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moo mm.“

BIOAVAILABILITY OF SORBED ORGANIC CONTAMINANTS
By

Jeong-Hun Park

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Civil and Environmental Engineering

2000

ABSTRACT
BIOAVAILABILITY OF SORBED ORGANIC CONTAMINANTS
By

J eong-Hun Park

The bioavailability of sorbed contaminants was investigated because it has been
an important issue in applying bioremediation technology to the restoration of
contaminated sites. Experimental and mathematical approaches were developed to
evaluate the bioavailability of contaminants sorbed by silica particles and natural soils.
Biokinetic parameters in column and batch assays were compared in order to evaluate the
role of cell attachment and ﬂow on biodegradation rates.

Commercially available silica, natural soils, and Ottawa sand were selected as
sorbents and 2,4-dichlorophenoxyacetic acid (2,4-D) and naphthalene were used as
sorbates/substrates. Four organisms capable of naphthalene degradation, and one for 2,4-
D degradation were studied. Sorption/desorption isotherms, biodegradation rate studies,
and bioavailability assays were conducted in batch systems. Bio-kinetic parameters were
also evaluated in both of batch and Ottawa-sand packed columns. A three-site desorption
model was developed to depict the desorption process of contaminants sorbed in natural
soils. A bioavailability model, which is counted for sorption, desorption, and
biodegradation of contaminants in both solid and liquid phases, was developed to

evaluate the bioavailability of sorbed contaminants.

This study demonstrates the existence of enhanced bioavailability for some sorbed
contaminants, which cannot be explained by desorption and liquid phase degradation alone.
Enhanced bioavailability of 2.4-D was observed in silica-slurry systems and an enhanced
transformation factor was introduced to express the increased biodegradation rate over that
expected from the liquid phase only. However, the bioavailability of sorbed naphthalene in
soils varied with the distribution coefficient and biokinetics. For the less sorptive soil, the
results could be explained by sequential desorption and degradation processes. For the
other soil, enhanced degradation was clearly observed for the organisms with ﬁrst-order
and Michaelis-Menten rates. No enhancement was found for the organism with zero-order
kinetics. In all soils, degradation of non-desorbable naphthalene, which could not be
removed by successive water extractions, was observed. Several explanations are explored
including the development of elevated substrate concentrations at the organism/sorbent
interface. Enhanced bioavailability could be described using a model formulation that
included sorbed-phase degradation.

Liquid-phase degradation rates using attached organisms in column experiments
were found to. be lower than with suspended cells in batch experiments. It is believed that
this results from at least two factors — exposed reduction of exposed cell surface area and

heterogeneity of cell distribution.

DEDICATION

To my wife, Hae-Sook Kim

ACKNOWLEDGMENTS

This achievement could not have been completed without the special assistance of
following people. My great thanks go to my major professor, Dr. Thomas C. Voice who
gave me guidance, encouragement and advice through the research. I appreciate my
committee’s professional help with expert knowledge: Dr. Stephen A. Boyd of the
Department of Crop and Soil Science for his introduction to soil organic chemistry; Dr.
Roger B. Wallace for helping me to understand groundwater systems; Dr. Syed
Hashsham for helping me to understand biological treatment processes; and Dr. Xianda
Zhao for introducing laboratory and experimental skills, and guiding direction through
the research (also my tennis partner).

I would also like to thank other people for their special help. Dr. Denise Kay did
the experiments for chapter 3 and helped me analyze the data. Dr. Paul Loconto and Mr.
Yanlyang Pan taught me the operation of the analytical instruments and helped with
method development, and secretarial personnel, Linda Steinman and Lori Hesse, assisted
me in many administrative aspects.

I cannot forget the initial laboratory help of Mr. Dave Berends and John
Zimmerman. I enjoyed the valuable information exchange on my research with my
fellow graduate student Stephen Callister (also my tennis partner), my ofﬁce mates Chris
Saffron (also my racquetball partner), Kyung-Hyuk Lee, and Dr. Jerry Wu. I appreciate

the encouragement and friendship of Dr. Jae-Woo Park in Anadigics Co. and his family.

I am grateful to all my family for their encouragement and support for my study
abroad. Especially, my heartfelt thanks go to my parents and my parents-in-law for their
unwavering love and support that encouraged me to complete this goal.

I owe a great dept to my lovely wife, Hae-Sook Kim. She has supported my study
by dint of rare diligence and perseverance, and managed our home hopefully and
enjoyably. I appreciate my lovely daughter, So-Young, and son, Ki-Lyang, for their

healthy and happy faces.

vi

TABLE OF CONTENTS

Chapter 1. Introduction and objectives .............................................................................. 1
Introduction ..................................................................................................................... 1
Objectives ........................................................................................................................ 2
References ....................................................................................................................... 4

Chaper 2. Literature review ............................................................................................... 5
Factors affecting sorption of chemicals ........................................................................... 5

Sorbent ......................................................................................................................... 5
Dissolved organic matter (DOM) ................................................................................. 7
Other factors ................................................................................................................. 7
Factors affecting bioavailability of sorbed chemicals ..................................................... 8
Sorbent ......................................................................................................................... 8
DOM .......................................................................................................................... 11
Substrate ..................................................................................................................... 12
Aging .......................................................................................................................... 12
Organisms ................................... 14
Conclusion ..................................................................................................................... 16
References ..................................................................................................................... 16

Chapter 3. Kinetic Modeling of Bioavailability for Sorbed-phase 2,4-

Dichlorophenoxyacetic Acid ............................................................................................ 21

Abstract .......................................................................................................................... 21

vii

Introduction ................................................................................................................... 22

Materials and methods ................................................................................................... 23
Results ........................................................................................................................... 29
Discussion ...................................................................................................................... 38
Acknowledgements ....................................................................................................... 41
References ..................................................................................................................... 41

Chapter 4. Development of a Kinetic Basis for Bioavailability of Sorbed Naphthalene in

Soil Slurries ....................................................................................................................... 43
Abstract .......................................................................................................................... 43
Introduction ................................................................................................................... 44
Materials and Methods .................................................................................................. 46
Theory ............................................................................................................................ 52

Three-site desorption model ....................................................................................... 52
Bioavailability model ................................................................................................. 53
Results ........................................................................................................................... 54
Discussion ...................................................................................................................... 66
Acknowledgements ....................................................................................................... 68
References ..................................................................................................................... 69

viii

Chapter 5 Bioavailability of Non-desorbable Naphthalene in Soil Slurries ..................... 72

Abstract .......................................................................................................................... 72
Introduction ................................................................................................................... 73
Materials and methods ................................................................................................... 74

Experimental .............................................................................................................. 74

Mathematical .............................................................................................................. 79
Results ........................................................................................................................... 83
Discussion .................................................................................................................... 101
Acknowledgements ..................................................................................................... 1 03
Refferences .................................................................................................................. l 03

Chapter 6. Comparison of Biodegradation Kinetic Parameters for Naphthalene in Batch

and Sand Column Systems by Pseudomonas Putida ...................................................... 106
Abstract ........................................................................................................................ 106
Introduction ................................................................................................................. 1 07
Materials and Methods ................................................................................................ 110

Materials ................................................................................................................... 110
Analytical Methodology ........................................................................................... 1 1 1
Batch experiments .................................................................................................... 112
Column experiments ................................................................................................ 113
Calculation of kinetic parameters ............................................................................. 117
Results and Discussion ................................................................................................ 119

Degradation and mineralization of naphthalene by suspended cells in batch systems

.................................................................................................................................. 119
Degradation and mineralization of naphthalene by attached cells in sand columns 126
External mass-transfer effects .................................................................................. 133
Internal mass-transfer effects ................................................................................... 136
Reference ..................................................................................................................... 139
Chapter 7 summary and conclusions ............................................................................. 142
Summary ...................................................................................................................... 142
Conclusions ................................................................................................................. 145
Appendices ...................................................................................................................... 146
Appendix A ..................................................................................................................... 147
Appendix B ..................................................................................................................... 153

LIST OF TABLES

Table 3- 1. Analysis of experimental results for a range of Rs. (0.12 - 0.43) at 1.2 x107

CFU ml'l ..................................................................................................................... 32
Table 3- 2. Analysis of experimental results for different initial concentrations and same

Rs. (0.25) at 5.6 x106 CFU m1" .................................................................................. 36
Table 4- 1 Soil characteristics' ................................................... ‘ ...................................... 47

Table 4- 2. Desorption site fraction and desorption rate coefﬁcient estimated by three-

site desorption model. ................................................................................................ 56
Table 4- 3. Each cell attachment percent in both soil Slurries. ........................................ 58
Table 4- 4. Kinetic parameters estimated in control and soil-slurry systems. .................. 59
Table 5- 1. Soil characteristics* ....................................................................................... 75

Table 5- 2. Desorption site fraction and desorption rate coefﬁcient obtained from
desorption, and non-desorption site fraction from series-dilution desorption
experiment. Each site fraction and desorption rate coefﬁcient were estimated from

non-linear regression analysis. ................................................................................... 87
Table 5- 3. Estimated each model parameters for NCIB. ................................................. 99
Table 5- 4. Estimated each model parameters for P. putida G7. ................................... 100
Table 6- 1. Experimental matrix for the column assays ................................................ 116

Table 6- 2. Comparison of kinetic parameters between batch and column experiments

.................................................................................................................................. 132
Table 6- 3. The predicted and measured values of efﬂuent naphthalene concentrations at

different cell densities .............................................................................................. 138
Table A- 1. Naphthalene Properties ............................................................................... 151
Table A- 2. Aqueous solubility of PAH compounds (Linz and Nakles, 1997) ............. 152

xi

LIST OF FIGURES

Figure 3- 1. Depicted model for disappearance of 2,4-D in slurry systems. Where, C and
S are sorbed and liquid phase concentration, respectively. fs is attached cell fraction.
Kd is distribution coefﬁcient. kl and ks are liquid and sorbed phase degradation rate
coefﬁcients, respectively. ........................................................................................... 26

Figure 3- 2. 2,4-D desorption from silica following a single dilution of the solution
phase. Initial liquid concentration was 483 pg L". The distribution coefﬁcient (K4)
was 1.4. Time points represent single measurements. .............................................. 31

Figure 3- 3. 2,4-D liquid-phase concentration versus incubation time. The two depicted
lines were plotted using following values of Rs, (0.19), [(4 (3.06), k; (0.0058), k,
(0.032), and f, (0.28). Inoculum was 1.2 x 107 CPU m1". ........................................ 33

Figure 3- 4. Enhanced transformation factor versus the ratio of silica to liquid. Estimated
line was plotted using average values of k,(0.0060), k,(0.027),f,(0.28) and Kd(3.41).
Inoculum was 1.2 x 107 CPU m1" .............................................................................. 34

Figure 3- 5. Initial depletion rate versus initial equilibrium concentration at a constant

silica to liquid ratio. Inoculum was 5.5 x 106 CPU ml". Values used for model; k1
(0.0045); k, (0.014); Kd (3.25); f, (0.28). .................................................................... 37

Figure 3- 6. The ratio effect of desorption rate coefﬁcient to degradation rate coefﬁcient
on liquid-phase concentration over incubation time. Ceq is calculated using EB
model with Ef value of one. C; is calculated using Eqs. 2 (overall mass balance
equation with k, value of zero) and 9 (ﬁrst-order desorption-rate equation). Kd value
is assumed 3.0. R,, is 0.1. k, is 0.006 min". .............................................................. 40

Figure 4- 1. Sorption isotherm of naphthalene in two—soil Slurries. ................................ 55

Figure 4- 2. Percent of desorption vs. desorption time. Three-site model was used for the
regression. .................................................................................................................. 60

Figure 4- 3. Total amount of naphthalene in SPCF and Kai-A soil Slurries as incubation
time of strain ATCC 17484 ........................................................................................ 61

Figure 4- 4. Total amount of naphthalene in Kal-A soil slurry as incubation time of strain
NP-Alk ....................................................................................................................... 62

xii

Figure 4- 5. Total amount of naphthalene in SPCF and Kai-A soil slurries as incubation
time of strain NCIB. ................................................................................................... 63

Figure 4- 6. Total amount of naphthalene in SPCF and Kal-A soil slurries as incubation
time of strain P. p.G7. ................................................................................................. 64

Figure 5- 1. Model IV description. keq, kneq and knd are deleted in Model I. kneq and knd
are deleted in Model II. knd is deleted in Model III ................................................... 82

Figure 5- 2. Sorption isotherm of naphthalene in four-soil slurries .................................. 85

Figure 5- 3. Liquid-phase concentration over desorption time. Three-site model was
used for the regression ................................................................................................ 86

Figure 5- 4.. Series dilution desorption in four soils ......................................................... 90

Figure 5- 5. Naphthalene disappearance under strain NCIB in SPCF soil slurry over
incubation time. .......................................................................................................... 91

Figure 5- 6. Naphthalene disappearance under strain NCIB in Rub-B81 soil slurry over
incubation time. .......................................................................................................... 92

Figure 5- 7. Naphthalene disappearance under strain NCIB in Rub-A soil slurry over
incubation time. .......................................................................................................... 93

Figure 5- 8. Naphthalene disappearance under strain NCIB in Kal-A soil slurry over
incubation time. .......................................................................................................... 94

Figure 5- 9. Naphthalene disappearance under strain G7 in SPCF soil slurry over
incubation time. .......................................................................................................... 95

Figure 5- 10. Naphthalene disappearance under strain G7 in Rub-BSl soil slurry over
incubation time. .......................................................................................................... 96

Figure 5- 11. Naphthalene disappearance under strain G7 in Rub-A soil slurry over
incubation time. .......................................................................................................... 97

Figure 5- 12. Naphthalene disappearance under strain G7 in Kal-A soil slurry over
incubation time. .......................................................................................................... 98

Figure 6- 1. Degradation of naphthalene, production of intermediates and carbon dioxide
in a batch experiment with free suspended cells. Protein content; 780 ug/L (2.15x107
CFU/mL), Initial substrate concentration; 520 ug/L of naphthalene. ...................... 120

xiii

Figure 6- 2. Estimation of degradation parameters of naphthalene in a batch experiment
with free suspended cells. Protein content; 420 ug/L (1.16x107CFU/mL), Initial
concentration of naphthalene; 445 ug/L, K,; 5.5 (i026) ug/L, km; 0.115 (i0.0004)
min", k0; 0.110(i-0.003) min". The values in the parentheses are the standard
deviations obtained by nonlinear regression. ........................................................... 123

Figure 6- 3. Estimation the initial mineralization rate of naphthalene in a batch
experiment with free suspended cells. Protein content; 420 ug/L (1.16x107CFU/mL),
Initial concentration of naphthalene; 445 ug/L, kcoz; 0.0068 (i0.00007) min". The
value in the parentheses is the standard deviation .................................................... 124

Figure 6- 4. Comparison of degradation rates of naphthalene in PBS and supernatant in
batch experiments. Protein content; 337 ug/L (9.3x106 CFU/mL), initial
concentration; 667 rig/L. Error bars indicate standard deviation. ............................ 125

Figure 6- 5. Estimation of degradation parameters of naphthalene in column experiments
with attached cells. Protein content; 2100 ug/L (1 .36><107 CFU/g sand), ﬂow rate;
3ml/min, inﬂuent concentrations; 506, 368, 221 ug/L, k0; 0.080 (i0.010) min", km;
0.0965 (i0.0005) min",K,; 49.6 (i1.4) ug/L. Error bars indicate standard deviations.
The values in the parentheses are the standard deviations obtained by nonlinear
regression. ................................................................................................................ 1 27

Figure 6- 6. Estimation of initial mineralization of naphthalene in column experiments
with attached cells. Protein content; 2100 ug/L (1 .36x107 CFU/g sand), ﬂow rate;
3ml/min, inﬂuent concentrations of naphthalene; 506, 368, 221 ug/L, sz; 0.0062
(i0.00013) min". The value in the parentheses is the standard deviation. .............. 128

Figure 6- 7. Flow rate effect on degradation of naphthalene in column experiments.
Column length; 5 cm, Protein content; 2490 ug/L (1.62x107 CFU/g sand), initial
naphthalene concentration; 498 ug/L, ﬂow rates; 3 mL/min, 2 mL/min, lml/min and
0.5 mL/min, Ks; 49.6 ug/L, km; 0.0965 min". Error bars indicate standard deviations.
.................................................................................................................................. 1 3 5

Figure A- 1. Degradation pathway from naphthalene to catechol (after Wackett, 1997)
.................................................................................................................................. 148

Figure A- 2. Ortho-cleavage pathway for catechol catabolism (aﬁer Rittman et al., 1994)
.................................................................................................................................. 149

Figure A- 3. Meta-cleavage pathway for catechol catabolism (after Rittman et al., 1994)
.................................................................................................................................. 150

xiv

CHAPTER 1. INTRODUCTION AND OBJECTIVES
INTRODUCTION

Organic contaminants in soils and sediments originate from a variety of
anthropogenic activities. Major contaminants such as benzene, toluene, ethylbenzene,
and xylene (BTEX), poly-aromatic hydrocarbons (PAHs), poly-chlorinated biphenyls
(PCBs), pesticides and herbicides are of environmental concern because of their
documented or suspected mutagenic and carcinogenic effects. Therefore, the fate of
these compounds at contaminated sites and the restoration of the sites are of high public
concern. Over 50,000 organic contaminated sites and several hundred thousand leaking
underground storage tanks were reported in United States (Singleton, 1994). The US
Environmental Protection Agency (EPA) has spent over a billion dollars each year from
1990 to clean up hazardous waste sites; a large portion of which are contaminated with
organic chemicals (Hileman, 1999).

Bioremediation is an emerging technology, which is attractive for cleaning up
these contaminants because it can treat them in-situ with little disturbance to the
contaminated matrix and because the contaminants can often be completely mineralized
to inorganic materials (Head, 1998). Also, bioremediation is relatively inexpensive
compared to incineration, soil washing, and pump and treat (Singleton, 1994; Hughes et
al., 1997). For example, Hughes et a1. (1999) reported that bioremediation was used
successfully and economically (compared to incineration: $55 million vs. $120 million)
to restore lagoon sediments containing mixture organic contaminants including PCBs,
PAHs, bezene, toluene, ethylbenzene, xylenes (BTEX), chlorinated solvents, and

pesticides. However, this technology is not widely adopted because of the uncertainty of

the success. PAHs and PCBs are composed of aromatic rings and are hydrophobic,
resulting in low aqueous solubility and a strong tendency to sorb to the matrix of organic
material in soil or to partition into oily phases at contaminated sites (Harayama, 1997).
Due to these chemical characteristics, the bioavailability of sorbed contaminants in
natural sorbents has been an important issue in applying bioremediation technology to
remediation of contaminated sites (Willumsem and Arvin, 1999). Bioavailability of
sorbed contaminants in soils plays a signiﬁcant role in successful bioremediation and it is
an important factor in determination of environmentally acceptable end-points (ES&T,
1998). Bioavailability is deﬁned as the extent of contaminant that is externally available

for utilization by organisms (Hamelink et al., 1994).

OBJECTIVES

This study was designed to investigate bioavailability of contaminants sorbed by
solids/materials. In order to investigate this phenomenon systematically, four main
objectives were developed. Each of the following chapters is dedicated to a single
objective.

The ﬁrst objective (chapter 3) was designed to develop experimental and
mathematical approaches to evaluate bioavailability of contaminants sorbed by an
artiﬁcial sorbent. Desorption rates were measured in the absence of organisms and
biodegradation rates were measured in the absence of solids. Combined
desorption/biodegradation experiments were then performed, and the results were
analyzed using a simple mathematical model incorporating three processes: desorption,

liquid-phase degradation and sorbed-phase degradation. This approach allowed us to

determine whether a sorbed-phase degradation mechanism was consistent with the
bioavailability data. 2,4-diehlorophenoxyacetic acid (2,4-D) was used as the sorbate and
substrate, silica as the sorbent, and F lavobacterium sp. FB4 as 2,4-D degrading organism.

The second objective (chapter 4) was designed to evaluate the effects of different
biokinetic processes on the bioavailability of sorbed naphthalene in natural soils. In this
study, four organisms (ATCC 17484, NP-Alk, Pseudomonas putida G7, and NCIB 9816-
4) haveing different degradation kinetics (zero-order, ﬁrst-order, and Michaelis-Menten)
were selected. Two of the organisms (ATCC 17484, NP-Alk) have been previously used
to study bioavailability of sorbed naphthalene based on mineralization. Pseudomonas
putida G7 and NCIB 9816-4 were selected because it was recently reported that they
were chemotactic to solid naphthalene.

The third objective (chapter 5) was designed to develop a mathematical model to
quantitatively describe the bioavailability. In this study, degradation assays were used
instead of mineralization assays, and two organisms (which were chemotactic to solid
naphthalene) and four soils (which have different organic carbon contents and different
distribution coefﬁcients of naphthalene) were selected. The developed model described
the relationship among sorption/desorption, and biodegradation of chemicals in soil
slurries. Model parameters for sorption, desorption and biodegradation of a dissolved
chemical were evaluated independently. Sorbed-phase degradation rate coefﬁcients,
which were an indicator to explicitly describe the enhanced bioavailability of the sorbed
chemical, were enumerated by non-linear regression ﬁtting of the model to experimental

data.

The fourth objective (chapter 6) was designed to evaluate the relationship
between kinetic parameters in both batch and column systems. This was accomplished
by selecting a sand/contaminant/organism combination that resulted in signiﬁcant
biomass attachment but only minimal contaminant sorption to the sand in the columns.
In addition, relatively high ﬂow rates and low total biomass levels were used to minimize
external and internal mass-transfer resistances, respectively. The effect of residence time
could be evaluated independent of ﬂow rate by using columns of different lengths, and it

became possible to simultaneously evaluate all of the degradation parameters.

REFERENCES

Hamelink, J. L., P. F. Landrum, H. L. Bergman and W. H. Benson. 1994.
“Bioavailability: Physical, chemical, and biological interactions.” Chelsea, Michigan:
Lewis Publishers.

Harayama, S. 1997. “Polycyclic aromatic hydrocarbon bioremediation design.” Current
Opinion in Biotechnology 8: 268-273.

Head, I. M. 1998. “Bioremediation: towards a credible technology.” Microbiology— UK
144: 599-608.

Hileman, B. 1999. “Research targets hazardous waste.” Chem. Eng. News 77: 24-25.

