Bioremediation of TCE at Savannah River - American Chemical Society

kinetic sorption, and predator grazing. We conclude that the total amount of TCE extracted or biodegraded in the. Savannah River demonstration was abo...
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Environ. Sci. Technol. 1997, 31, 3093-3102

Modeling in Situ Bioremediation of TCE at Savannah River: Effects of Product Toxicity and Microbial Interactions on TCE Degradation BRYAN J. TRAVIS* AND NINA D. ROSENBERG Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545

An in situ bioremediation field demonstration was performed at the U.S. DOE’s Savannah River site in 1992-1993 to remediate subsurface TCE contamination. This demonstration involved stimulating indigenous methanotrophic bacteria with injection of methane, air, and air-phase nutrients below the water table and vacuum extraction in the vadose zone. We model this field demonstration using TRAMPP, a numerical code that differs from those used in previous studies in that it includes both vadose and groundwater zones, unsteady air and water flow, limited nutrients, and airborne nutrients, in addition to toxicity, cometabolic kinetics, kinetic sorption, and predator grazing. We conclude that the total amount of TCE extracted or biodegraded in the Savannah River demonstration was about 25% higher than would have occurred by air sparging and vacuum extraction alone and that lower residual levels of TCE were achieved. Sensitivity analysis indicates that toxicity of the methanotrophs to TCE epoxides is an important factor affecting efficiency of remediation. Predation of methanotrophs by protozoa provide the best explanation for the oscillations in methanotroph population observed at the site, although competition, which we did not consider in our model, may also have played a key role. Neglecting the effects of microbial predation can lead to overestimates of TCE degradation on the order of 25%. Because rapid biodegradation only occurs where methanotrophs, TCE and “food” are all present, models that couple subsurface flow and transport with microbial processes are an important tool for assessing the effectiveness of bioremediation in field applications in which the environment is heterogeneous.

Introduction Contamination of groundwater and vadose zone soils with chlorinated solvents such as trichloroethylene (TCE) is a major national problem. A very promising cleanup technology is in situ bioremediation, the use of microbes to convert hazardous chemicals to environmentally benign products such as water, carbon dioxide, biomass, and salts. A field demonstration of bioremediation technology was performed at the U.S. Department of Energy’s Savannah River site in 1992-1993 (1-3). The technology employed a novel combination of injection of air, methane, N2O, and TEPO4 (triethyl phosphate) below the water table and vacuum extraction in the vadose zone, using a pair of subparallel horizontal wells. The object was to stimulate aerobic in situ bioremediation of TCE contamination in the vadose zone and the saturated zone by certain methanotrophs, methaneoxidizing bacteria which are capable of fortuitously come-

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tabolizing TCE under various conditions. TCE contamination occurred from the 1950s into the 1980s from a leaking process sewer line. Our goals are (1) to model the changes in methanotroph population and TCE concentration observed during the Savannah River field demonstration and (2) to examine the sensitivities of TCE biodegradation to key model biokinetic parameters. Models help us to understand complex subsurface remediation efforts such as those at Savannah River because they provide a mechanism for integrating the many different kinds of data involved (e.g., hydrological and microbiological). Moreover, a calibrated model can provide estimates of the temporal and spatial distribution of concentrations, pressures, and saturations everywhere in the subsurface region and can therefore be used to estimate important quantities which are difficult or impossible to measure in the field, such as the total mass of TCE biodegraded. This modeling study differs from previous modeling studies in that it includes both the vadose and groundwater zones, unsteady air and water flow, and limited nutrients and airborne nutrients, in addition to toxicity, cometabolic kinetics, predator grazing, and kinetic sorption. Previous models (4) have focused almost exclusively on steady saturated flow with waterborne delivery of nutrients or with nutrients in excess. None have considered predator grazing of microbes.

Conceptual Model At the Savannah River site, TCE contamination existed both above and below the water table. Consequently, in our model there are two fluid phases, water and air. Darcy’s law is applied to both phases with a different relative permeability versus saturation curve for each, as described by Brooks and Corey (5). Although nonaqueous phase TCE existed near the end of the leaking sewer line, there was no evidence for nonaqueous TCE at the site of the field demonstration (6) and so we assume that all the TCE in our system is dissolved in the water and air phases or sorbed onto soil grains. We assume that TCE, methane, oxygen, and nutrients can diffuse through the water-air interface rapidly enough so that the air and water concentrations are in local equilibrium and partitioning can be represented using Henry’s Law coefficients. Our model allows chemical species to diffuse through and to sorb onto soil grains. Diffusivities in soil grains are very small and so a time lag exists before equilibrium develops between fluid and solid phase concentrations. Roberts et al. (7) report that 10 days or more of monitoring can be required to obtain accurate laboratory estimates of Kd because of intraparticle diffusion. Our sorption model is a first-order rate model, as described by Semprini and McCarty (8). We assume that the microbes are immobile, existing in shallow biofilms attached to soil grains, and that solutes dissolved in groundwater are fully accessible to the microbes with no mass transfer delays. In keeping with previous modeling studies and with experimental evidence, our model uses multiplicative, Monod kinetics to describe microbial degradation of substrates (TCE and methane), oxygen, and nutrients. A triple Monod kinetics model for TCE degradation has been applied successfully in other studies (9). Recent studies (e.g., ref 10) indicate that pore clogging and permeability reduction can occur at high microbial densities. However, in our application, this effect is ignored because field measurements indicate that microbe counts at the site did not reach sufficiently high levels. Although the total bacterial count in soil samples from the site were on the order of 105-106 mL-1, the number of methanotrophs present before the demonstration was very

