotope fractionation under saturated flow conditions. Batch and column experiments were conducted to evaluate the potential influence of transport during Cr(VI) reduction by organic carbon. The δ53Cr data from the batch experiment were fit to a Rayleigh curve with a fractionation factor (α53) of 0.9965, whereas the column data were fit by a linear regression with α53 = 0.9979. The difference in α53 values was attributed to the presence of two Cr removal processes in the column experiment and the possible contribution of transport on the Cr isotope fractionation. Reactive transport models have been used to simulate isotope fractionation in contaminated systems, including sulfur isotope fractionation during sulfate reduction,28 carbon isotope fractionation during degradation of chlorinated hydrocarbons29−32 and other organic substances,33,34 and chlorine isotope fractionation in chlorinated hydrocarbons. Wanner et al.26,35 simulated Cr isotope fractionation associated with Cr reduction in field experiments. The reactive transport model

Chromium(VI) is a toxic and mobile groundwater contaminant that is derived both from naturally occurring geological sources and as a byproduct of industrial activities such as tanning and electroplating.1 In its oxidized form Cr(VI) occurs in groundwater as HCrO4−, CrO42−, and Cr2O72− oxyanions.2 The reduced Cr(III) form is an essential micronutrient that is sparingly soluble and thus less mobile in the subsurface;3 as such, remediation techniques have traditionally focused on promoting the reduction of Cr(VI) to Cr(III). A wide variety of materials can be used as electron donors for the reduction of Cr(VI), including aqueous Fe(II),4,5 granular zerovalent iron,6,7 organic carbon,8,9 and certain bacteria.10,11 Chromium has four stable isotopes, 50Cr, 52Cr, 53Cr, and 54 Cr, with natural abundances of 4.35%, 83.8%, 9.5%, and 2.37%. Various processes have been observed to fractionate Cr isotopes; in particular, the change in coordination during the transition from Cr(VI) to Cr(III) can cause a large change in isotope composition.12 During reduction, the lighter isotopes are preferentially reduced, resulting in an enrichment in heavier isotopes in the remaining Cr(VI) pool. Measurement of Cr isotope ratios in groundwater have been proposed as a method to track Cr(VI) migration processes and evaluate the performance of remediation activities.1,13 Values are expressed in terms of δ53Cr in units per mil (‰) relative to the initial isotope composition; positive δ53Cr values are indicative of mass-transfer processes.1 © 2012 American Chemical Society

Received: November 13, 2012 Accepted: November 16, 2012 Published: November 16, 2012 13311

dx.doi.org/10.1021/es3046235 | Environ. Sci. Technol. 2012, 46, 13311−13316

Environmental Science & Technology

Article

MIN3P has been previously used to simulate isotope fractionation associated with sulfate reduction in column experiments.28 In this paper, MIN3P is applied to the batch and column experiments previously described by Jamieson-Hanes et al.27 The objective of the modeling study is to assess the effects of Cr reduction and transport on Cr isotope fractionation. This objective is achieved through a more rigorous quantification of the isotope fractionation effects associated with Cr reduction and transport provided by the numerical modeling.

k=

52

k=

k=

keff

⎯→ ⎯

4Cr(OH)+2

+ 6H

54

50

k

(2)

→4

Cr(OH)+2

+

3CO32 −

+ 2H 2O

(3)

→ 453Cr(OH)+2 + 3CO32 − + 2H 2O

(4)

53

k

3CH 2O + 454CrO24 − + 6H+ 54

k

(5)

For a given isotope, the mass-dependent rate constants xk, where x refers to the mass number of the isotope, can be described as a function of the kinetic isotope fractionation factor, αx, the isotope ratio of the substrate, xRs, and the isotope ratio of the instantaneously derived product, xRp: 53 53

Rp =

kp

52

kp

=

− 53k s 52

− ks

keff = 50k +

52

⎛ IAP ⎞⎟ R = −keff ⎜1 − ⎝ K ⎠

= α5353R s (6)

An analogous equation can be derived for the isotopes. Employing a mass balance approach k + 53k +