Hughes, J. B., D. M. Beckles, S. D. Chandra and C. H. Ward. 1997. “Utilization of
bioremediation processes for the treatment of PAH-contaminated sediments.” Journal of
Industrial Microbiology & Biotechnology 18: 152-160.

Singleton, I. 1994. “Microbial Metabolism of Xenobiotics: Fundamental and Applied
Research.”J. Chem. Tech. Biotechnol. 59: 9-23.

Willumsem, P. A. and E. Arvin. 1999. “Kinetics of Degradation of Surfactant-

Solubilized Fluoranthene by a Sphingomonas paucimobilis.” Environ. Sci. Technol. 33:
2571-2578.

CHAPER 2. LITERATURE REVIEW
The purpose of this chapter is to review the factors affecting sorption of chemicals

to natural sorbents and bioavailability of sorbed contaminants.

FACTORS AFFECTING SORPT ION OF CHEMICALS

Sorption of organic chemicals by soil decreases the liquid-phase concentration in
the system. The decreased liquid concentration decreases the available amount of
contaminants to the organisms that can utilize only dissolved chemicals. Therefore, it is
generally known that sorption decreases bioavailability of contaminants, at least in
comparison to systems with the some contaminant mass and no soil. Factors affecting
sorption of organic chemicals are discussed below.
Sorbent

Mineral surfaces can be charged positively or negatively depending on the pH and
solution composition (Stumm and Morgan, 1995). The surface charge affects the sorption
of polar and ionic organic chemicals, such as pyridine and triazines (Laird and Fleming,
1999). Surface charge is established by exchanging of protons or metal ions that exist in
the solution. For example, the negatively charged surface of montmorillonite is converted
into a positive surface in the presence of hydroxy aluminium (Al(OH)x) (Sannino et al.,
1997). The modiﬁed surface promotes the adsorption of the negatively charged 2,4-
Dichlorophenoxiacetic acid (2,4-D) (Sannino et al., 1997). Sannino et al. also reported
that there was adsorption of 2,4-D into montmorillonite in phosphate buffer solution
(Sannino et al., 1997). They explained that the adsorption could not occur explicitly

because both 2,4-D and montmorillonite are negatively charged in the solution and both

of them repel each other preventing adsorption. However, they concluded that some
impurities of magnesium or calcium ions, which came from magnesium or calcium
phosphate, collect on montmorillonite surfaces. Therefore, the positive sites created by
magnesium and calcium ions are used as adsorption sites for 2,4-D anions.

The sorption of non-ionic organic compounds (N OCs) to soils strongly correlates
with soil organic matter (SOM) content of the soils (Novak, 1999) and the contaminant’s
relative hydrophobicity, which is described by its octanol-water partition coefﬁcient
(Voice and Weber, 1983). This is because SOM fractions as a separate sorbing phase,
with a capacity that normally overshadows that of mineral soil core. Sorption to the
mineral surface is typically thought of a surface phenomenon, and is non-linear and
competitive, while the sorption of chemicals into organic matter is known to follow a
partitioning that is a linear and a noncompetitive sorption process (Chiou et al., 1979).
The distribution coefﬁcient of a nonpolar organic compound is proportional to the
amount of SOM in the soil, because the amount of adsorption on mineral surface is
negligible compared to the amount of partitioning into SOM.

Aggregated clay has different sorption characteristics from the mineral surface
and SOM. Geerdink et a1 (1996) evaluated sorption onto three soil fractions: sand
(massive silicate particles), organic matter, and clay (agglomerates of clay platelets).
Sorption and desorption of the contaminants was fast in sand. Sorption into organic
matter was faster than into clay. The clay fraction exhibited extremely low desorption

rate for nonpolar chemicals; about 1000 times lower than the organic matter fraction.

Dissolved organic matter (DOM)

DOM enhances water solubility of organic pollutants and decreases the
distribution coefﬁcient of organic contaminants in soils and sediments (Chiou et al.,
1986). The effect of DOM on solubility and sorption of organic chemicals varies with the
properties of the chemicals. Plaehn et al.(1999) reported that the effect of DOM on
sorption and desorption of naphthalene to soil was not signiﬁcant. They concluded that
the negligible impact on the distribution coefficient of naphthalene resulted from being
only moderately hydrophobic. However the sorption of phenanthrene to humic acid,
which is composed of hydrophilic and hydrophobic moieties, was observed to increase
the solubility in a study by Ortega-Calvo and Saiz-Jimenez (1998). In Chiou et a1 study,
DOM strongly enhanced the solubility of DDT and 2,4,5,2’,5’-PCB which have low
water solubility (Chiou et al., 1986), while the solubility enhancement of lindane and
1,2,3-trichlorobenzene was negligible due to their highly water soluble properties. The
enhanced solubility of hydrophobic chemicals stems from partitioning of hydrophobic
chemicals into an intramolecular nonpolar organic environment provided by DOM
(Chiou et al., 1986). The main forces for binding of zenobiotic chemicals into DOM are
nonbonded forces (e. g., van del Waals) and hydrogen bonds (Schulten, 1999).

Other factors

Aging, temperature and pH are also important factors on sorption of organic
compounds. Aging increases the sorption of organic chemicals, allowing more time to
partition into the hard organic polymeric matrix and to sorb into micro-voids or
microporous minerals (e.g., zeolites) (Luthy et al., 1997). The distribution coefficient of a

hydrophobic organic contaminant typically decreases as temperature increases, because

the heat of sorption for many hydrophobic organic contaminants is negative, i.e sorption
is an exothermic reaction (Zhang et al., 1998). The enthalpy values (AHs) of sorption
become more negative as the size of the organic chemical increases. Piatt et al (1996)
have shown that AHs of pyrene was more negative than that of naphthalene, and the
distribution coefﬁcient of pyrene decreased more sensitively as temperature increased.
pH can increase or decrease sorption of ionic organic compounds due to changing the

charge of the mineral surface and ionic organic compounds (Laird and Fleming, 1999).

FACTORS AFFECTING BIOAVAILABILITY OF SORBED CHEMICALS

There has been a debate as to the occurrence of direct bioavailablility, which
means that organisms can utilize directly the sorbed chemicals (Marshman and Marshall,
1981; Subba-Rao and Alecander, 1982; Amador and Alexander, 1988; Grifﬁth and
Fletcher, 1991; Guerin and Boyd, 1992; Guerin and Boyd, 1993; Crocker et al., 1995;
Calvillo and Alexander, 1996; Guerin and Boyd, 1997; Ortega-Calvo and Saiz-Jimenez,
1998; Tang et al., 1998), and indirect bioavailability, where the sorbed chemicals can
only be utilized after desorption (Weber and Cole, 1968; Moyer et al., 1972; Steen et al.,
1980; Ogram et al., 1985; Shimp and Young, 1988; Smith et al., 1992; Weissenfels et al.,
1992; Shelton and Doherty, 1997). The bioavailability of sorbed chemicals appears to
depend on the properties of organisms used as well as the characteristics the sorbent and
experimental methodology.
Sorbent

The bioavailability of organic compounds sorbed to solids is dependent on

physical-chemical properties of the sorbents such as pore size distribution, aggregation,

hydrophobicity, and organic matter content. An inverse relationship has been suggested
between bioavailability and sediment organic carbon content, resulting from an increased
distribution coefﬁcient of organic chemical and a decrease in liquid-phase concentration.
Also, direct relationship between particle size and chemical availability has been reported
since ﬁne sediments increase the surface area available for adsorption (Knezovich et al.,
1987)

The effect of surface hydrophobicity on bioavailability was studied by Nam and
Alexander (Nam and Alexander, 1998). The mineralization of phenanthrene was rapid
and extensive in the presence of beads with hydrophilic surface such as glass and silica.
The rate of biodegradation in a bead slurry system was the same as a solids-free system,
suggesting that the bacterium readily degraded the sorbed phenanthrene to hydrophilic
surface. However, bioavailability of phenanthrene (through mineralization of
phenanthrene) was reduced by sequestration and sorption into hydrophobic sites inside
nanopores.

A few researchers have studied the relationship between organic matter content in
soils and bioavailability of partitioned contaminants. Guerin and Boyd (1997) reported
that no correlation existed between initial mineralization rate (IMR) or extent of
mineralization and soil organic matter content, while Ortega-Calvo et al (1997) reported
that the mineralization rate of phenanthrene was related to the content of organic matter
in soils; that is, the rates and extents of mineralization decreased with organic matter in
soils.

The physical characteristics of sorption sites have also been reported to affect

bioavailability of sorbed chemicals. Knaebel et al.(Knaebel et al., 1994) used dodecyl

linear alkylbenzene sulfonate (LAS), dodecyl linear alcohol ethoxylate(LAE), soditun
stearate and stearyl trimethylammonium chloride (STAC) as organic compounds, and
sand, montmorillonite, kaolinite, illite, and humics as sorbents in mineralization assays to
study bioavailability of sorbed organic compounds. The extent of bioavailability of
organic compounds sorbed on sand, kaolonite and illite was higher than that on
montmorillonite and humics. It is reported that this is because sand has exclusively
external binding sites, and kaolinite and illite are nonswelling clays whose binding
surface are primarily external. In contrast, montmorillonite is an expandable clay and
80% of the binding surfaces are interstitial. Externally sorbed organic compounds were
reported to be more accessible to organisms. In addition, nonporous sorbent provides
more enhanced bioavailability than sorbent with microporous.

The effects of humic acid (HA), “bare minerals (sematite and montmorillonite)”
(mineral with no HA coating) and mineral-HA complex on bioavailability of
phenanthrene were addressed by Laor et al (Laor et al., 1999). They used mixed culture
(dominated by Pseudomonas sp.) that was enriched from a coal tar contaminated soil. HA
did not enhance nor inhibit initial phenanthrene mineralization; that is, the ratios of initial
mineralization rate (IMR) in the presence of dissolved HA to that of the control were
close to one. While the mineral-HA complexes always stimulated the initial
mineralization of phenanthrene, resulting in a higher value than one for the ratios of IMR
in the presence of the mineral-HA complexes to that of the control. However, in the
presence of bare materials, IMR was either stimulated or inhibited. In conclusion, their
study suggested some important information on bioavailability. There was no evidence

that HA stimulated the mineralization by providing an addition carbon source to bacteria.

IMR value increased with sorbed phenanthrene to bare mineral and mineral-HA complex,
suggesting sorption of bacteria to phenanthrene-enriched surfaces might enhance IMR.
DOM

The DOM effect on bioavailability is more complex, depending on substrate,
characteristic and concentration of the DOM. The effect of DOM or HA on the
mineralization rate of naphthalene has been reported to be negligible (Meredith and
Radosevich, 1998; Plaehn et al., 1999), while humic acid signiﬁcantly increased the
mineralization rate of benzoic acid and phenylactic acid at low concentration (Amador
and Alexander, 1988). Ortega-Calvo and Saiz-Jimenez (Ortega-Calvo and Saiz-Jimenez,
1998) insisted that phenanthrene mineralization is enhanced, the extent of mineralization
is increased, and the acclimation period is shortened by the presence of dissolved humic
acids and humic acid-clay complex. Hatzinger and Alexander (Hatzinger and Alexander,
1995) also showed that the mineralization rate of phenanthrene was rapid in presence of
DOM. On the other hand, Laor et a1 (1999) reported that the IMR of phenanthrene was
not affected by the presence of HA. Haitzer et al (Haitzer et al., 1998) claimed that DOM
can decrease the bioavailability of organic chemicals in aquatic systems due to binding of
organic chemicals into DOM. Knaebel et al. reported that the initial mineralization of the
chemicals presorbed to humics much lower than those of clay complexes, because
binding between organic chemicals and humics is hydrophobic, and includes some
covalent interactions, while binding with clays involves ionic or hydrogen bonding
(Knaebel et al., 1994). The strong binding between organic chemicals and organic matter
would result in longer persistence of organic chemicals preventing utilization by

organisms.

The enhanced bioavailability of organic chemicals in the presence of
DOM can be explained by the sorption of chemicals into DOM; that is, the sorption into
DOM increases the chemicals’ concentration in the vicinity of the bacterial cells that can
physically attach to the DOM (Ortega-Calvo and Saiz-Jimenez, 1998). Inhibition in
presence of DOM results from decreased concentration of organic chemicals in aquatic
system due to binding of chemicals into DOM (Haitzer et al., 1998). Further research is
needed to clarify the relationship between DOM and bioavailability of organic chemicals
resulting from sorption, aging, DOM structure, organism characteristic, and organic
chemical property effects.
Substrate

The enhanced bioavailability of sorbed chemicals mainly happens in high
molecular substrate (Knezovich et al., 1987). For example, Grifﬁth and Fletcher reported
that strongly sorbed bovine serum albumin (BSA; molecular weight, ca. 68,000) was
rarely available to suspended cells, but was rapidly hydrolyzed by attached cells due to
excreted enzymes. And non-sorbed methyl-coumarinyl-amide—leucine (MCA-leucine;
molecular weight, ca. 385) was hydrolyzed faster by suspended than attached
cells(Grifﬁth and Fletcher, 1991). They suggested that when the sorption extent of a
substrate was low, it was easily accessible to suspended bacteria, in contrast, when the
sorption extent of a substrate was high it was more available to surface-attached bacteria
(Knezovich et al., 1987). The limit of molecular weight was not clearly deﬁned.
Aging
The aging effect on bioavailability of sorbed contaminant in soil, sediment and

aquifer materials is poorly understood and published articles on it are rare. It is reported

12

that the bioavailability of organic chemicals decreases with aging, the contact time
between organic chemicals and solid. For example, the bioavailability of chemicals such
as phenanthrene, atrazine (Chung and Alexander, 1998), 4-nitrophenol (Hatzinger and
Alexander, 1995), 1,2-dibromoethane (Steinberg et al., 1987), simazine (Scribner et al.,
1992) and native herbicide (Pignatello and Huang, 1991) declined over aging times.

If bioavailability can be predicted by physicochemical properties of sorbed
chemicals, the reasonable endpoint of bioremediation would be estimated by
physicochemical properties of sorbed chemicals. A clear correlation has not been
reported between bioavailability and physicochemical properties of sorbed chemicals.
Pignatello and Huang (Pignatello and Huang, 1991) found that the nonequilibrium site
sorption fraction of atrazine and metolachor increased and the bioavailability decreased
with aging time in ﬁeld. Hatzinger and Alexander reported that both bioavailability and
extractability of phenanthrene and 4-nitrophenol added to soil decreased with aging time
(Hatzinger and Alexander, 1995). However, Chung and Alexander found it was difﬁcult
to generalize the relationship between the bioavailability and the extent of extraction of
aged phenanthrene and atrazine sorbed to soil(Chung and Alexander, 1998). Carmichael
et al. reported that desorption rates of phenanthrene and chrysene freshly sorbed to soil
were much faster than the measured mineralization rates; however the desorption rates of
aged phenanthrene and chrysene in contaminated soil were equal to or slower than
mineralization rates (Carmichael et al., 1997). More research is required to evaluate the
relationship between bioavailability and physicochemical properties of sorbed organic

chemicals with and without aging.

l3

Contaminant aging effects with organic matter and humic materials have not been
extensively studied. One published study claimed that organic matter and humin aging
effect on mineralization of phenanthrene was not signiﬁcant compared to the soil aging
effect (Hatzinger and Alexander, 1995).

Organisms

Characteristics of organisms are an important factor on determining
bioavailability of sorbed chemicals. It is known that organisms, which secret
extracellular enzymes, can utilize the sorbed chemicals (Griffith and Fletcher, 1991). It is
also reported that some sorbed bacteria without extracellular enzymes have access to the
sorbed chemicals and are able to utilize sorbed chemicals (Guerin and Boyd, 1992;
Harms and Zehnder, 1995).

Kefford et a1. (1982) used three bacteria; Leptospira biﬂexa patoc 1, which
adheres reversibly, pigmented Serratia marcescens EF190, which adheres irreversibly,
and a non-pigmented hydrophilic mutant of EF 190, to study the effect of bacterial
adhesion on utilization of stearic acid coated on a glass surface (Kefford et al., 1982). The
hydrophobic pigmented Serratia strain had greater scavenging ability in removing sorbed
fatty acid than the hydrophilic non-pigmented mutant. They suggested that the strong
association of the hydrophobic strain with the solid allowed closer interaction to sorbed
stearic acid. Serratia initially showed a faster rate of removal, but the overall rate was
considerably slower than that of the Leptospira. It was suggested that Leptospira could
readily move to other surface areas as substrate was depleted in its vicinity, while Serratia
could not move to other surface site after depleting substrate near its locality due to its

strong attachment.

l4

Guerin and Boyd found that sorbed naphthalene was directly available to
Pseudomonas putida strain 17484, while it was not directly available to Alcaligenes sp.
Strain NP-Alk, suggesting bioavailability of sorbed contaminants is organism-speciﬁc
(Guerin and Boyd, 1992). They also suggested that an organism attached to the soil more
efﬁciently utilized sorbed naphthalene than a nonattaching strain (Guerin and Boyd,
1992). Tang et al (1998) reported that bacteria cultured on sorbed phenanthrene on a
solid easily mineralized the compound sorbed to polyacrylic beads or sediments, while
bacteria cultured on non-sorbed phenanthrene were not able to utilize sorbed
phenanthrene to the beads (Tang et al., 1998). They suggested that the microorganisms
grown on nonsorbed compounds might be inappropriate for evaluation of biodegradation
and bioremediation of sorbed contaminants. Calvillo and Alexander (1996) reported that
a microbial consortium mineralized biphenyl sorbed to polyacrylic beads, but pure
cultures of bacteria isolated from the consortium were not able to mineralize the sorbed
compounds, suggesting that cells from consortium were able to attach to the beads and
the attachment of cells was an important factor in mineralization of sorbed biphenyl
(Calvillo and Alexander, 1996). Harms and Zehnder showed that the rate of utilization
by Sphingomonas sp. of 3-dichlorodibenzofuran sorbed to porous Teﬂon was inﬂuenced
by the tendency of the bacterium to adhere to the sorbent, indicating bacteria adhesion is
an important factor in degradation of sorbed chemicals(Harms and Zehnder, 1995). In
aquatic environment, epibenthic and infaunal organisms may more accumulate sorbed
pollutants by direct contact than pelagic and epifaunal organisms (Knezovich et al.,
1987). The attachment of bacteria to sorbent appears to play a signiﬁcant role on the

bioavailability of sorbed chemicals.

However, no mechanisms on the direct uptake have been suggested and the
speciﬁc characteristics of the organisms such as cell surface structure, biodegradation

kinetics, morphology, and mobility at surface have not been explicitly studied.

CONCLUSION

The amount of sorption of organic chemicals into soil is dependent on properties
of the sorbent, characteristics of dissolved organic matter, aging, temperature, pH, and
the properties of sorbate. Generally, sorption decreases the bioavailability of organic
chemicals due to decreased liquid phase concentration of the chemicals. However, the
enhanced bioavailability of organic chemicals in the presence of the sorbent has been
reported. The bioavailability of sorbed chemicals is complex, depending on properties of
sorbent, DOM, and organism, and aging time. Surface sorption of organic chemicals and
the attachment of organisms have a positive correlation with the bioavailability of sorbed
chemicals, but mechanisms have not been clearly delaminated. More investigation is
needed to evaluate the relationship between bioavailability, the physicochemical

properties of sorbents and sorbed chemicals, and the characteristics of organisms.

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19

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CHAPTER 3. KINETIC MODELING OF BIOAVAILABILITY FOR SORBED-

PHASE 2,4-DICHLOROPHENOXYACETIC ACID

ABSTRACT

The degradation rate of 2,4-dichlorophenoxyacetic acid (2,4-D) was studied in
silica-slurry systems to evaluate the bioavailability sorbed-phase contaminant. After the
silica particles were saturated with 2,4-D, the system was inoculated with the 2,4-D
degrading microorganism F lavorbacterium sp. strain FB4. The disappearance rate of
2,4-D was measured and found to be greater than the rate predicted based upon liquid-
phase 2,4-D concentrations. A kinetic formulation, termed the enhanced bioavailability
model, was developed to describe the desorption and biodegradation processes in this
batch system. The approach assumes that 2,4-D resides in both the liquid and solid phases
and degradation occurs via both suspended and attached biomass. All biomass can
degrade liquid-phase 2,4-D at one rate, while only attached biomass can degrade sorbed
2,4-D at another rate. An enhanced transformation factor (Ef) was introduced to express
the increased biodegradation rate over that expected from the liquid phase only. This
approach was able to account for the increased degradation rates observed
experimentally. The results provide evidence that desorption to the bulk solution is not

prerequisite to degradation, and that sorbed substrate may be available for degradation.

2|

INTRODUCTION

Bioavailability of sorbed contaminants in soils and sediments affects the clean-up
time, cost, and end-point of bioremediation processes. Published results suggest that
bioavailability varies with the sorbent (Geerdink et al., 1996), substrate (Grifﬁth and
Fletcher, 1991) and organisms (Guerin and Boyd, 1992). Rigorous interpretation of such
results requires formulation of a conceptual model describing the component processes,
conducting experiments to allow quantiﬁcation of individual process rates, and
mathematical analysis of bioavailability data to evaluate the validity of the model
formulation. Potential interactions between processes must also be assessed. One critical
issue that arises in this approach is whether desorption is prerequisite to biodegradation,
or expressed alternatively, whether direct sorbed-phase degradation can occur. Some
researchers have reported that sorbed contaminant is not directly available to attached or
suspended cells (Steen et al., 1980; Ogram et al., 1985; Shimp and Young, 1988; Smith et
al., 1992; Weissenfels etal., 1992; Shelton and Doherty, 1997), while others concluded
that sorbed substrate can be directly utilized by attached cells (Guerin and Boyd, 1992;
Guerin and Boyd, 1993; Crocker et al., 1995; Calvillo and Alexander, 1996; Guerin and
Boyd, 1997; Ortega-Calvo and Saiz-Jimenez, 1998; Tang et al., 1998; Lahlou and J .J .,
' 1999; Laor et al., 1999; F eng et al., 2000 ). While these conﬂicting interpretations may
reﬂect differences related to the characteristics of the sorbents, substrates and organisms
employed, they may also result from inadequate experimental and mathematical
resolution of individual processes and the factors affecting them. For example, none of
the above studies reporting direct sorbed-phase degradation fully described desorption

kinetics, resulting in uncertainty in the substrate concentration driving degradation.