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low, on the order of 1-10 mL-1 (2). Total bacterial population density was estimated by AODC, acridine orange direct count, while MPN, most probable number, enumeration was used for the methanotrophs. Microbiological assays at the site (11) indicated that growth of the indigenous methanotroph population correlated with degradation of TCE. Methanotrophs can be divided into two types based on the form of MMO (methane mono-oxygenase) they can express. Both types can express pMMO, the particulate membrane-bound form, which is relatively ineffective for TCE degradation. Only type II can express sMMO, the soluble form which is very effective at degrading TCE. Abundant evidence indicates that type II methanotrophs were the dominant methanotrophic strain at the Savannah River site. This evidence includes phospholipid fatty acid (PLFA) assays from groundwater at the site (3, 12) and tests specific for sMMO activity which indicated large increases at the site during methane injection (3). Also, low available nitrogen would favor type II methanotrophs (14), since type IIs have nitrogenase while type Is do not, and nitrogen concentrations measured at the site were low, and decreasing, for most of the demonstration. Therefore, in our model, we assume that only type II methanotrophs expressing sMMO are responsible for TCE biodegradation. Since MMO is not highly specific, it can fortuitously degrade TCE as well as metabolize methane. Since the affinity of MMO for methane is greater than for TCE, however, if methane is present, the rate of degradation of TCE by MMO is reduced. Our model therefore includes competitive inhibition of TCE degradation in the presence of methane, following the formulation of Semprini and McCarty (8). Toxicity of TCE degradation products to methanotrophs is included according to the formulation of Chang and AlvarezCohen (9). The capacity of MMO to degrade TCE is limited due to general protein damage from activity of TCE epoxides (15). Nutrients, both nitrogen and phosphate, were added to the subsurface during the last 87 days of the Savannah River demonstration because growth of the methanotrophs at the site was believed to have become nutrient-limited (11, 12). A distinctive feature of the demonstration was that the nutrients, N2O and TEPO4, were added as gases rather than salts as is commonly the case. These nutrients rapidly partition from the air phase into the water phase and laboratory experiments indicated that methanotrophs from the site could use nitrogen and phosphorus efficiently when delivered in this manner (3). N2O and TEPO4 were added to the injected air at 0.07 and 0.007% by volume, respectively, in approximate stoichiometric balance with the average methane concentration during pulsing phases and typical elemental composition of microbes. In our simulations, for simplicity, we consider only nitrogen. We assume that when a microbe dies, its internal nutrient store is released slowly back into the environment for use by other microbes. An advantage of airborne delivery is that nutrients are distributed to greater distances through the air phase than as salt dissolved in water. A disadvantage, however, is that pulsing, a strategy which can be very effective in waterborne delivery systems (e.g., refs 8 and 16), is less effective here. Pulsing recognizes that competitive inhibition favors TCE degradation in the absence of methane. The diffusivity of methane is very small in water, so pulses of methane will remain separated spatially in the formation, allowing the pulsing strategy to work well. Methane diffusivity in the air phase, however, is about 10 000 times larger than in water, and discrete pulses of methane in air will not long remain spatially separated unless the time between pulses is very long, which would not be conducive to maintaining microbe counts at high levels. Another process we include in our model is protozoan grazing of bacteria. Protozoan predators include amoebae,

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various flagellates, and fungi, and are observed in many soils at significant numbers (103-105/g of dry soil weight). An increase in protozoan population in response to bacterial growth on hydrocarbon contamination has been documented recently (17). We found no published measurements at sites contaminated with chlorinated hydrocarbons; however, a limited sampling at the Savannah River site did find enhanced protozoan activity at several wells, and laboratory tests indicated that protozoans would in fact feed on methanotrophs (2). Menon et al. (18) show that protozoan predation of bacteria follows Monod kinetics. Our model includes a transport equation for predator species with Monod kinetics for grazing of microbes. A generalized description of the hydrogeology at the Savannah River site includes a sand unit, four major clay units, and a water table which lies about 40 m below the surface (19). These sediments are heterogeneous, varying greatly in thickness and continuity across the site. The in situ bioremediation demonstration began in late February, 1992, lasted 428 days, and consisted of seven injection and extraction phases. Details are given in Hazen (1, 2); see also ref 3.

