Environ. Sci. Technol. 2007, 41, 1432-1438
Reactive Transport Modeling of Trichloroethene Treatment with Declining Reactivity of Iron SUNG-WOOK JEEN,† K. ULRICH MAYER,‡ R O B E R T W . G I L L H A M , * ,† A N D DAVID W. BLOWES† Department of Earth Sciences, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, and Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
precipitation was found to be the main cause of passivation of iron, resulting in reduced treatment efficiency and progressive migration of the mineral precipitation front (7, 8). The objective of this study was to incorporate the effects of mineral precipitation on iron reactivity into a reactive transport model to improve our capabilities for predicting long-term performance of iron PRBs, particularly for reductive dechlorination of trichloroethene (TCE). An existing reactive transport code was modified and tested against observed data from long-term column experiments designed to assess the extent of secondary mineral formation and its effects on the performance of the iron. The model was further used to evaluate longevity of an iron PRB under various hydrogeochemical conditions.
Modeling Approach Evolving reactivity of iron, resulting from precipitation of secondary minerals within iron permeable reactive barriers (PRBs), was included in a reactive transport model for trichloroethene (TCE) treatment. The accumulation of secondary minerals and reactivity loss were coupled using an empirically derived relationship that was incorporated into an existing multicomponent reactive transport code (MIN3P) by modifying the kinetic expressions. The simulation results were compared to the observations from longterm column experiments, which were designed to assess the effects of carbonate mineral formation on the performance of iron for TCE treatment. The model successfully reproduced the evolution of iron reactivity and the dynamic changes in geochemical conditions and contaminant treatment. Predictions under various hydrogeochemical conditions showed that TCE would be treated effectively for an extended period of time without a significant loss of permeability. Although there are improvements yet to be made, this study provides a significant advance in our ability to predict long-term performance of iron PRBs.
Introduction Simulation of the processes occurring in granular iron permeable reactive barriers (PRBs) is complicated by the fact that various chemical reactions and transport processes occur simultaneously. Although previous models (1-3) have shown advances in representing such systems, one of the remaining issues is the declining reactivity of iron over time, which may affect long-term treatment efficiency and overall geochemical conditions in a PRB. This aspect has not been considered in a comprehensive manner because of the inherent complexity of interacting processes and difficulties in quantifying the processes that control reactivity evolution (4). Neglecting the declining reactivity of iron limits the applicability of reactive transport models for predicting longterm performance. The long-term performance of an iron PRB may be compromised if secondary minerals accumulate within the barrier. Primary concerns include decreases in porosity and hydraulic conductivity of the barrier (5) and a decrease in reactivity of iron materials (6). In particular, carbonate mineral * Corresponding author phone: (519) 888-4658; fax: (519) 7461829; e-mail:
[email protected]. † University of Waterloo. ‡ University of British Columbia. 1432
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Model and Modification. A general-purpose numerical model MIN3P (9) for multicomponent reactive transport in variably saturated porous media was modified for the purpose of this study. The formulation of the model is based on a partial equilibrium approach (10, 11), allowing inclusion of kinetic limitations on precipitate formation. A more detailed description of the model can be found in Mayer et al. (9). MIN3P was previously used to represent an iron PRB system at Elizabeth City, NC (1). The model calculated the amounts of secondary minerals precipitated within the barrier and the simulation results indicated that secondary mineral formation decreases porosity, primarily