Download Organic Compounds in Unsaturated Soil - Engineering

Organic Compounds in Unsaturated Soil
for
ENGG*6670
Fall 2002
Richard G. Zytner, Ph.D., P.Eng.
School of Engineering
University of Guelph
ACKNOWLEDGEMENTS
The information contained in this document has been prepared for private study only. It has been excerpted
from the following documents.
Arthurs, P.A., Stiver, W.H. and Zytner, R.G. (1995) Passive Volatilization of Gasoline From Soil,
Journal of Soil Contamination, 4(123-135).
Biswas, N., Zytner, R.G. and Bewtra, J.K. (1992) Model for Predicting PCE Desorption from
Contaminated Soils, Water Environment Research, Vol. 64(170-178).
Biswas, N., Zytner, R.G., McCorquodale, J.A. and Bewtra, J.K. (1991) A Numerical Model to Predict
the Movement of PCE in Unsaturated Soil, WASP, 60(361-380).
Brook, T.R., Stiver, W.H. and Zytner, R.G. (2000) Biodegradation of Diesel Fuel under VariousNitrogen
Addition Regimes, submitted J. of Soil and Contamination.
Gidda, T., Stiver, W.H. and Zytner, R.G., (1999) Passive Volatilization Behaviour of Gasoline in
Unsaturated Soils, Journal of Contaminant Hydrology, 39:137-159.
Guigard, S., Stiver, W.H. and Zytner, R.G. (1996) The Fate of Immiscible Chemicals in Unsaturated Soil,
Environmental Technology, 17:1123-1130.
Guigard, S., Stiver, W.H. and Zytner, R.G. (1996) Retention Capacity of Immiscible Chemicals in
Unsaturated Soils, Water, Air and Soil Pollution, 89(277-289).
Harper, B., Stiver, W.H. and Zytner, R.G. (1998) The Influence Of Water Content In Contaminant
Removal By SVE In A Silt Loam Soil, ASCE Journal of Environmental Engineering,
124(11):1047-1053.
Harper, B., Stiver, W.H. and Zytner, R.G. (2002) A Non-Equilibrium NAPL Mass Transfer Model for
SVE Systems, Journal of Environmental Engineering, accepted Sept., 2002
Laplante, T., Zytner, R.G. and Stiver, W.H. (2000) Supercritical Fluid Extraction From Soil Slurries, J.
of Supercritical Fluids.
Scheibenbogen, K., Zytner, R.G., Lee, H. and Trevors, J. (1994) Enhanced Removal of Selected
Hydrocarbons From Soil By PSEUDOMONAS AERUGINOSA UG2 Biosurfactants and Some
Chemical Surfactants, Chemical Technology & Biotechnology, 59(53-59).
Shewfelt, K. and Zytner, R.G. (2001) The Effects of Nitrogen Source and Supply on Bioventing of
Gasoline Contaminated Soil, NGWA Conference on Petroleum Remediation, Houston, TX,
Nov., pp. 265-272.
Zytner, R.G. (1994) Sorption of Benzene, Toluene, Ethylbenzene and Xylenes To Various Media,
Journal of Hazardous Materials, 38 (113-126).
Zytner, R.G., Biswas, N. and Bewtra, J.K. (1993) Retention Capacity of Dry Soils for NAPLs,
Environmental Technology, 14 (1073-1080).
Zytner, R.G. (1992) Adsorption - Desorption of Trichloroethylene in Granular Media, Water, Air and
Soil Pollution, 65(245-255).
Zytner, R.G., Biswas, N. and Bewtra, J.K. (1989) PCE Volatilized From Stagnant Water and Soil, ASCE
Journal of Environmental Engineering, 115(1199-1212).
Zytner, R.G. (1988) Fate of Perchloroethylene in Unsaturated Soil, Ph.D. Dissertation, Dept. of Civil
Engineering, University of Windsor.Smyth, T.J., Zytner, R.G. and Stiver, W.H. (1999) Influence
of Water on Supercritical Fluid Extraction Of Naphthalene From Soil, J. of Hazardous Materials,
B67:183-196
Zytner, R.G., Salb, A., Brook, T., Leunissen, M. and Stiver, W.H. (2001) Bioremediation Of Diesel Fuel
Contaminated Soil, Canadian Journal of Civil Engineering, 28(Suppl. 1)131-140.
Zytner, R.G., Hallman, M., Fernández Giménez, B., Jennings, R. and Leek, K. (2002) The Use of
Anhydrous Ammonia for Bioventing, Remediation Technologies Symposium 2002, Oct. 16 to
18, 2002, Banff, AB.
TABLE OF CONTENTS
1.0
OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 1 of 68
2.0
SOIL ENVIRONMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 1 of 71
3.0
SORPTION OF CHEMICALS BY SOIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 3 of 7
3.1
Adsorption Isotherms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 6 of 71
3.2
Adsorption Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 9 of 71
3.3
Soil-Water Partition Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 12 of 71
3.4
Desorption of Chemicals from Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 14 of 71
3.5
Application of Isotherm Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 16 of 71
4.0
RESIDUAL SATURATION OF NAPLS IN SOIL . . . . . . . . . . . . . . . . . . . . Page 17 of 71
4.1
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 18 of 71
4.2
Infiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 21 of 71
5.0
VOLATILIZATION FROM SOIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1
Passive Volatilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
Soil Vapour Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3
Soil Vapour Extraction - Mass Transfer Coefficients . . . . . . . . . . . . . . .
Page 23 of 71
Page 24 of 71
Page 25 of 71
Page 27 of 71
6.0
BIOREMEDIATION OF HYDROCARBONS . . . . . . . . . . . . . . . . . . . . . . .
6.1
Nutrient Addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2
Use of Anhydrous Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3
Use of Biosurfactants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Page 34 of 71
Page 35 of 71
Page 42 of 71
Page 47 of 71
7.0
SUPERCRITICAL FLUID EXTRACTION . . . . . . . . . . . . . . . . . . . . . . . . . . Page 47 of 71
8.0
MODEL DEVELOPMENT and NUMERICAL SOLUTION . . . . . . . . . . . . .
8.1
Aqueous Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2
Vapour Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3
Immiscible Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4
Sorbed Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5
Total Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.6
Non-Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Page 53 of 71
Page 53 of 71
Page 54 of 71
Page 54 of 71
Page 55 of 71
Page 55 of 71
Page 56 of 71
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 59 of 71
1.0
OVERVIEW
Organic chemicals enter the unsaturated soil environment through various activities including accidental
spills, leaking underground storage tanks, improper waste disposal and landfill leachate. The spilled
chemical then poses an immediate threat to air, soil, surface water and groundwater quality through
volatilization into the atmosphere, retention of the spilled chemical by the soil, runoff to surface waters and
infiltration to groundwater.
Spilled organic chemicals are often immiscible with water and are often thus are present as a non-aqueous
phase liquid (NAPL). The factors that influence a NAPL’s behaviour and fate in the unsaturated soil
environment can be physical, chemicalor biological. Significant processes involved are advection, diffusion,
adsorption, desorption, volatilization and chemical and biological degradation. The relative importance of
each process is dependant on the site conditions. This includes soil type, water content and temperature.
The following sections are overviews on the processes and conditions that affect NAPL behaviour in the
subsurface.
2.0
SOIL ENVIRONMENT
The soil environment consists of solid, liquid and gaseous phases, which combine to form various physical,
biological and chemical environments. In addition, different interfaces exist, gas:liquid, liquid:solid and
solid:gas, which increase the complexity of the soil environment [Walker, 1984].
The solid phase or aggregates as referred to by many, consists of minerals, amorphous precipitates and
organic particles. These constituents vary in composition, particle size distribution and particle surface area,
which also change with depth [Alexander, 1977 and Alrichs, 1972]. By noting the variation of soil with
depth, one is able to classify a particular soil. There are essentially three horizons in the soil profile, A, B
and C. The horizon A or the surface layer contains roots, small animals and the highest quantity of
microorganisms as the organic matter concentration is the highest. The concentration of these components
decreases in layers B and C as depth increases, with C being the parent material [Black, 1965 and Foth,
1978].
The organic matter contained in the soil is the remains of decomposed plants and animals. As the remains
decompose, complex substances are formed. These complexes include aromatic and unsaturated ring
structures, carboxyl, phenolic hydroxyl, alcoholic hydroxyl, carbonal, methoxyl and amino groups [Alrichs,
1972]. Felsot and Dahm, [1979] observed that because of these functional groups, organic matter
contributes 25-90 percent of the cation exchange capacity, CEC, in many types of soils. The CEC is
defined as the sum of the exchangeable cations of a soil [Black, 1965]. The measurement is usually
expressed as milli-equivalents of ions exchangeable per 100 grams of soil. This value indicates the number
of cations held by the organic matter and clay of the soil, which can be replaced reversibly by cations of
acid and salt solutions.
The physical parameters of the soil can be broken down into individual particles of silt, sand and clay
according to size: clay, 0-2 :m; silt, 2-50 :m; sand, 0.05-2 mm [Bouwer, 1978]. These particles make
soil_overview_03.wpd
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up only 40-80 percent of the soil matrix. The remaining volume is comprised of pores filled with water,
air and other gases.
The amount of pores in the soil matrix is dependent the soil classification. Clays generally have high percent
ages of small pores, whereas sand has a low percentage. Organic matter also contributes small pores to
the soil matrix. These small pores, or micropores as they are often called, can greatly enhance the soil
capabilities to hold water [Hamaker and Thompson, 1972], as they are not free draining. Roberts et
al., [1982], reports that the water held in the micropores is called the immobile domain, whereas the larger
pores which are free draining are classified as the mobile domain.
It should also be noted that many researchers refer to aggregates when discussing unsaturated soil.
Essentially, aggregates are a combination of physical and organic solids. They cause a challenge to site
remediation and modelling as the micro pores contained in aggregates can trap contaminant and water,
making the contaminant inaccessible to remediation processes.
Related to the physical characteristics of soil are various parameters. Table 1 gives an overview of the
parameters for different soil.
Table 1: Typical Soil Properties
Soil Type
Particle Dia.
mm
Porosity
%
Dry Bulk Density
kg/m3
Hydraulic Conductivitya
m/s
gravel
8 - 16
32
1800 - 2300
3 x 10-2 - 3 x 10-4
coarse sand
0.5 - 1.0
39
1400 - 2200
6 x 10-3 - 5 x 10-4
fine sand
0.125 - 0.25
43
1300 - 2000
2 x 10-4 - 2 x 10-7
silt
0.004 - 0.062
46
1100 - 1800
2 x 10-5 - 1 x 10-9
clay
< 0.004
42
800 - 1600
5 x 10-9 - 1 x 10-11
a - hydraulic conductivity refers to water movement through saturated soil, when soil unsaturated,
water movement referred to as capillary conductivity
The water phase in the soil matrix, consists of two components. One is the capillary water and the other
is the gravitational water. The gravitational water is affected only by gravity, while capillary water depends
on the polar nature of the water molecules and hydrogen bonding with the polar surface of the soil.
Capillary water is held with a tension of roughly one-third atmosphere [Alrichs, 1972]. When the water
content of the soil equals that of the capillary, the pores will contain large amounts of air and the soil will
be considered unsaturated. However, if the pore space is completely filled with water and has only
negligible amounts of air, the soil is considered saturated. Therefore, it can be seen that the gas and liquid
phases of the soil are closely tied together.
As the gas phase moves through the soil, water is displaced, while the reverse is true when water enters
the soil. However, it should be noted that the gas composition in the soil is different from the atmosphere.
This difference is mainly due to the oxygen consumption and carbon dioxide production by plant roots and
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soil microorganisms. The oxygen level in the soil hovers around 21 percent, with decreases related directly
to increases in carbon dioxide [Alrichs, 1972]. Studies have shown that the carbon dioxide in the soil air
varies from 0.3 to 3.0 percent, whereas in the atmosphere it remains around 0.03 percent. Furthermore,
as one travels deeper into the soil profile, the oxygen content decreases even further through restricted air
exchange [Hamaker and Thompson, 1972].
The microorganisms that exist in the soil include all types from the five major groups; bacteria,
actinomycetes, fungi, algae and protoza [Alexander, 1977], with bacteria being the most dominant. Their
respective concentrations depend on soil type, moisture content and concentration of organic matter. Table
2 shows the changes in concentrations of microorganisms with depth, which are directly related to the
organic matter present at each layer. Since organisms are attached to the soil particles either by
electrostatic attractions or their extracellular secretions, the number of microorganisms that move with the
water is severely restricted. This results in minimal biodegradation as one proceeds further down the soil
profile.
Table 2: Concentration of Microorganisms with Depth for a Typical Mineral Soil
[Alexander, 1977]
Depth
Organisms/gram of soil [thousands]
m
Aerobic
Anaerobic
Actinomycetes
Fungi
Algae
0.03-0.08
7,800
1,950
2,080
119
25
0.20-0.25
1,800
379
245
50
5
0.35-0.40
472
98
49
14
0.5
0.65-0.75
10
1
5
6
0.1
1.35-1.45
1
0.4
3
Goring et al., [1974] report that the optimum water content for microorganism growth is 50-75 percent
of field capacity. Therefore as the water content changes, so does the number of microorganisms. A
neutral pH is also favourable for most microorganisms, but some have been found to exist at a pH of
3.0. Furthermore, the microorganisms often exist in a substrate limited growth pat tern which takes off
when a new source of organic matter is present. An increase in temperature also stimulates activity up to
a point, whereas lower temperatures decrease their activity. One other important element is nutrients. If
for example insufficient nitrogen exists in the soil, a nitrogen source will be needed to increase the
microorganism biodegradation activity. A major challenge in in situ remediation is providing sufficient
amounts of nutrients and oxygen to the lower levels of the soil profile.
3.0
SORPTION OF CHEMICALS BY SOIL
Braids [1981] reports that majority of chemicals entering the soil environment are removed through
adsorption. This is also referred to as sorption, which is the combined affect of adsorption and absorption
[Burns et al., 1982]. However, in most studies absorption is considered minimal in soil and the sorption
process refers to adsorption. Adsorption can be stated as the condensation of gases on the soils free
surfaces, or the fixation of solutes from a solution on the surface of a solid [Morrill, et al., 1982]. These
interactions involve the interface between two phases; liquid:liquid, gas:liquid, gas:solid or liquid;solid
[Weber and Morris, 1963]. Since soil is the environment being studied, the interface of most concern is
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liquid:solid. With liquid:solid adsorption, the two main driving forces are [Walker, 1984],
solvophobic (or hydrophobic in aqueous systems) nature of the solute within the solvent
(i)
degree of affinity of a solute (or adsorbate) for the solid surface (or adsorbent)
(ii)
There are three different types of adsorption: exchange, physical and chemical. Rarely can soil adsorption
be limited to only one type. Adsorption can be positive or negative [Morrill et al., 1982]. Positive
adsorption occurs when there is an attraction between the adsorbate and the adsorbent, resulting in a higher
concentration of adsorbate at the surface-liquid interface than in the bulk solution. Negative adsorption,
commonly referred to as desorption, is the opposite situation with repelling of the adsorbate.
The interaction of the various adsorption mechanisms depends on the chemical family and soil type
[Darcel, 1984]. For example, hydrophobic chemicals will tend to accumulate in the soil organic phase
[Weber et al., 1983], as the water molecules are repelled. Preference is then given to these non-polar
chemicals, with high molecular mass, resulting in the weakly hydrophobic chemicals being rapidly
transported to the groundwater [Gambrell et al., 1984]. This phenomenon has also been observed by
Valocchi [1985]. The majority of chemicals found in the groundwater are weakly or moderately
hydrophobic, including PCE [Roberts et al., 1982]. Solubility is also vital as reported by Voice et al.
[1983]. The higher solubility makes it easier for the chemical to dissolve and percolate with water to the
groundwater. In other words, higher the insolubility, greater the adsorption [Isaacson and Sawhney, 1983
and Kenaga, 1980]. Solubility has been shown to increase with temperature, resulting in a lower
adsorption rate [Chiou et al., 1977].
With the soil matrix consisting of solid, liquid and gaseous phases, the heterogeneous nature greatly
influences the physical and chemical properties of the soil [Travis and Etnier, 1981]. The organic fraction
is very important, with the majority of adsorption occurring in it [Jury et al., 1984, Melcer, 1982, Kahn
et al., 1975 and Rippen et al., 1984]. Organic matter is also important in desorption, as it is seen that the
percentage of desorption decreases with an increase in organic matter [Dekkers, 1977]. Dekkers [1977]
reports that it would be desirable to know the composition of the soil organic matter to accurately predict
adsorption for a particular chemical. However, at present little is known about humic substances which
are the largest fraction of organic matter in soils. They are relatively high molecular mass [300 to 30000]
complex materials that are generally regarded as polymers of aromatic compounds having large surface
areas [Chiou et al., 1979]. Other organic substances are fulvic and humic acids which themselves can
rapidly adsorb organic compounds [Wang et al., 1978]. However, in some instances, adsorption by the
organic fraction may not apply and cation exchange capacity, CEC, pH or some other soil property may
influence adsorption [Zamani et al., 1984].