22

The purpose of this study was to develop an experimental and mathematical
approach to evaluate bioavailability, and speciﬁcally whether direct sorbed-phase
degradation occurs and at what rate if it does. To do this, desorption rates were measured
in the absence of organisms and biodegradation rates were measured in the absence of
solids. Combined desorption/biodegradation experiments were then performed, and the
results were analyzed using a mathematical model incorporating three processes:
desorption, liquid-phase degradation and sorbed-phase degradation. Using this approach,
we were able to determine whether a sorbed-phase degradation mechanism is consistent
with the bioavailability data and the rate of this process. Experimentally, we used 2,4-
dichlorophenoxyacetic acid (2,4-D) as the sorbate and substrate, silica as the sorbent, and
a 2,4-D degrading organism, Flavobacterium sp. F B4. The aquatic and systematic
herbicide 2,4-D is widely used on wheat, corn and sorghum to control broad-leaf weeds

and is a potential pollutant of groundwater (Sannino et al., 1997).

MATERIALS AND METHODS
The F lavobacterium sp. strain FB4 used in this study is a proteobacterium that is
able to internally metabolize 2,4-D as a sole carbon and energy source (Ogram et al.,
1985). Cells were grown until early stationary phase in minimal salts media ([MSM]
1419.6 mg NaZHPO4, 1360.9 mg KHZPO4, 0.3 mg (NH4)ZSO4, 50 mg MgSO4 7H20, 5.88
mg CaCl2 2HZO, 3.2 mg EDTA disodium salt, 2.78 mg FeSO4 7H20, 1.15 mg ZnSO4

7H20, 1.69 mg Mnso4 H20, 0.375 mg Cuso4 snzo, 0.233 mg Co(NO 6H20 and

92

0.1236 mg (NH 4)6Mo702 4 4H20 per liter of distilled water) that contained 400 ppm 2,4-

23

D. Cells to be used as inoculum were rinsed once and resuspended in phosphate buffered

saline (PBS).
Sterile stock solutions of radio-labeled 2,4-D (Sigma, 296 pure, 12.8 mCi mmol‘
l) were prepared in PBS. The solutions contained approximately 200,000 dpm ml.l ring-

U-[MC] 2,4-D with a ﬁnal concentration of 10 mg L4. The solutions were ﬁlter sterilized
and stored in ﬁve 100 ml portions in light shielded bottles at 4°C.

Uncoated silica was obtained from the J .T. Baker company. The irregular-shaped
particles have a size of 40 pm with 60-angstrom pores. To create the silica slurries, 2.5 g
silica was weighed into sterile a 10 ml Falcon® tube and 9 ml PBS were carefully layered
on top of the silica. The tubes were placed in a Speed Vac® for one minute. A
disposable sterile loop was used to re-suspend the silica. The silica was rinsed with 200
ml of PBS in 10 ml aliquots in a 25 ml glass column with a glass frit. After rinsing, the
silica was transferred to a 20 ml serum vial. The solution from the last rinse was used as
the supernatant control.

Sorption kinetics were evaluated to determine the silica-liquid contact time
necessary to reach apparent steady state in batch experiments. PBS and stock solutions
containing l4C-labeled 2,4-D were added to serum vials containing silica to achieve the
appropriate slurry density and contaminant concentration. The vials were placed on a
platform rocker and mixed well enough to prevent the silica from settling. The slurries
were sampled at prearranged time intervals and the samples were immediately
centrifuged at 11750 relative centrifuge force in a centrifuge ﬁlter vial to separate the
silica from the solution. After centrifugation the ﬁlter cup was separated from the vial

and the cap was cut off. The ﬁlter cup and vial were dropped into separate scintillation

24

vials containing 10 ml of scintillation cocktail ﬂuid. Activity was counted on a liquid
scintillation analyzer.

Desorption kinetics were measured by ﬁrst equilibrating silica and 2,4-D, diluting
the supernatant with 2,4-D-free PBS, and resuspending the particles. Liquid-phase and
sorbed-phase concentrations were measured over time as described above.

Biodegradation in the presence or absence of the sorbent was measured in similar
batch systems. After the system reached sorption steady state, it was sampled as
previously described to determine the sorbed and liquid-phase contaminant

concentrations. The slurry was then inoculated with a pure bacterial culture (FB4) at a

cell density of approximately 1x108 CFU ml”. Depletion of liquid phase 2,4-D was
monitored over time. Cell-free control vials were also prepared and monitored to examine
abiotic losses in the batch system. The liquid-phase degradation rate for 2,4-D was also
determined in the supernatant control solutions.

In the proposed model, shown schematically in Figure 3-1, the contaminant can
reside in two phases, solid and liquid, and degradation occurs via both suspended and
attached biomass. Distribution of 2,4-D between the two phases is described by an
equilibrium distribution coefﬁcient, as the kinetic experiments indicated that this process
is rapid and reversible. All of the biomass in the system can degrade liquid-phase
contaminant at one rate, while only an attached fraction degrades sorbed-phase
contaminant at another rate.

The linear sorption distribution coefﬁcient (Kd) decribes the relationship between

the sorbed-phase (Se) and liquid-phase (Ce) concentrations at equilibrium.

_5.

Kd——
Ce

Eq. 1

25

 

' Attached Biomass (fs)

 

       

 

 

 

 

‘ ,. Solid(S) "_ ~ .

 

 

2,4-D source

 

 

 

 

 

 

 

 

 

 

 

Suspended biomass (ﬂ)

 

 

 

Figure 3- 1. Depicted model for disappearance of 2,4-D in slurry systems. Where, C
and S are sorbed and liquid phase concentration, respectively. fs is attached cell
fraction. Kd is distribution coefﬁcient. k] and ks are liquid and sorbed phase

degradation rate coefﬁcients, respectively.

26

The overall mass balance equation of contaminant in the batch system is

expressed as:

dC dS
— V°—+m'— =VMkIC+m sks'S E . 2
(l d! dt) I f q

where C is the liquid-phase concentration of contaminant (pg L”); t is time (min); k1 is
the ﬁrst-order liquid-phase degradation rate coefﬁcient (min'l) determined from the
biodegradation assay; f, is the fraction of attached biomass in the system (unitless)
determined by plate-count techniques; k, is the ﬁrst-order sorbed-phase degradation rate
coefﬁcient (min'l); m is the sorbent mass (g), and V, is liquid volume (ml).

Under the sorption/desorption equilibrium assumption, the rate of change in the

sorbed phase can be calculated from the change in liquid phase concentration:

9:52:1(4112 W "7 Eq. 3
dt dt

Rearranging Eqs.2 and 3, the liquid—phase contaminant disappearance rate can be

expressed as:

dC
———=E-B-k1-C 13.4
dt 1 r q

where, EfiS the enhanced transformation factor (unitless),
El=1+RsI'Kd'fi'ks/kl Eq. 5
Bf is the bioavailability factor (Zhang et al., 1998),

1

B = ————- E . 6
’ 1+ 1a., - K. q
and R,, is solid to liquid ratio (g ml'l).
Ru = m/V: Eq. 7

Integrating Eq. 4, the liquid-phase contaminant concentration is expressed as

27

—E-B-k1-t
C=Co-e f ’ Eq.8

where C, is the initial liquid-phase concentration. We designated this approach as the
Enhanced Bioavailability (EB) model.

The solid/liquid distribution coefﬁcient, K4, was determined from the sorption
isotherm experiments. The liquid-phase degradation rate coefﬁcient, k1, was determined
from the silica-free biodegradation experiment using Eq. 4 when Ef and Bf are set to one.
The fraction of attached biomass, f5, was determined by comparison of the cell plate
counts from the initial inoculant and the liquid phase of the silica slurry. The ratio of
solid to liquid, Rs], was calculated from the silica weight and liquid volume. The
bioavailability factor, Bf, was calculated from Eq. 6. The enhanced transformation factor,
Ef, was determined from the change in liquid-phase concentration of contaminant in the
bioavailability assay using Eq. 8. The sorbed-phase degradation rate coefﬁcient, k,, was
calculated after E] was determined using Eq. 5.

If there is no sorbed-phase degradation by the attached biomass, k, is zero and Ef
is one. The equation reduces to the bioavailability equation (Bf model), that assumes
sorption/desorption equilibrium and only liquid-phase degradation (Zhang et al., 1998).
Bf varies between zero and one, with a value of one corresponding to a system with no
sorbent. If Efis equal to one, the microorganisms are only able to degrade contaminant in
liquid phase. Values of E] greater than one indicate a 2,4-D biodegradation rate faster
than that expected based on liquid-phase concentrations, whereas values less than one

indicate slower rates. In the later case, the value of k, will be negative.

28

RESULTS

The 2,4-D sorption isotherm was linear over the concentration range
employed in this study. Steady-state conditions were reached within 5 minutes and the
sorption distribution coefﬁcient (Kd) was 2.5 (i 1.0) ml g'l. Desorption was also rapid,
with the system reaching the value predicted by the sorption Kd by the ﬁrst time point at
30 seconds (Figure 3-2). Three consecutive dilution desorption experiments were
performed and no desorption hysteresis was observed. It was therefore concluded that
sorption/ desorption could be described as completely reversible and instantaneous for
this study.

Two sets of experiments were completed to evaluate the bioavailability of sorbed
2,4-D in slurry systems. In the ﬁrst set, the ratio of silica to liquid, R5,, was varied while
the amounts of 2,4-D were held constant. In the second set of experiments the initial 2,4-
D amount was varied while R3, was held constant. Since the experiments were performed
using cells harvested from independent cultures, cell activity varied slightly. Bio-kinetic
parameters were evaluated from silica-free controls and normalized for each set of
experiments.

Four silica to liquid ratios, 0.12, 0.19, 0.29, and 0.43 g ml", were used in the ﬁrst
experiment (Table 3-1). The amount of 2,4-D and volume of solution remained constant.
As a result, the initial liquid-phase 2,4-D concentrations were between 750 and 360 pg L'
l for the four ratios. An example of the concentration data and prediction by the Bf model
are shown in Figure 3-3. The Bf model consistently over-predicted liquid-phase
concentrations (i.e. under-predicted 2,4-D depletion), suggesting the need for an

additional or enhanced rate process. The enhanced transformation factor (Ef) was

29

determined from the 2,4-D concentration-time proﬁle using Eq. 8. It can be seen that the
added degradation described by this term results in a ﬁt consistent with the data. The
enhanced transformation factors (Ef) were greater than 1 for all silica to liquid ratios and

increased linearly with this ratio (Figure 3-4).

30

 

 

 

 

0 2 4 6 8 10 12 14 16 18

.5.“
-'— 0.60
E
3“ 0 55
E . I'M-L L—e— 9 4 o
=1
c. 0.50
: 0
5 0.45
a l
.2 E
El 0.40 g
h . i
E 9 Experiment data !
°
g 0.3 5 —. Expected line }
8 l
0.30 . 1
Q. ,
V;
N

Desorption time (min)

Figure 3- 2. 2,4-D desorption from silica following a single dilution of the solution

phase. Initial liquid concentration was 483 pg L”. The distribution coefﬁcient (K4) was

2.5(i1.0). Time points represent single measurements.

31

Table 3- 1. Analysis of experimental results for a range of Rs. (0.12 - 0.43) at 1.2 x107

 

 

 

 

 

 

 

CFU ml"
R. C.“ C.b k.“ Br k. E.
g 1111-" pg mL'l ug mL'1 min'1 - min'l -
0 0.92 0.92 0.0060
0.12 1.00 0.75 0.75 0.030 1.51
0.19 1.00 0.63 0.63 0.032 1.89
0.29 1.00 0.49 0.49 0.024 2.10
0.43 1.00 0.36 0.36 0.023 2.68

 

1 Initial solution concentration before adding silica
b initial concentration in liquid phase for degradation reaction.
c measured ﬁrst order degradation coefﬁcient in liquid phase

32

 

    
 

 

 

 

A 650 , ,
'8. r-
e ~~~~~~~~~~~~~
a 600 .........
.9 .........
5 550 ..............
G ~~~~~~~~
.9.
4..:
g 500
8 41- exp. data
9::
8 450 _ ------- Bf model

— EB model
i
N“ 400 . . . 1

0 10 20 30 40 50

Incubation time (min)

Figure 3- 3. 2,4-D liquid-phase concentration versus incubation time. The two depicted

lines were plotted using following values of Rs: (0.19), k, (0.0058), k, (0.032), and f,

(0.28). Inoculum was 1.2 x 107 CPU ml".

33

4.0 . .

 

3.5 _ 0 exp. data
—— Estimated

3.0 r

2.5 -

2.0 _ O

1.5 —

 

 

1.0 , ‘
0 0.2 0.4 0.6

 

Enhanced transformation factor(Ef)
0

Ratio of silica to liquid (g ml‘)

Figure 3- 4. Enhanced transformation factor versus the ratio of silica to liquid.

Estimated line was plotted using average values of k,(0.0060), k,(0.027), and f,(0.28).

Inoculum was 1.2 x 107 CPU ml”.

34

In the second experiment, four different 2,4-D amounts were used with a constant
silica to liquid ratio (R,, = 0.25) (Table3-2). This resulted in initial concentrations
between 70 to 550 pg L". Again, the Bf model signiﬁcantly underestimated initial 2,4-D
depletion rates while the EB model consistently ﬁt the results (Figure 3-5). The average

value of the enhanced transformation factor (Ef) was 1.70 i 0.15.

35

Table 3- 2. Analysis of experimental results for different initial concentrations and same

R,. (0.25) at 5.6 x106 CFU mrl

 

 

 

 

 

 

 

R.. C.“ C.b k.c Br k. Er
g mL'l pg mL" pg mL" min'1 - min'l -
0 0.93 0.93 0.0045
0.25 0.13 0.07 0.53 0.010 1.54
0.25 0.38 0.21 0.57 0.013 1.60
0.25 0.63 0.35 0.56 0.017 1.86
0.25 1.00 0.55 0.55 0.016 1.79

 

7‘ Initial solution concentration before adding silica
b initial concentration in liquid phase for degradation reaction.
° measured ﬁrst order degradation coefﬁcient in liquid phase

36

 

 

 

 

Q1 3.0 .
V., A — EB model
N - 2.5
6.5 g: ------- Bfmodel
3 E 2 0 0 exp. data
as .9 °
23 £3 0 »
.2 1.5 - ,,,,,,,
a a .........

1.0 ~ ,. '
3.9: .......
'O —* ''''''''
75 .E 0.5 - ,,,,,,,,
IE """"
s: .
H 0.0 ' l l A

0 200 400 600 800

Initial concentration of 2,4-D in liquid (ppb)

Figure 3- 5. Initial depletion rate versus initial equilibrium concentration at a constant

silica to liquid ratio. Inoculum was 5.5 x 106 CPU ml". Values used for model; k1

(0.0045); k, (0.014);};(028).

37

DISCUSSION

The proposed approach for assessing bioavailability deﬁnes a combination of
experimental and mathematical tools that can be used to describe biodegradation in the
presence of a sorbent. We have accounted for the dependence and the effect of both
desorption and biodegradation on liquid-phase concentration. By doing this we are able
to evaluate whether desorption and degradation are sequential processes, with
degradation dependent only on liquid-phase concentration. In our experimental system,
this was not the case. In all experiments degradation proceeded at a rate faster than
would be expected based on the liquid concentration alone.

The EB model presented in this study was developed using formulations for
reversible and instantaneous sorption/desorption processes and ﬁrst-order biodegradation
reactions in both liquid and solid phases, and the assumption that the liquid-phase
degradation rate coefﬁcient is not affected by the presence of solids. Since desorption
can never be truly instantaneous, the validity of this formulation rests on whether it is
signiﬁcantly faster than degradation. As shown in Figure 3-6, if the ratio of the
desorption rate coefﬁcient to the degradation rate coefﬁcient is larger than 10, the
concentration error would be less than 2 %. A desorption rate coefﬁcient, or (min'l), was

estimated using the following equation

d—S=—a-(S—Kd-C) Eq.9
dt

and found to be at least 30 min'I for the data shown in Figure 3-2. Because the liquid-
phase degradation rate coefﬁcients were less than 0.006 min", the rate ratio was greater

than 1000. Thus, the error is expected to be insigniﬁcant.

38

In order to describe the enhanced 2,4-D disappearance rate in the silica slurry
system, the enhanced transformation factor (Ef) was introduced to the previously
published Bf model. When the E] value is higher than 1, as in all cases from this study,
degradation is faster than expected based on liquid-phase concentrations. One possible
mechanistic interpretation of EfiS an enhanced system degradation rate resulting from
attached biomass accessing adjacent elevated concentrations of contaminant prior to
complete dilution in the liquid phase. This has been previously termed “direct sorbed-
phase utilization” (Guerin and Boyd, 1992; Guerin and Boyd, 1993). Another possibility
is an increase in bacterial metabolic rates resulting from the presence of the sorbent.
When considering the practical implication, the important conclusion is that bacterial
degradation in the presence of a sorbent may be faster than described by the independent

processes of desorption and liquid-phase degradation.

39

 

120 . . .

 

 

 

 

 

100 __;t:.::::
80 __ .........................................................
f; 60 ........ ,
:3
84 —-— (X/kl—l ()0
91$ 40 ........ oc/k,=10
U - ......... a/kl =1
20
............... a/kl =0 1
0 - . .
O 100 200 300

Incubation time (min)

Figure 3- 6. The ratio effect of desorption rate coefﬁcient to degradation rate coefﬁcient
on liquid-phase concentration over incubation time. C94 is calculated using EB model
with Ef value of one. C; is calculated using Eqs. 2 (overall mass balance equation with
k, value of zero) and 9 (ﬁrst-order desorption-rate equation). Kd value is assumed 2.5.

11,, is 0.1. k, is 0.006 min".

40

ACKNOWLEDGEMENTS
This work was supported, in part, by the Center for Microbial Ecology (National Science
Foundation, Grant DEB9120006), and by the Great Lakes and Mid-Atlantic Center for
Hazardous Substance Research (Environmental Protection Agency, Michigan

Department of Environmental Quality).

REFERENCES

Calvillo, Y. M. and M. Alexander. 1996. “Mechanism of microbial utilization of
biphenyl sorbed to polyacrylic beads.” Appl. Microbiol. Biotechnol 45: 383-390.

Cracker, F. H., W. F. Guerin and S. A. Boyd. 1995. “Bioavailability of Naphthalene
Sorbed to Cationic Surfactant-Modiﬁed Smectite Clay.” Environ. Sci. Techno] 29: 2953-
2958.

Feng, Y., J. H. Park, T. C. Voice and S. A. Boyd. 2000. “Bioavailability of Soil-Sorbed
Biphenyl to Bacteria.” Environ. Sci. Techno]. Accepted.

Geerdink, M. J., M. C. M. van Loosdercht and K. C. A. M. Luyben. 1996. “Model
for Microbial Degradation of Nonpolar Organic Contaminants in a Soil Slurry Resctor.”
Environ. Sci. T echnol 30: 779-786.

Grifﬁth, P. C. and M. Fletcher. 1991. “Hydrolysis of Protein and Model Dipeptide
Substrates by Attached and Nonattached Marine Pseudomonas sp. Strain NCIMB 2021 .”
App]. Environ. Microbiol 57: 21 86-2191 .

Guerin, W. F. and S. A. Boyd. 1992. “Differential Bioavailability of Soil-Sorbed
Naphthalene to Two Bacterial Species.” App]. Environ. Microbio] 58: 1142-1152.

Guerin, W. F. and S. A. Boyd. 1993. “Bioavailability of Sorbed Naphthalene to
Bacteria: Inﬂuence of Contaminant Aging and Soil Organic Carbon Content.” Sorption
and Degradation of Pesticides and Organic Chemicals in Soil. SSSA Special Publication
no 32: 197-208.

Guerin, W. F. and S. A. Boyd. 1997. “Bioavailability of naphthalene associated with
natural and synthetic sorbents.” Water Res. 32: 1504-1512.

Lahlou, M. and O.-C. J.J. 1999. “Bioavailability of labile and desorption-resistant
phenanthrene sorbed to montmorillonite clay containing humic fractions.” Environ.
Toxicol. Chem. 18: 2729-2735.

41

Laor, Y., P. F. Strom and W. J. Farmer. 1999. “Bioavailability of Phenanthrene
Sorbed to Mineral-Associated Humic Acid.” Wat. Res. 33: 1719-1729.

Ogram, A. V., R. E. Jessup, L. T. Ou and P. S. C. Rao. 1985. “Effects of Sorption n
Biological Degradation Rates of (2,4-Dichlorophenoxy)acetic Acid in Soils.” Appl.
Environ. Microbiol 49: 582-587.

Ortega-Calvo, J. J. and C. Saiz-Jimenez. 1998. “Effect of humic fractions and clay on
biodegradation of phenanthrene by a Pseudomonas ﬂuorescens strain isolated from soil.”
Appl. Environ. Microbiol. 64: 3123-3126.

Sannino, F., A. Violante and L. Gianfreda. 1997. “Adsorption-Desorption of 2,4-D by
Hydroxy Aluminium Montmorillonite Complexes.” Pestic. Sci 51: 429-435.

Shelton, D. R. and M. A. Doherty. 1997. “Estimating Losses of Efﬁcacy Due to
Pesticide Biodegradation in Soil: Model Simulations.” Soil Sci. Soc. Am. J 61: 1085—
1090.

Shimp, R. J. and R. L. Young. 1988. “Availability of organic chemicals for
biodegradation in settled bottom sediments.” Ecotoxicol Environ Saf 1 5: 31-45.

Smith, S. C., C. C. Ainsworth, S. J. Traina and R. J. Hicks. 1992. “Effect of sorption
on the biodegradation of quinoline.” Soil Sci. Soc. Am. J. 56: 737-746.

Steen, W. C., D. F. Paris and G. L. Baughman. 1980. “Effects of Sediment Sorption on
Microbial Degradation of Toxic Substances.” Contaminants and Sediments. Vol. 1. Ann
Arbor Sci. Pub], Ann Arbor, MI. 1: 477-482.

Tang, W. C., J. C. White and M. Alexander. 1998. “Utilization of sorbed compounds
by microorganisms speciﬁcally isolated for that purpose.” Appl. Microbiol. Biotechnol.
49: 117-121.

Weissenfels, W. D., H. J. Klewer and J. Langhoff. 1992. “Adsorption of Polycycleic
Aromatic-Hydrocarbons (PAHS) by soil particles- Inﬂuence ofn Biodegradatility and
Biotoxicity.” Appl. Microbio]. Biotechnol. 36: 689-696.