54

k

50

Cr and

1 + α50/52R 50/52 + α53/52R 53/52 + α54/52R 54/52

(12)

where x refers to the mass number of the individual Cr isotopes, [Cr] is the total Cr concentration (i.e., sum of the four isotopes), xk is determined through analogues of eqs 9 and 10 for the two-isotope system, and keff and K1/2 are calibrated to the experimental results. This type of rate expression simulated a near zero-order rate expression at high concentration and a first-order rate expression when the concentration approached or was below the value of K1/2. A first-order rate expression with respect to Cr was simulated in trial runs but did not reproduce the measured data well. Organic carbon is considered to be in excess in the batch experiment, and therefore the rate expression for Cr reduction did not include a dependence on organic carbon. Calcite and dolomite were allowed to precipitate or dissolve if present according to the rate expression:

3CH 2O + 453CrO24 − + 6H+

→ 454Cr(OH)+2 + 3CO32 − + 2H 2O

keff α54/52R 54/52

⎡ ⎤ [Cr] ⎥ R CH2O− xCr = − xk ⎢ ⎣ K1/2 + [Cr] ⎦

3CH 2O + 452CrO24 − + 6H+ 52

k=

MODEL PARAMETERS Batch Experiment. The batch experiment was simulated using nine geochemical components including oxidized and reduced Cr species, 52CrO42−, 53CrO42−, 52Cr(OH)2+, 53Cr(OH)2+, and major ions H+, CO32−, Ca2+, K+, and Mg2+. A total of 27 aqueous species were included to determine mineral solubilities, based on the WATEQ4F37 and MINTEQA238 databases. The primary reaction simulated is the irreversible reduction of Cr by organic carbon as described by eqs 3 and 4. The reaction rate is considered to be dependent on the concentration of Cr based on a hyperbolic rate expression:

3CH 2O + 450CrO24 − + 6H+

k

keff α53/52R 53/52 1 + α50/52R 50/52 + α53/52R 53/52 + α54/52R 54/52

■

where keff is the effective rate constant. To simulate massdependent isotope effects, eq 1 is expressed in terms of the four isotopes of Cr:

52

(9)

Although the approach described above provides a generalized formulation for simulating multiple isotope systems, the simulations presented in this paper describe a two-isotope model including 52Cr and 53Cr. This simplification is necessary because only these two isotopes were measured in the experimental system. Model results are expressed as δ53Cr in permil (‰) relative to the NIST SRM 979 Cr isotope standard.

(1)

→ 450Cr(OH)+2 + 3CO32 − + 2H 2O

keff 1 + α50/52R 50/52 + α53/52R 53/52 + α54/52R 54/52

(11)

+

+ 3CO32 − + 2H 2O

(8)

(10)

MODEL FORMULATION MIN3P36 is a multiphase, multidimensional reactive transport code applicable to geochemical problems involving kinetically controlled redox and mineral dissolution/precipitation reactions, along with equilibrium hydrolysis, aqueous complexation, ion exchange, and surface complexation reactions. Gibson et al.28 extended MIN3P to simulate isotope fractionation in a two-isotope sulfur system. Here a generalized formulation is employed, allowing for an unlimited number of isotopes for any element to be tracked. The four-isotope Cr system is used as an example. The reduction of chromate by organic carbon can be described as: 3CH 2O +

1 + α50/52R 50/52 + α53/52R 53/52 + α54/52R 54/52

53

■

4CrO24 −

keff α50/52R 50/52

50

(13)

where IAP is the ion activity product, K is the solubility constant, and keff is set to 4 × 10−9 mol (L bulk)−1 s−1, which is high enough to achieve near equilibrium conditions when the geochemical conditions are appropriate. In addition to calcite and dolomite, the Cr phases Cr(OH)3(am), K2Cr2O7, and K2CrO4 are included, although

54

Cr

(7)

the rate constants for each isotope reaction can be derived: 13312



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measured Cr concentrations and δ53Cr values were simulated well (Figure 1a), suggesting that the conceptual model

the phases K 2 Cr 2 O 7 and K 2 CrO 4 were found to be unimportant. To appropriately simulate the precipitation and potential dissolution of these minerals, each is simulated as two different minerals with different Cr isotopes. The rate expression for each mineral is given by: ⎛ IAP ⎞⎟ R = − xk ⎜1 − ⎝ K ⎠