Numerical Model Our model is embodied in a computer code called TRAMPP. TRAMPP (TRAcr3d with microbial and predator processes) is an extension of the TRACR3D code which simulates flow and transport of contaminants in porous media (20) and of the TRAMP code (21), which includes microbial dynamics but no predator grazing. TRAMPP considers two sets of equations which are solved simultaneously (Table 1). The first set includes the flow equations for unsaturated/saturated flow of air and water in heterogeneous porous media. These are time-dependent, 3D equations which are solved using an integrated finite difference, implicit time stepping, residual reducing, Newton-Raphson algorithm. The second set of equations in TRAMPP includes eight nonlinear, coupled, timedependent, two-phase transport equations, one each for oxygen, two nutrients, two substrates, and two microbe and one predator species. Only one of the two available equations for microbes was used in this study, for methanotrophs. In this study, we used the two nutrient equations to track native and added nitrogen separately, although one could use them to track two different nutrients (e.g., nitrogen and phosphorous). Material properties, such as porosity and permeability, can vary in space, and the model allows considerable flexibility in boundary conditions and the location of injection and extraction wells. Symbols are defined in Table 2. The numerical approach taken here is to use integrated finite difference approximations to the governing equations and to treat nonlinearities in an iterative fashion and use residual reduction as the convergence criterion, resulting in very small mass balance errors. Transport and catabolic reaction terms are separated through operator splitting but are made self-consistent through iteration. The components of the model have been tested successfully against analytical solutions and experimental results for a variety of flow, transport, and reaction conditions. A plan view schematic of the site is given in Figure 1a, showing the traces of the horizontal wells, the location of monitoring well MHT-4, and the orientation of the cross section (A′-A) used in this study for the model domain. We use this cross section because this region exhibited the greatest activity during the field demonstration, in terms of methanotroph population changes and TCE mineralization. The schematic of Figure 1b presents a vertical section of the target area (in a direction perpendicular to A′-A), indicating the approximate depths, thicknesses, and extent of the various hydrostratigraphic layers, the water table, and the subhorizontal traces of the two wells.

TABLE 1. Model Equations Flow Equations ∂ ∂ (fFa) + 3‚(Fab ua) ) Sa, (σFw) + 3‚(Fwb uw) ) Sw ∂t ∂t ka kw b ua ) - (∇πa + Fagˆ), b uw ) - (∇πw + Fwgˆ) µa µw

conservation of mass for air and water Darcy’s Law for air and water

Transport Equations (for species Z ) CH4, TCE, O2, N, M, MD, P) rate of change in advection concentration of species Z term ∂ ((σFw + fHz)[z]w) ) ∂t

(( (

)

Mo - M Y R + Rres - kdD[MD]w Mo + M CH4 CH4

)

d[z]s dt

(1 - )Fs

bioreaction term σFwRz

rate-limited sorption expression dispersion expression species velocity expression respiration expression bioreaction expression (CH4) bioreaction expression (TCE) bioreaction expression (O2)

)

Mo - M Y R - Rres + RTCE/Tc + kd[M]w Mo + M CH4 CH4 RMD ) -kd[M]w + kdD[MD]w RP ) kdP[P]w - kPQO2QM[P]w Qz ) [z]w/(GKz + [z]w), for z ) TCE, G ) 1 + [CH4]w/ICH4, RM ) -

sorption term

3‚(D h zFw∇[z]w) -

- 3‚(b uTz[z]wFw) +

where d[z]s/dt ) (KZd[z]w - [z]s)/τz z D h z ) σDw + fHzDaz b uTz ) b uw + Hzb ua, b uTM ) b uTMD ) 0 Rres ) kCQO2[C]w[M]w RCH4 ) kCH4QCH4QO2QN[M]w RTCE ) kTCEQTCEQO2QN[M]w RO2 ) Rres + kd[M]wQO2 + FCH4RCH4 + FTCERTCE

RN ) F N

dispersion term

bioreaction expression (N)

bioreaction expression (microbes) bioreaction expression (dead microbes) bioreaction expression (predators) otherwise, G ) 1

TABLE 2. Symbols  Π F µ σ τ a C Dz FZ FN f gˆ HZ ICH4 KZ k kZ

porosity fluid pressure density viscosity water-phase saturation diffusion time into/out of soil particles air phase (subscript) natural organic carbon molecular dispersivity for Z mass O2 consumed/substrate Z converted mass of nitrogen/bacterial mass air-phase saturation gravitational constant Henry’s Law coefficient for Z inhibition half saturation concentration CH4 half-saturation concentration for Z permeability maximum utilization rate of substrate Z

The model domain (Figure 2) includes the top 60 m of the subsurface and is 300 m in horizontal extent. The domain is divided into 1560 rectangular grid cells, 40 rows by 39 columns. Grid cells are uniform in thickness in the vertical direction and of variable thickness in the horizontal. The bottom boundary is impermeable. The top boundary is open to the atmosphere. Above the water table, the side boundaries are held fixed at initial conditions. The side boundaries below the water table are “free flow” (∂2/∂n2 ) 0). Fluid pressure and contaminant concentrations at the side boundaries are approximately what they would be if the horizontal domain were infinite in extent and the fluid concentrations at the present boundary locations were monitored. Initially, the soil units below the water table are completely water saturated, but after air injection begins, the upper part of the water table begins to desaturate. Our simulation includes seven injection and extraction phases (Table 3). Values for hy-

kC Kdz kd kdD kdP kP M MD MO N P QZ s S TC t b u w YCH4 Z

growth rate of microbes on C equilibrium sorption coefficient for Z death rate of microbes decay rate for dead microbes death rate of predators max utilization rate of microbes by predators microbe concentration as g/cm3 in water dead microbe biomass concentration maximum concentration of microbes nitrogen concentration in water predator species concentration Monod reaction term for species Z sorbed phase (subscript) source/sink term, g/s microbe TCE transformation capacity time fluid velocity, cm/s liquid water (subscript) microbial yield per unit CH4 consumed CH4, TCE, O2, N, M, MD, P

drogeologic and geochemical properties used in our simulations are listed in Table 4. We assume that the number of methanotrophs present at the start of the demonstration was very low, on the order of 1-10 mL-1. For oxygen, we assume an initial uniform pore water concentration of 9.2 mg/L, which represents equilibrium with air. There is no methane in the domain initially. Pore water nitrogen concentration is initially set at 1.4 mg/L (2). The initial distribution of TCE is very heterogeneous with the highest concentrations in the clay units. In addition to the TCE initially present in the model domain, TCE also enters the domain from a source outside the system. On the basis of the results of several simulations and field evidence for movement of TCE as vapor from an adjacent contaminated area (22), we estimate that ∼2 kg/day of TCE is drawn into the model domain from outside due to the vacuum at the extraction well. We approximate this