The cation exchange capacity, usually given in terms of milligram equivalents per 100 grams of soil, is a
measure of the readily exchangeable cations neutralizing negative charge in the soil. These charges may be
viewed as being balanced by either (i) an excess of ions of opposite charge and a deficit [or negative
adsorption] of ions of like charge, or (ii) the excess of ions of like charge, or (iii) the excess of ions of
opposite charge over those of like charge [Page et al., 1982]. Total CEC in arable soils varies from 0.5
to 50, being higher in organic soils [Roberts et al., 1982]. Some of the CEC sites change in number with
pH. The dominate exchange cations are Ca, Mg, K, N and Al [Cohen and Ryan, 1985]. Felsot and
Dahm [1979] report that the higher the CEC, the greater the adsorption. It is also reported that the
adsorption capability of a soil was more related to the organic content of the CEC than to CEC itself.
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Walker [1984] reports that the organic content contributes 25-90 percent of the CEC.
While change in pH affects the number of CEC sites, no correlation between changes in soil pH and
adsorption of non-polar chemicals has been reported [Walker, 1984]. The only change in adsorption,
related to pH variation, results when a change in soil components occurs. Many studies report pH values
but do not discuss how any change would affect adsorption. Hamaker and Thompson [1972] and Walker
[1984] eport that the effects of pH, organic matter, CEC and other soil properties are so interrelated that
it becomes extremely hard to separate their influences.
Organics can also be adsorbed by inorganics like sand and clay, when organic matter content is low
[McCarty et al., 1981]. This occurs through cation and anion exchange. In Canadian soils, anion
exchange is considered negligible as soil particles are predominantly negatively charged [Gambrell et al.,
1984]. The size of these particles is also important because the smaller the particle size, the more surface
area per unit volume is provided. This is especially evident with clay in which many binding sites are
provided [Walker, 1984]. Schwarzenbach and Westall [1984] observed reduced adsorption when they
washed the soil prior to use and observed reduced adsorption. The decrease in adsorption was attributed
to the washing out of the fines, which decreased the total surface area available for adsorption. However,
it should be noted that generally no agreement exists in the literature on particle size effect on adsorption
[Walker, 1984]. Karickhoff [1981] and Karickhoff et al. [1979] have stipulated that adsorption can also
be increased with an increase in organic carbon content as it also provides for additional binding sites.
As mentioned earlier, there are three types of adsorption; exchange, chemical and physical. Exchange
adsorption is the electrical attraction between the adsorbate and adsorbent, which allows ions in solution
to bind with sites on the soil surface [Weber, 1972]. Exchange adsorption includes both cationic
exchange and anion exchange [Morrill et al., 1982]. In chemical adsorption, a chemical bond is formed
between the adsorbate and adsorbent, preventing free movement of the molecule. In short term chemical
adsorption, less than twelve hours, the amount of adsorption is minimal with importance increasing with
time. Another term for chemical adsorption is chemisorption.
While chemisorption fixes a molecule, a physically adsorbed molecule can freely move around the surface.
Usually the first layer is chemically fixed and all succeeding layers are held by physical means. Physical
adsorption is attributed to van der Waals forces. These forces are weak and decrease rapidly with increase
in distance from the surface. Never the less, physical adsorption is very important for large molecules
whose shapes conform to adsorbing surfaces [Rao et al., 1979].
Besides these three types of major forces, there exist other minor forces such as hydrogen bonding and
hydrophobic interaction. Morrill et al. [1982] report that hydrogen bonding is significant for binding polar
organic molecules to clay surfaces. Even though various types of adsorption are known, no single
mechanism fully explains the adsorption of an organic molecule on soil particles. Instead it is felt that a
combination of different types of phenomenon affect the adsorption process and these can not be easily
differentiated, especially with heterogeneous soil [Bohn et al., 1979, Hamaker and Thompson, 1972 and
Hamaker, 1972].
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3.1
Adsorption Isotherms
Equilibrium equations or isotherms have been developed to help explain the adsorption process and allow
comparisons. These equations give a relationship between the solute in the liquid and solid phases when
equilibrium is reached. The equation relates the mass of solute adsorbed per unit mass of adsorbent to the
equilibrium concentration in the liquid phase. These equilibria are established by adding a known amount
of adsorbate to a known amount of adsorbent and determining the amount of adsorbate removed from the
liquid phase. The observed data are then used to generate appropriate correlation equations such as the
Langmuir Isotherm and the Freundlich Isotherm [Banerji et al., 1985, Briggs, 1981, Walker, 1984, La
Poe, 1985 and Elliot and Stevenson, 1977].
The Langmuir Isotherm was initially developed by Langmuir in 1916 for the adsorption of gases on solids
[Harter and Baker, 1977]. The development was based on three assumptions [Morrill et al., 1982]; (i)
energy of adsorption remains constant and independent of surface coverage, (ii) adsorption is on localized
sites with no interaction between adsorbate molecules and (iii) the maximum adsorption possible is a
complete monolayer. The original equation has been modified to explain adsorption from solution, and
is in the form:
(1)
where, X/M = mass of solute adsorbed per unit mass of adsorbent, :g/g
Q*
= mass of adsorbed solute per unit mass of adsorbent required to form a complete
monolayer on the surface, :g
b
= constant indicative of the energy of adsorption
C
= equilibrium concentration of solute in solvent, g/m3
However, limited use for this equation is found in the literature when discussing organic adsorption on soil.
La Poe [1985] reasoned that the Langmuir Isotherm was basically limited to monolayer adsorption, and
not multilayer, which occurred with organic chemicals.
The Freundlich Isotherm has been frequently used for the adsorption of organics on soil. It has the form;
(2)
where, X
M
Kf
Ce
nf
= mass of adsorbate adsorbed on adsorbent, :g
= mass of adsorbent, g
= equilibrium constant indicative of adsorptive capacity,[ug/g][L/mg]1/nf
= solution concentration at equilibrium after adsorption, mg/L
= constant indicative of adsorption intensity
Theoretically, this equation predicts that the adsorption will increase indefinitely. As a result, Eq. 2 should
not be extrapolated past the range of solute concentrations for which it was developed [Bohn et al., 1979,
Weber, 1972 and Belfort, 1980]. Furthermore, it does not reduce to a linear equation at low
concentrations as does the Langmuir Isotherm. Still, it has be used extensively in soil adsorption studies
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for a variety of organic chemicals, including PCE. Table 2 shows some of the constants found for various
chemicals in different soils [Friesel et al., 1984]. The reported correlation coefficients are quite good
indicating that the Freundlich Isotherm can be used successfully in soil adsorption for PCE and other
organics.
Many studies that used the Freundlich Isotherm, have reported nf values close to unity. In fact, the smaller
the value of 1/nf the higher the affinity between the adsorbate and adsorbent. However, when nf equals
one, the isotherm equation describes the distribution or partitioning between the two phases in terms of the
linear relationship:
(3)
where
Ce
Kp
= equilibrium concentration of solute, mg/L
= linear partition coefficient, [L/mg][:g/g].
Table 3: Freundlich Constants for Various Soils
Soil
Chemical
Kf
1/nf
r
TCE
6.6
1.08
0.98
Acid Peat
PCE
12.9
1.04
0.96
1,1,1-TCE
5.1
1.03
1.00
Acid Humic
Topsoil
TCE
PCE
1,1,1-TCE
3.0
10.4
5.1
1.16
1.12
1.01
0.99
0.94
0.99
Calcareous
Humic
Topsoil
TCE
PCE
1,1,1-TCE
2.0
5.8
1.3
0.93
0.91
1.00
1.00
1.00
0.98
Subsoil TCE
PCE
rich in
1,1,1-TCE
iron oxides
1.3
2.3
2.7
0.88
0.98
0.81
0.87
0.95
0.80
Clay
subsoil
TCE
PCE
1.9
0.5
0.70
0.95
0.81
0.70
Sand
subsoil
TCE
PCE
1.5
0.9
0.71
0.60
0.91
0.90
The linear partition equation has found wide use in describing organics in soil, especially in low
concentrations [Schwartzenbach and Westall, 1981, Kenaga, 1982 and Melcer, 1982] including PCE [La
Poe, 1985 and Roy and Griffen, 1985]. Karickhoff et al. [1979] report that Kp is relatively independent
soil_overview_03.wpd
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of soil mass present but is directly related to the organic carbon content. However, Weber et al. [1983]
and Karickhoff et al. [1979] report that solids concentrations affect Kp, while Bredehoft and Pinder
[1973] indicate that as adsorbates differ, so do correlation factors. Furthermore, Bredehoft and Pinder
[1973] also believe that Kp is inversely related to the solubility. These conflicting opinions reveal that each
organic chemical behaves differently in changing soil conditions, requiring appropriate studies for each
situation.
Due to differing opinions on the effect of soil type on Kp, several researchers attempted and were successful
in correlating adsorption with soil organic carbon content, OC, [Darcel, 1984b]. This was done by
normalizing Kp with OC, resulting in a soil-water partition coefficient, Koc. Koc is a measure of the
partitioning of a compound between an aqueous phase and a stationary phase, consisting mainly of humus
[Gambrell et al., 1984]. This is called a hydrophobic tendency in which the more hydrophobic a molecule
is, the greater it partitions from aqueous to organic media [McCall et al., 1981]. Non-polar molecules
like PCE primarily adsorb on soil through this mechanism [DeWalle et al., 1982].
Schwarzenbach and Westall [1981] and others have indicated that another parameter can also be used to
estimate Kp [Chiou et al., 1977 and Kahn et al., 1975]. This coefficient is called octanol water partition
coefficient, Kow. Karickhoff [1981] states that organic carbon in soil acts similarly to a solvent in a water:
immiscible solvent extraction. Therefore, a correlation was developed between Kp and Koc. This was
completed for a series of polycyclic aromatic compounds and chlorinated hydrocarbons that had water
solubilities ranging from 1 mg/L to 1000 mg/L. On correlation it was determined that;
(4)
where, Koc = organic carbon partition coefficient, L/mg
Kow = octanol water partition coefficient.
Then by applying organic carbon content, this equation can be written as;
(5)
Similarly, Schwarzenbach and Westall [1981] obtained the following relationship for natural aquifer
material;
(6)
All these equations predict Kp within a factor of two for non-polar organics in soil or sediment. However,
they are only truly valid for the type of compounds and their concentrations that were studied. Any
extrapolation beyond the upper limit can greatly increase the magnitude of error [Walker, 1984].
Another advantage of using Kow is that it may be calculated directly from water solubility by using the
simple relationship developed by Chiou et al. [1977]:
(7)
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where S = aqueous solubility of chemical in :mol/L.
For PCE, Chiou et al. [1977] determined a log[Kow] of 2.60 with a solubility of 3820 :mol/L at 25°C.
The World Health Organization, WHO, reported a log[Kow] of 2.88 at a temperature of 20°C [WHO,
1984]. While the majority of organics are within one order of magnitude, Mingelgrin and Gerstl [1983]
have shown that the less polar an organic, the more applicable is Kow for indication of soil uptake, since
chemicals with higher log[Kow] values are more readily adsorbed by soil [Kahn et al., 1975]. Jaffe and
Ferrara [1983] also report that the higher the Kow coefficient, the more accurate is the equilibrium model
for adsorption. Furthermore, if it is greater than 100, i.e. log[Kow] is between 2 to 3, the chemical can
be considered moderately hydrophobic [Roberts et al., 1982].
3.2
Adsorption Results
Zytner [1992] determined Freundlich Isotherms for the various soils listed in Table 4, with the adsorption
results in Table 5. The adsorption/desorption processes of TCE in different granular media are well
represented by the Freundlich Isotherm, for the range of aqueous concentrations studied. The organic
carbon content of the medium appears to be the most significant controlling factor in adsorption and
desorption. Both the adsorption and retention potentials of TCE increased with an increase in organic
carbon content. GAC had the highest retention potential of adsorbed TCE.
Table 4: Properties Of Media Used For Sorption/desorption Studies
Medium
Clay Soil
Sandy Loam Soil
Organic Top Soil
Peat Moss
GAC
N.A. Not available
Organic
C-%
0.25
1.0
11.7
49.4
74.1
CEC
meq/100 g
30.1
14.2
23.3
Approx. 150
N.A.
Surface Area
m2Cg-1
91
22
N.A.
0.4
1300
Table 5: Freundlich Coefficients For TCE Adsorption on Different Media
Medium
Kf
1/nf
r
1/nf
[mg/kg][L/mg]
Sandy Loam Soil
0.5
1.1
0.83
Organic Top Soil
13.5
0.81
0.96
Peat Moss
93.4
0.75
0.98
GAC
81076
0.526
0.98
Zytner [1994] determined the sorption and desorption characteristics of the major components of gasoline
for granular media listed in Table 4. Emphasis was placed on the sorption of benzene, toluene,
ethylbenzene and xylenes [BTEX], the aromatic hydrocarbons contained in gasoline. As shown in Figure
1 and Table 6, the Freundlich Isotherm satisfactorily described the sorption and desorption of dissolved
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BTEX on the media tested. The organic carbon content of the media was an important factor in both
sorption and desorption, withthe order of sorption preference being GAC, peat moss, organic top soil, clay
soil and sandy loam soil. The order of preferential sorption on component basis when comparing Kf values
for different media is toluene, m-, p- and o-xylene, ethylbenzene and benzene. This trend follows the log
Kow predications.
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Media
Table 6: Freundlich Sorption Coefficients
Benzene
Toluene
Ethylbenzene M, P-Xylene
Coef.
1.01
1.64
0.89
1.22
0.80
0.95
1.22
0.81
0.87
Totala
BTEX
0.87
3.08
0.96
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
0.83
7.58
0.98
0.34
18.74
0.89
0.60
30.82
0.99
0.79
11.49
0.99
0.81
10.36
0.99
0.83
74.06
0.95
0.93
63.99
0.96
1.49
13.89
0.98
1.56
9.25
0.97
0.97
39.44
0.94
0.94
0.82
0.91
1.20
0.45
0.93
1.24
0.44
0.97
1.17
0.38
0.93
1.00
0.66
0.95
Clay
1/nf
Kf
r
1.55
0.17
0.89
0.66
8.47
0.94
GAC
1/nf
Kf
r
0.51
19649
0.98
1.03
115974
0.91
Organic 1/nf
Kf
Top
r
Soil
0.78
2.97
0.96
Peat
Moss
1/nf
Kf
r
1.0
13.0
0.96
Sandy
Loam
Soil
*.*
1/nf
0.95
Kf
0.58
r
0.89
Value not determined
O-Xylene
100000
Mass Sorbed - ug/g
Peat Moss
10000
Organic Top Soil
Clay
1000
Sandy Loam Soil
100
10
10
100
1000
Equilibrium Concentration - mg/L
Figure 1: Sorption of Total BTEX on Different Media
a
Total BTEX - sum of all compounds
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3.3
Soil-Water Partition Coefficient
The soil-water partition coefficient, Koc, is useful in determining the mobility of organic chemicals in soil.
Koc is determined by normalizing the linear partition coefficient, Kp, with the organic carbon content of the
soil [Kenaga, 1980]. The soil-water partition coefficient becomes an important factor in adsorption studies
as adsorption is now related to a single factor, organic carbon content, which is independent of soil type.
Studies have shown that compounds with a Koc value of about 1000 are quite tightly bound to the organic
matter in the soil and are considered to be immobile [Kenaga, 1980]. Those chemicals with a Koc below
100 for a certain soil are considered moderately to highly mobile. Therefore, Koc is valuable in determining
the potential leachability of compounds through soil or their potential to bind to the soil.
Koc values reported in the literature for TCE include; Roy and Griffen [1985], Koc = 152 L/mg; La Poe
[1985], Koc = 183 L/mg and Jaffe et al [1983], Koc = 123 L/mg. However, Garbarini and Lion [1986],
Seip et al. [1986] and ORNL, 1980 all reported Koc values less than 100. Such a variation in Koc is
expected, as each soil consists of a complex matrix, i.e., organic carbon, content, surface area, CEC and
etc..
Comparison of the average Koc value [118 L/mg] determined by Zytner [1992] study to Kenaga's limits,
suggest that TCE has high mobility in soil. Therefore, based on the Koc values determined in this study and
reported elsewhere, TCE will quickly migrate into the groundwater, requiring quick action if a spill occurs.
For a comparison of mobility between TCE and PCE, their respective Koc values can be used. The TCEs
Koc value was 118 L/mg, while in Zytner et al. [1989], a Koc of 330 L/mg was determined for PCE.
Comparing these two Koc values suggests that TCE has a higher mobility in soil than PCE. The increased
mobility for TCE assists in explaining the higher incidence of TCE groundwater contamination for volatile
organic compounds [VOCs] [Fischer et al., 1987; Pye et al., 1983 and U.S. EPA, 1982]. In fact,
Kerfoot and Barrows [1981] reported that TCE had the highest ranking of all hazardous substances
identified in the groundwater at 546 Superfund Sites. A similar trend was observed in the Netherlands,
where 25% of all pumping stations tested positive for VOCs, with TCE being the most frequent at 67%
[Zoeteman et al., 1981]. These trends indicate that response to a TCE spill should be quick, to ensure
minimum migration of TCE into the soil and eventually the groundwater.
Zytner [1994] determined the soil-water partition coefficient for BTEX compounds as shown in Table 7.
