Environ. Sci. Technol. 2004, 38, 3864-3870
Quantification of Bacterial Chemotaxis in Porous Media Using Magnetic Resonance Imaging MIRA STONE OLSON,† R O S E A N N E M . F O R D , * ,‡ JAMES A. SMITH,† AND ERIK J. FERNANDEZ‡ Program of Interdisciplinary Research in Contaminant Hydrogeology, Departments of Civil Engineering and Chemical Engineering, University of Virginia, Charlottesville, Virginia 22904
Bacterial chemotaxis has the potential to enhance biodegradation of organic contaminants in polluted groundwater systems. However, studies of bacterial chemotaxis in porous media are scarce. In this study we use magnetic resonance imaging (MRI) for the noninvasive measurement of changes in bacterial-density distributions in a packed column at a spatial resolution of 330 µm as a function of time. We analyze both the diffusive and the chemotactic behavior of Pseudomonas putida F1 in the presence of the chemical stimulus trichloroethylene (TCE). The migration of motile bacteria in experiments without TCE was described using an effective motility coefficient, whereas the presence of TCE required addition of a nonzero chemotactic sensitivity coefficient, indicating a significant response to TCE. The need for a chemotactic sensitivity term was justified by a test for statistical significance. This study represents the first quantification of bacterial chemotactic parameters within a packed column. For conditions under which chemotaxis occurs in porous media, it may potentially be exploited to significantly improve rates of in situ pollutant biodegradation in the subsurface environment, particularly for pollutants dissolved in water trapped in low-permeability formations or lenses.
Introduction Microbial degradation of organic contaminants is often regarded as a principal remediation strategy for contaminated soil and groundwater. Conventional pump-and-treat remediation systems commonly used to restore polluted groundwater often leave contamination in regions of low permeability. When groundwater pumping ceases, contaminants slowly diffuse out from regions of low permeability into the surrounding groundwater. As organic contaminants diffuse from regions of low permeability into more permeable regions, a chemical gradient is created surrounding the lens of low permeability. Likewise, diffusion can cause concentration gradients at interfaces between aquitards and aquifers. Aquitards can act as long-term reservoirs for contaminants because of their sorptive properties and low permeabilities * Corresponding author phone: (434)924-6283; fax: (434)9822658; e-mail:
[email protected]. † Department of Civil Engineering. ‡ Department of Chemical Engineering. 3864
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and can be significant and widespread sources of slow diffusion of pollutants into more permeable regions (1). In situ bioremediation is often used for degrading contaminants not readily removed by pump-and-treat systems but is often limited by the transport of the bacteria to the contaminants they degrade (2). Many soil-inhabiting bacteria that degrade chemical contaminants are both motile and chemotactic (3, 4). Chemotaxis refers to the ability of bacteria to sense pollutant concentration gradients in water and preferentially swim toward regions of high pollutant concentration. Bacterial chemotaxis is often neglected in advection-dominated systems as it is usually assumed that the effects of bacterial motility are negligible compared to advection. However, bacteria can swim through aqueous media at speeds of 2040 µm/s or 1.7-3.5 m/day (5-7), comparable to or greater than typical groundwater flow rates of 1 m/day. Thus, particularly at low flow rates, chemotaxis may play an important role in bacterial transport. Previous studies report that chemotaxis can affect contaminant degradation in soil (8, 9), and a recent field study suggests that bacterial motility may be important in guiding subsurface microbial populations toward trapped chemical contaminants (10). Although bacterial chemotaxis is well documented in aqueous systems (11-13), it has only recently been observed in water-saturated porous media. Pedit et al. (14) experimentally demonstrate bacterial chemotaxis in porous media. Conventional capillary assays, in which the chemotactic response of bacteria can be quantified, were modified to include packed glass beads in both the