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are shown in Figures 3 and 4, respectively, along with field data. During phase one of the demonstration (refer to Table 3), the field methanotroph population remained at very low levels. There was a very rapid rise in the field TCE concentration to ∼300 ppm (by volume) at the extraction well, followed by a decline to ∼100 ppm. This spike in TCE concentration reflects the removal of readily accessible TCE prior to the breakthrough of fresh air and before mass transfer limitations became dominant. When the injection well was turned on at the start of phase two, flooding the subsurface with fresh air, the methanotroph population increased slightly in response to the higher oxygen content. The TCE concentration sharply increased at the start of phase two in response to the major readjustment of the flow field. The model results are in good agreement with the field data in phases one and two.

FIGURE 1. Generalized hydrogeology and location of horizontal wells: (a) map view and (b) cross section along the axis of wells. A-A′ represents a 2D cross section used for model domain. additional source by specifying a constant rate of injection of 2 kg/day of TCE on the right boundary through a 3 m vertical interval at a level slightly above the top of the tan clay (i.e., at ∼30 m depth) starting at day 150 (Figure 2). Biokinetic parameter values are listed in Table 5. These values are assumed to be constant in time and the subsurface temperature is assumed to be 20 °C. Two modeling results give us confidence in the hydrologic part of our model. The first is a report of a simulation of a helium tracer test performed prior to the bioremediation demonstration at the Savannah River site using the same horizontal wells (24). The conclusion of that study was that a large horizontal anisotropy, similar to the one we use here (implemented through the use of clay lenses and layers), was necessary to obtain results close to the field data. (Simulated helium concentrations were higher than the observed values; see Discussion.) In addition, early in our investigations, we simulated the pressure drop at the extraction well given the known air injection and extraction rates at the site. Using our model permeability field, we were able to match the observed pressure drop very well, indicating that large-scale geologic structure and permeabilities are modeled reasonably accurately. In this paper, we report the results of three simulations. In the first, we simulate bioremediation assuming the presence of predators. In the second, the same simulation is run without predation. To quantify the ratio of TCE degraded by in situ bioremediation to TCE removed by air injection and vacuum extraction alone, we also include a third simulation with the biokinetics part of the model turned off.

Model Results TCE and Methanotrophs at Wells. Model results for TCE concentration in the extraction well outflow and methanotroph and predator cell counts at monitoring well MHT-4

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In phases three and four, methane was added to the system (see Table 3) at 1 and 4%, respectively. The methanotroph population rose rapidly, plateauing at 2.5 × 104 cells/mL between 100-130 days, and then declined sharply. Both simulated methanotroph populations match the field data quite well up to 100 days; however, both continue to increase to ∼106 cells/mL by 140 days. The simulated methanotroph population in the no-predator case remains steady at ∼106 cells/mL. The with-predation simulation shows a sharp drop at day 144, similar to the drop observed in the field data 2 weeks earlier. We made numerous attempts to mimic the observed methanotroph plateau at 2.5 × 104 cells/mL at 100 days and crashes at 130 and 200 days by adjusting individual physical and biokinetic parameters. The only process in our model that could approximate the observed crashes was predation. Additional simulations indicate that the magnitude of a methanotroph population plateau between 104 and 106 cells/mL at 100 days is very sensitive to methane air phase concentrations between 0.75 and 1.0%, suggesting that minor differences in our model of the permeability field at the site could explain this discrepancy. The field TCE concentration at the extraction well fluctuated during phase three, averaging about 100 ppm. The largest decrease was between 150 and 160 days, corresponding to the rapid rise in methanotroph population that occurred at that time. TCE at the extraction well remained relatively level and then decreased linearly to ∼75 ppm beginning at about day 220. In general, the simulations track the average TCE levels at the extraction well closely, but do not capture the small time-scale variations seen in the field data. The drop beginning at about day 220 is matched, but occurs earlier than observed. During phase five, methane was absent from the injected air stream except for a few brief periods. The methanotroph population density dropped to less than 100 cells/mL. Both simulations show a drop in methanotroph population, but the levels predicted by the simulation with predation are much closer to the field observations. TCE concentration at the extraction well increased slightly to ∼80 ppm and the simulations capture this increase. Phase six was a period of regular pulsing of methane. Methanotroph population peaked again at ∼106 cells/mL during this period and then dropped off. In phase seven, nutrients were added to the injected air stream. Methanotroph levels continued to oscillate. Neither simulation matches population levels and oscillations perfectly during this time, but the simulation with predation is closer. As with the oscillations early in the demonstration, the only way our model could capture the variability in microbe counts was through the inclusion of predator grazing. Observed and simulated TCE at the extraction well remained very steady through phases six and seven. The modeled TCE concentrations from the no-predation simulation are slightly lower. This is because there are more methanotrophs present in

FIGURE 2. Model domain. The colors represent different hydrologic units: purple, sand; blue, clayey sand; green, sandy clay; and red, clay. Symbols: X, injection well; *, extraction well; arrow, external source of TCE.

