Environmental Technology, 2013 Vol. 34, No. 19, 2757–2764, http://dx.doi.org/10.1080/09593330.2013.788070
Deposition and transport of Pseudomonas aeruginosa in porous media: lab-scale experiments and model analysis Kyu-Sang Kwona , Song-Bae Kimb , Nag-Choul Choib , Dong-Ju Kimc , Soonjae Leea,c , Sang-Hyup Leec,d and Jae-Woo Choia∗ a Center for Water Resource Cycle Research, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea; b Environmental Biocolloid Engineering Laboratory, Program in Rural System Engineering, Seoul National University, Seoul 151-742, Republic of Korea; c Department of Earth and Environmental Sciences, Korea University, Seoul 136-701, Republic of Korea; d Graduate School of Convergence Green Technology & Policy, Korea University, Seoul 136-701, Republic of Korea
(Received 30 August 2012; final version received 18 March 2013 ) In this study, the deposition and transport of Pseudomonas aeruginosa on sandy porous materials have been investigated under static and dynamic flow conditions. For the static experiments, both equilibrium and kinetic batch tests were performed at a 1:3 and 3:1 soil:solution ratio. The batch data were analysed to quantify the deposition parameters under static conditions. Column tests were performed for dynamic flow experiments with KCl solution and bacteria suspended in (1) deionized water, (2) mineral salt medium (MSM) and (3) surfactant + MSM. The equilibrium distribution coefficient (Kd ) was larger at a 1:3 (2.43 mL g−1 ) than that at a 3:1 (0.28 mL g−1 ) soil:solution ratio. Kinetic batch experiments showed that the reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) at a soil:solution ratio of 3:1 were larger than those at a 1:3 ratio. Column experiments showed that an increase in ionic strength resulted in a decrease in peak concentration of bacteria, mass recovery and tailing of the bacterial breakthrough curve (BTC) and that the presence of surfactant enhanced the movement of bacteria through quartz sand, giving increased mass recovery and tailing. Deposition parameters under dynamic condition were determined by fitting BTCs to four different transport models, (1) kinetic reversible, (2) two-site, (3) kinetic irreversible and (4) kinetic reversible and irreversible models. Among these models, Model 4 was more suitable than the others since it includes the irreversible sorption term directly related to the mass loss of bacteria observed in the column experiment. Applicability of the parameters obtained from the batch experiments to simulate the column breakthrough data is evaluated. Keywords: bacteria transport; Pseudomonas aeruginosa; quartz sand; transport model; deposition
Introduction Bacteria transport in subsurface environments attracts considerable attention for the protection of drinking water supplies from pathogenic bacteria,[1] and application of bioaugmentation schemes to contaminated soils and aquifers.[2] It is known that the subsurface movement of bacteria is mainly controlled by the advective–dispersive transport and deposition to solid matrix.[3] The deposition appears to be a major control of the extent of bacterial movement in porous media. The deposition of bacteria on a solid matrix is affected by porous medium properties (e.g. surface charge and grain size), solution chemistry (e.g. pH and ionic strength) and surface characteristics of the bacteria (e.g. cell surface charge and hydrophobicity).[4,5] Pseudomonas aeruginosa is a gram-negative, rodshaped bacterium with a size range of 0.5 to 0.8 μm. It is ubiquitous in soil and water environments due to its minimal requirements for survival.[6] The bacterium has environmental significance with respect to public health
and environmental remediation. It is known to be a human pathogen under certain conditions [6] and also has the ability to degrade contaminants in the environment.[7–10] Understanding the fate and transport of the bacterium in the environment is therefore an important objective. The subsurface transport and attachment characteristics of bacteria have been investigated previously using P. aeruginosa under various conditions to examine the effect of bacteria cell hydrophobicity on its attachment to negatively charged polystyrene,[11] the suitability of DLVO theory for describing bacteria attachment on surfaces,[12] to determine the collision efficiency of bacteria in a rotating glass disk,[13] to apply colloid filtration theory to bacteria deposition on glass beads,[14] to study the influence of biosurfactant on the hydrophobicity and transport of bacteria,[15,16] to observe the effect of nutrient conditions on the transport and attachment of bacteria onto the surfaces,[17] to identify the impact of lipopolysaccharide types on the attachment of bacteria on solid substrata,[18]
∗ Corresponding author. Email: [email protected] This article was originally published with erroneous pagination. This version has been corrected. Please see Erratum (http://dx.doi.org/ 10.1080/09593330.2013.869380). © 2013 Taylor & Francis
2758
K-S. Kwon et al.
to explore the effect of physiological state on the adhesion of bacteria onto dolomite surfaces,[19] to observe the transport of alginate-encapsulated and non-encapsulated bacteria through soil,[20] and to investigate the transport of bacteria in sand columns.[21] However, quantitative information regarding the characteristics of bacterial deposition during transport in porous media is still limited. In the present study, the deposition and transport of P. aeruginosa in quartz sand has been investigated under static and dynamic flow conditions. For the static experiments both equilibrium and kinetic batch methods are used, while column tests are employed for the dynamic flow experiment. Batch data are analysed to quantify the deposition-related parameters. Column breakthrough data are analysed with different transport models to determine the parameters. In addition, the applicability of the parameters obtained from the batch experiments to simulate the column breakthrough data has been evaluated. Materials and methods Transport models Assuming that bacterial growth and decay are negligible, the one-dimensional advection–dispersion equation (ADE) with linear equilibrium attachment and kinetic reversible attachment in homogeneous and saturated porous media under steady-state flow condition (Model 1) is as follows [22]: 2
∂C D∂ C ρb vw ∂C = − − katt C + kdet S 2 ∂t R ∂x R ∂x θ ρs ρb ∂S = katt C − kdet S, θ ∂t θ
(1) (2)
where C is the concentration of bacteria suspended in the aqueous phase (M L−3 ), D is the hydrodynamic dispersion coefficient (L2 /T), vw is the pore-water velocity (L/T), R is the retardation factor, ρb is the dry bulk density of solid matrix (M L−3 ), θ is the water content, katt is the reversible deposition rate coefficient (1/T), kdet is the release rate coefficient (1/T), and S is the mass of bacteria attached per unit mass of solid matrix (M/M). The one-dimensional ADE for bacteria with two-site sorption (instantaneous equilibrium sorption and kinetic non-equilibrium sorption) (Model 2) can be presented as [23]: f ρb Ks ∂C ∂ 2C ∂C 1+ = D 2 − vw θ ∂t ∂x ∂x αρb − [(1 − f )Ks C − S] (3) θ ∂S = α[(1 − f )Ks C − S], (4) ∂t where f is the fraction of equilibrium sorption sites, and α is the first-order kinetic rate coefficient (1/T).