capillary tubes and the surrounding bacterial reservoir. Cell accumulations within the capillaries were compared and found to be higher in capillaries initially containing the attractant naphthalene than in the control capillaries without naphthalene, thereby demonstrating that chemotaxis can occur in porous media. Witt et al. (15) provide evidence that nitrate gradients in a packed column elicit a chemotactic response from Pseudomonas stutzeri KC cells under both flow and no-flow conditions. More quantitative studies of bacterial chemotaxis in porous media have been limited to modeled simulations (9, 16-18). Characterization of bacterial chemotaxis in porous media is important in the overall study of pollutant biodegradation and chemotaxis. Accurate predictions of bacterial transport in the presence of organic contaminants will allow us to more reliably assess the potential increase in contaminant degradation. Bacterial chemotaxis in porous media may have considerable impact on contaminant biodegradation in a natural system. As bacteria accumulate in regions with a chemical concentration gradient, they consume the contaminant as a carbon and energy source, thereby further increasing in number. Just as chemical reactions typically enhance the overall rate of mass transfer (19), the biodegradation of contaminant increases its rate of transfer to the aqueous phase. As the contaminant is consumed, a steep concentration gradient is maintained to further enhance the chemotactic response of the bacteria, thereby creating a positive-feedback loop. Thus, even small increases in bacterial concentration surrounding chemical concentration gradients may significantly impact the overall rate of contaminant removal. Previous experimental studies of bacterial chemotaxis in packed columns (15, 20-22) have focused on conventional column studies where breakthrough curves and column sectioning are the only data available. These studies have failed to quantify bacterial chemotaxis in terms of fundamental transport properties, largely due to problems with experimental techniques, mainly the lack of or poor spatial 10.1021/es035236s CCC: $27.50
2004 American Chemical Society Published on Web 06/05/2004
resolution of bacterial densities within porous media or the inability to separate the effect of chemotaxis from random motility and growth. We recently developed a noninvasive imaging technique for detecting changes in bacterial-density distributions within a packed column using magnetic resonance imaging (MRI) and verified that this technique is capable of quantifying bacterial motility in porous media (23). This approach enables us to quantify bacterial distributions within porous media at a relatively high resolution (330 µm) for the first time.
adjustable flow adapters (Biorad, Hercules, CA). The column was designed to accept influent at both ends, thereby forming an impinging plane midway along the column length, where flow was released via four holes drilled at equidistant points along the column perimeter. Nylon frits were applied along the inside of the holes to retain the porous media. The column was packed to a final bed length of 8 cm by filling it with buffer and adding the prewetted beads. Following assembly, the column was pretreated with unlabeled P. putida F1 cells and flushed with plain buffer.
The purpose of this work is to quantify the chemotactic response of P. putida F1 to TCE in a packed column. We use MRI to image changing bacterial concentrations over time as bacteria migrate from regions of high concentration to regions of lower concentration. We analyze how this migration changes in the presence of an increasing TCE gradient and employ computer simulations to derive transport parameters from observed data.
Magnetic Resonance Imaging Protocol. Column experiments were conducted in a 1.75T, 12-cm bore horizontal magnet (Nalorac Cryogenics Corp., Martinez, CA) and spectrometer (TecMag, Inc., Houston, TX) with gradient coils (Magnex Scientific, U.K.) of up to 20 G/cm. MRI procedures followed the protocol of Sherwood et al. (23). The imaging protocol was a T2-weighted, one-dimensional, x-slice spinecho sequence, programmed using MacNMR version 4.5.9 software (Tecmag, Inc.), with 30 TE times and a spatial resolution of 330 µm. T2 profiles were generated and converted to concentrations using MATLAB, as previously described (23).