TABLE 3. Injection/Extraction Schedule

TABLE 5. Biokinetics Parameters

phase

begin (day)

extraction (scfm)a

injection (scfm)a

1 2 3 4 5

1 21 56 161 241

240 240 240 240 240

6

290

240

7

341

240

no injection 200 air only 200 air, 1% CH4 by volume 200 air, 4% CH4 by volume 200 air only, 2% and 4% pulse CH4, 1 week each 200 air only; 4% CH4 by vol 8 h per 48 h cycle 200 air only; 4% CH4, 0.07% N2O, 0.007% TEPO4 by vol 8 h per 48 h cycle

a

parameter

scfm ) standard cubic feet per minute.

TABLE 4. Hydrogeologic and Physical Parameters clay horizontal permeability (darcy) vertical permeability (darcy) porosity (%) initial saturation (%) Brooks-Corey (5) parameters bubbling pressure (kPa) pore-size distribution index irreducible saturation (%)

0.001 0.0005 50 90

0.01 0.005 45 37

1 1 40 32

20 20 35 27

40 0.67 50

30 0.75 35

25 0.75 30

10 0.62 25

CH4 Henry’s Law coefficient (g/mL in air)/(g/mL in water) Dw (cm2/s) Da (cm2/s) τ (s) Kd (mL/g)

sandy clayey clay sand sand

TCE

27 0.37 10-5 10-5 0.23 0.1 106 (7) 0 2 ( 7)

O2

N

FCH4 FN FTCE ICH4 kC KCH4 kCH4 kd kdD kdP KM KN KO2 kP KTCE kTCE Mo C Tc Fc YCH4

value

notes, references for comparison

4.0 g (O2)/g (CH4) 0.125 g (N2)/g (M)

stoichiometry typical ratio of nitrogen in cells 0.3 g (O2)/g (TCE) stoichiometry 1.4 mg/L 9, 14 3 × 10-7 g/g/s required to balance initial [M]w of 10 cells/mL 0.4 mg/L 14 3 × 10-5 g (CH4)/g (M)/s 14 1.5 × 10-6/s ) 0.13/day 23 2.5 × 10-7/s fit 2.0 × 10-6/s ) 0.18/day 18 0.2 × 10-6 g/mL 18 1.0 mg/L fit 1.0 mg/L 23 0.4 × 10-5 g (P)/g (M)/s 18 0.1 mg/L see ref 9 5 × 10-5 g (TCE)/g (M)/s 9 3 × 10-5 g/mL max value at site < this value 10-6 g/mL 2 0.1 g (TCE)/g (M) dry weight 9 2.67 g (O2)/g (C) stoichiometry 0.50 g (M)/g (CH4) 23

P

29 1 10-5 10-5 10-6 0.14 0.1 0

0

0

this case, so more TCE is biodegraded and therefore unavailable for transport to the extraction well. Total Mass of TCE Extracted and Biodegraded. The total amounts of TCE extracted and biodegraded in situ in our simulation with predation included are 1867 and 730 kg, respectively, for a total TCE removed from the subsurface of 2597 kg; 28% of the TCE removed is removed via biodegradation. Without predation, the numbers are 1809 and 913 kg, for a total of 2722 kg; in this case 33% of the TCE removed is removed via biodegradation. Figure 5 plots the domainwide integrated mass of TCE extracted and biodegraded as a function of time for both simulations. This plot indicates that most of the TCE degradation occurred during the 4% methane phase (phase four), with additional degradation occurring during the nutrient addition phase (phase seven). Site data indicate that 2030 kg of TCE was vacuum extracted and that anywhere from 230 to 900 kg of TCE may have been

FIGURE 3. TCE concentration vs time at extraction well. Field data (points) are shown with simulation results for the with-predators case (solid line). The vertical dotted lines represent the boundaries between different phases of the field demonstration (see Table 3). The simulation TCE levels match the field data well, although the small time-scale variations in the field data are not captured. biodegraded in situ (Hazen, 1993), for a total amount of 22602930 kg of TCE removed. The model result for extracted TCE is very close to the observed values. The difference between model values (1809 and 1867 kg) and field value (2030 kg) for

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FIGURE 5. Integrated TCE extracted and biodegraded vs time for in situ bioremediation simulations. Curves represent with-predators case (solid line), no-predators case (dashed line), and air injection/ extraction only (diamonds). A total of 2030 kg of TCE was measured at the extraction well in the actual field demonstration, and 230900 kg of TCE is estimated to have been biodegraded in situ. The vertical dotted lines represent the boundaries between different phases of the field demonstration (see Table 3).