The one-dimensional ADE for bacteria with linear equilibrium deposition and kinetic irreversible deposition (Model 3) can be presented as [24]: vw ∂C D ∂ 2C ∂C − = − kir C, 2 ∂t R ∂x R ∂x
(5)
where kir is the irreversible deposition rate coefficient (1/T). The one-dimensional ADE with first-order kinetic reversible and irreversible deposition terms (Model 4) is [25]: ∂C ∂ 2C ∂C ρb = D 2 − vw − katt C + kdet S − kir C ∂t ∂x ∂x θ ρb ∂S ρb = katt C − kdet S θ ∂t θ
(6) (7)
The numerical solutions of the models are obtained by the Crank–Nicolson finite difference method along with the Thomas algorithm. The initial and boundary conditions used in the analysis are: C(x, 0) = S(x, 0) = 0 ∂C vw C0 at 0 < t ≤ t0 −D (0, t) + vw C(0, t) 0 at t > t0 ∂x ∂C (L, t) = 0, ∂x
(8) (9) (10)
where C0 is the influent bacterial concentration, L is the column length and t0 is the duration of bacterial injection. Organisms and culture preparation The bacterial strain P. aeruginosa KCCM–40269 was obtained from the Korea Culture Centre for Microorganisms, Seoul, Korea. Initially in a freeze-dried state, the bacteria were revived in 250 mL Erlenmeyer flasks containing 100 mL of Luria-Bertani (LB) medium over a period of two days. One millilitre of culture was then transferred to a volume of 500 mL LB broth and incubated at 30◦ C in an orbital shaker at 140 rpm. Cells in the late exponential growth phase were harvested, washed three times with deionized water and resuspended, and then adjusted to an optical density of 1.0 at 600 nm (OD600 ), giving a final bacterial concentration of approximately 2.04 × 108 CFU mL−1 . The unit conversion related to the cell counting between the cell density measured by spectrophotometer and plate counting method has been investigated in previous research.[26] In order to obtain the surface characteristics of the bacterial cells, the net surface electrostatic charge of the cells was measured using a zeta potential analyser (Zetasizer 3000HS, Malvern Instruments Ltd., UK). The zeta potential and electrophoretic mobility were determined for a cell concentration of 0.445 OD600 at 25◦ C, pH 6.8 and IS of 565 mM, and found to be 31.8 mV and −2.51 × 10−8 m2 /V /s, respectively. All glassware and materials used in the study
Environmental Technology were sterilized in an autoclave (twice at 121◦ C for 15 min at 121 lb pressure) to prevent any influence from other microorganisms. Equilibrium batch experiments Equilibrium batch experiments were conducted to obtain the adsorbed concentration with two different soil:solution ratios. The sand material mainly consisted of quartz with a negative charge, which was analysed by an X-ray diffraction spectrometer (MXP3A–HF22 ), and supplied by the manufacturer of Jumunjin silica. Before experimental use, the sand was washed using deionized water, and the wet sand was autoclaved for 20 min at 121 lb pressure, cooled at room temperature for 24 h, autoclaved again and oven dried at 70◦ C for 3–5 days. In the case of soil:solution = 1:3, 10 g of sterilized quartz sand (0.3–0.6 mm) was added to 50 mL vials containing 30 mL of bacteria solution at different concentrations (0.334, 0.419, 0.470, 0.548 and 0.726 OD600 ). To avoid any bacterial growth during the test, mineral salt medium (MSM) was adopted.[27] Preliminary tests showed that the cell density of P. aeruginosa KCCM–40269 remained stationary when cultured in MSM At an ionic strength of 565 mM. The vials were sealed using para film and placed in an air batch shaker at 140 rpm and a temperature of 30◦ C for 72 h. Samples were collected periodically and cell density was measured using a Heyios β UV spectrophotometer (Thermo–Electron Corporation) at 600 nm. In the case of soil:solution = 3:1, 45 g of sterilized quartz sand (0.3–0.6 mm) was added to 50 mL vials containing 15 mL of bacteria solution at a range of concentrations (0.876, 0.956, 0.991, 1.124 and 1.165 OD600 ). The same procedures were followed as in the case of 1:3 soil:solution. Kinetic batch experiments In contrast to the above equilibrium experiments, kinetic batch experiments were performed to investigate the change in adsorbed concentration with time. The kinetic batch experiments were also conducted at two different soil:solution ratios. All kinetic batch tests were run in duplicate. In case of soil:solution = 3:1, a 200 mL bacteria solution (1.019 OD600 ) was added to a 1000 mL Erlenmeyer flask with 600 g of sandy materials. The flasks were sealed and placed on an air shaker at 140 rpm and 30◦ C for 6 days. Samples were collected periodically and cell density was measured using a UV spectrophotometer at 600 nm. The same procedures were followed as in the case of 1:3 soil:solution. Column experiments Column experiments were conducted using a plexiglass column with a diameter of 2.5 cm and height 30 cm. A fresh column was packed for each experiment by placing
2759
sand under standing water to minimize air entrapment, and tamped down with a glass rod during filling to achieve a bulk density of 1.55 g cm−3 and porosity of 0.42. Porosity was determined by the volumetric method. The column packed with dried sand was added to a known volume of water, and the resulting increase in volume as determined by the increased water level represents the volume of the sediment. The volume of the voids was found by the difference in water level. The upward flow of deionized water from a sterile reservoir through the column was regulated using a variable speed pump (Fluid Metering, Syosset, NY, USA) located beyond the column outlet. Once a constant flow rate was established, a breakthrough experiment was performed for a conservative tracer (KCl) at a flow rate of 1.0 mL min−1 and injection time of 34.6 min. The initial concentration of conservative tracer was 11.6 mS cm−1 . As soon as the injection of the tracer solution was completed, deionized water with a very low ionic strength ( 0 mM) and pH of 7.0 was again introduced under steady-state flow conditions. Effluent samples were collected using a fraction collector (Model: RTRV II, Tucson, AZ, USA) at regular time intervals, and tracer concentrations were analysed using an electrical conductivity meter (ORION, Model: 130A, Germany) and then calibrated using the predetermined relationship between the electrical conductivity and the concentration of the conservative tracer. Bacterial transport experiments followed exactly the same procedure (flow rate of 1.3 mL min−1 and injection time of 34.6 min) as described in the case of the conservative tracer, except that the input concentration of bacteria equal to 1.0 OD600 , corresponding to 2.04 × 108 CFU mL−1 . In the column experiments, bacteria were suspended (1) in deionized water (exp. 1), (2) MSM (exp. 2), and (3) surfactant + MSM (exp. 3). A nonionic surfactant (Tween 20) was used in the experiment at a concentration of 0.1%. The analysis of effluent concentration was performed using the UV spectrophotometer.
Data analysis The equilibrium batch data were analysed to determine the retardation factor (R) with the following relationship: S = Kd Ceq R=1+
ρb Kd , θ
(11) (12)
where Kd is the equilibrium distribution coefficient (Vw /Ms ), and Ceq is the equilibrium concentration of bacteria suspended in the aqueous phase (M L−3 ). The kinetic batch sorption data were analysed to determine the reversible deposition rate coefficient (katt , 1/T) and the release rate coefficient (kdet , 1/T) using the following
2760
K-S. Kwon et al.
kinetic expression and mass balance equation: ∂C Ms = −katt C + kdet Sγ ∂t Vw Vw Vw C + Ms Sγ = Vw C0 → Sγ = (C0 − C), Ms
(13) (14)
where Ms is the mass of the soil and Vw is the volume of the solution. The transport parameters such as the pore-water velocity (vw ) and the hydrodynamic dispersion coefficient (D) were determined from the conservative tracer breakthrough data using Equation (15) for steady-state flow in homogeneous porous media: ∂Cs ∂ 2 Cs ∂Cs = D 2 − vw , ∂t ∂x ∂x
the sorbed concentration of contaminants with decrease in soil:solution ratio has been reported in the literature.[28,29] The equilibrium distribution coefficients (Kd ) obtained during the present study were in a range similar to the literature values. The value for P. putida EST4021 has been determined as 1.3 mL g−1 by Bengtsson and Carlsson,[30] who performed an equilibrium batch test on sand (< 0.63 mm) at a soil:solution ratio of 1:9 (1 g sand: 9 mL bacteria solution). Values for W8 (gram-positive rod) and S1 (gramnegative rod) have also been reported by Mills et al.,[31] with ranges of 0.56–6.11 and 0.55–3.72 mL g−1 , respectively, depending on the ionic strength of the solution. Mills et al. performed equilibrium batch experiments with quartz sand (0.36–0.42 mm) at a soil:solution ratio of 1:2 (12.5 g sand: 25 mL bacteria solution).