Zhang, W. X., E. J. Bouwer and W. P. Ball. 1998. “Bioavailability of hydrophobic
organic contaminants-effects and implications of sorption-related mass-transfer on
bioremediation.” Ground Water Monit. R. 18: 126-138.

42

CHAPTER 4. DEVELOPMENT OF A KINETIC BASIS FOR

BIOAVAILABILITY OF SORBED NAPHTHALENE IN SOIL SLURRIES

ABSTRACT

The degradation of naphthalene in soil-slurry systems was studied using four
different organisms and two soils. Organims with zero-order, ﬁrst-order, and Michaelis-
Menten rates were selected. The soils had substantially different sorption distribution
coefﬁcients. Sorption and desorption was evaluated in abiotic soil-slurry systems. The
desorption process was described by a model that accounts for equilibrium, rate-limited
and non-desorbing sites. Biodegradation parametes were measured in soil-extract
solutions. Bioavailability assays, inoculated soil-slurries, were conducted and both
liquid- and solid-phase naphthalene concentrations were measured over time. For the
less sorptive soil, the results could be explained by sequential desorption and degradation
processes. For the other soil, enhanced degradation was clearly observed for the
organisms with ﬁrst-order and Michaelis-Menten rates. Several explanations are
explored to explain these observations including the development of elevated substrate
concentrations at the organism/sorbent interface. No enhancement was found for the

organism with zero-order kinetics.

Key words- naphthalene, sorption, desorption, bioavailability, soil, model,

biodegradation, kinetics.

43

INTRODUCTION

Bioavailability of organic contaminants has been identiﬁed as a major limitation
to complete bioremediation of contaminated soils affecting clean-up time, cost, and the
end-point of the process (Head, 1998). The importance of bioavailability is evidenced by
numerous recent studies on this topic (Guerin and Boyd, 1992; Guerin and Boyd, 1993;
Crocker et al., 1995; Calvillo and Alexander, 1996; Guerin and Boyd, 1997; Ortega-
Calvo and Saiz-Jimenez, 1998; Tang et al., 1998; Laor et al., 1999; Park et al., 1999;
Feng et al., 2000; Park et al., 2000; Park et al., 2000). Most of this work has focused on
understanding the affects of soil sorption on ﬁrst-order mineralization. As a result of this
perspective, these studies have typically utilized very low contaminant concentrations
(e. g. less than 100 pg/L), only monitored C02 production in the presence and absence of
soil, and did not independently assess desorption processes.

In attempting to understand how desorption and biodegradation processes
interactively control bioavailability, it must be considered that biodegradation of sorbed
materials involves at least two rate processes: desorption and biodegradation. It is
understood that desorption is controlled by the concentration gradient across the
solid/liquid interface. The simplest view of biodegradation involves a rate driven by
liquid-phase contaminant, but different organisms and concentration regions result in
different rate formulations. Thus, the liquid-phase concentration of substrate is both a
result of, and the driving force for, both rate processes. The situation is further
complicated by the possibility of process interactions, for example if the presence of

organisms serves to either accelerate or retard desorption rates.

44

It has not been possible in previous studies to fully investigate these potential
process interactions for several reasons. First, desorption has not generally been
separately quantiﬁed. Second, when this has been attempted using mineralization data,
an assumption that CO2.

production equates with substrate disappearance is required. There is considerable
evidence in the literature, however, that contradicts this assumption for many of the
organisms and substrates that were used in bioavailability experiments (Whitman et al.,
1998; Willumsem and Arvin, 1999; Park et al., 2000). For example, Whiteman et al.
(1998) and Park et al. (2000) found that substrate disappearance was rapid, while C02
production lagged. As a result, the desorption driving force would be greater than that
expected by inferring liquid-phase substrate concentrations from C02 measurements.
Third, while ﬁrst-order kinetics have almost always been used to describe mineralization
rates, this may not be applicable to degradation rates.

In this study, a mathematical representation of bioavailability was developed by
measuring both rate processes independently, and investigating rate interactions in
systems with both processes active. Two soils, with different sorption capacities, and
four organisms having three different biokinetic formulations, were studied.
Experimental data were evaluated using coupled kinetic models. The overall purpose
was to understand the kinetic basis of bioavailability, including exploration of rate
interactions. Naphthalene was selected as a test contaminant, since it has often been used
as a model compound for PAHs, and this class of contaminant is a critical bioavailability

issue at numerous petroleum contamination sites.

45

MATERIALS AND METHODS

Soil
Two sandy loam soils were collected from a forest environment in Michigan:

SPCF (Spinks loamy sand) and Kalkaska-A. The soil samples were air-dried and sieved
through a US. Sieve Series #20 sieve (> 650 pm) to remove larger components. Prior to
use, the soils were sterilized by gamma irradiation (60Co source) at a dosage of 2 Mrad.
Following sterilization, sealed containers were maintained at room temperature. Before
the soils were used, 0.1g of each soil was placed on a nutrient agar plate and incubated at
30 °C for 3 days to verify sterility. No colony forming units were observed. Soil

characteristics are summarized in Table 4-1.

Organism and Growth conditions

 

Four naphthalene-degrading strains, Pseudomonas putida ATCC 17484,
Alcaligenes sp. NP-Alk, Pseudomonas putida G7 and Pseudomonas sp. NCIB 9816-4,
were used. ATCC 17484 and NP-Alk were selected because they were previously used
in mineralization assays to study bioavailability of sorbed naphthalene (Guerin and Boyd,
1992). G7 and NCIB 9816-4 were selected because it was reported recently that these
organisms are chemotactic to naphthalene (Grimm and Harwood, 1997). ATCC 17484
was obtained from the American Type Culture Collection, NP-Alk from S. A. Boyd at
Michigan State University, and G7 and NCIB9816-4 from C. S. Harwood at the

University of Iowa.

46

Table 4- 1 Soil characteristics'

 

Soils Organic content Mechanical characteristics
(%> 0%)
Organic matter Sand Silt Clay
SPCF 1.9 78 1 7 5
Kalkaska-A 3.9 90.6 7.7 1.7

 

TAnalysis completed by Plant and Soil Science Laboratory in Michigan State University.
SPCF: This was collected between 15 and 31 cm depth of forest in Michigan State University, East
Lansing, Michigan.
Kalkaska-A: This was collected at surface (0-6 cm depth) of forest in the northern half of Michigan’s lower
peninsula (The more detailed location was documented by Vance (Vance, 1984))

47

ATCC 17484 and NP-Alk were grown in 500 ml high-buffer broths
(HBB) composed of 2.0 g of NaCl, 3.0 g of (NH4)2HP04, 1.2 g of KH2P04, 3 mg of
MgSO4, 1 ml of fern'c quinate (Guerin and Boyd, 1992), 1 ml of vitamin solution (Wolin
et al., 1963), per liter of distilled water at pH 7.0. Naphthalene was added to the
sterilized liquid medium as a concentrated stock solution in acetone (200 g/L) to a ﬁnal
concentration of 200 mg/L. The liquid medium was inoculated by adding 5 ml of the
starved liquid culture (cell density of ~107 CFU/mL) to 500 mL medium, and stirred.
Growth was monitored by absorbance at 600 nm, and cells were harvested at a point
determined to correspond to early stationary phase based on a full growth curve. Cells
were separated by centrifugation (1900 x g, 20 min) and resuspended in a phosphate
buffer saline (PBS) solution before use. The PBS contained 8.5 g of NaCl, 0.6 g of
Na2HP04, and 0.3 g of KH2P04 per liter. This procedure was repeated three times to
ensure the removal of remaining naphthalene from the cell-growth medium. G7 and
NCIB 9816-4 were grown in 100 ml minimal mineral salt media (Harwood et al., 1994).
Solid naphthalene was added to the sterilized liquid at a ﬁnal concentration of 200 mg/L.
The liquid medium was inoculated by adding one ml of the starved liquid culture (cell
density of ~1 07 CF U/mL) to 100 mL medium, and stirred. Growth was monitored by
absorbance of 600 nm, and cells were harvested at a point determined to correspond to
early stationary phase. Cells were separated by centrifugation (1900 x g, 20 min) and
resuspended in a chemotaxis buffer (CB) (Harwood et al., 1994) before use. The CB
contained 13.6 g of potassium phosphate and 7.4 mg of EDTA per liter of distilled water,

and it was adjusted by 10 N NaOH solution to be pH 7. This procedure was repeated

48

three times to ensure the removal of remaining naphthalene from the cell growth medium.

All the cell suspensions were kept at room temperature and used within 3 hours.

Sorption, desorption and extraction
Naphthalene soil/water distribution coefﬁcients were measured

 

experimentally. An aliquot of each sterile soil (0.68 g of SPCF and 0.18 g of Kal-A) and
4.2 ml of CB containing l4C-naphthalene stock (in methanol) were prepared in 5 mL
screw cap vials with Teﬂon-lined septa. The soil/water ratios were carefully selected to
achieve approximately equal masses of naphthalene in both liquid and solid phases at end
of sorption period. The headspace was less than lmL in volume. Initial liquid-phase
concentrations ranged from 0 to 3,400 pg/L. Control vials without soil were also
prepared in triplicate. Vials were tumbled at 6 rpm for 2 days in the dark. After mixing,
each vial was centrifuged for 5 min at 1,200 x g to separate soil, and the supernatant was
sampled. The concentration of naphthalene in the supernatant was determined by liquid
scintillation counting (LSC) and high pressure liquid chromatography (HPLC). The
naphthalene concentration on soils were calculated by the difference between the initial
and ﬁnal liquid phase concentrations.

Desorption rates were measured in batch soil slurries with initial
naphthalene concentrations of 800 pg/lL. The vials were tumbled at 6 rpm for 2 days in
the dark. The ﬁnal concentration of naphthalene in the liquid phase was determined by
LSC, and the amount of sorbed naphthalene calculated by difference. The supernatant
was then decanted to the extent possible, the residual water determined by weight, and
naphthalene-free CB was added to the original volume. The vials were then tumbled

again at 6 rpm, with periodic liquid phase sampling and analysis.

49

Recovery studies were performed to determine extraction efﬁciencies.
Triplicate soil slurries were performed in the same manner as the desorption rate studies
except methanol was used as the desorption solution and the concentration was only
measured after 2 days of tumbling in the extraction solvent. Concentrations were

measured by HPLC, and efﬁciency calculated by mass balance.

Cell attachment assaJLs
Cell attachment was measured in duplicate soil slurries with initial liquid-

 

phase naphthalene concentrations of 800 pg/L. The vials, and controls were tumbled at 6
rpm for 2 days in the dark. The slurries and soil-free controls were inoculated to a
density 6f~107 CFU/mL

from an inoculum harvested during the early stationary phase. The inoculated
vials were tumbled at 6 rpm for 1 hr to facilitate cell attachment. The soil was then
allowed to settle quiescently for 2 hr and 0.1 mL of supernatant was removed for plate
counting. Cell attachment efﬁciency was calculated by difference between soil-slurries

and soil-free controls.

Bioavailability assays
Bioavailability assays were performed using soil slurries in 5 mL serum

 

vials with Teﬂon-lined septa. Using the same soilzwater ratio as in the desorption assays,
naphthalene was added to an initial liquid-phase concentration of approximately 800
pg/L, and the vials tumbled for 2 days in the dark. Sterility of the soil slurries was
checked by plating out the suspension on nutrient agar plates. The vials were then

inoculated with 0.05 mL of cell suspension harvested during the early stationary phase to

50

initiate biodegradation of naphthalene. Cell density in the serum vials was approximately
107 CFU/mL. Inoculated vials were tumbled at 6 rpm. At predetermined time intervals,
vials were removed and centrifuged at 1200 x g for 5 min to separate soil. One ml of
supernatant was transferred a vial containing 0.01 ml 10N NaOH to stop naphthalene
degradation, and analyzed by HPLC. The remaining supernatant was decanted and
extracted with methanol as described above to determine solid-phase concentrations by
HPLC.

Biodegradation rates were measured in soil-extract solutions. These were
prepared by tumbling soil slurries for 2 days at the same soil to water ratio as in the
desorption and bioavailability experiments. Kinetic data were obtained using the same
bioavailability assay procedure, except no soil was added to the vials. Both sterile-
CB/PBS and soil-extract controls were also used to monitor abiotic losses of naphthalene
during the sorption and degradation periods. All experiments were performed at room

temperature (24 i 1 °C) unless otherwise mentioned.

Chemical analysis
Naphthalene was analyzed by HPLC using a C13 reverse-phase column, an

 

80% acetonitrile/20% water mobile phase, and ﬂuorescence detection (280 nm excitation,
340 nm emission). The analytical detection limit was 0.2 pg/L. The detection limit for

LSC was less than 1 pg/L for the naphthalene solution used in this study.

51

THEORY

Three-site desorption model

Sorption and desorption processes have commonly been described using a model
with both equilibrium and kinetically-limited sites in the soil phase (van Genuchten and
Wagenet, 1989; Shelton and Doherty, 1997; Shelton and Doherty, 1997). There is
considerable recent evidence, however, that some fraction of the sorbed material either
does not desorb at all, or only desorbs very slowly (Ahn, 1998; Deitsch and Smith, 1999;
Tomson and Pignatello, 1999). In this study, a three-site model was developed by
assuming the solid is composed of equilibrium, non-equilibrium and non-desorption sites.
The non-desorption sites are deﬁned in this work as those containing sorbate that can not
be released to aqueous solutions during the experimental desorption period (3 days).
However, the non-desorbed naphthalene is extractable with methanol.

In the three-site model, equilibrium and non-desorption partitioning are described

Seq : feq ' Kd ' Cries /i Eq. 9
Snd = fnd ' Kd ' Ce(sorp) Eq. 10

while the release from non-equilibrium sites follows the ﬁrst-order expression

dSneq
dt

 

= a ' [freq ' Kr! ' Cries — Sneq] Eq. 1 1

where, Seq, Sneq and Snd (pg/kg) are the sorbed concentrations in solid, equilibrium
site, non-equilibrium site, and non-desorption site, respectively. Kd, (L/kg) is the
sorption distribution coefﬁcient, Cdcs (pg/L) is liquid phase concentration in a desorption

assay, C.,(sorp) is liquid phase concentration in sorption equilibrium. fcq is the equilibrium

52

site fraction, fneq is non-equilibrium site fraction, fnd is non-desorption site fraction, t is
desorption time, and on (min") is the ﬁrst order desorption rate coefﬁcient for non-
equilibrium sites. Kd, was calculated from the sorption isotherm. fcq, fncq, fnd and or were

estimated by non-linear regression analysis of desorption data.

Bioavailability model

The bioavailability models add biodegradation to the three-site desorption model.
In model I, biomass can utilize only liquid-phase contaminant. Degradation may be
described by zero-order, ﬁrst-order, or Michaelis-Menten kinetic expressions. In model
11, an additional degradation rate based on solid-phase concentrations is added. This
addresses what some investigators have described as direct solid-phase degradation
(Guerin and Boyd, 1992; Guerin and Boyd, 1993; Crocker et al., 1995 ; Calvillo and
Alexander, 1996; Tang et al., 1998), but might also represent any rate enhancement,
perhaps resulting from elevated concentrations at the cell/sorbent interface (Harms and
Zehnder, 1995; Ortega-Calvo and Sail-Jimenez, 1998; Feng et al., 2000). A ﬁrst-order
expression based on the concentration in the equilibrium sites is used for this rate.

Total disappearance rate in slurry systems for model 11 can be expressed

dC dS
— V-—+m-— =V-Rm+m-ke .5. E .12
(' dt dz) ' " " " q

where S (pg/kg) is the sorbed concentrations in solid. C (pg/L) is the liquid phase
concentration in a slurry system. m (kg) is the soil amount. V1 (L) is the liquid total
volume, keq is the ﬁrst order biodegradation coefﬁcients of sorbed contaminant. Rbio is
the liquid-phase biodegradation rate expression, which can be formulated as:

Rh... 2 ko - C for zero—order kinetics, Eq. 13

53

Rbiu = k1 - C for ﬁrst-order kinetics, and Eq. 14

km'C
K.-+C

Rbm =

 

for Michaelis-Menten kinetics, Eq. 15

where k0, k 1 and km are the zero-order, ﬁrst-order and maximum degradation rate
coefﬁcient for dissolved contaminant, respectively, and K5 is the half saturation
coefﬁcient.

In the system of equations above, k0, kl, km, and Ks were estimated by regression
of the soil-extract biodegration experiments, and keq by non-linear regression analysis of

the bioavailability data. For Model I a value of zero is used for keq"

RESULTS

Sorption of naphthalene was found to be linear for both soils (Figure 4-1). The
distribution coefﬁcients (Kd) were calculated by least squares regression and found to 4.3
L/kg for SPCF and 26 L/kg for Kal-A. Desorption was essentially complete within a few
hours, with the release of naphthalene from SPCF occurring somewhat faster than from
Kal-A. Coefﬁcients in the three-site model were determined by non-linear regression
(Table 4-2). It can be seen that the higher organic carbon soil (Kal-A) was found to have
a substantially larger proportion of equilibrium sites, but desorption from the non-
equilibrium sites was slower. The fraction of non desorption sites was essentially the
same in both soils, at approximately 0.13. Overall naphthalene recovery in the sorption-
desorption experiments was found to 99.0 (i 1.8) % for Kal-A and 99.0 (i 3.5) for SPCF.

No evidence of naphthalene degradation was observed.

54

 

  
  
 
 

 

 

 

 

25000
3 .
$3,, 8:25.60 ,0Ka|-Ai
‘7 20000 R2 = 0.96 lo SPCF
.8 4 A |
m
C
3 15000
.0
3
U)
“5 10000 -
C
.9
E
g 5000 s = 4.3 c
8 R2 = 0.98
O
o . .
0.0 500.0 1000.0 1500.0 2000.0

Concentration of naph. in liquid (ug/L)

Figure 4- 1. Sorption isotherm of naphthalene in two-soil slurries.

55

Table 4- 2. Desorption site fraction and desorption rate coefﬁcient estimated by three-

site desorption model.

 

Site fraction

 

 

N Desorption rate R2
. 09' coefﬁcient
3011 Desorbable desorption
feq fneq fnd or (mini)
SPCF 0.37 0.50 0.13 0.019 0.95
Kalkaska—A 0.70 0.16 0.14 0.0045 0.96

 

56

The attachment of the four bacterial strains to the two soils ranged from 13
% to 43 % (Table 4-3). No clear pattern of dependence on either soil or organism alone
was observed — attachment appeared to depend on both factors. From the soil extract
control rate data, it was found that naphthalene degradation followed a zero-order
relationship for ATCC 17484, and ﬁrst-order expression for NP-Alk and NCIB, and
Michaelis-Menten kinetics for G7. The appropriate rate parameters were determined from
these experiments.

The disappearance of naphthalene in the bioavailability assaies was the predicted
using these degradation rate parameters, and the distribution coefﬁcient from the sorption
isotherms, using model I with the appropriate biokinetic expression. Correlation
coefﬁcients describing the goodness of ﬁt for the predictions are shown in Table 4-4, and
plots comparing the predictions to the experimental data are shown in Figures 4-2 ~ 4-6.

To assess whether degradation related to the solid-phase concentration
occurs, model 11 was used to the ﬁt the experimental data by non-linear regression with
kcq as a ﬁt parameter. The improvements in correlation coefﬁcients were recorded, and
the F -test used to determine whether the use of model 11 results in a signiﬁcantly better
description of the data at the 95% conﬁdence level. Model 11 ﬁts are also shown in
Figures 4-3 ~4-6 and the model coefﬁcients in Table 4-4. In the ﬁgures presented, only
the total amount of naphthalene is shown, however separate plots of liquid and solid-

phase concentrations shown similar results.

57

Table 4- 3. Each cell attachment percent in both soil slurries.

 

Cell attachment %
cell SPCF Kal-A
average stdev average stdev
ATCC 25 12.5 13.4 6.3
NP-Alk 37.4 10.7 43.4 9.8
NCIB 14.0 16.8 16.9 15.2
G7 14.8 9.7 39.0 8.5

 

58

Table 4- 4. Kinetic parameters estimated in control and soil-slurry systems.

 

 

 

 

D d . Solid-
Bacter S .1 egra atron hase F-test result
ia 01 rate , degradati 0f 95%
parameters on rate" conﬁdence
k0 kc R2 levelm
9
Prediction 3.62 0 0.99
SPCF F
ATCC Regression 3.62 0.00061 0.99
Prediction 2.01 0 0.97
17484 Kal- A F
Regression 2.01 -0.00034 0.97
kl keq R2
NP- Prediction 0.00417 0 0.76
lk Kal-A . T
A Regressron 0.00417 0.00252 0.98
Prediction 0.0183 0 0.99
SPCF F
Regression 0.0183 0 0.99
NCIB
Prediction 0.0144 0 0.69
Kal-A T
Regression 0.0144 0.0103 0.99
k... Ks ks, R2
Prediction 4.94 36.4 0 0-96
SPCF F
P Regression 4.94 36.4 0.00441 0-99
putida . .
0, Prediction 6.83 36.4 0 0-90
Kal-A . T
Regress1on 6.83 36.4 0.0068 0-99

 

fDegradation rate parameters from soil-extract controls
"Solid-phase degradation rate from regression
"' F test results (01:0.05)
F: The difference of keq in between regression and prediction was not signiﬁcant at a conﬁdence level of
95%.
T: The difference of kcq in between regression and prediction was signiﬁcant at a conﬁdence level of
95%.

59

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

100
4
80% I Ti
: .
.2
g. 60 -
O
8’
‘45 40 " A
5° .
. — regressron
20 1
+ exp. data
0 n n a 4
0 1000 2000 3000 4000
Kal-A
100 I I I 1
4* 1a
80
f:
.2
E. 60 r
O
8
3 40 -
o B
.\° .
20_ _ regI‘eSSIOII
.1. exp. data
0 a n 1 n
0 1000 2000 3000 4000

Desorption time (min)

Figure 4- 2. Percent of desorption vs. desorption time. Three-site model was used for

the regression.

6O

 

6000 I I v I

.1
50001

A
O
O
O

  

 

   

 

Total amount, ng

 

 

 

 

 

3000 «
2000 . """ Prediction ‘ i “““““
— Regression
1000 - 4* Exp. data
0 . 4 . i ,
0 50 100 150 200 250
Kal-A

 

4000 . .