(14)

x

where k is determined analogous to eqs 9 and 10, and IAP is calculated using the total Cr(VI) or Cr(III) concentration, as appropriate. Column Experiment. The column was simulated as a onedimensional solution domain, 40 cm long, using 1 cm control volumes. The porosity and dispersivity were set to 0.40 and 0.019, respectively, based on values measured in similar columns.39 The water velocity through the column was set to the average measured value of 1.6 × 10−6 m s−1. Initial input water through the column contained no Cr. At 3 days simulation time, the input solution containing approximately 0.4 mmol L−1 of Cr was introduced to the column. The geochemical system used to describe the column experiment was the same as that described in the previous section for the batch experiment with the following exceptions. Jamieson-Hanes et al.27 determined an α53 value of 0.9979 for the column experiment, which is approximately in the middle of the range of previously published values.13,14,16−19,40 They hypothesized that intermediate value was due to a combination of Cr removal processes, including the reduction of Cr(VI) in solution, and sorption of Cr(VI) onto the organic carbon followed by reduction. In both cases Cr(VI) is reduced to Cr(III). As such, the column model includes two sets of Cr reduction reactions (eqs 3 and 4) to simulate two mechanisms, referred to here as mechanism A and mechanism B, but with different effective rate constants and different fractionation factors. Effective rate constants and fractionation factors for the two mechanisms were determined by model calibration. The column experiment exhibited an apparent decrease in Cr reduction rate over time, suggesting a dependence of the reaction rate on the availability of organic carbon. This is in contrast to the batch experiment, where no dependence on the availability of organic carbon is apparent. Therefore, for the column experiment, the reaction rates for both Cr reduction mechanisms were modeled with a first-order dependence on organic carbon, giving an overall rate expression: ⎡ ⎤ [Cr] ⎥ R CH2O− xCr = − xkfOC ⎢ ⎣ K1/2 + [Cr] ⎦

Figure 1. Results from batch experiment including (a) Cr(VI) (mmol L−1) concentrations and δ53Cr (‰) values over time, (b) δ53Cr (‰) with fraction ( f) of Cr(VI) remaining. Symbols represent measured data. Lines represent model results. The isotope fractionation factor used in the simulations was 0.9965.

described above was appropriate for simulating the system. Particularly, the hyperbolic dependence of the rate on total Cr(VI) concentrations seems appropriate. In addition, the model effectively simulated the fractionation of 53Cr as Cr(VI) was reduced (Figure 1b). The α53 value of 0.9965 is within the range of values determined by previous laboratory batch experiments using a variety of reductants.13,16−19,40 Column Experiment Simulation Results. The column experiment described by Jamieson-Hanes et al.27 was simulated to provide insight into the processes that contribute to the observed trends in Cr isotope fractionation. Inspection of the experimental data (Figure 2) indicates removal of Cr through two mechanisms with different removal rates. The column profile measurements (Figure 2a,b) show a gradual decrease in

(15)

where f OC is the volume fraction of organic carbon associated with a particular mechanism. In this context, f OC for mechanism B was presumed to be sorption sites or functional groups that can facilitate Cr reduction, although the exact mechanism is not known. Both keff and f OC for both mechanisms were calibrated to experimental results.

■

RESULTS AND DISCUSSION Batch Experiment Simulation Results. The batch experiment described by Jamieson-Hanes et al.27 was simulated to provide model verification and to assist in calibration of the model to the geochemical system for simulation of the column experiment. Jamieson-Hanes et al.27 determined an isotope fractionation factor of 0.9965 for the 53Cr/52Cr ratio. The time series of

Figure 2. Results from column experiment including (a) and (b) Cr(VI) (mmol L−1) and δ53Cr (‰) profiles at 18 days and 24 days, respectively, and (c) Cr(VI) (mmol L−1) concentrations and δ53Cr (‰) values in effluent samples over time. Water flow in the column is from bottom to top. Symbols represent measured data. Lines represent model results. The sharp increase in δ53Cr at approximately day 3 is due to the introduction of the Cr solution into the column at 3 days. 13313