in the up-gradient portions of the barrier. Mayer et al. (1) suggested that in a real system, the precipitation of secondary minerals would be less concentrated in the inflow area than predicted, because decreasing iron reactivity resulting from secondary mineral precipitation was not included in their study. Laboratory observations in recent column studies (7, 8) showed mineral precipitation fronts migrating across the columns over time, confirming that the reactivity of iron was decreasing as secondary minerals precipitated. Thus, to adequately reproduce these observations, it was necessary to incorporate the evolving reactivity of iron into the kinetic formulation of MIN3P. Inclusion of the evolving iron reactivity was accomplished by updating the reactive surface area of iron based on an empirically derived relationship between reactivity and secondary mineral accumulation. From the column studies of Jeen et al. (8), it was found that the reactivity decrease of iron can be described using an exponential function in terms of the accumulated total carbonate volume fraction:
S/S0 ) exp(-aX)
(1)
where S and S0 are the reactive surface area of iron at a particular time and the initial reactive surface area of iron (m2 iron L-1 H2O), respectively. X is the total carbonate volume fraction (volume of precipitates/bulk volume), and a is an empirical constant that relates iron reactivity loss to mineral precipitate accumulation. Declining reactivity is represented mathematically as a decline in reactive surface area of the iron; however, this may not be a physically accurate representation of the mechanism. A reduced number of reactive sites or declining rate of electron transfer across progressively thicker surface films could be other possible mechanisms (8). This equation can be further generalized if the accumulation of individual mineral phases is known:
∑ a φ (x,t))
S(x,t) ) S0 exp(-
i
i
(2)
i
10.1021/es062490m CCC: $37.00
2007 American Chemical Society Published on Web 01/12/2007
where S(x,t) is the reactive surface area of iron at a specific location along the flow path at time t (m2 iron L-1 bulk), S0 is the initial reactive surface area of iron (m2 iron L-1 bulk), Ri is the proportionality constant for mineral phase i, and φi(x,t) is the volume fraction of the mineral phase i at a specific location at time t (-). The proportionality constant for each mineral phase is related to the extent to which each mineral phase contributes to the decrease in iron reactivity. The model assumes that mineral phases exist together in one system and does not consider possible difference in effects that mineral phases may have if they exist separately. The kinetic module in MIN3P was modified by updating the reactive surface area at each node using eq 2. The updated reactive surface area is considered directly in both treatment and iron corrosion reactions, as reflected in eqs 3 and 4 of the following section. The rates of secondary mineral precipitation are indirectly affected by the reactivity decrease because of its influence on the evolution of solution composition and activity products in kinetic equations (eq 5 in the following section). As a result, all geochemical reactions within an iron PRB depend on each other, and thus the modified code is able to represent the dynamic evolution of geochemical conditions and contaminant treatment. Reaction Network and Model Parameters. 1. Chemical Reactions. The four laboratory columns of Jeen et al. (8) were simulated to evaluate the applicability of the modified code. The important chemical reactions in the columns include degradation of TCE, iron corrosion, and secondary mineral precipitation. Eleven aqueous components are required to describe the relevant chemical reactions: Ca2+, Cl-, CO32-, Fe2+, H+, H2(aq), TCE, cis-1,2-dichloroethene (DCE), vinyl chloride (VC), ethene, and H2O. A total of 12 aqueous complexes are also included for appropriate determination of mineral solubilities (Table S1, Supporting Information). Reduction-corrosion reactions are assumed to be irreversible, and all reactions are assumed to proceed in parallel. For the degradation of TCE, it is considered that about 5% is degraded by hydrogenolysis to cis-1,2-DCE and the remaining 95% is degraded to ethene via β-elimination (8). The reaction rate for TCE degradation is represented by the mixed-order kinetic expression:
d[TCE] [TCE] ) -kSA-TCE-Fe0‚S dt K1/2 + [TCE]
(3)
rate expressions based on transition state theory (14):
Rm i ) max
RH2O-Fe0 ) -max
{[
(
kSA-H2O-Fe0‚S 1 -
)] }
IAPH2O-Fe0 KH2O-Fe0
,0
(4)
where kSA-H2O-Fe0 is the rate constant of iron corrosion normalized to iron surface area (mol m-2 iron s-1), IAPH2O-Fe0 is the ion activity product, and KH2O-Fe0 is the equilibrium constant. An equilibrium constant of logKH2O-Fe0 ) -11.78 was calculated based on data from Reardon (12) and Stumm and Morgan (13). Secondary mineral precipitation reactions are assumed to be surface-controlled and are described by irreversible
IAPm i
-1
Km i
,0
(5)
where keff,i is an effective rate constant for the precipitation of mineral phase i (mol L-1 H2O s-1), IAPm i is the ion activity product, and Km i defines the corresponding equilibrium constant. It is assumed that once precipitates form they are not redissolved (irreversible precipitation), because allowing redissolution of secondary precipitates results in some recovery of reactivity of the iron at some locations and this adds more complexity to an already very complicated system. A preliminary simulation showed that only slight dissolution occurs at later times. The stoichiometries of the reduction-corrosion reactions are normalized with respect to zerovalent iron and are shown in Table S2 of the Supporting Information. Table S3, Supporting Information, lists the rate constants of the reactions for the various columns. For the initial reactive surface area, the physical surface area of iron, measured by the BET (15) method, was used for each column. Table S4, Supporting Information, summarizes secondary mineral precipitation reactions considered in the simulations, with the corresponding equilibrium constants. 2. Spatial Discretization, Physical Parameters, Initial and Boundary Conditions. The 0.5-m long columns were discretized using a spatial discretization interval of 0.01 m, giving a total of 51 grid points. The initial porosity was calculated for each column (0.51-0.55), with the corresponding volume fractions of Fe0 of 0.49-0.45. The initial hydraulic conductivity, calculated for each column, was in the range of 3.33 × 10-5 to 1.00 × 10-4 ms-1. Flow was simulated assuming fully saturated conditions with a second type (specified flux) boundary (1.38 × 10-5 ms-1) at the influent end and first type (specified head) boundary (zero hydraulic head) at the effluent end. Zero hydraulic head for the entire domain was specified as the initial condition for the flow simulation. The porosity and hydraulic conductivity were updated to reflect the changes during the simulation as secondary minerals precipitate and iron dissolves. The hydraulic conductivity was updated based on a normalized version of the Kozeny-Carmen relationship (16):
Kt ) where kSA-TCE -Fe0 is the rate constant of TCE normalized to iron surface area (mol m-2 iron s-1), [TCE] is the concentration of TCE (mol L-1 H2O), and K1/2 is the half-saturation constant (mol L-1 H2O), which corresponds to the TCE concentration at half-maximum transformation rate. The rate expression for iron corrosion by water is represented by a first-order dependence on iron surface area (12), and it is assumed that the reaction progress becomes inhibited when equilibrium conditions are approached (1):
{[ [ ( ) ]] } keff,i 1 -
[
φ3 (1 - φ)2
][ t
]
(1 - φ)2 φ3
initial
Kinitial
(6)
where Kt and Kinitial are the hydraulic conductivity at time t and initial hydraulic conductivity (ms-1), respectively, and φ is the porosity (-). The model, however, did not include the effect of the accumulation of hydrogen gas. The steady-state flow field was recalculated after each time step with the new permeability, maintaining the constant influent flux. A diffusion coefficient of 1.5 × 10-9 m2s-1 (17) and a longitudinal dispersivity of 9.9 × 10-4 m (from a bromide tracer test) were used as the transport parameters. Boundary conditions for reactive transport were third type (specified mass flux) at the influent end and second type (free exit) at the effluent end. The measured aqueous concentrations and pH of the source water for each column were