The values ranged between 26 and 656 LCkg-1, indicating that the BTEX compounds have high to
moderate mobility in soil. According to the Koc values measured, benzene has the greatest migration
potential, followed by toluene, m-, p- and o- xylene and ethylbenzene.
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Table 7: Koc Values [L@mg-1] For The Different Compounds
Medium
Koca
Lit. Koc[29]
Benzene
26 - 59
12 - 340
Toluene
65 - 151
13 - 710
Ethylbenzene
45 - 656
95 - 1095
M-, P-Xylene
44 - 320
110 - 1200
O-Xylene
38 - 324
48 - 540
Total BTEX
66 - 1232
Unavailable
a - X/M in :g/g
3.4
Desorption of Chemicals from Soils
Very few desorption studies have been performed on synthetic organics because considerable time is
required to conduct such studies [La Poe, 1985]. Desorption is determined by first allowing a solute to
attain equilibrium with a known mass of soil by adsorption. After equilibrium, the solution is removed and
replaced with a fresh solvent containing no solute. This new system is re-equilibriated and new X/M values
determined. The data are plotted to produce a desorption isotherm.
The desorption is believed to be a slower process than adsorption and losses due to volatilization and
degradation can occur [La Poe, 1985]. This can lead to an over estimation of the quantity of solute still
remaining adsorbed [Rao et al., 1979 and Rogers et al., 1980]. As a result of these difficulties,
Schwarzenbach and Westfall [1981] did not perform any desorption studies for the volatile organics they
studied, which included PCE. They felt the more one handled the adsorbent, the more errors could arise,
affecting the reliability of the results. Therefore, for desorption tests the methodology used is vital as has
significant impact on the results.
When the desorption studies are properly carried out, the isotherms do not necessarily overlap the
adsorption isotherm. This noncoincidence is referred to as hysteresis. The usual effect of hysteresis is that
desorption isotherms show higher desorptive capacity than adsorption capacity at lower equilibrium
concentrations [Felsot and Dahm, 1979, Hamaker, 1972, Koskinen, 1979 and Schwarzenbach and
Westall, 1981]. Other than unknown experimental losses, hysteresis can be attributed to non-attainment
of equilibrium or to changes in strength of adsorption during desorption over time. These two causes can
be interrelated and are hard to separate due to the soil's heterogeneity [Hamaker and Thompson, 1972].
Occasionally studies have been done to evaluate the breakthrough and elution curves. When they exhibit
tail curves, or asymemetrical curves, nonequilibrium is believed to exist [Rao et al., 1980]. This
nonequilibrium is also attributed to soil hysteresis. Schwarzenbach and Westall [1981] determined the
extent of hysteresis from the tailing effect without performing desorption tests.
Felsot and Dahm [1979] report that organic carbon content is important in desorption. They observed for
insecticides that the quantity of desorption decreased as organic carbon increased. More evidence for this
pattern was obtained by oxidizing organic matter and observing an increase in desorption. Others
[Hamaker et al., 1969, Hilton and Yuen, 1963 and Saha et al., 1969] have reported that if soil is dried
and then rewetted after the sorption phase, the sorbed chemical may be hard to extract. La Poe [1985]
soil_overview_03.wpd
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reported desorption isotherms above the sorption isotherm for PCE. This was not caused by slow
desorption kinetics but rather by slow adsorption kinetics. La Poe [1985] showed that the longer the
sorption study, the closer was the agreement between the adsorption and desorption isotherms, indicating
reversible action at concentrations between 0 and 150 :g/L. La Poe [1985] also suggests that the
negative adsorption of PCE can be attributed to the very hydrophobic nature of the soil being studied. This
causes the water molecules to be strongly attracted to the soil surfaces, producing significant portions of
the soil zones containing solute free water.
Zytner [1992] showed that the organic carbon content, CEC and surface area impacted the mass of TCE
desorbed. The ability to retain an organic chemical is based on the strength of bond developed between
the sorbent and sorbate [Roy and Griffen, 1985]. To understand or gain a feel for the retention capacity
of TCE by the media tested, the ratio of Kf to Kfd can be determined [Zytner et al., 1989]. The higher the
ratio, the greater is the retention of the chemical by the medium. However, when this ratio approaches units
or less, the medium has no retention capabilities. In other words, the medium exhibits total reversible
adsorption.
Table 8 contains the Kf to Kfd ratios for this study. As expected, GAC has the highest K f/K fd value because
of the high organic carbon content and large surface area. Likewise, sandy loam soil has the lowest
retention ratio as it has low adsorption and retention capacity of dissolved TCE.
Table 8: TCE Kf /Kfd Values For Media Studied
Medium
Kf/K fd
Sandy Loam Soil
0.2
Organic Top Soil
2.0
Peat Moss
2.6
Granular Activated Carbon
1016
Table 9 gives the desorption coefficients for the BTEX experiments. Table 9 shows that the Freundlich
Isotherm worked well as the r value was very high. Review of the coefficient values and the soil properties
given in Table 4, suggests that the mediums organic carbon content, CEC and surface area all affected
desorption. The ability to retain an organic chemical is based on the strength of the bond developed
between the sorbent and sorbate. To gain an understanding of the strength, the ratio of Kf/K fd is used. See
Table 10. Higher the ratio, greater the retention. When the ratio approaches 1, the medium in question
has no retention capabilities. Based on the values in Table 10, GAC has the highest retention, consistent
with the organic carbon content. Similar trends follow for the remaining media, except for peat moss, which
should have been next. It is expected that experimental error occurred. Further investigation is warranted.
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Table 9: BTEX Freundlich Desorption Coefficients
Media
Coef
Clay
1/nfd
Kfd
r
Benzene
1.07
2.53
0.98
Toluene
1.01
2.88
1.00
Ethylbenzne
*.*
*.*
*.*
M, P-Xylene
1.01
2.82
0.99
O-Xylene
1.00
2.81
1.0
GAC
1/nfd
Kfd
r
0.96
1981.0
0.91
0.97
1596.0
0.99
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
*.*
Organic
Top
Soil
1/nfd
Kfd
r
1.0
2.48
1.0
1.0
2.54
1.0
1.0
2.6
1.0
1.0
2.42
1.0
1.0
2.54
1.0
Peat
Moss
1/nfd
Kfd
r
1.0
38.74
1.0
1.0
39.33
1.0
1.01
40.76
1.0
1.0
37.80
0.99
0.99
40.13
1.00
Sandy
Loam
Soil
*.*
1/nfd
1.0
Kfd
2.12
r
1.0
Value not determined
1.0
2.18
1.0
1.0
2.30
0.98
1.0
2.07
1.0
1.0
2.18
1.0
Table 10: BTEX Kf /Kfd Values For Media Studied
Media
Clay
GAC
Organic Top Soil
Peat Moss
Sandy Loam Soil
3.5
Benzene
0.1
9.9
1.2
0.3
0.3
Toluene
3.0
72.0
3.0
1.9
7.1
Ethylbenzene
*.*
*.*
7.2
1.6
0.3
M, P-Xylene
0.3
*.*
11.9
0.4
0.2
O-Xylene
0.3
*.*
4.8
0.2
0.2
Application of Isotherm Data
Adsorption isotherms can be used to determine the mass of soil contaminated with a dissolved organic
compound. By knowing or estimating the mass of chemical spilled and the dissolved chemical
concentration, the mass of contaminated soil can be calculated. This mass of soil can then be treated in
situ or excavated for disposal. If necessary, it is also possible to determine the mass of chemical spilled
if the mass of contaminated soil can be estimated.
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4.0
RESIDUAL SATURATION OF NAPLS IN SOIL
Non-aqueous phase liquids (NAPLs) are constantly being released into the environment through chemical
spills, improper waste disposal practices and leaking underground storage tanks [Asano, 1985; Pye et al.,
1983]. Once released into the soil environment, these NAPLs migrate toward the groundwater. The
nature and quantity of NAPL reaching the groundwater depends upon the properties of the NAPL and the
soil [Short, 1985; Palmer, 1987; Feenstra and Cherry, 1988].
When a NAPL is released into the subsurface, it flows through the unsaturated zone of the soil toward the
groundwater under the influence of gravity. This migration is a complex process as the NAPL may exist
in gaseous, sorbed, dissolved and immiscible phases in the unsaturated zone. Several complex models
requiring numericalsolutions have been developed to explain this migration [Pinder and Abriola, 1986; Zhu
et al., 1991].
The problem with a NAPL release into the subsurface is that a fraction of it will eventually reach the
groundwater, causing groundwater contamination. To minimize the amount of NAPL reaching the
groundwater, it is necessary to remove the immiscible phase from the subsurface as soon as possible.
Therefore, the knowledge of the maximum penetration of the chemical into the soil is of great interest.
However, it is difficult to predict the migration of the NAPL in the unsaturated zone because it must reach
a minimum saturation concentration in the porous medium before flow begins [Schwille, 1984]. This cannot
be generalized for every situation as the residual capacity values of the NAPLs differ for different soil
combinations. The lack of experimental data further complicates the issue [Thomson et al., 1992; Schwille,
1988].
The NAPL migration in unsaturated soil is dependant on a number of factors: properties of the soil,
properties of the NAPL, volume of NAPL spilled, time period over which the spill occurred and area of
infiltration of the chemicals.
Soil properties that affect chemical behaviour are the soil's intrinsic permeability, the soil's pore size
distribution and the soil-chemical interfacial tensions. The intrinsic permeability controls the flux for a given
pressure head. The pore size distribution and interfacial tensions contribute to the pressure potential of the
liquids present.
Important NAPL properties include density, kinematic viscosity, surface tension and vapour pressure. The
density of the chemical will dictate its behaviour once it reaches the groundwater table. Once a lighterthan-water non-aqueous phase liquid (LNAPL) reaches the groundwater it will spread laterally along the
capillaryfringe and may eventually depress natural groundwater levels. Whereas, a denser-than-water nonaqueous phase liquid (DNAPL) will displace water and continue its downward migration under pressure
and gravity forces.
The kinematic viscosity (<) of a fluid is the key factor in determining the fluid's velocity (conductivity) in dry
soil. The conductivity of the soil for a specific fluid is given by the intrinsic permeability of the soil multiplied
by the acceleration due to gravity and divided by the fluid's kinematic viscosity. Freeze and Cherry [1979]
provide that the intrinsic permeability of a soil is a property of the media only and therefore, in a given
media, less viscous fluids will have higher conductivities. A NAPL with low viscosity will penetrate into the
soil_overview_03.wpd
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unsaturated zone more rapidly than a NAPL with a high viscosity. Using this information, Schwille [1984]
has shown that light heating oil with <=4 mm2s-1 would migrate four times slower than water, while
trichloroethylene with <=0.4 mm2s-1 would move 2.5 times faster that water. Kinematic viscosity of water
is 1 mm2s-1.
The surface and interfacial tensions will effect the pressure potentials in the soil and thus the driving force
for migration. The vapour pressure will control the soil-air migration of chemicals in the subsurface.
Mercer and Cohen [1990] have reviewed a number of models for predicting NAPL behaviour in the
unsaturated zone. The models are useful for conceptualization but the data for their application is generally
lacking. The models can be divided into three classes: (i) interphase mass transfer approach, which
considers interphase partitioning of NAPL between water and vapour phases [Abriola and Pinder, 1985;
Corapcioglu and Pinder, 1987], (ii) immiscible phase approach, whichcouples the equations for the waterNAPL-gas system and includes constitutive relations for saturation and relative permeability, and (iii) sharp
interface approach, or a piston flow model, which develops a time-distance profile for NAPL transport
based on Darcy's Law. The last approach is similar to the approach employed in the Green and Ampt
model for water flow.
Kessler and Rubin [1985] proposed a model for the short term migration of oil spills and found that
available data concerning water infiltration was useful for determining parameters needed to describe oil
flow in the unsaturated zone. In a subsequent study, Rubin and Mechrez [1989] performed laboratory
experiments to validate this approach of using water infiltration data to determine oil infiltration. The
conversion of water infiltrationparameters to oil infiltration parameters was based on the physicalproperties
of water and oil, such as surface tension and viscosity.
Reible et al. [1990] outlined a simplified model for one dimensional infiltration of NAPL through the
unsaturated zone. They found that the infiltration of an immiscible chemical into the unsaturated zone could
be predicted using only the spill volume and area, the intrinsic permeability, the retention capacity and the
capillary rise height of the infiltrating liquid. The model was validated by carrying out experiments and a
good correlation was found between the predicted and experimental results.
Cary et al. [1989] modelled a number of soil column infiltration experiments using a simplified explicit finite
difference code for three phase flow in a one-dimensional system. Model predictions were good for
infiltration experiments in loamy sand and silt loam but less satisfactory for experiments in sand. Cary et
al. [1989] also used simple multiphase flow code to predict oil infiltration and redistribution in unsaturated
soils. The calculated infiltration times were greater than those measured experimentally. The model under
predicted the infiltration and redistribution curves for water but accurately predicted the curves for mineral
oil. However, the authors recognized the need for more experimental data concerning the organic liquid
conductivity in unsaturated soils at a variety of soil water contents.
As a NAPL migrates downward in the unsaturated zone, it leaves behind residual liquid in the soil pores
[residual saturation] due to the surface tension effects. This residual saturation may become a future source
of contamination through transport by infiltrating water and the migration of vapour plumes originating from
the residual saturation [Mercer and Cohen, 1990, Mackay and Cherry, 1989 and Cohen et al., 1987].
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The ability of the unsaturated zone to retain NAPL has been measured and reported as [de Pastrovich et
al., 1979; Schwille, 1984; Wilson and Conrad, 1984]:
RC = srC0oC1000
(8)
where
RC = retention capacity, litres of NAPL per m3 of medium
sr = residual saturation, volume of NAPL/volume of voids
0o = soil porosity, fraction
Values of residual saturation, sr, have been reported between 0.1 to 0.2 in the unsaturated zone and 0.15
to 0.5 in the saturated zone [Schwille 1984; Hoag and Marley 1986; Anderson 1988]. In the unsaturated
zone, residual saturation increases with decreasing intrinsic permeability, effective porosity and moisture
content. Schwille [1988] reports that while there exists numerous measurements of residual oil retention
in porous media, information is limited for other chemicals and solvents.
4.1
Theory
Retention capacity [RC] is defined as the volume [or mass] of NAPL retained by a volume [or mass] of
Consider that the bulk soil volume, Vs, is given by:
soil.
Vs = Vw + Va + Vss
(9)
where,
Vw = Volume of water, L
Va = Volume of air, L
Vss = Volume of solids, L.
If the soil is dry, i.e. no bound water is present, the volume of voids in the soil, Vv, L is given by:
Vv = Va
(10)
or
Vv = 0oVs
(11a)
= 0oCM/Ds
(11b)
where,
M = dry mass of soil, g
Ds = bulk dry density of soil, g/L
Letting the volume of pure chemical retained by a soil, Vc, L, be proportional to the soil void volume, Vv,
the retention of chemical can be expressed as:
Vc % Vv
(12)
Defining fv as the fraction of voids filled with chemical, and using Eq. 11a in Eq. 13, Vc can be expressed
as:
Vc = fv0o[M/Ds]
(13)
Since the volume of a spilled chemical is usually reported on a mass basis,
Mc = DcVc
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Page 18 of 70
where,
Mc = mass of chemical, g
Dc = density of chemical, g/L
Combining Eq. 13 and Eq. 14,
Mc = fv0oDc[M/Ds]
or
Mc/M = fv0o[Dc/Ds]
or
RC
= fv0o[Dc/Ds]
(15)
(16a)
(16b)
The Mc/M relationship given in Eq. 16a is defined as the retention capacity, RC, of a chemical in dry soil
in g/g. The term fv in Eq. 16a is the same as the residual saturation, sr, term defined earlier [Schwille, 1984;
Hoag and Marley, 1986; Anderson, 1988].
Zytner et al. [1993] measured retention capacity values in the laboratory for three non-aqueous phase
liquids [NAPLs], PCE, TCE and gasoline. The dry soils studied were sandy loam, clay, organic top soil
and peat moss. For the conditions tested, it was determined that higher the NAPL's density and the soil's
porosity and lower the soil bulk density, greater is the retention capacity. Consistently higher retention
capacities were obtained for PCE with a density of 1622 g/L, than TCE and gasoline, with respective
densities of 1456 g/L and 750 g/L. Similar behaviour has been observed by other researchers [Schwille,
1984; Hoag and Marley, 1986; Anderson, 1988]. Similar trends were also observed by Schwille [1984],
Hoag and Marley [1986] and Anderson [1988], who reported that retention capacity increased with an
increase in soil porosity.
To obtain a correlation between the retention capacity, soil properties and NAPL properties, the retention
capacity (Mc/M) values were plotted versus 0o(Dc/Ds). The data showed that the retention capacity
increased linearly with an increase in the 0 o(Dc/Ds) values, and can be expressed by the following
correlation:
RC = 1.05[0o[Dc/Ds] - 0.15
(17)
where, RC = retention capacity [mass of chemical/ mass of soil] - g/g.
The r2 value of the 42 observations used to obtain Eq. 17 is 0.997, suggesting a good fit. Equation 17 can
be simplified by forcing the regression through zero and changing the coefficient [1.05] to 1.0 to obtain:
RC = [0o[Dc/Ds]
(18)
with an r2 value of 0.993.