(15)
Results and discussion Equilibrium batch data Figure 1 shows the adsorption isotherms for P. aeruginosa on quartz sand. The two isotherms attained at different soil:solution ratios resulted in a linear relationship. The equilibrium distribution coefficient (Kd ) at a soil:solution ratio of 1:3 was 2.43 mL g−1 and the calculated retardation factor (R) is 9.98 under a bulk density of 1.55 g cm−3 and porosity of 0.42. The equilibrium distribution coefficient at 3:1 was 0.28 mL g−1 , and the resulting retardation factor was 2.05. This shows that the equilibrium distribution coefficient at 1:3 is larger than at 3:1. An increase in
Kinetic batch data Figure 2 shows that the kinetic batch sorption data for P. aeruginosa in quartz sand were examined at different soil:solution ratios along with a model fit using a kinetic reversible sorption model (Equations (13) and (14)). At a soil:solution ratio of 1:3, bacterial cell concentration declined sharply to about 0.72 OD600 within 24 h and then levelled off. The reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) were determined as 1.22 × 10−3 and 1.05 × 10−3 1 min−1 , respectively. In (a) 1.2 Bacteria conc. OD600
where Cs is the concentration of conservative tracer in the aqueous phase (M /Vx ). The mass recovery (Mr ) of bacteria was quantified using Equation (16): ∞ ∫0 Cdt Mr = (16) C 0 t0
Soil : soltuion = 1 : 3
1.0 0.8 0.6 0.4
ka = 1.22E-3 (1/min) kd = 1.05E-3 (1/min) R2 = 0.97
0.2 0.0 0
2.5
20
40
60
80
100
120
140
Time (h)
Soil : solution = 1 : 3 Soil : solution = 3 : 1
(b) 1.2 2.0 1.5
Bacteria conc. OD600
Sorbed concentration, S (mg/g)
3.0
Kd = 2.43 ml/g
1.0 Kd = 0.28 ml/g
0.5 0.0 0.0
Soil : soltuion = 3 : 1
1.0
ka = 1.64E-3 (1/min) kd = 1.33E-3 (1/min) R2 = 0.97
0.8 0.6 0.4 0.2 0.0
0.2
0.4
0.6
0.8
1.0
Equilibrium concentration, Ceq (mg/ml)
Figure 1. Linear sorption isotherms for Pseudomonas aeruginosa on quartz sand at different soil:solution ratios.
0
20
40
60 80 Time (h)
100
120
140
Figure 2. Kinetic batch sorption data for Pseudomonas aeruginosa on quartz sand and the model fit with the kinetic reversible sorption model:(a) soil:solution = 1:3; (b) soil:solution = 3:1.
Environmental Technology
Figure 3.
2761
Breakthrough curves of Pseudomonas aeruginosa in quartz sand column under different solutions.
the case of 3:1 soil:solution ratio, bacteria concentration also decreased rapidly to about 0.56 OD600 within 24 h and levelled off thereafter. The reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) were determined as 1.64 × 10−3 and 1.33 × 10−3 1 min−1 , respectively. The reversible deposition (katt ) and release (kdet ) rate coefficients are larger than those at a soil:solution ratio of 1:3.
tailing can be attributed to the presence of surfactant, which enhances the movement of bacteria through quartz sand. The reduction of bacteria attachment on the surfaces of porous media in the presence of a nonionic surfactant (Tween 20) has been reported for Alcaligenes paradoxus on beads and quartz sand [34–36] and Hydrogenophaga flava ENV735 on silica quartz sand.[37,38]
Transport model analysis Column data The BTCs for bacteria obtained from the column experiments at different leaching solutions are presented in Figure 3. The BTCs showed well-defined single peaks with different sized tailings. The BTC for bacteria suspended in deionized water had a peak concentration of 0.80 and mass recovery of 0.83 with medium sized tailing. For bacteria in the MSM, the BTC had minimal tailing with a peak concentration of 0.56 and mass recovery of 0.58. The difference between the two BTCs can be attributed to the increase in ionic strength from 0 mM (deionized water) to 565 mM (MSM). Due to the increase in ionic strength, the tailing effect is minimized. This phenomenon can be attributed to the decrease in the distance between bacteria and quartz sand surfaces in similarly charged diffuse layers, resulting in an incremental bacteria attachment on the sand surface. [13,32,33] The lowered peak concentration and mass recovery owing to the increment in ionic strength in our study confirms this explanation. In the case of the bacteria suspended in the surfactant and MSM, the BTC had a peak concentration of 0.53 and mass recovery of 0.75, with long tailing. Relative to the case in the MSM only, the peak concentration was similar, but the mass recovery increased considerably. Most importantly, a marked tailing effect was observed, but the peak concentration and mass recovery were still lower than in deionized water, even though the tailing effect increased. This distinct
The transport models (Models 1–4) are applied to fit the column data of bacteria (Figure 4). The sorption parameters obtained are summarized in Table 1. The transport parameters such as the pore-water velocity (vw ) and the hydrodynamic dispersion coefficient (D) determined from the conservative tracer data are 0.515 ± 0.001 cm min−1 and 0.283 ± 0.089 cm2 min−1 , respectively. As presented in Figure 4(a), (b) and (d), the Models 1, 2 and 4 fit well to the observed bacteria BTCs and describe the tailing effect well. However, the retardation factors from Model 1 (R = 1.0) are different from those of Model 2 (R = 3.7 − 4.7). In Model 4, the BTCs are described well without incorporation of retardation factor into the model. However, Model 3 cannot describe the tailing effect observed in the BTC even if the coefficients of determination (R2 ) are high (Figure 4(c)). This defect in Model 3 comes from the incorporation of equilibrium sorption (retardation) into the transport model instead of the reversible kinetic sorption. The tailing effect in the BTC can be described properly by the kinetic reversible sorption term.[3,4] It should be noted that Models 1 and 2 contains reversible sorption terms, including equilibrium sorption (retardation) and reversible kinetic sorption, while Model 4 has both reversible and irreversible kinetic sorption terms. Even though these models are adequate for describing the bacteria BTC, Model 4 is the most suitable since it includes
2762
Figure 4.
K-S. Kwon et al.
Model fitting to column data (a) Model 1; (b) Model 2; (c) Model 3; (d) Model 4. Table 1.
Model 1 Exp. 1 Exp. 2 Exp. 3 Model 2 Exp. 1 Exp. 2 Exp. 3 Model 3 Exp. 1 Exp. 2 Exp. 3 Model 4 Exp. 1 Exp. 2 Exp. 3
Parameters from the fitting of transport models to column data of bacteria. R
ka (1/min)
kd (1/min)
1.0 1.0 1.0
2.63E-3 8.22E-3 1.03E-3
2.40E-3 6.00E-4 4.80E-3
kir (1/min)
f
4.32E-5 1.71E-5 1.28E-2
2.72E-3 4.52E-3 6.86E-3
2.09E-3 5.46E-5 5.49E-5
the irreversible sorption term directly related to the mass loss of bacteria observed in the column experiment. The simulation results of Model 4 using sorption parameters (ka , kd ) from the kinetic batch experiments performed in the MSM solution are comparable with column data of bacteria suspended in the MSM solution (Figure 5). For
R2 0.96 0.96 0.95
3.7 4.7 4.1 1.0 1.0 1.0
α (1/min)
4.60E-5 5.58E-5 1.10E-4
0.97 0.96 0.99
3.66E-3 7.90E-3 9.18E-3
0.97 0.96 0.96
1.45E-3 2.71E-3 3.07E-3
0.97 0.96 0.99
the irreversible sorption coefficient, the parameter value (kir = 2.71 × 10−3 ) obtained from the transport model analysis is used in the simulation. The simulation results are demonstrated in Figure 5. The first (ka = 1.22 × 10−3 ; kd = 1.05 × 10−3 ) and second data sets (ka = 1.64 × 10−3 ; kd = 1.33 × 10−3 ) are from soil:solution ratios of 1:3 and
Environmental Technology
2763
Figure 5. Comparison of column data for Pseudomonas aeruginosa suspended in the MSM solution with the simulation of Model 4 using sorption parameters obtained from the kinetic batch experiments.
3:1, respectively. The simulated peak concentrations (0.83 and 0.82) are greater than that observed (0.56), indicating that the parameters from the batch tests are underestimated in simulating the column conditions.
underestimated in simulating the column conditions and that equilibrium sorption and kinetic reversible sorption are not appropriate.
Acknowledgements Conclusions In this study we have investigated bacterial sorption and transport in quartz sand under static and dynamic flow conditions by applying P. aeruginosa as a model bacterium. For the static experiment, both equilibrium and kinetic batch tests were performed at a 1:3 and 3:1 soil:solution ratio. For dynamic flow conditions, column tests were performed with bacteria suspended in (1) deionized water, (2) mineral salt medium and (3) mineral salt medium with surfactant. The sorption parameters have been determined by fitting batch and column experimental data. The results show that the equilibrium and kinetic sorption parameters vary depending on the soil:solution ratio in the batch system. This makes clear that estimation of the adsorption parameters for predicting bacterial transport phenomena should consider the condition of a porous medium. In column test, the ionic strength of solution and surfactant affect bacterial sorption on soil and hence control tailing and mass loss of bacteria during transport. The bacterial transport has been explained using transport models assuming a variety of sorption processes, (1) kinetic reversible, (2) two-site, (3) kinetic irreversible and (4) kinetic reversible and irreversible. Among these four models, the kinetic reversible and irreversible sorption model describes bacterial sorption more effectively than the others. The equilibrium and kinetic sorption parameters obtained from the batch experiments have been applied to simulate bacterial transport through the column. The simulated peak concentrations are greater than those observed, indicating that the parameters from the batch tests are
The authors acknowledge that this study was supported by the Korea Institute of Science and Technology (KIST) institutional program (2E23943) and by the Korea Environmental Industry & Technology Institute funded by the Ministry of Environment, Korea (grant number: G112–00056–0004–1).
