Transport and activity of dissimilatory metal-reducing bacteria in porous media for the remediation of
heavy metals and chlorinated hydrocarbons
by Robin Gerlach
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of
Philosophy in Engineering
Montana State University
© Copyright by Robin Gerlach (2001)
Abstract:
Dissmiilatory metal-reducing bacteria (DMRB) are capable of reducing a wide range of metals and the
injection of DMRB is considered for the cleanup of contaminated soil and groundwater. The success of
such an approach, among other factors, relies upon the bacterial transport through porous media and the
contaminant transformation by DMRB.
Long term starvation of Shewanella algae BrY, the model DMRB used in this research, allows for
significantly improved transport through quartz sand porous media columns. However, the reasons for
the improved transport remain unclear. Changes in cell size, net electrostatic charge, hydrophobicity,
buoyant density, and effective diffusion coefficient do not provide a sufficient explanation and this
question will have to remain the focus of future research. In addition, it became evident that the
employed mathematical model based on the colloid filtration theory is not capable of appropriately
describing scale and physiology dependent effects on bacterial transport through porous media. A
complete understanding of the processes and factors influencing the porous media transport of starved
bacteria is still lacking and will remain as the focus of future research.
The reduction of chromium [Cr(VI)], a compound of significant environmental concern at many
Department of Energy, Department of Defense, and Environmental Protection Agency Superfund sites,
can be facilitated by DMRB. Starved S. algae BrY cells can be resuscitated into an actively
metabolizing state in the presence of ferric iron [Fe(III)], which is abundant in the environment. Active
S. algae BrY cells can either directly reduce Cr(VI) or produce surface reactive ferrous iron [Fe(II)].
Fe(II) chemically reduces and precipitates Cr(VI) eliminating it from contaminated water. S. algae BrY
cells can also potentially contribute towards the long term reactivity of zero valent iron subsurface
barriers. The microbial reduction of Fe(III) allows for the removal or activation of surface associated
corrosion products and results in increased transformation rates of carbon tetrachloride, another
widespread environmental contaminant.
The results summarized in this dissertation indicate that the injection of starved DMRB is a promising
technology for the remediation of subsurface environments contaminated with heavy metals and
chlorinated hydrocarbons. TRANSPORT AND ACTIVITY OF DISSIMILATORY METAL-REDUCING BACTERIA IN POROUS
MEDIA FOR THE REMEDIATION OF HEAVY METALS
AND CHLORINATED HYDROCARBONS
by
Robin Gerlach
A dissertation submitted in partial fulfillment
of the requirements for the degree
of
Doctor of Philosophy
in
Engineering
I
MONTANA STATE UNIVERSITY
Bozeman, Montana
April 2001
© COPYRIGHT
by
Robin Gerlach
2001
All rights reserved
Ii
APPROVAL
of a dissertation submitted by
Robin Gerlach
This dissertation has been read by each member of the dissertation committee and has been found
to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency,
and is ready for submission to the College of Graduate Studies.
/a -
Dr. Donald A. Rabem
(Signature)
j Cl
(Date)
Approved for the College of Graduate Studies
Dr. Bruce R. McLeod
(Date)
Ni
STATEMENT FOR PERMISSION TO USE
In presenting this dissertation in partial fulfillment of the requirements for a doctoral degree at
Montana State University, I agree that the Library shall make it available to borrowers under the rules of
the Library. I further agree that copying of this dissertation is allowable only for scholarly purposes,
consistent with “fair use” as prescribed in the U.S. Copyright Law. Requests for extensive copying or
reproduction of this dissertation should be referred to Bell & Howell Information and Learning, 300 North
Zeeb Road, Ann Arbor, Michigan 48106, to whom I have granted “the exclusive right to reproduce and
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Signature
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iv
ACKNOWLEDGEMENTS
I thank my advisors Al Cunningham and Frank Caccavo Jr. for their support and interest in my
research project. Their guidance, encouragement, and breadth of knowledge have contributed significantly
to the completion of this dissertation and my development as a researcher. Thanks also to the remaining
committee members Anne Camper, Marty Hamilton, and Warren Jones who were always available for
helpful discussions.
My appreciation goes to all students, staff, and faculty of the Center for Biofilm Engineering (CBE) at
Montana State University (MSU) who made the past four years of my life extremely interesting and
enriching. Special thanks goes to John Neuman, our laboratory manager, without whom the CBE would
only be half of what it is now and to Kathy Lange, one of the most sincere people I have ever met.
Following former and current CBE students deserve special mentioning: Stephanie Douthit, Patricia Stoots,
Julie Eyre, Chris Wend, and John Komlos who have all played an important role in my scholarly and
personal development.
' ,
The assistance of following MSU undergraduate students is acknowledged: Jennifer Nyman, Kara
Boettcher, and Christina McKinstry.
I also want to thank the Characklis family: Bill Characklis for creating a vision and founding the CBE
in Bozeman, Montana; Nancy Characklis and John Austin for adopting me as their surrogate son and for
always being there for me; and lastly Greg Characklis (and Joe Hughes) who encouraged me to seek out the
CBE for my dissertation work. May I also not forget the care and support which I received from Al
Cunningham’s wife Sam.
My parents, however, deserve the biggest thanks. Without their love and care, their children and
grandchildren would have not become the caring and intelligent people they are today.
This work was supported by the Center for Biofilm Engineering at Montana State UniversityBozeman, a National Science Foundation supported Engineering Research Center (NSF Cooperative
Agreement. EEC-8907039). The hospitality of the Microbiology Department at the University of New
Hampshire and the Department of Chemical Engineering at Washington State University is acknowledged.
TABLE OF CONTENTS
1.
INTRODUCTION...........................................................................................................••........................I
Bacterial Transport Through Porous Media for Bioaugmentation (Research Goal I ) ............................ 4
Parameters Influencing Bacterial Transport Through Porous Media............................................... 5
Influence of Solution Chemistry on Bacterial Transport Through Porous Media..................... 5
Influence of Porous Media Properties and Hydraulic Conditions on Bacterial Transport
Through Porous M edia............................................................... - ....................................-6
Influence of Cell Properties on Bacterial Transport through Porous Media.............................7
Implications for Bioaugmentation Strategies......................... ..........................................................9
Bacterial Starvation as a Transport Enhancement Strategy............................................................ 10
Permeable Reactive Subsurface Barriers and Microbial Metal-Reduction (Research Goal 2)...............12
Research Goals...................................................................................................................................... 16
Objectives.......................................................................................................
17
2.
ENHANCING TRANSPORT OF SHEWANELLA ALGAE BrY THROUGH POROUS
MEDIA BY STARVATION...... ........................................................................................................... 18
Abstract.............. ....................................................................................................................................18
Introduction.... ....................................................................................................................................... 18
Quantification of Bacteria in Porous Media...................................................................................19
Materials and Methods.... .......................................................................................................................21
Strain and Culturing Methods........................................................................
21
Transport studies............................................................................................................................. 21
Porous Media Sampling.................................................................................................................. 23
Modeling........................................................................................................................................ 23
Results and Discussion..........................................................................................................................26
Short (30 cm) Column Experiments.............................................................................................. 26
Breakthrough of Bacteria......................................................................................................... 27
Distribution of Bacteria in the Pore Water............................................................................. 28
Sorption of Bacteria Along the Flowpath................................ ................................................29
Recovery of Bacteria from Columns....................................... ................................................31
Large (3 m) Column Experiments................................................................................................. 33
Breakthrough of Bacteria....................................................................................................... 34
Distribution of Bacteria in the Pore Water........................................ ......................................35
Sorption of Bacteria Along the Flowpath............................................................................... 37
Recovery of Bacteria from Columns...................................................................................... 38
Modeling and Scale Up Issues...................................................................................................... 39
Influence of Influent Cell Concentration and Fluid Velocity........................................................40
Conclusions......................................... :.................................................................................................41
Additional Research Needs.................... ........................................................................................ 42
3.
CHANGE OF TRANSPORT-RELATED CELL PROPERTIES DURING STARVATION
OF SHEWANELLA ALGAE BrY......... .................................................................................................. 45
Abstract....................... .......................................................................................................................... 45
Introduction............................................................................................................................................ 45
Materials and Methods.........................................................................................................................48
Strains and Culturing Methods......................................................................................................48'
vi
Cell Quantification and Cell Imaging.............................................................................................49
Hydrophobicity.................... .....................1.................................................................................... 49
Zeta Potential and Diffusion Coefficient......................................................................................... 50
Buoyant Density.............................................................................................................................. 50
Adhesion Assay............................................................................................................................... 50
Transportability Through Porous Media......................................................................................... 51
Results and Discussion............................................................................. :............................................ 51
Cell Quantification.......................................................................................................................... 52
Hydrophobicity and Zeta Potential.................................................................................................. 53
Diffusion Coefficient and Buoyant Density.................................................................................... 55
Modeling Results............................................................................................................................ 55
Influence of Diffusion Coefficient and Buoyant Density, Calculation of Tj.............................55
Calculation of Collision Efficiencies a ...................
57
Applicability of Filtration Model to 3 m Column Results...............................................................58
Batch Adhesion Assays as a Predictive Tool.................................................................................. 59
Conclusions.............................................................................................................................................61
4.
BIOGEOCHEMICAL ELIMINATION OF CHROMIUM(VI) FROM CONTAMINATED
WATER...................................................................................................................................................64
Abstract........................
64
Introduction...........................................................................................
64
Materials and M e th o d s............................. ................................................... :...................................... 67
Experimental.............................
67
Test Organism and Culture Methods....................................................................................... 67
Amorphous Iron Coating.......................................................................................................... 68
Batch Experiments, Iron Reduction.........................................................................................68
Batch Experiments, Chromium Reduction...............................................................................69
Regeneration of Ferrous Iron in the Presence of Chromium Precipitates and Repeated
Chromium Reduction........................................................................................................ 69
Column Experiments...................................................................................
70
Analytical........................................................................................................................................ 71
Iron Quantification............................................................. i....................................................71
Chromium Quantification........................................................................................................ 72
Results and Discussion....!...................................................................................................................... 73
Microbial Fe(III) Reduction............................................................................................................ 73
Cr(VI) Reduction by Microbially Produced Fe(II) ......................................................
75
Regeneration of Surface Reactive Ferrous Iron in the Presence of Chromium Precipitates...........78
Iron and Chromium Reduction in Columns.................................................................................... BI
Conclusions.............................................................................................................................................85
5.
DISSIMILATORY IRON-REDUCING BACTERIA CAN INFLUENCE THE REDUCTION
OF CARBON TETRACHLORIDE BY IRON METAL........:.............................................................87
Abstract...................................................................................................................................................87
Introduction.............................................................................................................................................87
Materials and Methods.....................
89
Organism and Culture Conditions................................................................................................... 89
CT Reduction......................................
90
Iron Reduction........................................... ..................................................................................... 9 1
Adhesion of BrY to Iron...........................................
91
Analytical Methods....................................................................................... '•............................... 91
Results and Discussion................... .-...................................................................................................... 92
Conclusions......................................................................................................................................... -.95
vii
6.
CONCLUSIONS.....................................................................................................................................97
Implications for Field Applications.......................................................................................
REFERENCES CITED.......................................................................................
99
102
I
viii
LIST OF TABLES
Table
PaSe
2.1. Summary of parameters for bacterial transport experiments in 30 cm porous media
columns filled with 40 mesh quartz sand.......................
26
2.2. Normalized breakthrough concentrations, absolute, and relative recoveries of cells from
30 cm columns........................................................................................................
33
2.3. Su m m ary of parameters for bacterial transport experiments in 3 m porous media columns
filled with 40 mesh quartz sand............ :................... .•................................................................. -34
2.4. Normalized breakthrough concentrations, absolute, and relative recoveries of cells from
3 m columns.................................................................................................................................... 39
3.1. Parameters used for the calculation of normalized breakthrough concentrations in 30 cm
columns as shown in Figure 3.1............................................................................................
3.2. Estimated single collector efficiencies (rj) and collision efficiency factors (a) for starved
and vegetative S. algae BrY cells, 30 cm columns....................................... .......................
3.3. Estimated single collector efficiencies (77) and collision efficiency factors (a) for starved
and vegetative S. algae BrY cells, 3 m columns................ ..................................................
I
4.1. Adhesion of Y algae BrY cells to HEPES-pretreated iron and Fe(O)................................
ix
LIST OF FIGURES
Figure
1
Page
LI. Examples of permeable reactive barrier designs.............................................................................13
1.2. Reactive Media employed in permeable reactive barriers....................................................... ....14
2.1. Normalized breakthrough concentration (c/c0) of starved and vegetative cells in the
effluent of 30 cm long sand..,...................... *..................................................................................27
2.2. Normalized concentrations of starved and vegetative cells of S. algae BrY in the pore
water of 30 cm long columns..........................................................................................................29
2.3. Sorption of starved and vegetative cells of S. algae BrY to quartz sand along the flowpath
of 30 cm long columns......................................................................'.............................................. 31
2.4. Normalized breakthrough concentration of starved and vegetative cells of Shewanella
algae BrY in the effluent of 3 m long columns...............................................................................35
2.5. Normalized concentrations of starved and vegetative.cells of S. algae BrY in the pore
water of 3 m long columns....................... ...................................................................................... 37
2.6. Sorption of starved and vegetative cells of S. algae BrY to quartz sand along the flowpath
of 3 m long columns........................................................................................................................38
3.1. Predicted, normalized, steady-state breakthrough concentrations through 30 cm quartz
sand columns................................................................................................................................... 47
3.2. UV fluorescence images o f Shewanella algaeBiY-gfp cells.........................................................52
3.3. Concentration of total cells, culturable cells, and absorbance at 600 nm during starvation
of Shewanella algae BrY................................................................................................................ 53
3.4. Change in hydrophobicity index and zeta potential during starvation of Shewanella algae
BrY.................................................................................................................................................. 54
3.5. Change of apparent buoyant density and apparent diffusion coefficient during starvation
of Shewanella algae BrY................................................................................................................ 55
3.6. Change in adhesivity and transportability during starvation of Shewanella algae BrY..................59
3.7. Plot of fractional recovery from 30 cm quartz sand columns over the fraction of cells
adhered in batch experiments......................................................................................................... -60
4.1. PTFE, stainless steel column assembly for studying microbial Fe(III)-reduction and
Cr(VI) reduction in flow through systems....................................................................................... 71
4.2. Production of Fe(II) from amorphous Fe(III) oxyhydroxide-coated sand by starved and
vegetative cells of S. algae BrY...................................................................................................... 74
X
4.3. Cr(VI) transformation in vials containing microbially reduced Fe-coated sand and active
S. algae BrY, microbially reduced Fe-coated sand (autoclaved to eliminate microbial
activity), and oxidized Fe-coated sand........................................................................................... 76
4.4. Direct Cr(VI) transformation by active S. algae BrY in the absence of sand, in the
presence of quartz sand, and in the presence of iron-coated sand.................................................. 78
4.5. Mass ofFe(II) over time in vials containing iron oxyhydroxide-coated sand as determined
by separate analysis of aqueous and solid phase in the vials.......................................................... 80
4.6. Cr(VI) transformation in vials with initially exhausted Cr(VI)-reduction capacity after
reinoculation and incubation with S. algae B rY ............................................................................ 81
4.7. Normalized breakthrough curves of a fluorescein tracer and its corresponding Cf(VI)
breakthrough data through columns containing biologically produced Fe(II)............................... 83
4.8. Processes governing the fate of Cr(VI) in environments containing surface associated
iron and dissimilatory metal-reducing bacteria (DMKB)................................................................84
5.1. Carbon tetrachloride degradation and chloroform production by passivated Fe(O),
passivated Fe(O) with active S. algae BrY.cells,, and active S. algae BrY cells alone.................... 93
5.2. Production of Fe(II) by passivated Fe(O), passivated Fe(O) with active S. algae BrY cells,
and passivated Fe(O) with heat-killed S. algae BrY cells............................................................... 94
xi
ABSTRACT
Dissimilatory metal-reducing bacteria (DMRB) are capable of reducing a wide range of metals
and the injection of DMRB is considered for the cleanup of contaminated soil and groundwater. The
success of such an approach, among other factors, relies upon the bacterial transport through porous media
and the contaminant transformation by DMRB.
Long term starvation of Shewanella algae BrY, the model DMRB used in this research, allows for
significantly improved transport through quartz sand porous media columns. However, the reasons for the
improved transport remain unclear. Changes in cell size, net electrostatic charge, hydrophobicity, buoyant
density, and effective diffusion coefficient do not provide a sufficient explanation and this question will
have to remain the focus of future research. In addition, it became evident that the employed mathematical
model based on the colloid filtration theory is not capable of appropriately describing scale and physiology
dependent effects on bacterial transport through porous media. A complete understanding of the processes
and factors influencing the porous media transport of starved bacteria is still lacking and will remain as the
focus of future research.
The reduction of chromium [Cr(VI)], a compound of significant environmental concern at many
Department of Energy, Department of Defense, and Environmental Protection Agency Superfund sites, can
be facilitated by DMRB. Starved S. algae BrY cells can be resuscitated into an actively metabolizing state
in the presence of ferric iron [Fe(III)], which is abundant in the environment. Active S. algae BrY cells can
either directly reduce Cr(VI) or produce surface reactive ferrous iron [Fe(II)]. Fe(II) chemically reduces
and precipitates Cr(VI) eliminating it from contaminated water. S. algae BrY cells can also potentially
contribute towards the long term reactivity of zero valent iron subsurface barriers. The microbial reduction
of Fe(III) allows for the removal or activation of surface associated corrosion products and results in
increased transformation rates of carbon tetrachloride, another widespread environmental contaminant.
The results summarized in this dissertation indicate that the injection of starved DMRB is a
promising technology for the remediation of subsurface environments contaminated with heavy metals and
chlorinated hydrocarbons.
I
CHAPTER I
INTRODUCTION
Every year, large quantities of organic and inorganic compounds are released into the environment
as the result of human activities. These releases can be deliberate or accidental. Due to their toxicity and
recalcitrance in the environment, many of these compounds can accumulate to unacceptable levels and pose
a severe risk to humans and the environment. Davis (1972) was one of the first to suggest the application of
bacteria in the remediation of contaminated groundwater. The use of indigenous or exogenous bacteria for
the cleanup of contaminated groundwater has since become a widely applied approach.
In situ bioremediation has become one of the preferred technologies for the cleanup of
contaminated sites since it avoids the need for excavation and subsequent disposal of hazardous waste.
While the bacterial degradation of fuel hydrocarbons and polyaromatic hydrocarbons is well documented
(Alexander, 1999; Crawford and Crawford, 1996), the use of bacteria for the cleanup of soils and
groundwater contaminated with chlorinated hydrocarbons (U.S.EPA, 2000) and metals (Evanko and
Dzombak, 1997; McCullough et al., 1999) remains the focus of much research.
The increased need and interest for biological metal and radionuclide remediation is partially
based on more than 50 years of using nuclear energy for both peaceful and military purposes. Many
Department of Energy (DOE) and Department of Defense (DOD) sites are contaminated with hazardous
and radioactive waste. These sites create a legacy that is estimated to cost the DOE alone more than US $
60 billion over the next 10 years (McCullough et al., 1999). The contamination at many DOE facilities is
spread over large areas and deep below the surface (McCullough et al., 1999). Conventional treatment
strategies, such as pump and treat or excavation with subsequent treatment, might not be practical,
economical, or advisable due to the long term liability that might be created. Thus, the development of
alternative strategies for the treatment of DOE sites can potentially save billions of dollars.
2
Two subsurface remediation strategies have received much attention over the recent years and
have demonstrated potential for the development of economically viable, long term, low maintenance
cleanup strategies.
1.
The use of permeable reactive barriers (PRBs). PRBs established downstream of
contaminated sites have proven to effectively prevent off-site migration of chlorinated
hydrocarbons, metals, and radionuclides (Scherer et ah, 2000; U.S.EPA, 1998; Yuyun and
Allen, 1999).
2.
The metabolism of dissimilatory metal-reducing bacteria (DMRB). DMRB have
demonstrated potential for the remediation of groundwater contaminated with chlorinated
hydrocarbons, heavy metals, and radionuclides (Amonette et al., 2000; McCullough et al,
1999; Workman et al., 1997).
Dissimilatory metal-reducing bacteria (DMRB) gain energy for growth by coupling the oxidation
of organic compounds or hydrogen to the dissimilatory reduction of ferric iron [Fe(III)] and other oxidized
metals. DMRB can enzymatically reduce large amounts of a wide range of metal ions, including Fe(III),
Cr(VI) and U(VI) (Harding, 1997; Lovley, 1993; Lovley, 1995b). Although the direct enzymatic reduction
of heavy metals by DMRB potentially allows for the development of remediation strategies, the indirect
reduction of soluble contaminants such as Cr(VI), U(VI), and carbon tetrachloride might be more important
and feasible in subsurface environments (Fredrickson and Gorby, 1996a; Harding, 1997; McCullough et al.,
1999). DMRB can reduce a wide range of ferric iron minerals to produce redox reactive ferrous iron
[Fe(II)] (Caccavo, Jr. et al., 1992). Ferrous iron can chemically react with soluble contaminants including
Cr(VI), U(VI), and carbon tetrachloride (Amonette et al., 2000; Eary and Rai, 1988; McCullough et al.,
1999).
Over the last decade, it has become apparent that the biological and geochemical processes
described above can govern the fate of metals and radionuclides in the subsurface. Consequently, a number
of governmental agencies have begun to support research addressing the fate of heavy metals and
radionuclides in the subsurface. The DOE, for instance, established the Natural and Accelerated
3
Bioremediation Research (NABIR) program in 1997. The NABIR program focuses on the development of
a strong theoretical foundation for the assessment and remediation of DOE sites contaminated with
mixtures of metals and radionuclides. The high interest in the biogeochemical processes involved also
recently led to a five day symposium on “Chemical-Biological Interactions in Contaminant Fate”,
organized by the Division of Environmental Chemistry at the 220th American Chemical Society National
Meeting in Washington D.C. (August 20-24, 2000). The majority of the presentations focused on the
influence of iron minerals and dissimilatory metal-reducing bacteria on the fate of chlorinated
hydrocarbons, heavy metals, and radionuclides. The presentations clearly demonstrated the importance of
metal-reducing bacteria in contaminated subsurface environments.
The idea of utilizing DMRB to establish and maintain permeable reactive barriers (PRB) in the
subsurface is intriguing. DMRB, present in the subsurface or injected into the subsurface, could be
stimulated to produce redox reactive ferrous iron from indigenous or injected ferric iron. The produced
ferrous iron would chemically react with chlorinated hydrocarbons, Cr(VI), U(VI), and other contaminants.
However, reports of field trials evaluating the use of DMRB for the establishment of redox reactive
subsurface barriers are lacking. Thus, this research project is designed to improve the basis for future field
demonstrations involving the use of DMRB for environmental cleanup.
The major research goals of this dissertation are:
1.
The development of a strategy for the enhancement of bacterial transport through porous
media, and
2.
The assessment of using dissimilatory metal-reducing bacteria for the in situ bioremediation
of heavy metals and chlorinated hydrocarbons.
These two m ain research goals are reflected in the structure of this dissertation. The first part of
the introduction reviews the processes and parameters governing bacterial transport through porous media.
This review is the basis for Chapters 2 and 3, which evaluate bacterial starvation as a promising strategy for
the bacterial transport enhancement (Chapter 2), and document the changes in transport-related bacterial
cell properties during long term starvation (Chapter 3). The second part of the introduction provides an
4
overview of the current literature on permeable reactive subsurface barriers and microbial metal-reduction.