Equations 17 and 18 satisfactorily correlate the retention capacity with the soil and NAPL properties,
indicating that retention capacity is a function of the soil's physical properties and the NAPL's chemical
properties. This has also been suggested by other researchers [Feenstra and Cherry, 1988; Schwille,
1984]. Further investigation is required to see if Eq. 18 can be applied to NAPLs not tested in this study.
Additional investigation is also required to more clearly define the inter-relationships between soil's
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physical/chemical properties and the NAPL's chemical properties.
Guigard et al. [1995] showed that an important parameter in the movement of immiscible chemicals
through soil is the retention capacity, by studying the retention capacities for two chemicals, n-hexane and
tetrachloroethylene [PCE] in three soils at varying soil water contents. The retention capacities were
determined using prepared laboratory scale soil columns using two experimental techniques: (i)
saturation/drainage experiments where the soil columns were saturated with the chemicals and allowed to
drain freely for 24 h, and (ii) spill simulations where a known amount of chemical was spilled on the surface
of the soil column and allowed to infiltrate for one hour. Results show that the retention capacities on a
volume basis were independent of chemical type. However, the retention capacities did decrease with
decreasing porosity and increasing soil water content. The decrease of retention capacity with respect to
moisture was significant, with the decreases ranging from 38% to 94%. The implications of this are rapid
penetration into the subsurface. Retention capacities obtained from spill simulations were consistently lower
than those obtained by the saturation/drainage experiments due to hysteresis.
4.2
Infiltration
Guigard et al. [1996] completed laboratory simulations with hexane and PCE in prepared soil columns.
Both infiltration times and liquid front movement were measured as was the influence of soil type and soil
water content on the spill behaviour. Infiltration times for both chemicals into a given soil were similar. The
chemicals infiltrated fastest into the more permeable soil. The liquid front movement in air dry soils followed
a log-log relationship with time that is similar to the Green and Ampt model.
Figures 2 and 3 show that the wetting front movement can be described by the Green and Ampt model if
chemical is present at the top of the soil column. The assumptions are that the moving front is sharp with
uniform concentrations across the horizontal from. A 1D expression can be used to represent the
movement, which some call “plug flow”. Normally the Green and Ampt model limit its applicability to front
movement while liquid remains present at the surface (i.e., ponding). From Figures 2 and 3, it is clear that
after ponding has ceased, the front movement slows down appreciably during a period referred to as
redistribution. During this redistribution period, the value in the Green and Ampt model is limited to a
conservative or knowingly over predictive estimation of the position of the wetting front.
Increasing the soil water content had a significant effect on both the infiltration times and liquid front
movement: the infiltration times increased, while the chemical front moved faster through the soil column.
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Figure 2: Hexane Liquid Front Movement in Air Dry Soil
Figure 3: PCE Liquid Front Movement in Air Dry Soil
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5.0
VOLATILIZATION FROM SOIL
Volatilization can be defined as the loss of chemicals from any surface to the vapour phase, followed by
movement in to the atmosphere [Spencer et al., 1982]. The potential to volatilize depends on the
chemicals vapour pressure as well as environmental conditions and factors that exist at the solid-air-water
interface.
Henry's law is used to explain the transfer between the liquid and gas phases due to volatilization. It is a
valid approximation for many environmental applications which take place at atmospheric pressure and
temperature. The law states that at a constant temperature, the mass of gas dis solved in a given volume
of a solvent is directly proportional to its partial pressure in the gas phase in equilibrium with the solution
[Yurteri et al., 1987]:
pi = KHi @CLi
(19)
At atmospheric pressures the gas phase approaches ideal behaviour, allowing one to express the law as:
Hi = KHi /RTe = CGi/CLi
(20)
where, pi
KHi
CLi
CGi
R
Te
Hi
= partial pressure of component i, atm
= Henry's law constant for i, m3-atm/mole
= equilibrium liquid phase concentration of i, mole/m3
= equilibrium gas phase concentration of i, mole/m3
= universal gas constant, atm-m3 /mole K
= equilibrium temperature, K
= dimensionless Henry's law constant for i.
Namkung and Rittmann [1987] studied two publicly owned treatment works and observed that the higher
the Henry's law constant, the greater the rate of volatilization. However, Yurteri et al. [1987] observed
that Henry's law constant could be affected by the presence of salts, surfactants and humic material.
Therefore, it is important to understand the nature of the impurities present and their effects on Henry's Law
constant and the volatilization rate.
When a synthetic chemical is spilled on an impervious surface or soil that does not drain quickly,
volatilization can be expressed by Ficks first law of diffusion [Gowda and Lock, 1984]:
F = KL [C SL -C L] = KG [C G - C SG ]
(21)
where
KL
KG
CL
CG
CSL
CSG
= mass transfer coefficients, m/day
= mass transfer coefficients, m/day
= concentrations in the bulk liquid, g/m3
= concentrations in the bulk gas phase, g/m3
= liquid phase concentrations at the interface, g/m3
= liquid phase concentrations at the interface, g/m3
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5.1
Passive Volatilization
Gasoline spilled into unsaturated soil migrates into the subsurface under the influence of gravity until the
entire volume is dispersed into the soil pores [Zytner et al., 1993]. This gasoline, commonly referred to
residual saturation, remains present in the soil until it volatilizes into the atmosphere, is transported further
into the subsurface by infiltrating water, or is biologically degraded.
Numerous options for cleanup of gasoline-contaminated surface soils exist [Kostecki and Calabrese,
1989]. Options include soil vapour extraction [Khan and Cruse, 1990], chemical degradation [Khan and
Cruse, 1990], excavation and landfill, in situ bioremediation [Dean-Ross et al., 1992; English and Loehr,
1991], bioventing [Dupont, 1993], surfactant flushing [Zalidis et al., 1991] and through passive
volatilization. Passive volatilization describes the natural evaporation of the contaminant from soil, and
includes the following engineered modifications; covering excavations to facilitate venting and excavating
the soil and land spreading it [Donaldson et al., 1992].
A significant spill fate is passive volatilization. It is the natural evaporative loss that can lead to atmospheric
health and explosion risk. Passive volatilization is also an important remediation option in itself or as part
of a soil vapour extraction system; the most common remediation technique for gasoline. In soil vapour
extraction, air preferentially flows through the higher permeabilitymaterial, rendering the removal of gasoline
from low permeability zones an essentially passive volatilization process. Having reliable passive
volatilization rates would assist in understanding and quantitatively predicting the behaviour of a spill.
To date, experimental work on passive volatilization is limited. Fine and Yaron [1993], Galin et al. [1990]
and Acher et al. [1990] all reported that the increased volatilization in sand is directly related to increased
permeability. Soils such as clays, which exhibit higher porosities but have pore size distributions skewed
towards smaller pores, show lower volatilization rates than sand. Johnson and Perrott [1990] showed that
in a fine silty clay contaminated with gasoline, at water contents approaching 90% of saturation, there was
reduced vapour-phase diffusion of the contaminant. Goss [1993], Batterman et al. [1995] and Shonnard
and Bell [1993] showed that volatilization fluxes increased with the addition of small amounts of water to
dry soils due to reduced sorption.
Experimental evidence also suggests that convective movement can enhance volatilization from soil. Two
such effects are noted. Spencer et al. [1982] showed that water evaporation can create sufficient suction
to pull contaminated water to the surface of the soil. This increases the flux of dissolved contaminants
towards the surface and aids volatilization into the atmosphere. Accordingly, the effect applies most
strongly to more polar contaminants that have an appreciable solubility in water. A second capillary rise
effect was noted by Arthurs et al. [1995] and Smith et al. [1994], who found that the immiscible phase
itself can rise in soils. This 'wicking' effect can transport chemicals to the soil surface without the aid of
water evaporation, making it easier for the contaminants to volatilize into the atmosphere.
In general, studies on the volatilization of gasoline from soils are limited. Donaldson et al. [1992]
conducted gasoline volatilization experiments on a loamy sand during both spring and summer seasons.
Overall volatilization losses were consistently higher during the summer experiments, a result of higher soil
temperatures and the resulting increase in vapour pressure of the gasoline components. Jarsjo et al. [1994]
compared volatilization rates of kerosene from various soils at 27oC and 5oC, and found that the fraction
soil_overview_03.wpd
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of kerosene volatilized was 2 to 3 times higher at 27oC than 5oC. Passive volatilization is an inexpensive
remediation option as natural mechanisms are used to remove the bulk of gasoline. The gasoline migrates
to the soil surface by convection due to bulk gasoline concentration gradients and due to diffusion in the
gaseous or liquid gasoline due to individual component concentration gradients. However, limited
information is available in the literature on the volatilization rates.
Research completed by Arthurs et al. [1994] showed that passive volatilization rate of gasoline is
dependent on the soil and chemical type, wicking behaviour, and depth of gasoline in soil. Wicking is a
significant mechanism. The time required to deplete the overall gasoline concentration in the soil to 40%
of the initial concentration ranged from 0.25 to 10 days for the three soils. Ottawa Sand was the fastest
[6 h], followed by Delhi Loamy Sand [160 h] and Elora Silt Loam [240 h]. Observation of individual
components indicated that a wicking mechanism was contributing to the gasoline flux towards the
atmosphere. Based on the results, volatilization rate volatilization rate increases with increasing vapour
pressure [n-Heptane, followed by Toluene, n-Octane, Ethylbenzene, m-Xylene and n-Hexadecane].
Gidda et al [1999] reported some interesting findings that are applicable to the passive volatilization of
gasoline from unsaturated soil. Immiscible phase movement to the surface, commonly referred to as
wicking, is a significant contributor to passive volatilization, and most significant at higher initial gasoline
contents. The initial gasoline content, and hence wicking, plays a larger part in volatilization behaviour than
soil type. There appears to be a threshold level of approximately 5% residual gasoline content in soil at
which this process ceases. Findings also show that wicking occurs for an extended period of time in under
sub-zero temperatures as it takes longer to reach the threshold level.
Solubility limits and freezing of gasoline components cause precipitation to occur at the soil surface. As a
result, the surface gasoline fraction consists of both solid and liquid gasoline, which maintains the driving
force necessary for wicking to continue until the threshold level of 5% is attained.
Volatilization behaviour from wet soils is dependent on the soil type, where water impacts both the diffusive
and wicking movement of the gasoline. Soils with larger pores, like Delhi Loamy Sand and Elora Silt Loam
maintain the interconnected pore structure more easily, allowing for increased volatilization. However for
Windsor Clay Loam, at water contents of 20 and 30%, the water enters the many fine pores which in turn
can trap gasoline, reducing the passive volatilization rate.
Sub-zero temperatures result in a decrease in the total fraction of gasoline lost when compared to the room
temperature experiments. This also holds true for the wet soil. Sub-zero temperatures impact diffusion
based on reduced chemical vapour pressures.
5.2
Soil Vapour Extraction
Soil vapour extraction (SVE) is an attractive technique for the remediation of unsaturated soils
contaminated with volatile petroleum products. Conceptually, SVE is a simple process in which vacuum
induced airflow is used to enhance the removal of volatile organic compounds (VOCs). Field studies have
shown that SVE can remove large quantities of VOCs from a variety of soil types [Coia et al., 1985;
Crow et al., 1987; Hutzler et al., 1988; Gibson et al., 1993]. Unfortunately, SVE performance often
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deteriorates rapidly as evidenced by an appreciable decline in soil vapour concentrations and contaminant
mass removal rates, sometimes within days after start-up [DiGuilo, 1992; Crow et al., 1987]. When
airflow is interrupted for a period of time and then restarted, the observed vapour concentrations on restart
return to high levels but again decline rapidly as air flow continues. As such, it is difficult to predict the time
required to achieve a given cleanup level.
To gain a better understanding of the SVE process, the removal of VOCs from coarse grained soils by
SVE has been investigated in a number of laboratory scale studies [Thornton and Wooten, 1982; Baehr
et al., 1989]. The experimental evidence suggests that in the presence of a non-aqueous phase liquid
(NAPL), venting behaviour is controlled by equilibrium. Several numerical and analytical models based
on the assumption of local phase equilibria have been developed for coarse grained soils to predict
contaminant removal by SVE [Baehr et al., 1989; Rathfelder et al., 1991; Ho and Udell, 1994].
Non-equilibrium behaviour has been investigated in recent experimental studies with coarse grained soils.
Wilkins et al. [1996] showed that the rate of mass transfer from the NAPL decreased with decreasing
mean grain size of the soil particles. Hayden et al. [1994] in an investigation of multi-component NAPL
removal, indicated that mass transfer limitations begin to develop when a compound was nearly depleted
from the NAPL. Ho and Udell [1994] investigated the effects of permeability differences on mass transfer
in a two-layered soil system. Berdnston and Bunge [1991] suggested that in the absence of a NAPL, mass
transfer was limited by diffusion at the air-water interface.
With respect to fine grained soils, there have been comparatively few experimental investigations of SVE.
Gierke et al. [1992] investigated the effect of water content on chemical removal from sand and a
manufactured aggregated soil material. For the aggregated soil in the absence of a NAPL, both the air
velocity and water content were important factors limiting the rate of mass transfer. Fine and Yaron [1993]
showed that both the water content and the degree of aggregation influence the venting of kerosene from
a variety of soils.
Several non-equilibrium contaminant transport models have been developed to gain insight into mass
transfer limitations. The simplest models employed first-order mass transfer relationships to describe mass
transfer between the vapour and NAPL or dissolved phases [Rathfelder et al., 1991; Armstrong et al.,
1994]. It is believed that the mass transfer limitations were the result of a combination of diffusion from low
to high permeability soil layers associated with advective air flow, diffusion through water filled pores, mass
transfer resistance at the air-water interface and desorption kinetics [Brussseau, 1991; Gierkeet al., 1992;
Hayden et al., 1994]. More complicated models were developed based on a 2-domain [mobile/immobile]
approach to account for differences in behaviour between the air filled macropores and the water filled
micropores [Brusseau, 1991; Gierke et al., 1992; Ng and Mei, 1996].
Harper et al. [1998] studied the extraction of single and binary volatile organic contaminants from a silt
loam soil at three different water contents. Transport of chemicals through the soil columns was strongly
influenced by the water content. Figures 4 shows the venting behaviour of the binary system experiments.
The breakthroughs for both components were quite sharp. The observed dimensionless breakthrough times
(dotted vertical lines) for toluene and m-xylene, 6500 and 16000, compare favourably with their respective
ideal values of 7100 and 14000, where dimensionless time corrects for differences in air flow rate and the
initial amount of a contaminant. Further, this ideal dimensionless breakthrough time is equal to the liquid
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density divided by the saturated vapour density.
1
Air Dry
0.1
When equilibrium conditions exist as well as
Toluene
ideal plug flow behaviour with no dispersion,
m-Xylene
breakthrough occurs as a step function going
ideal m-Xylene time (14000)
from 1 to 0 in an instant. As shown in Figure 4,
ideal Toluene time (7100)
under air dry conditions, the dimensionless
16% WC
static period
concentration stayed at 1 until breakthrough of
toluene. Then the dimensionless m-xylene
concentration fluctuates around 0.9 until
breakthrough. It can again be concluded that
equilibrium conditions were attained while the
bulk of the contaminant was removed. Post
analysis revealed that the concentrations in the
22% WC
soil had fallen to less than 10 :g/g which was
below most clean-up levels (Oliver et al.,
1996). Thus, when ideal breakthrough occurs,
it can be stated that equilibrium conditions
static period
ideal m-Xylene time (14000)
prevailed throughout the extraction procedure.
ideal Toluene time (7100)
This results in almost complete removal of the
contaminant with minimal tailing. At the
20
40
60
80
100
120
140
intermediate water content [16 wt%],
J x 1000
equilibrium conditions were predominant for the
Figure 4: SVE Dimensionless Effluent
early removal but mass transfer limits were
Concentrations for Binary Mixture
apparent as NAPL saturations were reduced.
At the highest water content [22%], mass
transfer limitations controlled even with the majority of the initial NAPL contamination still present.
0.01
0.001
0.0001
0.00001
1
0.1
C/C
*
0.01
0.001
0.0001
0.00001
1
0.1
0.01
0.001
0.0001
0.00001
5.2
Soil Vapour Extraction - Mass Transfer Coefficients
In practice, the performance of SVE systems is less than ideal. An appreciable decline in effluent vapour
concentrations and mass removal rates is often observed within days after start up, followed by extended
tailing (DiGiulio, 1992; Stinson, 1989). As a result of deteriorating SVE performance, contaminant levels
remain above clean up targets and increased clean up costs are incurred from pumping large volumes of
air at low contaminant concentrations for extended time periods. Predicting SVE performance and cleanup
time is difficult due to limited information on site characteristics, complexities of the subsurface, factors
causing tailing, and control of contaminant mass transfer by several interacting processes. Currently, design
and operation is still based on empirical knowledge and a better understanding of the complexities is
required (Poulsen et al., 1998).