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3). An α53 value of 0.9965 from the batch experiments is assumed in the simulations for the slower Cr(VI) removal mechanism, mechanism A. Good agreement between the observed and simulated isotope fractionation in the samples that are subject only to mechanism A suggests that this assumption is reasonable. These observations include the samples at the influent end of the column profiles and the column effluent data after 30 days (Figure 2). In Figure 3, these data points are those with a fraction greater than 0.8. Although the α53 value of 0.9965 appears to fit the data reasonably well, the model is not particularly sensitive to this parameter. However, a sensitivity analysis (Figure S1, Supporting Information) would suggest that the maximum α53 value for mechanism A would be approximately 0.9975. Several simulations were conducted with a range of values for α53 for mechanism B (Figure 3). Based on results from the profile at 24 days and the column effluent samples, a reasonable fractionation factor for mechanism B is α53 = 0.9992. The results from the profile at 18 days do not correspond as well to these fractionation factors and suggest greater fractionation at 18 days. Model Interpretation. The reactive transport simulations of the column experiment employed a two-mechanism Cr removal approach where the first mechanism (A) simulated Cr removal with an α53 value of 0.9965. This fractionation factor is consistent with previous studies of Cr reduction13,16−19,40 and with the batch experiment of Jamieson-Hanes et al.27 using similar organic matter. The second mechanism (B) employed in the simulations required an α53 value of 0.9992 to reasonably match the experimental data, suggesting that an alternate mechanism may be responsible for Cr removal. Sorption of Cr is reported to result in little fractionation,14 whereas recent reports suggest that there may be an inverse relationship between reduction rates and the degree of Cr isotope fractionation.19 Park et al.41,42 proposed two mechanisms for Cr removal in batch experiments with various forms of biomass. The two mechanisms are described as direct reduction in solution and reduction following sorption to the biomass. Although the experiments studied here are performed at a pH higher than that of the experiments of Park et al.41,42 (neutral pH vs pH 2, respectively), a similar two-mechanism process is apparent. Ellis et al.14 demonstrated that sorption of Cr on γ-Al2O3 and goethite resulted in little Cr isotope fractionation. Simulation results suggest that in the column experiments both apparent mechanisms impart some fractionation, although the nature of the mechanism is not constrained by the model. The batch and column experiments of Jamieson-Hanes et al.27 showed distinctly different behavior. Specifically, the batch

the chromium concentration in the lower half of the column (influent end) followed by a more pronounced decrease in the Cr concentration in the upper half of the column (effluent end). The profile at 18 days (Figure 2a) appears to show the break in Cr removal rate earlier in the column (approximately 18 cm into the column) compared to the profile at 24 days (approximately 24 cm into the column), suggesting an advancing front as available organic carbon is consumed. This advancing front is manifested in time series effluent data (Figure 2c) as a breakthrough in Cr concentration from 20 to 30 days. In addition, Cr concentrations in the column effluent data (Figure 2c) continually increase from 30 to 88 days, suggesting a continued decrease in the Cr removal capacity, apparently due to further consumption of organic carbon. The Cr concentrations in the simulation are controlled by four parameters, namely the keff and f OC for each of the two removal mechanisms (Table 1). Mechanism A is the slower Table 1. Kinetic rate Parameters for Column Experiment Simulations parameter

mechanism A

mechanism B

keff (mol (L bulk)−1 s−1) f OC α53

1 × 10−5 5 × 10−5 0.9965

3 × 10−4 8 × 10−5 0.9992

mechanism associated with the gradual decrease in Cr concentrations at the influent end of the profiles, and the gradual increase in Cr concentrations after 30 days in the time series (effluent) data. The value of keff for this mechanism is calibrated to the slope in Cr concentration at the influent end of the column profiles and to the concentrations observed in the time series data after 30 days. The value of f OC is calibrated to the changing rate of Cr removal over time, as shown by the change in the slope of Cr concentrations at the influent end of the columns between 18 and 24 days and by the gradual increase in Cr concentrations in the effluent data after 30 days. Mechanism B is associated with the more pronounced decrease in Cr concentrations observed at the effluent end of the column profiles, the low concentrations of Cr observed in the column effluent data prior to 20 days, and the breakthrough of Cr concentrations observed from 20 to 30 days. The value of keff for this mechanism is calibrated to the observed Cr concentrations at the effluent end of the column profiles, and f OC is calibrated to the position of the Cr breakthrough observed in the effluent data. Jamieson-Hanes et al.27 noted that the observed isotope fractionation in the column experiment followed a linear trend on a plot of δ53Cr versus the fraction of Cr remaining (Figure

