Development and Application of Models of Chemical Fate
in Canada
Modelling the Fate of Substances in Sludge-Amended Soils
Report to Environment Canada
CEMN Report No. 200502
Prepared by:
Lauren Hughes, Eva Webster, Don Mackay, James Armitage, Frank Gobas
Canadian Environmental Modelling Network
Trent University
Peterborough, Ontario K9J 7B8
CANADA
Development and Application of Models of Chemical Fate in
Canada
Report to Environment Canada
Contribution Agreement 2004-2005
Modelling the Fate of Substances in Sludge-Amended Soils
July, 2005
Prepared by:
Lauren Hughes, Eva Webster, Don Mackay, James Armitage, Frank Gobas
Canadian Environmental Modelling Network
Trent University
Peterborough, ON
K9J 7B8
EC Departmental Representative:
Don Gutzman
Head, Exposure Section
Chemical Evaluation Division, Existing Substances Branch
Environment Canada
Place Vincent Massey, 14th Floor
Hull, PQ
K1A 0H3
EC Contracting Authority:
Robert Chenier
Environmental Protection Service
Chemical Evaluation Division
351 St. Joseph Blvd 14th Floor
Hull, PQ
K1A 0H
Executive Summary
The Biosolids-Amended Soil: Level IV (BASL4) model is described and evaluated. The
model calculates the fate of chemicals introduced to soil in association with contaminated
biosolids amendment. Processes of chemical degradation, volatilization, leaching,
diffusion, sorbed phase transport due to bioturbation, and the degradation of the organic
matter (OM) present in the soil and amendment are quantified. Chemical is introduced to
the soil either directly or as a component of biosolid amendments. It can be applied to
the surface of the soil, injected into a deeper layer of soil, or ploughed into surface and
deeper layers. Applications of biosolids or neat chemical can occur at user-specified
times during the simulation, as can ploughing events. BASL4 was applied to three
dissimilar chemicals: DDT, benzene, and 2,4-D for a 365-day simulation period. Model
results were qualitatively as expected for each substance. Several six-month simulations
of DDT fate in different amended and unamended soil scenarios demonstrate the
capabilities of the model. The model is also evaluated by comparison with a number of
field and laboratory studies. Validation is difficult because of the scarcity of suitable
experimental data, but the model has been shown to give results that are regarded as
being in reasonable accord with observations. BASL4’s screening-level plant, worm and
shrew sub-models are described and evaluated by comparison with experimental data.
The model is regarded as being useful for assessing the long-term, year-to-year fate and
possible build-up of chemicals in sludge-amended soils as well as for estimating risk of
biotic uptake and bioaccumulation in soil-dwelling organisms.
ii
Table of Contents
Executive Summary .......................................................................................................... ii
Table of Contents ............................................................................................................. iii
List of Tables ..................................................................................................................... v
List of Figures.................................................................................................................... v
1
Introduction............................................................................................................... 1
1.1
Background ......................................................................................................... 1
1.2
Sludge-Amended Soils........................................................................................ 1
2
Biosolid-Amended Soil Model.................................................................................. 2
2.1
BASL4 Background............................................................................................ 2
2.2
Model Structure and Required Inputs ................................................................. 2
2.3
Sequence of Model Calculations ........................................................................ 4
2.4
Model Parameters ............................................................................................... 5
2.4.1
Z Values ...................................................................................................... 5
2.4.2
Transport and Degradation Reaction Processes (D Values) ....................... 6
2.5
Solution of the Differential Equations ................................................................ 8
3
Biotic Models ........................................................................................................... 10
3.1
Bioavailability................................................................................................... 10
3.2
Plant Models ..................................................................................................... 10
3.2.1
Introduction............................................................................................... 10
3.2.2
EQP Plant Models..................................................................................... 11
3.2.3
Dynamic Carrot Root Model .................................................................... 11
3.3
Soil Fauna Models (Worms and Shrews) ......................................................... 12
3.3.1
Introduction............................................................................................... 12
3.3.2
Biotic Model Parameters........................................................................... 13
3.3.2.1
Z Values .............................................................................................. 13
3.3.2.2
D Values.............................................................................................. 14
3.3.3
Model Calculations ................................................................................... 18
4
BASL4 Applications and Results........................................................................... 19
4.1
Introduction....................................................................................................... 19
4.2
Illustrative Results ............................................................................................ 19
4.2.1
Partitioning................................................................................................ 19
4.2.2
Long Term Fate......................................................................................... 22
4.3
Comparison With Reported Soil Data .............................................................. 29
4.3.1
Sanchez et al (2004).................................................................................. 29
4.3.2
Jianlong et al (2004).................................................................................. 31
4.3.3
McLachlan et al (1996)............................................................................. 31
4.3.4
Petersen et al (2003).................................................................................. 32
4.4
Screening-Level Biota Models Evaluation ....................................................... 34
4.4.1
EQP Plant Model ...................................................................................... 34
4.4.2
TLEQ Carrot Root Model......................................................................... 36
4.4.3
Soil Fauna Models .................................................................................... 37
4.4.3.1
Model Parameterization ...................................................................... 37
4.4.3.2
D Values.............................................................................................. 39
iii
4.4.3.3
EQP and SSB Model Results.............................................................. 41
4.4.3.4
DYB Model Results............................................................................ 43
5
Discussion................................................................................................................. 47
References........................................................................................................................ 49
iv
List of Tables
Table 1: Physical chemical Properties at 25 ˚C (from Mackay et al 2000). The mineral
matter-water partition coefficient, KMW, was set to 1 L/kg for all substances.
Table 2: Soil properties used in BASL4 simulations. All values are estimates based on
Mackay (2001), except where otherwise noted.
Table 3: BALS4 Results – Comparison of partitioning and fates of some chemicals
Table 4: Application rates (Wild et al, 1992).
Table 5: Mean concentrations of chemical in sewage sludge (Wild et al, 1992).
Table 6: Lipid and water volume fractions of BASL4 plants, estimated from Fryer et al,
(2003) unless otherwise indicated.
Table 7: EQP predicted and measured plant-soil concentration ratios (kg soil solids/kg of
plant dw) (Wild et al, 1992).
Table 8: Illustrative results of the EQP model. Plant concentrations are in µg/kg dry
weight.
Table 9: Calculated uptake characteristic times for selected chemicals.
Table 10: Dynamic carrot model α values and predicted and measured plant-soil
concentration ratios (kg soil solids/kg of plant dw) after 82 days (Wild et al, 1992).