Experimental Section Bacteria/Attractant. Pseudomonas putida F1, obtained from Dr. Caroline Harwood at the University of Iowa, was selected for this study because when induced with toluene it exhibits chemotaxis to trichloroethylene (TCE) and other environmental pollutants (3). TCE was chosen as the chemical attractant because of its environmental persistence and its prevalence in regions of low permeability. It is degraded by P. putida (24, 25) but cannot be used as a growth substrate (3). Cells were grown in Luria broth from a -70 °C glycerol stock (40% v/v); 0.5-1% of this seed culture was then used to inoculate a sealed media bottle containing a 50:50 (v/v) mixture of Hutner’s Mineral Base (18, 26) and Luria broth. Media bottles were sparged with oxygen, and approximately 10 mM toluene was added to the vapor phase. Cells were grown to an optical density at 590 nm (Abs590) of 1.0 (12-16 h) and inspected for motility at 400× using a Zeiss Std 16 microscope. A 10% (v/v) dilution of random motility buffer (23), a phosphate buffer that does not support growth, was used for all experiments. Immunomagnetic Labeling. A purified monoclonal antibody specific to P. putida, developed and provided by Dr. Maribel Ramos-Gonzalez at the Estacion Experimental del Zaidin CSIC (27), was diluted to a strength of 1:500 and tested for attachment to P. putida F1. Magnetite was obtained as a suspension of 50-60 nm ferrofluid particles from Immunicon Corp. (Huntington Valley, PA). The procedure for attaching magnetite particles to P. putida F1 was adapted from that of Nakamura et al. (28) and is described elsewhere (23). Briefly, the antibody was reduced with dithiotreitol (DTT, Pierce Chemical, Rockford, IL), and the ferrofluid particles were derivatized with the heterobifunctional cross-linker N-succinimidyl-3(2-pyridyldithio) propionate (SPDP, Pierce Chemical, Rockford, IL). The antibody and magnetite were then combined and incubated with P. putida F1 cells harvested at an O.D590 of 1.0 at a volumetric ratio of 10:1 (bacterial suspension: antibody and magnetite mixture). Following 2 h of equilibration, the unattached conjugated antibody was removed from the suspension of labeled bacteria by filtration and rinsing. Labeled bacteria were resuspended in buffer. Cell Enumeration. The acridine orange direct count (AODC) method (29) was used to determine cell concentrations of all bacterial suspensions in these experiments. Column Assembly. Column assembly and packing is described in detail elsewhere (23). Glass-coated polystyrene beads (SoloHill Engineering, Inc., Ann Arbor, MI) with a size distribution of 250-300 µm were wet-packed into a specially designed plastic column with an i.d. of 1.5 cm and two
Agarose Plug Assays. Agarose plug assays (3, 30) were used as a screening test to verify chemotaxis of aqueous suspensions of P. putida F1 to TCE. Plugs containing 2% low-melting-temperature agarose (NuSieve GTG Agarose, FMC Bioproducts, Rockland, Maine), random motility buffer, and 10% (vol/vol) TCE were melted in a 70 °C water bath. Drops of melted agarose were placed on a microscope slide with two cover slips placed on either side, approximately 2 cm apart. A third cover slip was then placed on top of the two cover slips to form a chamber. Cells were harvested, resuspended in random motility buffer, and then flooded into the chamber surrounding the agarose plug. The chemotactic response was observed at 30-s intervals for 15 min at 8× magnification. Control experiments were conducted in an identical manner, omitting TCE from the agarose plug. Random Motility Experiments. Bacterial random motility experiments were conducted to examine the diffusion-like migration of P. putida F1 cells without the influence of an attractant. After pretreating the column with unlabeled bacteria, labeled bacteria were introduced onto one-half of the column at a flow rate of 2 mL/min as plain buffer was pumped onto the other half at the same flow rate. Both streams exited the column at its midpoint, creating an initial step change in bacterial concentration at the midpoint of the column. Once the step change was established, flow was halted and spatial bacterial concentration profiles were collected for 15-16 h under no-flow conditions using MRI. Bacterial transport is represented as follows:
∂b µ0,eff ∂2b ) ∂t ∂x2
(1)
where b is bacterial concentration (cells/mL), is porosity, and µ0,eff is the effective motility coefficient of bacteria (cm2/ s). Observed data were matched to the solution of eq 1 by adjusting the lumped effective random motility term, µ0,eff/. Chemotaxis Experiments. Labeled bacteria were introduced onto one-half of the packed column at a flow rate of 2 mL/min, while an aqueous TCE solution at Csat (8.4 mM) was introduced onto the other half at the same flow rate, creating opposing initial step changes in bacterial concentration and TCE concentration. Once flow ceased, bacterial migration was monitored for approximately 15-16 h using MRI. Bacterial concentration profiles were measured every 35 min for the duration of the experiment. VOL. 38, NO. 14, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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The governing equation for one-dimensional bacterial transport with no flow is given by