Figure 3. δ53Cr (‰) with fraction (f) of Cr(VI) remaining for column profile samples at 18 and 24 days, and column effluent samples over the duration of the experiment. Symbols represent measured data. Lines represent simulation results with various values of α. 13314



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simulations more precisely represent the observed trends in Cr concentrations and isotope ratios than was possible using simpler approaches. Furthermore, the simulations were able to provide further insight into the isotope fractionation associated with the Cr removal processes. These study results are consistent with previous work that suggests that Cr removal and the associated isotope fractionation is not a straightforward process and that multiple mechanisms may be involved. The application of Cr isotopes to quantify Cr removal at contaminated field sites or in Cr remediation systems will require spatially and temporally distributed data sets that can accurately represent these multiple mechanisms. The simulations highlight the need to further understand the mechanisms controlling Cr attenuation and reduction, and the effects of these mechanisms on Cr isotope ratios, so that field data can be appropriately interpreted and Cr removal rates accurately quantified.

experiment appeared to be dominated by a single, slow Cr removal mechanism with an α53 value of 0.9965. On the contrary, the column experiment showed two distinct Cr removal regimes with different fractionations. The organic carbon material used in the batch and column experiments were both derived from municipal compost, and although they would have similar characteristics, a degree of chemical heterogeneity would be expected. The composition of the organic material was not characterized for each experiment. The organic composition of municipal composts is a function of the input material and composting processes, both of which would be expected to vary over time. The similarity in the material used in each of the experiments might lead to the hypothesis that the differences between the batch and column experiments are due to transport processes. Berna et al.16 suggest a “reservoir effect” to explain lower than expected δ53Cr values in a Cr-contaminated groundwater plume. However, the simulations suggest that a significant reservoir effect is not evident. The reservoir effect is essentially a mixing process that will more heavily influence the leading edge of a plume. However, the simulations demonstrate the distinctly different regimes before and after Cr breakthrough, both in Cr concentrations and δ53Cr, which could not be represented by simulating increased mixing. Ellis et al.14 suggested that a small equilibrium fractionation resulting from sorption (e.g., 0.1 ‰) would be amplified during transport. This modeling study employed a kinetic fractionation for both Cr removal mechanisms but cannot explicitly rule out other mechanisms. However, it is apparent from the kinetic modeling that significant fractionation factors are associated with each mechanism. Furthermore, a reversible equilibrium fractionation is not consistent with experimental data, which showed that Cr(VI) is the dominant Cr species in solution, whereas Cr(III) was associated with the solid phase.27 Previous studies on Cr isotope fractionation have often employed Rayleigh-type or linear fractionation models to explain observed trends. This study shows that these models may not be adequate and should be used with caution. Jamieson-Hanes et al.27 applied a linear fractionation model to arrive at a α53 value of 0.9979 for the column experiment studied here and concluded that the difference between the batch and column fractionation factors implied a mixed mechanism, such as direct reduction alongside sorption. Although this conclusion is generally consistent with the simulation results, the simulations show that a sorption mechanism without fractionation cannot explain the observed trends. Furthermore, the simulations demonstrate that the observed trend in isotope fractionation can be described by a compound two-mechanism Rayleigh process (Figure 3). The simulation results provide a reasonable fit to the Cr concentration and δ53Cr data, suggesting that the twomechanism hypothesis is a plausible representation of the true system. We note, however, that the solution is subject to chemical and physical heterogeneity within the column that are not included in the simulation. Thus, derived simulation parameters such as reaction rates and fractionation factors are not considered fundamental constants but rather effective values that represent the column overall. In fact, the variability shown in the column data, and therefore the variability in the quality of the model fit, is apparently due to spatial heterogeneities and apparent temporal changes. Implications. The simulations illustrate the value of applying reactive transport models to complex systems. The

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ASSOCIATED CONTENT

* Supporting Information S

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant awarded to D. W. Blowes, and an Ontario Research Foundation - Research Excellence Grant awarded to D. W. Blowes, C. J. Ptacek, and R. T. Amos. Additional support was provided through a NSERC Alexander Graham Bell Canada Graduate Scholarship and an Ontario Graduate Scholarship (OGS) awarded to J. Jamieson-Hanes.

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REFERENCES

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Reactive Transport Modeling of Chromium Isotope Fractionation

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