used as the influent chemical compositions. The initial condition affects the simulation results only at very early time; thus, several orders of magnitude lower concentrations for each constituent, relative to the expected concentrations, were specified as the initial condition for reactive transport. Detailed input parameters for each column are summarized in Table S5 of the Supporting Information. Constraining Model Parameters. With the large number of model parameters, difficulties with non-unique parameter VOL. 41, NO. 4, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Model Parameters for Each Column. A (D.I. H2O + 10 mgL-1 TCE)
column log keffa (mol L-1 H2O s-1)
CaCO3(s) (aragonite) Fe2(OH)2CO3(s) Fe(OH)2(am)
R1d R2e R3f
NAg NA -8.42 NA NA 70.0
B (100 mgL-1 CaCO3 + 10 mgL-1 TCE)
C (100 mgL-1 CaCO3 + -1 10 mgL TCE, coarse iron)
D (500 mgL-1 CaCO3 + 10 mgL-1 TCE)
-7.14 (-7.89 to -6.83)b (-7.86 to -6.89)c -10.79 -8.68 75.0 2.0 NA
-7.44 (-8.03 to -7.28)b (-8.18 to -7.30)c -10.89 -8.68 85.0 5.0 NA
-6.93 (-6.20 to -6.03)b (-7.26 to -6.11)c -9.75 -8.68 55.0 2.0 NA
a Calibrated (see text for details). b Calculated using the rate expression described in Plummer et al. (19). c Calculated directly from the difference in calcium concentration versus time similar to the method of Reddy et al. (20). d Proportionality constant for CaCO3(s) (aragonite). e Proportionality constant for Fe2(OH)2CO3(s). f Proportionality constant for Fe(OH)2(am). g NA: Not applicable.
sets in the numerical solutions were anticipated. This potential problem was limited to a degree by the interconnected nature of the reactions and was further minimized using the following procedure. First, the rate constants for TCE degradation were determined by fitting the simulated concentration profiles to the earliest profiles of observed concentrations. The rate constant of iron corrosion for each column was calculated from the measured maximum rate of gas generation, based on the method suggested by Reardon (12, 18), assuming that hydrogen is the only gas collected, and was then adjusted to give improved fits. Second, the effective rate constants for secondary mineral precipitation reactions were obtained by fitting the initial profiles of alkalinity, calcium, dissolved iron, and pH simultaneously (Table 1). The rate constant for CaCO3(s) was about 1-2 orders of magnitude higher than reported in Mayer et al. (1); however, it should be noted that the reported value was obtained by fitting to reproduce the field chemical compositions. For comparison, a reaction rate expression for calcite dissolution and precipitation as a function of temperature and PCO2 (19) was used to calculate CaCO3(s) precipitation rates for each column. CaCO3(s) precipitation rates were also calculated directly from the difference in calcium concentration versus time using the method proposed by Reddy et al. (20). The estimated CaCO3(s) precipitation rates from the two methods were generally comparable with the rate constants obtained from the simulations (Table 1). The rate constant for Fe2(OH)2CO3(s) was comparable to that of siderite reported in Mayer et al. (1), although the range between different columns was wide (Table 1). For Fe(OH)2(am), the same rate constant as reported in Mayer et al. (1) was used, except for column A, which received 10 mgL-1 TCE in deionized water, where a slightly higher value was used (Table 1). Fe(OH)2(am) was assumed to be the initial phase for representative iron (hydr)oxide, although Fe(OH)2(am) is not stable and will convert to magnetite. Fe(OH)2(am) was not considered to be a passivating mineral for the columns receiving dissolved CaCO3, because the amount was significantly less than the carbonate minerals. Finally, the proportionality constants (Ri of eq 2) were determined for each mineral phase by fitting all geochemical profiles over time (TCE, pH, alkalinity, etc.) with single values of the constants. Because the simulation results are highly dependent on the previous geochemical conditions at each time step and the chemical compositions are interdependent, fitting of all geochemical profiles simultaneously with a single set of parameters was challenging. However, despite this difficulty, generally good profile matches were obtained.