FIGURE 6. Simulated TCE concentration vs time profiles at MHT-4. Solid line represents bioremediation simulation, predation included. Dashed line represents simulation with no microbial activity (air injection/extraction only). The bioremediation simulation predicts residual levels 1 order of magnitude lower. The vertical dotted lines represent the boundaries between different phases of the field demonstration (see Table 3). FIGURE 4. Methanotroph and predator populations vs time at well MHT-4: (a) Field data, (b) simulated methanotroph populations for with-predators case (solid line) and without-predators case (dashed line), (c) simulated predator population. The vertical dotted lines represent the boundaries between different phases of the field demonstration (see Table 3). The simulation with predation included is a better match to the field data. TCE removed from the subsurface through vacuum extraction can be attributed entirely to the differences in concentration at the extraction well during the first 3 weeks (Figure 3). The amounts biodegraded (730 and 913 kg) lie within the middle to upper part of the field estimate. According to the simulation, mass of TCE biodegraded per unit mass of methane consumed by microbes is 0.13-0.15. These model results indicate that protozoan grazing could reduce the amount of TCE biodegraded by about 20%, a significant factor. This adverse affect on the rate of biodegradation may lead to underestimation of remediation times at other sites if predator grazing is not considered. The amount of TCE extracted by air sparging and vacuum extraction alone in our simulation without microbial activity

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is 2095 kg. The amount of TCE removed through in situ bioremediation in the model is 24% higher for the withpredators case and 30% higher without predator grazing, a significant enhancement of the removal rate of TCE. Our simulations show that in addition to removing a greater total amount of TCE from the system, in situ bioremediation generally results in lower residual levels of TCE than in situ air stripping. Figure 6 compares the modeled TCE concentrations at a point for in situ air stripping alone and for in situ bioremediation, predation included. In the in situ bioremediation case, the microbes reduce the level of TCE relative to the air stripping case by an order of magnitude, to about 30 µg/L, close to observed values, and this occurs rapidly after methane injection begins and the methanotroph population increases. A rebound occurs during those periods when microbes are not growing, due to slow release of TCE from soil grains and to transport of TCE from neighboring contaminated regions. Spatial Distributions. Figures 7-11 provide pictures of the modeled spatial distribution of various parameters at the site. Figure 7 shows the water saturation field at 200 days, by which time a steady-state saturation field has been

FIGURE 7. Water saturation at 200 days. Note the significant desaturation zone that develops near the injection well.

FIGURE 8. Modeled (a) methanotroph and (b) predator populations for bioremediation simulation (with-predation). At 140 days, methanotroph growth is limited to a rectangular region close to the injection well. By 280 days, the region of growth is more extensive, with the greatest activity in the upper left part of the domain where methane has accumulated. At 420 days, microbial levels outside the near-field region are limited by the lack of methane. Stimulated by the added nitrogen during phase seven, the microbes near the injection well are nearly consuming all the methane, preventing wider distribution. The predators are shown at their population peak at 250 days. established. Significant desaturation occurs due to air injection below the initial water table. By 200 days, water saturation levels have dropped to 50-60% in a triangular region that extends over 100 m on either side of the injection well and, at its deepest, 3 m below the injection point. The clays near the injection well are partially desaturated to 7075%, opening up pore space and allowing TCE to escape. Figure 8a displays the simulated methanotroph population at 140, 280, and 420 days. At 140 days, methanotroph growth occurs over a roughly rectangular region 70 m wide by 15 m deep centered on the injection well. At 280 days, the microbe population near the injection well has declined greatly,

reflecting protozoan grazing, but the region of growth is more extensive, reflecting the further spread of methane from the injection well. At 280 days, the peak activity is in the upper left part of the domain where methane has accumulated, but protozoa have not increased significantly. The distribution of microbes at 420 days is very similar to that seen at 140 days. The distribution of methanotrophs is very similar at 140 and 420 days for both the with-predators and the nopredation simulations, but differ markedly at 280 days. In the with-predators case, the methanotroph population in the central region of the domain is greatly reduced as a consequence of the peak in predator population at 250 days

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FIGURE 9. Modeled distribution of air-phase methane for bioremediation simulation (with-predation). Clay lenses, the extraction well, and active microbes close to the injection well strongly affect the distribution of methane.

FIGURE 10. Modeled distribution of dissolved nitrogen at 420 days for bioremediation simulation (with-predation). To convert from milligram per liter (liquid phase) shown here to percent by volume (air phase), multiply by 0.1 in this case. Air-phase delivery successfully disperses nitrogen over a wide region.

FIGURE 11. Modeled distribution of dissolved TCE at the end of the bioremediation simulation (with-predation). TCE levels near the injection well have been reduced to low (ppb) levels. Outside the desaturated region (see Figure 7), low permeability clays block air flow. (see Figure 4). In the no-predators case, the methanotroph population in this central region remains high. Figure 8b shows the simulated predator distribution at 250 days. Figure 9 shows the simulated distribution of methane at 200 and 400 days. At 200 days, methane has spread for considerable distances horizontally. The presence of clay lenses, which restrict vertical flow and the presence of the extraction well on the right side of the domain strongly affect the transport of methane such that it accumulates on the left side. Some methane exits through the domain boundaries. At 400 days, methane is present only near the injection well. During the pulsing phases, the average methane concentration is reduced by a factor of six and microbes in the vicinity of the injection well are consuming all the methane, prevent-