References [1] Foppen JWA, Schijven JF. Transport of E. coli in columns of geochemically heterogeneous sediment. Water Res. 2005;39:3082–3088. [2] Da Silva MLB, Alvarez PJJ. Enhanced anaerobic biodegradation of benzene-toluene-ethylbenzene-xyleneethanol mixtures in bioaugmented aquifer columns. Appl Environ Microb. 2004;70:4720–4726. [3] Hornberger GM, Mills AL, Herman JS. Bacterial transport in porous media: evaluation of a model using laboratory observations. Water Resour Res. 1992;28:915–923. [4] Fontes DE, Mills AL, Hornberger GM, Herman JS. Physical and chemical factors influencing transport of microorganisms through porous media. Appl Environ Mircob. 1991;57:2473–2481. [5] Gannon JT, Manilal VB, Alexander M. Relationship between cell surface properties and transport of bacteria through soil. Appl Environ Microb. 1991;57:190–193. [6] Hardalo C, Edberg SC. Pseudomona aeruginosa: assessment of risk from drinking water. Crit Rev Microbiol. 1997;23: 47–75. [7] El-Naggar MA, El-Aasar SA, Barakat KI. Bioremediation of crystal violet using air bubble bioreactor packed with Pseudomonas aeruginosa. Water Res. 2004;38: 4313–4322. [8] Kim SB, Park CH, Kim DJ, Jury WA. Kinetics of benzene biodegradation by Pseudomonas aeruginosa: parameter estimation. Environ Toxicol Chem. 2003;22:1038–1045.
2764
K-S. Kwon et al.
[9] Kumar M, Gupta SK, Garg SK, Kumar A. Biodegradation of hexachlorocyclohexane-isomers in contaminated soils. Soil Biol Biochem. 2006;38:2318–2327. [10] Yin B, Gu JD, Wan N. Degradation of indole by enrichment culture and Pseudomonas aeruginosa Gs isolated from mangrove sediment. Int Biodeter Biodegr. 2005;56: 243–248. [11] van Loosdrecht MCM, Lyklema J, Norde W, Schraa G, Zehnder AJB. The role of bacterial cell wall hydrophobicity in adhesion. Appl Environ Micob. 1987;53:1893–1897. [12] Sen TK. Processes in pathogenic biocolloidal contaminants transport in saturated and unsaturated porous media: a review. Water Air Soil Pollut. 2011;216:239–256. [13] Martin RE, Hanna LM, Bouwer EJ. Determination of bacterial collision efficiencies in a rotating disk system. Environ Sci Technol. 1991;25:2075–2082. [14] Martin RE, Bouwer EJ, Hanna LM. Application of cleanbed filtration theory to bacterial deposition in porous media. Environ Sci Technol. 1992;26:1053–1058. [15] Bai G, Brusseau ML, Miller RM. Influence of a rhamnolipid biosurfactant on the transport of bacteria through a sandy soil. Appl Environ Microb. 1997;63:1866–1873. [16] Cappello S, Crisari A, Hassanshahian M, Genovese M, Santisi S, Yakimov MM. Effect of a bioemulsificant exopolysaccharide (EPS2003 ) on abundance and vitality of marine bacteria. Water Air Soil Pollut. 2012;223:3903–3909. [17] Mueller RF. Bacterial transport and colonization in low nutrient environments. Water Res. 1996;30:2681–2690. [18] Makin SA, Beveridge TJ. The influence of A-band and B-band lipopolysaccharide on the surface characteristics and adhesion of Pseudomonas aeruginosa to surfaces. Microbiology. 1996;142:299–307. [19] Grasso D, Smert BF, Strevett KA, Machinist BD, van Oss CJ, Giese RF, Wu W. Impact of physiological state on surface thermodynamics and adhesion of Pseudomonas aeruginosa. Environ Sci Technol. 1996;30:3604–3608. [20] Duarte KR, Freitas AC, Pereira R, Pinheiro JC, Gonçalves F, Azaari H, El Azzouzi M, Zrineh A, Zaydoun S, Duarte AC, Rocha-Santos TAP. Treatment of olive oil mill wastewater by silica-alginate-fungi biocomposites. Water Air Soil Pollut. 2012;223:4307–4318. [21] Leon-Morales CF, Leis AP, Strathmann M, Flemming HC. Interactions between laponite and microbial biofilms in porous media: implications for colloid transport and biofilm stability. Water Res. 2004;38:3614–3626. [22] McCaulou DR, Bales RC, McCarthy JF. Use of shortpulse experiments to study bacteria transport through porous media. J Contam Hydrol. 1994;15:1–14. [23] Lindqvist R, Cho JS, Enfield CG. A kinetic model for cell density dependent bacterial transport in porous media. Water Resour Res. 1994;30:3291–3299.
[24] Harvey RW, Garabedian SP. Use of colloid filtration theory in modeling movement of bacteria through a contaminated sandy aquifer. Environ Sci Technol. 1991;25: 178–185. [25] Hendry MJ, Lawrence JR, Maloszewski P. The role of sorption in the transport of Klebsiella oxytoca through saturated silica sand. Ground Water. 1997;35:574–584. [26] Kim DJ, Chung SG, Lee SH, Choi JW. Relation of microbial biomass to counting units for Pseudomonas aeruginosa. AFR J Microbiol Res. 2012;6(21):4620–4622. [27] Kim DJ, Choi JW, Choi NC, Mahendran B, Lee CE. Modeling of growth kinetics for Pseudomonas spp. during benzene degradation. Appl Microbiol Biot. 2005;69:456–462. [28] Harter RD, Naidu R. An assessment of environmental and solution parameter impact on trace-metal sorption by soils. Soil Sci Soc Am J. 2001;65:597–612. [29] Kim DJ, Choi NC, Kim SB. Quantification of irreversible benzene sorption in sandy materials. Hydrol Process. 2004;18:3229–3234. [30] Bengtsson G, Carlsson C. Degradation of dissolved and sorbed 2,4-dichlorophenol in soil columns by suspended and sorbed bacteria. Biodegradation. 2001;12:419–432. [31] Mills AL, Herman JS, Hornberger GM, DeJesús TH. Effect of solution ionic strength and iron coatings on mineral grains on the sorption of bacterial cells to quartz sand. Appl Environ Microb. 1994;60:3300–3306. [32] Chen G, Zhu H. Bacterial deposition in porous medium as impacted by solution chemistry. Res Microbiol. 2004;155:467–474. [33] Gannon J, Tan Y, Baveye P, Alexander M. Effect of sodium chloride on transport of bacteria in a saturated aquifer material. Appl Environ Microb. 1991;57:2497–2501. [34] Gross MJ, Logan BE. Influence of different chemical treatments on transport of Alcaligenes paradoxus in porous media. Appl Environ Microb. 1995;61:1750–1756. [35] Johnson WP, Martin MJ, Gross MJ, Logan BE. Facilitation of bacterial transport through porous media by changes in solution and surface properties. Colloid Surface A. 1996;107:263–271. [36] Li Q, Logan BE. Enhancing bacterial transport for bioaugmentation of aquifers using low ionic strength solutions and surfactants. Water Res. 1992;33:1090–1100. [37] Dong H, Rothmel R, Onstott TC, Fuller ME, DeFlaun MF, Streger SH, Dunlap R, Fletcher M. Simultaneous transport of two bacterial strains in intact cores from oyster, Virginia: biological effects and numerical modeling. Appl Environ Microb. 2002;68:2120–2132. [38] Cheng ZW, Chen JM, Chen DZ, Zhang LL. Biodegradation of methyl tert-butyl ether in a bioreactor using immobilized Methylibium petroleiphilum PM1 cells. Water Air Soil Pollut. 2011;214:59–72.