This overview provides the background for the Chapters 4 and 5, which describe the potential use of
DMRB to establish and maintain redox reactive zones in the subsurface capable of the continuous
precipitation of .Cr(VI) (Chapter 4) and the potential of DMRB to influence the performance of zero valent
iron redox reactive subsurface barriers (Chapter 5).
Bacterial T ransport Through Porous Media for Bioaugmentation (Research Goal I)
The transform ation rates achievable by stimulation of the indigenous microflora at contaminated
subsurface sites are often insufficient to develop economically viable remediation technologies. An
insufficient number of indigenous microorganisms capable of degrading the compounds of interest or the
inability to effectively stimulate the indigenous microflora are commonly encountered reasons for low
transformation rates (Vogel, 1996; Walter, 1997). Therefore, bioaugmentation, the injection of
I
microorganisms which can biotransform environmental contaminants, has been proposed and implemented
(Dybas et al., 1998; Ellis et ah, 2000; McCullough et al., 1999; Salanitro et al., 2000; Steffan et al., 1999).
Unfortunately, the injection of previously cultivated bacteria into subsurface formations can cause
excessive biofilm growth in injection wells. The resulting clogging of well screens poses a major problem
in the development of subsurface bioaugmentation strategies (Molz et al., 1986; Nelson et al., 1985,
Malusis et al., 1997; Maxwell and Baqai, 1995). Thus, for the successful development of subsurface
bioaugmentation strategies, the ability to avoid excessive adhesion, limit biofilm growth, and to facilitate
bacterial transport through porous media needs to be improved. Much research has focused on the
development of strategies for the enhancement of bacterial transport through porous media. However, a
promising technology applicable to a wide range of bacterial strains and to different hydrogeological
conditions has yet to be developed.
A bacterial strain or consortium chosen for subsurface bioaugmentation should have the following
characteristics. The microorganisms should easily obtain regulatory, approval for injection. The
microorganisms should be easily transported to avoid the plugging of injection wells and readily distributed
5
over large areas. The cells should yet be adhesive enough to distribute themselves along the flowpath. They
should be compatible with the indigenous microflora, i.e. they should not negatively influence the existing
microflora, but should be able to survive and actively metabolize the compounds of interest. Bacterial
strains or consortia for bioaugmentation should be inexpensive to obtain and easy to culture.
The following literature review on bacterial transport through porous media will justify the choice
of bacterial starvation as a means for enhancing bacterial transport through porous media.
Parameters Influencing Bacterial Transport Through Porous Media
Bacterial transport is typically governed by aqueous phase advective movement coupled with
retardation by adhesion onto surfaces and straining or trapping in interstitial pores. Bouwer et al. (2000),
Mills (1997), Lawrence and Hendry (1996), and Harvey (1991) provide excellent reviews of the factors
influencing bacterial transport through porous media. The numerous parameters influencing bacterial
transport and adhesion in porous media can be grouped into three major categories, I) solution chemistry,
2) porous media characteristics and hydraulic conditions, and 3) properties of the bacterial cells. These
categories are discussed in the following paragraphs.
Influence of Solution Chemistry on Bacterial Transport Through Porous Media. A wide range of
solute characteristics has been reported to influence bacterial adhesion to surfaces and transport through
porous media. An increase in ionic strength has been correlated with increasing attachment (Pontes et al„
1991; Gannon et al., 1991b; Jewett et al., 1995; Mills et al., 1994; Scholl et al., 1990; Tan et al., 1994) and
is usually attributed to the compression of the electrostatic double layer in the presence of high ion
concentrations (Das and Caccavo Jr., 2000; Deshpande and Shonnard, 1999; Mills et al., 1994). Scholl and
Harvey (1992), Kinoshita et al. (1993), and Jewett et al. (1995) report the effect of changes in pH values on
bacterial transport and attachment, however, no uniform results are found. Increased attachment with
decreasing pH is reported by Scholl and Harvey (1995) and Kinoshita et al. (1993), while Jewett et al.
(1995) state that changes in pH from 5.5 to 7 do not affect bacterial transport through porous media
6
columns. Temperature has also been shown to influence bacterial transport, however the effect seems to be
case dependent. While Bellamy et al. (1985) describe higher cell removal at higher temperatures, Sarkar et
al. (1994) describe lower removal at higher temperatures. Johnson and Logan (1996) investigated the
influence of dissolved and sediment organic matter (DOM, SOM) on bacterial transport through porous
media columns and describe an increase in travel distance in the presence of SOM. This increase in travel
distance is attributed to an increase in negative surface charge of the iron-coated quartz sand through the
addition of SOM. Like SOM, surfactants or dispersants can also result in decreased attachment and
therefore facilitate the transport of bacteria through porous media. This is most likely due to decreased cell
porous media interactions in the presence of surface active compounds. However, the activity or viability of
the bacteria may be influenced negatively by the addition of such compounds (Goldberg et al., 1990; Gross
and Logan, 1995b; Jackson et al., 1994; Paul and Jeffrey, 1984; Sarkar et al., 1994).
Influence of Porous Media Properties and Hydraulic Conditions on Bacterial Transport Through
Porous Media. A number of porous media characteristics and hydraulic conditions have been reported to
influence bacterial adhesion and transport through porous media. For instance, the pore size, grain size, and
their distributions can influence bacterial transport through porous media (Fontes et al., 1991; Marlow et
al., 1991; Sharma and Mclnemey, 1994; Smith et al., 1985; Wood and Ehrlich, 1978). Sharma and
McInemey (1994) report that penetration rates of non-motile strains increase linearly with the size of the
glass beads used, however, penetration rates of motile strains become independent with bead diameters of
398 |tm or larger. Presumably, the lower specific surface area provided by larger grains and the higher
hydraulic conductivity of the coarse grained porous medium allow for increased transport (Fontes et al.,
1991; Marlow et al., 1991). Wood and Ehrlich (1978) and Smith et al. (1985) attribute enhanced bacterial
transport to preferred flowpaths, which are provided by fractures or macropores present in the native
material. The soil mineralogy (e.g. presence of Fe-minerals) and the organic matter content can influence
the porous media surface charge and surface hydrophobicity, respectively. Both, the surface charge and the
surface hydrophobicity, can influence bacterial adhesion to surfaces and transport through porous media.
7
The presence of positively charged surfaces like Fe-minerals in soils is reported to lead to increased
attachment of bacterial cells, which carry a net negative charge at neutral pH values (Johnson and Logan,
1996; McCaulou et ah, 1994; Mills et ah, 1994; Scholl and Harvey, 1992; Scholl et ah, 1990). Hydrophobic
surfaces have been described to increase the number of cells adhering in batch and column systems
(Absolom et ah, 1983; Fletcher and Loeb, 1979; McCaulou et ah, 1994) however, hydrophilic cells might
show decreased attachment to hydrophobic surfaces (Paul and Jeffrey, 1985).
In addition to the surface chemistry, the physical characteristics, such as surface roughness, can
significantly influence attachment regardless of the prevailing chemistry (Geesey and Costerton, 1979;
Mueller, 1996). Thus, it is not surprising that hydrodynamic conditions have also been found to play a very
important role in bacterial adhesion to surfaces. Rijnaarts et ah (1993) and Scheuerman et ah (1998)
suggest that the hydrodynamics can become much more important in view of bacterial attachment than the
physicochemical conditions.
The interstitial fluid velocity can vary widely when cells are actively injected into the subsurface.
The interstitial fluid velocity depends on the pumping rates during injection and the radial distance from the
point of injection. Most authors report an increase in breakthrough and dispersal of bacteria at higher
interstitial fluid velocities (Gannon et ah, 1991b; Marlow et ah, 1991; Sarkar et ah, 1994; Tan et ah, 1994),
although Camesano and Logan (1998) report higher dispersal at low pumping velocities and Gross et ah
(1995) observe no influence of fluid velocity on bacterial transport through porous media.
Tnflnenne of Cell Properties on Bacterial Transport through Porous Media. A number of bacterial
cell properties have an influence on bacterial adhesion and bacterial transport through porous media. The
cell surface charge, often measured as zeta potential, has been shown to influence bacterial adhesion to
surfaces. Cell adhesion has been correlated mostly inversely but also directly with surface net electrostatic
charge (Gilbert et ah, 1991; Sharma et ah, 1985; VanLoosdrecht et ah, 1987a). Cell hydrophobicity has
been reported to influence bacterial adhesion to surfaces and transport through porous media (Caccavo, Jr.
et ah, 1997; DeFlaun et ah, 1999; Fletcher and Marshall, 1982; VanLoosdrecht et ah, 1987b) and separate
8
mechanisms have been found for the bacterial adhesion to hydrophilic and hydrophobic surfaces (Paul and
Jeffrey, 1985). Both, the hydrophobicity and net electrostatic charge, are influenced by the type of surface
molecules present on the outside of the cells. Proteins and (lipo)polysaccharides are believed to influence
bacterial adhesion to surfaces and transport through porous media but it cannot yet be predicted how certain
surface molecules influence bacterial transport and adhesion (Abbott et al., 1983; Caccavo, Jr., 1999;
Caccavo, Jr. et al., 1997; Costerton et al., 1978; Fletcher, 1976; Fletcher and Floodgate, 1973; Rijnaarts et
al., 1996; Williams and Fletcher, 1996).
Cell motility and chemotaxis can have an influence on transport through porous media. While the
influence of bacterial motility on bacterial transport through porous media seems to be mostly negligible at
flow velocities typically encountered in aquifers (Barton and Ford, 1995; Barton and Ford, 1997; Camper et
al., 1993), bacterial motility is suggested to result in increased dispersal of cells (Camesano and Logan,
1998) and in increased penetration under static conditions (Jenneman et al., 1985; Reynolds et al., 1989).
Cell appendages, such as flagella and pill, have also been shown to become important during initial
bacterial attachment to surfaces. This influence is attributed to an increase in collisions due to the greater
effective diameter, an increase in effective diffusivity, and to the ability of the bacterial appendages to
reach across the boundary layer between the liquid and the solid phase (Busscher et al., 1990a; Busscher et
al., 1990b; Busscher et al., 1992; Mueller, 1996; O'Toole and Kolter, 1998a; O'Toole and Kolter, 1998b;
Piette and Idziak, 1991). Das and Caccavo suggest that the flagellum acts as a motility-independent adhesin
(Das and Caccavo, unpublished results).
Other bacterial cell properties correlated with transport through porous media include the cell size
and cell shape. Smaller and more spherical cells are commonly thought to be transported better through
porous media than larger and elongated cells (Fontes et al., 1991; Gannon et al., 1991a; Weiss et al., 1995).
Harvey et al. (1997) show that the buoyant density of bacterial cells can influence the sedimentation rates
of bacteria in porous media environments, but it is considered negligible at ambient groundwater flow
velocities (Corapcioglu and Haridas, 1984).
9
Many of the cell properties listed above are influenced by the physiological state of the bacteria
and can be significantly different for the same bacterium depending on the environmental conditions
(Grasso et al., 1996; VanLoosdrecht et al., 1987a). Thus, controlling the physiology of a bacterial strain
before injection offers great potential for the development of effective bacterial transport strategies.
Tmnlications for Bioausmentation Strategies
The review of the current literature implies that bacterial transport through porous media is
influenced by a combination of numerous parameters. The importance of any single parameter is specific to
any given situation and cannot be predicted easily. Camper et al. (1993) and Harvey (1997) state that
bacterial transport experiments should be performed with the aquifer material of interest and as close as
possible to the expected conditions in the field since the determination of individual characteristics of cells
or the porous medium will not allow an accurate prediction. A good overview on how to design and
standardize bacterial transport experiments is given by Harvey (1997).
The examination of the parameters influencing bacterial adhesion to surfaces and transport
through porous media makes evident that only a very limited number of parameters can be effectively
manipulated in field scale applications. Attempts to change the solution chemistry or the physicochemical
properties of the porous medium are likely to become an economical and technological challenge. Thus,
manipulating parameters falling into these two categories might be extremely difficult or not advisable. In
addition to the economical and technological challenges, lowering the ionic strength or changing the pH of
groundwater could lead to undesirable changes in the groundwater chemistry potentially resulting in
increased contaminant mobility. Manipulating the physicochemical properties o f the aquifer by for instance
hydraulically or pneumatically fracturing the aquifer is difficult to control over large areas and might result
in increased contaminant transport through zones with high hydraulic conductivity. Manipulating and
controlling the bacterial inoculum and the interstitialfluid velocity appear to have the greatest potential o f
economical and technological success in field scale subsurface bioaugmentation strategies.
10
Bacteria are commonly cultured in large amounts before injection in bioaugmentation projects.
The cultivation step allows for the manipulation of the inoculum in many different ways. Strains with a
decreased tendency to adhere to surfaces can be selected or bacteria can be harvested at a specific growth
rate. Procedures on how to select for such strains have been described in Caccavo et al. (1997) and DeFlaun
et al. (1990, 1999). Unfortunately, these procedures can be time consuming and might not be successful for
every bacterial strain or situation. Transport enhancement strategies which are potentially applicable to any
bacterial strain and consortium are more desirable. The following paragraph will indicate that a potentially
applicable strategy involves the use of starved bacteria.
Bacterial Starvation as a Transport Enhancement Strategy
Starvation is believed to be a common survival mechanism for bacteria which cannot form spores
or cysts (Hood and MacDonell, 1987; Kjelleberg et al., 1993b; Tabor et al., 1981). Starvation of bacteria
can result in radical size reduction and a rapid decrease in metabolic activity until the bacteria approach
complete dormancy (Kjelleberg, 1993a). Starved bacteria can survive for years in the absence of nutrients
(Amy and Haldeman, 1997; Kjelleberg, 1993a) and the improved transport of metabolically dormant cells
is believed to contribute towards the presence of bacteria in the deep subsurface (Fredrickson and Onstott,
1996b; Lappin-Scott and Costerton, 1990). Starved bacteria can be resuscitated relatively rapidly by the
addition of suitable nutrients (Amy and Morita, 1983; Cunningham et al., 1997; Kjelleberg, 1993a;
Novitsky and Morita, 1978), making bacterial starvation a potentially effective means of facilitating
transport of through porous media for a number of engineering applications (Bouwer et al., 2000;
Cunningham et al., 1997; Cusack et al., 1992; Gerlach et al., 1998; Lappin-Scott and Costerton, 1992;
Lappin-Scott et al., 1988a; Lappin-Scott et al., 1988b; MacLeod et al., 1988; Sharp et al., 1999).
The injection, resuscitation, and subsequent plugging of high permeability zones in oil bearing
formations using starved bacteria is demonstrated in the literature (Cusack et al., 1992; Lappin-Scott and
Costerton, 1990; Lappin-Scott et al., 1988a; Shaw et al., 1985). The use of starved bacteria results in
significant improvements in secondary oil recovery since the bacteria travel deeper into the high
11
permeability zones and form bacterial plugs upon resuscitation, which force the injected water through the
low permeability zones to recover residual oil more efficiently.
Starved bacteria have also been used in a series of laboratory and field scale experiments
demonstrating the possibility to form hydraulic groundwater barriers. Starved cells derived from bacteria
known to produce large amounts of extracellular polysaccharides (EPS) were injected into porous media
and resuscitated. The bacteria are reported to form thick biofilms upon resuscitation and to decrease the
porous media permeability dramatically (Cunningham, 2000; Cunningham et al., 1997).
It is well known that a number of changes occur during long term starvation of non spore-forming
bacteria. A large amount of KNA and protein appear to be degraded rapidly at the onset of starvation
(Kaplan and Apirion, 1975b; Pine, 1965), however synthesis of new proteins appears to be required for
long term survival of starving cells (Groat et al., 1986; Hengge-Aronis, 1993; Kaplan and Apirion, 1975a;
Kjelleberg et al., 1993b; Reeve et al., 1984). These proteins are believed to be part of a general stress
response, often involving RpoS, and allow bacteria to become more stress resistant and more efficient
scavengers (Cappelier et al., 2000; Fischer et al., 1998; Foster and Spector, 1995; Matin, 1991; Matin et al.,
1989; Spector, 1998; Spector et al., 1999; Svensater et al., 2000; Teich et al., 1999; Zinser and Kolter,
1999; Zinser and Kolter, 2000). Increased stress resistance is verified by the observation that starved cells
survived better under a range of stress conditions than their vegetative counterparts (Albertson et al., 1990;
Hartke et al., 1994; Nystrom et al., 1988; Nystrom et al., 1990; Nystrom et al., 1992) and that slow growing
cells are better adapted for starvation stress survival than their fast growing counterparts (Muller and Babel,
1996).
In the section “Influence of Cell Properties on Bacterial Transport through Porous Media” cell
surface properties were discussed, which are known to influence bacterial attachment to surfaces and
transport through porous media. It was discussed that the growth state of bacteria and the presence of
nutrients can influence attachment. Thus, it is not surprising that bacterial starvation can significantly
influence the attachment of bacteria to surfaces. Short term starvation of bacteria can result in an increased
tendency to attach to surfaces (Dawson et al., 1981; Kjelleberg, 1984) whereas long term starvation (weeks
12
to months) may decrease bacterial attachment and enhance bacterial transport through porous media
(Bouwer et ah, 2000; Cusack et ah, 1992; Gerlach et ah, 1998; Lappin-Scott and Costerton, 1992; LappinScott et ah, 1988a; Lappin-Scott et ah, 1988b; MacLeod et ah, 1988; Sharp et ah, 1999).
It is one goal of this research to develop and evaluate strategies for the enhancement of bacterial
transport through porous media, which are potentially applicable to field relevant scales, a wide range of
bacterial strains, and different hydrogeological conditions. Ideally, such a strategy should allow to culture a
large number of bacteria inexpensively, which are mobile enough to transport over large distances and yet
adhesive enough to attach to soil particles. This would allow establishing a sessile population capable of
transforming dissolved and sorbed contaminants. Long term nutrient starvation appears to provide such a
widely applicable strategy for the enhancement of bacterial transport through porous media.
The motivation for using the DMRB, Shewanella algae BrY, for these studies is explained in the
following section.
Permeable Reactive Subsurface Barriers and Microbial Metal-Reduction !Research Goal 2)
The use of permeable subsurface treatment zones (or' barriers) established downstream of
contaminated areas provides potential for preventing the off-site migration of contaminants without
severely influencing the natural groundwater flow. Permeable reactive barriers (PRBs) can provide long
term control of a contamination problem, if their reactivity is maintained over extended periods of time.
PRBs are commonly constructed by excavating trenches perpendicular to the groundwater flow direction
and refilling these trenches with the reactive material of choice. These systems represent semi-passive
approaches, which minimize the exposure of operating personnel to potentially hazardous compounds.
Permeable reactive barriers can be made in a number of different designs and consist of many
different materials (Figure 1.1 and Figure 1.2, Gavaskar et ah, 1998; Sacre, 1997; Scherer et ah, 2000;
U.S.EPA, 1997; U.S.EPA, 1998; Yuyun and Allen, 1999).
13
Reactive
I Cell
Funnel
Reactive
Cell
Plume
Plume
' Pea
Gravel
(a) Continuous reactive barrier configuration
Reactive
v Cellx
Plume
Pea
Gravel
(b) Funnel-and-gate configuration
Funnel
Caisson
Gate I
Plume
Caisson
Gate 2
Funnel
(c) Multiple caisson gates
Figure 1.1.
Reactive
Pea
Medium I Gravel
Reactive
Medium 2
(d) Serial reactive media
Examples of permeable reactive barrier designs (from Gavaskar et al., 1998).
The most commonly used material is zero valent iron (Fe(O)). Fe(O) has been employed in
permeable reactive subsurface barriers which are capable of remediating a wide range of contaminants
including chlorinated organics (R-Cl, Eykholt and Davenport, 1998, Gatpagar et al., 1997, Gillham and
O'Hannesin, 1994, Helland et al., 1995, Johnson et al., 1996, Johnson et al., 1998, Roberts et al., 1996,
Sayles et al., 1997, Siantar et al., 1996), heavy metal and radionuclide oxyanions, such as chromate (CrO42',
Gould, 1982, Cantrell et al., 1995) and oxycations, such as uranyl (UO22*) (Fiedor et al., 1998),
nitroaromatics (R-NO2, Cao et al., 1999, Agrawal and Tratnyek, 1996), and nitrate (NO3", Huang et al.,
1998, Till et al., 1998, Siantar et al., 1996). The corrosion of Fe(O) provides the electrons necessary for the
reduction of these contaminants according to the following half reactions.
Fe(O)
-»Fe2+ + 2e~
(Eq.1.1)
Fe2'
->Fe3' + e-
(Eq.1.2)
The electrons provided react with the different contaminants according to following half reactions.
R-Cl + 2 e~ + H+
CrO42" + 3 e" + 8 H+
UO22' + 2 e~
R-H + CE
-> Cr3' + 4 H2O
-> UO2
(Eq. 1.3)
(Eq. 1.4)
(Eq. 1.5)
14
R-NO2 + 6 e" + 6 H+
NO)" + 8 e" + 9 H+
R-NH2 + 2 H2O
(Eq. 1.6)'
-> NH3 + 3 H2O
microbes (2 %)
peat (6 %)
(Eq. 1.7)12
chitosan beads (2 %)
hydrogen sulfide (2 %)
other (17 %)
Figure 1.2.
Reactive Media employed in permeable reactive barriers, adapted from Sacre (1997).
The simplicity, low maintenance requirements, and robustness of the approach have made Fe(O)
PRBs one of the preferred technologies for the remediation of groundwater contaminated with chlorinated
hydrocarbons and heavy metals.
However, no reliable data on the long term performance of Fe(O) PRBs are available yet.
Laboratory studies simulating long term performance of Fe(O) indicate that contaminant degradation rates
can either decrease (Devlin et al., 1998, Gatpagar et al., 1997) or more rarely increase (Holland et al., 1995)
1 Other potential reaction products include nitroso and hydroxylamino compounds. (Klausen et al., 1995)
2 Other potential reaction products are nitrogen gas and nitrite (Till et al., 1998)
■ 15
as a function of time. This temporal effect on contaminant degradation rates is usually attributed to the
development of corrosion products that passivate the metal surface and influence electron transport to the
contaminant (Helland et al, 1995; Johnson and Tratnyek, 1994; Odziemkowski et ah, 1998).
A wide range of iron corrosion products has been found in laboratory and field studies. The
corrosion products commonly found in the presence of contaminated groundwater include iron
(hydroxides such as goethite (Pratt et al., 1997), magnetite (Bonin et al., 1998; Odziemkowski and
Gillham, 1997; Odziemkowski et al., 1998), maghemite (Peterson et al., 1997), hematite (Pratt et al., 1997),
mixed chromium-iron-hydroxides (Powell et al., 1995a, Blowes et al, 1997b), green rust (Odziemkowski
and Gillham, 1997; Odziemkowski and Gillham, 1997), and siderite .(Mackenzie et al., 1995). The
corrosion product distribution appears to be dependent on the type of iron used, contaminant present,
geochemical conditions, and time of exposure to contaminated groundwater.
Fe(O) PRBs are usually established using trench and fill methods to place the iron into the
subsurface. However, the installation can become uneconomical at depths of more than 12 to 15 meters and
alternative methods for establishing permeable reactive zones in the subsurface have been proposed (Yuyun
and Allen, 1999). Since injection wells can be installed much deeper than trenches the injection of colloidal
Fe(O) particles to establish redox reactive zones has been proposed (Cantrell and Kaplan, 1997a; Cantrell et
al., 1997b; Cantrell et al., 1995). A group at Pacific Northwest National Laboratory (PNNL) has
demonstrated and patented a method using dithionite (S2O42") as a chemical reductant (AmOnette et al.,
1998; Amonette et al., 1994; Istok et al., 1999; Scott et al., 1998). The dithionite is injected into the
subsurface in a pH 11 bicarbonate buffer and reduces indigenous ferric iron to ferrous iron, which
chemically reacts with heavy metals, radionuclides, and chlorinated hydrocarbons. This technology has
been demonstrated at DOS's Hanford site in Richland, WA and at an army base in Fort Lewis, WA (Beets,
1998; Fruchter et al., 1997):
Alternatively, indigenous ferric iron could be reduced by microorganisms. Dissimilatory metalreducing bacteria (DMRB) are widely distributed in both pristine and contaminated terrestrial, aquatic and
subsurface environments and can reduce many iron minerals found in the environment and on passivated
i?