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ing wider distribution. Figure 10 shows the simulated distribution of the added nitrogen at 420 days. The highest concentrations are near the injection well, but nitrogen has spread for a considerable distance horizontally. Our model shows that air phase delivery successfully disperses nitrogen over a wide region. At the end of our simulation demonstration, microbial activity (Figure 8) is limited by lack of methane, not lack of nutrient. Throughout the demonstration, there is a gradual reduction of TCE throughout the domain, except for the clays (Figure 11). The clays near the injection well, however, are being desaturated by the injected air pressure (∼1 atm overpressure) to a sufficient degree to allow removal of TCE. By the end of the field demonstration (428 days), the 100 m

TABLE 6. Sensitivities of TCE Removal to Key Parameters full simulation biokinetics only

kCH4

KCH4

KN

kTCE

KTCE

ICH4

TC

kP

1.86 1.83

-1.00 -0.94

-0.42 -0.45

-1.20 -1.08

0.06 0.07

-0.004 -0.008

-1.43 -1.30

-0.24 -0.27

wide × 20 m thick central region has been remediated to low levels (Figure 11). The contaminated region at the bottom is not remediated because a low-permeability clay layer lies about 3 m below the injection well and blocks most downward air flow. Sensitivity Analysis. We determine the sensitivity of the enhanced removal of TCE due to microbial degradation through a series of simulations in which biokinetic parameters of the system were varied one at a time. The parameters varied are kCH4, KCH4, KN, kTCE, KTCE, ICH4, TC, and kP. Sensitivity is defined as

(

)(

)

∆TCE - ∆TCEbase Xbase ∆TCEbase X - Xbase

where X is a biokinetic parameter, ∆TCE is the total TCE removed by extraction and biodegradation minus the total TCE extracted when biokinetics is not operating, and the subscript base refers to the simulation of the Savannah River site as described previously in this paper, with protozoan grazing. Resulting sensitivities are listed in Table 6. The first row of numbers are the sensitivities as defined above. The second row of numbers are the sensitivities for biodegradation only (vapor extraction differences neglected). These results are specific to the Savannah River site since the amount of TCE removed is a function of the injection/extraction strategy, the site hydrogeology, and the initial distribution of TCE. Since our model is nonlinear, these sensitivities strictly apply only to a limited part of the parameter space. The relative rankings of the sensitivities, however, are likely to hold more generally and be somewhat site independent. There are two main conclusions that can be drawn from this sensitivity analysis. First, and the most obvious, is that TCE degradation is strongly sensitive to the factors controlling the rate at which microbes can grow (e.g., kCH4, KCH4, and KN). Second, toxicity is also quite important. There is a strong sensitivity to kTCE, the maximum utilization rate for TCE, but it is a negative correlation because TCE degradation products are toxic to methanotrophs. The faster TCE is degraded, the more toxic products are created and the more microbes are killed. These results imply that using bacteria that are not damaged by TCE, or that can even utilize TCE for energy, will be much more effective than simply using bacteria with higher growth rates. Sensitivity to predators (kP), although significant, was not as strong as to toxicity.

Discussion The simulations presented here support the observations that removal rate of TCE was enhanced by biostimulation of methanotrophs. However, no attempt was made to find an optimal field operation. Several modeling results imply that a more efficient operation was likely possible. For example, much of the TCE between the injection and extraction wells was already removed by the time nutrients were added. Also, the area of high TCE removal rate was limited by the reduced spatial distribution of methane during pulsing. Optimization algorithms, such as developed by Lang (16), could be used in conjunction with our model to develop a more efficient field operation, by determining the best distribution and location of wells, injection/extraction schedules, and pulsing patterns for growth substrates and nutrients. Novel variations may be found; for example, in a very simple optimization model (25), we found that pulsing nutrients, as well as methane, and systematically changing the length of the time

interval between pulses could extend the area of high removal rate. Further use of transport models coupled with optimization algorithms has considerable potential for reducing remediation time as well as operational costs. Despite the good agreement between our model and field results, it is important to keep in mind the limitations of such models. First, our knowledge of the microbial processes is incomplete, especially with respect to predation of methanotrophs in soils contaminated with chlorinated hydrocarbons, where little work has been done. The various rate coefficients used in the model are only approximately correct. For example, kCH4 can change over time as bacteria apparently adapt, within limits (e.g., ref 23). Another important factor is soil heterogeneity. We have captured reasonably well the larger scale variations, but small scale heterogeneity can also induce additional channeling. Channeling could result in methane concentrations being lower or higher at some points than expected. Simulations indicate, for example, that the initial plateau level in the microbe population counts at MHT-4 (see Figure 4 at 100 days) are very sensitive to methane concentrations in the 0.75-1% range, a range of variation that could easily be effected by soil heterogeneity and channeling. Further, our 2D model does not capture the full 3D nature of the site, leading to more opportunity for inaccuracy. We believe this explains why our simulated methane concentrations at the extraction well are higher than the observed values. We attribute these higher (1-2×) levels to both vertical buoyancy of methane, a lighter-than-air gas (a factor not included in the model), and 3D geometric effects. The injection well extends about 30 m beyond the end of the extraction well, and moreover, the two wells are subparallel. Methane leaving the end of the injection well will not experience as strong a pressure gradient toward the extraction well as methane leaving near the beginning of the injection well. This leads to greater dispersion and reduced concentrations, which could only be captured in a full 3D model. We believe this also explains the too-high results for helium reported in ref 24. Perhaps the most important factor, however, is the complex microbial ecology. The balance between type I and type II methanotrophs may have changed during the demonstration. We believe type II was dominant during most of the demonstration for reasons discussed earlier, but type I population was not directly monitored during the demonstration. Also, a number of microbial species other than methanotrophs were measured in site soil samples. These included several nitrogen transformers whose population densities also responded to the air, methane and especially the nutrient injection campaign (2). The population changes in all cases were characterized by oscillatory rises and declines rather than a steady maintained increase. The collective effect of these species on methanotroph dynamics is uncertain. Although we have focused on predation rather than competition as a mechanism for generating the kind of oscillatory behavior seen in the methanotroph population at the Savannah River field site, we note that competition and predation are similar in terms of mathematical formulation and mathematical dynamics. Perhaps the more important point is that our modeling indicates that microbial interactions of some kind, predation or competition, appear necessary to explain the field data. It is in the area of microbial ecology that coupled transport and biodegradation models need most improvement.