∂b µ0,eff ∂2b ∂(vcb) ) ∂t ∂x2 ∂x
(3)
where τ represents the tortuosity of the porous medium. This adjustment accounts for the restricted geometry of a porous medium, which disrupts the natural diffusive behavior of the bacteria. The chemotactic velocity is defined as follows
vc )
Kc 1 χ0,eff ∂a 3 (K + a)2 ∂x c
(4)
where χ0,eff is the effective chemotactic sensitivity parameter (cm2/s), Kc is the chemotaxis receptor constant (mM), and a is the aqueous concentration of TCE (mM). TCE gradients were calculated from the following description of solute diffusion
∂a DTCE ∂2a ) ∂t τ ∂x2
(5)
where DTCE is the bulk diffusion coefficient of TCE (cm2/s). Equations 2, 4, and 5 were solved numerically in MATLAB using a Crank-Nicholson finite difference scheme and fit with experimental data for the lumped parameters µ0,eff/ and χ0,eff/. Growth was neglected in the model formulation because the bacteria were suspended in a minimal media without a carbon source to support growth. Decay was neglected because experiments ran no longer than 16 h and because visual observations of cells swimming at the conclusion of experiments did not appear different from observations at the onset of experiments in terms of numbers and activity of swimming cells. Sorption was also omitted because pretreating the column with unlabeled bacteria was previously shown to minimize sorption of labeled bacteria (23). Although P. putida F1 can degrade low concentrations of TCE, degradation was also not included in this formulation because it does not appear to be significant at TCE concentrations above ca. 0.3 mM (25). The effects of TCE toxicity on bacterial motility were excluded from the model formulation because bacterial motility was observed to be similar at the conclusion of experiments with and without the presence of TCE. In addition, swimming was observed in samples of bacteria suspended in a saturated aqueous solution of TCE. The first data set (collected at approximately 35 min) was input and used as the initial condition for model simulations, and no flux boundary conditions were assigned to the ends of the column. Parameter values used in the computer simulations are listed in Table 1. Kc was estimated from the concentration of TCE at which P. putida F1 cells accumulated in a concentrated band around a TCE-containing agarose plug. The column tortuosity for TCE was approximated from solute tracer tests conducted in the packed column. The effective diffusion of a MnCl2 solution was measured through a packed column using MRI and compared to its bulk diffusion. The retardation factor for 3866
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Kc (mM) DTCE (cm2/s)a τTCE v (cm/s)b a0 (mM)
(2)
where vc is the chemotactic velocity (cm/s). The first term on the right-hand side of eq 2 represents the diffusion-like random motility of the bacteria, while the second term accounts for the advective chemotactic response. Analogous to molecular diffusion, the effective motility coefficient can be related to the bulk aqueous motility coefficient, µ0, according to
µ0,eff ) µ0 τ
TABLE 1. Parameter Values used in Simulations
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a
1.0 9.4 × 10-6 2.0 0.0044 8.4
Desk Reference, Hayduk and Laudie, 1974.
b
Harwood et al., 1989.
manganese was observed by comparing the transport of manganese to that of a conservative tracer and then incorporated into the tortuosity calculation. Statistical Analysis. A statistical F-test was performed to evaluate the significance of the effective chemotactic sensitivity term, χ0,eff/, used to describe the behavior of bacteria in the presence of a TCE gradient. Two models for describing bacterial transport were compared. The first (called the lowerorder model) incorporates bacterial random motility only and was simulated using eq 1. The second (the higher-order model) incorporates both bacterial random motility and chemotaxis and is described using eqs 2, 4, and 5. The F-statistic was evaluated using the following formula from Milton and Arnold (31)
F(k-m,n-k-1) )
{SSElow - SSEhigh}/{(k - m)} {SSEhigh}/{(n - k - 1)}
(6)
where k and m are the numbers of independent variables in the higher-order and lower-order models, respectively, and n is the number of data points used for analysis. SSEhigh and SSElow are the sums of squared errors for the higher-order and lower-order models, respectively, and are defined as follows
SSE ) (yi - ym)2
(7)
where yi represents the experimental data and ym the model prediction at the corresponding axial position in the column.