16
Fe(O) (Caccavo, Jr. et al., 1992; Fredrickson and Gorby, 1996a; Heijman et al., 1995; Heijman et al., 1993;
Kostka and Nealson, 1995; Lovley, 1995b; Nealson and Saffarini, 1994; Roden and Zachara, 1996).
DMRB can also reduce a large number of heavy metals and radionuclides enzymatically and therefore offer
a potentially useful mechanism for the remediation of contaminated subsurface environments (Beets, 1998;
Fredrickson and Gorby, 1996a; Fruchter et al., 1997; Lovley, 1995a; Lovley, 1995b; Lovley, 1997).
Alternatively, DMRB can produce ferrous iron from indigenous ferric iron minerals in the subsurface,
which in turn chemically reduces carbon tetrachloride (CL) to chloroform (CF) and Cr(VI) to Cr(III)
(Amonette et al., 2000; Caccavo, Jr. et al., 1996; Gorby et al., 1994; Gorby et al., 1995).
In summary, the potential use of DMRB in subsurface remediation has gained considerable
interest from engineers, microbiologists, and geochemists. DMRB are known to influence the geochemistry
of subsurface environments and have the potential to be part of successful strategies for the bioremediation
of heavy metal and radionuclide contaminated subsurface environments. DMRB are also likely to contact
existing subsurface remediation installations such as Fe(O) PRBs and to influence their performance.
Research Goals
This research is designed to evaluate the use of dissimilatory metal reducing bacteria (DMRB) for
the bioaugmentation of contaminated subsurface environments. Two major research needs are identified
based on the literature review.
1.
The transport of DMRB through porous media has to be enhanced and injection well
clogging avoided.
2.
The applicability of DMRB to mediate the precipitation of heavy metals and the
dechlorination of chlorinated hydrocarbons has to be evaluated.
This dissertation addresses these needs by utilizing the dissimilatory metal-reducing bacterium,
Shewanella algae BrY, as the model organism. The following four objectives are addressed in the four
main chapters of this dissertation.
17
Objectives
1.
Starvation of Shewanella algae BrY is evaluated as a strategy for the enhancement of
bacterial transport through porous media.
2.
The reasons for the improved transport of starved S. algae BrY bacteria are elucidated by
monitoring changes in transport related cell properties during nutrient starvation.
3.
The possibility of using S. algae BrY to establish and maintain redox reactive zones for the
elimination of chromium(VI) from contaminated groundwater is tested.
4.
The potential influence of S. algae BrY on existing remediation systems based on zero valent
iron is evaluated.
18
CHAPTER2
ENHANCING TRANSPORT OF SHEWANELLA ALGAE BrY
THROUGH POROUS MEDIA BY STARVATION
Abstract
The successful bioaugmentation of contaminated subsurface environments relies on the effective
transport and distribution of bacteria through porous media. This chapter provides evidence that starvation
of the dissimilatory metal-reducing bacterium Shewanella algae BrY results in a significant enhancement
of bacterial transport. Intermediate scale column studies (30 cm and 3 m length) were conducted to
compare the transport of starved and freshly cultivated (vegetative) cells of S. algae BrY through quartz
sand columns. Plate counts were utilized to estimate bacterial numbers and activity. The fractional recovery
(number of cells recovered in the effluent compared to number of cells injected) and the normalized
breakthrough concentration were statistically significantly greater for starved cells in both 30 cm and 3 m
long columns. The distribution of starved cells sorbed to the sand along the flowpath was in addition more
homogeneous for starved cells. It was concluded that bacterial starvation facilitates the transport and
distribution of S. algae BrY through porous media.
Introduction .
The literature review in Chapter I suggests that nutrient starvation is potentially a reliable way to
control the physiological state of bacteria and thus many transport-related parameters, including the cell
size, shape, surface charge, hydrophobicity, buoyant density, type and amount of cell surface molecules,
the presence of pili and flagella, and thus the motility and chemotaxis of cells. Starvation of bacteria results
in radical size reduction and a rapid decrease in metabolic activity until the bacteria approach complete
19
dormancy (Kjelleberg, 1993a). Starved bacteria can survive for years in the absence of nutrients but can be
resuscitated relatively rapidly by the addition of suitable nutrients (Amy and Haldeman, 1997; Kjelleberg,
1993a). Short term starvation (hours to days) of bacteria can result in increased attachment to surfaces
(Dawson, et al, 1981; Kjelleberg, 1984) while long term starvation (weeks to months) can decrease
attachment and enhance bacterial transport through porous media significantly (Bouwer et al., 2000;
Gerlach et al., 1998; Lappin-Scott and Costerton, 1992; Sharp et al., 1999). The injection of starved
bacteria has been suggested to promote plugging of subsurface formations for enhanced secondary oil
recovery (Cusack et al., 1992; Lappin-Scott et al., 1988a; Lappin-Scott et al., 1988b; MacLeod et al., 1988)
and for groundwater containment (Cunningham et al., 1997). This chapter describes the results of transport
experiments and the derivation of parameters which quantitatively describe the transport of starved and
vegetative cells of Shewanella algae BrY through porous media.
Quantification of Bacteria in Porous Media
The quantification of bacteria in groundwater and soil poses one of the greatest challenges in
microbiology. There is a lack of. established methods for the reliable quantification of bacterial cell
numbers and activity in porous media, which are applicable to field scale investigations. The methods
employed in laboratory and field scale investigations include traditional plate counts, most probable
number (MPN) methods, direct microscopic counts, the use of genetically engineered bacteria containing
fluorescent markers (e.g. green fluorescent protein (gfp), Burlage et al., 1996) or an antibiotic resistance
marker (Berry and Hagedom, 1991), radiolabeled cells (DeFlaun et al., 1999; Gross et al., 1995a),
ferromagnetic antibody aided concentration (Johnson et al., 2001; Zhang et al., 1999), and 13Cdabeled cells
together with isotope selective mass spectrometric measurements (DeFlaun et al., 1997). Some of these
methods provide powerful tools for the quantification of bacteria in soil columns in the laboratory but are
not practical for field scale applications due to regulatory concerns (i.e. radiolabeled cells or antibiotic
markers). The use of ferromagnetic antibodies and 13C labeling allow for the selective enumeration of cells
in groundwater but cannot effectively assess the number or activity of sorbed cells. Recent developments in
20
molecular biology have significantly improved our ability to selectively detect and quantify bacteria in
environmental samples. However, affordable methods to reliably quantify bacterial activity in porous
media are still under development.
Bacterial plate counts have been used in a number of research projects evaluating the bacterial
transport through porous media (Alexander et al, 1991). Plate counts of attached bacteria require the
desorption of bacteria before culturing, which is commonly achieved by shaking, blending, or sonicating a
soil sample in the presence of a buffered solution. The desorption of bacteria from soils is an important step
since it represents a trade-off between complete desorption of bacteria and bacterial injury or decay due to
stress factors during the desorption procedure. Stress due to buffer constituents, such as surfactants, of
agitation procedures can significantly influence the outcome of the subsequent culturing step. Stevenson
(1958) already reported that increasing the sonication time during an ultrasound aided desorption procedure
increases the number of bacteria recovered from soils. However, extensive sonication can lead to a sharp
decrease in viable cell numbers most likely due to heating and disruption of the cell integrity by ultrasound.
It is well known that bacterial plate counts underestimate the number of bacterial cells especially when
attempting to quantify the number or activity of bacteria sorbed to surfaces (Wagner et al., 1993; Zuberer,
1994). However, since plate counts provide a lower estimate of bacterial numbers and activity, they were
utilized in this research. It can be assumed that the reported numbers commonly underestimate the number
of active bacteria since plate counts do not account for viable but not culturable (VBNC) cells.
Experiments comparing the transport of starved and vegetative cells of Shewanella algae BrY
were performed on two different scales. First, short columns (30 cm length) were utilized to develop a test
system for the relatively rapid assessment of bacterial transport in porous media. Larger scale experiments
involving columns of 3 m length were utilized to test the applicability of the parameters derived from the
short column experiments and to test the starvation transport enhancement strategy on a more field relevant
scale.
21
Materials and Methods
Strain and Culturing Methods
Cultures of Shewanella algae BrY (formerly Shewanella alga BrY, Caccavo, Jr. et al., 1992) were
maintained on tryptic soy agar (40 g L"1; Difco, Detroit, MI) at room temperature. Cells were aerobically
grown to the late exponential, early stationary phase in tryptic soy broth (30 g L'1; Difco, Detroit, MI) at
room temperature for 15 hours on a rotary shaker at 150 rpm. The cells were harvested by centrifugation
(5860 x g, 20 minutes, 4 °C), washed twice in phosphate buffered saline solution (PBS; 8.5 g L"1 NaCl,
0.96 g L"1K2HPO4, 0.61 g L"1KH2PO4, pH 7.0), and resuspended in PBS. Washed S. algae BrY cells were
either used directly (vegetative state) or starved by aseptically stirring on a magnetic stir plate at room
temperature as described by Caccavo et al. (1996). Starved cells were harvested after 7 weeks of starvation
by centrifugation and resuspended in PBS before incorporation into the transport experiments.
Transport studies
Cells of S. algae BrY, starved or vegetative, were injected into porous media columns filled with
40 mesh quartz sand (Unimin Corp., Emmet, ID). The columns were established using commercially
available polyvinylchloride (PVC) pipe. Columns of two different dimensions were used, 3 m long and
2.54 cm in diameter, and 30 cm long and 1.5 cm in diameter. The column inlets and outlets were layered
with nylon mesh, and pea gravel to prevent settling of sand into the inlet tubing and washout of sand
through the outlet tubing. All columns were equipped with sample ports at the influent and effluent points
and a number of columns were equipped with additional sample ports evenly distributed along the length of
the column. The columns were packed wet to avoid the inclusion of air pockets and facilitate reproducible
packing. The water level was maintained just below the top of the column during packing by periodically
draining part of the water by gravity. The columns were always packed by slowly pouring the porous
22
medium into the water filled column to the desired settled bed height. The columns were operated upflow
using a constant head tank or a peristaltic pump for 30 cm and 3 m long columns, respectively.
The pore volume and hydrodynamic dispersion coefficient of each column were determined
utilizing Na-fluorescein (Aldrich, Milwaukee, WI) as a tracer and by fitting the experimental data to an
analytical solution of the advection dispersion equation described below (Van Genuchten and Alves, 1982).
The interstitial fluid velocity (v) in the bacterial transport experiments was calculated using the measured
flowrate, the cross sectional area of the column, and the porosity, which was estimated from the tracer
studies.
The bacterial concentrations were expressed as fractions of their influent concentrations in each
experiment and were plotted as a function of the number of pore volumes eluted from the columns. In
addition, the fractional recovery was calculated from the influent and effluent data. The fractional recovery
was defined as the number of bacteria in the effluent of the column (integral of the effluent concentration
'
over time) divided by the total number of bacteria injected into the column (integral of the influent
concentration over time).
The columns were prepared for inoculation by flushing with 2 pore volumes 0.5 % NaOCl (dilute
commercial bleach), I pore volume sterile PBS, 2 pore volumes 0.01 M sodium thiosulfate to eliminate
residual chlorine and 2 pore volumes sterile PBS. A commercial kit (EACH, Loveland, CO) was used to
ensure residual chlorine concentrations below the detection limit (0.05 mg L"1) in the effluent of the
columns. Plate counts routinely performed on effluent samples after this treatment did not show any
growth. During the transport experiments, the columns were inoculated with approximately two pore
volumes of starved or vegetative cells of S. algae BrY suspended in PBS. The transport of bacteria was
monitored by sampling the influent, effluent, and the pore water along the flowpath of the columns for
culturable cells. Aliquots (0.1 mL and I mL for 30 cm and 3 m columns, respectively) were taken using
sterile, disposable I mL syringes equipped with 22 gauge needles. Dilutions in PBS were performed
immediately and colony forming units were determined on tryptic soy agar (40 g L"1; Difco, Detroit, MI;
23
incubation for 24 hours at 30°C). The reported values are the arithmetic means of the three appropriate
plate counts.
Porous Media Sampling
The distribution of sorbed cells throughout the columns was estimated at the end of the
experiments by sectioning the columns and analyzing the sand cores for culturable cells after desorption
from the sand. The 30 cm columns were divided into six sections of each five centimeter length while the
3 m long columns were sectioned into ten 30 cm long sections. The desorption of cells was accomplished
by diluting a desorption solution, described by Camper et al. (1985), 1:5 in PBS. Higher concentrations of
the desorption solution appeared to inhibit growth of S. algae BrY cells on TSA plates. An aliquot (5 ml) of
this solution was added to I g of sand in a test tube, which was vortexed for I second. The test tube was
placed on a horizontal shaker (150 rpm) for 30 min and then vortexed again for 3 seconds. The supernatant
was sampled immediately after coarse particles had settled and plate counts on tryptic soy agar were
performed after dilution in PBS.
Modeling
Bacterial transport through porous media has often been described using clean bed filtration
models. Yao et al. (1971) suggested a set of equations describing the removal of suspended particles in a
porous media bed. Improved and alternative clean bed filtration models were developed subsequently
(Logan et al., 1993; Logan et al., 1995; Rajagopalan and Tien, 1976; Tien and Payatakes, 1976). Logan et
al. (1995) summarized, clarified, and compared clean bed filtration model equations and proposed the
following steady state solution.
c
— = exp
3 (1- 0 )
-arjx
2
(Eq. 2.1)
24
In Equation 2.1, c is the effluent. cell concentration (cells length"3), C0 the influent cell
concentration (cells length"3), dc is the diameter of the spherical collectors (length), 0 the porosity, a the
collision efficiency factor (i.e. the number of collisions of suspended bacteria with collectors which result
in adhesion of a bacterium compared to the number of total collisions), Tj is the single collector efficiency
(i.e. the rate at which particles strike a single collector divided by the rate at which particles flow towards
the collector), and x is the distance into the porous medium (length). Assuming spherical collectors, the
overall single collector efficiency (77) can be calculated as follows (Logan et al., 1995).
.
n = A N ~! + ^
n
2r+ N
g
(Eq. 2.2)
The three terms involving NPe, Nr, and N0 represent the removal processes based on diffusion,
interception, and gravitational settling, respectively (Logan et al., 1995). They can be calculated from
(Eq 2.3)
N pe =
(Eq 2.4)
[pp-p)gd2p
(Eq 2.5)
IS jiiv
where dp is the diameter of the bacteria (length), Dp the particle diffusion coefficient (length2
time"1), Ps and p are the densities of the bacteria and the fluid (mass length"3), respectively, g is the
gravitational constant (length time"2), jl the fluid viscosity (mass length1 time *), and v the interstitial fluid
velocity (length time"1). Dp can be calculated from
JcT
D = 0 .1 0 5 1 -— —
Pd,
(2.6)
with k the Boltzman constant, T the absolute temperature (Logan et al., 1995).
The steady state solution (Equation 2.1) is based on the application of the advection dispersion
equation (Equation 2.7) assuming first order removal of particles from the suspension.
25
dc
d 2c
dc ,
-= ^ -Y -V -—
•
dx
dx
(Eq. 2.7)
^att
D in Equation 2.7 is the hydrodynamic dispersion coefficient (length2 time"1).
Equation 2.7 describes the filtration process using a simple first order reaction term with katt
(time"1) as rate coefficient. Additional terms for cell death or lysis can be included. However, cell death and
lysis were considered negligible in this study since plate counts in the influent remained relatively constant
and commonly varied less than 10 % during the time of the transport experiment. .
Dispersion can commonly be neglected when calculating particle collision efficiencies in porous
media beds at steady state (Unice and Logan, 2000). Thus, Equation 2.7 becomes at steady state.
dc
(Eq 2.8)
c
v
dx
Integration yields an equation containing a first order attachment coefficient in distance (kaU/v,
length"1), which can be estimated from the suspended cell concentration profile over the length of the
column.
— = e x p (-^ x )
C0
'
(E q 2 .9 )
V
The comparison of Equations 2.1 and 2.9 finally reveals that
(Eq. 2.10)
2
Jcatt was estimated independently from two different sets of data; from suspended cell concentration
profiles over the length of the column after 2 pore volumes and by fitting an analytical solution of Equation
2.7 (Equation 2.11, Van Genuchten and Alves, 1982, p. 60) to the effluent concentration profiles over time
observed in the transport experiments. The analytical solution (Equation 2.11) was also utilized to model
the experimental results.
c(x,t)=Y
(v-u)x
exp
v
2D
gy/c
y
x —ut
ijo t
(v +
+ exp
v
m) x
2D
g%/c
/
x+ut
24Ft
(Eq. 2.11)
26
where
\0.5
(Eq. 2.12)
U - V
v
Results and Discussion
Short GO cm') Column Experiments
,
Triplicate experiments utilizing 30 cm long columns were performed for both starved and
vegetative cells. The dispersion coefficient and porosity which were estimated from tracer studies varied
between 0.63 cm12 m in' and 2.83 cm2min'1, and 46 % and 50 %, respectively. The interstitial fluid velocity
varied between 1.84 cm min ' and 3.09 cm min ' and the average influent concentration between 6.86 x IO7
CFU m l/' and 2.65 x IO8 CFUm l/' (Table 2.1).
Table 2.1.
'
Su m m ary of parameters for bacterial transport experiments in 30 cm porous media
columns filled with 40 mesh quartz sand
katt
kan6
■ 7.79 x IO7
0.0018
n.d.3
1.86
2.65 x IO8-
0.0008
0.0005
0.46
2.41
1.43 x IO8
0.0014
0.0011
2.83
0.48
2.51
8.75 x IO7
0.0029
n.d.
1.98
0.50
2.27
6.86 x IO7
0.0023
0.0020
2.32
0.50
1.84
9.84 x IO7
0.0022
0.0017
D1
Q2
Vi
starved
0.63
0.50
3.09
cells
1.21
0.50
1.67
vegetative
cells
Co45
.
1Dispersion coefficient [cm2 min"'], estimated from tracer studies
2porosity, estimated from tracer studies
3 interstitial fluid velocity [cm min ']
4 influent cell concentration [CPU m l/']
5first order attachment coefficient [s '] estimated using Equation 2.7
6 first order attachment coefficient [s '] estimated using Equation 2.9
7 n.d.: not determined
27
Breakthrough of Bacteria. Starved cells of Shewanella algae BrY were detected in the effluent of
the columns at higher normalized concentrations than vegetative cells (Figure 2 .1). The average normalized
breakthrough concentration for starved cells was 38 ± 6 % compared to 13 ± 1% for vegetative cells. The
higher breakthrough of starved cells is also reflected in greater fractional recoveries (20 ± 3%) compared to
vegetative cells (6 ± I %). A multiple regression analysis was performed to evaluate the effects of bacterial
physiology (i.e. starved vs. vegetative cells), influent cell concentration, and interstitial fluid velocity on the
fractional recovery and normalized breakthrough concentration of Shewanella algae BrY after transport
through 30 cm quartz sand columns. The regression analysis revealed that, even after correcting for the
potential influence of influent cell concentration and interstitial fluid velocity, the physiological state of the
cells significantly influenced the transport behavior of cells in the column experiments. The resulting Pvalues of P = 0.006 and P = 0.005 for fractional recovery and normalized breakthrough concentration,
respectively, clearly indicated the statistically significant difference between starved and vegetative cells of
S. algae BrY.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
pore volumes eluted
Figure 2.1.
Normalized breakthrough concentration (c/c0) of starved ( ♦ , ■, A) and vegetative (O ,
□, A ) cells of Shewanella algae BrY in the effluent of 30 cm long quartz sand columns
and corresponding model fits (Equation 2.7, solid lines).
28
The corresponding model fits provided additional information about the transport behavior of
. starved and vegetative Shewanella algae BrY cells (Figure 2.1). Vegetative cells appeared to break through
slightly earlier (i.e. at 0.95 and 0.91 pore volumes) than predicted by the model in two columns. The third
column, however, showed a delayed breakthrough (at 1.14 pore volumes) for vegetative cells in
comparison to the model predictions. Starved cells were detected in the effluent approximately at (one
column) or after the predicted times (two columns, at 1.08 and 1.14 pore volumes). Since the experiments
were not designed to determine the exact time of breakthrough, it was difficult to derive definitive
statements about the time of breakthrough. An early breakthrough of cells was maybe due to size dependent
exclusion since the effective porosity for particles can be significantly lower than for solutes (Bales et al.,
1989; Bitton et al., 1974; Morley et al., 1998). Since an early breakthrough was observed more frequently
for the larger vegetative cells, pore size exclusion may have influenced the transport of S. algae BrY in the
quartz sand columns.
Distribution of Bacteria in the Pore Water. The enhanced transport of starved S. algae BrY cells
was also reflected in the profiles of suspended cell concentrations for starved and vegetative cells after the
injection of 2 pore volumes of cell suspension (Figure 2.2). A total of only four columns was considered for
this analysis because plate counts for the third set of columns were unsuccessful. Using a least square
method to fit Equation 2.9 to the experimental data, first order sorption coefficients (Arozz) of 5.3 x IO"4 s"1
and 1.1 x IO"3 s"1 were obtained for starved cells (Revalues: 0.580 and 0.870) and 2.0 x IO"3 s"1 and 1.7 x
IO'3 s"1 for vegetative cells (R2-Values: 0.930 and 0.932). Despite the low regression coefficients for the
starved cell concentration profiles, the smaller Aozz values provided supporting evidence that starved cells of
S. algae BrY transport better through the quartz sand columns than their vegetative counterparts. A steady
state model allowing for a bimodal distribution of first order sorption coefficients (Bolster et al., 2000) was
applied to fit the experimental data but did not result in obvious improvements (data not shown). The Aozz
values estimated using Equation 2.9 were in the same range, but consistently lower than the Aozz values
derived from fitting the analytical solution of Equation 2.7 to the experimental data (see Table 2.1). These
29
differences might be due to the uncertainties in estimating the steady state breakthrough concentration from
the experimental data (Figure 2.1), which was necessary for the calculation of katt using Equation 2.7. In
addition, Equation 2.9 assumes steady state conditions, which might not have been achieved completely
after two pore volumes in the 30 cm long columns. Since steady state could not be assumed, the ka„ values
derived from fitting Equation 2.7 were used for subsequent calculations. The kalt values were used to
predict the breakthrough of starved and vegetative cells through the 3 m long columns.
0.6
■
distance into column [cm]
Figure 2.2.
Normalized concentrations of starved ( ♦ , ▲) and vegetative (□, O) cells of S. algae BrY
in the pore water along the flowpath of 30 cm long quartz sand columns after injection of
approximately 2 pore volumes of cell suspension. Model fits (Equation 2.9) are
represented by the solid lines.