Results and Discussion Evaluation of the Modified Model. The essential objective of this study was to incorporate the declining reactivity of 1434
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iron resulting from mineral precipitation into the reactive transport model and to evaluate the importance of reactivity evolution on contaminant treatment. Therefore, comparative simulations with and without including the proposed relationship between mineral volume fraction and reactivity of iron (eq 2) were conducted. Assuming that the reactivity of iron decreases solely because of the depletion of iron, the reactive surface area of iron (S) in the kinetic expressions in MIN3P can be updated using a two-third power-relationship, which was used previously to describe the fluid/rock interaction over geologic time spans (21):
S ) S0
( ) φFe0
0 φFe 0
2/3
(7)
where S0 is the initial reactive surface area of iron (m2 iron 0 0 L-1 bulk), and φFe 0 and φFe are the initial and current volume fractions of Fe0 (-). If eq 7 is used to update the reactive surface area of iron, the simulated TCE profiles for column D, which received 500 mgL-1 CaCO3 + 10 mgL-1 TCE, change only slightly over time (Figure 1a), because the loss of iron resulting from treatment and corrosion reactions is small and thus the reactivity remains almost constant. Other profiles (data not shown), such as pH, alkalinity, calcium, and dissolved iron, also change only slightly over time. The accumulation of aragonite is most prominent near the influent end of the column, and it does not move further into the column over time (Figure 1b). More importantly, the accumulation of aragonite, along with Fe2(OH)2CO3(s) (data not shown), is concentrated near the influent end of the column such that complete clogging occurs by Day 139. In contrast, after incorporation of the reactivity loss resulting from mineral precipitation, the simulation shows migration of the TCE profiles over time (Figure 1c), with similar migration of other profiles (see Figure 2). While aragonite still accumulates near the influent end, it spreads further into the column over time (Figure 1d), decreasing the possibility of clogging. Comparison with the Results of the Column Experiments. As an example of the comparison with laboratory observations, Figure 2 shows the measured and simulated profiles of TCE, pH, alkalinity, and calcium for column D (500 mgL-1 CaCO3 + 10 mgL-1 TCE). The TCE profiles are generally in good agreement (Figure 2a), showing the progressive passivation of the iron. TCE profiles at later times do not migrate as rapidly as the early profiles, indicating that the iron surfaces maintain some reactivity even after the surfaces are coated by substantial amounts of carbonate minerals. The pH is controlled by iron corrosion and
FIGURE 1. (a) TCE and (b) aragonite volume fraction simulated with “two-third” relationship (see text for details). (c) TCE and (d) aragonite volume fraction simulated with the proposed relationship between mineral volume fraction and reactivity of iron (eq 2). carbonate precipitation at early times, and is significantly affected by passivation of the iron at later times (Figure 2b). At early times, pH increase occurs near the influent end; however, because this region loses reactivity over time, pH increases are delayed further into the column. Eventually the pH profiles become almost flat. Similarly, the consumption of alkalinity and calcium occurs mainly in the region near the influent end at early times (Figure 2c and d). As time progresses, the decline in alkalinity and calcium occurs further into the column and the removal rates also decline. During model calibration, the calcium profiles were used to determine the aragonite precipitation rate because calcium removal is controlled primarily by this mineral. Subsequently, the Fe2(OH)2CO3(s) precipitation rate was determined by matching the alkalinity profiles with a fixed value for the aragonite precipitation rate constant. Dissolved iron concentration profiles were also used to constrain the Fe2(OH)2CO3(s) precipitation rate. As a result, the mineral precipitation rates were relatively well constrained. For the proportionality constants for aragonite and Fe2(OH)2CO3(s), better fits were obtained by using a higher value for aragonite than for Fe2(OH)2CO3(s) (Table 1). Because a higher value of the proportionality constant represents a greater contribution to the reactivity loss, it can be hypothesized that, in this system, aragonite reduces the reactivity of iron more than Fe2(OH)2CO3(s). The simulations for other columns were conducted using model parameters similar to those for column D (Table 1) and the simulation results are generally consistent with the laboratory data (see Figures S1-S3, Supporting Information). Prediction of the Long-Term Performance of Iron PRBs under Various Hydrogeochemical Conditions. It is not