Results and Discussion Plug Assays. Five plug assay experiments were performed with P. putida F1 and TCE, and three control experiments were run without TCE. Two representative experiments are presented in Figure 1. Bacterial responses to TCE, as seen by characteristic chemotactic bands of cell accumulation and depletion, were first observed 30 s after introduction of the bacteria and were evident throughout the duration of the experiments. Initially bands intensified but then gradually became less bright as they grew wider and traveled further away from the TCE-containing agarose plug. As previously observed (3), bacteria accumulated a short distance away from the plug, seemingly because the cells require an optimal concentration of TCE lower than that immediately surrounding the plug. Faint bands of bacterial accumulation and depletion were observed as early as 2 min into the control experiments, immediately adjacent to the plugs. None of these bands grew as dark or distinct as the bands surrounding the TCEcontaining plugs nor did they travel further away from the plug over time. For these reasons and because of evident bacterial infiltration into the plug, it is unlikely that this response was due to chemotaxis. Instead, we believe that bacteria diffusing into the agarose plug experienced a lower diffusion coefficient than those in the surrounding aqueous solution and therefore accumulated around the edge of the plug. Diffusion into the agarose plug was less likely to occur in the TCE-containing experiments because the high concentration of TCE in the plug may act as a repellent to the bacteria (32).
FIGURE 1. Chemotactic response of P. putida F1 to an agar plug (dark circular region) containing 10% (vol/vol) TCE (A) and no TCE (B) at 0 (i), 1 (ii), and 2 min (iii) after addition of the bacterial suspension. A bright band of bacterial accumulation and a dark band of depletion in A are seen in response to TCE diffusion from the agar plug.
FIGURE 2. Concentration profiles of TCE from model of diffusion (thin line, left axis) and dimensionless bacterial concentration from light scattering (bold line, right axis) 3 min after surrounding a TCE-containing agarose plug with suspended bacteria. Plug assays were also used to estimate an appropriate range of values for the chemotaxis receptor constant for P. putida F1 to TCE, Kc, used in model simulations (Table 1). The chemotaxis receptor constant was estimated to approximate the equilibrium dissociation constant for the chemoreceptor, Kd, used to describe chemotaxis. However, the specific receptor for TCE has not yet been identified, and therefore, the true value of Kd is not known for P. putida and TCE. Recent work on chemotactic sensitivity suggests that the true receptor dissociation constants should be determined using specific receptor mutants (33). Bands of maximal bacterial concentration generally originated at a TCE concentration of approximately onehalf the original TCE concentration in the plug (Figure 2), assuming uniform TCE diffusion from the circular agarose plug (19). According to a qualitative parameter study by Ford and Lauffenburger (34), bacterial accumulations peak at onehalf of the original attractant concentration when the dimensionless parameter a0/Kd equals approximately 10, where a0 is the maximum attractant concentration. This information was used to develop a set of probable values of Kc ranging from 5% to 15% of the maximum TCE concentra-
tion used in the plug assays, in this case the aqueous solubility of TCE. From these values, a single value of an estimate for Kc that resulted in the best model fit for experimental concentration profiles was selected from several modeled simulations. Random Motility Experiments. Experiments were conducted at cell concentrations on the order of 108-109 cells/ mL. Duplicate random motility experiments demonstrated the migration of motile, chemotactic bacteria in the absence of a chemical attractant. Concentration profiles were simulated by numerically solving eq 1, the resultant reduction of eq 2 when the chemotaxis term is set to zero. Concentration profiles were collected every 35 min and fitted separately to numerical solutions for individual predictions of the random motility coefficient for each temporal profile. Concentration data from the first profile, collected at 35 min, were input and used as the initial condition for modeled simulations in order to account for changes in the original step change after 35 min of static conditions. Representative concentration profiles with averaged results (1 standard deviation of the fitted random motility coefficient, µ0,eff/, for one experiment are depicted in Figure 3. For clarity, only three VOL. 38, NO. 14, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Dimensionless concentration profiles for motile and chemotactic P. putida F1 in a packed column at 35 (9), 456 (2), and 913 min (×) after onset of a random motility experiment. Data at 35 min are used as initial input for model simulations. Dark curves are averaged best-fit solutions to model eq 1 with µ0,eff/E ) 1.2 ( 0.32 × 10-7 cm2/s at 456 (- - -) and 913 min (‚ ‚ ‚). Lighter curves (- ‚ -) represent the standard deviation of µ0,eff/E.