Table 11: BASL4 dynamic worm and shrew sub-model parameter values (Armitage et al,
2004).
Table 12: Selected D values for worms at Ochten (mol Pa-1 h-1).
Table 13: Selected D values for shrews at Ochten (mol Pa-1 h-1).
Table 14: Observed worm and soil concentrations at Ochten (Hendriks, 1995), EQP and
SSB worm model results. Observed concentrations are geometric means.
Table 15: Observed worm and soil concentrations at Gelderse Poort (Hendriks, 1995),
EQP and SSB worm model results. Observed concentrations are geometric means.
Table 16: BASL4 shrew sub-model results for Ochten. Observed BMFs calculated from
geometric mean concentrations (Hendriks et al, 1995).
Table 17: BASL4 shrew sub-model results for Gelderse Poort. Observed BMFs
calculated from geometric mean concentrations (Hendriks et al, 1995).
List of Figures
Figure 1: Chemical transport and transformation processes in BASL4.
Figure 2: BASL4 dynamic worm model.
Figure 3: BASL4 dynamic shrew model.
Figure 4: BASL4 results for DDT in soil with no organic matter degradation. Both layers
have identical initial concentrations. Note the small range of concentrations in (a). The
upper layer is represented by a solid line and the lower layer by a dashed line.
Figure 5: BASL4 results for DDT in soil with organic matter degradation. Both layers
have identical initial concentrations. The initial rise in concentration is due to the
degradation of the fast-degrading OM. The upper layer is represented by a solid line and
the lower layer by a dashed line.
Figure 6: BASL4 results for DDT in soil with degrading organic matter. There is an
initial concentration of DDT in the surface layer only. A single plough event occurred at
approximately 2 months into the simulation. Both layers have the same fractions of fast-
v
and slow-degrading OM, thus (d) is identical to Figure 3(d). The upper layer is
represented by a solid line and the lower layer by a dashed line.
Figure 7: BASL4 results for DDT in soil with degrading organic matter, both layers
having identical initial concentrations. Biosolid amendment is added to the second layer
(by injection) 2 months into the simulation at a rate of 2.0 x 105 kg/ha. The biosolid is
50% fast- and 50% slow-degrading OM, and has a DDT concentration of 0.3 mg/kg. The
upper layer is represented by a solid line and the lower layer by a dashed line.
Figure 8: BASL4 results for DDT in soil with degrading organic matter, an initial
concentration of 0.1 mg/kg dw in both layers, 2.0 x 105 kg/ha biosolid amendment added
at 1 month, and ploughing occuring at 5 months. The biosolid is 50% fast- and 50%
slow-degrading OM, and has a DDT concentration of 0.3 mg/kg. Note the small range of
concentrations. The upper layer is represented by a solid line and the lower layer by a
dashed line.
Figure 9: The loss of fenitrothion, diazinon, and dimethoate from non-sterlized soil as a
function of time. The crosses are the experimental results of Sanchez et al (2004). The
heavy lines are the model results using the fitted half-lives, and the thin lines are the
model results using half-lives taken from the handbook of Mackay et al (2000).
Figure 10: The loss of DMP, DEP, DBP, and DnOP in sludge-augmented soil. The
model results are shown as solid lines and the experimental results as points.
Figure 11: The loss of OCDD during a field study from 1972 to 1990. The crosses
indicate experimental data, the thin line is model results assuming the 2300-day half-life
as suggested by Mackay et al (2000) and the thick line a half-life of 5000 days.
Figure 12: The loss of OCDD during an 18-year field study from 1972 to 1990. The
crosses indicate experimental data, the four lines indicate model results for OCDD
degradation half-lives of 40, 20, 10 and 5 years.
Figure 13: Selected D values (mol Pa-1 h-1) for a) worms and b) shrews at Ochten.
Figure 14: Observed (Belfroid et al, 1994) and predicted worm concentrations
Figure 15: Predicted worm (solid line) and shrew (dashed line) concentrations at Ochten.
Figure 16: Predicted worm (solid line) and shrew (dashed line) concentrations at Gelderse
Poort.
vi
1
Introduction
1.1
Background
This report was prepared as part of the “Development and Application of Models of
Chemical Fate in Canada” contribution agreement between Environment Canada (EC)
and the Canadian Environmental Modelling Network (CEMN). This report describes the
development of a model of the dynamic fate of chemicals in soil. The sludge-amendment
of soils is described, some previous modelling efforts are outlined and the BASL4 model
sequence, data requirements and calculations are detailed. The plant, worm and shrew
sub-models included in BASL4 are described. Illustrative results for the abiotic biosolidamended soil model are presented followed by an account of the results of validation
efforts. The biotic sub-models of BASL4 are also evaluated by comparison with
experimental and field study results. The model performance and possible future
directions of BASL4 are discussed.
1.2
Sludge-Amended Soils
Biosolid amendments derived from sewage sludge are widely used to ameliorate
agricultural soils. They consist largely of organic matter and contain quantities of
nitrogen and phosphorus similar to those found in livestock manure and other fertilizers
(Petersen et al, 2003) making them valuable nutrient sources for crops. Their high
organic matter content makes them effective soil conditioners, and they have been
successfully used in land reclamation projects to improve the structure and fertility of
damaged and depleted soils at surface strip mines and large construction sites (Brown et
al, 2003). Land application of treated sewage sludge also provides an inexpensive,
beneficial, and arguably more environmentally sound alternative to disposal in landfills
or by incineration.
Biosolids are the product of treated household, commercial and industrial wastewaters,
and as such are susceptible to contamination by a broad spectrum of substances. A large
part of the organic material present in biosolids is due to human excreta, and therefore
consists of a complex mixture of fats, proteins, carbohydrates, lignin, amino acids,
sugars, cellusoses, humic material and fatty acids, as well as live and dead
microorganisms (Rogers, 1996). These organic materials provide sorption sites for
hydrophobic substances.
The fate of heavy metals in sludge-amended soils has been studied extensively (Illera et
al, 2000; Walter et al., 2002) and heavy metal concentrations in agricultural amendments
are now regulated in Europe, Canada and the USA. Other toxic substances that remain and in some cases are concentrated - in sludge after secondary and tertiary wastewater
treatment processes are in various stages of investigation and regulation. These
substances include known endocrine disrupters (Environment Canada, 2002), persistent
organic pollutants, and a variety of potentially pathogenic microorganisms (Horan et al,
2004).