Early SVE contaminant transport models, based on venting experiments involving the removal of NAPL
containing contaminants from granular soils, employed local phase equilibria to describe interphase mass
transfer (Marley and Hoag, 1984; Baehr et al.,1989; Ho et al, 1994). Experimental results suggested that
in the presence of NAPL, there was sufficient contact between the flowing air and immobile NAPL to attain
chemicalequilibrium. Although attractive for their computational efficiency and ease of implementation, local
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equilibrium models provide an incomplete description of most field scenarios. These models, however, are
valuable screening tools to evaluate the potential use of SVE as a remedial option.
The next generation of SVE contaminant transport models were non-equilibrium based incorporating
various rate limiting mechanisms to explain field observations and improve predictive capabilities. These
early non-equilibrium contaminant transport models considered the effects of soil heterogeneity on
contaminant mass transfer in the creation of preferential airflow pathways. The controlling mechanism for
contaminant mass transfer was diffusion through a low permeability non-advective layer adjacent to a layer
of higher permeability with advective air flow (Johnson et al., 1990; Ho and Udell, 1991). These models
offered a simple yet effective description of the influence of soil heterogeneity on mass transfer limitations
at the field scale.
A number of SVE non-equilibrium models have been presented employing first order interphase mass
transfer kinetics to describe single and multicomponent contaminant removal from unstructured soils
(Rathfelder et al., 1991; Lingineni and Dhir, 1992; Armstrong et al.1994; Karan et al. 1994). The first
order models were applied to both NAPL and non-NAPL containing systems and incorporated mass
transfer resistances involving single and multiple-phase pairs. With the exception of Karan et al. (1994),
constant mass transfer coefficients were assumed in all cases. A requirement of this modelling approach
was the evaluation of mass transfer coefficients, which in the absence of effective correlations for soils, had
to be determined by curve fitting.
Other non-equilibrium model efforts adopted a more rigorous mechanistic approach to quantify the effects
of aggregate formation on contaminant mass transfer in structured soils. These models employed a two
domain (mobile/immobile) approach incorporating several mass transfer resistances including radial
diffusion, interphase mass transfer kinetics and desorption kinetics to describe the removal of single
components fromthree phase (air-water-solid) containing systems. The mobile domain consisted of the airfilled macropores and the immobile domain consisted of the water-filled micropores. Gierke et al. (1992)
considered advection and diffusion in the air-filled domain, radial diffusion in water-filled spherical
aggregates and first order mass transfer kinetics between the two domains. Ng and Mei (1996) presented
a similar model to Gierke et al. (1992), but differed by assuming instantaneous inter-domain mass transfer.
Brusseau (1991) assumed first order kinetics between the immobile water and air-filled pore domains with
instantaneous and rate limited desorption within domains. Campagnolo and Akgerman (1995) presented
a comprehensive multi-component, multi-domain model, which included a NAPL phase and 4 solid phase
domains:gas-filled mineralparticles, water-filled mineral particles, microbial aggregates and organic matter.
The two multi-domain models all performed well when tested against laboratory data and in the case of
Campagnolo and Akgerman (1995) against field data.
Contaminant mass transfer in SVE systems is controlled by a complex series of inter-related processes
dependent on several parameters including soil type, permeability, particle size distribution, organic carbon
content, water content, contaminant composition, contaminant physical and chemical properties and air
pore velocity amongst others. Although much insight into the factors controlling contaminant mass transfer
in SVE has been derived fromprevious investigations, the scope of conditions investigated, is still somewhat
narrow. Process data is especially lacking for the removal of NAPL containing contaminants from
structured soils, even though such conditions are likely to be encountered in the field.
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5.3.1
Model Development
The contaminant transport model developed for the column venting experiments describes the removal of
a multicomponent organic contaminant from soil distributed amongst four phases: vapour, NAPL, aqueous
and solid. The wetting fluid, as is typical for almost all air- NAPL-water-systems, was water, which was
assumed to engulf the solid particles. The NAPL phase as the fluid of intermediate wettability, forms a layer
adjacent to the water layer with the remainder of the pore space filled by air.
The vapour, NAPL and aqueous phases were assumed to follow applicable ideal behaviour. Ideal gas
behaviour was assumed for the soil vapour as the venting experiments were performed at room temperature
and the pressure drops across the soil columns were less than 500 Pa (Massman, 1989). The soil gas, due
to the low column pressure drops, was treated as an incompressible fluid. The aqueous phase was treated
as a Henry’s Law ideal solution considering the low aqueous solubilities of the four organic compounds
used in the venting experiments and the corresponding negligible deviations from ideal behaviour. The
NAPL phase was assumed to obey Raoult’s Law ideal solution behaviour based on the similarities in
chemical structure for toluene, m-xylene and trimethylbenzene, all being methyl substitute single ring
aromatic compounds. Hexane as part of the quaternary mixture will exhibit slight non-ideal character, but
for simplicity was assumed to behave ideally within the NAPL mix. Isothermal conditions were assumed.
Evaporative water losses and bio-degradation of the organic compounds were negligible. Postexperimental water contents measured across the length of the soil columns differed from pre-experimental
values by less than 5%. Biological activity was monitored by the measurement of CO2 levels in influent and
effluent air over the course of a venting experiment and negligible differences were observed. The
observed overall mass balance was excellent.
The soil vapour was assumed to be the only mobile phase as the applied external pressure gradient was
too low to induce flow in either the NAPL or aqueous phases (Falta et al.,1989). Constant superficial gas
velocity was assumed as constant air flow rates were maintained over the course of the venting
experiments. The air-filled porosity was set as a constant as the effects of NAPL depletion were
negligible.
The other process contributing to vapour phase contaminant transport was molecular diffusion. Mechanical
dispersion, an important process affecting liquid transport in porous media, was assumed to make a
negligible contribution to vapour phase contaminant transport, on the basis of the almost 4 order of
magnitude difference between gaseous and liquid molecular diffusivities (Benson et al.,1993).
Contaminant transport by molecular diffusion was assumed to obey Fick’s Law and Knudsen diffusion
along with species coupling effects associated with multi-component diffusion were ignored. Fick’s Law
provides an accurate description of transport by molecular diffusion for the dilute vapour phase
concentrations and the medium textured soil encountered in the venting experiments (Thorstenson and
Pollock, 1989). The binary molecular diffusion coefficient was corrected for the porosity and tortuosity
of the porous medium based on the empirical relationship of Millington and Quirk (1961).
Local equilibrium has been applied between the NAPL, water and solid phase concentrations. Linear
equilibrium partition coefficients have been used to relate the concentrations in these three phases at all
times. The aqueous/NAPL partition coefficient was defined as the molar aqueous solubility divided by the
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molar NAPL density. Equilibrium partitioning between the aqueous and solid phases was assumed to be
dominated by sorption of the organic compounds to the soil organic matter. This process was described
by the linear adsorption isotherm model where the sorption partition coefficient is given as the product of
the organic carbon partition coefficient and the organic carbon fraction of the soil (Karickhoff et al., 1979).
Mass transfer limitations in the contaminant transport model were described using an overall volumetric
mass transfer coefficient. The rate of mass transfer is expressed as the product of concentration driving
force and an overall volumetric mass transfer coefficient. The overall volumetric mass transfer coefficient
incorporates in a lumped manner the resistance that prevails in each of the phases of the system and the
interfacial contact area between the phases. This lumped resistance combines the diffusional resistance
within the soil’s organic matter, diffusional resistances within water-filled pores, diffusional resistance within
the NAPL layer and resistances across the various phase interfaces. However, as a lumped parameter no
attempt is made to quantify, or provide resolution between these resistances.
Contaminant removal in the soil venting columns was thus described by two contaminant transport
equations including one for the mobile vapour phase and a second for the immobile NAPL-aqueous-solidphases. The vapour phase contaminant transport equation is given by Equation 22:
(22)
where,
2g
q
x
Dej
Kga
t
Cgj
Kgl
Clj
j
= air volumetric fraction (m3"m-3)
= superficial air velocity (m"h-1)
= spatial coordinate (m)
= effective molecular diffusion coefficient (m2 "h-1)
= overall air/NAPL volumetric mass transfer coefficient (h-1)
= time (h)
= vapour phase concentration (mol"m-3)
= air / NAPL partition coefficient (m3 "m-3)
= NAPL concentration (mol"m-3)
= species index
In the second contaminant transport equation for the NAPL, aqueous and solid phases, the local
equilibrium assumption allowed the accumulation term to be expressed in terms of the NAPL concentration
as follows:
(23)
where
2l
2w
Kwl
Ksl
= volumetric NAPL content (m3"m-3)
= volumetric water content (m3"m-3)
= NAPL / aqueous phase partition (m3"m-3)
= sorbed / NAPL partition coefficient (m3"kg-1)
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Db
= bulk density of the porous medium (kg"m-3)
The overall volumetric mass transfer coefficient has been modelled as a linear function of the local NAPL
content as described in Equation 24:
(24)
where
2lo
m
Kgamin
= initial NAPL volumetric fraction (m3"m-3 )
= adjustable parameter capturing dependence of the overall volumetric mass transfer
coefficient on the NAPL content (h-1)
= overall volumetric mass transfer coefficient in the absence of a NAPL, which essentially
describes the air-water transfer resistance (h-1)
The above equation was selected for a number of simple reasons. First was the inability of a constant mass
transfer coefficient to capture the observed venting behaviour as will be discussed later. This included an
inability to capture the observed outlet concentrations while a substantial NAPL content remained in the
system. Thus, it was recognized that a declining mass transfer coefficient was necessary. During the period
in which the NAPL remains in the system the bulk of the contamination is in this NAPL phase. Thus, the
mass transfer coefficient must be linked to the prevailing NAPL content. The simplest two-parameter
relationship is a linear one between the mass transfer coefficient and the NAPL content. As the volumetric
NAPL content changes withtime, the overall mass transfer coefficient has an indirect temporal dependence.
5.3.2 Mass Transfer Coefficients
Table 11 gives all the calculated mass transfer coefficients obtained for the single, binary and quaternary
cases run at the various water contents. Figures 5, 6, 7 and 8 show the resulting fits using these coefficients.
The figures show that the variable mass transfer coefficient provided an excellent fit of all nine data sets.
The relationship could fit the sharp breakthroughs of the air dry experiments (2.7wt%), the increase
spreading of the middle water content(15-16 wt%) experiments and the
extended tailing of the high water content (20-22 wt%) experiments. The
overall mass transfer coefficient and the corresponding fitting parameters were
dependent on the soil’s water content.
For the binary and quaternary experiments, the same two fitting parameters
were used for all of the components within a given experiment.
Figure 5: Toluene Behaviour
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Table 11: Mass Transfer Coefficients Parameters (m and Kgamin ) and Fitting Parameters (ASSRD)
Air Dry
Middle Water Content
High Water Content
Experiment
Replicate
m
(h-1)
Kgamin
(h-1)
ASSRD
m
(h-1)
Kgamin
(h-1)
ASSRD
m
(h-1)
Kgamin
(h-1)
ASSRD
Single
A
950
80
1.3
380
2.1
0.32
120
0.1
0.88
B
285
65
2.3
210
8
0.12
38
1
0.1
avg
617
72
295
5
79
0.55
A
450
75
0.4
225
8
0.49
35
0.8
1.2
B
360
120
0.6
315
18
0.52
125
0.8
1.24
C
420
35
1.2
NA
NA
NA
NA
NA
NA
avg
410
77
270
13
80
0.8
A
130
0.4
0.41
205
0.3
0.38
115
0.02
5.05
B
148
1.7
0.4
148
0.6
0.27
120
0.01
1.47
C
265
0.45
0.45
330
7
0.88
95
0.05
2.33
228
2.6
110
0.03
Binary
Quaternary
avg
181
0.85
NA = not applicable
ASSRD = Average sum of squared relative deviations
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For the binary case both components (toluene and m-xylene) were fit equally well
with this single set of parameters. For the quaternary, three of the four
components were fit very well while the tailing of the fourth component (hexane)
was poorly handled in the middle water and high water content experiments. The
greater mass transfer resistance observed for hexane was attributed to its much
lower affinity for the water phase relative to the other three components. To
extend the success of this model to handle complex mixtures requires the
development of a relationship for the two parameters as a function of the physicalchemical properties of each component. The overall trend is a slight decline in the
magnitude of the initial mass transfer coefficient with increasing soil water content
and a nearly two orders of magnitude decline for the final mass transfer Figure 6: Quaternary Dry
coefficient. The final mass transfer coefficient for the quaternary air dry case is the
only exception to these trends. Further work is necessary to explore this
difference.
Figure 9 illustrates the observed mass transfer coefficient parameters as a function
of the soil’s water content for the three different contaminant mixtures. The
parameters presented are the overall volumetric mass transfer coefficient at a
NAPL content of 0.04 m3"m3 (i.e., m /2 ol+ Kgamin) and the overall volumetric
mass transfer coefficient at a NAPL content of 0 m3 "m3 (i.e., Kgamin). For
comparison purposes, the value for single AD cases were normalized to a NAPL
content of 0.04 m3"m3 (actual experiment run at 0.17 m3"m3). These essentially
represent the initial and final values for the overall volumetric mass transfer
coefficient. As a first approximation there was excellent agreement for the three
different contaminant mixtures between the initial and final mass transfer
coefficients over the range of water contents investigated. The overall trend trend
is a slight decline in the magnitude of the initial mass transfer coefficient with
increasing soil water content and a nearly two orders of magnitude decline for the
final mass transfer coefficient. The final mass transfer coefficient for the quaternary
air dry case is the only exception to these trends. Further work is necessary to
explore this difference.
It is valuable to compare the observed mass transfer coefficients to values
reported in the literature even though differences in soils, soil packing, flowrates
and contaminant mixtures may be substantial. Karan et al. (1994) reported
air/NAPL overall volumetric mass transfer coefficients of 252 to 432 h-1 for the
removal of n-octane from a silt clay soil and glass beads. It is encouraging that
these values are of the same order as the initial mass transfer coefficient observed
in this work. The range of Kgamin values are comparable to the values of air/water
mass transfer coefficients for TCE removal from sand reported by Armstrong et
al. (1994), 0.036 to 36 h-1 and by Cho and Jaffe (1990), 0.02 to 63 h-1.
Figure 7: Quat. Semi-wet
Figure 8: Quat. Wet
Figure 9: m and Kgamin
The good agreement of the variable Kga model with the experimental data suggests that for these
experimental conditions, the mass transfer resistance declined as a function of the NAPL content. Two
possible explanations for the decline in the overall mass transfer coefficient are the diminishing contact area
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between the vapour and NAPL phases and the increasing path length fromthe interface between these two
phases to the bulk airflow pathways. If the NAPL was present as a perfect sphere then the interfacial area
would follow a 2/3 power on the NAPL content. If the NAPL is present as a film around a spherical
particle the power relationship would be expected to be lower than 2/3. If the NAPL is present in a
perfectly cylindrical capillary tube then the path length would increase linearly with decreasing NAPL
content. In the field the NAPL will prevail in the soil pores in a number of different spatial configurations
leading to a power dependency that is a weighting of all of the possible simplistic pictures. The success of
the linear model should be interpreted as a clear case for inclusion of a mass transfer coefficient functionality
on the prevailing NAPL content. The success should not be interpreted as evidence that the correct power
is 1.0. A better fit may be achieved by making the power an additional parameter in the fitting exercise.
6.0
BIOREMEDIATION OF HYDROCARBONS
Diesel fuel contaminated soil is a major environmental concern. It is the second most frequently treated
contaminant after benzene at USEPA superfund projects [Buswell, 1994]. A typical source is leaking
underground storage tanks at service stations [Atlas et al., 1995]. Diesel fuel is a complex mixture of
hydrocarbons consisting of approximately 30% alkanes, 45% cyclic alkanes and 24% aromatics
[Frankenberger et al., 1989]. As a complex mixture, all aspects of the site cleanup process from
assessment to remediation become more difficult.
Bioremediation is an attractive remediation technique for diesel fuel and it can be accomplished as either
an in situ and ex situ process. In situ treatment can include many different types of enhancements. The
most common include increasing oxygen availability and the addition of nutrients. Increasing oxygen
availability may be accomplished by air sparging (bioventing), the addition of hydrogen peroxide through
injection wells or an infiltration gallery [Fiorenza et al., 1991; Ryan et al., 1991], tilling in a land farm style
operation, or amending the soil with bulking agents such as straw, mulch, or wood chips. In a few cases,
a site is enhanced through the addition of microbial strains that are specifically capable of degrading the
contaminant.
Ex situ or bioreactors, involve similar enhancements as in in situ but also include the excavation of the soil
and transferring it to a reactor. The advantage of a reactor situation is that through mixing it becomes easier
to uniformly distribute oxygen and nutrients and it is possible to run at elevated temperatures. Thus,
increased degradation rates can be realized at the expense of greater capital and operating costs.
Selection of the appropriate technology and enhancement depends on solubility, volatility and sorptive
ability of the contaminant, location and extent of the contamination, hydrogeology of the site and goal of
the remediation project, i.e., source remediation or plume control [Fiorenza et al., 1991]. However,
treatment effectiveness can be poor if an incorrect enhancement has been chosen. Thus, it is important to
have a good understanding of the bioremediation process and specific site characteristics in order to
increase the odds of success. To aid in understanding the process, biodegradability tests are conducted
in the laboratory. One of the techniques available is respirometry [Steinhart, 1995].