TABLE 2. Summary of Experimental Results and Parameters random motility experiments µ0,eff/ ) (10-7 cm2/s) χ0,eff/ ) (10-6 cm2/s) significance level of chemotaxis model no. of profiles analyzed
chemotaxis experiments
trial 1
trial 2
trial 1
trial 2
1.2 ( 0.3 NA NA
1.7 ( 0.4 NA NA
NA
NA
27
17
3.2 ( 0.5 7.0 ( 0.6 99.9% (457 min) 99.95% (949 min) 29
1.5 ( 0.3 9.4 ( 1.5 99.95% (561 min) 99.95% (913 min) 27
a
µ0,eff/ and χ0,eff/ represent the lumped effective motility and chemotactic sensitivity coefficients, respectively, averaged over the number of profiles analyzed and are presented with one standard deviation.
FIGURE 4. Dimensionless concentration profiles for motile and chemotactic P. putida F1 in a packed column at 35 (9), 457 (2), and 949 min (×) after onset of a chemotaxis experiment. TCE was introduced to the left side of the column. Data at 35 min are used as initial input for model simulations. Dark curves are averaged best-fit solutions to model eqs 2, 4, and 5 with µ0,eff/E ) 3.2 ( 0.53 × 10-7 cm2/s and χ0,eff/E ) 7.0 ( 0.61 × 10-6 cm2/s at 457 (- - -) and 949 min (‚ ‚ ‚). Lighter curves (- ‚ -) represent one standard deviation of µ0,eff/E and χ0,eff/E. concentration profiles are depicted. For both experiments, fitted values of the random motility coefficient for each temporal profile were averaged. Results from both trials are listed in Table 2. Chemotaxis Experiments. Duplicate chemotaxis experiments were conducted in separately packed columns, and concentration profiles were collected every 35 min. Numerical solutions to eqs 2, 4, and 5 were used to fit values of µ0,eff/ and χ0,eff/ for each temporal concentration profile (Table 2). Averaged results (1 standard deviation are illustrated in Figure 4 for one of the experiments. Data from the initial 3868
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profile, collected 35 min after the onset of the experiment, were used as the initial input for all simulations. Similarly, an initial profile was generated for TCE after 35 min of noflow conditions, assuming decay from a step change at the column midpoint at zero min and the diffusion coefficient and tortuosity values given in Table 1. Fitted parameters, µ0,eff/ and χ0,eff/, affected simulated concentration profiles in a different manner, and therefore, it was possible to fit both parameters independently. Changing the random motility coefficient affected the extent of the penetration of the diffusing bacteria, whereas modifying the chemotactic
FIGURE 5. Dimensionless concentration profiles for motile and chemotactic P. putida F1 in a packed column 949 min (b) after onset of a chemotaxis experiment. Curves are averaged best-fit solutions to model eqs 2, 4, and 5 with µ0eff/E ) 2.5 × 10-7 cm2/ s and χ0,eff/E ) 6.3 × 10-6 cm2/s (s) or χ0,eff/E ) 0 ((- - -)). sensitivity coefficient altered the magnitude of the increase in bacterial concentration at the interface. A single Kc value that resulted in the best model fit for several concentration profiles was selected from within the range determined from plug assay analyses. Although the model was not as sensitive to changes in Kc as it was to changes in µ0,eff/ and χ0,eff/, there was a noticeable effect on the location of maximum bacterial accumulation within the column. Increasing the Kc value predicted the bacteria would accumulate at a higher TCE concentration. Although the possibility of bacterial repulsion by high concentrations of TCE was considered, we did not include an analysis of this potential effect because bacteria and TCE were initially introduced onto opposite sides of the column, and therefore, only a small fraction of the bacteria encountered the highest concentrations of TCE where toxicity effects may have been a concern. The average random motility in the chemotaxis experiments is slightly higher in one trial than that observed in the random motility experiments, devoid of TCE. All other trials match well. It is possible that this deviation is due to batchto-batch differences in cell swimming properties. For example, if cells grown in one batch swim faster than in another, we would expect to measure a higher random motility coefficient. Statistical Significance of Chemotactic Sensitivity Coefficient. As seen by comparing Figures 3 and 4, bacterial transport through a packed column is altered by the presence of TCE. There is an enhanced migration of cells toward the initially cell-free half of the column when TCE is present in that region. There is no evidence that the presence of TCE caused any changes in bacterial attachment to or detachment from the beads. Following each experiment, the column was flushed with buffer and imaged once more to ensure that no labeled bacteria remained in the column or on the beads. Numerical simulations of the higher-order model (which includes a chemotaxis term) and the lower-order model (which assumes no chemotaxis) were compared to experimental data sets from chemotaxis experiments to determine whether the altered bacterial migration in the presence of TCE may be attributed to chemotaxis. Results seen in Figure 5 illustrate the improved fit of model simulations to experimental data when chemotaxis was included in the bacterial transport model. The following hypotheses were tested, according to Milton and Arnold (31)