1
In Ontario, sewage biosolid application rates must not exceed 8 tonnes of solids per
hectare over a five year period. Further restrictions apply depending on the nutrient
content and metal concentration of the soil. (OMAFRA, 2005). Typical biosolid
application rates in the UK are comparable to those of Ontario (Smith, 1996). In
Germany, biosolids applications are limited to 5 t/ha over three years (McLachlan et al,
1996). It is believed that screening level models can aid in the process of regulating
chemical concentrations in biosolid amendments intended for disposal or agricultural or
land reclamation use. The model described here was developed for this purpose.
2
Biosolid-Amended Soil Model
2.1
BASL4 Background
The Biosolid-Amended Soil: Level IV (BASL4) model is an extensive modification of
the Soil model described in Mackay (2001), which in turn is a modification of a
published herbicide model (Mackay and Stiver, 1991) based on work by Jury et al.
(1983). The Soil model is freely available from the Canadian Environmental Modelling
Centre website (www.trentu.ca/cemc). It describes the equilibrium fate of a chemical in a
single layer of soil and includes volatilization, leaching and reaction processes. Other
models of chemical fate in soil are described in Webster et al (2003).
In addition to its simulation of chemical partitioning, transport and degradation in sludgeamended soils, BASL4 calculates screening-level estimates of plant, worm, and shrew
concentrations for evaluating risk associated with potential land-applied biosolid
scenarios. These sub-models are intended to provide estimates of chemical uptake by
crops and soil-dwelling organisms in continuing contact with biosolids-amended soils.
The plant sub-model is an instantaneous EQuilibrium Partitioning (EQP) model, i.e., it
assumes that the plant’s roots and foliage achieve equilibrium (equifugacity) with the soil
pore water at all times. A single-process dynamic carrot root model is also included,
which is a Transpiration-Limited fractional EQuilibrium (TLEQ) model.
The worm and shrew sub-models presented here include EQP models as well as steadystate and dynamic fugacity versions of steady-state rate-constant models developed and
tested by Armitage (2004).
2.2
Model Structure and Required Inputs
BASL4 is designed to model Type 1 chemicals (Mackay, 2001) which may partition
appreciably into all media. The chemical properties required by the model are the molar
mass of the chemical (MW, g/mol), its degradation half-life in soil (τ½, d), solubility (S,
g/m3), vapour pressure (P, Pa), organic carbon-water partition coefficient (KOC, L/kg),
mineral matter-water partition coefficient (KMW, L/kg), and the temperature at which the
data apply (T, ˚C). The logarithm of the octanol-water partition coefficient, logKOW, is
required by the model for estimates of biotic uptake.
2
BASL4 models a soil compartment consisting of two well-mixed layers. Different soil
compositions can apply to the first (surface) and the second (deeper) layer. Each layer
consists of air, water, organic matter (OM), and mineral matter (MM). OM is further
divided into fast-, slow- and non-degrading fractions. The user is interrogated for the
volume fractions of pore air and water in each layer. For simplicity, these are assumed to
remain constant throughout the simulation. The user defines the fraction of OM made up
of organic carbon (OC). The initial mass fractions of fast- and slow-degrading OC are
specified by the user as are their respective degradation half-lives (τ½-F and τ½-S, d). Any
remaining fraction is considered non-degradable. The field area (A, ha), air boundary
layer thickness (YB, mm), leaching rate (mm/d), bioturbation rate (cm/yr), molecular
diffusivities of the pore air and water (BA and BW, m2/h), densities of organic matter and
mineral matter (ρOM and ρMM, kg/m3), layer depths (m), and the effective diffusion
distances in each layer (Y1 and Y2, m) are also required. The effective diffusion distances
can be estimated as half the layer depth for the first layer and the distance between the
mid-points of the first and second layers for the second layer. The user can specify a
bioavailable fraction of chemical, B, as described in section 3.1.
Three guilds of vegetation are described by the EQP plant sub-models: leafy tubers (such
as carrots), grasses or sedges, and coniferous trees. The guilds are assumed to differ only
in their root and foliage lipid, water, and non-lipid organic matter (NLOM) volume
fractions. The volume fractions of lipid and water in the foliage and roots of each guild
of plant are required inputs. The foliage and root densities of each guild of plant are
assumed to be 1000 kg/m3. The only additional parameter required for the transpirationlimited equilibrium carrot root sub-model (TLEQ) is the water uptake or transpiration
rate in L/day. Plant concentrations are calculated automatically at the end of each time
step.
The EQP worm and shrew sub-models assume instantaneous equilibrium partitioning
between the organism’s tissue and the surrounding soil. The lipid and water volume
fractions, volume (m3), and bulk density (kg/m3) of an individual organism are the
required inputs to the models. The shrew EQP sub-model is described in this report but
is not included in the BASL4 model.
Chemical uptake in worms and shrews is assessed by the steady-state (SSB) and dynamic
(DYB) bioaccumulation models which assume equilibrium partitioning between the soil
and the water, lipid and NLOM fractions of the organism. In addition to those
parameters required by the EQP worm sub-model the OM absorption efficiency (volume
of soil OM digested per volume ingested), the volumetric rates (m3/day) of air
respiration, water turnover, and soil solid ingestion, and the rate constants (day-1)
characterizing chemical metabolism, growth dilution, and reproductive loss are required
for the SSB and DYB worm models. Similarly, the additional required parameters for the
shrew model are the volumetric rates of respiration, water intake, dietary intake and
incidental soil intake; the metabolic, growth dilution and reproductive loss rates; and the
gut absorption efficiencies of lipid, water, NLOM, and soil OM. The SSB worm submodel is not included in the BASL4 model, but is discussed in this report for comparison
3
purposes. EQP worm and SSB shrew calculations are executed automatically while both
DYB sub-models must be selected by the user.
2.3
Sequence of Model Calculations
The physical properties of the chemical are defined and the nature of the two-layer soil
compartment is specified as described above. The initial concentrations of the chemical
in each layer of the soil compartment is input in units of mg/kg soil dry weight (dw).
Chemical is assumed to achieve an equilibrium phase distribution in the soil immediately
upon application. The atmosphere above the soil is considered to have zero chemical
concentration, thus there are no inputs from the atmosphere. The model treats the soil
compartment as being at constant temperature corresponding to the temperature of the
chemical data input at the start of the simulation.