Respirometry allows the measurement of hydrocarbon biodegradation rates through changes in O2 and CO2
soil_overview_03.wpd
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levels via stoichiometry [Hickey, 1995]. This can be accomplished with a variety of techniques such as
pressure changes in sealed bioreactors or the direct measurement of oxygen addition [Mahendraker and
Viraraghavan, 1995; Naziruddin et al., 1995]. Subsequent monitoring O2 and CO2 in the field allows for
possible timing of tilling events.
However, the literature has shown that diesel fuel is more persistent in the field than in the lab. Compounds
readily degraded in the lab in a few weeks were still found in actual contaminated soil samples even after
a number of years [Steinhart, 1995]. Following successful lab trials, Cutright [1995] reported minimal
degradation in the field. Sturman et al. [1995] and Atlas [1995] indicate that these failures result from a
failure to account for and understand the scale-dependant variables like mass transport limitations, spatial
heterogeneity and competing microorganisms.
An additional challenge facing some northern sites [including northern Canada, Scandinavia and northern
Russia] is low temperatures. Biodegradation is frequently temperature-limited at most times during the year
[Kerry, 1993]. Additional research at low temperatures is needed, including whether nutrients are
required [Sparrevik and Breedveld, 1997; Reynolds et al., 1997].
Zytner et al. [2000] completed field and laboratory studies to study the influence of temperature and
oxygen on the bioremediation of diesel fuel contaminated soil. Field data was obtained from a landfarm
located in Northern Ontario, while the laboratory experiments were conducted using bioreactors containing
diesel spiked soil and contaminated soil from the field site.
Table 12 contains the average TPHC concentrations (wet basis) measured in the field and the
corresponding standard deviation. Using these average TPHC concentrations, an overall TPHC mass loss
of 21% was observed between the two sampling intervals for the various depths. The largest losses (58%)
occurred in the top 10 cm, while for the lower two soil depths, losses were approximately 10%.
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Table 12: Average TPHC Data Measured in Field on a Wet Basis
Visit #1 - June 22-23, 1996
Segment No. a
0 to 10 cm Below Grade
10 to 20 cm Below Grade
20 to 30 cm Below Grade
Conc. [mg/kg]
Std. Dev.
Conc. [mg/kg]
Std. Dev.
Conc. [mg/kg]
Std. Dev.
12300
3590
18200
3580
25300
8370
2
9840
2360
21500
840
22600
4040
3
10000
3790
30300
6850
29600
11400
4
8320
2420
15400
4500
17500
2970
5
13400
3220
20400
2960
23600
2200
6
12700
1810
18400
2770
18200
1440
7
13900
3250
14800
2080
18700
2340
8
12800
870
22500
4040
25400
3610
9
8750
1220
20400
3780
23000
5070
Avg for depth
11300
2500
20200
3490
22700
4600
1
Avg for Landfarm
18000
Visit #2 - August 4, 1996
Segment No b
0 to 10 cm Below Grade
10 to 20 cm Below Grade
20 to 30 cm Below Grade
Conc. [mg/kg]
Std. Dev.
Conc. [mg/kg]
Std. Dev.
Conc. [mg/kg]
Std. Dev.
1
7320
2970
14200
8330
6920
5120
2
3350
810
16400
3380
25800
10800
3
3130
60
17200
2650
36000
2570
4
2060
470
21200
1830
13200
3600
5
10300
6860
18900
1520
25400
5620
6
6320
200
14200
1020
13400
9190
7
5070
660
17800
2900
18000
5010
8
2450
260
11100
4370
17500
7210
9
3730
1240
23200
4890
29000
4820
Avg for depth
4850
1500
17100
3430
20600
5990
0.007
0.0043
0.01
Avg for Landfarm
14200
Decay Rates (1/d)
Avg for depth
0.022
0.009
Avg for Landfarm
a - five samples for each segment; b - three samples for each segment
0.0038
0.005
Table 12 also gives the average first order rate constants as a function of depth based on the observed
losses of TPHC. First order rate constants were used as they best describe field degradation [Kampbell
and Wilson, 1991 and Naziruddin et al., 1995]. For all depths, the average TPHC first order rate
constants ranged from 0.022 to 0.0043 d-1. Similar rate constants were obtained for the individual
compounds. Finally, the overall rate constant was calculated as 0.005 d-1, which indicates that the overall
rate was impacted by the lower losses at the 10 to 20 cm and 20 to 30 cm depths.
The observed contaminant loss is due to a combination of abiotic and biotic mechanisms. Abiotic
mechanisms are volatilization and leaching. Volatilization was measured, in a related project, at
approximately 0.5% of the original contamination [Fitzgerald, 1997]. Modelling of volatilization using a
modification of the Behaviour Assessment Model [Jury et al., 1984] projected a 2% loss. This low
volatilization loss is consistent with a full scale bioventing operation, in which over 90% of the diesel
removal could be attributed to biodegradation, while only 10% was due to volatilization [Downey et al.,
1995].
Laboratory degradation rates were quantified based on changes in the total petroleum hydrocarbons
concentrations and some individual components, and by monitoring oxygen consumption and carbon
dioxide evolution. The first order rate constants were obtained and seen to be a strong function of oxygen
levels and operating temperature. Modest degradation continued at 2°C.
Using the degradation data, correlation was developed based on a combination of a Monod type
expression to account for oxygen dependencies and a modified form of van’tHoff- Arrhenius relationship
[Tchobanoglous and Schroeder, 1987] to account for the temperature dependencies.
(25)
where, k
O2
T
= first order rate constant, 1/d
= oxygen content in soil, v%
= desired temperature, °C
Figure 10 gives a comparison of the field results with the laboratory results and shows for the upper level,
warm conditions, the TPHC losses are in reasonable agreement. Essentially, the lab first order rate
constants at high oxygen content and 25°C are within a factor of 1.5 of the field degradation constants for
the surface soil. At the lower depths, comparison of rates was within a factor of 10. The slower
degradation rate was due to the lower oxygen levels measured in the deeper soils. As such, more frequent
or possibly more effective tilling is required to bring the contaminants closer to the surface and to maintain
porosity and thus oxygen diffusion. Alternatively, additional bulking agents may be of value or spreading
of the soil over a larger area to have less depth.
Review of the literature revealed similar studies. Reynolds et al. [1997] reported two diesel degradation
rates for a landfarm in Alaska. The site was monitored for 1 year, in which it was frozen from late
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September to late May; 0.003 d-1 without
LAB k1*
FIELD k1*
nutrients and 0.0 0 5 d-1 with nutrients. * temps from 15 to 28 C
* temps at 25 C
-1
Sparrevik and Breedveld [1997] also
10
studied diesel in the field for 1 year in
Norway and reported a degradation rate
of 0.004 d-1 (average temperature 8°C).
(0.022)
0 to 10 cm
For comparison on lab studies,
(0.0144) High O2
Elektorowicz [1994] reported a diesel
(0.0112)
degradation rate of 0.03 d-1 [room
20 to 30 cm
(0.0084)
Low O2
(0.0043)
temperature], while Margesin and
Schinner [1997] reported degradation
10 to 20 cm
(0.0038)
-1
-1
rates varying from 0.019 d to 0.027 d
at 25°C.
All these values compare
-3
10
favourably, suggesting that the
bioreactor/respirometer used in this study Figure 10: Field and Laboratory Rate Constants (1/d)
is capable of providing useful data..
However, Geerdink et al. [1996] measured first order rate constants in the lab [at 30 °C] of approximately
13 d-1 for fresh diesel and for n-hexadecane. This range in degradation rate reinforces the importance of
accurately simulating biodegradation rates in the lab as a function of site conditions, including soil type, age
of contamination, nutrient addition, temperature and availability of oxygen. If not, incorrect decisions may
be made in the field.
6.1
Nutrient Addition
Diesel fuel is a major soil contamination problem. It is estimated that over 250,000 underground storage
tanks are leaking diesel fuel in the United States alone (Buswell, 1994; Atlas et al., 1995). Researchers
have found that bioremediation is an effective method at removing diesel fuel from contaminated soil (Baker
et al., 1993; Downey et al., 1995; Frankenberger et al., 1989; Shen and Bartha, 1994; Walworth and
Reynolds, 1995; Widrig and Manning, 1995).
Bioremediation is a process whereby micro-organisms use the organic substances in soil (including diesel
fuel) as a carbon and energy source. The organisms require an electron acceptor such as oxygen (aerobic
environment), to produce carbon dioxide and water while degrading the contaminants. The growth of new
cells may also occur, with the intake of nutrients such as nitrogen, phosphorous, potassium, and essential
micro-nutrients.
Many soils contaminated with petroleum hydrocarbons are nutrient limited. This phenomenon is caused
by excessive carbon loading from the fuel without any significant nutrient inputs. Nitrogen and phosphorus
are often identified as the limiting factors for biodegradation (Walworth & Reynolds, 1995). Nitrogen is
of particular concern since it is mobile in the environment. Soils can lose nitrogen in the form of nitrate
leaching, ammonia volatilization, and denitrification to gaseous species including N2O and N2 (Hamid &
Mahler, 1994; Ismailov, 1983; Whitehead & Raistrick, 1990). In fact, Xu et al. (1995) found that soils
contaminated with petroleum fuels lose nitrogen at faster rates than uncontaminated soils, primarily due to
denitrification.
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Nitrogen may be added in a variety of forms including nitrate and ammonium compounds, urea, and urea
oligomers (controlled release fertilizers). The nitrate and ammonium ions are the readily available forms of
nitrogen responsible for microbial nutrition (Gottscalk,1985), whereas urea and urea oligomers must be
initially degraded to release ammonium ions.
The various forms of nitrogen have unique advantages and disadvantages in the bioremediation field.
Nitrate, ammonia and urea have been used extensively in the agricultural industry for years and the
behaviour of these compounds is well documented. All have relatively high water solubilities and are
available to the microbial consortium in soil (Walworth & Reynolds, 1995). Urea oligomers have been
used in the horticultural community (i.e. turf grass industry) for about a decade, but information regarding
their behaviour in soil is limited. Some of the different forms of nitrogen that have been used by researchers
for bioremediation are listed in the Table 13.
Table 13: Various Nitrogen Sources Listed in the Bioremediation Literature
Nitrogen Source
NH4Cl
Contaminant Degraded
Fuel Oil
Diesel Fuel
NH4NO3
Oil Sludge
Diesel Fuel
KNO3
NaNO3
Urea
Phenol
Gasoline
Petroleum Mix
Diesel Fuel
Urea Oligomers
Fuel Oil
Reference
Bauer et al., 1994
Elektorwicz, 1994
Widrig and Manning, 1995
Dibble and Bartha, 1979
Frankenberger et al., 1989
Huessmann, 1995
Walworth and Reynolds, 1995
Hoyle et al., 1995
Kampbell and Wilson, 1991
Jain et al., 1992
Shen and Bartha, 1994
Wang et al., 1990
Flathman et al., 1994
Currently, there is limited standardized information that allows for comparison of the various nitrogen
sources at different levels for effective bioremediation. Consequently, Brook et al.[2000] conducted
laboratory studies using respirometers containing field contaminated soil that were amended with different
sources and levels of nitrogen. The contaminated soil was monitored for oxygen consumption, carbon
dioxide generation and depletion of total petroleum hydrocarbon (TPHC) levels.
The respiration data for all of the experiments was converted to equivalent TPHC degradation assuming
n-hexane as a typical compound in the mixture of total petroleum hydrocarbons. Many authors believe
TPHC degradation is best described as a first order reaction and as such the raw TPHC and converted
respiration data were used to calculate first order rate constants (Hickey, 1995; Kelly and Cerniglisa,
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1995). The first order rate constants are presented in Table 14 for all nitrogen sources and carbon to
nitrogen ratios.
Table 14: TPHC, O2 and CO2 First Order Rate Constants (x 10-4 1/d)
C:N
Nitrogen
TPHC
O2
Source^
NN
26
2.6
0.83403
U
250
13
UO
72
8.5
AN
39
0.65
KN
45
1
AS
320
20
40:1
U
110
13
UO
92
8.9
AN
120
4.1
KN
57
1.8
AS
190
9.9
^
NN– no nitrogen; U–urea; UO– urea oligomers;
AN– ammonium nitrate; KN– potassium nitrate;
AS– ammonium sulfate
CO2
1.9
34
25
11
9.1
14
16
19
19
1.6
19
The degradation rate constants measured by loss of TPHC were consistently higher than the degradation
rate constants determined based on oxygen consumption and carbon dioxide evolution data. The carbon
dioxide rates ranged from a factor of 3 to 30 times lower than the rates measured by TPHC loss. The rates
measured by oxygen consumption ranged from a factor of 8 to 60 times lower than the rates measured by
TPHC loss.
The rate constants measured by TPHC loss, oxygen consumption and carbon dioxide generation will agree,
provided that the hydrocarbons are all completely mineralized through the degradation process. In practical
terms for a fuel containing largely carbon and hydrogen atoms only, mineralized means that all of the carbon
is released as carbon dioxide and all of the hydrogen as water. Reasons for discrepancy include incomplete
mineralization, denitrification processes and soil carbonate interactions.
Incomplete mineralization of the diesel fuel generates hydrocarbons that are partially stabilized and that
generally become more polar. Once the sample extraction process can no longer efficiently extract these
compounds they become considered completely degraded, even though they have not consumed their full
complement of oxygen nor released all of their carbon as carbon dioxide. The result is higher observed
degradation rates based on TPHC loss than observed based on respiration rates. The TPHC degradation
rates may be considered to overestimate the cleaning rates as some of the non-extractable compounds may
still represent an incremental environmental risk. The respiration based degradation rates underestimate
the process as some of the non-extractable compounds have become stable (or nearly so) constituents of
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the soil’s ‘natural’ organic matter.
One of the possible interferences with the measurement of oxygen consumption as measured by the
pressure sensors is denitrification. Any reactors that contained a significant quantity of nitrate may be
susceptible to denitrification, such that the nitrates are converted to gaseous nitrogen compounds, such as
N2, N2O and NO2. The use of nitrate as an electron acceptor would decrease the quantity of diatomic
oxygen utilized by the microbes during degradation. Furthermore, any gaseous nitrogen formed would
decrease the calculated oxygen consumption, by increasing the pressure in the reactors. However, if all
of the nitrogen in the fertilizer used in the 20:1 applications was released as nitrogen gas, the result would
be to decrease the observed oxygen based rate constants by only 5 x10-4 d-1. Measurements of nitrogen
content of the soil clearly show that nitrogen losses were modest and thus denitrification could not have
been a significant factor in influencing the oxygen measurements.
In contrast, the low carbon dioxide production may have resulted from reactions between the carbon
dioxide and soil carbonates found in the alkaline soil (Baker et al., 1993; Brady, 1990; Hickey, 1995).
The carbon dioxide generated may react with the carbonates in the soil and consequently will not be
absorbed in the potassium hydroxide solution. This would result in the pH analysis of the KOH in the test
tubes underestimating the actual carbon dioxide evolution. This process is less likely to be responsible for
the difference in the rate constants since the pH of the soil is not extremely high (Hickey, 1995).
TPHC Decay Rate (1/d)
Review of the TPHC rate constants showed that all treatments resulted in increased degradation above the
control experiments with no nitrogen
addition. As seen in Figure 11, the
0.04
degradation rate constants were
C:N=20:1
C:N=40:1
dependent on both the source of nitrogen
0.03
and the C:N ratio. At the 20:1 carbon to
nitrogen ratio, the nitrogen supplement
enhanced degradation rates by between a
0.02
factor of 1.5 (ammonium nitrate) to 12
(ammonium sulphate) times relative to the
0.01
control (no nitrogen). While urea,
ammonium and nitrate have all been
0
reported as being available to the
NN
U
UO
AN
KN
AS
Nitrogen Treatment
microbial consortium in soil; ammonium is
the preferred source of nitrogen from an
energy standpoint. This is due to the fact
that it is already in a reduced form, where Figure 11: TPHC Decay rates vs Nitrogen Treatment
as nitrate must be first reduced prior to assimilation into amino acids (Walworth & Reynolds, 1995).
For three of the nitrogen supplements decreasing the nitrogen supply (a C:N ratio of 40:1) increased the
degradation rates. This is suggestive of an optimal supply of nitrogen with the optimal level dependent on
the nitrogen source.
Figure 11 also illustrates the dependence of the TPHC decay rate constant on the source of nitrogen and
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shows the impact the average total nitrogen level has on decay. For each level of total nitrogen there is an
indication of the split between the ammonia and nitrate forms as percentage of total nitrogen that is NH3
(see Table 4 for the value). For the 40:1 carbon-to-nitrogen ratio, it is apparent that the degradation rate
constant increases only when the average ammonia levels increases. Nitrogen treatments that have lower
levels of ammonia (AN and KN) do not have the same degree of rate enhancement. The same trends are
apparent for the 20:1 carbon-to-nitrogen ratio. Wren et al. (1994), in the study of crude oil, noted that
degradation starts faster when in the presence of ammonia as compared to nitrate, provided that the soil
is alkaline. The soil used in this study would be considered alkaline as the pH was 7.9.
Dibble and Barth (1979) and Huessman (1995) raise the question regarding microbial inhibition to nitrates.