Deposition and transport of Pseudomonas aeruginosa in porous media: lab-scale experiments and model analysis Kyu-Sang Kwona , Song-Bae Kimb , Nag-Choul Choib , Dong-Ju Kimc , Soonjae Leea,c , Sang-Hyup Leec,d and Jae-Woo Choia∗ a Center for Water Resource Cycle Research, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea; b Environmental Biocolloid Engineering Laboratory, Program in Rural System Engineering, Seoul National University, Seoul 151-742, Republic of Korea; c Department of Earth and Environmental Sciences, Korea University, Seoul 136-701, Republic of Korea; d Graduate School of Convergence Green Technology & Policy, Korea University, Seoul 136-701, Republic of Korea
(Received 30 August 2012; final version received 18 March 2013 ) In this study, the deposition and transport of Pseudomonas aeruginosa on sandy porous materials have been investigated under static and dynamic flow conditions. For the static experiments, both equilibrium and kinetic batch tests were performed at a 1:3 and 3:1 soil:solution ratio. The batch data were analysed to quantify the deposition parameters under static conditions. Column tests were performed for dynamic flow experiments with KCl solution and bacteria suspended in (1) deionized water, (2) mineral salt medium (MSM) and (3) surfactant + MSM. The equilibrium distribution coefficient (Kd ) was larger at a 1:3 (2.43 mL g−1 ) than that at a 3:1 (0.28 mL g−1 ) soil:solution ratio. Kinetic batch experiments showed that the reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) at a soil:solution ratio of 3:1 were larger than those at a 1:3 ratio. Column experiments showed that an increase in ionic strength resulted in a decrease in peak concentration of bacteria, mass recovery and tailing of the bacterial breakthrough curve (BTC) and that the presence of surfactant enhanced the movement of bacteria through quartz sand, giving increased mass recovery and tailing. Deposition parameters under dynamic condition were determined by fitting BTCs to four different transport models, (1) kinetic reversible, (2) two-site, (3) kinetic irreversible and (4) kinetic reversible and irreversible models. Among these models, Model 4 was more suitable than the others since it includes the irreversible sorption term directly related to the mass loss of bacteria observed in the column experiment. Applicability of the parameters obtained from the batch experiments to simulate the column breakthrough data is evaluated. Keywords: bacteria transport; Pseudomonas aeruginosa; quartz sand; transport model; deposition
Introduction Bacteria transport in subsurface environments attracts considerable attention for the protection of drinking water supplies from pathogenic bacteria,[1] and application of bioaugmentation schemes to contaminated soils and aquifers.[2] It is known that the subsurface movement of bacteria is mainly controlled by the advective–dispersive transport and deposition to solid matrix.[3] The deposition appears to be a major control of the extent of bacterial movement in porous media. The deposition of bacteria on a solid matrix is affected by porous medium properties (e.g. surface charge and grain size), solution chemistry (e.g. pH and ionic strength) and surface characteristics of the bacteria (e.g. cell surface charge and hydrophobicity).[4,5] Pseudomonas aeruginosa is a gram-negative, rodshaped bacterium with a size range of 0.5 to 0.8 μm. It is ubiquitous in soil and water environments due to its minimal requirements for survival.[6] The bacterium has environmental significance with respect to public health
and environmental remediation. It is known to be a human pathogen under certain conditions [6] and also has the ability to degrade contaminants in the environment.[7–10] Understanding the fate and transport of the bacterium in the environment is therefore an important objective. The subsurface transport and attachment characteristics of bacteria have been investigated previously using P. aeruginosa under various conditions to examine the effect of bacteria cell hydrophobicity on its attachment to negatively charged polystyrene,[11] the suitability of DLVO theory for describing bacteria attachment on surfaces,[12] to determine the collision efficiency of bacteria in a rotating glass disk,[13] to apply colloid filtration theory to bacteria deposition on glass beads,[14] to study the influence of biosurfactant on the hydrophobicity and transport of bacteria,[15,16] to observe the effect of nutrient conditions on the transport and attachment of bacteria onto the surfaces,[17] to identify the impact of lipopolysaccharide types on the attachment of bacteria on solid substrata,[18]
∗ Corresponding author. Email: [email protected] This article was originally published with erroneous pagination. This version has been corrected. Please see Erratum (http://dx.doi.org/ 10.1080/09593330.2013.869380). © 2013 Taylor & Francis
2758
K-S. Kwon et al.
to explore the effect of physiological state on the adhesion of bacteria onto dolomite surfaces,[19] to observe the transport of alginate-encapsulated and non-encapsulated bacteria through soil,[20] and to investigate the transport of bacteria in sand columns.[21] However, quantitative information regarding the characteristics of bacterial deposition during transport in porous media is still limited. In the present study, the deposition and transport of P. aeruginosa in quartz sand has been investigated under static and dynamic flow conditions. For the static experiments both equilibrium and kinetic batch methods are used, while column tests are employed for the dynamic flow experiment. Batch data are analysed to quantify the deposition-related parameters. Column breakthrough data are analysed with different transport models to determine the parameters. In addition, the applicability of the parameters obtained from the batch experiments to simulate the column breakthrough data has been evaluated. Materials and methods Transport models Assuming that bacterial growth and decay are negligible, the one-dimensional advection–dispersion equation (ADE) with linear equilibrium attachment and kinetic reversible attachment in homogeneous and saturated porous media under steady-state flow condition (Model 1) is as follows [22]: 2
∂C D∂ C ρb vw ∂C = − − katt C + kdet S 2 ∂t R ∂x R ∂x θ ρs ρb ∂S = katt C − kdet S, θ ∂t θ
(1) (2)
where C is the concentration of bacteria suspended in the aqueous phase (M L−3 ), D is the hydrodynamic dispersion coefficient (L2 /T), vw is the pore-water velocity (L/T), R is the retardation factor, ρb is the dry bulk density of solid matrix (M L−3 ), θ is the water content, katt is the reversible deposition rate coefficient (1/T), kdet is the release rate coefficient (1/T), and S is the mass of bacteria attached per unit mass of solid matrix (M/M). The one-dimensional ADE for bacteria with two-site sorption (instantaneous equilibrium sorption and kinetic non-equilibrium sorption) (Model 2) can be presented as [23]: f ρb Ks ∂C ∂ 2C ∂C 1+ = D 2 − vw θ ∂t ∂x ∂x αρb − [(1 − f )Ks C − S] (3) θ ∂S = α[(1 − f )Ks C − S], (4) ∂t where f is the fraction of equilibrium sorption sites, and α is the first-order kinetic rate coefficient (1/T).