Amendment is considered to be 100% (dry) organic matter, and the chemical
concentration in the amendment is specified in units of mg/kg dry amendment. Although
in practice most biosolids contain water and liquid sludge can be injected into or sprayed
onto a field, the model assumes that upon application the layer into which the amendment
was applied is instantly mixed, and the volume fractions of water and air adjust
immediately to remain consistent with the values originally defined by the user. This
simplification is regarded as acceptable because the water content of the biosolids and
soil probably adjust to a common value within some hours after application. The increase
in the soil's water retention capacity with increased organic matter is ignored. Layer
depths change as a result of changes in the quantity of organic matter present but these
changes are considered sufficiently small that diffusion distances remain constant.
Leaching and diffusion rates thus remain unchanged throughout the simulation. The
model does not simulate runoff.
The fugacity method is used to model the fate and distribution of the chemical between
the phases. Z values (mol m-3 Pa-1) are deduced in all relevant media and the initial layer
fugacities are determined. Initial transport and transformation D values (mol Pa-1 h-1) are
calculated for leaching, sorbed phase transport (bioturbation) between the layers,
volatilization, diffusion in air and water phases between the layers, and degradation of the
chemical in each layer (Figure 1).
An integration time step is suggested based on the shortest characteristic response time of
the chemical transport and transformation, OM loss, and biota uptake and elimination
processes.
The model is designed to run for a simulation period of a single growing season because
in the present version there is no facility for entering changing temperatures and other
seasonal effects. Year-to-year accumulation, however, may be determined by multiple
runs of the model in which the user manually determines any losses that may occur
during the winter months. Alternatively, the user may choose to run the model for many
months to obtain an estimate of long-term accumulation. It should be noted that, in
4
general, degradation and transfer processes will tend to be slower during the colder
months. For a three-year simulation it may be appropriate to run the model for less than
36 months and assume that, for example, two months of winter may be equal to one
month of summer. The model would thus be run for 30 months.
During the simulation, up to three separate applications of the chemical are possible. The
user specifies the times at which the applications occur, and selects whether the
application is pure chemical or contaminated biosolid amendment in each case. In both
cases, the user defines the amount to be added in units of kg/ha, and chooses whether to
add it to either the first or the second layer. If the application is biosolid, the user must
also enter the concentration of chemical in the biosolid in mg/kg (dw) and the mass
fractions of fast- and slow-degrading OC that it comprises. The final simulation option is
the frequency of ploughing events, during which the layers are mixed and then separated
into two layers of identical composition. The soil compartment can be ploughed up to six
times at user-specified times during the simulation.
Concentrations of chemical in biota are calculated at the end of each time step. No
changes in mass of chemical in the soil are deduced for any of the screening-level biotic
sub-models. Chemical taken up by the plants, worm or shrew is therefore not considered
removed from the soil. Likewise, chemical lost by the biota through excretion and
respiration is not returned to the soil. Modelling of these processes would require data on
population densities, i.e. number of organisms per square metre.
The input data and initial conditions of the simulation are recorded. Results are reported
periodically throughout the simulation in tabular and graphical form and may be saved
for further analysis.
All equations used in the model may be viewed, but not changed, by the user. This
transparency of the model allows the user to follow each calculation in as much detail as
desired.
2.4
Model Parameters
2.4.1
Z Values
The model is written in fugacity format as described by Mackay (2001). It begins by
calculating the fugacity capacities of the different sub-compartments of the soil. For air:
ZA =1/RT
(1)
where R is the gas constant (8.314 J K-1 mol-1) and T is the temperature (K). ZW for water
is the reciprocal of the chemical's Henry's constant, H (Pa m3 mol-1), which is determined
from the ratio of the vapour pressure and solubility of the chemical using
H = P × MW /S .
5
(2)
ZOM for organic matter is calculated from KOC, and ZMM for mineral matter from KMW as
follows:
ZOM = FOC/OM KOC ZW
ZMM = KMW ZW
(3)
(4)
where FOC/OM is the mass fraction of OC in the soil OM.
The fugacity in layer i is calculated from the quantity of the chemical in the layer, Mi
(mol), and the sum of the sub-compartment volume and Z value products, VZT,i (mol
Pa-1), where the summation includes air, water, OM and MM, i.e.
VZT,i =
∑V
i, j
Zj
(5)
j
fi = Mi / VZT,i
(6)
From the fugacity, the concentrations in each sub-compartment can be calculated as Zj fi
(mol/ m3) and the amounts as VjZj fi (mol).
The soil's fugacity capacity is characterized by a bulk Z value for each layer determined
as follows:
ZBulk,i = VZT,i /Vi
(7)
It is noteworthy that as OM degrades, the fugacity tends to increase because of
decreasing VZ values, corresponding to the loss of sorption capacity in the soil. This loss
is also reflected by a gradual decrease in ZBulk,i. The rate of decrease of ZBulk,i depends on
the value of ZOM,i for the chemical; the more highly sorptive the chemical, the more
rapidly the bulk Z value decreases with decreasing OM volume. The soil gradually
becomes less effective as a "solvent" for the chemical.
2.4.2
Transport and Degradation Reaction Processes (D Values)
Figure 1 shows the various transport and transformation processes affecting the chemical
in the soil. In each case the process rate is expressed as a D value (mol Pa-1 h-1) which is
essentially a flux rate constant, the rate being Df (mol/h). D values apply to both transport
and decay processes.
The first layer loses chemical by five processes: volatilization to the air above the soil
compartment, leaching to the second layer, sorbed phase transport (bioturbation) to the
second layer, diffusion in pore air and pore water to the second layer, and degrading
reactions. The second layer loses chemical by sorbed phase transport and diffusion to the
first layer, by leaching, and by degrading reactions. Chemical is lost from the system by
degradation in each layer, by volatilization from the top layer, and by leaching from the
lower layer.
6
Figure 1: Chemical transport and transformation processes in BASL4.
The volatilization process is described in Mackay (2001) and based on an approach
suggested by Jury et al (1983). Effective air and water diffusivities, BEA and BEW, are
calculated from the molecular diffusivities, BA and BW, and the volume fractions of air
and water, vA and vW respectively, in the soil using the Millington-Quirk equation:
BEA = BA vA10/3 / (vA + vW)2
BEW = BW vW10/3 / (vA + vW)2
(8)
(9)
The diffusion D values are then
DA, i = BEA A ZA / Yi
DW, i = BEW A ZW / Yi
(10)
(11)
where A is the area of the field (m2), Y is the diffusion path length and the subscript i
corresponds to layer 1 or 2. A mass transfer coefficient, kV (m/h), can be calculated as BA
(m2/h) / YB (m). The D value that characterizes mass transfer across the boundary layer is
DE = A kV ZA
(12)
This D value occurs in series with the sum of the air-in-soil and water-in-soil diffusion D
values to give an overall volatilization D value,
DV = 1 / ( 1/DE + 1/(DA + DW))
7
(13)
The leaching D value is calculated using:
DL = GL ZW
(14)
where GL is the volumetric leaching rate (m3/h) which is the product of the field area in
m2 and the leaching rate in m/h. The leaching rate is assumed to be the same for both soil
layers.