To further interpret the results the data was analysed using SYSTAT (1992) to explore the best fit of the
degradation rate constant as a function of nitrate, ammonia and total nitrogen.
First all the k values as a function of NH3 and NO3 were analysed for both carbon to nitrogen ratios. The
regression resulted in:
[
]
[
]
k = 0.032 NH 3 − 0.003 NO3 + 0.004
where
k
NH3
NO3
r 2 = 0.80
(26)
= TPHC decay rate (1/d)
= ammonia concentration in soil (mg/g)
= nitrate concentration in soil (mg/g)
Even though small, the coefficient for the nitrate term is negative, supporting the hypothesis of nitrate
inhibition. Figure 2 was then plotted to see how well the measured and predicted k values compare, with
the straight line being the desired fit. While the comparisons appear favourable, it was believed that the
total nitrogen values high in nitrate were impacting the regression The r2 value improved and the coefficient
for the nitrate value increased, with minimal change to the other coefficients. While the trend is encouraging,
further research is required to confirm the occurrence of nitrate inhibition and evaluate the concentration
at which it starts to dominate.
The completed study on weathered diesel fuel in silt-clay soil has shown that nitrogen is an important
parameter in biodegradation reactions, since nitrogen addition increased the biodegradation rates in all
cases. Furthermore, ammonia-nitrogen was seen to be responsible for the highest degradation rates, with
ammonium sulfate and urea treatments showing the highest decay rates. The analysis also suggests the
occurrence of nitrate inhibition, but further research is required determine the seriousness of the inhibition
and the concentration at which it occurs.
6.2 Use of Anhydrous Ammonia
Bioventing was first developed in the 1970’s to address some of the problems associated with SVE
(Leeson and Hinchee, 1997). Like SVE, it is an in situ remediation technology that uses air extraction to
remove contamination. However, where the intent of SVE is to volatilize as much of the contaminant as
possible, bioventing uses low or intermittent air flow rates to produce oxygen-rich conditions in the vadose
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zone (Hickey, 1995) and stimulate indigenous microbial degradation of the hydrocarbon contaminant.
Bioventing has several advantages over SVE including reduced operating costs, elimination of the need for
post-treatment of the air stream, and degradation of residual contamination left by SVE so that clean up
criteria can be met. In addition, bioventing is a true remediation technology in that the contaminant is
degraded rather than removed.
There are several environmental parameters that influence bioventing performance. These include soil
moisture content, pH, nutrient content and availability, oxygen content, temperature, toxicity and
bioavailability of the contaminant, and physical characteristics of the soil matrix. Although the design of
bioventing systems is necessarily site-specific to some extent, researchers have reached a general consensus
on the optimum values of some of the important environmental conditions. For example, there is general
agreement in the literature that many bioventing sites are nutrient limited, especially in terms of nitrogen and
phosphorous. Reported carbon-nitrogen-phosphorous (CNP) ratios presented in many papers vary widely,
from 100:10:1 to 1000:10:1. However, a major question that exists is how to deliver the appropriate
amount of nutrient to produce optimum biodegradation conditions in the field.
Several nitrogen sources such as ammonium nitrate, ammonium sulphate, potassium nitrate and urea
oligimers have been investigated. Ammonia based nitrogen has a shorter lag time and higher degradation
rate because the microorganisms require less energy for microbial metabolism (Walworth and Reynolds,
1995; Jorio, 2000). Unfortunately, ammonium salts cause the soil pH to decrease in poorly buffered
systems (Wrenn et al., 1994; Jackson and Perdue, 1999; Foght et al., 1999). The benefit of nitrate
compounds is no pH change, while more nitrate is required to achieve the same degradation with a longer
lag time (Wrenn et al., 1994). However, the addition of excess nitrate-nitrogen can be inhibitory in some
cases (Brook et al., 2001). All these sources of nitrogen can be solubilised for subsurface injection.
Unfortunately, practice has shown that the application is non-uniform, especially when the contaminated
site is kept operational. One of the options for providing the required nitrogen is the use of anhydrous
ammonia.
Anhydrous ammonia (AA) is popular in agricultural applications and can be added in either gaseous or
liquid form (Cronce and Cagnetta, 1996). Of the two forms, gaseous has the greatest potential as it could
be applied through an existing SVE/bioventing system and should easily disperse through the soil. Then
upon contact with the water, the AA dissolves and becomes ammonium as shown in Equation 1, the ideal
form for microorganisms (Shewfelt and Zytner, 2001) .
NH3 + H2O <------> NH4+ + OHThe disadvantages of using AA are the potential rise in soil pH as the reaction causes an initial alkaline
environment in the ammonia retention zone, where the pH of the soil can temporarily rise above 9 at the
point of highest concentration. Since ammonia has a pka value of 9.3 (McVickar et al, 1966), at a pH of
6 the equilibrium of ammonia (NH3) to ammonium (NH4+) is 0.1% to 99.9% respectively, while at a pH
of 9 the equilibrium is 50% to 50% (Whitman, 2002).
One additional concern is the safety of using AA in terms of handling because of potential dangers
associated with the gaseous form (MSDS, 2001). However, AA is the second highest form of nitrogen
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fertilizer sold in Canada as 635,388 metric tonnes of AA were sold in Canada in 2000 (Agriculture and
Agri-food Canada, 2001). Thus, proper safety procedures are well established and widely available
(Johnston et al, 2002) and should not hinder the use of AA.
Upon receiving the soil, it was sieved to remove larger soil particles, then mixed to ensure homogeneity.
The water content of the soil was adjusted to 15% by adding the appropriate amount of ultra-pure water.
The nitrogen content of the soil was then adjusted by adding the appropriate amount of NH4Cl or AA to
attain a C:N ratio of 10:1. This level was based on the previous work of Shewfelt and Zytner (2001),
where it was determined that ammonium worked best at a 10:1 ratio for a different soil.
To add the NH4Cl to the soil, calculations were performed to determine the amount of NH4Cl powder
required to obtain a C:N ratio of 10:1. Once calculated, the powder was dissolved in the water to be
added to bring the soil water content to 15%. Calculation of the required amount of AA assumed that it
would all be converted to NH4+ once in the soil based on the work of Cronce and Cagnetta (1996) on
TCE contaminated soil. Addition of the AA to the soil was accomplished through extraction of AA from
a Tedlar bag using a 50mL gas-tight syringe, and subsequent injection beneath the soil layer. The soil was
then gently mixed to distribute the AA.
After the addition of the nitrogen, the soil was divided into approximately 150 g samples for use in the
respirometers. At each stage of sample preparation, two 30 g samples of soil were removed to provide
information on the initial conditions. The samples were sealed and stored at 4oC if intended for microbial
analysis, and at –15oC if intended for TPH analysis.
Biodegradation rates for the conditions tested were measured in respirometers originally designed and
developed by Law (1996). The respirometers (Figure 12) function by measuring oxygen consumed and
carbon dioxide produced by the aerobic respiration of the indigenous soil microbial population. The
respirometers consist of Teflon-sealed 1L glass jars equipped with pressure transducers, which took
readings every 30 minutes that were recorded via a data-logging program. These readings were later
converted to an amount of oxygen remaining in the headspace of the jar. All respirometers were placed
in an incubator set at 25oC.
The carbon dioxide evolved during microbial metabolism
of the gasoline is trapped in a vial of KOH solution.
Oxygen consumption as measured by the calibrated
transducers, which measure the decrease in pressure
inside the respirometer over a specified length of time.
Bioventing conditions were maintained through an aeration
tube, which could be opened to the atmosphere by a plug
valve once oxygen concentration inside the respirometer
dropped below 18%.
Plug Valve
Pressure Transducer
Cord to Datalogger
Teflon Stopper
1L Glass Bottle
Aeration Tube
KOH Solution
Contaminated Soil
Figure 12: Schematic of Respirometer
In total, 27 respirometers were available to run simultaneously. Each of the completed experiments
employed 11 bioreactors. Ten of these reactors contained soil samples with identical treatments and were
incubated in duplicate for 2, 5, 10, 15 or 30 d. One reactor contained no soil, and acted as a
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thermobarometer to account for fluctuations of internal pressure due to atmospheric changes.
Normally the amount of carbon dioxide evolved through the degradation process would be determined by
measuring the pH shift of the KOH solution (Shewfelt and Zytner, 2001). Similarly, the amount of oxygen
consumed during degradation could be related to TPH degradation through the stoichiometric reaction of
a representative hydrocarbon. However, for this study, the intent was only to measure the TPH decay and
compare the results of AA and NH4Cl. Accordingly, the only purpose of the KOH solution was to ensure
the proper functioning of the respirometers by preventing the build-up of CO2.
The extent of TPH degradation was determined by measuring the difference between the initial and final
TPH content of the soil in each reactor. Soil samples were extracted using methylene chloride, and the TPH
content was determined using a gas chromatograph equipped with a flame ionization detector (GC-FID).
The GC-FID was calibrated by constructing a five-point calibration curve using known concentrations of
commercial gasoline (aged for 24 h) in methylene chloride.
In order to observe any acidification of the soil, the soil pH was measured before and after each incubation
as well. Soil pH was measured in a 1:1 slurry of soil and ultra-pure water using a pH meter.
The microbial population of the soil samples before and after incubation was measured by plating serial
dilutions of sodium pyrophosphate-extracted soil. R2A growth medium was used for heterotrophic plate
counts, and Bushnell-Haas (BH) media was used for counts of hydrocarbon degrading bacteria. The BH
plates were incubated in the presence of commercial gasoline as the sole carbon source. No attempt was
made to isolate or characterize the bacteria; only population data was obtained for total heterotrophic and
petroleum hydrocarbon degraders.
The use of a mathematical model confirmed that AA could easily be injected into the soil through the
bioventing well. The results indicated that sparge times of approximately 30 minutes at 2 atm (absolute)
were required to attain an AA concentration of 0.15 kg/m3 at the outer edge of the radius of influence (7.5
m), having a pressure of 1 atm. The radius of influence for the subsurface condition being simulated was
based on field measurements for SVE. Increasing the inlet pressure increased the resulting concentration,
but safety concerns and ammonium needs were exceeded. Consequently, the choices of inlet concentration
should be based on the contaminant concentration in the system, while the pressure should be based on
the available equipment and/or time constraints.
The degradation component of the model worked very well. The time to consume the AA in the system
depends on the ammonia and contaminant concentrations in the system. Based on an assumed contaminant
level of 1500 mgcontaminant/kgsoil and an AA application of 0.15 kg/m3, the levels of AA in the system drop
to approximately 80% of the starting value in 30 days, indicating a reasonable residual for effective
degradation.
The NH4Cl experiment showed very little decrease in pH with time, with the biggest pH drop being to 7.7.
Similar trends were observed by Shewfelt and Zytner (2001) who studied the effectiveness of ammoniumnitrogen vs. nitrate-nitrogen, and combinations of the two. All the values are within the pH range
considered ideal for bacterial growth (Huesmann, 1994). However, the AA experiment developed some
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acidification, with the pH getting as high as 9.2, which is not an uncommon result when dealing with the
application of this particular compound (Whitman, 2002).
The first-order biodegradation rates calculated from the TPH analysis, giving the following values:
NH4Cl: 0.028 d-1
<
AA: 0.023 d-1
<
Shewfelt and Zytner (2001) for NH4Cl: 0.081 d-1
<
With both treatments having identical application rates, it can be seen that AA has potential. Comparison
of the NH4Cl results with the earlier work of Shewfelt and Zytner (2001). Comparison of the values shows
that the current study had a lower degradation rate. However, it must be noted that the two soils were from
separate sites with dissimilar contamination levels; current study approximately half the contamination. It
is also highly likely that the two soils have differing ages of contamination. Soil that has been contaminated
for a longer period of time makes bioremediation more difficult, thus affecting the overall degradation rate.
Microbial tests showed that there was an increase in microbial activity of the total heterotrophs (R2A
growth media) and hydrocarbon degraders (BH growth media) for the NH4Cl, with the population going
to 105 to 106 cfu/g. Similar growth was observed by Shewfelt and Zytner (2001). Similar tests done with
the AA treatments showed a reduction of microbial activity, with about 104 cfu/g measured. Even though
less microbial activity, the AA had reasonable degradation.
For this test, the amount of AA added was controlled. However, analytical results of the amended soil
showed that approximately half the amount of the desired ammonium was present. Review of the
application procedure suggests that sufficient AA gas escaped from the treated soil, reducing the amount
of ammonium in the soil. Future tests will need to adjust for this in order to provide satisfactory nutrient
conditions in the respirometers. However, it must be noted that the AA test attained similar degradation
results when compared with the current NH4Cl, with only half as much ammonium present in the soil. This
suggests that AA is a promising candidate for the use in bioventing.
The modelling results showed that AA could easily be injected into the soil at a reasonable pressure of 2
atm to supply nitrogen to the indigenous microorganisms. The simulations also showed that a sparge time
of 30 minutes would suffice and provide a reasonable AA concentration of 0.15 kg/m3 in the soil. Using
a conservative contaminationlevel, the simulations showed that the AA would decrease to 80% of the initial
value after 30 d.
Laboratory results demonstrate that AA is successful in acting as a primary nitrogen source, with a
degradation rate equal to that of NH4Cl, even with half as much ammonium. Two challenges seem to be
the level of acidification that develops and the amount of AA that needs to be added to the soil to obtain
the desired concentration. The loss of AA from the soil, which is associated with easy migration needs to
be overcome for enhanced success in the future.
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6.3 Use of Biosurfactants
Biosurfactants have the potential to remove hydrocarbons from soil. Their application increases solubility
and reduces surface tension to permit their washing from soil. Thsy also biodegrade afterwards leaving no
traces. Scheibenbogen et al. [1994] demonstrated that rhamnolipid biosurfactants, produced by
Pseudomonas aeruginosa [UG2] have the ability release hydrocarbons from soil, making pump and treat
a possible option again. Both aliphatic and aromatic hydrocarbons were effectively removed from soil
columns without clogging, a typical problem for chemical surfactants.
7.0 SUPERCRITICAL FLUID EXTRACTION
Supercritical Fluid Extraction (SFE) is one of a number of innovative soil remediation technologies being
developed. SFE is an extraction process which utilizes the solubilizing power and the rapid mass transfer
characteristics of supercritical fluids (SCFs) to remove contaminants. McHugh and Krukonis [1994]
provide an excellent introduction to the field of supercritical fluids.
The supercritical fluid chosen is almost always carbon dioxide as supercritical carbon dioxide (SCCO2)
requires modest operating conditions (Tc = 31°C; Pc = 7.4 MPa) and it can easily be separated from the
solute by depressurization [Laitinen et al., 1994]. Additionally, carbon dioxide is cheap, available, and has
minimal environmental impact.
On an analytical scale, various contaminants have been removed from soil using SFE, including PCBs,
PAHs, DDT, phenolics and metals [Brady et al., 1987; Andrews et al., 1990; Akgerman et al., 1992].
With respect to soil remediation, Groves et al. [1985] indicated that the focus of research has been on
factors that control the rate of extraction on a small scale. Laitinen et al.[1994] recently reviewed the latest
advancements on site remediation. Information is still lacking on thermodynamic and kinetic data.
The distribution coefficient is a fundamental parameter of interest with regard to the development of the
SFE process as it indicates the feasibility of the extraction process [Roop et al., 1989]. It also can be used
to estimate the amount of CO2 required. The literature contains limited information on SCCO2-soil
distribution coefficients [Andrews et al., 1990; Erkey et al., 1992; Gray et al., 1995].
Mass transfer coefficients between the soil and bulk supercritical fluid depend on both an internal resistance
and an external film resistance. The internal resistance is often characterized as an effective diffusion
coefficient through a porous structure [Wu and Gschwend, 1986]. The external film resistance is
dependent on Reynold’s and Schmidt’s numbers as in most solvent extraction systems but also depends
on the Grashof number owing to a greater importance of natural convection [Debenedetti et al., 1986].
Measurements of mass transfer coefficients for soil - supercritical fluid systems are limited. Madras et al.
[1994] and Montero et al. [1995] have fitted breakthrough curves for extraction from a dry soil.
Water content of the soil is one factor which has not yet been studied in any detail. Low et al. [1994]
found little impact as a result of water contents up to 10% by weight on SFE of diesel from loam and silt
soils. Champagne and Bienkowski [1995] found no statistical differences in the equilibrium distribution with
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soils up to 10% by weight water content. However, Akgerman and Yeo [1993] were only able to recover
11% of the naphthalene from a soil slurry. Water is believed to hinder extraction of non-polar compounds
by acting as a barrier to carbon dioxide penetration [Camel et al., 1993].
Soil at a contaminated site may have a water content that is nearly dry through to 40% by mass. Drying
soil in a laboratory setting may be viable but drying tonnes of soil is unlikely to be practical. In addition,
a soil washing operation may be one of the first units in an overall treatment process and this will lead to
water contents well in excess of 50%. Therefore, the influence of water on the supercritical fluid extraction
of soil needs to be addressed.
Smyth et al., (1999)determined the distribution coefficient, Kcs (gsoil/gCO2), for Delhi Loamy Sand for
different water content and mixing conditions. The results are summarized in Table 15. All calculations
were done on a mass basis to avoid any discrepancies that may arise due the variability in the volume of
CO2 as a result of changes in temperature and pressure.