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Acknowledgments This research was funded by the Office of Technology Development, within the Department of Energy’s Office of Environmental Management, under the Non-Arid Soils Volatile Organic Compounds Integrated Demonstration. Additional support was provided by Los Alamos National Laboratory Directed Research and Development.

Literature Cited (1) Hazen, T. C. Westinghouse Savannah River Company report WSRC-RD-91-23 April, 1992, revision; 1992. (2) Hazen, T. C. Westinghouse Savannah River Company report; October 26, 1993. (3) Brockman, F. J.; Payne, W; Workman, D. J.; Soong, A.; Manley, S.; Hazen, T. C. J. Hazard. Mater. 1995, 41, 287. (4) Sturman, P. J.; Stewart, P. S.; Cunningham, A. B.; Bouwer, E. J.; Wolfram, J. H. J. Contam. Hydrol. 1995, 19, 171. (5) Brooks, R. H.; Corey, A. T. Proc. Am. Soc. Civil Eng. 1966, IR2(92), 61. (6) Pruess, K. Lawrence Berkeley National Laboratory document LBL32418; 1993. (7) Roberts, P. V.; Hopkins, G. D.; Mackay, D. M.; Semprini, L. Ground Water 1990, 28, 591. (8) Semprini, L.; McCarty, P. L. Ground Water 1992, 30, 3. (9) Chang, H. L.; Alvarez-Cohen, L. Environ. Sci. Technol. 1995, 29, 2357. (10) Jennings, D. A.; Petersen, J. N; Skeen, R. S.; Peyton, B. M.; Hooker, B. S.; Johnstone, D. L.; Yonge, D. R. In Bioaugmentation for Site Remediation; Hinchee, R. E., et al., Eds.; Battelle Press: Columbus, OH, 1995; pp 97-103. (11) Pfiffner, S. M.; Palumbo, A. V.; Phelps, T. J.; Hazen, T. C. J. Ind. Microbiol. In press.

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(12) Bowman, J. P.; Jimenez, L.; Rosario, I.; Hazen, T. C.; Sayler, G. S. Appl. Environ. Microbiol. 1993, 59, 2380. (13) Pfiffner, S. M.; Phelps, T. J.; Palumbo, A. V. In Bioremediation of Chlorinated Solvents; Hinchee, R. E., et al., Eds., Battelle Press: Columbus, OH, 1995; pp 263-271. (14) Graham, D. W.; Chaudhary, J. A.; Hanson, R. S.; Arnold, R. G. Microbiol. Ecol. 1993, 25, 1. (15) Oldenhuis, R.; Oedzes, J. Y.; van der Waarde, J. J.; Janssen, D. B. Appl. Environ. Microbiol. 1991, 57, 7. (16) Lang, M. M. Ph.D. Dissertation; Stanford University, 1995. (17) Sinclair, J. L.; Kampbell, D. H.; Cook, M. L.; Wilson, J. T. Appl. Eviron. Microbiol. 1993, 59, 467. (18) Menon, P.; Becquevort, S.; Billen, G.; Servais, P. Microbiol. Ecol. 1996, 31, 89. (19) Eddy, C. A.; Looney, B. B.; Dougherty, J. M.; Hazen, T. C.; Kaback, D. S. Westinghouse Savannah River Company report WSRC-RD91-21; 1991. (20) Travis, B. J.; Birdsell, K. H. Los Alamos National Laboratory document LA-11798-M; 1991. (21) Travis, B. J. Los Alamos National Laboratory document LA-UR93-479, 1993. (22) Looney, B. B.; Hazen, T. C.; Kaback. D. S.; Eddy, C. A. Westinghouse Savannah River Company report WRSC-RD-91-22; 1991. (23) Semprini, L.; McCarty, P. L. Ground Water 1991, 29(3), 365. (24) Robinson, B. A.; Rosenberg, N. D.; Zyvoloski, G. A.; Viswanathan, H. Los Alamos National Laboratory Report LA-12781-MS; 1994. (25) Travis, B. J.; Rosenberg, N. D. Los Alamos National Laboratory Report LA-12789-MS; 1994.

Received for review December 9, 1996. Revised manuscript received April 4, 1997. Accepted July 14, 1997.X ES9610186 X

Abstract published in Advance ACS Abstracts, September 1, 1997.