McLachlan (2002) suggests a bioturbation velocity of the soil of 0.3 cm/yr estimated
assuming a bulk soil density of 1.3 kg/m3 and an average annual surface accumulation of
40,000 kg/ha. This velocity is converted to a volumetric transfer rate GB (m3/h), and the
corresponding D value is then given as
DB,i = GB ZBulk, i
(15)
This implies that mineral matter, organic matter, and sorbed chemical move between
layers in both directions. The rate is slow but can be significant in the long term.
The reaction D value is calculated from the degradation half-life, τ½ (h), or degradation
rate constant, kR = ln(2)/τ½ ≅ 0.693 /τ½ (h-1), as follows:
DR,i= VZi kR
(14)
In reality, it is likely that different rates apply to chemical degradation in different phases,
but these data are rarely available, thus an overall rate constant is applied.
A total D value for all loss processes is calculated for each layer:
DT,1 = DA,1 + DW,1 + DV + DL,1 + DB,1 + DR,1
DT,2 = DA,2 + DW,2 + DL,2 + DB,2 + DR,2
(15)
(16)
where the subscripts A, W, V, L, B, R denote the processes of air diffusion, water
diffusion, volatilization, leaching, bioturbation, and degradation reaction respectively.
Note than there is no direct volatilization from layer 2. The total D values characterize the
sum of the chemical loss processes for each layer.
2.5
Solution of the Differential Equations
Solution of the differential equations is accomplished by numerical integration using a
time step, ∆t (h), selected by examination of the characteristic times of chemical loss in
each layer, namely VZT,i/DT,i, as well as the characteristic times of OM degradation and
biotic processes when appropriate. Amendment or chemical addition and ploughing
occur at the beginning of each time step when indicated by the user. When chemical or
biosolid amendment is added, the masses of chemical (M1 and M2, mol) and OM are
8
increased directly. Soil properties and chemical distribution between phases are then
adjusted accordingly.
The masses of fast-degrading and slow-degrading OM lost by degradation are calculated
for the time increment as
∆mi,F = kF mi,F ∆t
∆mi,S = kS mi,S ∆t
(17)
(18)
respectively, in which the rate constants kS and kF (h-1) are determined from the half-lives
defined by the user. There is no change in the mass of non-degrading OM. Quantities of
OM exchanged between layers by bioturbation processes are calculated using the GB
(m3/h) values described above. The volumes of fast-, slow- and non-degrading OM
transported from each layer during a time step due to these processes are calculated using
three equations of the form
∆vi,j,B = GB vi,j∆t
(19)
where vi,j is the volume fraction in layer i of the jth-degrading component of OM. Or, in
terms of the change in masses
∆mi,j,B = ρOMGBvi,j∆t
(20)
where ρOM is the density of organic matter. The new soil composition is again adjusted
accordingly, as are the new VZ values.
The finite difference equations for the chemical mass balance are calculated using Euler’s
method:
∆M1 = ( ( DB,2 + DA,2 + DW,2) f2 - DT,1 f1 )∆ t
∆M2 = ( ( DB,1 + DA,1 + DW,1 + DL,1 ) f1 - DT,2 f2 )∆ t
(21)
(22)
and the new chemical amounts in each layer are calculated using these. Equation 6 is
used to calculate the new layer fugacities from the new values of VZT,i and Mi. Similarly,
ZBulk,i and the changing D values are recalculated at each time step. This deviates from
previous dynamic modelling efforts where compartment volumes, D values, and Z values
remain constant with time and the change in the amount of chemical is calculated from a
change in fugacity.
The cumulative quantities of degraded and added OM, as well as the losses and additions
of chemical are calculated to provide a mass balance check, i.e. the initial quantity plus
any additions are compared to the sum of the present inventory and the losses.
9
3
Biotic Models
3.1
Bioavailability
Bioavailability has been the subject of considerable research with respect to soils and
sediments. As a contaminated soil ages, the contaminant may become less available to
soil-dwelling organisms due to chemical sequestration in organic and mineral matrices.
A physical explanation is that the chemical slowly diffuses into or out of the humic
materials that constitute the organic matter of the soil. Some chemicals may diffuse so
deeply into the organic phase that it requires a prolonged period of time for the chemical
to be released either from the soil solid matrices to the pore air and water or from the
ingested soil solids to the gut contents of an organism from which it becomes available
for transfer into the organism’s tissue.
It has been suggested (Belfroid et al, 1994) that the fraction of chemical freely dissolved
in soil pore water is rapidly depleted by soil-dwelling organisms. This results in a rapid
decrease in the fugacity of the soil, followed by a period of slow desorption and
dissolution of the substance from the solid phase into the pore water. The net effect of
this is that only a fraction of the total chemical in the soil is available for uptake during
that period of equilibration. It is thus likely that bioavailability depends on the desorption
kinetics and the history of the chemical in the soil.
Expressing these phenomena mathematically requires detailed information on the
structure of the organic matter, and the diffusion and desorption rate parameters. The
simplest approach to include an allowance for bioavailability is to arbitrarily view the
sorbed chemical as existing in two forms: a fully and immediately bioavailable form and
an entirely non-bioavailable form. The fraction of the total that is available can then be
defined based on experimental data.
The total concentration of chemical in the soil, C (mol/m3), and Z value are known. The
fugacity in layer i is then C/ZBulk,i. If the fraction that is bioavailable is B (e.g., 0.1 or
10%) then it is only this fraction that can exert a fugacity over a period of days and it can
become rapidly depleted. Essentially, fi is reduced to fiB. This can be included in the
model by increasing ZBulk,i to ZBulk,i/B, thus the product fiZBulk,i remains C and the total
mass of chemical remains accounted for.
3.2
Plant Models
3.2.1
Introduction
Equilibrium partitioning models are used here to give screening-level estimates of plant
uptake of chemicals from contaminated soils.
Several studies of organic chemical uptake in plants suggest that high KOW compounds
present in soil remain bound to the soil organic matter (OM) and are usually not found in
significant quantities in plants other than in the root peel of some relatively high-lipid-
10
content tubers, such as carrots (Wild et al, 1992; O’Connor et al, 1991; Duarte-Davidson
et al, 1996). Duarte-Davidson et al (1996) suggest that compounds of logKOW ≈ 1.0-2.5
are the most likely to be taken up into the xylem and phloem and transported to different
parts of the plant. These chemicals are most suited to the EQP models.