 

 

 

 

3000
01)
t:
E
g 2000-
f§
O
i“ 1000 B
0 a r 1 n
0 50 100 150 200 250

Incubation time (min)

Figure 4- 3. Total amount of naphthalene in SPCF and Kal-A soil slurries as incubation

time of strain ATCC 17484

61

Kal-A

 

 

 

 

 

 

 

 

 

4000 . . . T
3000
DD
1::
5
32000 11*
m 0 O
3 ------ Predictron
131000. — Regress1on
*1 Exp. data
0 n n a A
0 50 100 150 200 250

Incubation time (min)

Figure 4- 4. Total amount of naphthalene in Kal-A soil slurry as incubation time of

strain NP-Alk

62

SPCF

 

 

 

 

 

 

 

 

 

 

 

 

 

4000 a . .
.1
------ Predrction
@3000” —- Regressron
:- 11 Exp. data
:3
$2000.
"53‘
O
E" 1000 ~
0 L a L L
0 50 100 150 200 250
Kal-A
4000 ‘ . . .
3000-
CD
1:
5 .
g2000-
rs ~~~~~~~~~~~~
o ..........
5‘ 1000i at- 1
0 n n .L n
0 50 100 150 200 250

Figure 4- 5. Total amount of naphthalene in SPCF and Kal-A soil slurries as incubation

time of strain NCIB.

Incubation time (min)

63

SPCF
4000 r . T

 

 

  
  

------ Prediction
-— Regression
1* Exp. data

3000 -

 

 

  

 

‘\
“
§

‘5
\
‘§
\

Total amount, ng
N
O
o
O

 

 

 

1000 r ~~~~~
A ~~~~~
0 n a n
0 50 100 150
Kal-A

 

4000, .

3000 r

Total amount, ng
N
o
O
O

O
O
O

 

 

 

 

0 50 100 l 50
Incubation time (min)

Figure 4- 6. Total amount of naphthalene in SPCF and Kal-A soil slurries as

incubation time of strain P. p.G7.

64

For strain ATCC 17484, the disappearance of dissolved and sorbed
naphthalene was well predicted by Model I and the use of Model 11 was not justiﬁed
statistically (Figure 4-3). For strain NP-Alk (only Kal-A soil was studied), Model I
predicted higher than observed concentrations. A good ﬁt to the data could be obtained
with Model 11, indicating the need for additional or enhanced degradation (Figure 4-4).
This was supported by the F-test results (Table 4-4). The two soils performed differently
with strain NCIB. The soil with the lower Kd value (SPCF) was well described by Model
I, while that with the higher sorption capacity was ﬁt better by Model 11 (Figure 4-5).
Similar results were obtained from strain G7 (Figure 4-6). Note that even though the line
predicted from model I was slightly above the experimental data, the estimated keq
(0.00441 min") value was not statistically signiﬁcant from zero at a conﬁdence level of

95 % (Table 4-4, Figure 4-6a).

65

DISCUSSION

The assumptions used in Model I (which was used for the predictions) were that
a) desorption could be described by a model incorporating equilibrium, rate-limited and
non-desorption sites, b) desorption parameters developed in abiotic experiments were
valid in the bioavailability assays, c) biodegradation is dependent upon only liquid-phase
concentrations, and d) degradation rates in developed in soil-extract solutions were
applicable to the bioavailability assays. This model predicted the behavoir in all of the
experiments using the lower organic content soil, SPCF. However, it underestimated the
disappearance rate of naphthalene for three organisms (NP-Alk, NCIB, and G7) with the
higher organic-content Kal-A soil. This provides clear evidence that the simple
sequential desorption-degradation model using independent process parameters is not
universally applicable to systems in which both processes occur.

In this study, the observed enhancement was described using a second
degradation process dependent upon solid-phase substrate concentrations. There are
several possible mechanistic explanations for this result. The ﬁrst is that degradation by
some portion of the biomass is linked to solid-phase substrate concentrations. This might
result from what has been termed “direct solid phase degradation”, or equivalently, from
zones of concentration higher than in the mixed-liquid-phase, such as might occur at the
interface between attached organisms and the soil. The second is that the liquid phase
degradation process is somehow enhanced by the presence of soil. We have attempted to
account for other chemical species that might desorb and stimulate degradation by

measuring the biokinetic parameters in soil extracts. However, if these species are

66

depleted by degradation, further release might occur in the soil slurries, thereby further
stimulating degradation. A third possibility is that the biomass serves to increase the
desorption rate coefﬁcient. It is difﬁcult to postulate a mechanism by which this occurs.
We therefore conclude that degradation rate enhancement occurs with some soil-
organism combinations, but the precise cause of this remains elusive.

In an attempt to ﬁirther distinguish between these explanations, predictions were
made assuming the same fraction of non-desorption sites, but the balance of sorbed
material was in equilibrium with the liquid. This represents the limiting case for
enhanced desorption rates. A slight change in the predictions was observed, but
degradation rate enhancement was still clearly evident. Thus the explanation that
biomass increases desorption can not alone explain the enhancement in degradation. This
result also indicates that it may not be possible to distinguish between the ﬁrst two
explanations. This is because there is no mathematical difference between an increase in
the liquid-phase degradation rate and formulation of a second rate process based on the
solid-phase concentration, when the two concentrations are in equilibrium.

Degradation rate enhancement was only observed with the more sorptive
soil. In this soil, the solid-phase concentrations are approximately six times higher for a
given liquid-phase concentration. As a result, experiments with the less sorptive soil are
approximately six times less sensitive to rate enhancement. It is entirely possible,
therefore, that rate enhancement occurs with this soil, but is not observed because this
process represents a negligible contribution to the overall rate. This is perhaps
substantiated by the result with G7 and SPCF, where a slight enhancement can be seen in

the plot (Figure 6), but this was not found to be statistically signiﬁcant (Table 4-4).

67

It was also observed that degradation rate enhancement did not occur even
with the more sorptive soil for the ATCC 17484 organism. The kinetics of this reaction
are described by a zero-order formulation, indicating that the degradation rate is limited
by a factor other than substrate concentration. Thus, we probably should not expect any
enhancement due to the presence of a sorptive solid phase because if concentration is not
controlling the rate direct access to the solid or locally higher concentrations are not
likely to produce an effect.

Two of the bacterial strains used in this effort, ATCC 17484 and NP-Alk, have
been used in previous bioavailability studies (Guerin and Boyd, 1992; Guerin and Boyd,
1997). Guerin and Boyd reported that the bioavailability of sorbed naphthalene was
different for two organisms, that is, sorbed naphthalene was directly available to ATCC
17484 but not available to NP-Alk. In the present study, the opposite result was found.

We did not attempt to directly address the causes for this apparent contradiction,
but conclude that differences in the two approaches could cause this. In the previous
work, only C02 production was measured as the focus was on changes in mineralization
rates. In this study, desorption and biodegradation were independently quantiﬁed and
both dissolved and sorbed naphthalene were directly measured over the incubation period
because the objective was on mechanistic interactions. In addition, experimental

conditions such as the soil used and substrate concentrations, were substantially different.

ACKNOWLEDGEMENTS

68

This research was supported, in part, by the Center for Microbial Ecology
(National Science Foundation, Grant DEB9120006) and by the Great Lakes and Mid-
Atlantic Center for Hazardous Substance Research (Environmental Protection Agency,

Michigan Department of Environmental Quality).

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van Genuchten, M. T. and R. J. Wagenet. 1989. ““Two-site/two-region models for

99 99

pesticide transport and degradation: theoretical development and analytical solutions .
Soil Science Society of America Journal 53: 1303-1310.

Whitman, B. E., D. R. Lueking and J. R. Mihelcic. 1998. “Naphthalene uptake by a
Pseudomonas ﬂuorescens isolate.” Can. J. Microbio]. 44: 1086-1093.

70

Willumsem, P. A. and E. Arvin. 1999. “Kinetics of Degradation of Surfactant-

Solubilized Fluoranthene by a Sphingomonas paucimobilis.” Environ. Sci. Technol. 33:
2571-2578.

Wolin, E. A., M. J. Wolin and R. S. Wolfe. 1963. “Formation of Methane by Bacterial
Extracts.”J. Biol. Chem. 238: 2882-2886.

Zhao, X., and Voice, T. (2000). "Assessment of bioavailaibility using a multicolumn
system." Environmental Science & Technology, 34(8), 1506-1512.

71

CHAPTER 5 BIOAVAILABILITY OF NON-DESORBABLE NAPHTHALENE IN

SOIL SLURRIES

ABSTRACT

The degradation of naphthalene was studied in soil-slurry systems, and a
quantitative model was developed to evaluate the bioavailability of sorbed-phase
contaminant. Four soils with different organic matter contents were used as sorbents.
Two naphthalene degrading organisms, Pseudomonas putida G7 and NCIB 9816-4, were
also selected. Sorption isotherms, single and series-dilution desorption studeis were
conducted to evaluate distribution coefﬁcients, to estimate desorption parameters, and to
measure the non-desorbed amount of naphthalene. Biodegradation kinetics were
measured in soil extract solutions and rate parameters developed. For the bioavailability
assays, sorption equilibrium was established, and the systems were inoculated.
Naphthalene concentrations in both sorbed and dissolved phases were measured over
time. Enhanced bioavailability, as evidenced by faster than expected degradation rates,
was observed in soils with the higher sorption distribution coefficients. These
observations could be desrcribed by using a model formulations that included solid-phase

degradation. In all soils, degradation of non-desorbable naphthalene was observed.

72

INTRODUCTION

Numerous researchers have made the assumption, either explicitly or implicitly,
that sorbed contaminants are not directly available for biodegradation (Ogram et al.,
1985; Scow et al., 1986; Shimp and Young, 1988; Scow et al., 1989; Scow and Hutson,
1992; Estrella et al., 1993; Tabak et al., 1994; Shelton and Doherty, 1997; Zhang et al.,
1998). The most commonly accepted conceptual model is that sorbed contaminant ﬁrst
desorbs, and then degrades. Recently, other studies have suggested that adsorbed
substrate may be directly available for degradation by attached cells (Guerin and Boyd,
1992, 1993,1997; Crocker etal., 1995; Calvillo and Alexander, 1996; Tang et al., 1998;
Ortega-Calvo and Saiz-Jimenez, 1998). It should be noted that all of the evidence for
direct availability is derived from mineralization studies. This approach, while useful for
demonstrating the effects of sorbing materials on complete degradation, is somewhat
limited in its potential to elucidate the underlying mechanims. Amongst these limitations
are that CO2 measurements do not indicate whether the mineralized material originates in
the sorbed or dissolved phases, may not directly reﬂect substrate disappearance, and
provide only an indicator of the combined effects of desorption and degradation, rather
than of the individual processes. Central to this issue is that to independently evaluate a
system controlled by multiple rate processes, multiple time-concentration data sets are
required. Finally, the availability of irreversbly sorbed contaminant to biodegradation
has not been explicitly studied.

In the present study, substrate depletion, instead of mineralization, was used to
investigate the bioavailability of sorbed naphthalene. Sorption and degradation could be

tracked independently by measuring naphthalene concentrations in both the sorbed and

73

dissolved phases. Models were developed to quantitatively analyze the data.
Naphthalene was selected as a test contaminant, since it has often been used as a model
compound for PAHs, and this class of contaminant is a critical bioavailability issue at
numerous petroleum contamination sites.

MATERIALS AND METHODS

Experimental

Four sandy-loam soils with organic matter contents from 1 to 4% were collected
from forest environments in Michigan. Soil samples were air-dried and sieved through a
US. Sieve Series a #20 sieve (> 650 pm) to remove larger components. Prior to use, the
soils were sterilized by gamma irradiation (60Co source) at a dosage of 2 Mrad.
Following sterilization, sealed containers were maintained at room temperature. Before
the soils were used, 0.1g of each soil was placed on a nutrient agar plate and incubated at
30 °C for 3 days to verify sterility. No colony forming units were observed. Soil

characteristics are summarized in Table 5-1.

74

Table 5- 1. Soil characteristics*

 

 

Organic Mechanical Distribution
Soils matter content characteristics coefﬁcient
(°/o) (°/o)

Sand Silt Clay Kd

SPCF 1 .9 78 1 7 5 4.3
Rubicon-BS1 1.4 87 8.2 4.7 8.8
Rubicon-A 2.0 89 8.4 2.7 1 1.0
Kalkaska-A 3.9 91 7.7 1.7 25.6

 

* Analysis completed by Plant and Soil Science Laboratory in Michigan State University.
SPCF. This was collected in 15-31 cm depth of forest in Michigan State University, East Lansing,

Michigan.

Rubicon-BS1 (subsurface in 15-31 cm depth), Rubicon-A (surface in 0-6 cm depth), and Kalkaska-A
(surface in 0-6 cm depth). These were collected at uncultivated forest in the northern half of Michigan’s
lower peninsula (The detail location was documented by Vance (Vance, 1984)).

75

Two naphthalene-degrading strains, Pseudomonas putida G7 and Pseudomonas
sp. strain NCIB 9816-4, were selected because these organisms have been reported to be
motile and chemotactic to naphthalene (Grimm and Harwood, 1997; Grimm and
Harwood, 1999). G7 and NCIB 9816-4 were grown in 100 mL minimal mineral salt
media (Harwood et al., 1994). Solid naphthalene was added to the sterilized liquid at a
ﬁnal concentration of 200 mg/L. The liquid medium was inoculated by adding one mL
of starved liquid culture (cell density of ~107 CFU/mL) to 100 mL medium, and stirred.
Growth was monitored by absorbance at 600 nm, and cells were harvested at a point
determined to correspond to early stationary phase based on a full growth curve. Cells
were separated by centrifugation (1900 x g, 20 min) and resuspended in a chemotaxis
buffer (CB) (Harwood et al., 1994) before use. The CB contained 13.6 g/L of potassium
phosphate and 7.4 mg/L of EDTA, and was adjusted by ION NaOH to pH 7. This
procedure was repeated three times to ensure the removal of remaining naphthalene from
the cell grth medium. All the cell suspensions were kept at room temperature and
used within 3 hours.

Naphthalene soil/water distribution coefﬁcients were measured experimentally.
An aliquot of each sterile soil (0.68g of SPCF, 0.57g of Rub-BSI, 0.39g of Rub-A, and
0.18g of Kal-A) and 4.2 ml of CB containing ”C-naphthalene stock (in methanol) were
prepared in 5 mL screw-cap vials with Teﬂon-lined septa. The soil/water ratios were
carefully selected to achieve approximately equal masses of naphthalene in both liquid
and solid phases at end of the sorption period. The headspace was less than lmL in

volume. Initial liquid-phase concentrations ranged from 0 to 3,400 pg/L. Control vials

76

 

without soil were prepared in triplicate. Vials were tumbled at 6 rpm for 2 days in the
dark. Aﬁer mixing, each vial was centrifuged for 5 min at 1200 x g to separate soil, and
the supernatant was sampled. The concentration of naphthalene in the supernatant was
determined by liquid scintillation counting (LSC) and high pressure liquid
chromatography (HPLC). The naphthalene concentrations on soils were calculated by
the difference between the initial and ﬁnal liquid-phase concentrations.

Desorption rates were measured in batch soil slurries with initial
naphthalene concentrations of 800 pg/L. The vials were tumbled at 6 rpm for 2 days in
the dark. The ﬁnal concentration of naphthalene in the liquid phase was determined by
LSC, and the amount of sorbed naphthalene calculated by difference. The supernatant
was then decanted to the extent possible, the residual water determined by weight, and
naphthalene-free CB was added to the original volume. The vials were then tumbled
again at 6 rpm, with periodic liquid-phase sampling and analysis.

Series-dilution desorption assays were conducted to measure the amount
of non-desorbable naphthalene. Six replicate soil slurries were prepared for each soil
using the desorption procedure described above. The slurries were tumbled for 24 hours,
the supernatant decanted, sampled and analyzed for naphthalene, and the bottles reﬁlled
with naphthalne-free CB to the original volume. This procedure was repeated for a total
of 6 successive desorption periods. The non-desorbable naphthalene was determined
using a methanol extraction of the soil following this series dilution procedure. Recovery
studies were performed to determine extraction efﬁciencies. Triplicate soil slurries were

prepared in the same manner as the desorption rate studies except methanol was used as

77

the desorption solution. The concentration was measured after 2 days of tumbling by
HPLC, and the efﬁciency calculated by mass balance.

Cell attachment was measured in duplicate soil slurries with initial liquid-
phase naphthalene concentrations of 800 pg/L. The vials, and controls were tumbled at 6
rpm for 2 days in the dark. The slurries and soil-free controls were inoculated to a
density of ~1 07 CFU/mL

from an inoculum harvested during the early stationary phase. The inoculated
vials were tumbled at 6 rpm for 1 hr to facilitate cell attachment. The soil was then
allowed to settle quiescently for 2 hr and 0.1 mL of supernatant was removed for plate
counting. Cell attachment efﬁciency was calculated by difference.

Bioavailability assays were performed using soil slurries in 5 mL screw-
cap vials with Teﬂon-lined septa. Using the same soil to water ratio as in the desorption
assays, naphthalene was added to an initial liquid-phase concentration of approximately
800 pg/L, and the vials tumbled for 2 days in the dark. Sterility of the soil slurries was
checked by plating out the suspension on nutrient agar plates. The vials were then
inoculated with 0.05 mL of cell suspension harvested during the early stationary phase to
initiate biodegradation of naphthalene. Cell density in the serum vials was approximately
107 CFU/mL. Inoculated vials were tumbled at 6 rpm. At predetermined time intervals,
vials were removed and centrifuged at 1200 x g for 5 min to separate soil. One ml of
supernatant was transferred a vial containing 0.01 ml 10N NaOH to stop naphthalene
degradation, and analyzed by HPLC. The remaining supernatant was decanted and

extracted with methanol as described above to determine solid-phase concentrations by

HPLC.

78

 

Biodegradation rates were measured in soil-extract solutions. These were
prepared by tumbling soil slurries for 2 days at the same soil to water ratio as in the
desorption and bioavailability experiments. Kinetic data were obtained using the same
bioavailability assay procedure, except no soil was added to the vials. Both sterile-CB
and soil-extract controls were also used to monitor abiotic losses of naphthalene during
the sorption and degradation periods. All experiments were performed at room
temperature (24 i 1 °C) unless otherwise mentioned.

Naphthalene was analyzed by HPLC using a C13 reverse-phase column, an
80% acetonitrile/20% water mobile phase, and ﬂuorescence detection (280nm excitation,
340nm emission). The analytical detection limit was 0.2pg/L. The detection limit for

LSC was less than 1 pg/L for the naphthalene solution used in this study.

Mathematical

Four related kinetic models employing different assumptions for the
biodegradability of different contaminant pools were used to evaluate the experimental
results. The liquid phase concentration is increased by desorption of sorbed contaminant
and decreased by biodegradation. All models assume that soils have three types of
desorption sites: equilibrium, non-equilibrium and non-desorption sites (Park et al.,
2000)

S = Set] + Sneq + Sm] Eq. 16

where S (pg/kg) is the total sorbed contaminant concentration, which is

subcripted for the different solid phase compartments (eq for equilibrium, neq for non-

79

equilibrium, and nd for non-desorption). Desorption from equilibrium sites is described
by a linear partitioining model

Sequeq-Ka-C Eq.17

where C (pg/L) is the liquid-phase concentration, feq is the equilibrium site
fraction and K; (L/kg) is the sorption distribution coefﬁcient. A ﬁrst-order linear driving
force formulatio is used to describe desorption from non-equilibrium sites:

J» = -a - (Sim, — freq . Kd - C) Eq. 18

where J., is the desorption ﬂux, fneq is non-equilibrium site fraction in desorbable
site, and or (min") is ﬁrst-order desorption rate coefﬁcient.

In model I, biomass can utilize only liquid-phase contaminant. Model 11 allows
biomass to also utilize contaminant in equilibrium sites on the solid phase. Non-
equilibrium, or rate limited sites, are available in model 111 , and all three sites, including
the non-desorption sites, can be accessed in model IV (Figure 5-1). The biodegradation
rate coefﬁcients for the various contaminant pools are independent. The mathematical
basis for model IV is developed below. Models 1, II and III are simpliﬁcations of this
model, achieved mathematically by setting the appropriate degradation rate coefﬁcients

to zero. The total disappearance rate in slurry systems for model IV can be expressed as

—(M'%+m'§')=l/l‘Rbiri+m'keq'Seq+m'/qu'Sm'q+M'kml'Sml Eq.19

where m (kg) is the mass of soil, V. (L) is the liquid volume, t is time and kgq,
kneq, and knd are the ﬁrst order biodegradation coefﬁcients of sorbed contaminants in
equilibrium, non-equilibrium and non-desorption sites respectively. Rbio is the liquid—

phase biodegradation rate expression, which can be formulated as:

80

R6... = k1 -C for ﬁrst-order kinetics, and Eq. 20

k...-C
Ks‘i’C

Rbio = for Michaelis-Menten kinetics, Eq. 21

 

where kl and km are the ﬁrst-order and maximum degradation rate coefﬁcient for
dissolved contaminant, respectively, and K, is the half saturation coefﬁcient.

Mass balances on the non-equilibrium and non-desorption sites, produces

 

 

dSneq
=Jn—kne °Sne E .22
dt " q q
“5”" = —knd . 5...: Eq. 23
dt

In the system of equations above, Kd was calculated from sorption isotherm data,
feq, fneq, fnd and or were estimated by non-linear regression analysis of the desorption rate
data, k1, km, and Ks by regression of the soil-extract biodegradation experiments, and

keq, km,q and knd by non-linear regression analysis of the bioavailability data.

For model I, keq, kneq and knd are set to zero, for model 11 kneq and knd are

set to zero, and for model 111, knd is set to zero.

81

 

ATTACHED BIOMASS

SOLID (S)
Nonequilibr "1.: ?- Equilibrium
ium (f...) (f...)

 

 

 

 

 

 
   
 
    
        

 
    

     
  

 

Non-
desorption
(fnd)

    

 

 

 

 

aminant

 

 

 

 

 

 

 

 

V
SUSPENDED BIOMASS

 

 

 

Figure 5- 1. Model IV description. keq, kneq and knd are deleted in Model I. kneq and knd

are deleted in Model 11. knd is deleted in Model 111.