The one-dimensional ADE for bacteria with linear equilibrium deposition and kinetic irreversible deposition (Model 3) can be presented as [24]: vw ∂C D ∂ 2C ∂C − = − kir C, 2 ∂t R ∂x R ∂x
(5)
where kir is the irreversible deposition rate coefficient (1/T). The one-dimensional ADE with first-order kinetic reversible and irreversible deposition terms (Model 4) is [25]: ∂C ∂ 2C ∂C ρb = D 2 − vw − katt C + kdet S − kir C ∂t ∂x ∂x θ ρb ∂S ρb = katt C − kdet S θ ∂t θ
(6) (7)
The numerical solutions of the models are obtained by the Crank–Nicolson finite difference method along with the Thomas algorithm. The initial and boundary conditions used in the analysis are: C(x, 0) = S(x, 0) = 0 ∂C vw C0 at 0 < t ≤ t0 −D (0, t) + vw C(0, t) 0 at t > t0 ∂x ∂C (L, t) = 0, ∂x
(8) (9) (10)
where C0 is the influent bacterial concentration, L is the column length and t0 is the duration of bacterial injection. Organisms and culture preparation The bacterial strain P. aeruginosa KCCM–40269 was obtained from the Korea Culture Centre for Microorganisms, Seoul, Korea. Initially in a freeze-dried state, the bacteria were revived in 250 mL Erlenmeyer flasks containing 100 mL of Luria-Bertani (LB) medium over a period of two days. One millilitre of culture was then transferred to a volume of 500 mL LB broth and incubated at 30◦ C in an orbital shaker at 140 rpm. Cells in the late exponential growth phase were harvested, washed three times with deionized water and resuspended, and then adjusted to an optical density of 1.0 at 600 nm (OD600 ), giving a final bacterial concentration of approximately 2.04 × 108 CFU mL−1 . The unit conversion related to the cell counting between the cell density measured by spectrophotometer and plate counting method has been investigated in previous research.[26] In order to obtain the surface characteristics of the bacterial cells, the net surface electrostatic charge of the cells was measured using a zeta potential analyser (Zetasizer 3000HS, Malvern Instruments Ltd., UK). The zeta potential and electrophoretic mobility were determined for a cell concentration of 0.445 OD600 at 25◦ C, pH 6.8 and IS of 565 mM, and found to be 31.8 mV and −2.51 × 10−8 m2 /V /s, respectively. All glassware and materials used in the study
Environmental Technology were sterilized in an autoclave (twice at 121◦ C for 15 min at 121 lb pressure) to prevent any influence from other microorganisms. Equilibrium batch experiments Equilibrium batch experiments were conducted to obtain the adsorbed concentration with two different soil:solution ratios. The sand material mainly consisted of quartz with a negative charge, which was analysed by an X-ray diffraction spectrometer (MXP3A–HF22 ), and supplied by the manufacturer of Jumunjin silica. Before experimental use, the sand was washed using deionized water, and the wet sand was autoclaved for 20 min at 121 lb pressure, cooled at room temperature for 24 h, autoclaved again and oven dried at 70◦ C for 3–5 days. In the case of soil:solution = 1:3, 10 g of sterilized quartz sand (0.3–0.6 mm) was added to 50 mL vials containing 30 mL of bacteria solution at different concentrations (0.334, 0.419, 0.470, 0.548 and 0.726 OD600 ). To avoid any bacterial growth during the test, mineral salt medium (MSM) was adopted.[27] Preliminary tests showed that the cell density of P. aeruginosa KCCM–40269 remained stationary when cultured in MSM At an ionic strength of 565 mM. The vials were sealed using para film and placed in an air batch shaker at 140 rpm and a temperature of 30◦ C for 72 h. Samples were collected periodically and cell density was measured using a Heyios β UV spectrophotometer (Thermo–Electron Corporation) at 600 nm. In the case of soil:solution = 3:1, 45 g of sterilized quartz sand (0.3–0.6 mm) was added to 50 mL vials containing 15 mL of bacteria solution at a range of concentrations (0.876, 0.956, 0.991, 1.124 and 1.165 OD600 ). The same procedures were followed as in the case of 1:3 soil:solution. Kinetic batch experiments In contrast to the above equilibrium experiments, kinetic batch experiments were performed to investigate the change in adsorbed concentration with time. The kinetic batch experiments were also conducted at two different soil:solution ratios. All kinetic batch tests were run in duplicate. In case of soil:solution = 3:1, a 200 mL bacteria solution (1.019 OD600 ) was added to a 1000 mL Erlenmeyer flask with 600 g of sandy materials. The flasks were sealed and placed on an air shaker at 140 rpm and 30◦ C for 6 days. Samples were collected periodically and cell density was measured using a UV spectrophotometer at 600 nm. The same procedures were followed as in the case of 1:3 soil:solution. Column experiments Column experiments were conducted using a plexiglass column with a diameter of 2.5 cm and height 30 cm. A fresh column was packed for each experiment by placing
2759
sand under standing water to minimize air entrapment, and tamped down with a glass rod during filling to achieve a bulk density of 1.55 g cm−3 and porosity of 0.42. Porosity was determined by the volumetric method. The column packed with dried sand was added to a known volume of water, and the resulting increase in volume as determined by the increased water level represents the volume of the sediment. The volume of the voids was found by the difference in water level. The upward flow of deionized water from a sterile reservoir through the column was regulated using a variable speed pump (Fluid Metering, Syosset, NY, USA) located beyond the column outlet. Once a constant flow rate was established, a breakthrough experiment was performed for a conservative tracer (KCl) at a flow rate of 1.0 mL min−1 and injection time of 34.6 min. The initial concentration of conservative tracer was 11.6 mS cm−1 . As soon as the injection of the tracer solution was completed, deionized water with a very low ionic strength ( 0 mM) and pH of 7.0 was again introduced under steady-state flow conditions. Effluent samples were collected using a fraction collector (Model: RTRV II, Tucson, AZ, USA) at regular time intervals, and tracer concentrations were analysed using an electrical conductivity meter (ORION, Model: 130A, Germany) and then calibrated using the predetermined relationship between the electrical conductivity and the concentration of the conservative tracer. Bacterial transport experiments followed exactly the same procedure (flow rate of 1.3 mL min−1 and injection time of 34.6 min) as described in the case of the conservative tracer, except that the input concentration of bacteria equal to 1.0 OD600 , corresponding to 2.04 × 108 CFU mL−1 . In the column experiments, bacteria were suspended (1) in deionized water (exp. 1), (2) MSM (exp. 2), and (3) surfactant + MSM (exp. 3). A nonionic surfactant (Tween 20) was used in the experiment at a concentration of 0.1%. The analysis of effluent concentration was performed using the UV spectrophotometer.
Data analysis The equilibrium batch data were analysed to determine the retardation factor (R) with the following relationship: S = Kd Ceq R=1+
ρb Kd , θ
(11) (12)
where Kd is the equilibrium distribution coefficient (Vw /Ms ), and Ceq is the equilibrium concentration of bacteria suspended in the aqueous phase (M L−3 ). The kinetic batch sorption data were analysed to determine the reversible deposition rate coefficient (katt , 1/T) and the release rate coefficient (kdet , 1/T) using the following
2760
K-S. Kwon et al.
kinetic expression and mass balance equation: ∂C Ms = −katt C + kdet Sγ ∂t Vw Vw Vw C + Ms Sγ = Vw C0 → Sγ = (C0 − C), Ms
(13) (14)
where Ms is the mass of the soil and Vw is the volume of the solution. The transport parameters such as the pore-water velocity (vw ) and the hydrodynamic dispersion coefficient (D) were determined from the conservative tracer breakthrough data using Equation (15) for steady-state flow in homogeneous porous media: ∂Cs ∂ 2 Cs ∂Cs = D 2 − vw , ∂t ∂x ∂x
the sorbed concentration of contaminants with decrease in soil:solution ratio has been reported in the literature.[28,29] The equilibrium distribution coefficients (Kd ) obtained during the present study were in a range similar to the literature values. The value for P. putida EST4021 has been determined as 1.3 mL g−1 by Bengtsson and Carlsson,[30] who performed an equilibrium batch test on sand (< 0.63 mm) at a soil:solution ratio of 1:9 (1 g sand: 9 mL bacteria solution). Values for W8 (gram-positive rod) and S1 (gramnegative rod) have also been reported by Mills et al.,[31] with ranges of 0.56–6.11 and 0.55–3.72 mL g−1 , respectively, depending on the ionic strength of the solution. Mills et al. performed equilibrium batch experiments with quartz sand (0.36–0.42 mm) at a soil:solution ratio of 1:2 (12.5 g sand: 25 mL bacteria solution).
(15)
Results and discussion Equilibrium batch data Figure 1 shows the adsorption isotherms for P. aeruginosa on quartz sand. The two isotherms attained at different soil:solution ratios resulted in a linear relationship. The equilibrium distribution coefficient (Kd ) at a soil:solution ratio of 1:3 was 2.43 mL g−1 and the calculated retardation factor (R) is 9.98 under a bulk density of 1.55 g cm−3 and porosity of 0.42. The equilibrium distribution coefficient at 3:1 was 0.28 mL g−1 , and the resulting retardation factor was 2.05. This shows that the equilibrium distribution coefficient at 1:3 is larger than at 3:1. An increase in
Kinetic batch data Figure 2 shows that the kinetic batch sorption data for P. aeruginosa in quartz sand were examined at different soil:solution ratios along with a model fit using a kinetic reversible sorption model (Equations (13) and (14)). At a soil:solution ratio of 1:3, bacterial cell concentration declined sharply to about 0.72 OD600 within 24 h and then levelled off. The reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) were determined as 1.22 × 10−3 and 1.05 × 10−3 1 min−1 , respectively. In (a) 1.2 Bacteria conc. OD600
where Cs is the concentration of conservative tracer in the aqueous phase (M /Vx ). The mass recovery (Mr ) of bacteria was quantified using Equation (16): ∞ ∫0 Cdt Mr = (16) C 0 t0
Soil : soltuion = 1 : 3
1.0 0.8 0.6 0.4
ka = 1.22E-3 (1/min) kd = 1.05E-3 (1/min) R2 = 0.97
0.2 0.0 0
2.5
20
40
60
80
100
120
140
Time (h)
Soil : solution = 1 : 3 Soil : solution = 3 : 1
(b) 1.2 2.0 1.5
Bacteria conc. OD600
Sorbed concentration, S (mg/g)
3.0
Kd = 2.43 ml/g
1.0 Kd = 0.28 ml/g
0.5 0.0 0.0
Soil : soltuion = 3 : 1
1.0
ka = 1.64E-3 (1/min) kd = 1.33E-3 (1/min) R2 = 0.97
0.8 0.6 0.4 0.2 0.0
0.2
0.4
0.6
0.8
1.0
Equilibrium concentration, Ceq (mg/ml)
Figure 1. Linear sorption isotherms for Pseudomonas aeruginosa on quartz sand at different soil:solution ratios.