Chemicals with lower KOW (i.e., logKOW < 1) are generally too lipophobic to enter the
root system from soil pore water and those with higher KOW (logKOW > 2.5) are sparingly
soluble in plant xylem and phloem fluids. The latter, therefore, are most likely to be
found sorbed to the lipid-like material of the plant, e.g. the waxy cuticle of a leaf or the
lipid content of a root surface. For this reason, a simple dynamic carrot root model was
included in BASL4 in which carrot uptake of chemical is limited by the transpiration rate
of the carrot. Since carrot roots have relatively high lipid contents, they will give worstcase scenario concentrations of the more hydrophobic substances in plants in general.
3.2.2
EQP Plant Models
Z values for plant type m (i.e. leafy tuber, grass/sedge, or conifer) are calculated as
follows:
Zm,n = [(vL-m,n + 0.035vNLOM-m,n)KOW + vW-m,n]ZW
(23)
where the subscript n indicates either the root or the foliage of the plant, and vL-m,n, vW-m,n,
and vNLOM-m,n are volume fractions of lipid, water, and NLOM respectively. This equation
implies that the sorptive capacity of NLOM is 0.035 that of lipid matter (lipid being
equivalent to octanol) as suggested by Armitage (2004). The volume fraction of NLOM
is calculated from the volume fractions of water and lipid as (1 - vL-m,n - vW-m,n).
It is assumed that the plant tissues are at equilibrium with the pore water in the soil.
Recalling that the fugacity of the soil changes with time, the following equation is used to
evaluate root and foliage concentrations at each time step in the model from the fugacity
in layer 1:
Cm,n (µg/g wet weight) = f1Zm,nMW×1000/ρm,n
(24)
where Cm,n is the concentration of chemical in the part n of plant m and ρm,n is the density
of part n of plant m (kg/m3). Dry weight concentrations are estimated using
Cm,n (µg/g dw) = Cm,n (µg/g ww) /(1- vW-m,n.)
3.2.3
(25)
Dynamic Carrot Root Model
The concentration of hydrophobic chemicals in plants can be overestimated because of
the relatively slow transport of these substances from the soil pore water into the plant
root. The dynamic carrot root model accounts for this by considering that the rate of
water uptake in the carrot is likely the most important limiting factor on the carrot’s rate
of chemical uptake. In fugacity terms this can be expressed by a D value that is the
11
product of the water uptake rate (GSR, m3/h) and the Z value of the substance in water.
This water uptake rate is controlled by the plant’s transpiration rate. If the root has
constant volume VR (m3) and Z value ZR then the rate of fugacity fR increase in the root
will be given by
d (VR Z R f R )
= DSR f S
dt
(26)
where fS is the soil and pore water fugacity and DSR is the soil to root D value. This
assumes no loss from the root. Integrating from an initially zero fugacity and with
constant fS gives
fR = fS(1 – exp(-DSRt/VRZR))
(27)
fR = α(t)fS
(28)
or
where α is the fraction of the soil’s fugacity achieved by the carrot root at time t. The
quantity VRZR/DSR is the characteristic time, tchar, of uptake. It can be shown that
DSR / VRZR = GSRZW /(VRZR)
= GSRZW /{VR[(vL + 0.035vNLOM)ZL + vWZW]}
= GSR /{VR[(vL + 0.035vNLOM )KOW + vW]} = 1/tchar
(29)
where GSR is the transpiration rate. Trapp (2001) suggests a rate of approximately 1 L/d,
or 4.2 × 10-5 m3/h for carrots. The carrot root volume is assumed to be 10-4 m3 or 100
cm3. Equation 27 is used to calculate the carrot root fugacity at the end of each time step.
This assumes fS does not change appreciably for the length of the simulation or for at
least a time comparable to tchar.
3.3
Soil Fauna Models (Worms and Shrews)
3.3.1
Introduction
Worms and shrews were the soil fauna chosen for modelling on the basis that data are
available to parameterize and test the model as a result of the study by Armitage (2004).
Three fugacity-based models were developed and applied.
The simplest is an EQP model in which the organisms are assumed to achieve
equilibrium with the soil. This implies that the fugacity of the chemical in the organism
equals that of the soil. The only new information required is the Z value of the chemical
in the organism.
The second is a steady-state bioaccumulation (SSB) model in which the steady-state
conditions of the organism with respect to the soil is calculated. This requires that the
12
chemical uptake and release processes be expressed as functions of the soil and organism
fugacities, and in the case of the shrew the fugacity of its food (worms). At steady-state
the uptake and release rates are equal and the organism fugacity at which this occurs can
be calculated.
Each process rate is again quantified as a D value. This results in an algebraic equation
that can be solved for the organism’s fugacity and hence its concentration. The advantage
of this model is that it becomes clear which processes are most important to the
organisms uptake and elimination of chemical.
Third is an unsteady-state or dynamic bioaccumulation (DYB) model that gives the time
course of uptake and clearance and can deduce the concentrations in the organisms as
they change as a result of changing concentration in the soil. The information required by
the DYB model is the same as that required for the SSB model. The solution of the
differential equations describing the uptake and clearance processes is accomplished by
numerical integration. If the organism is exposed to constant soil fugacity for a long
period it will approach the concentration as calculated by the SSB model.
3.3.2
Biotic Model Parameters
3.3.2.1 Z Values
The organism Z value is calculated as
Zj = [(vL-j + 0.035vNLOM-j)KOW + vW-j]ZW
(30)
where the subscript j is replaced with O to indicate the worm (oligochaeta) or M to
indicate the shrew (mammalia) and the organism’s volume fraction of NLOM is
calculated as it is for the plant model.
Two worm concentrations, one for each layer of soil, are calculated for each time step.
The worm is assumed to spend its time indiscriminately between the two soil layers and
therefore an average concentration weighted according to the layer depths is taken as the
final concentration in the worm. These concentrations are determined using
CO,i (µg/g ww) = fiZOMW×1000/ρO
(31)
where fi is the fugacity of soil layer i, and ρO is the density of the worm. For comparison
with experimental results, the worm’s lipid normalized concentration is estimated as
CO (µg/g lipid) = CO (µg/g ww) / vL-O.