82

RESULTS

Sorption of naphthalene for all four soils was linear over the experimental range
of liquid-phase equilibrium concentrations (0 to 1000 pg/L for Kal-A, Rub-A and Rub-
BS1, and 0 to 1700 pg/L for SPCF) (Figure 5-2). Distribution coefﬁcients ranged from
4.3 to 25.6 mL/g (Table 5-1). From the desorption rate proﬁles it can be seen that there is
an instantaneous release of contaminant, followed by a period of rate limited desorption
(Figure 5-3). Also shown on these plots is a horizontal dashed line indicating the amount
that would be expected in solution for completely reversible desorption of all of the
sorbed material. All of the soils stopped releasing additional material well before
reaching this level. The distribution of desorption sites obtained from the three-site
model was remarkably similar for three soils (Kal-A, Rub-A and Rub-BSI) with average
fractions of 0.71 , 0.16 and 0.13 for the equilibrium, non-equilibrium and non-desorption
sites. Desorption rate coefﬁcients for the non-equilibrium sites were essentially the same
for these soils. There was a greater fraction of rate-limited sites in the SPCF soil, but the
desorption rate coefﬁcient was approximately ﬁve times larger. Soil had three-site model
(Park et al., 2000) was used to estimate the desorption rate coefficients and each site
fraction for all four soils. The correlation factors ranged from 0.91 to 0.97 (Figure 5-3).
Analyzing the three-site model regression, desorbable fraction ranged from 0.85 to 0.89
(Table 5-2). The non-desorption site ranged from 0.11 to 0.15. The desorption rate
coefﬁcient on non-equilibrium site ranged from 0.00011 to 0.019 min". Overall

naphthalene recoveries in the sorption/desorption experiments were 99.0 (i 1.8) % for

83

Kal-A, 98.0 (i 1.7) % for Rub-A, 99.2 (i 5.2) % for Rub-BS], and 99.0 (i 3.5) for

SPCF.

84

25000

20000

—L
0|
0
O
O

1 0000

Equilibrium cone. of naph. in solid (ug/kg)

5000 4

 

 

0.0

l o Kal-A
I :1 Rub-A

i x Rub-BS1

joSPCF

 

l

 

 

f

500.0 1000.0

1500.0
Equilibrium cone. of naph. in liquid (ug/L)

Figure 5- 2. Sorption isotherm of naphthalene in four-soil slurries.

85

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Table 5- 2. Desorption site fraction and desorption rate coefﬁcient obtained from

desorption, and non-desorption site fraction from series-dilution desorption experiment.

Each site fraction and desorption rate coefﬁcient were estimated from non-linear

regression analysis.

 

Site fraction

Series-dilution

 

desorption
- Non- Non- Desorption rate
S l
01 Desorbable desorbable desorbable coefficient
- -1
feq fneq fnd fnd (1 (mm )
SPCF 0.37 0.50 0.13 0.11 0.019
Rubicon-BS1 0.76 0.13 0.11 0.12 0.0045
Rubicon-A 0.66 0.19 0.15 0.17 0.0039
Kalkaska-A 0.70 0.16 0.14 0.18 0.0045

 

87

The series dilution experiments show clear evidence of the existence of a non-
desorption fraction, as the desorption isotherms all lie signiﬁcantly above the sorption
data (Figure 5-4). This fraction can be calculated from the intercept of the desorption
isotherm and the initial solid-phase concentration (Table 5—2). The calculated value was
remarkably close to that found by regression analysis. This result was further conﬁrmed
by extraction of the soil after the last dilution. It was also found that the slope of the
desorption isotherm was essentially the same as the sorption isotherm, providing
experimental validation of the assumption of this equivalence used in the regression
analysis.

The degradation kinetics in the soil extract solutions followed by ﬁrst order
kinetic for NCIB and the Michaelis-Menten equation for G7. Rate parameters are shown
in Tables 5-3 and 5-4.

Data from the bioavailability assays for both organisms and all four soils are
shown in Figures 5-5 ~ 5-12. The depletion of liquid-phase substrate can be seen to
occur over the ﬁrst 400 minutes for all combinations. Solid-phase concentrations
continue to decrease after this time however. In all cases total solid-phase contaminant
concentrations dropped to levels below the non-desorbable amount, shown by the dashed
horizontal line. Thus it appears that more contaminant was removed from the solids in
these inoculated systems than could be accomplished in abotic systems.

Also shown in these ﬁgures are predictions of both the liquid- and solid-
phase data using model I with liquid-phase degradation parameters from the

biodegradation assays. Liquid-phase data are reasonably well predicted by model I for

88

the two soils with the lower distribution coefﬁcients (SPCF and Rub-BS1), but not for the
other two. Model I could not predict the solid-phase data well for any of the
combinations, however, especially at later times. These predicted curves reached a
plateau at or near the measured non-desorbable amount. Fitting the data using models 11
and 111 provided a better ﬁt of the liquid-phase data, but were not able to describe solid-
phase data below the non-desorbable amount. Regression using model IV was able to
provide a reasonable description of both liquid- and solid-phase data for all combinations

over the entire time period. Model parameters are shown in Tables 5-3 and 5-4.

89

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Table 5- 3. Estimated each model parameters for NCIB.

 

 

Degradation
Soil Model rate Solid-phase degradation rate" R2
parameterst
k1 k¢q kneq knd
(min’l) (min’l) (min'l) (min'l)
Model I N/A N/A N/A 0.989
Model II 0 N/A N/A 0.989
SPCF 0.01 831
Model III 0 0 N/A 0.990
Model IV 0 0 0.0015 0.996
Model I N/A N/A N/A 0.975
R b B Model II 0.05206 0.0083 N/A N/A 0.977
u —
Model III 0 0.0074 N/A 0.986
Model IV 0 0.0039 0.0014 0.999
Model I N/A N/A N/A 0.321
Model II 0.01842 0.052 N/A N/A 0.980
Rub-A
Model III 0.023 0.027 N/A 0.985
Model IV 0.023 0.012 0.00032 0.990
Model 1 N/A N/A N/A 0.836
1 Model II 0.0144 0.0103 N/A N/A 0.988
Ka -A
Model III 0.0103 0 N/A 0.988
Model IV 0.0098 0 0.00040 0.995

 

'Degradation rate parameters from soil-extract controls

"Solid-phase degradation rate from regression

99

Table 5- 4. Estimated each model parameters for P. putida G7.

 

 

Degradation
Soil Model rate Solid-phase degradation rate" R2
parameterst
km Ks keq kneq knd

Model I N/A N/A N/A 0.987

Model II 4.938 36.37 N/A N/A 0.987

SPCF Model III 0 0 N/A 0.987
Model IV 0 0 0.00056 0.990

Model I N/A N/A N/A 0.972

Rub-B Model II 9.958 36.37 0 N/A N/A 0.977
Model III 0 0 N/A 0.977

Model IV 0 0 0.00091 0.988

Model I N/A N/A N/A 0.899

Model II 4.490 36.37 0.0078 N/A N/A 0.991

Rub-A Model III 0.0066 0.0020 N/A 0.993
Model IV 0.0070 0.00087 0.00042 0.999

Model I N/A N/A N/A 0.946

Model II 6.826 36.37 0.0068 N/A N/A 0.985

Kal’A Model 111 0.0068 0 N/A 0.985
Model IV 0.0067 0 0.00043 0.991

 

WDegradation rate parameters from soil-extract controls
"Solid-phase degradation rate from regression

100

DISCUSSION

The desorption proﬁles show clearly that three types of desorption occur:
equilibrium release as evidenced by the elevated solution concentrations at the ﬁrst time
point, rate limited release shown by the increases over the next several time points, and a
non-desorbable fraction indicated by failure to reach the expected equilibrium levels.
The presence of the non-desorbable fraction is supported by the series dilutions
experiments, indicating that this material could not be removed by six successive water
extractions. Conﬁrmation that this material was actually sorbed, rather than resulting
from a loss, is provided by the methanol extractions and the essentially complete closure
of the sorption/desorption mass balance. Thus it appears that not only is the use of the
three-site model consistent with the desorption rate data, but the data provide information
sufﬁcient to estimate the rate parameters.

The ability of model I to ﬁt the liquid-phase concentration data for the SPCF and
Rub-B81 soils suggests that most of the degradation occurs in the liquid-phase, as
assumed in the model (Figures 5-5, 5-6, 5-9 and 5-10). This result does not eliminate the
possibility of degradation based on the solid phase contaminant, but if this occurs it is
apparently not signiﬁcant. Because these soils have relatively low sorption capacities,
solid—phase concentrations are low, and rates based on these compartments would also be
expected to be low. It is seen that model I does not predict the liquid-phase rate proﬁles
very well for the higher capacity soils (Figures 5-7, 5-8, 5-11 and 5-12). Models II and
III, which incorporate degradation of the reversibly-sorbed material, provide successively

better ﬁts of the liquid-phase rate proﬁles for these soils (Table 5—3 and 5-4). Apparently,

lOl

the higher concentrations of sorbed substrate result in signiﬁcant increases in degradation
rates. This extends recent published evidence of enhanced mineralization rates (Guerin
and Boyd, 1992; Guerin and Boyd, 1993; Guerin and Boyd, 1995; Guerin and Boyd,
1997; Ortega-Calvo and Saiz—Jimenez, 1998; Laor et al., 1999), suggesting that substrate
degradation can also be enhanced over that expected on the basis of liquid-phase
concentration alone.

This result is consistent with what has been labeled “direct solid-phase
degradation” by some researchers, and other explanations such as elevated concentrations
at the sorbent/organism interface and stimulation of liquid-phase rates by the presence of
solids (Guerin and Boyd, 1992; Guerin and Boyd, 1993; Guerin and Boyd, 1995; Harms
and Zehnder, 1995; Guerin and Boyd, 1997; Ortega-Calvo and Saiz-Jimenez, 1998; Laor
et al., 1999; F eng et al., 2000). The results using models I, II and III do not address these
alternative explanations. They simply indicate that rate enhancement occurs and that the
behavior of systems involving both desorption and degradation can not necessarily be
predicted based on independent assessments of these processes. All three models provide
the same prediction of the long-term behavior of liquid-phase concentrations: that
substrate levels drop below the detection limit within 400 minutes.

The solid-phase concentration data provide some additional insights, however.
The total amount of contaminant in the solid phase continued to decrease after«liquid
phase was depleted, dropping below the amount known to be held in the non-desorption
sites. Thus it appears that the naphthalene that could not be removed by exhaustive
aqueous extraction, was removed in the presence of bacteria. This addressed

mathematically by model IV, which incorporates degradation of the non-desorption

102

material. It can be seen that this formulation provides a good description of the
experimental data.

We offer two possible explanations for this unexpected result. First, the
presence of bacteria may serve to extract the non-desorbable fraction, much as an organic
solvent would. Living bacterial cultures continually produce a wide array of soluble
organic materials, and it is possible that some of these can facilitate desorption. Second,
the bacteria may able to degrade this material directly, with desorption. This could occur
through direct partitioning to the cell membrane, or via degradation by extracellular
enzymes. The present study was not designed to discriminate between these
explanations, and further research in this area may be warranted. Nonetheless, it can be
concluded that material that was non-desorbable in these experimental systems was

bioavailable.

ACKNOWLEDGEMENTS

We thank Dr. Harwood in University of Iowa for providing the bacteria.

REFF ERENC ES

Estrella, M. R., M. L. Brusseau, R. S. Maier, I. L. Pepper, P. J. Wierenga and R. M.
Miller. 1993. “Biodegradation, Sorption, and Transport of 2,4-Dichlorophenoxyacetic
Acid in Saturated and Unsaturated Soils.” Appl. Environ. Microbiol 59: 4266-4273.

Feng, Y., J. H. Park, T. C. Voice and S. A. Boyd. 2000. “Bioavailability of Soil-Sorbed
Biphenyl to Bacteria.” Environ. Sci. Technol. 34: 1977-1984.

Grimm, A. C. and C. S. Harwood. 1997. “Chemotaxis of Pseudomonas spp. to the
Polyaromatic Hydrocarbon Naphthalene.” Appl. Environ. Miceobiol. 63: 41 1 1—41 15.

103

Grimm, A. C. and C. S. Harwood. 1999. “NahY, a Catabolic Plasmid-Encoded
Receptor Required for Chemotaxis of Pseudomonas putida to the Aromatic Hydrocarbon
Naphthalene.” J. Bacterial. 1 81 : 33 10-33 16.

Guerin, W. F. and S. A. Boyd. 1992. “Differential Bioavailability of Soil-Sorbed
Naphthalene to Two Bacterial Species.” Appl. Environ. Microbial 58: 1142-1152.

Guerin, W. F. and S. A. Boyd. 1993. “Bioavailability of Sorbed Naphthalene to
Bacteria: Inﬂuence of Contaminant Aging and Soil Organic Carbon Content.” Sorption

and Degradation of Pesticides and Organic Chemicals in Soil. SSSA Special Publication
no 32: 197-208.

Guerin, W. F. and S. A. Boyd. 1995. “Maintenance and Induction of Naphthalene
Degradation Activity in Pseudomonas putida and an Alcaligenes sp. under Different
Culture Conditions.” Appl. Environ. Microbial 61: 4061-4068.

Guerin, W. F. and S. A. Boyd. 1997. “Bioavailability of naphthalene associated with
natural and synthetic sorbents.” Water Res. 32: 1504-1512.

Harms, H. and A. J. B. Zehnder. 1995. “Bioavailability of Sorbed 3-
Chlorodibenzofuran.” Appl. Environ. Microbial 61: 27-33.

Laor, Y., P. F. Strom and W. J. Farmer. 1999. “Bioavailability of Phenanthrene
Sorbed to Mineral-Associated Humic Acid.” Wat. Res. 33: 1719-1729.

Ogram, A. V., R. E. Jessup, L. T. On and P. S. C. Rao. 1985. “Effects of Sorption n
Biological Degradation Rates of (2,4-Dichlorophenoxy)acetic Acid in Soils.” Appl.
Environ. Microbial 49: 582-587.

Ortega-Calvo, J. J. and C. Saiz-Jimenez. 1998. “Effect of humic fractions and clay on
biodegradation of phenanthrene by a Pseudomonas ﬂuorescens strain isolated from soil.”
Appl. Environ. Microbial. 64: 3123-3126.

Park, J. H., X. Zhao and T. C. Voice. 2000. “Development of a kinetic basis for
bioavailability of naphthalene in soil slurries.” Water Res. Submitted: .

Scow, K. M. and J. Hutson. 1992. “Effect of Diffusion and Sorption on the Kinetics of
Biodegradation Theoretical Considerations.” Soil Sci. Soc. Ame. J 56: 119.

Scow, K. M., S. K. Schmidt and M. Alexander. 1989. “Kinetics Of Biodegradation Of
Mixtures Of Substrates In Soil.” Soil Biol. Biochem. 21: 703.

Scow, K. M., S. Simkins and M. Alexander. 1986. “Kinetics of Mineralization of
Organic Compounds at Low Concentration in Soil.” Appl. Environ. Microbial 51: 1028.

Shelton, D. R. and M. A. Doherty. 1997. “A model describing pesticide bioavailability
and biodegradation in soil.” Soil Sci. Soc. Am. J61: 1078 — 1084.

104

Shimp, R. J. and R. L. Young. 1988. “Availability of organic chemicals for
biodegradation in settled bottom sediments.” Ecotoxicol Environ Saf 1 5: 31-45.

Tabak, H. H., C. Gao, L. Lai, X. S. Yan, S. Pfanstiel, L. S. Kim and R. Govind. 1994.
“Determination Of Bioavailability and Biodegradation Kinetics Of Phenol and
Alkylphenols In Soil.” 554: 51.

Zhang, W. X., E. J. Bouwer and W. P. Ball. 1998. “Bioavailability of hydrophobic
organic contaminants-effects and implications of sorption-related mass-transfer on
bioremediation.” Ground Water Monit. R. 18: 126-138.

105

CHAPTER 6. COMPARISON OF BIODEGRADATION KINETIC
PARAMETERS FOR NAPHTHALENE IN BATCH AND SAND COLUMN
SYSTEMS BY PSEUDOMONAS PUT IDA
ABSTRACT

Kinetic parameters for the degradation of naphthalene by Pseudomonas putida
(ATCC 17484) were estimated in both batch and column assays, in order to evaluate the
role of cell attachment and flow on biodegradation rates. Suspended cells and cells
attached to Ottawa sand were used under a variety of biomass levels, column ﬂow-rates,
and substrate concentrations. The maximum speciﬁc utilization coefﬁcient (kn,), the
zero-order reaction coefﬁcient (k0) for degradation, and zero-order reaction coefficient
(sz) for mineralization differed by factors similar to the calculated reduction in exposed
cell surface area. In batch systems, degradation followed zero-order kinetics across the
entire concentration range, while the columns exhibited decreased rates at concentrations
less than 100 (pg/L), describable by Michaelis-Menten kinetics. This is reﬂected in
elevated values of the half-saturation constant, K5, in columns. We offer the explanation
that this may result from reactive-heterogeneity within the porous media, imposing a
distribution of length-scales for transfer of substrate to the cell surfaces. Well-mixed
batch systems are expected to have both shorter and more uniform transfer distances.
This explanation was also shown to be consistent with observations of the effects of ﬂow
rate and biomass levels in column experiments. When kinetic parameters obtained in
batch system are used for prediction of degradation in columns, at least two factors —
exposed reduction of exposed cell surface area and heterogeneity of cell distribution —

will likely reduce overall column degradation rates.

106

INTRODUCTION

Biokinetic parameters are commonly measured in batch systems using suspended
cells in liquid medium. One of the advantages of this approach is that mass-transfer
limitations are minimized and the resultant kinetic parameters are thought to reﬂect the
“intrinsic” biodegradation capability of the organisms. It is further assumed that the more
complicated soil environments in which biodegradation processes may occur can be
described by incorporating appropriate formulations for ﬂow through the porous media,
mixing, cell attachment, mass-transfer to and from cell surfaces, and sorption/desorption.
It is commonly assumed that the basic biokinetic processes are unaltered by system
conﬁguration and the parameters measured in soil-free suspended-cell assays can be
applied directly in these dissimilar systems. Finding little evidence in the literature to
support this assumption, this effort was designed to evaluate whether there are
differences between biodegradation rates by suspended-cells in well-mixed batch systems
without solids and attached-cells in sand columns.

Bacterial adhesion effects on biodegradation or cell activity have been studied in
batch systems by van Loosderacht et al. (van Loosdrecht et al., 1990) These
investigators classiﬁed the potential inﬂuences of interfaces into two categories: direct
and indirect. Direct inﬂuences include changes in the structure and permeability of the
cell membranes as the result of adhesion to solid surfaces. Indirect inﬂuences involve
alteration of cell activity because of changes in the composition of the medium resulting
from sorption and desorption phenomena at the interfaces, and the physical geometric
structure around an attached organism. van Loosderacht et a1. did not ﬁnd conclusive

evidence that the adhesion of cells to solid surfaces directly inﬂuences bacterial

107

metabolism. They concluded that differences are largely indirect, resulting from
decreases in the “apparent” substrate afﬁnity because of a reduction in the cell surface
exposed to the bulk solution and an increased mass-transfer resistance to the non-exposed
surface (N gian et al., 1977; McLaren, 1978; Ellwood et al., 1982; Firestone, 1982; Bright
and Fletcher, 1983; Dolﬁng, 1985; Myrold and Tiedje, 1985; Caldwell and Lawrence,
1986; Jeffrey and Paul, 1986; Focht and Shelton, 1987). It should be noted that all of
these studies were conducted in well-mixed systems and may not be applicable to
degradation in ﬂow through porous media.

A few recent investigations have attempted to explore the differences between
batch (free cells) and column (adhered cells) experiments. Harms and Zehnder (Harms
and Zehnder, 1994) studied the effect of substrate diffusion on the biodegradation rate of
dibenzofuran and 3-chlorodibenzofuran by attached and suspended Sphingomonas sp.
strain HH19K. A batch system was used to obtain kinetic parameters including
maximum speciﬁc growth rate (um) and the half-saturation coefﬁcient (K,) for suspended
cells, and columns packed with glass beads were used to obtain Ks for the attached cells.
They found that KS increased as surface coverage of cells increased and as bulk ﬂow rates
decreased, while the maximum speciﬁc activity was 15% to be the same in the both
systems. Kelly et al. conducted both suspended culture batch and quartz-sand-packed
column experiments to measure biodegradation kinetic parameters for benzene, toluene
and xylene, using a mixed culture of organisms derived from creosote-contaminated soil
(Kelly et al., 1996). They found that the maximum speciﬁc growth rate was similar for
both systems but it was not possible to estimate half-saturation coefﬁcients for the

columns. Mass-transfer limitations in the columns were not explicitly addressed.

108

Estrella et al. studied the biodegradation rate parameters for 2,4-dichlorophenoxyacetic
acid by indigenous soil microorganisms in batch systems and soil-packed columns under
different ﬂow and biomass conditions (Estrella et al., 1993). They reported that the
maximum speciﬁc growth rate coefﬁcients were greater in the column systems.
However, because of limitations in their mathematical formulation, it was necessary for
them to gum; the same yield coefﬁcient and half-saturation constant for both batch and
column systems.

The objective of the present study was to evaluate the relationship between kinetic
parameters in both batch and column systems without the assumptions that have been
necessary in previous column studies. This was accomplished by selecting a
sand/contaminant/organism combination that resulted in signiﬁcant biomass attachment
but only minimal contaminant sorption to the sand in the columns. In addition, relatively
high ﬂow rates and low total biomass levels were used to minimize external and internal
mass-transfer resistances, respectively. Finally, by using columns of different lengths,
the effect of residence time could be evaluated independent of ﬂow rate, and it became
possible to simultaneously evaluate all of the degradation parameters.

Naphthalene was selected as the substrate for this study. As one of the
components in petroleum products such as gasoline and diesel fuel, naphthalene
contamination of soil and groundwater is not uncommon. Because naphthalene sorbs
signiﬁcantly to soils with relative low organic content, bioavailability of this compound
has been studied by several researchers (Guerin and Boyd, 1992; Guerin and Boyd, 1995;
Zhao and Voice, 2000). However, in order to fully understand the interactions between

sorption and biodegradation processes, each processes must be evaluated independently.

109

This study was designed speciﬁcally to understand naphthalene degradation rates per se,
so that interactions between biodegradation and sorption/desorption could be addressed in
a subsequent study, with an overall goal of understanding the bioavailability of

naphthalene in the soil environment (Zhao and Voice, 2000).