Sorption of Bacteria Along the Flowpath. The distribution of sorbed cells throughout the 30 cm
columns at the end of each experiment further supported the hypothesis that starved cells were better
transported, less retarded, and more homogeneously distributed throughout the columns (Figure 2.3). The
30
number of cells desorbed per g of sand in each 5 cm section was normalized to the total number of cells
injected to allow comparison between the different columns. It was evident that vegetative cells sorb more
strongly than starved cells within the first few cm of the columns. The relative number of vegetative cells
(CFU) detected per g of sand decreased from 7.17 x IO"3 ± 2.41 x IO"3 g"1 in the first 5 cm section to 3.63 x
IO"3 ± 4.82 x IO"4 g"1 (51 % of sorbed bacteria detected in first 5 cm of column) at 15 to 20 cm into the
column and then remained constant. The quantification limit of this method was approximately
LS x l O4 CFU g'1 sand or approximately IO"6 g"1. Starved cells appeared to be more homogeneously
distributed along the flowpath. The normalized concentration of starved cells decreased only slightly from
3.22 x IO"3 ± 2.24 x 10"4 g"1 within the first 5 cm of the column to 2.49 x IO 3 ± 3.97 x IO^g"1 (77 %)
between 15 to 20 cm into the column and remained constant through the remainder the column. The large
error bars obtained for sorbed vegetative cells in the influent region of the columns suggested a relatively
high variability in the behavior of the three different vegetative cell cultures. Differences in the transport
behavior of freshly cultivated cells were also noted by Johnson and Logan (1996) who reported significant
day to day variations in transport behavior of equivalently treated cell cultures. The surface coverage o f the
quartz sand with cells was less than 0.5 %. This estimate was established under the assumption that all
cells, which were not detected in the effluent of the columns, adhered to the first 5 cm of the porous ,media
column. An equivalent surface area for vegetative cells was calculated using the cell dimensions (2.2 mm x
0.63 mm) given by Caccavo et al. (1996). The total surface area covered by vegetative cells was then
compared to the total surface area of the quartz sand in the first five centimeter of the column (15 g x 0.168
m2 g"1, as determined by surface area analysis using nitrogen gas).
31
1.0%
0 . 8%
0.4%
0 .2%
0 .0%
distance into column [cm]
Figure 2.3.
Sorption of starved ( ♦ ) and vegetative (■) cells of S. algae BrY to quartz sand along the
flowpath of 30 cm long columns. The sorbed cell concentrations were normalized to the
total number of cells injected into each column. The averages of each three different
columns and their corresponding standard errors of the mean are plotted.
Recovery of Bacteria from Columns. The normalized effluent concentrations, fractional
recoveries, total recoveries of cells from the experimental systems, and the distribution of the recovered
cells in the effluent, sorbed to sand, and present in the pore water are listed in Table 2.2.
As mentioned above, the normalized breakthrough concentration and fractional recoveries were
significantly higher for starved cells than for vegetative cells. Total recoveries of cells from the
experimental systems were limited using the desorption and plate count methods described. Between 37 %
and 69 % of the total number of cells injected into each column were recovered by analyzing the effluent,
the sand, and the pore water separately. Total recoveries were around 40 % with the exception of one case
in which 69 % of the vegetative cells added were accounted for. No clear difference was observed between
32
the ability to recover starved or vegetative cells (2-sample t-test P = 0.482). Such low and inconsistent
recoveries were reported by other researchers who used plate counts for the enumeration of bacteria in
porous media transport experiments (Alexander et ah, 1991; Camper et ah, 1993; Fontes et al., 1991; Tan et
ah, 1994). Since a decay of cells in the feed reservoir was not observed (the commonly observed variations
remained within 10 % of the average influent concentration), the inability to recover all bacteria from the
porous media columns might have been due to incomplete recovery of bacterial cells sorbed to the quartz
sand. Cells may have been injured during the desorption procedure and were thus not recoverable using the
plate count method employed. Bacterial decay or a change of bacterial cells into the viable but not
culturable state during the passage through the porous media column might have also caused incomplete
recoveries. The relative distribution of the recovered cells to the three phases (cells in the effluent, sorbed
to sand, and in the pore water) appeared to be significantly different for starved and vegetative cells. While
36 ± 6 % of the recovered starved cells were detected in the effluent, only 15 ± 3 % of the recovered
vegetative cells were detected there (2-sample t-test P = 0. 031). Relative recoveries of sorbed bacteria were
higher for vegetative cells (72 ± 3 % compared to 47 ± 8 % for starved cells; P = 0.039). No significant
difference was observed for the relative number of bacteria recovered in the pore water (17 + 4 % and 13 ±
I % for starved and vegetative cells, respectively; P = 0.261). These distributions further supported the
hypothesis that starved cells of S. algae BrY were better transported, less retarded, and more
homogeneously distributed through porous media.
33
Table 2.2.
Normalized breakthrough concentrations, absolute, and relative recoveries of cells from
30 cm columns.
total
replicate normalized fractional
breakthrough recovery recovered
concentration in effluent
fraction of
recovered
cells
detected on
sand 1
fraction of fraction of
recovered recovered
cells
cells
detected in detected in
effluent1 pore water 1
Starved
I
34%
17%
37%
50%
31%
19%
Cells
2
45%
22%
42%
37%
43%
20%
3
35%
20%
48%
53%
36%
12%
vegetative
I
14%
6%
43%
75%
12%
13%
Cells
2
12%
5%
69%
70%
18%
12%
3
13%
7%
40%
71%
15%
14%
1 fractions calculated based on the total recovery of cells from the system. I.e. distribution of recovered
cells to the three phases: Sorbed (detected on sand), transported through column (detected in effluent),
and present in pore water at the end of the experiment (detected in pore water)____________________
Large (3 mi Column Experiments
In order to develop economically viable bioaugmentation strategies, it is necessary to predict and
control the transport of bacteria through porous media at field relevant scales. Intermediate scale
experiments, utilizing 3 m long (2,54 cm diameter) columns were performed in duplicate to evaluate scale
dependent effects on bacterial transport and to test the ability of the filtration model to predict bacterial
transport at larger scales. A summary of the column parameters is given in Table 2.3.
34
Table 2.3.
Summary of parameters for bacterial transport experiments in 3 m porous media columns
filled with 40 mesh quartz sand
D1
e2
3.85
0.49
cells
1.98
vegetative
cells
starved
^
Co4
katt
katt
3.98
2.57 x IO7
0.000552
n.d.7
0.47
3.89
1.40 x IO8
0.000453
0.0006
3.64
0.50
3.98
3.86 x IO7
3.02
0.48
3.43
7.60 x IO7
. 0.0029
0.00091
n.d.7
0.0027
1Dispersion coefficient [cm2 min"1], estimated from tracer studies
2porosity, estimated from tracer studies
3 interstitial fluid velocity [cm min'1]
4 influent cell concentration [CPU mL"1]
5first order attachment coefficient [s"1] estimated using Equation 2.7
6 first order attachment coefficient [s'1] estimated using Equation 2.9
7 n.d.: not determined__________________________________
Breakthrough of Bacteria. The trends in concentrations of culturable cells in the effluent of the 3
m long columns were in agreement with the 30 cm scale results (Figure 2.4). However, the breakthrough
occurred at lower concentrations since the bacteria had to pass through a longer column causing greater
removal. The breakthrough of starved S. algae BrY cells occurred at higher concentrations (10 ± 3 %) than
for their vegetative counterparts (0.5 ± 0.7 %) and the fractional recoveries were 4 ± I % for starved and
0.2 ± 0.3 % for vegetative cells. Despite the obvious visual difference between the breakthrough curves of
starved and vegetative cells, two sample t-tests indicated only a low statistical significance for the observed
differences in the normalized breakthrough concentration (P = 0.136) and fractional recovery (P — 0.079)
of starved and vegetative cells. The low statistical significance was mainly due to the limited number of
samples available (n = 4) and the high variability in normalized breakthrough concentrations and fractional
recovery for vegetative cells. Multiple regression analyses could not be performed due to the limited
number of samples available. It should also be noted that a steady state was possibly not completely
achieved after two pore volumes in the 3 m long columns. The effluent cell concentrations appeared to still
35
increase when the experiments were terminated after two pore volumes of bacterial cell suspension were
injected.
The model fits (solid lines in Figure 2.4) indicated that both starved and vegetative cells tended to
break through slightly later than the model predicted. It is possible that retardation of cells occurred in the
columns, which would explain the delayed breakthrough (Gannon et al., 1991b; Harvey et al., 1989;
McCaulou et al., 1994). Retardation can occur independently from the attenuation of cells (Harvey et al.,
1989) and it might be that retardation, although it might have occurred in the smaller scale columns, was
only observable consistently in the larger scale columns.
0.1 %
0.001 %
0 . 00001%
pore volumes eluted
Figure 2.4.
Normalized breakthrough concentration of starved ( ♦ , ■) and vegetative (O , □) cells of
Shewanella algae BrY in the effluent of 3 m long quartz sand columns and corresponding
model fits (Equation 2.7, solid lines).
Distribution of Bacteria in the Pore Water. Concentration profiles of suspended cells after two
pore volumes were acquired for two columns, one for starved cells, one for vegetative cells (Figure 2.5).
The kal, value obtained for starved cells using Equation 2.9 was 6.2 x IO^ s ' (R2 = 0.964) and 2.7 x IO3S 1
36
(R2 = 0.998) for vegetative cells. In contrast to the 30 cm long columns, the Icatt values estimated using
Equation 2.9 were higher than the corresponding katt values derived from fitting the analytical solution of
Equation 2.7 to the experimental data (see Table 2.3). The difference between the first order sorption
coefficients estimated with Equation 2.9 and Equation 2.7 was already discussed for the 30 cm long
columns and might have been due to incomplete recovery of bacteria in the pore water or the effluent of the
columns.
Although the katt values derived for 30 cm and 3 m long columns appeared to fall in a similar
range, the katt values derived from the short columns could not predict the breakthrough concentrations in
the 3 m long columns. Using the average katt derived from the. 30 cm long columns, the predicted
normalized breakthrough concentration for 3 m long columns was 0.01 % and 0.0000009 % for starved and
vegetative cells, respectively. These predicted concentrations did not agree with the normalized
breakthrough concentrations of starved (10 %) and vegetative cells (0.5 %) observed in 3 m column ■
experiments. Due to the exponential relationship of Equation 2.9 slight changes in katt resulted in significant
changes in the effluent concentration. Scale dependent effects on bacterial transport through porous media
were described by Harvey et al. (1993) and Martin et al. (1996). Harvey et al. (1993) stated the importance
of small and large scale variability of heterogeneities on the results of bacterial transport, Martin et al.
(1996) observed decreases in the collision efficiency factor (<% also called sticking efficiency), and thus
decreasing Icatl with increasing distance into the porous media columns. These observations indicated that
the estimation of katt and a from laboratory scale experiments do not necessarily allow the accurate
prediction of field scale results. In addition, a number of researchers recently suggested the existence of
subpopulatiohs with different transport behaviors in monoclonal cultures (Albinger et al., 1994). The
presence of a subpopulation of sticky bacteria resulted in a higher than predicted number of bacteria
adhering to the porous media grains close to the injection point while the less sticky subpopulation traveled
extremely well through the porous medium, resulting in higher than expected effluent concentrations.
37
O
50
100
150
200
250
300
distance into column [cm]
Figure 2.5.
Normalized concentrations of starved ( ♦ ) and vegetative (■) cells of S. algae BrY in the
pore water along the flowpath of 3 m long quartz sand columns after injection of
approximately 2 pore volumes of cell suspension. Model fits (Equation 2.9) are
represented by the solid lines.
Sorption of Bacteria Along the Flowpath. The distribution of sorbed cells throughout the columns
at the end of each experiment (Figure 2.6) showed the same trend as for the 30 cm column experiments.
Vegetative cells appeared to attach more strongly to the quartz sand close to the injection point than starved
cells. The normalized cell concentrations of vegetative cells decreased rapidly with distance into the
column. Between 120 cm and 150 cm into the column, the relative concentration of vegetative cells sorbed
to sand had dropped to approximately 0.5 % (9.31 x 10"6 g ') of the concentration detected in the first 30 cm
of the column (1.71 x IO 3 g '). The relative concentration of starved cells sorbed to sand between 120 cm
and 150 cm into the column (6.35 x IO"5 g"1) was at 9 % of the concentration detected in the first 30 cm
(7.28 x IO"4 g"1). The relative sorbed cell concentrations in the last 30 cm of the column (270 cm to 300 cm
section) was 2.75 x IO"6 g"1(0.2 % of sorbed cell concentration in first 30 cm of column) and 1.54 x 105 g '
38
(2 %) for vegetative and starved cells, respectively. As in the short column experiments, large standard
errors were observed for sorbed vegetative cells, indicating higher culture to culture variability for
vegetative cells. As in the 30 cm columns, the surface coverage of the quartz sand with vegetative cells was
less than 0.5 %.
distance into column [cm]
Figure 2.6.
Sorption of starved ( ♦ ) and vegetative (■) cells of S. algae BrY to quartz sand along the
Oowpath of 3 m long columns. The sorbed cell concentrations were normalized to the
total number of cells injected into each column. The averages of each two different
columns and their corresponding standard errors of the mean are plotted.
Recovery of Bacteria from Columns. Table 2.4 lists the normalized effluent concentrations,
fractional recoveries, total recoveries of cells from the experimental systems, and the distribution of the
recovered cells in the effluent, sorbed to sand, and present in the pore water.
39
Table 2.4.
Normalized breakthrough concentrations, absolute, and relative recoveries of cells from
3 m columns
total
replicate normalized fractional
breakthrough recovery recovered
concentration in effluent
fraction of
recovered
cells
detected on
sand 1
Fraction of
recovered
cells
detected in
effluent1
fraction of
recovered
cells
detected in
pore water 1
starved
I
8%
4%
51%
91.5%
7%
0.97%
cells
2
12%
5%
38%
86.3%
13%
1.15%
vegetative
I
0.004%
0.0006%
37%
99.9% ■
0.002%
0.11%
cells
2
1.0%
0.4%
55%
98.7%
0.7%
0.63%
1 fractions calculated based on the total recovery of cells from the system. I.e. distribution of recovered
cells to the three phases: Sorbed (detected on sand), transported through column (detected in effluent),
and present in pore water at the end of the experiment (detected in pore water)____________________
The normalized breakthrough concentration and fractional recoveries were compared above
(“Breakthrough of Bacteria"). The total recoveries of cells from the 3 m long columns fall in the same
range as for the 30 cm columns; between 37 % and 55 % of the total number of cells injected into each
column were recovered. The relative distribution of recovered cells to the three phases (effluent, sorbed to
the sand, and pore water) also followed the same pattern that was described for the 30 cm long columns.
Due to the limited number of total samples (n = 4) and the high variability in the behavior of the vegetative
cell cultures, significance levels of two sample t-tests remained low. The fraction of recovered vegetative
cells ,sorbed to the quartz sand (99 ± I %) was higher than for starved cells (89 ± 4 % ; P = 0.160). This was
balanced by a higher fraction of recovered starved cells in the effluent (10 ± 4 %) compared to vegetative
cells (0.4 ± 0.5 %; P = 0.163). The difference between the fraction of recovered starved and vegetative
cells detected in the pore water was less significant than for the other two phases (P = 0.222).
Modeling and Scale Up Issues
In order to apply the. starvation transport enhancement strategy in the field, predictions about the
transport behavior of starved cells must be made on field relevant scales. The first order attachment
40
coefficients (Icatt) estimated in this work and their observed decrease at larger scales agreed with the '
observations made by others (McCaulou et al., 1995; Schijven et ah, 1999). The scale dependency of Icath
however, did not allow to predict the breakthrough of starved or vegetative cells through 3 m long columns
based on 30 cm column results. A scale dependency of Icalt was described and is known to limit the
applicability of the simple first order attachment terms to predict results on larger scales (Martin et al.,
1996). In addition, the transport model used was derived for spherical collectors. The quartz sand used in
this study was irregularly shaped and this might have contributed to the observed discrepancies between the
experimental data and the modeling predictions at both, the 30 cm and the 3 m scale. Another potential
explanation for the inability to model the experimental results using the filtration theory with first order
attachment is population variability within the bacterial cultures. The presence of subpopulations with
different transport behaviors and the observed scale dependent effects would require the application of
transport models, which could potentially account for spatial and intrapopulation heterogeneities of
transport related parameters. Detailed studies recently revealed the presence of subpopulations within
monoclonal cultures (Albinger et al., 1994; Baygents et al., 1998; Bolster et al., 2000; Simoni et al., 1998)
and thus multi-parameter models might be more appropriate to describe bacterial transport through porous
media. However, for the development of a conceptual model describing population- and scale-dependent
effects, very specific knowledge of the parameters influencing bacterial transport is required. This research,
however, did not allow to reveal specific mechanisms that might be responsible for the inability to use short
columns in order to predict bacterial transport at larger scales. Thus, the choice of a more appropriate
transport model was not obvious and was not pursued in this research.
Influence of Influent Cell Concentration and Fluid Velocity
Additional improvements in bacterial transport through porous media could be achieved by
optimizing other parameters which are known to influence bacterial transport and which can be controlled
in field applications. Preliminary experiments evaluating the influence of influent cell concentration and
interstitial fluid velocity on the transport of starved S. algae BrY were performed in 30 cm long columns. A
41
change in influent cell concentration from 2.28 x IO6 CFU ml'1to 1.27 x IO7 CFU ml"1resulted in a change
in fractional recovery from 21 % to 17 % for the low and high cell concentration, respectively. A change in
interstitial fluid velocity from approximately 3 cm min"1 to approximately 13 cm min"1 only led to an
increase in fractional recovery from 17 % to 19 % for low and high fluid velocity, respectively. These data
suggest that the control of influent cell concentration and fluid velocity can potentially influence the
bacterial transport through porous media but their importance appears to be low compared to the influence
of starvation.
Conclusions
A major problem that is preventing the widespread implementation of bioaugmentation strategies
is the tendency of bacteria to strongly adhere to the surfaces in the immediate vicinity of the injection point.
Biofouling of these areas can lead to a failure of the injection system and limited dispersion of the bacterial
inoculum in the subsurface. The use of starved bacteria might overcome this problem since starved bacteria
typically adhere less, transport farther, and distribute more homogeneously through porous media than
vegetative cells. The promising potential of starved cells for bioaugmentation strategies is indicated by
higher fractional recoveries (3.2 fold higher for 30 cin columns, 22.6 fold higher for 3 m columns), higher
normalized breakthrough concentrations (2.9 and 19.9 fold), and lower first order attachment coefficients
Qcatt, 1.8 fold lower for 30 cm columns, and 3.2 fold lower for 3 m columns). In addition to their improved
transport behavior, starved cells are metabolically dormant. Their metabolic dormancy and lag phase before
regaining full metabolic activity would allow for the simultaneous or subsequent injection of nutrients
without the danger of immediate biofouling of the employed injection system.
There is sufficient evidence to assume that transport enhancement by bacterial long term
starvation is not limited to Shewanella algae BrY but is a rather general starvation response across many
bacterial strains (Amy and Haldeman, 1997; Kjelleberg, 1993a; Lappin-Scott and Costerton, 1990). Thus,
the starvation transport enhancement strategy could be applicable to a large number of bacterial strains with
42
a variety of metabolic capabilities. If successfully developed, the starvation transport enhancement strategy
would provide the practitioner with a versatile tool for the development of bioaugmentation technologies.
Additional Research Needs
In order to better understand the transport behavior of S. algae BrY cells, the parameters
governing the transport of starved bacteria have to be identified. According to the filtration theory
(Equation 2.10) Jcalt is influenced by 77 and a, v, and 0. While the fluid velocity (v) and the porosity (0) are
independent from the inoculum used, 77 and a account for properties of the bacteria. The single collector
efficiency (77) can be calculated using Equations 2.2 through 2.6. The cell diameter (dp), diffusion
coefficient (Dp), and buoyant density (pp) are the cell properties which can account for differences in 77
between starved and vegetative cells. The collision efficiency (a) incorporates all physicochemical
interactions between the bacteria and the porous medium, and in addition any inaccuracies in the
calculation of 77 (Martin et ah, 1996). Equations for the calculation of a, however are not available for
bacteria. This indicates that the currently existing filtration models do not yet account for these
physicochemical interactions and inaccuracies.
Changes in cell shape, surface charge, hydrophobicity, and the presence of pili and flagella are
likely to influence a and therefore influence bacterial attachment to surfaces and transport through porous
media. It was considered important to monitor the change of several of these bacterial cell properties in an
attempt to identify parameters responsible for the starvation transport enhancement. The results of these
investigations and their implications are summarized in Chapter 3.
Once a better understanding of the factors governing the transport of starved S. algae BrY cells
has been gained, a model accounting for these factors must be developed. However, until the processes
governing bacterial transport through porous media are sufficiently understood and appropriate models
developed, transport experiments should be conducted at field relevant scales, with the bacteria and
subsurface material of interest, and by mimicking field conditions as closely as possible.
.43
Since there is not necessarily a clear relationship between attenuation and retardation of bacteria in
porous media it is extremely difficult to extrapolate from column breakthrough data to the distribution of
sorbed cells (Harvey et ah, 1989). Therefore, the development of more accurate methods for the
quantification of sorbed bacteria and their activity is essential to develop a thorough understanding of
bacterial transport through porous media. A more complete recovery of cells from porous media would
allow to better estimate the distribution of cells along the flowpath and therefore allow to describe the
adhesion processes better. Using plate counts, between 30 % and 60 % of the injected cells remained
unaccounted for in this study. Such a low recovery allows for much speculation and can result in the
development of mathematical models which, although they accurately describe the experimental data,
might not be based on a thorough understanding of the processes involved.
Since the activity of sorbed bacteria is the most important factor determining the success of
bioaugmentation strategies, it would be desirable to develop procedures which allow for the enumeration
and activity assessment of sorbed bacteria simultaneously. The recent development of molecular techniques
has rapidly improved our ability to selectively detect and quantify bacteria in environmental samples. The
most commonly used suite of molecular methods for the identification and quantification of bacterial
numbers and activity consists of selective ribosomal KNA (rRNA) and DNA extraction, quantitative
polymerase chain reaction (qPCR) and reverse transcriptase PCR (RT-PCR), denaturing gradient gel
electrophoresis (DGGE) fingerprinting, and nucleic acid sequencing. The detection and quantification of
rRNA and rDNA (DNA encoding for the rRNA) could be utilized for quantification and activity
assessments. The rRNA content of cells can be correlated with bacterial activity while the amount of rDNA
estimates bacterial numbers (Schafer et al., 2001). Consequently, the rRNA to rDNA-ratio can be used as
an estimate for the specific activity of bacterial cells (Duineveld et al., 2001). Unfortunately, the techniques
currently available are I) mostly designed for aqueous samples, 2) still fairly work intensive, and 3) require
the use of expensive equipment and supplies (qPCR equipment and fluorescent labels). The current
development of nucleic acid microarrays might provide an opportunity to develop these methods more
44
inexpensively. Successfully developed microarray technologies might allow for the inexpensive and
selective enumeration and activity assessment of bacterial cells in environmental samples.
45
CHAPTER 3
CHANGE OF TRANSPORT-RELATED CELL PROPERTIES DURING
STARVATION OF SHEWANELLA ALGAE BrY
Abstract
The enhanced transport of long term nutrient starved bacteria has often been attributed to the
decrease in average cell size during starvation. However, according to the filtration theory, cell size alone is
not sufficient to explain the difference in transportability between starved and vegetative cells of
Shewanella algae BrY. This chapter documents the changes of a number of transport related cell properties
o f Shewanella algae BrY during carbon and nitrogen starvation. The cell buoyant density decreased during
starvation, the apparent diffusion coefficient and the cell surface charge (measured as zeta potential)
increased but no change in hydrophobicity'was observed. According to the filtration model applied, the cell
size, buoyant density, and diffusion coefficient of starved cells could partly explain the improved transport
behavior of starved cells through porous media while the transport of vegetative cells was drastically
overpredicted. The observed slight changes in cell surface charge and hydrophobicity were not sufficient to
explain the difference between starved and vegetative cells. It was concluded that parameters beyond the
ones investigated appear to be responsible for the differences in porous media transport of starved and
vegetative S. algae BrY cells and that a better understanding of these parameters must be obtained before
an appropriate mathematical model can be developed.