expected that the model will precisely match the geochemical changes over time in a PRB under field conditions; nevertheless, the ability to represent the trends, and in many cases, to match the column data, suggests that the model could be useful for estimating long-term performance of PRBs over a range in hydrogeochemical conditions. The empirical nature of the reactivity update in the model, however, should be recognized when the simulation results are interpreted. Simulations for four different cases were performed to examine the change in performance over time under differing hydrogechemical conditions (Figure 3 and Figures S4 and S5 of the Supporting Information). As a base case, the performance of a 0.5-m-thick iron PRB, treating 10 mgL-1 TCE with a groundwater composition of 100 mgL-1 CaCO3 and a velocity of 0.1 md-1, was simulated. The model parameters were taken from those of column C, because it received water of the same composition as the base case and also the coarse material used in column C is more commonly used in the field. Because groundwater velocity is about 23 times slower than that of the column experiments, the residence time of TCE was much greater than in the columns for the same travel distance. As a result, TCE is completely treated within 0.15 m of the influent surface of the barrier, even after 40 years of operation (Figure 3a). For the same reason, mineral precipitation occurs closer to the influent surface of the barrier. The volume fractions of carbonate minerals are greatest at about 0.02 m from the influent surface and the sum approaches about 0.35 after 40 years of operation (Figures S4a and S5a, Supporting Information). However, in the region where carbonate precipitation is most prominent, dissolution of iron is also most significant. As a result, porosity loss is somewhat compenVOL. 41, NO. 4, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Measured and simulated profiles for column D (500 mgL-1 CaCO3 + 10 mgL-1 TCE): (a) TCE, (b) pH, (c) alkalinity, and (d) calcium. Note that the calcium was not measured on Day 4 and thus was not compared with the simulation result. sated, such that the porosity does not decline to less than 0.3 (from the initial value of 0.55) after 40 years of operation. For the second case, the groundwater velocity was increased to 0.2 md-1, keeping the groundwater composition the same as that for the base case (100 mgL-1 CaCO3). Because the residence time is decreased and twice the amount of CaCO3 is supplied in the same period of time compared to the base case, the TCE profiles are shifted further into the barrier, and thus TCE persists until about 0.3 m from the influent surface after 40 years of operation (Figure 3b). The pattern of carbonate mineral accumulation is similar to that of the base case, although the position of the precipitates is shifted further into the barrier (Figures S4b and S5b, Supporting Information). For the third case, the groundwater velocity was the same as that of the base case (0.1 md-1), but the composition was increased to 200 mgL-1 CaCO3. The increased CaCO3 concentration favors aragonite precipitation. As a result, the TCE profiles are shifted furthest into the barrier (to about 0.4 m from the influent surface after 40 years of operation, Figure 3c). Predominance of aragonite precipitation rather than Fe2(OH)2CO3(s) formation near the inflow portion of the barrier (Figure S4c, Supporting Information) results in greater reactivity loss and thus less accumulation of Fe2(OH)2CO3(s) in that region (Figure S5c, Supporting Information). The porosity loss is more or less evenly distributed throughout the barrier, and porosity is not reduced to less than 0.38 by 40 years of operation. The final simulation was conducted with the same groundwater composition and velocity as the base case (100 1436
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mgL-1 CaCO3 and 0.1 md-1, respectively), but with a oneorder-of-magnitude smaller reactive surface area of iron. Because of the dependency of the TCE reaction rate constant on the reactive surface area of iron (eq 3), the initial TCE profile is shifted further into the barrier compared to the base case (Figure 3d). However, because of lower accumulation of carbonate minerals (Figures S4d and S5d, Supporting Information), the rate at which the TCE profile migrates is slower than that in the base case. Even with a reactive surface area one order-of-magnitude smaller than that measured in the laboratory, degradation of TCE is still relatively fast at a typical groundwater velocity. In addition, accumulation