MATERIALS AND METHODS

Materials

Pseudomonas putida (ATCC 17484), which has been established to grow on
naphthalene as a sole source of carbon and energy was obtained from the American Type
Culture Collection (Guerin and Boyd, 1995). Bacteria were grown in 500 mL high-
buffer broth (HBB) composed of 2.0 g of NaCl, 3.0 g of (NH4)2HPO4, 1.2 g of KH2P04,
3 mg of MgSO4, 1 mL of ferric quinate (Blakemore etal., 1979), 1 mL of vitamin
solution (Wolin et al., 1963), per liter of distilled water at pH 7.0. Naphthalene was
added to the sterilized liquid medium as a concentrated stock solution in acetone (200
g/L) to a ﬁnal concentration of 200 mg/L. The liquid medium was inoculated by adding
5 mL of starved liquid culture to 500 mL medium, and stirred. Growth was monitored by
absorbance at 600 nm, and cells were harvested at a point determined to correspond to
early stationary phase based on a full growth curve. Cells were separated by
centrifugation (1900 x g, 20 min) and resuspended in phosphate buffer saline (PBS)
solution before use. The PBS contained 8.5 g of NaCl, 0.6 g of NazHPO4, and 0.3 g of
KH2P04 per liter and had an ionic strength of 0.16 M. This procedure was repeated three
times to ensure the removal of remaining naphthalene in the cell grth medium. The

cell suspension was stored at room temperature and used within 2 hours.

110

To produce the best estimates of the biodegradation rates in column experiments,
the effects sorption to the solids were minimized. Ottawa sand was selected as the solid
medium because sorption of naphthalene to the sand was negligible, having a measured
distribution coefﬁcient (Kd) of 0.089 mL/ g. The following characteristics of the sand
were measured: bulk density of 1.64 g/mL; particle size distribution, 26.3% coarse sand
(0.5-0.7 mm), 73.2% medium sand (0.25-0.5 mm), 0.5% ﬁne sand (0.18-0.25 m);
organic matter content below the detection limit of 0.03%; and pH of 6.5. The sand was
placed in sealed container and was sterilized by gamma irradiation at a dosage of 2 Mrad
in a cobalt-60 irradiator (Phoenix Memorial Laboratory, University of Michigan, Ann
Arbor, Michigan). The sealed sand was stored at 25 °C. Before the sand was used,
microbial growth was examined by plating 0.1 g sand on a nutrient agar plate. There was
no colony formation after 4 days of incubation at 30 °C.

Analytical Methodology

Concentrations of naphthalene, ”C02 and intermediate degradation products were
determined at each time point in the batch and column assays. One-mL samples were
placed into 10 mL of liquid scintillation cocktail for determination of total 14C activity by
liquid scintillation counting. A second l-mL sample was centrifuged (1200 x g, 10 min.)
to separate sand and biomass and the supernatant analyzed for naphthalene by high
pressure liquid chromatograph (HPLC) or gas chromatography (GC). In the HPLC
method, separation was accomplished using a C13 reverse-phase column and an 80%
acetonitrile in water mobile phase under isocratic conditions with a ﬂow rate of 1
mL/min. Naphthalene was detected by ultraviolet absorption at 225 nm. Using an

injection volume of 50 uL the detection limit was 2 ug/L. The GC methodology utilized

111

the headspace technique reported by Voice and Kolb (Voice and Kolb, 1993). Separation
was accomplished using a PE-624 capillary column followed by detection using a
photoionization detector (PID). The detection limit was 17 pg/L.

Mineralization rates for naphthalene were evaluated by measuring the production
of 14C02. To accomplish this, naphthalene was ﬁrst removed from the sample by stirring
with a magnetic stirring bar for 2 hours at 700 rpm after adjusting pH to above 12 to
minimize the volatilization of C02. This procedure was found to remove greater than
95% of the naphthalene from prepared standards. After the sample was acidiﬁed to less
than pH 2.0 with 0.2 mL of 6N HCl, ”CO; was trapped within 300 hr. of base (0.1 M
KOH) suspended from a tube stopper inside a plastic cap. After overnight degassing, 280
uL of the KOH was transferred to scintillation vials with 10 mL of liquid scintillation
cocktail. The radioactivity was measured by liquid scintillation counting. The efﬁciency
of trapping l4C02 was found to be greater than 90% using NaII'4CO3 standards (Plaehn et
al., 1999). Subsequent to C02 release, a one-mL aliquot of the remaining solution was
transferred to a vial which contained 10 mL scintillation cocktail to measure the amount

of MC contained as intermediate degradation products.

Batch experiments

Degradation parameters were determined under two batch experimental
conditions: sand-free solutions of phosphate buffer saline (PBS), and sand-free solutions
with the supernatant from the sand-PBS mixture. In both types of experiments, ten mL of
re-suspended cells (107-108 CFUs/mL) were placed into 20 mL serum vials. A stirring

bar was added to the vial to provide mixing. Biodegradation was initiated by spiking an

112

aliquot of l4C-labeled naphthalene stock (in methanol) into the vials at an initial
concentration of 400-700 ug/L. Two types of control vials, prepared by adding NaOH to
prevent degradation, were used as a check for losses. One contained only PBS and the
other contained PBS with dispersed cells. The naphthalene-spiked samples were mixed
at 800 rpm on a magnetic stirrer. Each bottle was removed at predetermined time
intervals and biodegradation was stopped by increasing pH to greater than 12 with
injection 0.1 mL of 10 N NaOH. Naphthalene concentrations were measured by GC or
HPLC and the activity of 14C02 and intermediate products were monitored by liquid
scintillation counting. All experiments were performed at 24 i 1 °C.

To test the effect of soil organic matter that might be released from the sand to the
aqueous solution, a degradation-rate experiment was conducted using PBS that had been
in contact with the sand. 200 g of sterilized sand was mixed with 200 mL PBS and
tumbled for 2 hours at 6 rpm. The mixture was centrifuged at 2100 X g for 20 min to
separate the sand and supernatant. The supernatant was then used in the degradation
experiment, as described above, and compared to the results from PBS that had not been

in contact with the sand.

Column experiments

Sand columns were constructed from stainless steel tubing (1D. 1.1 cm) and
SwagelokTM ﬁttings in lengths of 5, 10, 15, and 20 cm. Porous stainless-steel frits
(thickness, 3 mm: pore size, 0.02 mm) were placed at each column end to prevent loss of
sand. All column parts were sterilized by autoclaving prior to packing, and the sterilized

sand was added to the columns under a laminar ﬂow hood. Sand columns were saturated

113

and washed with 5 pore volumes of PBS solution at a ﬂow rate of 10 mL/min. The
average porosity of the packed column was 0.38 determined by a weight measurement
before and after water saturation of the columns.

The columns were inoculated by pumping 2.67 pore volumes of cell suspension at
a ﬂow rate of 5 mL/min using a syringe pump. Flow was stopped for at least 15 minutes
to facilitate attachment. Nonattached cells were then washed out by ﬂushing the columns
with 3 pore volumes of PBS solution at a ﬂow rate of 3 mL/min. l4C-labeled naphthalene
solution (68,000 DPM/mL) was pumped through the inoculated sand columns.

The experimental matrix included four column lengths, four ﬂow rates, and four
cell densities (Table 6-1). Four column lengths and three inﬂuent concentrations at a
constant cell density were designed for kinetic parameter estimation. Four different-ﬂow
rates at one column length, one cell-density level and one inﬂuent concentration were
used to evaluate the effect of velocity on degradation rate. Four different cell densities at
one column length and one ﬂow rate were used to evaluate of the effect of biomass level
on degradation rate. In all cases, the naphthalene concentrations in the efﬂuent were
stable after 3-pore volumes of solution were fed.

Efﬂuent samples were collected directly into 20-mL headspace vials, which
contained NaOH to stop biodegradation. Naphthalene losses resulting from sorption or
bacterial contamination were evaluated using non-inoculated packed columns, which
were prepared in the same way as the inoculated columns, at two different ﬂow rates.
After three pore volumes, inﬂuent and efﬂuent concentrations were equal, indicating that

sorption to the system was complete and there were no fortuitous losses.

114

The amount of attached biomass was determined by the difference between
protein measurements of the inoculum and the efﬂuent during the wash step, using
bovine serum albumin as a standard protein (Pierce, 1989). 33i3% of the inoculum cells
were attached to the sand for all column lengths. To study the distribution of biomass in
the sand column, sand samples were removed from three 10-cm columns after washing,
divided into three segments, digested in 4.5 mL of 0.1 N NaOH solution for 2 hours, and
the protein content measured. No signiﬁcant differences were found in the distribution of

cells in the inlet, middle and outlet one-third portions of the three columns.

115

Table 6- 1. Experimental matrix for the column assays

 

 

 

 

 

 

 

Protein
Column . Cell Density Inﬂuent
Experiment Flow rate concentration .
length _ (CFU/g concentratron
(mL/mrn) (pg/L in pore
(cm) Sand) (Hg/L)
volume)
506
5 3 2100 1.36x107 368
221
506
10 3 2100 1.36x107 368
Kinetic 221
parameter 506
estimation 7
15 3 2100 1.36x10 368
221
506
20 3 2100 1.36x107 368
221
0.5
Flow rate 1 7
5 2490 1.62x 10 498
effect 2
3
6353 4.13><107 699
Cell 7
3071 1.99x10 613
density 10 3
2065 1.34x107 613
effect
1006 6.53x106 613

 

116

To estimate the dispersion coefﬁcient and effective porosity in the columns, a
step-increase of tritiated water (3H20) (81,000 DPM/mL) was pumped through a control
sand column at several ﬂow rates ranging from 0.5 to 3 mL/min. The efﬂuent was
sampled every 10 sec until the concentration was same as the inﬂuent as measured by
liquid-scintillation counting. The dispersivity and effective porosity were found to be
0.11 cm and 0.38 respectively, using the solution to the equation for one-dimensional

saturated homogeneous ﬂow in porous media (Freeze and Cherry, 1979).

Calculation of kinetic parameters

The non-growth Michaelis-Menten equation was used to calculate the half-
saturation coefﬁcient (K,, ug/L) and maximum speciﬁc utilization coefficient (km, min")
in both batch and column assays. The non-growth assumption was based upon several
experimental conditions designed to minimize the potential for growth: nitrogen was not
included in the PBS solution, the amount of inoculation was large when compared with
the naphthalene concentration, and the experimental time was relatively short. The
validity of this assumption was also examined by measuring the cell concentrations
between before and after one experiment; no signiﬁcant differences were found. The non-

growth equation can be used directly for batch experiments:

_ dC _ kaoC

—_ E.1
dt K,+C (q)

where X0 is initial protein content (pg/L), C is naphthalene concentration in liquid
(pg/L), and t is time (min). In columns, the advection/dispersion equation was used to

formulate the system mass balance, with a Michaelis-Menten reaction term:

117

dC dC 2 dC kaoC
_ = D __ V _ _
dt alx2 dx K.- + C

 

 

(Eq. 2)

where R is the retardation coefﬁcient, D is the dispersion coefﬁcient (cmz/sec)

and V is average pore velocity (cm/sec). At steady state, Eq. 2 can be simpliﬁed to:

ac2 _VdC _ kaaC

D __
dxz dx Ks'l’C

 

 

(EQ- 3)

Because dispersion was small when compared with advection in these systems (D

= 0.01 cmz/sec), it can be ignored, and Eq. 3 can be further simpliﬁed:

£_kaaC
dx Ky+C

 

(EQ- 4)

Converting the independent variables length (x) and pore velocity (V) to residence

time (t,), Eq. 4 becomes:

 

dC _ kaaC

— — 5.5
dt, K.+C (Q)

In batch systems, the terms km and Ks were estimated simultaneously for each
batch experiment using concentration-time data and Eq. 1. In the column experiments,
this was not possible because each column only produces one steady-state concentration,
so the parameters were estimated from multiple experimental conditions (initial
naphthalene concentration, ﬂow rate and column length). In both cases, estimations were
accomplished using the nonlinear regression algorithm in the ModelMaker software
package (Cherwell Scientiﬁc Publishing, Oxford, United Kingdom).

If the value of K5 is signiﬁcantly smaller than the value of C, then Eqs. 1 & 5 can
be simpliﬁed to a zero-order relationship and the zero-order reaction coefﬁcient, k0 (

ug/L-min) can be determined from:

118

 

E — koXo (for batch) (Eq. 6)
— :1: = ktho (for column) (139- 7)

The initial production of CO2 was found to follow a zero—order relationship in

both systems (see Figures 6-3 and 6-6). The parameter kc02 ( ug/L-min) was estimated

 

from:
1%: = kcozX: (for batCh) (EQ- 8)
dgﬂ = kcoZXo (for column) (Eq. 9)

where C002 is the concentration of produced CO2, (pg/L as naphthalene).

RESULTS AND DISCUSSION

Degradation and mineralization of naphthalene by suspended cells in batch systems

It can be seen in Figure 6-1 that naphthalene was rapidly removed within the ﬁrst

ﬁve minutes in the batch experiments. An increase in measurable intermediates mirrored

naphthalene disappearance until the point of naphthalene depletion. C02 production was

much more gradual, and continued to increase after the concentration of intermediates
peaked. The recovery of total l4C-activity was greater than 90% throughout the

experiment.

119

120

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

:3 A
c 100 ., RecoAy‘lgry % x H
-8
:3
'8 80
U)
.8 Naphthalene
:2 60
o
:1
.3
. 40
§
#8 Intermediate
U- 20
8 co, . >
U 0 ; 1— ; -

o 1 2 3 4 5

Time (min)
,3 120
a.
c:
,O 100 Recovery
1: A". x
.2
8 80' Intermediate
.9 +
g 60
'1:
.8
° 40
g Naphthalene 002
f3 3
U: 20
c:
0
U o
100 150 200 250 300
Time (min)

 

Figure 6- 1. Degradation of naphthalene, production of intermediates and carbon
dioxide in a batch experiment with free suspended cells. Protein content; 780 ug/L
(2.15x107 CF U/mL), Initial substrate concentration; 520 ug/L of naphthalene.

A: Initial incubation time within 5 min
B: Extended incubation time up to 300 min

120

This sequence indicated that the ﬁrst step in the process, removal of naphthalene,
was not rate-limiting with respect to the overall mineralization process. Several possible
intermediates can be formed during the degradation of the naphthalene. Barnsley
reported that ATCC 17484 is able to transform naphthalene to 1,2-dihydroxy-
naphthalene, which is then converted to catechol. Catechol can be further utilized
following an artha-cleavage pathway (Barnsley, 1976, Appendix A). Because this study
focused on the degradation of the naphthalene, identiﬁcation of the intermediates was not
attempted. However, it was noted that the intermediates were found to be hydrophilic
and nonvolatile, were not retained (i.e same retention time with water) on a C13 reverse-
phase HPLC column in this separation condition, and absorbed light in the 220 nm UV.

The maximum speciﬁc utilization rate coefﬁcient, km, and the half-saturation
constant, K,, were estimated as 0.115 (i0.004) min’1 and 5.5 (i026) ug/L , respectively
(Figure 6-2). Because of the extremely low value of K5, the disappearance of the
naphthalene could be adequately described as a zero-order reaction and the rate
coefﬁcient, k0, was estimated to be 0.110 (i0.003) min". The production of C02 also
followed a zero-order relationship and the coefﬁcient, kcoz, was 0.0068 (i0.00007) min'1
(Figure 6-3). Correlation coefﬁcients (r2) were 0.99 for the Michaelis-Menten equation,
0.99 for the zero—order equation, and 0.98 for the mineralization of naphthalene. Guerin
and Boyd studied the naphthalene utilization activity of ATCC 17484 and estimated the
utilization activity to be in the range 0.0025 - 0.015 min], which is one magnitude less
than our value (k0=0.11 min") (Guerin and Boyd, 1995). This may be explained by
experimental differences between the two studies. Guerin and Boyd utililized much

greater initial concentrations (25,600 vs. 450 ug/L) and performed the degradation

121

experiments in quartz cuvettes without mixing, possibly resulting in oxygen transfer
limitations. We utilized serum bottles and continuous stirring. In addition, we analyzed
naphthalene chromatographically (HPLC and GC), whereas the earlier study monitored
UV absorption at 277 nm, which may be biased by other light absorbing species.

To determine whether leachable material from the sand could have affected
degradation rates, batch studies using PBS and sand supernatant were compared. As can
be seen in Figure 6-4, there was no apparent difference in disappearance of naphthalene

between two solutions.

122

500 ~ —— _ — — —.

1' 5'1

450 0 Experimental data

400 l

 

Regression

350 .

300 .

 

250

200 -

150 1

Concentration of naphthalene (ug/I.)

100 -

50‘

 

0 2 4 6 8 1O 12 14 16

Time (min)

Figure 6- 2. Estimation of degradation parameters of naphthalene in a batch experiment

with free suspended cells. Protein content; 420 ug/L (1 .16x107CFU/mL), Initial
concentration of naphthalene; 445 ug/L, K,; 5.5 (i026) pg/L, km; 0.115 (i0.0004) min’

', k0; 0.110(i0.003) min". The values in the parentheses are the standard deviations

obtained by nonlinear regression.

123

40.0. ,7, 7 a- , a”? ,2 .7 7 7 7. .2

350 l . 9 Experimental data]

   

—Regression i
, - - —— —~ 0

 

 

Naphthalene concentration mineralized (ng/ ml)
8
C

Time (min)

Figure 6— 3. Estimation the initial mineralization rate of naphthalene in a batch
experiment with free suspended cells. Protein content; 420 ug/L (1.16x107CFU/mL),
Initial concentration of naphthalene; 445 ug/L, kco2; 0.0068 (1000007) min". The value

in the parentheses is the standard deviation.

124

800.0 .
700.0
600.0 .
500.0 .
400.0 .
300.0 .
200.0
100.0 .
0.0

   
   

” A ‘suﬁérﬁaaat;
0 PBS 1

. -—-- Simulation

Concentration (ug/I.)

 

 

 

Time (min)

Figure 6- 4. Comparison of degradation rates of naphthalene in PBS and supernatant in
batch experiments. Protein content; 337 ug/L (9.3x106 CFU/mL), initial concentration;

667 ug/L. Error bars indicate standard deviation.

125

Degradation and mineralization of naphthalene by attached cells in sand columns
A series of column assays were conducted to evaluate naphthalene degradation
and mineralization rates by attached cells in sand columns (Table 6-1). The residence
time was calculated based upon the ﬂow rate and pore volume of each column. Using
Eq. 5, the half-saturation constant, Ks, and maximum speciﬁc utilization rate, kn”, were
estimated to be 49.6 (3.1.4) ug/L and 0.0965 (i0.0005) min", respectively (Figure 6-5).
The zero-order reaction coefﬁcient for degradation, k0, was calculated to be 0.080
($0.010) min'l using the data points when the efﬂuent naphthalene concentration was
greater than 100 ug/L (Figure 6-5) and the zero-order reaction coefﬁcient of
mineralization, kw, was 0.0062 (i0.00013) min'1 (Figure 6-6). Correlation coefficients
(r2) were 1.00 for the Michaelis-Menten equation, 0.97 for zero-order degradation, and

0.94 for mineralization.

126

600.0

 

 

 

 

 

 

 

,_ TREgression for zero-order
equaﬂon
. Co=506 ug/L
. l
500 0 . Co=368 ug/L
Co=221 ug/L
S 4000 - Regression for Michaelis-
\ Menten equation
or: , -_ , ,
3
c:
8 000
a 3 . a
I:
c:
a
u
8
U 200.0
100.0
0.0 . g
0.0 1.0 2.0 3.0 4.0 5.0 6.0
Time (min)

Figure 6- 5. Estimation of degradation parameters of naphthalene in column

experiments with attached cells. Protein content; 2100 ug/L (1.36x107 CF U/g sand),
ﬂow rate; 3m1/min, inﬂuent concentrations; 506, 368, 221 ug/L, k0; 0.080 (i0.010) min'
', km; 0.0965 (:0.0005) min",K,; 49.6 (i1.4) jig/L. Error bars indicate standard

deviations. The values in the parentheses are the standard deviations obtained by

nonlinear regression.

127

3O

 

25
20
1 5

10

0 Experimental data i

0.0

—— Regression

 

 

 

Naphthalene concentration mineralized (ng/ ml)

Figure 6- 6. Estimation of initial mineralization of naphthalene in column experiments
with attached cells. Protein content; 2100 ug/L (1.36x107 CF U/g sand), ﬂow rate;
3ml/min, inﬂuent concentrations of naphthalene; 506, 368, 221 ug/L, kco2; 0.0062

($000013) min". The value in the parentheses is the standard deviation.

128

Comparing rate coefﬁcients in both batch and column systems, it could be seen
that R0 was reduced by a factor of 27% and km2 by 8% in columns when compared to
batch systems (Table 6-2). The decreases in ko and kw2 were found to be signiﬁcant at
the 98% and 56% conﬁdence levels, respectively. It has been suggested that the exposed
surface area of attached cells is less than suspended cells, which can reduce degradation
rates (Harms and Zehnder, 1994). To estimate the effect of this surface area reduction,
we measured the dimensions for the rod-shaped cells (0.5 pm diameter x 1.2 pm length).
Assuming monolayer-cell coverage (the portion of the sand surface covered by cells is
less than 0.5 %), the attached cell presented 17 % less area to bulk solution. This value
must be treated as only approximate, because we were not able to account for cell
clustering or irregularities in the sand surface. It was noted that this value was similar to
the reductions in k0 and km, which were reduced by 27% and 16% respectively. Because
the k0, km, and kw2 values represent a series of mass-transfer and reaction processes
occurring within the cell membrane, the dependence on surface area likely has a
mechanistic basis.

Activity decreases with attached bacteria have also been observed in batch assays
by other workers (Gordon et al., 1983; Jeffrey and Paul, 1986). Jeffery and Paul (Jeffrey
and Paul, 1986) found that the thymidine incorporation rate and the ATP/DNA ratio of
free suspended cells were greater than those of attached cells. They ascribed the reduced
rates to physiological differences between the two systems, primarily a reduction in the
amount of cell surface available for nutrient uptake. Gordon et al. (Gordon et al., 1983)
reported that attached cells had a decreased metabolic efﬁciency and that the rate of

respiratory metabolism was slower than that of suspended cells in their assays. They

129

suggested several possibilities including reduced availability of inorganic nutrients or
oxygen, pH effects, and reduction of exposed cell surface area.