Introduction
Chapter 2 clearly demonstrated that starved Shewanella algae BrY cells are transported better
through porous media than their vegetative counteiparts. However, explanations for the improved
46
transportability of starved cells are lacking. Changes in cell size and cell shape have been correlated with
transport through porous media (Bitton et al, 1974; Fontes et ah, 1991; Gannon et ah, 1991a; Weiss et ah,
1995) and were suggested to be at least partly responsible for the improved transport of starved bacteria
(Lappin-Scott and Costerton, 1992; Lappin-Scott et al., 1988b).
However, based on the filtration theory, the differences in cell size between starved and vegetative
cells of Shewanella algae BrY were not sufficient to explain the observed differences in bacterial transport.
Caccavo et al. (1996) reported a change in cell size from approximately 2.2 pm x 0.63 pm (length
x width) to more spherical cells of approximately 1.04 pm x 0.63 pm during starvation. Based on the one­
dimensional filtration theory (Equation 2.1), this change would lead to an only slightly increased
breakthrough (c/c0) of starved cells. Figure 3.1 shows the steady state concentration in the effluent of 30 cm
long columns for particles in the range from 0.1 pm to 5 pm. A list of the parameters used in these
calculations is given in Table 3.1. According to these calculations, starved cells of S. algae BrY break
through the 30 cm long porous rhedia columns at approximately 47.2 % of the influent cell concentration
while vegetative cells are predicted to break through at 46.6 %. The experimental data summarized in
Chapter 2 show slightly lower breakthrough concentrations for starved cells (38 %) and significantly lower
concentrations for vegetative cells (13 %). Thus, parameters other than the cell size are likely to govern the
transport of starved and vegetative S. algae BrY cells through porous media columns.
Equation 2.1 suggests that, if the filtration theory and the parameters applied here are appropriate
for the description of bacterial transport through porous media, changes in the single collector efficiency
(77) or the collision efficiency factor (a) must be responsible for the observed changes in normalized
breakthrough concentrations (c/c0). The single collector efficiency (77) is a measure for the rate at which
particles strike porous media grains and is influenced by the size, diffusion coefficient, and buoyant density
of the bacterial cells (see Equations 2.2 through 2.6). The collision efficiency factor (ti) is a measure for the
number of collisions, which result in the adhesion of particles to the porous media grains. The collision
efficiency factor is also referred to as sticking efficiency in bacterial transport experiments. In contrast to Tj
there is no theoretical basis available for the calculation of (X, it allows to account for physicochemical
47
interactions between the bacteria and the porous media grains (Martin et ah, 1996) and should remain
between zero and one (Logan et ah, 1995).
Figure 3.1.
Predicted, normalized, steady-state breakthrough concentrations through 30 cm quartz
sand columns of particles with diameters between 0.1 pm and 5 pm according to the
filtration theory (Equation 2.1, Logan et ah, 1995). The arrows indicate the calculated
breakthrough concentrations for starved and vegetative cells of S. algae BrY, whose sizes
were reported to be 1.04 pm and 2.2 pm, respectively (Caccavo, Jr. et ah, 1996). The
curve was generated with the values listed in Table 3.1.
The observed differences in porous media transport between starved and vegetative cells and the
inaccurate model predictions discussed in Chapter 2 make it necessary to investigate the parameters which
potentially influence 77 and a, and thus the transport and adhesion of bacteria in porous media. The
diameter (dp), buoyant density (pp) and diffusion coefficient of the cells (Dp) are known to influence 77 (see
Equations 2.2 through 2.6). Changes in cell hydrophobicity, net electrostatic charge, character and
properties of surface molecules, and the presence of flagella and pili are likely to influence the ability of
cells to adhere to surfaces after a collision has occurred, i.e. influence the collision efficiency or stickiness
of cells (a).
48
Table 3.1.
Parameters used for the calculation of normalized breakthrough concentrations in 30 cm
columns as shown in Figure 3.1.
Param eter
Value
Parameter
Value
P '
998.2 kg nr3
V
2 cm min"1
Pp
1044 kg m"3
g
9.80665 m s"2
dc
0.5 mm 1
P
1.002 x IO"3 kg m"1 s"
T
293.16 K
0
0.5
k
1.3048 x IO23 JK"1
a
I2
4
0.1 |tm to 5 |rm
X
0.30 m
1 effective filtration size (manufacturer information, Unimin Coip., Emmet, ID)
2 a= I indicates complete destabilization of the bacterial suspension, i.e. every collision of
a bacterial cell with a porous media grain results in the removal of the bacterium from the
cell suspension.______________________________________________
The changes in cell diameter during starvation of S. algae BrY were previously described by
Caccavo et al. (1996). These data were used for the development of Figure 3.1. The research summarized in
this chapter describes the changes of cell numbers (colony forming units, direct cell counts, and absorbance
at 600 run), hydrophobicity, apparent diffusion coefficient, electrostatic charge, apparent buoyant density,
adhesivity to quartz sand, and transportability through 30 cm quartz sand columns of Shewanella algae
BrY over a starvation period of 7 weeks. The findings are expected to improve the understanding of
bacterial transport enhancement by starvation and contribute towards the development of a model
appropriately describing the transport of starved and vegetative bacteria through porous media.
Materials and Methods
Strains and Culturing Methods
Cultures of Shewanella algae BrY were maintained and grown as described in Chapter 2. Starved
cells were harvested weekly by centrifugation and resuspended in PBS for all assays, except, for
microscopic direct counts, viable cell counts, and absorption measurements, which were performed with
49
samples taken directly from the starvation cultures. Shewanella algae BrY-gfp (gfp = green fluorescent
protein) was graciously provided by Yuri Gorby (Pacific Northwest National Laboratory, Richland, WA).
S. algae BrY-gfp was cultured and maintained in the presence of 20 mg L"1kanamycin. ■
Cell Quantification and Cell Imaging
Cells were quantified using absorbance measurements at 600 nm, microscopic direct counts, and
dilution plate counts using tryptic soy agar (TSA, Difco). Absorbance measurement were performed using
a Shimadzu spectrophotometer (UV-2101 PC). Microscopic direct counts were performed after fixing cells
in 4 % formaldehyde solution and staining cells on dark polycarbonate filters (Whatman, Clifton, NJ) with
50 mg L'1 acridine orange or 10 mg L"1 4,,6-diamidino-2-phenylindole (DAPI) solution for one to five
minutes. The filters were examined using a Nikon Eclipse E800 epifiuorescence microscope with a
mercury lamp UV source. The filter blocks used had an excitation/emission bandwidth of 450-490
nm/>515 nm and 340-380 nm/435-495 nm for acridine orange and DAPI, respectively. Electronic images
of S. algae BrY-gfp cells were acquired using the same microscope with mercury lamp, the 450-490
nm/>515 nm filter block and a charge coupled device (CCD) camera (Princeton Instruments, Tenton, NJ).
Cell sizes were estimated manually from the acquired images by measuring cell width and length and
comparing it to the appropriate scale bar. Plate counts were performed after dilution in PBS by spreading
0.1 TnT. of bacterial cell suspension onto tryptic soy agar. Plates were incubated at 30 °C for 24 hours and
colonies were counted using a 5x magnification glass.
Hvdronhobicitv
Changes in cell hydrophobicity were estimated using the bacterial adhesion to hydrocarbons
(BATH) assay (Rosenberg, 1984; Rosenberg et al., 1980). In brief, cell suspensions (5 mL) with an
absorbance (600 nm) between 0.120 and 0.180 were added to acid washed screw cap vials. Hexadecane (2
mL, Fisher, Pittsburgh, PA, previously filtered through a 0.2 pm membrane) was added, the vials were
50
closed, and vortexed for 30 seconds. The hexadecane and aqueous phases were allowed to separate for 15
minutes and an aliquot of the aqueous phase was transferred carefully to a cuvette using a glass pasteur
pipette. The absorbances (600 run) of the treatments were compared to control treatments lacking
hexadecane. All measurements were conducted in triplicate. The hydrophobicity index (HI) was calculated
according to
H I-
A (AtOnm,initial - A (AMnm9Jinal
(Eq. 3.1)
A (AMnm9Initial
. Zeta Potential and Diffusion Coefficient
The zeta potential and the apparent diffusion coefficient of the cells was determined from cell
suspensions in PBS at pH 7.0 and 25 °C using a Zetasizer 2c (Malvern Instruments). Zeta potentials and
apparent diffusion coefficients were determined from the average of 10 measurements.
Buoyant Density
The apparent buoyant density was calculated using the density gradient centrifugation procedure
described by Harvey et al. (1997). In brief, 2.5 m L o f a bacterial suspension was carefully layered on top of
40 mL of a Percoll/PBS solution at pH 7 (Sigma, St. Louis, MO) and the tubes were centrifuged at 15,000 x
g for 60 minutes. Immediately after centrifugation, the position of the bacterial bands in the tubes was
compared to bands formed by brightly colored beads of known density (DMB-10, Sigma, St. Louis, MO).
Adhesion Assay
Adhesion studies were performed as described previously (Caccavo, Jr. et al., 1997). In brief, cell
suspensions in PBS were added to 2 mL mirocentrifuge vials containing 0.1 g of quartz sand (approximate
surface area 0.0168 m2). The vials were placed on a horizontal shaker at 150 rpm for 30 minutes,
centrifuged at 300 x g for 5 minutes, and the supernatant was sampled for total microscopic cell counts
51
using acridine orange. Cell concentrations between 1.17 x IO7 and 1.93 x IO8 cells per ml were used and the
adhesion assays were performed in triplicate. The number of adhered cells was calculated by subtracting
the number of unadhered cells from the number of total cells. Adhesion of cells to the tubes and cell lysis
were accounted for with controls of adhesion assay buffer without quartz sand.
Transportability Through Porous Media
T r an sp o rt
experiments were performed with vegetative cells, and cells starved for one, four, and
seven weeks in the same manner as described in Chapter 2.
Results and Discussion
Figure 3.2 shows images of Shewanella algae BrY-gfp cells during long term starvation. Cells
immediately after culturing and washing were between 1.8 pm and 3.0 pm in length and approximately 0.6
Iim in width. Cells fluoresced brightly when observed under ultraviolet (UV) light with the appropriate
filter set. In the upper left comer of image (a) ongoing cell divisions were captured. Two cells were
separating from each other at the end of a cell division cycle at the time the image was acquired. S. algae
BrY-gfp cells shrunk over time and were approximately 1.5 pm in length and 0.6 pm in width after 7
weeks but still fluoresced due to the presence of gfp (image b). The cells appeared to be perfectly spherical
with an approximate diameter of 0.6 pm after 6 months of starvation (image c). S. algae BrY-gfp cells still
fluoresced after 6 months and could be resuscitated on tryptic soy agar (data not shown). Visual
observation indicated that S. algae BrY-gfp cells were motile until the third week but mostly non motile at
longer starvation times.
52
I l
10 |im
Figure 3.2.
UV fluorescence images of Shewanella algae BrY gfp cells immediately after culturing
and washing (a), after 7 weeks of starvation (b), after 6 months of starvation (c). All
images were obtained from live specimen without additional staining.
Cell Quantification
Viable cells from Shewanella algae BrY starvation cultures were estimated as colony forming
units on tryptic soy agar. The concentration of viable cells slightly increased during the first week of
starvation from initially 3.83 x IO9 ± 1.33 x IO8 CFU mL"1 to 4.93 x IO9 ± 4.84 x IO8 CFU mV1 (Figure
3.3). The concentration of cells decreased to 2.10 x IO8 ± 1.04 x IO7 CFU mL"1 after seven weeks of
starvation. Initially total cell counts were approximately ten times higher (3.15 x IO10 ± 2.05 x IO9 cells
mL"1) than the concentration of colony forming units. The total concentration of cells decreased slightly
during the first week to 2.77 x IO10 ± 2.02 x IO9 cells mL 1and was approximately 2.5 times higher than the
concentration of viable cells. The numbers further decreased over time and reached 4.98 x IO9 ± 1.05 x IO8
cells mL 1 after 7 weeks, which was approximately 25 times higher than the concentration of colony
forming units at that time. The absorbance (600 run) of the cell suspensions decreased almost linearly from
1.22 ± 0.06 to 0.172 ± 0.01 over seven weeks of starvation. These results concur with the observations by
Caccavo et al. (1996) who reported a decrease in viable cell numbers during starvation. The change in the
53
ratio of culturable to total cells from initially ten to approximately 25 after seven weeks of starvation
indicated an increase in the fraction of cells that were physically intact but not culturable. Resuscitation
methods other than plate counts on full strength tryptic soy agar might have allowed for a better recovery of
S. algae BrY cells from long term starvation, however, the development of improved recovery methods
was not focus of this research.
1.5
1.2
E
0.9
I
E
c
S-
5
"O
0.6
I
S
S
£
O
0.3
0.0
0
1
2
3
4
5
6
7
Time [weeks]
Figure 3.3.
Concentration of total cells (♦ ), culturable cells (■), and absorbance at 600 nm (▲)
during starvation of Shewanella algae BrY. Error bars represent the standard error of the
means (n = 3).
Hvdrophobicitv and Zeta Potential
The change in hydrophobicity and zeta potential during starvation is shown in Figure 3.4. The
hydrophobicity index appeared to slightly increase during the first 3 weeks of starvation but decreased
again starting in week 4. It remained between 27 % and 42 % until week 5 and dropped slightly to 18 ±
13 % after 7 weeks of starvation. The standard errors obtained using the BATH assay, however, were too
54
large to determine definite trends. An analysis of variance was performed to obtain pooled variances for a
two sample t-test. The t-test failed to indicate a statistically significant difference between the initial and
final hydrophobicity indices (P = 0.66).
The zeta potential slightly increased over the 7 week period. Initially -39.5 ± 1.6 mV were
measured, which agrees with the value of -37.7 ± 0.7 mV reported by Caccavo et al. (1997). The zeta
potential remained at approximately -40 mV until week 4 but then increased to reach -33.3 ± 2.8 mV after
7 weeks. However, this change in zeta potential could not explain the observed transport improvement
reported in Chapter 2. A change of net electrostatic charge to more positive values should have resulted in
increased electrostatic interaction between the negatively charged quartz sand and the bacterial cells
(Sharma et al., 1985). However, as shown in Chapter 2, the transport of S. algae BrY cells was improved
and not inhibited by starvation. Thus, electrostatic interactions were not likely to be responsible for the
improved transport of starved S. algae BrY cells through quartz sand at circumneutral pH.
TL -30
100 %
5
50%
time [weeks]
Figure 3.4.
Change in hydrophobicity index ( ♦ ) and zeta potential (■) during starvation of
Shewanella algae BrY. Error bars represent the standard error of the means (n —3 and
n=10 for hydrophobicity index and zeta potential, respectively).
55
Diffusion Coefficient and Buoyant Density
The apparent diffusion coefficient and the apparent buoyant density both changed slightly during
starvation (Figure 3.5). The diffusion coefficient increased from 2.7 x IO"9 ± 1.11 x IO"10 cm2 s ' to 4.79 x
IO 9 ± 1.43 x IO"'9 cm2 s ' after seven weeks and the apparent buoyant density decreased from 1.044 g cm"3
to 1.020 g cm"3.
I
I
I
C
O
■a
I
1.02
time [weeks]
Figure 3.5.
Change of apparent buoyant density ( ♦ ) and apparent diffusion coefficient (■) during
starvation of Shewanella algae BrY. Error bars represent the standard error of the means
(n = 3 and n=l0 for buoyant density and diffusion coefficient, respectively).
Modeling Results
Influence of Diffusion Coefficient and Buoyant Density. Calculation of
Tl.
The measured values
for the cell diameter (1.04 ]im and 2.2 pm, Caccavo et al. 1996), buoyant density (1.020 g cm"3 and 1.044 g
cm"3), and diffusion coefficient (2.7 x 109 cm2 s ' and 4.79 x 109 cm2 s ') of starved and vegetative cells
56
(respectively) were used to calculate the theoretical breakthrough concentrations according to Equations 2.1
through 2.5. All other values were used as listed in Table 3.1.
The observed increase of the apparent diffusion coefficient and the decrease in buoyant density
alone were not sufficient to explain the differences in breakthrough concentrations of starved and
vegetative cells. The breakthrough concentrations calculated for starved cells (45.4 %) were slightly closer
to the experimentally observed breakthrough (38 %) after accounting for buoyant density (pp) and diffusion
coefficient (Z)p) indicating that pp and Dp have a slight influence on the transport behavior of bacterial cells.
However, the model failed to predict the breakthrough of vegetative cells. A normalized breakthrough
concentration of 42.0 % was predicted and only 13 % were observed in the 30 cm column experiments
summarized in Chapter 2.
,
The determination of buoyant density (pp) and diffusion coefficient (Dp) also allowed the
calculation of the single collector efficiencies (77) for starved and vegetative cells. However, the differences
in 7j between starved (7.62 x IO-4 ± 1.30 x IO"4, see Table 3.2 for listing) and vegetative cells (9.37 x IO"4 ±
1.24 x 10"4) could only account for part of the differences in breakthrough concentrations and were not
statistically significantly different (two sample t-test, P = 0.190). Thus, if all other parameters of the model
were correct, changes in the collision efficiency factor (a) must have been responsible for the differences
between starved and vegetative cells.
Table 3.2.
Estimated single collector efficiencies (77) and collision efficiency factors (a) for starved
and vegetative S. algae BrY cells, 30 cm columns.
starved
vegetative
n
a
%
a
0.000637
1.846
0.000794
2.660
0.000896
1.006
0.001008
2.494
0.000752
1.284
0.001008
2.418
57
Calculation of Collision Efficiencies a. The estimation of 77 for each column allowed the
calculation of a values for starved and vegetative cells using the first order attachment coefficients
estimated in Chapter 2 (Table 2.1) and Equation 2.10. The or values obtained for starved cells (1.38 ± 0.43)
were statistically significantly lower than for vegetative cells (2.55 ± 0.12, P = 0.049). This difference in a
values, indicated that differences in the sticking efficiencies (ti) were partly responsible for the observed
differences in porous media transport of starved and vegetative cells, assuming that all other values used for
the calculations, such as cell size were correct (see Table 3.2 for list of orvalues).
The sticking efficiency (or) was introduced into the filtration theory to account for
physicochemical interactions between the particles (bacteria) and the porous media grains (Martin et al.,
1996). However, since there is no theoretical basis available for the calculation of or, it is commonly
estimated from filtration experiments. It, thus, also indirectly serves to account for any inaccuracies in the
filtration theory. Since the filtration model was derived for spherical collectors, such inaccuracies can
derive from the application of the filtration model to porous media grains that are irregularly shaped like,
the quartz sand used in this study. According to the filtration theory (Logan et al., 1995), values for a
should remain between zero and one. However, values of a greater than one have been reported to be
necessary to accommodate for differences in model predictions and experimental data (Baygents et al.,
1998; Logan et al., 1995; Martin et al., 1996). It was concluded that the filtration theory, as applied here,
was not completely appropriate for describing the experimental data from bacterial transport experiments
(Chapter 2) unless values of greater than one were assigned to the sticking efficiency (ti).
The cell surface hydrophobicity and the net electrostatic charge could have potentially influenced
the physicochemical interaction between bacteria and porous media grains, and therefore a. However, as
discussed above, no significant changes in cell surface hydrophobicity were observed using the BATH
assay. However, since the BATH assay measurements were associated with high standard errors, future
research should evaluate other methods that allow to estimate cell surface hydrophobicity more accurately.
Hydrophobic interaction chromatography utilizing hydrophobic resins such as phenyl- or octyl-sepharose is
58
potentially suitable for this purpose. The observed changes in zeta potential to less negative values during
starvation cannot explain the increased transport of starved bacteria either. A change of cell surface charge
to less negative values should have resulted in increased cell quartz sand interactions and thus decreased
transportability.
Applicability of Filtration Model to 3 m Column Results
The measurement of the buoyant density (Pp) and diffusion coefficient (Dp) were used to predict
the transport through the 3 m long columns. However, as discussed in Chapter 2, the model drastically
underpredicted the breakthrough concentrations for both starved and vegetative cells. While breakthrough
concentrations of 0.04 % were predicted for starved cells, 10 % ± 3 % were observed in the experiments
described in Chapter 2. The breakthrough of vegetative cells was also underpredicted; a normalized effluent
concentration of 0.017 % was predicted but 0.4 % ± 0.5 % were observed.
While t] was not significantly different for starved and vegetative cells (starved: 5.37 x 10"4 ± 0;
vegetative 5.85 x 10"4 ± 2.88 x IO"5, see Table 3.3), a values were lower for starved cells (0.46 + 0.06) than
for vegetative cells (1.38 ± 0.87). However, a two sample t-test failed to indicate a statistically significant
difference between the a values of starved and vegetative cells (P = 0.374) due to the high variability
observed in the transportability of vegetative cells. The a values calculated for the 3 m columns were
significantly smaller than the ones calculated for 30 cm long columns (see Table 3.2). A decrease of
collision efficiency factors (a) with increasing distance into a porous medium was also observed by Martin
et al. (1996) and is a further indication for scale dependent effects on bacterial transport.
Table 3.3.
Estimated single collector efficiencies (if) and collision efficiency factors (d) for starved
and vegetative S. algae BrY cells, 3 m columns.
vegetative
starved
n
Ctf
V
a
0.000537
0.502
0.000505
1.995
0.000537
0.412
0.000606
0.767
59
Batch Adhesion Assays as a Predictive Tool
The tendency of S. algae BrY cells to adhere to quartz sand in batch systems was monitored in
order to provide a tool that would potentially allow to screen bacterial strains for their applicability to the
starvation transport enhancement strategy. The fraction of cells adhered to quartz sand in batch experiments
and the fractional recovery of cells after passage through 30 cm quartz sand columns are shown in Figure
3.6. The adhesion assay showed a significant decrease in the fraction of cells adhered to quartz sand over
the first two weeks of starvation (t-test with pooled standard errors, P = 0.05) and the fraction attached
remained below 10 % in average during the seven weeks of starvation. It should be noted that the adhesion
assay yielded high standard errors and negative adhesion values after two and five weeks, i.e. a higher cell
number was observed in the treatments containing quartz sand than in the controls. It is obvious, though,
that there was an inverse relationship between the number of cells adhered to quartz sand and the fractional
recovery of cells (Figure 3.6).
100%
80 % . ,
0
) o
C
«)
- 20 %
time [weeks]
Figure 3.6.
Change in adhesivity (fraction of cells adhered to quartz sand, ♦ ) , and transportability
(fractional recovery of viable cells after passage through 30 cm of quartz sand, □) during
starvation of Shewanella algae BrY. Error bars represent the standard error of the means.