0
20
40
60 80 Time (h)
100
120
140
Figure 2. Kinetic batch sorption data for Pseudomonas aeruginosa on quartz sand and the model fit with the kinetic reversible sorption model:(a) soil:solution = 1:3; (b) soil:solution = 3:1.
Environmental Technology
Figure 3.
2761
Breakthrough curves of Pseudomonas aeruginosa in quartz sand column under different solutions.
the case of 3:1 soil:solution ratio, bacteria concentration also decreased rapidly to about 0.56 OD600 within 24 h and levelled off thereafter. The reversible deposition rate coefficient (katt ) and the release rate coefficient (kdet ) were determined as 1.64 × 10−3 and 1.33 × 10−3 1 min−1 , respectively. The reversible deposition (katt ) and release (kdet ) rate coefficients are larger than those at a soil:solution ratio of 1:3.
tailing can be attributed to the presence of surfactant, which enhances the movement of bacteria through quartz sand. The reduction of bacteria attachment on the surfaces of porous media in the presence of a nonionic surfactant (Tween 20) has been reported for Alcaligenes paradoxus on beads and quartz sand [34–36] and Hydrogenophaga flava ENV735 on silica quartz sand.[37,38]
Transport model analysis Column data The BTCs for bacteria obtained from the column experiments at different leaching solutions are presented in Figure 3. The BTCs showed well-defined single peaks with different sized tailings. The BTC for bacteria suspended in deionized water had a peak concentration of 0.80 and mass recovery of 0.83 with medium sized tailing. For bacteria in the MSM, the BTC had minimal tailing with a peak concentration of 0.56 and mass recovery of 0.58. The difference between the two BTCs can be attributed to the increase in ionic strength from 0 mM (deionized water) to 565 mM (MSM). Due to the increase in ionic strength, the tailing effect is minimized. This phenomenon can be attributed to the decrease in the distance between bacteria and quartz sand surfaces in similarly charged diffuse layers, resulting in an incremental bacteria attachment on the sand surface. [13,32,33] The lowered peak concentration and mass recovery owing to the increment in ionic strength in our study confirms this explanation. In the case of the bacteria suspended in the surfactant and MSM, the BTC had a peak concentration of 0.53 and mass recovery of 0.75, with long tailing. Relative to the case in the MSM only, the peak concentration was similar, but the mass recovery increased considerably. Most importantly, a marked tailing effect was observed, but the peak concentration and mass recovery were still lower than in deionized water, even though the tailing effect increased. This distinct
The transport models (Models 1–4) are applied to fit the column data of bacteria (Figure 4). The sorption parameters obtained are summarized in Table 1. The transport parameters such as the pore-water velocity (vw ) and the hydrodynamic dispersion coefficient (D) determined from the conservative tracer data are 0.515 ± 0.001 cm min−1 and 0.283 ± 0.089 cm2 min−1 , respectively. As presented in Figure 4(a), (b) and (d), the Models 1, 2 and 4 fit well to the observed bacteria BTCs and describe the tailing effect well. However, the retardation factors from Model 1 (R = 1.0) are different from those of Model 2 (R = 3.7 − 4.7). In Model 4, the BTCs are described well without incorporation of retardation factor into the model. However, Model 3 cannot describe the tailing effect observed in the BTC even if the coefficients of determination (R2 ) are high (Figure 4(c)). This defect in Model 3 comes from the incorporation of equilibrium sorption (retardation) into the transport model instead of the reversible kinetic sorption. The tailing effect in the BTC can be described properly by the kinetic reversible sorption term.[3,4] It should be noted that Models 1 and 2 contains reversible sorption terms, including equilibrium sorption (retardation) and reversible kinetic sorption, while Model 4 has both reversible and irreversible kinetic sorption terms. Even though these models are adequate for describing the bacteria BTC, Model 4 is the most suitable since it includes
2762
Figure 4.
K-S. Kwon et al.
Model fitting to column data (a) Model 1; (b) Model 2; (c) Model 3; (d) Model 4. Table 1.
Model 1 Exp. 1 Exp. 2 Exp. 3 Model 2 Exp. 1 Exp. 2 Exp. 3 Model 3 Exp. 1 Exp. 2 Exp. 3 Model 4 Exp. 1 Exp. 2 Exp. 3
Parameters from the fitting of transport models to column data of bacteria. R
ka (1/min)
kd (1/min)
1.0 1.0 1.0
2.63E-3 8.22E-3 1.03E-3
2.40E-3 6.00E-4 4.80E-3
kir (1/min)
f
4.32E-5 1.71E-5 1.28E-2
2.72E-3 4.52E-3 6.86E-3
2.09E-3 5.46E-5 5.49E-5
the irreversible sorption term directly related to the mass loss of bacteria observed in the column experiment. The simulation results of Model 4 using sorption parameters (ka , kd ) from the kinetic batch experiments performed in the MSM solution are comparable with column data of bacteria suspended in the MSM solution (Figure 5). For
R2 0.96 0.96 0.95
3.7 4.7 4.1 1.0 1.0 1.0
α (1/min)
4.60E-5 5.58E-5 1.10E-4
0.97 0.96 0.99
3.66E-3 7.90E-3 9.18E-3
0.97 0.96 0.96
1.45E-3 2.71E-3 3.07E-3
0.97 0.96 0.99
the irreversible sorption coefficient, the parameter value (kir = 2.71 × 10−3 ) obtained from the transport model analysis is used in the simulation. The simulation results are demonstrated in Figure 5. The first (ka = 1.22 × 10−3 ; kd = 1.05 × 10−3 ) and second data sets (ka = 1.64 × 10−3 ; kd = 1.33 × 10−3 ) are from soil:solution ratios of 1:3 and
Environmental Technology
2763
Figure 5. Comparison of column data for Pseudomonas aeruginosa suspended in the MSM solution with the simulation of Model 4 using sorption parameters obtained from the kinetic batch experiments.
3:1, respectively. The simulated peak concentrations (0.83 and 0.82) are greater than that observed (0.56), indicating that the parameters from the batch tests are underestimated in simulating the column conditions.
underestimated in simulating the column conditions and that equilibrium sorption and kinetic reversible sorption are not appropriate.
Acknowledgements Conclusions In this study we have investigated bacterial sorption and transport in quartz sand under static and dynamic flow conditions by applying P. aeruginosa as a model bacterium. For the static experiment, both equilibrium and kinetic batch tests were performed at a 1:3 and 3:1 soil:solution ratio. For dynamic flow conditions, column tests were performed with bacteria suspended in (1) deionized water, (2) mineral salt medium and (3) mineral salt medium with surfactant. The sorption parameters have been determined by fitting batch and column experimental data. The results show that the equilibrium and kinetic sorption parameters vary depending on the soil:solution ratio in the batch system. This makes clear that estimation of the adsorption parameters for predicting bacterial transport phenomena should consider the condition of a porous medium. In column test, the ionic strength of solution and surfactant affect bacterial sorption on soil and hence control tailing and mass loss of bacteria during transport. The bacterial transport has been explained using transport models assuming a variety of sorption processes, (1) kinetic reversible, (2) two-site, (3) kinetic irreversible and (4) kinetic reversible and irreversible. Among these four models, the kinetic reversible and irreversible sorption model describes bacterial sorption more effectively than the others. The equilibrium and kinetic sorption parameters obtained from the batch experiments have been applied to simulate bacterial transport through the column. The simulated peak concentrations are greater than those observed, indicating that the parameters from the batch tests are
The authors acknowledge that this study was supported by the Korea Institute of Science and Technology (KIST) institutional program (2E23943) and by the Korea Environmental Industry & Technology Institute funded by the Ministry of Environment, Korea (grant number: G112–00056–0004–1).