H0: The lower-order model is appropriate (χ0 ) 0)
(8)
H1: The higher-order model is needed (χ0 * 0)
(9)
For both chemotaxis experiments, H0 was rejected by comparing the computed F-statistic (eq 6) to the F3,23
distribution (31), and we therefore conclude that incorporating the chemotaxis term improved the original model of bacterial transport in the presence of TCE. Levels of significance, often called the “P-values”, for these tests indicate high levels of confidence in this prediction and are listed in Table 2 for two time intervals. The predicted effective chemotactic sensitivity coefficients derived from our chemotaxis experiments (Table 2) seem to match well with historical reports. Given that previous predictions of the chemotactic sensitivity coefficient are reported for aqueous systems, we converted experimental effective parameters to comparable bulk parameters. Chen et al. (35) suggest that both transport coefficients, random motility and chemotactic sensitivity, should be reduced proportionally in porous media. If we assume a bulk random motility coefficient for P. putida F1 of 1.3 × 10-5 cm2/s (7), we can use eq 3 to estimate a tortuosity, τ, for the packed column as experienced by the bacteria using the fitted lumped parameter µ0,eff/. Relating this tortuosity to the measured effective chemotactic sensitivity coefficient, again employing eq 3, we calculate values of 2.8 × 10-4 and 8.1 × 10-4 cm2/s for the bulk chemotactic sensitivity coefficients in the two chemotaxis trials. These values for P. putida F1 and TCE are slightly higher than previous predictions for P. putida G7 and naphthalene of 7.2 × 10-5 cm2/s (11) and for P. putida PRS2000 and 3-chlorobenzoate of 1.9 × 10-4 cm2/s (18) but on average fall within the same order of magnitude. The chemotactic velocity of the bacteria depends on both the attractant concentration and the attractant gradient (eq 4) and therefore varies both temporally and spatially along the length of the column. At any point in time, the maximal chemotactic response is experienced in the region with the greatest chemical gradient, near the column midpoint. Over the course of these experiments, the maximum chemotactic velocity varied by approximately 2 orders of magnitude. As demonstrated here, bacterial chemotaxis has a statistically significant effect on bacterial migration through porous media in the presence of a chemical concentration gradient. Although the P. putida F1 used in this study were unable to degrade TCE appreciably or consume it as a growth substrate, we assume that the effect of chemotaxis will only be intensified when applied to more readily degraded pollutants such as toluene and benzene. Knowing that bacterial chemotaxis can impact the migration of bacteria toward organic contaminants, remediation strategies may consider not only the degradative abilities of bacteria but also their chemotactic abilities. In this way, chemotaxis may play a significant role in natural as well as engineered subsurface remediation. VOL. 38, NO. 14, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Acknowledgments The authors gratefully acknowledge Dr. Juli L. Sherwood and Dr. R. Nick Keener for valuable assistance with experimental protocol and MRI and Dr. Maribel Ramos-Gonzalez for supplying the antibody. Funding for this research was provided by the National Science Foundation, the Virginia Water Resources Research Council Graduate Research Award, and a Horton Research Grant from the American Geophysical Union. Additional support for M. Olson was provided by the National Science Foundation Graduate Research Fellowship, the Environmental Protection Agency STAR Fellowship, the American Association of University Women Selected Professions Engineering Dissertation Fellowship, and the Environmental Research and Education Foundation Fiessinger Scholarship Award. We also thank three anonymous reviewers whose helpful comments improved the manuscript.
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Received for review November 6, 2003. Revised manuscript received March 10, 2004. Accepted May 3, 2004. ES035236S