60
A linear relationship was suggested after the average fractional recovery from 30 cm porous media
columns was plotted against the average fraction of cells adhered to quartz sand in the batch adhesion
studies (Figure 3.7). The regression analysis resulted in a correlation coefficient of R2 = 0.9421 and a slope
of (fractional recovery) x (fraction adhered) ' = -0.2365. Thus, a decrease in the fraction of cells adhered to
quartz sand in batch experiments resulted in an increase in fractional recovery during transport through 30
cm quartz sand columns. These results imply that it might be possible to use less time- and cost-intensive
batch experiments to qualitatively predict the transport behavior of cells through porous media. However,
to obtain quantitative results, the reproducibility of adhesion assays will have to be improved. The
acquisition of sorption isotherms would potentially allow for better quantitative assessments. However, the
acquisition of sorption isotherms for bacterial cultures is expected to be approximately as work intensive as
column studies and therefore would have only little potential as a screening tool. In addition, batch
adhesion experiments are not likely to emulate the potential influence of hydrodynamics and kinetics on
bacterial adhesion in porous media columns.
fractional recovery = -0.2365 x (fraction adhered in batch) + 0.2413
R2 = 0.9421
™
0.1
fraction adhered in batch
Figure 3.7.
Plot of fractional recovery from 30 cm quartz sand columns over the fraction of cells
adhered in batch experiments.
61
Conclusions
The research summarized in this chapter demonstrated that, according to the filtration theory,
changes in cell size, buoyant density, and diffusion coefficient could partly explain the enhanced transport
of starved cells. The remaining differences between starved and vegetative cells had to be accounted for by
modifying the collision efficiency factor (a), which had to take on values of greater than one. Although
values of a greater than one have.been reported in the literature, they are conceptually not appropriate to
use in the filtration model. Thus, the applicability of the filtration theory for bacterial transport through
porous media in its cunrently applied form has to be questioned and the development of a mathematical
model capable of appropriately describing bacterial transport through porous media is necessary. Such a
model should be capable of appropriately describing the observed scale- and physiology-dependent
changes influencing the transport and attachment behavior of bacterial cells, which were described in
Chapter 2 and this chapter.
The observed changes in cell hydrophobicity and zeta potential were not sufficient to explain the
differences in sticking efficiency between starved and vegetative cells. Research elucidating the change of
other potentially transport related cell properties is ongoing. Currently scanning and transmission electron
microscopy (SEM, TEM) are utilized to observe the fate of flagella during long term starvation of S. algae
BrY. The SEM and TEM work is performed by Alice Dohnalkova at the Environmental and Molecular
Science Laboratory (EMSL) located at Pacific Northwest National Laboratory (PNNL, Richland, WA).
Caccayo et al. (1992) reported that vegetative cells of Shewanella algae BrY have a single polar flagellum.
The presence of flagella could significantly increase the effective diameter of vegetative cells and therefore
result in a greater removal of cells according to the filtration theory. Long term starvation might result in
the loss of the flagellum and could therefore provide an explanation for the transport differences observed
for starved and vegetative cells. Preliminary results utilizing a flagella deficient mutant (S. algae BrY-NE,
which was kindly provided by Amitabha Das from PNNL), however indicate that the absence of the
flagellum, though it potentially increases the transportability of vegetative cells, does not result in the same
62
increase in transportability as for starved cells (data not shown). Mueller (1996) discussed the influence of
flagella and cell motility on the attachment of Pseudomonas fluorescens to the surface of microscopic flow
cells. The higher adsorption rate coefficient of the flagellated, motile strain was attributed to superior
transport properties. The non-motile strain however showed higher sticking efficiencies suggesting that the
flagellum was not necessary for attachment but increased the specific adsorption rate coefficient. Das and
Caccavo in contrast suggest that the flagellum acts as a motility-independent adhesin for Shewanella algae
BrY (Das and Caccavo, unpublished results). Mueller (1996) however states that the hydrodynamic
conditions can significantly influence the attachment of bacteria and it is likely that the hydrodynamic
conditions in porous media are different from microscopic flow cells. Thus, the change of other parameters
might be responsible for the improved transport behavior of starved cells.
A more thorough characterization of the cell surface of S. algae BrY during starvation might
provide additional information about the change of transport related cell surface properties. Information
about protein and carbohydrate patterns on the cell surface could potentially be obtained using X-ray
Photoelectron Spectroscopy (XPS) and Time of Flight Secondary Ion Mass Spectroscopy (ToF SIMS) as
described by Caccavo et al. (1997). In addition, an alternative method should be applied to verify the
hydrophobicity results obtained with the BATH assay (e.g’ hydrophobic interaction chromatography).
In view of potential field applications of the starvation transport enhancement strategy, the
starvation time should be optimized to produce cells with optimal transport properties in the least amount
of time. The fractional recoveries observed for S. algae BrY (Figure 3.6) suggest that similar transport
efficiencies can be achieved after four and seven weeks of starvation. Thus, four weeks of starvation might
be sufficient to achieve a significant improvement in bacterial transport. Seven weeks of starvation time
were initially chosen because previous work by Caccavo et al. (1996) indicated an almost constant cell size
after seven weeks of starvation.
A thorough observation of the results by Caccavo et al. (1996) indicate that after five weeks of
starvation the iron reductase activity reached levels that were below the detection limit of the assay utilized.
One can therefore speculate that, if the iron reductase activity is an indicator for the overall metabolic
63
activity and if metabolic activity is necessary for irreversible attachment, the decrease in enzyme activity
might also be responsible for the decreased attachment of starved S. algae BrY cells to the quartz sand in
porous media columns. Additional research is necessary to test this hypothesis.
64
CHAPTER4
BIOGEOCHEMICAL ELIMINATION OF CHROMILM(VI) FROM CONTAMINATED WATER
Abstract
Ferrous iron [Fe(II)] reductively transforms heavy metals in contaminated groundwater and the
bacterial reduction of indigenous ferric iron [Fe(III)] to Fe(II) has been proposed as a means of establishing
redox reactive barriers in the subsurface. The reduction of Fe(III) to Fe(II) can be accomplished by
stimulation of indigenous dissimilatory metal-reducing bacteria (DMRB) or injection of DMRB into the
subsurface. The microbially produced Fe(II) can chemically react with contaminants such as Cr(VI) to form
insoluble Cr(IH)-precipitates.
In batch and column experiments, the DMRB Shewanella algae BrY reduced surface-associated
ferric iron to ferrous iron, which chemically reduced highly soluble Cr(VI) to insoluble Cr(HI). Once the
chemical Cr(VI)-reduction capacity of the Fe(H)ZFe(HI) couple in the experimental systems was exhausted,
the addition of S. algae BrY allowed for the repeated reduction of Fe(III) to Fe(H), which again reduced
Cr(VI) to Cr(III).
The research presented herein indicates that a biological process using DMRB allows the
establishment of a biogeochemical cycle that facilitates chromium precipitation. Such a system could
provide a means for establishing and maintaining remedial redox reactive zones in Fe(III)-bearing
subsurface environments.
Introduction
Chromium(VI) compounds have been used extensively in the manufacture of alloys, the
electroplating industry, the manufacture of dyes and pigments, the preservation of wood, in the leather
65
tanning industry, and as corrosion inhibitors in conventional and nuclear power plants (Hayes, 1997;
Langard, 1980). Due to leakage, poor storage, and improper disposal practices, Cr(VI) has become one of
the most frequently detected contaminants at hazardous waste sites (National Research Council, 1994;
Watts, 1997). The spills are often extensive and the frequent presence of co-contaminants such as
chlorinated aliphatics, radionuclides, metals, and organic solvents makes Cr(VI) remediation an
environmental challenge (James, 1994; Watts, 1997; Pagilla and Canter, 1999).
Cr(VI) is highly soluble and therefore easily transported with the ground, water (Bartlett and
James, 1988). It is toxic and mutagenic to humans and other organisms (Committee on Biologic Effects of
Atmospheric Pollutants, 1974; Gibb, 1999; Keith and Telliard, 1979; Watts, 1997). In its reduced trivalent
state [Cr(III)], chromium forms insoluble hydroxides (Hug and Laubscher, 1997; Richard and Bourg, 1991)
and remains immobile under most environmentally-relevant pH and redox conditions (Blowes et al., 1997a;
James, 1996; Lantz, 1992; Pohland et al., 1993; Richard and Bourg, 1991). Cr(III) is less toxic than Cr(VI)
and is neither readily taken up by organisms or plants nor transferred through the food chain (Hayes, 1997;
National Research Council, 1994).
Traditional remediation technologies such as excavation with subsequent treatment or Pump and
Treat have often proven to be expensive and can create a long term liability for the responsible party
(Mackay and Cherry, 1989; Seaman et al., 1999). Technologies to treat large volumes of soils and
groundwater inexpensively are lacking. Passive treatment systems, such as subsurface barriers, offer an
alternative to traditional cleanup technologies. Subsurface barriers minimize the need for expensive
operating equipment and minimize human exposure to contaminants. Treatment systems relying on
geochemical processes have attracted increased attention in recent years.
Permeable reactive subsurface barriers made from iron metal have been successfully used for the
elimination of a variety of contaminants including chlorinated solvents (O'Hannesin and Gillham, 1998),
heavy metals (Cantrell et al., 1995; Gould, 1982), and radionuclides (Fiedor et al., 1998). The iron metal is
usually introduced into the aquifer using trench and fill methods. However, the installation of trenches can
become uneconomical at depths of more than approximately 12 m (Scott et al., 1998; Fortner, 1995). Thus,
66
alternative technologies allowing for the establishment of redox reactive zones in the subsurface have been
proposed and implemented in the field. These technologies involve the injection of small Fe(O) particles
under high pressure or after hydraulic or pneumatic fracturing of the aquifer (Landis and Vidumsky, 1999;
Cantrell et al., 1997b).
In situ redox manipulation has also been suggested for sites where trench and fill methods are
impractical (Seaman et al., 1999). Cr(VI) can be reduced by ferrous iron, sulfides, and natural organic
compounds (Wittbrodt and Palmer, 1996) and the reduction of Cr(VI) in several field studies has been
attributed to the presence of ferrous iron (Anderson et al., 1994; Henderson, 1994; Pagilla and Canter,
1999; Patterson et al., 1997). Industrially, the addition of ferrous iron as a reductant represents a common
treatment scheme for Cr(VI) contaminated water (Eary and Rai, 1988) and the use of ferrous iron in
subsurface barriers for the remediation of chromium has been proposed (Blowes et al., 1997a; Powell et al.,
1995b). During a field demonstration, at the Department of Energy (DOE) site in Hanford (WA), an
inorganic reductant (sodium dithionite, NazSzO^ was injected into the aquifer and allowed to reduce
indigenous Fe(III)-minerals to Fe(II) (Scott et al., 1998). The dithionite-containing carbonate-bicarbonate
buffer (pH 11) was injected into the subsurface and reduced between 60% and 100% of the Fe(III) present
in the Hanford sediments. The resulting reductive capacity of the reduced aquifer material was calculated to
be sufficient for the treatment of 51 to 85 pore volumes (or 7 to 12 years) of groundwater at I ppm Cr(VI)
and 9 ppm dissolved oxygen (Scott et al., 1998).
Dissimilatory Metal-Reducing Bacteria (DMRB) have been found to use a wide range of iron
minerals as electron acceptors at ambient groundwater pH (Caccavo, Jr. et al., 1992; Fredrickson and
Gorby, 1996a; Roden and Zachara, 1996). DMRB are widely distributed in pristine and contaminated
terrestrial, aquatic, and subsurface environments (Lovley, 1995b). They gain energy for growth by coupling
the oxidation of organics or H2 to the dissimilatory reduction O fF e (III) minerals and other metals.
Gorby et al. (1994) and Amonette et al. (2000) demonstrated that DMRB can reduce Fe(III)
oxides, which can in turn reduce carbon tetrachloride (CT) to chloroform (CF) and Cr(VI) to Cr(III)
according to the following reactions.
67
2 Fe2+ + CCl4 + H+ -> 2 Fe3+ + CHCl3 + CF
(Eq 4.1)
3 Fe2+ + CrO42" + 5 H+ -> 3 Fe3++ Cr(OH)3J / + H2O
(Eq 4.2)
Depending on the Fe(H)ZCr(VI) ratio and reaction time, reaction (2) will result in the
precipitation of pure chromium-hydroxides or mixed iron-chromium-hydroxides with
iron and chromium in different ratios (i.e. FexCr3.x(OH)3, x=0,l,2,3) (Cary et al., 1977;
Powell et al., 1995b)._____________________________________________________
If DMKB can repeatedly generate surface reactive ferrous iron from indigenous iron minerals in
the subsurface, they could potentially be used to establish in situ bioreactors or permeable reactive barriers.
A viable remediation strategy based on this approach must provide continuous reduction and precipitation
of chromium from contaminated groundwater. The continuous reduction of Cr(VI) relies upon the renewal
of Fe(II) through microbial reduction of the Fe(IH) present. To the author’s knowledge the ability of
DMRB to repeatedly generate surface reactive Fe(II) for the chemical reduction of Cr(VI) to Cr(HT) in the
presence of iron-chromium precipitates has not yet been demonstrated. Laboratory-scale model systems
were used to evaluate the potential for such a process.
Materials and Methods
Experimental
. Test Organism and Culture Methods. Shewanella algae BrY (Caccavo, Jr. et al,, 1992) was used
as the model DMRB in these studies. S. algae BrY was maintained on tryptic soy agar (40 g L"1, Difco
Laboratories) at ambient temperature and grown to the late log, early stationary phase in tryptic soy broth
(30 g L"1, Difco Laboratories) at 150 rpm for 15 hours at ambient temperature. Cultures were centrifuged at
5,860 x g for 20 minutes at 4 0C, washed twice in phosphate buffered saline solution (PBS) to remove
nutrients, and resuspended in the same buffer (Caccavo, Jr. et al., 1996). S. algae BrY cells immediately
after this treatment were defined as vegetative cells and were used after resuspension and dilution in
68
oxygen-free minimal medium. Starved cells were obtained by aseptically stirring cell suspensions on
magnetic stir plates at room temperature. Cells were harvested after 7 weeks. Centrifugation and
resuspension in oxygen-free minimal medium served to remove cell debris from the starvation cultures
(Caccavo, Jr. et al., 1996). Starved cells were used in parts of this study because it was previously
demonstrated that starved cells are transported more effectively through porous media than vegetative cells
and would therefore have an advantage in subsurface bioaugmentation with DMRB (Chapter 2; Bouwer et
al., 2000).
Amorphous Iron Coating. Artificially iron-coated sand was obtained by soaking 200 g of quartz
sand (40 mesh, Unimin Corp., Emmet, ID) for several days in twice its volume of I N HNO3, subsequent
washing with distilled water until the pH of the rinsewater exceeded 5.0, and mixing with 400 mL of a
suspension of amorphous iron(III)oxyhydroxide (Lovley and Phillips, 1986). This suspension was prepared
by dissolving 13.8 g FeCl3 in 400 mL of deionized water and slowly adjusting the pH to 7.0 using NaOH.
A layer of clear liquid formed after the mixture of sand and iron(IH)oxyhydroxide was briefly shaken and
allowed to settle. The clear liquid was decanted after approximately 12 hours of settling time and replaced
with an equal volume of 0.1 mM NaCl at pH 7.0. The entire overlying suspension was decanted after an
additional 12 hours, leaving only the sand. The above procedure was repeated four additional times over
the course of 96 hours. The coated sand was washed to remove loosely attached iron, allowed to air-dry,
and passed through a 2 mm sieve to break up larger aggregates. The coating procedure yielded a coating of
amorphous iron(III)oxyhydroxide of about 1.3 + 0.12 mg Fe(HI) per gram of sand. No Fe(II) was detected
on the sand using the analytical method described below.
Batch Experiments. Iron Reduction. Oxygen free minimal medium containing NaHCO3, 2.5;
NH4Cl, 1.5; KH2PO4, 0.6; KC1, 0.1 (in grams per L of deionized water); vitamins 10 mL per L, and trace
minerals 10 mL per L (Balch et al., 1979; Lovley and Phillips, 1988) amended with 10 mM lactate was
prepared under an N2=CO2 (80:20) gas atmosphere (Balch and Wolfe, 1976). Oxygen free minimal medium
(9 mL) and I mL of bacterial culture, either starved or vegetative, were added to test tubes containing I g
69
of iron-coated sand and the vials were crimp sealed using thick butyl rubber stoppers (Bellco Glass,
Vineland, NJ). All manipulations were performed under an oxygen-free N2/CO2 purge. The resulting
bacterial concentrations were approximately 1.5 x IO7 colony forming units (CPU) per mL. Control vials
were established lacking either bacteria or lactate and all vials were incubated statically in the dark.
Triplicate sets of vials were sacrificed periodically to determine the amount of ferrous iron in the
supernatant and on the sand separately.
Batch Experiments. Chromium Reduction. A subset of vials containing biologically generated
Fe(II) was autoclaved to eliminate microbial activity and thus to distinguish between chemical and
biological Cr(VI)-reduction. Cr(VI) (0.15 mg) was injected from a K2CrO4 stock solution to a final
concentration of 15 mg L"1. Cr(VI) concentrations in the aqueous phase were monitored over time as
described below. Vials were sacrificed at the end of each experiment and analyzed for Cr(VI) and total Cr
in the aqueous and solid phase separately. Controls containing oxidized iron oxyhydroxide coated sand
were established to account for abiotic losses of Cr(VI).
Experiments evaluating the ability of S. algae BrY to reduce Cr(VI) directly were also performed.
Cr(VI) was injected into vials containing vegetative cells of S. algae BrY cells in oxygen free minimal
medium supplemented with 10 mM lactate and Cr(VI) concentrations were monitored oyer time.
Regeneration of Ferrous Iron in the Presence of Chromium Precipitates and Repeated Chromium
Reduction. A set of vials containing microbially reduced iron was sterilized by autoclaving and then
repeatedly injected with a Cr(VI) 'solution until their reduction capacity for Cr(VI) was exhausted. The
capacity of these vials to reduce Cr(VI) was regarded as exhausted if 48 hours after the last injection of
0.05 mg Cr(VI), more than 0.05 mg/vial (=5 mg L"1) Cr(VI) were still detected. Vials were sacrificed for
analysis of Fe(II), total Fe, Cr(VI), and total Cr after exhaustion of the Cr(VI) reduction capacity. A subset
of the remaining vials was reinoculated with S. algae BrY to evaluate the potential for regeneration of
reduced iron through the addition of DMRB. Reinoculated vials were sacrificed over time for iron analysis.
70
Vials that showed evidence of Fe(II) production were autoclaved and their ability to repeatedly reduce
Cr(VI) was tested as described above.
Column Experiments. Porous media columns were established to evaluate the microbially
mediated geochemical elimination of Cr(VI) from contaminated waters in flow through systems (Figure
4.1). Polytetrafluoroethene (PTFE) columns, 5 cm in length, fitted with stainless steel nuts and ferrules, and
PTFE and Tygon® tubing (Cole Palmer) were established. Each column was filled with seven grams of
iron-coated sand. A PTFE grid was placed at the inlet and outlet o f each column to prevent washout of sand
into the outlet or settling of sand into the inlet tubing. The columns were operated upflow with a peristaltic
pump at flowrates of 4 mL min"1and up to 15 columns were operated simultaneously via a manifold.
Each column was inoculated with approximately 6 x IO8 CFU of starved S. algae BrY and the
columns were operated in semi-batch mode. Approximately 20 mL of oxygen-free minimal medium
supplemented with 10 mM lactate was pumped through each column every two days. Columns were
sacrificed over time, and the sand was analyzed for Fe(II) and total Fe. After evidence OfFe(II) production,
a Cr(VI) solution was injected concurrently with a tracer (fluorescein) at a flowrate of 1.16 mL min"1,
resulting in a residence time of approximately 6 min in the columns. Inlet and outlet concentrations of
Cr(VI) and the tracer concentrations were monitored over time. The columns were sacrificed at the
conclusion of the experiment and analyzed for both total Cr and Cr(VI).
A subset of columns was reinoculated with starved S. algae BrY cells after depletion of the Cr(VI)
reduction capacity and the ability of the columns to reduce Cr(VI) was assessed as described above after
allowing adequate time for iron reduction.
71
r*— :
.
=4
T
—
I
Sam pling ports
Fe-coated sand O2Tree N2ZCO2
(80/20) gas
Z
Stainless steel, PTFE column
assembly
0.2 pm filters
Injection ports
------------ O —
Tygoif tubing
Valve
Manifold
—Oxygen-free minimal medium
+ 10 mM lactate
Figure 4.1.
PTFE, stainless steel column assembly for studying microbial Fe(III)-reduction and
Cr(VI) reduction in flow through systems. Each column contained 7 g of iron coated sand
and up to 15 columns were operated simultaneously. A reduced copper catalyst at 400 °C
served to remove residual oxygen from the nitrogen/carbon dioxide gas mixture.
Analytical
Iron Quantification. Amorphous iron-coated sand was analyzed for both total iron and ferrous iron
[Fe(II)]. The method described by Lovley and Phillips (Lovley and Phillips, 1987; Lovley and Phillips,
1988) was modified as follows. Instead of 0.5 M HC1, 2.5 M HCl was used, which proved to be sufficient
for the solubilization of all Fe from the sand after incubation on a horizontal shaker for 30 minutes at 150
rpm. Fe(II) was determined by adding 5 mL of 2.5 N hydrochloric acid to I g of iron coated sand. Total
iron was quantified by adding 5 mL of 0.25 N hydroxylamine solution in 2.25 N hydrochloric acid to I g of
iron coated sand. Aliquots of the acid extracts were added to 5 mL of a solution of I g ferrozine (Sigma, St.
72
Louis, MO) in I L 750 mM HEPES buffer at pH 7 and the absorbance was determined
spectrophotometrically at 562 nm.
Chromium Quantification. Aqueous Cr(VI) was quantified using diphenylcarbazide (Urone,
1955). A 500 (jL sample was added to 10 mL 0.2 N sulfuric acid and vortexed. A diphenylcarbazide (DPC)
solution (0.5 mL, 0.25% of DPC in 100% acetone) was added and the absorbance was read at 540 nm.
Cr(VI) was extracted from the sand using a modified procedure described by Vitale et al. (1994).
A 0.28 M Na2CO3ZO-S M NaOH solution (5 mL) was added to I g of sand, mixed, and heated to 90-95 °C
for 60 minutes. The vials were swirled repeatedly during the heating process and deionized water was
added, if needed, to replace losses due to evaporation. The vials were allowed to cool to room temperature
and the total volume was filtered through a 0.2 pm nylon membrane (Fisher Scientific, Pittsburgh, PA).
The pH was adjusted to 7.5 using concentrated HNO3 and 0.5 mL of the DPC solution was added. The pH
was adjusted to I with concentrated H2SO4 and the total volume of the sample was adjusted to 10.5 mL
with 0.2 N H2SO4 before the absorbance was read at 540 nm.
Aqueous phase total Cr was determined after filtration using a modified standard method
(Anonymous 1992). One drop of methyl orange indicator solution was added to I mL of sample.
Concentrated NH4OH was added until the solution turned yellow, diluted H2SO4 (1:2 from concentrated
H2SO4) was added until the indicator turned red, and the total volume was adjusted to approximately 5 mL.
Boiling chips were added, the vial was closed with a rubber stopper, and a 20 gauge needle was inserted as
exhaust; the solution was heated to a boil. One drop of a KMnO4 solution (40 g L"1) was added and boiling
was continued. If fading occurred, another drop of KMnO4 was added and boiling was continued for at
least 2 minutes after addition of the last drop of KMnO4. Sodium azide (NaN3, 5 g L"1) was added in 0.1
mL increments and boiling continued until the color disappeared. The vials were allowed to cool after
addition of I drop of concentrated H3PO4. Concentrated H2SO4 was used to adjust the pH to 1.0 and the
whole content of each vial was filtered through a 0.2 pm nylon filter. The volume was adjusted to 10 mL
using 0.2 N H2SO4 and 0.5 mL of DPC solution were added before measuring the absorbance at 540 nm.
73
Total Cr from the sand was determined analogously substituting the I mL liquid sample with I g
of Fe-coated sand.