Static periods were included in six of the twelve experiments. For the water contents of 10% and less, the
concentration in the exhaust carbon dioxide was the same following the static period as before the static
period, indicating that the system was operating at equilibrium between the soil and the carbon dioxide at
the point of initiating the static period. Therefore, the distribution coefficients provided, in Table 14, are
equilibrium descriptors of the system for water contents of 10% and less.
The average equilibrium distribution coefficient for water contents between 0% and 10% was 0.92 (±0.31)
g/g. This compares favourably with the value of 1.7 (±0.7) g/g for the same soil, air dry, measured at 35°C
and 10.7 MPa by Gray et al.. The difference in the two observed partition coefficients is largely explained
by the difference in solubility at the two different supercritical conditions (0.014 mol/mol at 35°C/10.7MPa
vs 0.0096 at 42°C/10MPa ).
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Table 15: Distribution and Mass Transfer Results
Water
Content
(%)
Experiment
Label
Distribution Coefficient (g/g)
Maximum
Average
0/M/C
1.5
1
1A/M/C
1.7
1
1B/M/S
0.8
0.59
3
3/M/C
1
0.81
5
5A/M/C
1.5
1.1
5B/M/S
1
0.68
5C/NM/S
1.6
1.6
5D/NM/S
2.1
1.2
10
10A/NM/C
0.65
0.53
10B/NM/S
1.3
0.73
20
20A/NM/C
0.64
_
20B/NM/S
0.15
0.09
M - mixed; NM - nonmixed; C - continuous; S - static period
Mass Transfer Coefficient
(k ov a) x 109 m3 s-1 g-1ds
0
0.5
1.4
1.3
2.1
1.8
1.2
0.0073
The equilibrium distribution coefficient for bone dry soil (0/M/C) is the same as the other low water content
values. This is unexpected as it is recognized that sorption to bone dry soil increases due to the availability
of mineral sites [Chiou and Shoup, 1985]. The oven dried soil likely absorbed some moisture during the
necessary handling to contaminate the soil and transfer it to the extraction vessel. In a test, the oven dried
soil increased to a water content of 0.11% when exposed to ambient air for 43 minutes. Since the handling
of 0/M/C soil was less than 43 minutes, the partition coefficient is independent of water content over the
water content range of 0.11% to 10%.
The observed distribution coefficients for water contents of 20% are lower than the Kcs values for 10%
water or less. The reported distribution coefficients have included the unknown naphthalene with the
carbon dioxide samples. Although this has been done for consistency, it is likely somewhat less valid for
the 20% water content situation. The efficiency of the carbon dioxide sampling was running at around 60%
during the lower water content cases. To explain the unknown naphthalene the carbon dioxide sampling
efficiency would have to have dropped to about 40% for run 20B and to about 0.2% for run 20A. In
addition, with the higher concentrations in the residual soil it is reasonable that modest errors in soil sampling
could explain a substantial portion of the unknown naphthalene. Thus, the distribution coefficients reported
in Table 5 for the 20% case are likely overestimates.
The mass transfer of the contaminant from the soil to the supercritical fluid is crucial to the development of
SFE as a full scale process. The effect of water on mass transfer coefficients has not been quantified.
Therefore, an attempt was made to determine values for the mass transfer coefficients for water contents
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from air dried to 20%.
Overall mass transfer coefficients were calculated for each of the experimentalruns involving a static period.
Table 15 summarizes the values determined for each experiment with the mass transfer coefficient defined
based on:
(27)
where
N
Mds
Ds
Cs
CCO2
k ov a
= mass transfer rate (gN/s),
= mass of dry soil (gds),
= soil density (gds/m3),
= naphthalene soil concentration (gN/gds),
= naphthalene concentration in SCCO2 (gN/gCO2), and
= overall mass transfer coefficient (m3@s-1@g-1ds).
Equation 27 is consistent with the conventional form of a mass transfer equation but has been written to
explicit identify that all of the parameters are to be used on a mass basis rather than the conventional volume
basis. A mass basis is preferred in this application for three reasons: one, the amount of soil in a system
is usually measured by mass rather than by volume; two, the area for transfer likely scales with the mass
of soil in the system rather than the volume of the system particularly when the relevant domain extends all
the way to soil slurries; and three, for supercritical fluids specifying the mass of the fluid is open to less
ambiguity than specifying the volume of the fluid.
The resulting overall mass transfer coefficients for the low water content cases averaged 1.6 x10-9 m3 s-1
g-1ds. For the 20% water content case the value is 7.3 x10-12 m3 s-1 g-1ds. This decrease by at least a factor
of 200 as the water content increases from 10% to 20% is believed that this lower value is largely due to
water bridging between particles of a packed bed of soil. These results indicate that the soil tested allows
for easy extraction of contaminants when the soil water content is below 10%.
LaPlante et al. (2000) complete SFE work with a soil slurry. Experiments were operated two modes; nomix during dynamic flow and mixing during static periods. The no mix operating mode achieved equilibrium
for water contents from 0 to 10 wt%. Mixing during static periods achieved equilibrium for water contents
from 15 to 50 wt%, while continuous mixing achieved equilibrium for water contents from 50 to 200 wt%.
The average equilibrium distribution coefficient obtained for soil water contents up to 10% dry mass basis
was 2.4 gs/gCO2 (± 43 %). These values were all measured using the no mixing mode of operation. The
variability is fairly large and is due to a combination of variability between the small batches of soil used,
the experimental noise within a run and the variability in supercritical conditions between experiments. This
value is in excellent agreement with the values reported in the literature for similar extraction conditions.
Smyth (1996) reported distribution coefficients of 0.81 to 2.1 gs/gCO2 for the extraction of naphthalene from
Delhi loamy sand of up to 10% water content. Extracting naphthalene from a similar soil matrix, Gray et
al. (1995) reported equilibrium distribution coefficients ranging from 1.7 to 3.9 gs/gCO2. Using a silt/clay
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soil witha moisture content of 17.2%, Montero et al. (1996) reported an equilibrium distributioncoefficient
of 1.3 gs/gCO2.
The average equilibrium distribution coefficient for the water contents between 15% and 50%, measured
using mixing during the static periods, was 3.9 gs/gCO2 (±29%). This value is higher than that observed for
the lower water content range (0-10%) and there may be evidence of a weak trend with water content
within the 15 to 50% range. However, given the variability observed it is difficult to conclude whether the
difference is real.
Theoretically the equilibrium distribution coefficient will be affected by the soil’s water content as a result
of water competing for active sorption sites and some water dissolving in the SCF (Reindl et al.1994; Firus
et al., 1997; Laitinen et al., 1994). However, the maximum affect should be realized with only a modest
amount of water in the soil. In addition, both affects will be relatively small due to the non-polar character
of naphthalene as a solute. Thus, a strong continually increasing dependence on the soil’s water content
would not be anticipated.
Equilibrium was achieved without the use of a static period for dynamically operated, mixed systems, in
which baffles were employed to improve the mixing regime within the extractor. This was observed for soil
water contents of 50% to 200% dry mass basis. The improved mixing system was capable of providing
sufficient soil / SC-CO2 contact to allow the mass transfer process to proceed at a significant rate.
The average equilibrium distribution coefficient for the experiments operated with continuous mixing was
8.1 gs/gCO2 (±25%). This value is much higher than measured for the drier soils and than measured when
operating under the different modes of operation. Based on the level of experimental noise observed at
the end of these runs as discussed earlier it is doubtful that the equilibrium distribution coefficients are
indeed this high.
Upon obtaining equilibrium distribution coefficients for all
soil water content extractions up to 200% dry mass basis,
it was then possible to calculate mass transfer coefficients
from the resulting concentration data. In the few
experiments in which a reliable equilibrium distribution
coefficient was not measured, an assumed value was used.
The calculated mass transfer coefficient was found to change
less than 10% based on changing the assumed value by a
factor of 2.
Figure 13: Mass Transfer Coefficients No Mix
The no mix mode lead to a fairly steady decline in mass
transfer coefficients as the water content increased from air
dried to 50 wt% as shown in Figure 13. Similarily, Akgerman et al. (1993), Camel et al. (1993) and
Scheussinger et al. (1996) have observed increasing soil water contents hindering the extraction process
by introducing a barrier between the extracting fluid and the contaminant. Smyth et al. (1999) argue that
water bridging between soil aggregates is a plausible reason for the strong dependence on water content.
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The next mode of operation involved mixing during the
static periods. This second mode of operation did not
require any modifications to the vessel and thus was
readily implemented to test whether mass transfer
coefficients could be improved. As can be seen in
Figure 14, the mass transfer coefficients were
observed to be approximately an order of magnitude
higher for the water contents tested using this mixing.
Figure 14: Mass Transfer Coefficients Mixed Static
In this second mode of operation, the mixing was
created using a stir bar in an unbaffled extractor. It is
recognized that this form of mixing leads to vortex
formation an is generally regarded as fairly ineffective in both two and three phase systems (Oldshue, 1983;
Wong et al., 1987). However, the significant improvement in mass transfer observed motivated the
development of a continuous mixing system.
The third mode of operation involved modification of the vessel to introduce baffles, to introduce SC-CO2
to the bottom and to provide deentrainment. The baffles were added to break up the vortex and thus
improve mixing effectiveness. The introduction of the SC-CO2 to the bottom was to avoid short-circuiting.
The deentrainment device was added to separate the SC-CO 2 from the soil slurry and thus allow
continuous operation.
The deentrainment system worked well as the glass
wool at the vessel outlet remained free of soil in all
six experiments. Also the effectiveness of mixing and
contact was significantly improved as evidenced by
the observed mass transfer coefficients (Figure 15).
At the 50% water content, the mass transfer
coefficients are approximately the same as the air
dried values. As the water content increases to
200%, the coefficients decrease but do remain within
a factor of two of the air dried values. This suggests
Figure 15: Mass Transfer Coefficients - Dynamic that slurried soil extraction can be achieved with
minimal effort, indicating the potential for a
continuously operated SFE system exists. A continuously operated SFE system, in which contaminants
may be extracted at a rate similar to that of the dry soil condition, will substantially reduce supercritical soil
remediation costs. It is recommended that further supercritical soil remediation research focus on the
development of pilot or full scale, SFE systems in which slurried soil is continuously pumped to the extractor
unit.
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8.0 MODEL DEVELOPMENT and NUMERICAL SOLUTION
The contaminant transport model to be presented considers a uniform soil system in which a chemical
contaminant is distributed among four phases: NAPL, vapours, aqueous or sorbed. The wetting fluid, as
for most air/NAPL/water systems in soil, is assumed to be water.
Figure 16 shows a typical soil cross-section
after a NAPL spill. Following the spill, all
the chemical will disperse in the soil,
eventually coming to rest. Then if it is
assumed that water is applied at the
surface, e.g., rainfall, there will be
movement of the dissolved phase. If it is
further assumed that the water is moving
slow enough to ensure equilibrium
conditions, a 1 D transport equation can be
developed to represent the migration of the
dissolved phase in the vertical direction.
Figure 16: Soil Cross-Section
Following is the development of such an
equilibrium model, where a total mass
balance is completed for each phase.
8.1 Aqueous Phase
A dissolved chemical moving through soil will be affected by dispersion and advection. In addition their
may be some mass transfer limitations and decay. The resulting equation describing 1 D transport is given
by:
(28)
where
Ca
qa
Da
t
z
:a
= aqueous concentration, kg m-3
= superficial velocity of aqueous phase through soil, m s-1
= dispersion coefficient of dissolved chemical in soil, m2 s-1
= time, s
= spatial coordinate, m
= decay rate of dissolved chemical, s-1
Now, by assuming constant dispersion and knowing the moisture content [2], the mass of chemical per unit
volume of soil can be determined by:
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(29)
8.2
Vapour Phase
Similar to the aqueous phase, a transport equation can developed to account for dispersion, advection and
mass transfer, giving:
(30)
where
Cv
qv
Dv
t
z
:v
= vapour concentration, kg m-3
= superficial or Darcian velocity of vapour through soil, m s-1
= dispersion coefficient of dissolved chemical in soil, m2 s-1
= time, s
= spatial coordinate, m
= decay rate of dissolved chemical, s-1
Again, by assuming constant dispersion, knowing the volumetric air content [0] and applying Henry’s Law
for partition between the aqueous and vapour phase, Cv=KHCa, the mass of chemical per unit volume of
soil can be determined by:
(31)
8.3
Immiscible Phase
Similar to the aqueous phase, a transport equation can developed to account for dispersion, advection and
mass transfer, giving:
(32)
where
Ci
qi
Di
t
=
=
=
=
immiscible phase concentration, kg m-3
superficial velocity of immiscible phase through soil, m s-1
dispersion coefficient of dissolved chemical in soil, m2 s-1
time, s
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z
:i
= spatial coordinate, m
= decay rate of dissolved chemical, s-1
Again, by assuming constant dispersion, knowing the volumetric immiscible content [N] and applying Ki
as the immiscible partition coefficient between the aqueous and NAPL phase, the mass of chemical per unit
volume of soil can be determined by:
(33)
8.4
Sorbed Phase
The amount of chemical sorbed per unit volume is expressed by:
(34)
where
Cs
$
:s
= sorbed concentration of chemical, kg kg-1
= bulk density of soil, kg m-3
= decay rate of sorbed chemical in soil, s-1
Since linear partition coefficient [K p] can be used to relate adsorption to dissolved concentration, Cs=KpCa.
Thus the mass of chemical is:
(35)
8.5
Total Mass
The total mass of chemical per unit volume of soil can be written as:
Total Mass = Dissolved Mass + Vapour Mass + Immiscible Mass + Sorbed Mass
(32)
or
(37)
soil_overview_03.wpd
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or
(38)
The development of the local equilibrium model is based on the instantaneous attainment of chemical
equilibrium in the 4 phases. This requires additional constraints, including Raoult’s Law of ideal solution
behaviour, equilibrium partitioning between the vapour and NAPL phases:
(39)
Raoult’s Law is appropriate for describing vapour liquid phase equilibria where components have
appreciable mole fractions. A dimensionless Raoult’s Law partition coefficient dependent on the NAPL
concentration was obtained by combining Equation 39 with the ideal gas law and the definition of a mole
fraction. From the ideal gas law, the molar concentration is related to partial pressure by:
(40)
The NAPL phase mole fraction can be expressed in terms of molar concentrations by:
(41)
where
Nc = number of components
In addition, the NAPL volumetric fraction must sum to unity as expressed in the following equation for the
NAPL phase:
(42)
where vj is the molar specific volume [m3 mol-1 ].
Finally, there is the need of mass balance, where:
(43)
where R
8.6
= soil porosity
Non-Equilibrium
Many situations arise where equilibrium conditions do not apply. As such mass transfer relationships must
be incorporated into the transport model. Specifically,
soil_overview_03.wpd
Page 55 of 70
(44)
(45)
(46)
where
(a
Csat
(v
Cv-sat
(i
= mass transfer coefficient for NAPL to water, s-1
= maximum solubility of NAPL, kg m-3
= mass transfer coefficient for water to air, s-1
= maximum vapour solubility of NAPL, kg m-3
= mass transfer coefficient for NAPL to air, s-1
Many forms of the mass transfer coefficient exist. For example, Thomson [1991] believes that the mass
transfer coefficient is function of each phases pore volume raised to an exponent. The following equation
gives the mass transfer relationship for NAPL to water:
(47)
In work completed by Harper et al. [2000] on soil vapour extraction, it was conceptualized, based on the
experimental observations, that as the NAPL is being removed, the remaining NAPL becomes more
isolated from the air flow pathways. As a result, the mass transfer resistance, in the form of diffusion
through immobile water and stagnant air in both the micropores and the macropores increases. The effects
of NAPL depletion was taken into account by expressing the mass transfer coefficient, Kga , as a linear
function of the fractional NAPL volume relative to the initial NAPL volume:
(48)
where
Nt
No
(amax
(amin
= NAPL volumetric fraction at any time t, m3/m3
= initial NAPL volumetric fraction, m3/m3
= maximum mass transfer coefficient for NAPL to water, s-1
= minimum mass transfer coefficient for NAPL to water, s-1
Although this expression is arbitrary with no specific physical basis, it seems reasonable that in the absence
of a NAPL, conditions would remain more or less constant. Further work on this expression by
Zytner[2000] has suggested further modifications as seen below:
soil_overview_03.wpd
Page 56 of 70
(49)
where
0t
0o
pkmax
pkmin
= vapour volumetric fraction at any time t, m3/m3
= initial vapour volumetric fraction, m3/m3
= exponent
= exponent
Using Eq. 49 it was possible to reasonably model the behaviour of SVE in a sandy loam under air dry
conditions as shown in Figure 17. The coefficients used are: (amax = 18 h-1; pkmax = 3; (amin = 440 h-1
and pkmin = 5. Further work is ongoing to obtain a more universal experession.
Concentration - g/m^3
50
40
30
Mod-T
20
Exp-T
10
0
0
5
10
15
20
25
Time - h
Figure 17: Sandy Loam Soil - Air Dry
soil_overview_03.wpd
Page 57 of 70
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