References [1] Foppen JWA, Schijven JF. Transport of E. coli in columns of geochemically heterogeneous sediment. Water Res. 2005;39:3082–3088. [2] Da Silva MLB, Alvarez PJJ. Enhanced anaerobic biodegradation of benzene-toluene-ethylbenzene-xyleneethanol mixtures in bioaugmented aquifer columns. Appl Environ Microb. 2004;70:4720–4726. [3] Hornberger GM, Mills AL, Herman JS. Bacterial transport in porous media: evaluation of a model using laboratory observations. Water Resour Res. 1992;28:915–923. [4] Fontes DE, Mills AL, Hornberger GM, Herman JS. Physical and chemical factors influencing transport of microorganisms through porous media. Appl Environ Mircob. 1991;57:2473–2481. [5] Gannon JT, Manilal VB, Alexander M. Relationship between cell surface properties and transport of bacteria through soil. Appl Environ Microb. 1991;57:190–193. [6] Hardalo C, Edberg SC. Pseudomona aeruginosa: assessment of risk from drinking water. Crit Rev Microbiol. 1997;23: 47–75. [7] El-Naggar MA, El-Aasar SA, Barakat KI. Bioremediation of crystal violet using air bubble bioreactor packed with Pseudomonas aeruginosa. Water Res. 2004;38: 4313–4322. [8] Kim SB, Park CH, Kim DJ, Jury WA. Kinetics of benzene biodegradation by Pseudomonas aeruginosa: parameter estimation. Environ Toxicol Chem. 2003;22:1038–1045.
2764
K-S. Kwon et al.
[9] Kumar M, Gupta SK, Garg SK, Kumar A. Biodegradation of hexachlorocyclohexane-isomers in contaminated soils. Soil Biol Biochem. 2006;38:2318–2327. [10] Yin B, Gu JD, Wan N. Degradation of indole by enrichment culture and Pseudomonas aeruginosa Gs isolated from mangrove sediment. Int Biodeter Biodegr. 2005;56: 243–248. [11] van Loosdrecht MCM, Lyklema J, Norde W, Schraa G, Zehnder AJB. The role of bacterial cell wall hydrophobicity in adhesion. Appl Environ Micob. 1987;53:1893–1897. [12] Sen TK. Processes in pathogenic biocolloidal contaminants transport in saturated and unsaturated porous media: a review. Water Air Soil Pollut. 2011;216:239–256. [13] Martin RE, Hanna LM, Bouwer EJ. Determination of bacterial collision efficiencies in a rotating disk system. Environ Sci Technol. 1991;25:2075–2082. [14] Martin RE, Bouwer EJ, Hanna LM. Application of cleanbed filtration theory to bacterial deposition in porous media. Environ Sci Technol. 1992;26:1053–1058. [15] Bai G, Brusseau ML, Miller RM. Influence of a rhamnolipid biosurfactant on the transport of bacteria through a sandy soil. Appl Environ Microb. 1997;63:1866–1873. [16] Cappello S, Crisari A, Hassanshahian M, Genovese M, Santisi S, Yakimov MM. Effect of a bioemulsificant exopolysaccharide (EPS2003 ) on abundance and vitality of marine bacteria. Water Air Soil Pollut. 2012;223:3903–3909. [17] Mueller RF. Bacterial transport and colonization in low nutrient environments. Water Res. 1996;30:2681–2690. [18] Makin SA, Beveridge TJ. The influence of A-band and B-band lipopolysaccharide on the surface characteristics and adhesion of Pseudomonas aeruginosa to surfaces. Microbiology. 1996;142:299–307. [19] Grasso D, Smert BF, Strevett KA, Machinist BD, van Oss CJ, Giese RF, Wu W. Impact of physiological state on surface thermodynamics and adhesion of Pseudomonas aeruginosa. Environ Sci Technol. 1996;30:3604–3608. [20] Duarte KR, Freitas AC, Pereira R, Pinheiro JC, Gonçalves F, Azaari H, El Azzouzi M, Zrineh A, Zaydoun S, Duarte AC, Rocha-Santos TAP. Treatment of olive oil mill wastewater by silica-alginate-fungi biocomposites. Water Air Soil Pollut. 2012;223:4307–4318. [21] Leon-Morales CF, Leis AP, Strathmann M, Flemming HC. Interactions between laponite and microbial biofilms in porous media: implications for colloid transport and biofilm stability. Water Res. 2004;38:3614–3626. [22] McCaulou DR, Bales RC, McCarthy JF. Use of shortpulse experiments to study bacteria transport through porous media. J Contam Hydrol. 1994;15:1–14. [23] Lindqvist R, Cho JS, Enfield CG. A kinetic model for cell density dependent bacterial transport in porous media. Water Resour Res. 1994;30:3291–3299.
[24] Harvey RW, Garabedian SP. Use of colloid filtration theory in modeling movement of bacteria through a contaminated sandy aquifer. Environ Sci Technol. 1991;25: 178–185. [25] Hendry MJ, Lawrence JR, Maloszewski P. The role of sorption in the transport of Klebsiella oxytoca through saturated silica sand. Ground Water. 1997;35:574–584. [26] Kim DJ, Chung SG, Lee SH, Choi JW. Relation of microbial biomass to counting units for Pseudomonas aeruginosa. AFR J Microbiol Res. 2012;6(21):4620–4622. [27] Kim DJ, Choi JW, Choi NC, Mahendran B, Lee CE. Modeling of growth kinetics for Pseudomonas spp. during benzene degradation. Appl Microbiol Biot. 2005;69:456–462. [28] Harter RD, Naidu R. An assessment of environmental and solution parameter impact on trace-metal sorption by soils. Soil Sci Soc Am J. 2001;65:597–612. [29] Kim DJ, Choi NC, Kim SB. Quantification of irreversible benzene sorption in sandy materials. Hydrol Process. 2004;18:3229–3234. [30] Bengtsson G, Carlsson C. Degradation of dissolved and sorbed 2,4-dichlorophenol in soil columns by suspended and sorbed bacteria. Biodegradation. 2001;12:419–432. [31] Mills AL, Herman JS, Hornberger GM, DeJesús TH. Effect of solution ionic strength and iron coatings on mineral grains on the sorption of bacterial cells to quartz sand. Appl Environ Microb. 1994;60:3300–3306. [32] Chen G, Zhu H. Bacterial deposition in porous medium as impacted by solution chemistry. Res Microbiol. 2004;155:467–474. [33] Gannon J, Tan Y, Baveye P, Alexander M. Effect of sodium chloride on transport of bacteria in a saturated aquifer material. Appl Environ Microb. 1991;57:2497–2501. [34] Gross MJ, Logan BE. Influence of different chemical treatments on transport of Alcaligenes paradoxus in porous media. Appl Environ Microb. 1995;61:1750–1756. [35] Johnson WP, Martin MJ, Gross MJ, Logan BE. Facilitation of bacterial transport through porous media by changes in solution and surface properties. Colloid Surface A. 1996;107:263–271. [36] Li Q, Logan BE. Enhancing bacterial transport for bioaugmentation of aquifers using low ionic strength solutions and surfactants. Water Res. 1992;33:1090–1100. [37] Dong H, Rothmel R, Onstott TC, Fuller ME, DeFlaun MF, Streger SH, Dunlap R, Fletcher M. Simultaneous transport of two bacterial strains in intact cores from oyster, Virginia: biological effects and numerical modeling. Appl Environ Microb. 2002;68:2120–2132. [38] Cheng ZW, Chen JM, Chen DZ, Zhang LL. Biodegradation of methyl tert-butyl ether in a bioreactor using immobilized Methylibium petroleiphilum PM1 cells. Water Air Soil Pollut. 2011;214:59–72.