Results and Discussion
Microbial FetIID Reduction
S. algae BrY can use Fe(III) as electron acceptor to grow in the absence of oxygen with lactate as
the electron donor. Starved and vegetative cells of S. algae BrY reduced surface associated ferric iron
[Fe(III)] to ferrous iron [Fe(II)] over time (Figure 4.2). The reduction was faster in vials inoculated with
vegetative bacteria than in those inoculated with starved bacteria. Vegetative cells reduced approximately
90% (1.19 ± 0.09 mg) of the ferric iron available on the sand (1.3 ± 0.12 mg) within 24 hours. Starved cells
exhibited a slower rate of Fe(III) reduction and reduced approximately 80% (1.04 ± 0.02 mg) of the ferric
iron within 10 days. Control vials lacking either lactate or S. algae BrY cells showed no evidence of Fe(II)
production, indicating that the observed reduction was due to electron donor-dependent microbial activity.
It has been shown that starved cells of S. algae BrY reduce soluble ferric pyrophosphate slower
than vegetative cells (Caccavo, Jr. et a l, 1996). The data presented here are consistent with these results.
Although starved cells of S. algae BrY reduce surface-associated Fe(III) at a slower rate than vegetative
cells, the amount of Fe(II) produced by starved cells after 10 days is only slightly lower than the amount
produced by vegetative cells.
Most of the Fe(II) produced (> 95%) remained associated with the sand surfaces as observed by
separate analyses of the supernatant and the sand (data not shown). The fact that most of the reduced iron
remains surface-associated is important in view of a possible field application since surface-associated
Fe(II) is more reactive with dissolved contaminants than dissolved Fe(II) (Kriegman-King and Reinhard,
1994). Furthermore, in aquifers dissolved Fe(II) would be transported out of the system over time with the
flowing groundwater minimizing the efficacy of this technology.
74
Tim e [hours]
Figure 4.2.
Production of Fe(II) from amorphous Fe(III) oxyhydroxide-coated sand by starved ( • )
and vegetative cells (■) of S. algae BrY. Every system contained I g of sand coated with
1.3 ± 0.12 mg of Fe(III) initially. Controls lacking either lactate (A) or S. algae BrY (D)
did not produce significant amounts of Fe(II). Error bars represent the standard error of
the means (n=3).
The injection of DMRB into the subsurface and the stimulation of DMRB in the subsurface to
enhance contaminant transformation has been proposed (Caccavo, Jr. et al., 1996, Gorby et al., 1994). Here
it was demonstrated that starved S. algae BrY cells, which can be transported through porous media better
than vegetative cells (Chapter 2; Bouwer et al., 2000), can reduce surface-associated Fe(III) and produce
surface-associated, reactive Fe(II). Thus zones of surface reactive Fe(II) could be established in
contaminated aquifers by stimulating naturally-occurring DMRB or by bioaugmenting the contaminated
subsurface with starved DMRB.
75
CrfVrD Reduction by Microbiallv Produced FeCID
The reduction of Cr(VI) by Fe(II) has been documented using soluble Fe(II) (Seaman et ah, 1999)
and surface associated Fe(II) in natural aquifer material (Caccavo, Jr. et ah, 1996; Gorby et ah, 1994;
Kriegman-KingandReinhard, 1994).
Cr(VI) concentrations decreased immediately after injection in the experimental vials containing
biologically produced, surface-associated Fe(II) (Figure 4.3). The reaction of Fe(II) with Cr(VI) occurred
so fast in these vials that at the first sampling event after 30 seconds, the concentration of Cr(VI) was
already significantly lower than the control concentration of 15 mg L"1. Vials that were sterilized
immediately before the injection to eliminate microbial activity showed the same initial decrease, providing
evidence that chemical processes are governing the reduction of Cr(VI) by Fe(II) initially. Control vials
containing oxidized iron oxyhydroxide-coated sand did not show a significant decrease in Cr(VI) over the
duration of the experiment, indicating that sorption of Cr(VI) to the iron-coated sand was negligible. The
initial increase in Cr(VI) concentrations in the control vials (see Figure 4.3) can be attributed to incomplete
mixing before the first sampling event. Since the reaction of Cr(VI) with Fe(II) was expected to occur
rapidly, the systems were sampled as soon as possible after addition of Cr(VI), i.e. after 30 seconds. Thus,
there was only a limited time available for mixing and the resulting incomplete mixing might account for
initially lower Cr(VI) concentrations.
Based on the stoichiometry of reaction (2), the vials contained sufficient Fe(II) to completely
reduce the Cr(VI) added (0.15 mg, resulting in an initial concentration of 15 mg L'1). However, 11 minutes
after the injection of Cr(Vl), the reduction of 15 mg L'1 Cr(VI) remained incomplete. This incomplete
conversion of Cr(VI) could be due to mass transport limitations within the experimental systems or the
presence OfFe(II) sites which were not reactive with Cr(VI). Powell et al. (1995) stated that the reactivity
of surface-associated Fe(II) can vary depending on the mineralogy. Although no mineralogical analyses
were performed during this research, the white color of the precipitate during biological iron reduction and
the high carbonate content of the buffer suggest that siderite (FeCO3) formed at least partly on the sand
grains. Siderite has been described as only moderately reactive with dissolved phase contaminants relative
76
to other iron minerals (Agrawal and Tratnyek, 1996; Blowes et al., 1997a). This may explain the fraction of
moderately and non reactive Fe(II) in the batch vials. Diffusion limitations (Agrawal and Tratnyek, 1996)
and the accumulation of chromium precipitates can also limit Cr(VI) reduction by surface reactive Fe(II).
h
150-
2 4 hours
Tim e [min]
Figure 4.3.
Cr(VI) transformation in vials containing (■) microbially reduced Fe-coated sand and
active S. algae BrY, (# ) microbially reduced Fe-coated sand (autoclaved to eliminate
microbial activity), and (A) oxidized Fe-coated sand. The reaction of Fe(II) with Cr(VI)
occurred so fast that at the first sampling event, the concentration of Cr(VI) was already
significantly lower in vials containing Fe(II) than in control vials. The target
concentration of 15 mg L'1 is indicated by (O). Error bars represent the standard error of
the means (n = 3).
The vials were analyzed for total chromium and Cr(VI) at the conclusion of each experiment. The
majority (> 95%) of the chromium recovered from the sand was recovered as Cr(III) (data not shown)
demonstrating that Cr(VI) was transformed to Cr(III) in the presence of biologically produced surface
associated Fe(II).
77
Complete conversion of Cr(VI) within 24 hours was only observed in the vials containing active S.
algae BrY cells. This decrease suggests either direct reduction of Cr(VI) by S. algae BrY or an indirect
mechanism in which S. algae BrY reproduced ferric iron resulting from reaction (2) to ferrous iron, which
then again reduced Cr(VI) to Cr(III). Direct microbial reduction of Cr(Vl) by metal-reducing bacteria and
soil indigenous microbial populations has been reported (Bader et al., 1999; Bopp and Ehrlich, 1988;
Turick et al., 1998). It was demonstrated here that direct microbial reduction of Cr(VI) by active S. algae
BrY is possible, although significantly slower than the chemical reduction with surface reactive Fe(II).
Regardless of whether surfaces were available (quartz sand or iron-coated sand) or not, active S. algae BrY
cells did not significantly reduce 5 mg L"1 Cr(VI) within 11 minutes (Figure 4.4). However, the
concentration of Cr(VI) dropped to below I mg L"1 in all vials containing S. algae BrY after 24 hours,
indicating slow direct microbial reduction of Cr(VI) by S. algae BrY. As mentioned previously, at the first
sampling event after 30 seconds, the target concentration of 5 mg L"1 Cr(VI) was not observed consistently
in the vials, presumably due to incomplete mixing. The concentrations stabilized to approximately 5 mg L"1
Cr(VI) after 1-2 minutes into the experiment and repeated mixing. Compared to the systems containing
microbially produced Fe(II) (Figure 4.3), the direct microbial reduction of Cr(VI) was much slower. These
observations concur with Caccavo et al. (1996), Eary and Ray (1988), and Fendorf and Li (1996) who
reported that the reduction of Cr(VI) by dissolved and surface associated ferrous iron is rapid compared to
the direct microbial reduction.
Reactions with surface associated Fe(II) usually occur within minutes to hours at environmentally
relevant pH values and temperatures. These reactions can require stoichiometric or excess amounts of
ferrous iron (Davis and Olsen, 1995; Eary and Rai, 1988; Seaman et al., 1999). Fe(II) has been found to
reduce Cr(VI) at pH values of less than 10 (Palmer and Wittbrodt, 1991), and the rate of reduction
increases with decreasing pH (Powell et al., 1995b; Eary and Rai, 1991). Because the pH of ground water
rarely exceeds 10, the reaction should be feasible in field situations and has been proposed for the treatment
of industrial waste water and groundwater containing Cr(VI) (Eary and Rai, 1988; Seaman et al., 1999).
78
I
O
24 hours
Time [min]
Figure 4.4.
Direct Cr(VI) transformation by active S. algae BrY (▲) in the absence of sand, (D) in
the presence of quartz sand, and ( • ) in the presence of iron-coated sand. The time
between injection of bacteria and Cr(VI) (minutes) was not sufficient for significant
Fe(II) production in the vials. The target concentration of 5 mg L"1 is indicated by (O).
Error bars represent the standard error of the means (n = 3).
Regeneration of Surface Reactive Ferrous Iron in the Presence of Chromium Precipitates
These experiments were designed to determine whether surface reactive Fe(II) can be regenerated
in the presence of Fe(III)Cr(III)precipitates formed during the reduction of Cr(VT) by Fe(II). Surface
reactive Fe(II) was produced initially by inoculating vials containing amorphous iron(oxy)hydroxide coated
sand with starved S. algae BrY. Figure 4.5 shows the total mass of Fe(II) detected in the vials over the
duration of the experiment. More than 90% (1.19 ± 0.10 mg) of the ferric iron available in the vials (1.3 ±
0.12 mg) was reduced to Fe(II) in the absence of Cr(VI), 20 days after the inoculation with starved S. algae
BrY. The Fe(II) containing vials were autoclaved after 20 days to eliminate microbial activity and
repeatedly injected with a Cr(VI) solution until their reduction capacity for Cr(VI) was exhausted. Only
79
approximately 40% (0.15 mg Cr(VI) per vial) of the theoretically reducible Cr(VI) was reduced over a
period of twelve days (day 20 to day 32). Destmctive analysis of triplicate vials revealed that the majority
of the chromium detected on the sand (> 95%) was present as Cr(III) suggesting that Cr(VI) was reduced to
Cr(IH) which precipitated (data not shown). The incomplete reduction of Cr(VI) indicates the presence of
slowly or non reactive fractions of Fe(II).
' However, the conversion of Cr(VI) to Cr(III) could only account for approximately 80% of the
decrease in ferrous iron concentration (from 1.19 ± 0.10 mg to 0.78 + 0.10 mg) indicating that ferrous iron
was possibly oxidized by compounds other than Cr(VI). Despite the fact that syringes were flushed with
oxygen-free gas before sampling, trace amounts of oxygen could have been injected into the vials and lead
to the oxidation of Fe(II) to Fe(IH).
After the Cr(VI) reduction capacity of the vials was exhausted (day 32), a subset of the remaining
vials was reinoculated with S. algae BrY to evaluate the possible regeneration of surface reactive ferrous
iron through the addition of DMRB. Fe(H) increased from 0.78 ± 0.10 mg per vial to 1.10 ± 0.07 mg per
vial in reinoculated vials only between day 32 and day 55 (Figure 4.5). Vials, which were not reinoculated,
did not show an increase but rather a slight decrease to 0.73 ±0.15 mg Fe(II), which might be due to slow
reactions of Cr(VI) with Fe(II) in these vials. Cr(VI) was still detectable in these vials at day 55 while no
Cr(VI) was detected in reinoculated vials. Although the amount of total Fe(H) in the reinoculated vials
(1.10 ± 0.07 mg) was lower than the amount of Fe(II) produced during the initial incubation in the absence
of Cr-precipitates (1.19 ± 0.10 mg) it is clear that S. algae BrY was able to re-reduce a significant amount
of the Fe(IH) which was generated during the prior injection of Cr(VI).
80
Tim e [days]
Figure 4.5.
Mass of Fe(II) over time in vials containing iron oxyhydroxide-coated sand as determined
by separate analysis of aqueous and solid phase in the vials. ( • ) after inoculation with
active S. algae BrY (day 0 - 20), (A) after autoclaving and repeated chromium injection,
(■) after autoclaving, repeated chromium injection, and reinoculation with S. algae BrY
(day 32 - 55). Error bars represent the standard error of the means (n = 3).
Reinoculated vials were examined for their ability to reduce Cr(Vl) a second time after production
of Fe(II). Cr(VI) was injected to a concentration of 15 mg L ' and Cr(VI) concentrations were monitored
over time. Cr(VI) concentrations in reinoculated vials decreased immediately after injection (Figure 4.6)
and only approximately I mg L"1 Cr(VI) was detected after 30 seconds. The rate of Cr(Vl) reduction in
these vials was even faster than during the first cycle (compare to Figure 4.3) where approximately 5 mg
L 1 remained after 11 minutes. No decrease in Cr(VI) was observed in vials which were not reinoculated.
The final concentration of approximately 18 mg L"1in these vials is the result of residual Cr(VI) in the vials
before the injection of Cr(VI) from the stock solution.
These results suggest that S. algae BrY can regenerate the reduction capacity of iron minerals by
producing surface reactive Fe(II) from Fe(III) even in the presence of Cr(III)-precipitates. It is likely that
81
the rate and extent of Fe(II) production in natural systems, before and after precipitation of Cr(III), will
depend on the type of iron mineral present, the concentration and ability of DMRB to reduce the Fe-Crmineral phases after precipitation of Cr, and on the time allowed for Fe-reduction.
Tim e [min]
Figure 4.6.
Cr(VI) transformation in vials with initially exhausted Cr(VI)-reduction capacity (S)
after reinoculation and incubation with S. algae BrY, (■) without reinoculation. The
Cr(VI) spike increased the initial Cr(VI) concentration in the vials by 15 mg L"1
(indicated by O). Error bars represent the standard error of the means (n = 3).
Iron and Chromium Reduction in Columns
In order to develop the basis for a viable restoration technology, Cr(VI) must be reductively
precipitated by surface reactive Fe(II) in flow through systems at short contact times. Column studies were
designed to more closely approximate field situations, in which the contact time of Cr(VI) with biologically
produced surface-associated Fe(II) is limited. Columns inoculated with starved S. algae BrY were fed in
semi-batch mode and were sacrificed periodically to analyze for Fe(II).
82
Fe(II) was produced over time (data not shown), albeit at a significantly slower rate than in the
batch experiments described above. Starved S. algae BrY reduced more than 95% of the Fe(III) present in
the columns after 2 months as compared to 80% after 7 to 10 days in batch experiments. The production of
ferrous iron in the columns indicated that starved S. algae BrY colonized the Fe-coated sand and utilized
the surface-associated Fe(III) as electron acceptor.
After evidence of Fe(II) production, a Cr(VI) solution (6.6 ±0.1 mg L"1) was injected into the
columns concurrently with a tracer (Fluorescein, Na-salt). Influent and effluent concentrations of the tracer
and Cr(VI) were monitored over time. The data were expressed as fractions of their influent concentration
and were plotted as a function of the number of pore volumes eluted from each column. Figure 4.7 shows
the tracer breakthrough curves of three different columns with their corresponding model fits (Fetter 1993)
and their corresponding Cr(VI) breakthrough data. It is evident that one of the columns had a higher
dispersivity likely due to differences in packing of the sand. However, despite this difference in tracer
breakthrough it is evident that the breakthrough of Cr(VI) in all three columns was clearly retarded as
compared to fluorescein. Statistical examination of the breakthrough curves using a two sample xg-test
(Gibbons, 1971, pp. 131-137) clearly indicates that in all three columns containing microbially produced
Fe(II), the breakthrough of Cr(VI) was retarded (xg-test, P value = 0.05).
The mass of Cr(VI) retarded in the columns was calculated on the basis of the breakthrough curves
for each column, approximating complete breakthrough of Cr(VI) after four pore volumes- (see Figure 4.7).
The mass of Cr(VI) in the effluent of the column (integral of the effluent concentration over time) was
compared with the mass of Cr(VI) injected into the column (integral of the influent concentration over
time). As in the batch experiments only a fraction of the stoichiometrically possible mass of Cr(VI) was
retarded in each column, however, the fraction of chromium retarded in the columns was even smaller (I +
0.7% (n=3) of the total reduction capacity for Cr(VI)) than for the batch studies (approximately 40% of the
total reduction capacity for Cr(VI)).
83
pore volumes eluted
Figure 4.7.
Normalized breakthrough curves of a fluorescein tracer (■, A, ♦ ) (corresponding model
fits indicated by solid lines) and its corresponding Cr(VI) breakthrough data (D, A , O )
through columns containing biologically produced Fe(II). The influent Cr(VI)
concentration was 6.6 ± 0.1 mg L"1 for every column.
It is possible that the short contact time in the columns (approximately 6 minutes) is not sufficient
for more extensive Cr(VI) reduction to occur. Mass transport limitations, the presence a slowly or non
reactive Fe(II)-fraction (such as siderite), and the accumulation of chromium precipitates on surface
reactive sites could be responsible for the slow and incomplete reaction. The sand was analyzed for total
chromium and Cr(VI) by sacrificing triplicate columns after the injection of Cr(Vf). More than 95% of the
chromium was recovered as Cr(III) (data not shown), indicating that the retardation of chromium was due
to reductive precipitation within the columns.
Three different processes are likely to be responsible for the reductive precipitation of chromium.
Cr(Vf) may react chemically with surface reactive Fe(II) or active S. algae BrY cells may directly reduce
Cr(VI) to precipitate Cr(III). These processes in addition to DMRB mediated biogeochemical cycling of
Fe(III) back to Fe(II) (process 2 in Figure 4.8) may explain the attenuated increase of Cr(VI) towards the
84
end of the experiment. All three processes together with the limited reduction capacity of the columns
would cause the slow but continued reductive precipitation of chromium within the columns and would
result in Cr(VI) effluent concentrations that approach the influent concentration asymptotically. It is likely
that all three processes occur within the columns and therefore it is surprising that of the total reduction
capacity of surface associated Fe(II) for Cr(VI), only approximately 1% is utilized after approximately 4
pore volumes and that Cr(VI) concentrations in the effluent are almost equal to the influent concentration.
QjMRtT)
e
Cr(VI)
-> Cr(III)
Fe(Il)sur
©F
e(III) sur
(^dmreT)
Figure 4.8.
Processes governing the fate of Cr(VI) in environments containing surface associated
iron and dissimilatory metal-reducing bacteria (DMRB). (I) Direct Cr(VI)-reduction by
DMRB; (2) chemical reduction of Cr(VI) by surface associated ferrous iron (Fe(H)sur)
and biological re-reduction (regeneration) of surface associated ferric iron (Fe(III)sur) to
Fe(II)sur by DMRB.
The remaining columns were reinoculated after depletion of the Cr(VI) reduction capacity to
evaluate the possibility of regenerating the reactivity of the columns by injection of S. algae BrY.
Approximately two months after reinoculation, Fe(II) production in these columns was evident and Cr(VI)
was injected a second time concurrently with fluorescein. The normalized breakthrough curves were very
85
similar to those plotted in Figure 4.7 (data not shown) indicating that the reduction capacity of the columns
was regenerated by the reinoculation of S. algae BrY..
Conclusions
The results presented here suggest the possibility of artificially establishing a biogeochemical
cycle (Figure 4.8) in Cr(Vl) contaminated subsurface environments. DMKB reduce ferric iron [Fe(III)] to
ferrous iron [Fe(II)], which in turn reduces Cr(VI) to Cr(III) while Fe(H) is oxidized (back) to Fe(HI).
DMRB can then re-reduce the Fe(III) to Fe(H) to close the biogeochemical cycle.
The use of DMRB potentially allows the establishment and maintenance of redox reactive zones
(permeable subsurface barriers) in Fe(IH)-bearing subsurface -environments. The applicability of this
technology to any given field site will depend on our ability to produce surface reactive Fe(II) from the iron
minerals present in the aquifer. This can be achieved by stimulating indigenous microorganisms or, if
microbial iron reduction cannot be sufficiently supported, by our ability to augment the existing
populations with metal-reducing bacteria. The use of starved metal-reducing bacteria has potential for such
an approach. Starved bacteria are transported better through porous media (Chapter 2; Bouwer et al, 2000)
and, as shown here, can produce surface reactive Fe(II).
However, like in most biofemediation strategies, there are limitations and concerns that have to be
considered. High initial Cr(VI) concentrations or the presence of other toxic compounds might slow down
or prevent biological iron and chromium reduction. High or low pH values as well as a lack of electron
donor can also hinder biological iron reduction and, subsequently, chromium reduction.
The process described herein does not remove chromium from the aquifer but immobilizes it by
precipitation. Although Fe(III)Cr(IH)hydroxides and pure Cr(HI)Iiydroxide have very low solubilities and
are unlikely to result in chromium concentrations above the drinking water limit for chromium, the
reoxidation of Cr(IH) compounds is a concern. Although microbially mediated reoxidation of Cr(IH) is not
a proven concern in systems containing natural soil material (James, 1994, Bader et al., 1999), manganese
oxides have been found to quickly oxidize Cr(IH) in soils (Rai and Zachara, 1988, Bartlett and James,
86
1979). Therefore, the application of oxidants such as'potassium permanganate or hydrogen peroxide,
commonly used in certain remediation schemes, can potentially lead to a remobilization of immobilized
chromium.
Future research is needed to address the identification of mineral phases developing during the
microbial reduction of iron (hydr)oxides and iron chromium (hydr)oxides. The stability of these
precipitates will be assessed and the recycling, frequency of the ferric/ferrous iron redox couple in the
presence of Cr(VI) will be evaluated in batch and flow through systems.
87
CHAPTER 5 .
DISSIMILATORY IRON-REDUCING BACTERIA CAN INFLUENCE THE REDUCTION OF
CARBON TETRACHLORIDE BY IRON METAL
Reproduced with permission from Gerlach, R., Cunningham, A.B. & Caccavo, F. Jr. (2000): Dissimilatory
Iron-Reducing Bacteria Can Influence the Reduction of Carbon Tetrachloride by Iron Metal.
Environmental Science and Technology 34 (12): 2461-2464. Copyright 2000 American Chemical Society.
Abstract
Little is known about the long term performance of zero valent iron (Fe(O)) subsurface barriers.
Groundwater exposure induces corrosion processes that can passivate the Fe(O) surface and decrease barrier
reaction rates. This chapter presents evidence that dissimilatory iron-reducing bacteria (DIRB) can
stimulate the rate of carbon tetrachloride (CT) transformation in the presence of corroded iron. The DERB,
Shewanella algae BrY, adhered to the corroded Fe surfaces that showed little or no capacity to transform
CT. The addition of BrY to these systems with decreased CT transformation rates resulted in increased
ferrous iron concentrations and increased CT transformation to chloroform (CF). The results suggest that
DIRB can have an influence on the long term performance of Fe(O) barriers.