(32)
Shrews are considered to be at equilibrium with the top layer of soil. The shrew
concentration is calculated similarly to that of the worm using
CM (µg/g ww) = f1ZMMW×1000/ρM
13
(33)
CM (µg/g lipid) = CM (µg/g ww) / vL-M
(34)
where f1 is the fugacity of the top (first) layer of soil.
3.3.2.2 D Values
The dynamic and steady-state process models offer insight into the chemical uptake and
elimination mechanisms of the organisms. The processes quantified by the BASL4 submodels are depicted in Figures 2 and 3 for the worm and shrew respectively.
Air respiration
(diffusive)
Water respiration
Metabolism
WORM TISSUE
(lipid + NLOM + water)
(diffusive)
Reproductive losses
Growth Dilution
Diffusion (instant)
Soil solid ingestion
WORM GUT
Fecal egestion
(soil solids)
Figure 2: BASL4 dynamic worm model.
As in the EQP model, chemical uptake in worms is assumed by the SSB and DYB
models to be governed by equilibrium partitioning between the soil and the water, lipid,
and non-lipid organic matter fractions of the worm. Here the worm is modelled as a
separate compartment (Figure 2) which takes up chemical from the soil by passive
diffusion from the pore air and water, and by ingestion of soil solids. The processes
responsible for chemical loss from the worm compartment are diffusion to pore air and
water, fecal egestion, metabolism, and reproductive losses. Worm concentration is also
affected by growth dilution. Worm growth is not explicitly included as a change in body
size but is rather approximated by a growth dilution factor as is done in other biota
models such as the Fish Model (Mackay, 2001).
Uptake of chemical through respiration results from passive diffusion of soil pore air
through the skin of the worm. The D value characterizing this exchange is given by
DUA−O = G A−O Z A E A−O
14
(35)
Air respiration
(diffusive)
Metabolism
SHREW TISSUE
(lipid + NLOM + water)
Reproductive losses
Growth Dilution
Diffusion (instant)
Soil solid ingestion
Worm tissue ingestion
SHREW GUT
(soil solids)
Water intake
Fecal egestion
Urination
Figure 3: BASL4 dynamic shrew model.
where GA-O is the respiration rate (m3/h) of the worm and EA-O the efficiency of chemical
uptake from the air. EA-O is assumed to be 0.7 in this model (Armitage, 2004). The D
value for water diffusion is calculated similarly:
DUW −O = GW −O Z W EW −O
(36)
where GW-O is the water uptake rate (m3/h) and EW-O the efficiency of chemical uptake
from water. EW-O is calculated from the empirical formula suggested by Armitage (2004)
as
EW −O =
1
1.85 + 155 K OW
(37)
The worm’s diet is assumed to consist entirely of soil solids. The worm ingests both OM
and MM in the same proportions as they occur in the soil. Chemical passes through the
gut wall of the worm into its tissue according to
DUD −O ,i = G D −O vOM ,i Z OM E D −O
(38)
where GD-O is the soil solids ingestion rate (m3/h), vOM,i is the volume fraction of OM in
the soil solids in layer i and ED-O is the worm’s chemical uptake efficiency from ingested
OM. ED-O is assumed to be 0.1 in this model (Armitage, 2004).
The D values of elimination to air and water are given by
15
DEA−O = DUA−O
DEW −O = DUW −O
(39)
(40)
respectively. Here the BASL4 sub-model differs from Armitage (2004) in that a separate
hypotonic urination rate is not required as it is assumed that water excretion rates are
equal to water intake rates. Fecal elimination is characterized as follows:
D EF −O ,i = DUD −O ,i (1 − AOM −O )
(41)
where AOM-O is the fraction of OM in the gut that is assimilated by the worm.
BASL4 requires rate constants to account for concentration decreases in the worm due to
metabolism (kM-O), growth (kG-O), and reproductive losses (kR-O). The corresponding D
values are calculated as follows:
DM-O = kM-OVOZO
DG-O = kG-OVOZO
DR-O = kR-OVOZO
(42)
(43)
(44)
where VO is the volume of the worm (m3) and the rate constants units are converted to h-1.
These may be set to zero for a conservative estimate. Total uptake and elimination D
values are the sum of the individual processes:
DUT −O ,i = DUA−O + DUW −O + DUD −O ,i
(45)
D ET −O ,i = D EA−O + D EW −O + D EF −O ,i + DM −O + DG −O + D R −O .
(46)
In the shrew SSB and DYB sub-models, chemical uptake by the shrew occurs through
respiration and via the gut (Figure 3). The shrew eliminates chemical through respiration,
urination and fecal egestion. The shrew concentration is affected by metabolism of the
chemical, growth dilution and reproductive losses (for females). This model does not
consider the effects of lactation. The simplifying assumption is made that the entire diet
of the shrew consists of worms and that all soil taken up incidentally is surface layer soil
solids present in the gut of the ingested worm.
Chemical uptake and release process D values for the shrew model are deduced as they
are for the worm model. For respiration,
DUA− M = G A− M Z A E A− M
(47)
DEA-M = DUA-M
(48)
and
where GA-M is the respiration rate (m3/h), EA-M the shrew’s chemical uptake efficiency
from air, and the subscripts U and E indicate respectively uptake and elimination
16
processes. EA-M is assumed to be 0.7 (Armitage, 2004). Similarly for water uptake and
elimination,
DUW − M = GW − M Z W EW − M
(49)
DEA-M = DUA-M
(50)
and
where EW-M is arbitrarily set to 0.7 (comparable to the respiratory and dietary
efficiencies). As is the case for the dynamic worm sub-model, a separate urination rate is
not required by BASL4.
Dietary uptake consists of both worm tissue and soil in the worm gut. A single value,
ED-M, is used to characterize the shrew’s uptake efficiency of chemical from worm tissue
(including lipid, NLOM, and water fractions of the worm) and it is assumed that the
uptake efficiency from soil OM is 80% of ED-M (Armitage, 2004). These uptake D values
are calculated as follows:
DUD − M = G D − M Z O E D − M
DUS − M = vOM ,1G S − M Z OM (0.8 E D − M )
(51)
(52)
where GD-M and GS-M are the ingestion rates (m3/h) of worm tissue and soil respectively.