of secondary minerals and the resulting porosity loss is significantly decreased. This result may suggest that less reactive iron material may perform more effectively than iron of higher reactivity in the long term. In summary, for the cases tested, TCE is completely treated within an iron PRB of 0.5 m thickness for a period of 40 years, and porosity does not decrease to the extent that severe clogging would occur near the influent surface. Implications. The identity and properties of mineral phases are important factors affecting predictive capabilities. For example, based on mineralogical examination (22), Fe2(OH)2CO3(s) was included in the simulation as an iron carbonate phase. Because this mineral has a relatively low density, if another iron carbonate phase with a higher density, such as siderite, accumulates instead of Fe2(OH)2CO3(s), the simulated porosity loss will be substantially decreased. Mineral precipitation rates are also significant. It was found that mineral precipitation is not only dependent on the
FIGURE 3. Predicted TCE profiles (a) with a composition of 100 mgL-1 CaCO3 at a groundwater velocity of 0.1 md-1; (b) with a composition of 100 mgL-1 CaCO3 at a groundwater velocity of 0.2 md-1; (c) with a composition of 200 mgL-1 CaCO3 at a groundwater velocity of 0.1 md-1; and (d) with a composition of 100 mgL-1 CaCO3 at a groundwater velocity of 0.1 md-1, and with a one-order-of-magnitude smaller reactive surface area of iron. S represents the initial reactive surface area of the iron (m2 iron L-1 bulk).
absolute value of the precipitation rate but also on its relative value in relation to precipitation rates of other mineral phases. A slow precipitation rate for one mineral phase may be beneficial for the reduction of contaminants; however, at the same time, it may result in faster accumulation of other mineral phases. To obtain more reasonable estimates for expected precipitation rates, feasibility tests using site groundwater should be conducted in the initial stage of a PRB design. These tests should be supplemented by numerical modeling. Simple calculation of longevity of an iron PRB inferred from the column flow rate and chemical composition may be misleading. Accumulation of carbonate minerals may not be proportional to the influent concentration. For example, higher dissolved CaCO3 concentrations favor aragonite precipitation rather than Fe2(OH)2CO3(s), resulting in a different evolution of geochemical conditions over time. Higher influent CaCO3 concentration may result in an initially faster accumulation of aragonite; however, because of a greater reactivity loss caused by aragonite, the overall loss in porosity for some period of time may be less than is the case for lower influent CaCO3 concentrations. The simulation results of this study successfully demonstrate the concept of the evolving reactivity of iron and reproduce the measured geochemical profiles with reasonable accuracy. Thus, the model has promise as a predictive
tool that can be used in the initial stages of designing iron PRBs. However, use of the model for prediction of long-term performance should be approached with considerable caution. Applicability of the various rate constants to field conditions is uncertain and the effects of other oxidants (e.g., SO42-, NO3-, O2) and other potential secondary mineral precipitates (e.g., iron sulfides) were not considered. Preferential flow also was neglected. While these effects could be included through future testing and development, the model, as presently formulated, will be most relevant in circumstances where carbonate precipitation is the dominant geochemical process.
Acknowledgments Funding for this research was provided from the NSERC/ DuPont/EnviroMetal Industrial Research Chair held by R.W.G. and from a Canadian Water Network grant held by D.W.B. Additional funding for S.-W.J. was provided by the Korean Government Scholarship Program.
Supporting Information Available Tables of equilibrium constants for complexation reactions, reaction stoichiometries and rate constants of reductioncorrosion reactions, equilibrium constants for mineral dissolution-precipitation reactions, and input parameters used in the simulations; comparisons with the results of the column VOL. 41, NO. 4, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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experiments (other than column D); figures of the predicted aragonite and Fe2(OH)2CO3(s) volume fractions. This material is available free of charge via the Internet at http:// pubs.acs.org.
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Received for review October 17, 2006. Revised manuscript received December 5, 2006. Accepted December 6, 2006. ES062490M