Introduction
Permeable, reactive, subsurface barriers of zero valent iron (Fe(O)) have been successfully
employed for the remediation of contaminated groundwater. Fe(O) is effective in degrading or transforming
a wide range of contaminants including chlorinated organics (Eykholt and Davenport, 1998; Gatpagar et
al., 1997; Gillham and O'Hannesin, 1994; Helland et al., 1995; Johnson et al., 1998; Johnson et al., 1996;
Roberts et al., 1996; Sayles et al., 1997; Siantar et al., 1996), heavy metal oxyanions (Cantrell et al., 1995;
Gould, 1982) and oxycations (Fiedor et al., 1998), nitroaromatics (Agrawal and Tratnyek, 1996; Cao et al.,
r
88
1999), and nitrate (Huang et al., 1998; Siantar et a l, 1996; Till et al., 1998). The corrosion of Fe(O)
provides the electrons necessary for the reduction of these contaminants.
Laboratory studies simulating long term performance of Fe(O) have indicated that contaminant
degradation rates can either decrease (Devlin et al., 1998; Gatpagar et al., 1997) or more rarely increase
(Helland et al., 1995) as a function of time. This temporal effect on contaminant degradation rates is
usually attributed to the development of corrosion products that develop on the metal surface (Helland et
al., 1995; Johnson and Tratnyek, 1994; Scherer et al., 1998). Johnson and Tratnyek (1994), Scherer et al.
(1998), and Odziemkowski et al. (1998) have proposed that the accumulation of corrosion products on
Fe(O) surfaces either inhibits contaminant access to the metal surface or forms new sites for contaminant
adsorption, reaction, and catalysis. A decrease in reaction rates can be due to the accumulation of thick
layers of amorphous corrosion products on Fe(O) surfaces, which may function as a physical barrier
between dissolved contaminants and the underlying reactive sites. The accumulation of highly redox
reactive corrosion products such as green rust, however, can also significantly contribute to the reduction of
contaminants in Fe(O) systems (Erbs et al., 1999).
A wide range of Fe(O) corrosion products has been found in laboratory and field studies, and
corrosion product distribution appears to be dependent on the type of Fe(O) used, the contaminant present,
geochemical conditions, and the time of exposure to contaminated groundwater. The corrosion products
commonly found in the presence of contaminated groundwater include goethite (ct-FeOOH; Pratt et al.,
1997), magnetite (Fe3O4; Bonin et al., 1998; Odziemkowski and Gillham, 1997; Odziemkowski et al.;
1998; Peterson et al., 1997), maghemite Cy-Fe2O3; Peterson et al., 1997), hematite (Ctr-Fe2O3; Pratt et al.,
1997), mixed chromium-iron-hydroxides .(Blowes et al., 1997b; Powell et al., 1995a), green rust (mixed
Fe(II)-Fe(III)-hydroxy salts; Odziemkowski and Gillham, 1997), and siderite (FeCO3; Mackenzie et al.,
1997).
Many of these iron minerals are used by dissimilatory iron-reducing bacteria (DIRB) as electron
acceptors (Caccavo, Jr. et al., 1992; Fredrickson and Gorby, 1996a; Heijman et al., 1995; Heijman et al.,
1993; Kostka and Nealson, 1995; Roden and Zachara, 1996). DIRB are widely distributed in both pristine
89
and contaminated terrestrial, aquatic, and subsurface environments (Lovley, 1995b) and are thus likely to
contact Fe(O) banners in situ. DIRB gain energy for growth by coupling the oxidation of organic matter to
the dissimilatory reduction OfFe(III) minerals.
Gorby et al. (1994) demonstrated that DIRB can reduce structural Fe(HI) oxides, which can in turn
reductively dechlorinate carbon tetrachloride (CT) to chloroform (CF). The bacterial reduction of Fe(III) to
Fe(II) allowed for the following reaction to occur:
2 Fe2++ CCl4 + H+
2 Fe3+ + CHCl3 + Cf
(Eq. 5.1)
DIRB have also been shown to influence the corrosion of mild steel by solubilizing the protective
ferric iron film on the passivated metal surface (Obuekwe et a l.,1981a; Obuekwe et al., 1981b). A decrease
in corrosion layer thickness due to solubilization of corrosion products in Fe(O) systems could also lead to
increased reaction rates of iron metal with chlorinated aliphatics (Devlin et al., 1998; Gatpagar et al., 1997;
Scherer et al., 1998).
It was hypothesized that DIRB can influence the performance of Fe(O) subsurface barriers by
adhering to corroded iron and reducing Fe(III) corrosion products to Fe(II)-compounds. The reduction of
Fe(IH) to Fe(II) can lead to the formation of surface reactive Fe(II) species or result in the removal of
passivating ferric precipitates on the Fe(O) surface. Either one of these processes will potentially increase
the reductive transformation of carbon tetrachloride (CT).
Materials and Methods
Organism and Culture Conditions
Shewanella algae BrY (Caccavo, Jr. et al., 1992) was used as a model DIRB in these studies. S.
algae BrY was maintained on tryptic soy agar (40 g D'1, Difco Laboratories) at ambient temperature. S.
algae BrY cultures were grown in tryptic soy broth (30 g L"1, Difco Laboratories) at 28 °C and 150 rpm for
15 hr. Cultures were centrifuged at 5,520 x g for 20 minutes at 4 °C, washed once in anaerobic 10 mM
90
HEPES (N-[2-hydroxyethyl]piperazme-N’-[2-ethanesulfonic acid]) buffer (pH 7.0) amended with 2.5 g
liter1 NaCl and suspended in the same buffer. All buffers were made anaerobic by boiling and cooling
under a constant stream of O2-Ifee N2 (Balch and Wolfe, 1976). All manipulations were performed under
an O2-Ifee N2 purge.
CT Reduction
The ability of S. algae BrY to influence CT reduction by passivated Fe(O) was examined. Fe(O)
powder (20 mg, 100 mesh, Fisher Scientific) was added to 10 ml of aerobic HEPES buffer (pH 7.0), sealed
in culture tubes under an aerobic headspace and vigorously shaken on a wrist action shaker for
approximately 40 hours. This method Was found to be efficient for pre-exhausting the CT-reducing
capacity of the Fe(O) powder. Following pretreatment, the tubes were purged with O2-Ifee N2 for 15
minutes by lowering a stainless-steel canula into the bottom of each tube and letting the anaerobic gas
percolate through the aqueous phase. Anaerobic sodium lactate (final concentration 10 mM) and either
heat-killed or active cells of S. algae BrY (final concentration of IO10 cells ml"1) were added to the tubes
using degassed syringes and needles. The tubes were then filled to their maximum capacity (27.5 ml) with
anaerobic HEPES buffer to minimize loss of CT into the headspace. The gas-sparging canula was slowly
removed from the tube as a PTFE-Iined silicon septum was inserted. Control tubes containing either heatkilled or active S. algae BrY cells without iron powder were also established. The experiments were
initiated by injecting 0.5 ml of a CT (Sigma) stock solution into the tubes. The stock solution was made by
adding I ml of pure phase CT to 40 ml of deionized water. Excess aqueous phase was allowed to escape
from the tube by allowing it to equilibrate with a N2 filled syringe. Aqueous phase samples (0.1 ml) were
taken with a gas-tight syringe through the PTFE lined septum immediately after CT addition and
periodically over the duration of the experiment. At each time-point, the sampling-syringe was flushed with
N2 and approximately the same volume
O f N 2 was
injected into each tube. The sample was injected into I
ml of hexane and allowed to equilibrate for at least 10 minutes on a horizontal shaker (150 rpm). The
hexane phase was then used for gas chromatographic analysis. The total headspace volume at the end of the
91
experiment was less than 5 % of the vessel volume, ensuring that less than 4 % of CT remaining and less
than 0.5 % of CF produced in the tubes were in the headspace (using the Henry constants given by Gossett,
1987). All CT reduction treatments were conducted in triplicate.
Iron Reduction
The ability of S. algae BrY to use Fe(O) corrosion products as a terminal electron acceptor was
examined in an experiment analogous to that described above, omitting the CT and replacing the PTFElined septa with blue butyl rubber stoppers. The initial cell concentration was 1.60 x IOld cells ml"1.
Sampling was performed by removing aqueous phase-aliquots, with a disposable syringe and 22-gauge
needle. Samples were injected directly into 0.5 N HCl for ferrous iron analysis. All iron reduction
treatments were conducted in triplicate.
Adhesion of BrY to Iron
Adhesion studies were performed as described previously (Caccavo, Jr. et al., 1997) with the
exception that the Fe(III) oxide-bicarbonate buffer was replaced with Fe(O)-HEPES buffer. Anaerobic tubes
of HEPES-pretreated iron were prepared as described above. A final concentration of 4.2 x IO8 cells ml"1
was used and adhesion assays were performed in triplicate.
Analytical Methods
CT, CF, and dichloromethane (DCM) were analyzed using a HP 6890 gas chromatograph (GC)
equipped with an electron capture detector. An aliquot (10 pi) of hexane phase of the liquid-liquid extracts
was injected onto a 60/80 Carbopack B/l % SP-1000 column (8 ft x 1/8 in). The GC was operated under the
following conditions: Injector temperature of 275 0C, initial temperature of 100 °C, increased at 15 0C
min"1 up to 200 °C, detector temperature of 250 °C, and Na flow of 40 ml min"1. The GC was controlled by
HP Chem Software, Vers. A.04.01. This method permitted for the detection of concentrations of less than
92
IOjlg
r1of CT, CF, or DCM. Ferrous iron analysis was performed with Ferrozine as described by Lovley
and Phillips (1987).
Results and Discussion
A number of studies have suggested that contact between DIKB cells and insoluble Fe(III)
minerals is a necessary prerequisite for Fe(III) mineral reduction (Arnold et al., 1988; Caccavo, Jr. et al.,
1992; Caccavo, Jr. et al., 1996; Lovley and Phillips, 1988; Tugel et al., 1986). The ability of S. algae BrY
to catalyze reactions on the surface of Fe(O) particles would thus be predicated on the ability of S. algae
BrY cells to adhere to those particles. The results in Table 4.1 demonstrate that 82 % of the S. algae BrY
cells adhered to 2 mg mV1 of HEPES-pretreated iron, while cells were unable to adhere to untreated Fe(O).
When 20 mg ml"1 of HEPES-pretreated iron were provided, more than 99 % of the cells adhered while only
10 % adhered to 20 mg ml'1 of untreated Fe(O). The difference in the ability of S. algae BrY to adhere to
each of these forms of Fe(O) is most likely due to the presence of Fe(III) oxides on the surface of the
corroded iron. Previous studies have demonstrated that DIKB preferentially adhere to Fe(III) coated
surfaces (Caccavo, Jr. et al., 1997; Grantham and Dove, 1996). The results of the adhesion experiments
suggest that DERB would be more likely to catalyze reactions on the surface of corroded iron than on the
surface of untreated Fe(O).
Table 4.1.
Adhesion of S. algae BrY cells to HEPES-pretreated iron and Fe(O)
treatment
% adhered cells
2 mg HEPES-pretreated iron
82.2 + 6.2
2 mg Fe(O)
-5.4 ± 8.2
20 mg HEPES-pretreated iron
99.3 + 5.8
20 mg Fe(O)
10.6 ± 6.3
1
numbers represent the means of three replicate assays ± the
standard errors of those means
93
Following adhesion, active S. algae BrY cells were capable of restoring the capacity of HEPESpretreated iron to transform CT to CF. After 20 hours of incubation CT concentrations were statistically
significantly lower (t-test, P-value 5 0.025) in treatments with active S. algae BrY than in controls
containing HEPES-pretreated iron without cells or cells without iron (Figure 5.1A). In fact, neither S. algae
BrY nor HEPES-pretreated iron caused significant transfoimation of CT. No direct CT transformation by
BrY was observed, which agrees with previous work by Gorby et al. (1994) and Workman et al. (1997).
Workman et al. (1997) were able to stimulate CT transformation in the presence of BrY through the
addition of Vitamin B12. However, the levels of Vitamin B12 used by Workman et al. (1997) were much
higher than normally observed in natural systems and are unlikely to have existed in these experiments.
40
60
Time [hours]
Figure 5.1.
0
20
40
60
80
100
Time [hours]
(A) Carbon tetrachloride degradation and (B) chloroform production by (■) passivated
Fe(O), ( • ) passivated Fe(O) with active S. algae BrY cells and (▲) active S. algae BrY
cells alone. Error bars represent the standard error of the means (n = 3).
Treatments containing both HEPES-pretreated iron and active cells also accumulated more CF
than the controls (Figure 5. IB) (Ps0.0003). Approximately 60 % of the added CT were converted to CF in
tubes containing both HEPES-pretreated iron and S. algae BrY. No dichloromethane (DCM) or other
transformation products were detected in any of the setups.
94
Active cells of S. algae BrY were capable of generating ferrous iron with lactate as an electron
donor and HEPES-pretreated iron as a sole terminal electron acceptor (Figure 5.2). These experiments were
conducted in the absence of CT since the presence of CT could have resulted in instantaneous re-oxidation
of microbially produced Fe(II) to Fe(III), thus masking the production of Fe(II) by DIRB. The sum of
dissolved and colloidal Fe(II) was measured in these systems. Dissolved Fe(II) is reported to be a weak
reductant for CT transformation to CF (Doong and Wu, 1992) while surface associated Fe(II) is thought to
be a stronger reductant (Kriegman-King and Reinhard, 1992; Kriegman-King and Reinhard, 1994;
Matheson and Tratnyek, 1994). Using conventional extraction methods, however, it is difficult to measure
the quantity of adsorbed Fe(II) selectively, because the grains of Fe(O) provide a large reservoir of
extractable metal ions (Johnson et al., 1998). Figure 5.2 clearly shows that, over a period of ca. 6 days, the
treatments with active S. algae BrY cells resulted in significantly higher Fe(II) concentrations than either
control (P^ 0.027).
Time [hours]
Figure 5.2.
Production of Fe(II) by (■) passivated Fe(O), (# ) passivated Fe(O) with active S. algae
BrY cells and (▲) passivated Fe(O) with heat-killed S. algae BrY cells. Error bars
represent the standard error of the means (n = 3).
95
Conclusions
The influence of microorganisms on tire performance of Fe(O) subsurface barriers has only
recently been recognized. Methanogenic and denitrifying bacteria have been shown to have a positive
effect on the degradation of CT and CF by Fe(O) (Novak et al., 1998; Till et ah, 1998; Weathers et al.,
1997) .
It is reported here for the first time that DIKB can influence the transformation of a chlorinated
aliphatic in the presence of passivated iron. The results indicate that DERB can have a positive influence on
the performance of zero valent iron by restoring the reactivity of passivated iron to transform CT to CF.
The work described herein did not allow for the identification of the exact reaction mechanism. It
is possible that DIRB restore the reactivity of corroded iron by producing surface-bound reactive Fe(II)
sites through microbial reduction of Fe(III) precipitates or by reductive dissolution of Fe(III) corrosion
products. Reductive dissolution of ferric iron precipitates would lead to a decrease in corrosion layer
thickness, which could increase the mass transport of contaminants to surface reactive sites or facilitate the
electron transport from the Fe(O) to reaction sites on the corrosion product-covered surface of the iron
grains (Scherer et al., 1998). Thin passive films composed of magnetite, maghemite, and other mixed
valent (hydr)oxides have been modeled as semiconducting FeaO3 doped with Fe(II) (Schmuki et al., 1995).
Thicker corrosion layers tend to be amorphous with fewer semiconductor characteristics (Scherer et al.,
1998) . The reductive dissolution of amorphous iron (oxy)hydroxides from the grain surface would lead to a
decrease in corrosion layer thickness and could result in enhanced electron transport from the underlying
iron metal to surface reactive sites.
More research is needed to identify the mechanisms by which DIRB influence the performance of
Fe(O). Specifically designed experiments involving surface analytical equipment should allow for the
identification of the reaction mechanisms. Future research should also address the role of iron-reducing
bacteria in pilot and field scale barriers. DIKB could be isolated from core samples and their effects could
be evaluated under more field realistic conditions (alkaline pH, field-weathered iron material).
96
A thriving DIRB population in Fe(O) barriers could also have a negative effect on the reactivity of
Fe(O). The development of thick biofilms could inhibit mass transport of dissolved contaminant to the iron
surface (Grady and Lim, 1980; Revsbech and Jorgensen, 1986), thus lowering the overall reactivity of
Fe(O) barriers. Although currently not observed in field applications, excessive microbial growth within
subsurface barriers could potentially lead to a decrease in porosity, which could result in hydraulic failure
of the barrier. DIRB activity in Fe(O) barriers could also result in enhanced dissolution of reactive corrosion
products thus decreasing the overall reactivity of Fe(O) subsurface barriers.
97
CHAPTER 6
CONCLUSIONS
The research summarized in this dissertation was designed to evaluate the use of dissimilatory
metal-reducing bacteria (DMRB) for the bioaugmentation of subsurface environments contaminated with
heavy metals and chlorinated hydrocarbons.
Two important aspects determining the success or failure of bioaugmentation strategies were
evaluated including the effective delivery of DMRB into the subsurface and the biogeochemical influence
of DMRB on the fate of contaminants. The four objectives listed in the Introduction were addressed in four
separate but interrelated research projects, which are summarized in the four main chapters of this
dissertation. This chapter summarizes the findings, discusses their implications, and identifies future
research needs.
Chapter 2 compared the porous media transport of starved and vegetative cells of Shewanella
algae BrY, the model DMRB used in this dissertation. Bacterial long term starvation had been suggested as
1a transport enhancement strategy previously to this work, but a quantitative comparison had not been
attempted. Based on the fractional recovery of cells (i.e. the total number of bacteria transported through a
porous media column divided by the total number of cells injected) starved cells were transported better
through porous media than vegetative cells. A 3.2 fold increase in fractional recovery was observed in
30 cm long columns for S. algae BrY cells, which were previously starved for 7 weeks. The longer (3 m)
columns showed an approximately 23 fold increase in fractional recovery for starved cells. In addition,
starved cells were more homogeneously sorbed to the quartz sand along the flowpath while vegetative cells
tended to preferentially sorb to sand close to the influent side of the columns. The preferential sorption of
vegetative cells to the vicinity of the injection point could potentially cause the clogging of injection wells
in field situations, a well known problem in the application of bioaugmentation technologies.
98
The research described in Chapter 3 was designed to evaluate potential reasons for the transport
enhancement by starvation. A number of cell properties known to influence bacterial transport through
porous media was monitored over seven weeks of starvation and an existing mathematical model, based on
the colloid filtration theory, was used to evaluate their importance. Changes in cell size, buoyant density,
diffusion coefficient, hydrophobicity, and net electrostatic charge could only partly explain the transport
enhancement by starvation. However, a comparison of the results of batch adhesion assays with the
fractional recoveries from 30 cm long columns suggested that less labor intensive batch experiments might
provide an effective screening tool. Such a tool would allow to screen bacterial strains for their starvation
transport enhancement potential without the need to perform labor intensive column studies.
Chapters 2 and 3 suggest additional experimental work, which will improve the understanding of
physiological changes and mechanisms responsible for the transport enhancement by bacterial starvation.
The influence of flagella, the change in surface characteristics of the cells, and the relationship between
metabolic activity and bacterial transport through porous media should be evaluated. Once the transport
mechanisms are understood, efforts should be focused on the development of an appropriate mathematical
model. The employed model based on the filtration theory was not easily adapted to accurately describe
scale- and physiology-dependent effects on bacterial transport through porous media. Methods for the
reliable, selective, and quantitative assessment of bacterial numbers and activity in porous media should be
developed in order to facilitate the model development process. Chapter 2 suggests a method based on the
use of molecular techniques, which, if successfully developed, could provide the necessary information in
the future.
In view of potential field applications, the starvation time should be optimized. The results
summarized in this dissertation suggest that for Shewanella algae BrY four weeks of starvation result in
bacteria that transport approximately as well as bacteria starved for seven weeks. However, the
concentration of bacteria in the starvation cultures were approximately five fold greater after four weeks
than after seven weeks, which would allow to produce larger numbers of bacteria from the same initial
volume of cells.
99
Chapters 4 and 5 focused on the ability of Shewanella algae BrY cells to directly and indirectly
influence the fate of heavy metals and chlorinated hydrocarbons in the subsurface.
Chapter 4 described the resuscitation of starved S. algae BrY into actively metabolizing cells in
the presence of surface associated iron minerals. Active S. algae BrY cells reduced chromium(VI) directly
and in addition allowed the establishment of a biogeochemical cycle involving surface reactive iron which
facilitated the precipitation of chromium(VI) from contaminated water. A research project, based on these
findings is currently addressing the potential feasibility of this technology by characterizing the mineral
phases developing during iron and chromium reduction and by evaluating the stability of the chromium
precipitates under different redox conditions.
Chapter 5 described the potential influence of DMRB on the performance of zero valent iron
(Fe(O)) permeable reactive barriers (PRBs). DMRB are ubiquitous in the environment and therefore likely
to come in contact with existing PRBs. However, the potential effects of DMRB on Fe(O) PRBs had not
been evaluated previously to this work. The microbial reduction of corrosion products, which accumulated
on the Fe(O) surface by S. algae BrY cells was presumably responsible for the increase in reaction rates of
Fe(O) with carbon tetrachloride. Chapter 5 was published in “Environmental Science and Technology” and
is reprinted in this thesis with permission of the American Chemical Society. This publication has
stimulated widespread interest and a number of funding requests and research projects in the scientific
community. Drs. Michelle Scherer and Pedro Alvarez at the University of Iowa are currently investigating
the influence of DMRB on the mineralogy and reactivity of Fe(O) in the presence of chlorinated aliphatics.
Research groups at the Oakridge National Laboratory and MSE Technology Applications (Butte, MT) are
interested in the effect of DMRB on the performance of Fe(O) PRBs for uranium elimination from
groundwater.
Implications for Field Applications
The author of this dissertation is convinced that the research summarized herein contributes
towards an improved understanding of bacterial transport through porous media. Continued research in this
100
area will contribute towards the development of a widely applicable technology for subsurface
bioaugmentation.
There is ample evidence that long term nutrient starvation of bacteria enhances the transport of a
number of different bacterial strains and the knowledge about starvation responses suggests that starvation
could be a generally applicable strategy for transport enhancement. The development of a generally
applicable transport enhancement strategy would allow for the use of any bacterium of interest. Indigenous
and exogenous bacterial strains and potentially whole consortia could be used. The bacteria could be
starved after isolation and enrichment, and injected into contaminated subsurface environments to
accelerate bioremediation processes.
The ultimate success of bioaugmentation strategies will be based on the survival and activity of
the injected microorganisms. The survival and activity of these organisms is influenced by many factors
including nutrient availability, moisture content, physical and chemical characteristics of the environment
(pH, toxicity, electron acceptors, etc.), predation, parasitism, and competition with the indigenous microbial
population. Future research should, therefore, focus on further improvement of bacterial transport strategies
and on the development of environmentally benign, however competitive and metabolically active inocula.
Research focusing on the optimization of fluid velocity and inoculum concentration for bacterial
transport and the use of site groundwater or artificial groundwater for starvation might lead to further
improvements of the starvation transport enhancement strategy.
Future research should evaluate the bacterial transport, bacterial activity, and long term stability of
chromium precipitates on bench- and meso-scale. The long term stability of chromium precipitates will be
addressed in bench scale experiments in a collaborative research project between the Idaho National
Environmental and Engineering Laboratory (INEEL) and the Center for Biofilm Engineering at Montana
State University. In view of a potential field scale application alternative carbon sources should be
evaluated. An excess of lactate was used in the studies described. The type and amount of carbon source
added to any subsurface environment should be optimized to minimize costs and the danger of excessive
microbial growth close to the injection well. The transport of starved Shewanella algae BrY through porous
101
media, their resuscitation in the presence of iron minerals, and the reaction of ferrous iron with Cr(VI)
should be evaluated on a larger scale. A meso-scale subsurface testing facility such as the VEGAS in
Stuttgart (Germany) would be ideal for this purpose.
102
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