In the present study, we had no reason to believe that the decrease in cell activity
was caused by limitations in the availability of the electron acceptor, substrate, or other
nutrients in the bulk solution of the column systems. Air-saturated PBS was used in both
systems. Because the maximum initial concentration of the naphthalene was less than
700 rig/L, dissolved oxygen was sufﬁcient, remaining more than 7.0 mg/L during the
entire course of the experiment. Sorption of naphthalene to the sand and column
components was negligible after 3 pore volumes passed the columns (veriﬁed in non-
inoculated packed columns). pH effects on sand surface were eliminated because the
sand was thoroughly washed and equilibrated with PBS before inoculation, and ATCC
17484 was found to have same activity at both the original sand pH of 6.5 and the buffer
pH of 7.0. The degradation rate of naphthalene was also tested in the supernatant, and
was found to be the same as that in PBS (Figure 6-4). The reduction of exposed cell
surface was therefore considered to be the primary factor in the decrease in cell activity
as reﬂected by the values of k0 and km.

The differences between batch and column experiments became more problematic
at low concentrations (i.e., substrate concentrations less than 100 ug/L), corresponding to
large levels of removal. While the batch systems appeared to follow zero—order kinetics
across the entire concentration range (Figure 6-2), the columns deviated from this model
below approximately 100 ug/L (Figure 6-5). The behavior at low concentrations is
described by the half-saturation coefﬁcient, K,. These values were signiﬁcantly greater

in the columns than those in batch systems (Table 6-2). Harms and Zehnder also found

130

greater K, values for degradation of dibenzofuran and 3-chlorodibenzofuran by attached
cells of Sphingomonas sp. strain HHl9k. They attempted to model this considering the
change in diffusive ﬂux resulting from the smaller surface area of attached cells.
However, their experimental K, values were much greater than theoretical predictions.
They concluded that the discrepancy could have resulted from the assumptions used in
their calculations, non-unifonn distribution of cells within the column, variations in the
boundary layer across which diffusive transport occurs, and the presence of preferential
ﬂow paths.

The dispersion coefﬁcient, which reﬂects hydraulic heterogeneity, was evaluated
in the tritium tracer study in the non-inoculated packed columns. As noted previous, the
dispersion term in Eq. 3 was negligible when compared with the advection term.
However, it is possible that reactive heterogeneity might occur in the inoculated columns
as a result of the differences between the distribution of cells and the paths of water ﬂow.
Reactive-heterogeneity implies that all ﬂow paths do not necessarily contain the same
biomass and therefore do not offer the same reactivity. This effect is not captured by the
dispersion coefﬁcient, and the advection-dispersion equation assume that all paths are

similarly reactive.

131

Table 6- 2. Comparison of kinetic parameters between batch and column experiments

 

 

 

 

k0 km Ks kcoZ
(min’l) (min'l) (pg/L) (min'l)
0.110 0.115 5.5 0.0068
Batch , , , ,
(i0.003) (£00004) (i026) (i0.00007)
0.080 0.0965 49.6 0.0062
Column , , , ,
(i001) (i0.0005) (i1.4) (i0.00013)
Comments
(comparing 27 % .
. l6 % reduction 9-fold increase 8 % reduction
column to batch reduction
values)

 

*: Standard deviation given by ModelMaker software during nonlinear regression.

132

The sand pore structure can be viewed as a “bundle of capillary tubes” of different
sizes and shapes (Tindall and Kunkel, 1999). Cells will colonize the most favorable
locations, which may be far from the primary ﬂow paths, so as to avoid shearing effects.
Thus, the solution to cell transfer distance for the substrate in packed columns may be on
the order of several sand particle diameters. In a suspended-cell system, the distance is
the aqueous boundary layer surrounding the cell, which will likely be much smaller.
Furthermore, the variation in transfer distance will be much greater in columns, as cells
may reside both close to and far from the various ﬂow paths. The result is essentially
increased dispersion of substrate in inoculated columns, where substrate in some paths
encounters fewer cells than in others. Thus, reactive-heterogeneity decreases reaction
efﬁciency and increases the relative substrate concentration in the efﬂuent over that
which would be expected from the assumption of uniform reactivity implicit in the
advection-dispersion equation. The result is greater measured/ﬁt K, values. Thus K, is
not an intrinsic parameter. Rather, it is a lumped mass-transfer parameter that describes
the overall process of transport from the bulk ﬂuid to the cell surface, and is always

dependent upon the nature of the system in which degradation occurs.

External mass-transfer effects

The naphthalene concentrations in the efﬂuents at different retention times were
measured to evaluate the effect of external mass transfer in the pore space on degradation
rates (Figure 6-7). Also shown is the predicted curve using the degradation parameters
obtained from the previous column experiments with the assumption that overall

degradation rates are not dependent on the liquid ﬂow rate. It appeared that this

133

assumption was true for ﬂows of 1 mL/min or greater, based on the coincidence of the
measured values and the simulation for all but the lowest ﬂow rate. At 0.5 mL/min,
efﬂuent concentrations were greater than predicted. Harms and Zehnder reported that the
half-saturation coefﬁcient (K,) increased as ﬂow rate decreased, producing greater-than-
expected efﬂuent concentrations (Harms and Zehnder, 1994). Tros et al. suggested that
this was the result of channeling and immobile water, because channeling reduces
residence time and mass-transfer resistance increases in immobilized water regions (Tros
et al., 1998). In the present study, however, the immobilized-water region was less than
5% of the pore volume for all pore-water velocities used, as determined by the tritium
tracer study. Furthermore, Li et al. has shown that the immobile water fraction was
determined by the sand structure but was independent of the velocity (Li et al., 1994),
while Grifﬁoen et al. reported mobile water fraction was constant or decreased with
increasing pore water velocity in their literature review (Grifﬁoen et al., 1998). We
therefore conclude that the effects observed in our data did not result from immobile

water.

134

600.0

 

.5 Flow; rat—azai'rilrrin =
9 . Flow rate: 2ni/m'n
500.0 . A Flow rate: 1rrﬂm'n ;
0 Flow rate: 0.5mi/m'n‘
l_Simuiation !

   

400.0

300.0

200.0

Efﬂuent concentration (ug/ L)

100.0

 

 

0.0

 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Residence Time (min)

Figure 6- 7. Flow rate effect on degradation of naphthalene in column experiments.
Column length; 5 cm, Protein content; 2490 ug/L (1.62x107 CFU/g sand), initial
naphthalene concentration; 498 ug/L, ﬂow rates; 3 mL/min, 2 mL/min, lml/min and 0.5

mL/min, K,; 49.6 jig/L, km; 0.0965 min". Error bars indicate standard deviations.

135

As discussed previously, K, is a lumped mass-transfer parameter describing
transport to the cell surface. It is commonly observed that the external mass-transfer
resistance increases with decreases in pore-velocity, as evidence by correlation between
mass-transfer coefﬁcients and Reynolds numbers (Weber and DiGiano, 1996). Although
these results do not provide a mechanistic basis for our observations, the dependence of
K, on velocity is not unexpected. We suggest that this dependence may be another
manifestation of reactive-heterogeneity of bioactivity. As velocity decreases, the
residence time in all of the capillary pathways will increase proportionally. The result
should be increased removal of substrate. However, in highly reactive pathways (i.e
pathways with more cells), this increase will result in depletion of the substrate before the
ﬂow reaches the column exit. Any bio-reactivity in the pathway after the point of
depletion is effectively wasted. This wasted activity does not contribute to removal in the
column system, and thus the integrated removal rate on a biomass basis is reduced. The
result is that column efﬂuent concentrations will be greater than would be expected by

the implicit assumption that all of the biomass is active.

Internal mass-transfer effects

Internal mass-transfer limitations (within the biological matrix) might also affect
degradation rates because cells interior to the matrix may see a reduced substrate ﬂux.
Such a limitation would result in rates that are not strictly proportional to biomass levels,
but drop below expected rates. This possibility was investigated by inoculating the sand
columns with four different cell densities (Table 6-1). The predicted values in Figure 6-3

were calculated using the degradation parameters estimated from previous sand column

136

experiments in Figure 6-5. There were no signiﬁcant differences between the measured
and predicted values for the low-cell-density cases (1006 and 2065 ug/L). As the cell
density increased, the measured values were greater than the predicted values (Table 6-3)
indicating some internal mass-transfer resistance. Even at the highest inoculation levels,
the cells theoretically covered less than 1% of the sand surface. Harms and Zehnder also
reported a similar result: half-saturation coefﬁcients (K,) for DF and 3CDF degradation
by strain HH19k increased as cell density increased in their columns (Harms and

Zehnder, 1994).

137

Table 6- 3. The predicted and measured values of efﬂuent naphthalene concentrations at

different cell densities

 

 

Protein density Cell Density Predicted value Measured value Difference
(118/L) (CPU/g sand) (lug/L) (us/L) (118/L)
6353 4.13x107 70 192 (:509)‘ 122
3071 1 .99x107 294 370 (:99) ‘ 76
2065 1.34x107 397 419 (i127) " 22
1006 6.53x1o6 503 510 (:94) ‘ 7

 

* Standard deviation of three measurements.

There is another factor that could cause the higher naphthalene concentrations in
the effluent at greater inoculation densities: increased cell washout. However, the optical
density of the efﬂuent did not increase and the effluent cell density was below the
detection limit (5 x 105 CFU/mL) after inoculation in all of the columns, and the
degradation rate was stable after the acclimation period. These observations suggest that
cell detachment was negligible.

It should be noted that this result might also derive from reactive-heterogeneity.
At increased biomass levels substrate depletion will occur earlier in the most reactive
ﬂow paths. Thus, even more of the bioactivity would be wasted, as compared to smaller
biomass levels, and the overall inefficiency would be greater. The result, as discussed

above, would be greater than expected K, values and effluent naphthalene concentrations.

138

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Metabolism in Pseudomonads Metabolizing Naphthalene and Salicylate.” J. Bacterial
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Blakemore, R. P., D. Maratea and R. S. Wolfe. 1979. “Isolation and pure culture of a
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720-729.

Bright, J. J. and M. Fletcher. 1983. “Amino Acid Assimilation and Respiration by
Attached and Free-living Population of a Marine Pseudomonas sp.” Microb. Ecol 9: 215-
226.

Caldwell, D. E. and J. R. Lawrence. 1986. “Growth kinetics of Pseudomonas
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Dolﬁng, J. 1985. “Kinetics of methane formation by granular sludge at low substrate
concentrations.” Appl. Environ. Microbial. 22: 77-81.

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141

CHAPTER 7 SUMMARY AND CONCLUSIONS
SUMMARY
The objective of this study was to investigate the bioavailability of sorbed
contaminants in solids, which has been an important issue in applying bioremediation
technology to the restoration of contaminated sites. In order to accomplish this, four
main objectives were pursued in sequence.
The ﬁrst objective was to develop an experimental and mathematical approach to

evaluate bioavailability of contaminants sorbed in artiﬁcial solids (silica particles).

 

Desorption rates of 2,4-dichlorophenoxyacetic acid (2,4-D) were measured in the absence
of organisms and the biodegradation rates were measured in the absence of the solids.
After the silica particles were saturated with 2,4-D, the system was inoculated with the
2,4-D degrading microorganism F lavorbacterium sp. strain FB4. The disappearance rate
of 2,4-D was measured and found to be greater than the rate predicted based upon liquid-
phase 2,4-D concentrations. A kinetic formulation, termed the enhanced bioavailability
model, was deve10ped to describe the desorption and biodegradation processes in this
batch system. The approach assumes that 2,4-D resides in both the liquid and solid phases
and degradation occurs via both suspended and attached biomass. A11 biomass can
degrade liquid-phase 2,4-D at one rate, while only attached biomass can degrade sorbed
2,4-D at another rate. An enhanced transformation factor (Ef) was introduced to express
the increased biodegradation rate over that expected from the liquid phase only. This
approach was able to account for the increased degradation rates observed
experimentally. The results provide evidence that desorption to the bulk solution is not

prerequisite to degradation, and that sorbed substrate may be available for degradation.

142

The second objective was to evaluate the effects of biodegradation kinetics on the

 

bioavailability of sorbed naphthalene. Degradation assays were used instead of
mineralization assays. Four different organisms with zero-order, ﬁrst-order, and
Michaelis-Menten rates, and two soils with different sorption distribution coefﬁcients
were selected. Sorption and desorption was evaluated in abiotic soil-slurry systems. The
desorption process was described by a model that accounts for equilibrium, rate-limited
and non-desorbing sites. Biodegradation parametes were measured in soil-extract
solutions. Bioavailability assays, inoculated soil-slurries, were conducted and both
liquid- and solid-phase naphthalene concentrations were measured over time. For the
less sorptive soil, the results could be explained by sequential desorption and degradation
processes. For the other soil, enhanced degradation was clearly observed for the
organisms with ﬁrst-order and Michaelis-Menten rates. No enhancement was found for
the organism with zero-order kinetics.

The third objective was to develop a quantitative mathematical model to describe
the bioavailability of chemicals sorbed by natural soils. Two naphthalene degrading
organisms (which were chemotactic to solid naphthalene) and four soils with different
distribution coefﬁcients were selected. Sorption isotherms and single/series dilution
desorption experiments were conducted to evaluate distribution coefﬁcients, to estimate
desorption parameters, and to measure the non-desorbable amount of naphthalene.
Biodegradation kinetics were measured in soil extract solutions and rate parameters
developed. For the bioavailability assays, sorption equilibrium was established, and the
systems were inoculated. The naphthalene concentrations in both sorbed and dissolved

phases were measured over time. Enhanced bioavailability, as evidenced by faster than

143

expected degradation rates, was observed in soils with the higher sorption distribution
coefﬁcients. These observations could be described by using a model formulation that
included solid-phase degradation. A portion of the sorbed naphthalene could not be
desorbed by successive water extractions, but was available for biodegradation.

The fourth objective was to evaluate the relationship between kinetic parameters

 

in batch and column systems, in order to assess the role of cell attachment and ﬂow on

 

biodegradation rates. Under this objective, Ottawa sand-packed-columns were used to
evaluate the effects of ﬂuid ﬂow, soil to water ratio, cell attachment/detachment, and the
mixing of contaminants, on the degradation of naphthalene by Pseudomonas putida
(ATCC 17484). Column results were compared to degradation rates found in well-mixed
batch systems with suspended cells. The maximum speciﬁc utilization coefﬁcient (km),
the zero-order reaction coefﬁcient (k0) for degradation, and zero-order reaction
coefﬁcient (koo2) for mineralization differed by factors similar to the calculated reduction
in exposed cell surface area. In batch systems, degradation followed zero-order kinetics
across the entire concentration range, while the columns exhibited decreased rates at
concentrations less than 100 (pg/L), describable by Michaelis-Menten kinetics. This is
reﬂected in elevated values of the half-saturation constant, K,, in columns. This may

result from reactive-heterogeneity within the porous media, imposing a distribution of

expected to have both shorter and more uniform transfer distances. This explanation was i

)I

‘t‘
also shown to be consistent with observations of the effects of ﬂow rate and biomass

levels in column experiments. When kinetic parameters obtained in batch system are

used for prediction of degradation in columns, at least two factors — exposed reduction of

144

exposed cell surface area and heterogeneity of cell distribution — will likely reduce

overall column degradation rates.

CONCLUSIONS

There is enhanced bioavailability of sorbed contaminants, which cannot be
explained by desorption and liquid-phase degradation alone.

The bioavailability of sorbed contaminants in natural soils is dependent on
biokinetic characteristics as well as the sorption distribution coefﬁcient.

Contaminants held in non-desorption sites, may be available for
biodegradation.

Biodgradation rate may be reduced in columns due to factors including the

reduction of exposed cell surface area and heterogeneity of cell distribution.

145

APPENDICES

146

APPENDIX A

Biodegradation pathway of naphthalene
Naphthalene properties

PAHs’ solubility

147

naphthalene

NADH + O 2 + H +
N AD + naphthalene-lJ-dioxygenase

HO H

O H cis -l ,2-dihydroxy-1 ,2-dihydronaphthalene

NAD +
N ADH + H +% l,2-dihydroxy-l,2-dihydronaphthalene dehydrogenase
O H

 

1,2-dihydroxynaphthalene

3g

2-hydroxychromene-Z-carboxylate isomerase

O H
/ C 00 trans-o -hydroxy-benzylidenepyruvate
( tHBP)

H20

0 2 “'\ ’
1,2-dihyd roxy-naphthalene dioxygenase
O O H _
O O
l 2-hydroxychromene-2-carboxylate
l / (HCCA)

tHBPA hydratase-aldolase
CH 3C0COO '

0 H
salicylaldehyde
C H 0

NAD +
N ADH H salicylaldehyde dehydrogenase
+ +

OH
.: salicylate
COO
NADH+2H + +02+>l
salicylate hydroxylate
NAD + +H20+CO 2

O H
catechol
0 H

Figure A- 1. Degradation pathway from naphthalene to catechol (after Wackett, 1997)

148

02
OH // coon coon coon
O\C_O O\C:O
catechol-1,2- \ COO“ \ —

Hdioxygenase 4- oxoadipate
catechol cis, cis-muconic acid muconohctone “101' hctone
H20
5
O O\
9 9 .I coon
H00CCH2CH2—CCH2C—S—C0A H00CCH2CH2—C—CH2COOH ‘—> coon
3-oxoadipyl-COA
3-oxoad'pate
CoASH j CoASH (B- ketoad'pate)
‘i ii ‘3
I l
CH3C—S—C0A + H00CCH2CH2—C—S—C0A HOOCCHZCHz—C—OH
acetyl-CoA succinyl-CoA succiiate
TCA cycle TCA cycle

Figure A- 2. Ortho-cleavage pathway for catechol catabolism (after Rittman et al.,

1994)

149

HCOOH
H2O fonnic acid

CO” .2 W0”
Q::::L catechol-2, 3- £80” CH2 COOH

dioxygenase
catechol
2-hyOdroxymuconic
semialdehyde
H20
OH 0 ll)
1
CH3CHCH2C—COOH CH2=CHCH2C-COOH

4-hydroxy-2-oxovaleric acid 2-oxopent-4-eneoate

l 4-hydroxy-2-oxovalerate aldolase

NADH CoASH CoASH CO2
+ HJ’ NAD+ \ l
0 O 0
II E 5 '1 'l I(I)
CH3C—S_COA CH3C-H + CH3C—COOH ‘ CH3C—S—COA
acetyl-CoA acetaldehyde pyruvate acetyl-COA
TCA cycle TCA cycle

Figure A- 3. Meta-cleavage pathway for catechol catabolism (after Rittman et al., 1994)

150

Table A- 1. Naphthalene Properties

 

 

 

 

Param Value (unit) Description reference
eters
M.W 128 (g/mol) Molecular weight
Tm 80.6 (°C) Melting point Schwarze
K’H 0.0197 Dimensionless Henry’s law constant nbach et
(mol L,.m“)/(mo1Lw") al., 1992

 

Wm 2.5 x 104 (mol Data") or Solid naphthalene solubility

(s) 32 (mg me“) At 25 °C and 1 atm

 

Cws‘“ 8.7 x 10‘4 (mol Lwatcr'r) or Subcooled liquid naphthalene

0) 111 (mg L....") solubility

At 25 °C and 1 atrn

 

Kow 2300 (Lwatcr/Loctamﬂ) Octanol-water distribution coefﬁcient Chiou,

Log K,,. = -O.87 log cwsat (1) + 0.68 1998

 

K0m 182 (meu/kgom) Organic matter-water distribution
coefﬁcient
Log Kom = 0.904 log KOW — 0.779

Log K,,n = -0729 log cwsat (1) + 0.001

 

 

K0C 313 (mecr/kgoc) Organic carbon-water distribution
coefﬁcient

1.72 Kom

(Using organic carbon is 58% of

organic matter, OM=OC x 1.74)

 

 

 

 

151

 

Table A- 2. Aqueous solubility of PAH compounds (Linz and Nakles, 1997)

 

Maximum Aqueous

 

 

 

 

 

PAHS Solubility* (mg/L)
2-Ring Naphthalene 30.0
3-Ring Acenaphthene 3.42
Acenaphthylene 3.93
Anthracene 1.29
Fluorene 1.90
Phenanthrene at 21 °C 0.816
4-Ring Benzo(a)anthracene at 24 °C 0.0100
Chrysene 0.00600
Fluoranthene 0.265
Pyrene at 26 °C 0.160
5-Ring Benzo(a)pyrene 0.00380
Benzo(b)ﬂuoranthene 0.0140
Benxo(k)ﬂuoranthene ' 0.00430
Dibenzo(a,h)anthrancene 0.000500
6-Ring Benzo(g,h,l)perylene 0.000260
Indeno( 1 ,2,3-cd)pyrene 0.000530

 

* Pure single compound solubility in water; at 25 °C unless otherwise mentioned.

152

APPENDIX B

F -test in statistics

153

F test1 definition
Comparison of two variances; A variance ratio test used to decide whether two
independent estimates of variance can reasonably be accepted as being two

estimates of the variance of a single normally distributed universe.

F= 3.3/st
Where S32: the larger variance estimate

Sb2= the smaller variance estimate

F test for a group of setsz.

_ (RSSiz - RSSI - RSSz)/(df12 — dfi — dfz)
RSSiz/dfiz

 

F

Where

RSS(or SSE)=X(yi-"yi)2; Residual sum of square.
SST=Z(yi-'yi)2; Total sum of square

yi=datum point.

“yi=predicted point by equation.

'yi=mean of data.

RSS1: Residual sum of square at ﬁrst set

RSS2: Residual sum of square at second set

RSS|2= Residual sum of square at combined set

 

1 Anderson, KL. 1987. Practical Statistics for Analytical Chemists. Van Nostrand Reinhold, New York,
New York.

154

df1= degree of freedom at ﬁrst set (nl-pl)

df2= degree of freedom at second set (n2-p2)

df12= degree of freedom at combined set (n.+n2-p12)
p=parameter used in the equation.

n=number of data

F test for equations3

F _ (RSS/ - Rssr) /(df; — dfr)
_ RSS/ /dﬁ

 

RSSr= Residual sum of square (or Error sum of squares) at full equation
RSS,= Residual sum of square (or Error sum of squares) at reduced equation
dfr= degree of freedom at full equation (n-pf)

df,= degree of freedom at reduced equation (n-p,)

(ex. See the book: it says SSE (sum of square of error) instead RSS)
p=parameter used in the equation.

n=number of data

 

2 Communication with Denise P. Kay

3 Neter, J., W. Wasserrnan, G.A. Wlitrnor. (1993) . Applied Statistics. 4"I ed. Boston ; Allyn and Bacon.

0r Jay L. Devore. (1995) Probability and Statistics for Engineering and the Sciences. 4th ed. Duxbury Press.
p561.

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