ED-M can be estimated using the empirical relationship (Armitage, 2004)
E D−M =
1
6.87 × 10 × K OW + 1.116
−9
(53)
Fecal egestion is also treated in two parts. As in equation 41 for the worm, the egestion of
soil is calculated using
DES − M = DUS − M (1 − AOM − M )
(54)
where the subscript ES-M is the egestion of soil solids by the shrew. The fugacity
capacity of the non-soil fecal matter is
Z F − M = ((v L − F + 0.035v NLOM − F )K OW + vW − F )Z W
(55)
where vL-F, vNLOM-F and vW-F are the volume fractions of lipid, NLOM and water in the
non-soil part of the shrew’s feces. These are calculated from GD-M and the absorption
efficiencies AL-M, ANLOM-M, AW-M using
v L − F = G D − M v L −O (1 − AL − M ) G F − M
v NLOM − F = G D − M v NLOM −O (1 − ANLOM − M ) G F − M
vW − F = G D − M vW −O (1 − AW − M ) G F − M
17
(56)
(57)
(58)
where GF-M is the feces (not including soil solids) excretion rate (m3/h) and is given by
G F − M = G D − M (1 − v L −O AL − M − v NLOM −O ANLOM − M − vW −O AW − M )
(59)
The second egestion D value is therefore
DEF − M = GF − M Z F − M .
3.3.3
(60)
Model Calculations
The differential equation governing the change in fugacity of the worm is
VO Z O
df O ,i
dt
= DUT −O ,i f i − DET −O ,i f O ,i
(61)
where fO,i is the instantaneous fugacity of the worm in layer i. If the worms are
considered to be free of chemical at the start of the simulation and the soil fugacity is
constant, dfO,i/dt will be positive until the worm achieves a fugacity high enough that the
two terms on the right of equation 61 (i.e., total uptake and elimination rates) cancel each
other out. This is the steady-state fugacity of the worm, and is calculated in the SSB
model by setting the left-hand side of (61) to zero and solving for fO,i. If the total uptake
and elimination D values are equal, the steady-state fugacity of the worm will be equal to
the soil’s fugacity.
Integration of dfO,i/dt for the DYB model is accomplished by expressing the two Df terms
as fluxes and converting (61) to a finite difference equation:
∆f O ,i =
( Φ U − O ,i − Φ E − O ,i )
VO Z O
× ∆t
(62)
where ∆t is the length of time step (h), and ΦU-O,i and ΦE-O,i are the total uptake and
elimination fluxes (mol/h). The fugacity of the worm at the end of each time step is
given by
f O , i = f O , i + ∆f O , i
(63)
Φ U −O ,i = DUT −O ,i f i
(64)
Φ E −O ,i = D ET −O ,i f O ,i
(65)
and the total fluxes are
The DYB shrew model is analogous to the one described above. The fluxes are slightly
more complicated than in the worm model as some of the uptake depends on the fugacity
18
of the soil and some of it depends on the fugacity of the worm. The total fluxes are
summarized as
Φ U − M = (DUA− M + DUW − M + DUS − M ) f 1 + DUD − M f O
Φ E − M = (DEA− M + DEW − M + DES − M + DEF − M ) f M
(66)
(67)
where f1 is the fugacity of the first layer of soil and fO is a weighted average of fO,1 and
fO,2. The shrew’s fugacity is determined after the calculation of the soil fugacity and the
worm’s fugacity. The concentrations (in units of µg/g wet weight and µg/g lipid) of the
worms and shrews are calculated using equations analogous to equations 31 and 32. The
steady-state lipid-normalized biomagnification factors (BMF) for the shrews are
calculated as
BMF = CM (µg/g lipid) / CO (µg/g lipid)
(68)
or, in fugacity terms, BMF = fM / fO.
4
BASL4 Applications and Results
4.1
Introduction
Evaluation of the model for its ability to predict concentrations accurately is difficult
because of the lack of experimental data. Although there have been studies of chemical
fate in soils subject to addition of amendments, there is often a lack of data on application
rates, soil characteristics, and concentrations over a sufficient time period.
4.2
Illustrative Results
Illustrative model results are included here for the purpose of demonstrating the model’s
treatment of chemicals with diverse physical properties as well as its range of
applications.
All chemical and soil properties used in the following simulations are reported in Tables
1 and 2 respectively.
4.2.1 Partitioning
One-year simulations of DDT, benzene, and 2,4-D were performed for illustrative
purposes. These chemicals are known to exhibit very different behaviour in soil. Layer
depth and diffusion distance are the only physical differences between the layers. All
substances were present as initial concentrations of 1 mg/kg dw in each layer of soil. No
ploughing or additions occurred during the simulation.
A time step of 6 hours was used for all three chemicals based on the smallest of the
calculated characteristic times.
19
Table 1: Physical chemical Properties at 25 ˚C (from Mackay et al 2000, except where noted).
The mineral matter-water partition coefficient, KMW, was set to 1 L/kg for all substances.
Name
CAS
Molar
mass
g/mol
Water
solubility
g/m3
Vapour
pressure
Pa
KOC
log
KOW
L/kg
Half-life in soil,
days
Handbook
Fitted
DDT
50-29-3
354.5
0.0031
0.00002
2.4×105
6.19
3837
-
Benzene
71-43-2
78.1
1780
12700
83
2.13
50 b
-
2,4-D
94-75-7
221.0
890
5.6×10-5
20
2.81
15
-
Fenitrothion
122-14-5
277.25
30
0.0054
17
3.4
4
20
Diazinon
333-41-5
304.4
60
0.008
820
3.3
70
30
Dimethoate
60-51-5
229.28
25000
0.0011
16
0.8
10
10
DMP
131-11-3
194.2
4000
0.22
54
2.1
23
3.5
DEP
84-66-2
222.26
1100
0.133
317
2.4
20
6.5
DBP
84-74-2
278.35
11.2
0.0019
2.2 × 104 a
4.7
23
8.5
DnOP
117-84-0
390.56
0.0005
1.33×10-5
1.4×107
8.1
12
28
OCDD
3268-87-9
460
7.4×10-8
1.1×10-10
6.5×107
8.2
2300
3650
DEHP
117-81-7
394.54
0.4
0.00001
1.0×105
8
20
20
NP
104-40-5
220.36
5.5
0.07
2.1×105
5.7
n/r
100
Naphthalene
91-20-3
128.18
31
10.4
961 a
3.37
71
-
Phenanthrene
85-01-8
178.20
1.10
0.02
1.52×104 a
4.57
230
-
Pyrene
129-00-0
202.30
0.132
0.006
6.2×104 a
5.18
710
-
Benzo[a]pyrene
50-32-8
252.32
0.0038
7×10-7
4.5×105 a
6.04
710
-
PCB-99
38380-01-7
326.43
0.016
0.003
1.0×106 a
6.4
infinite
-
PCB-110
38380-03-9
326.43
0.02
0.002
7.5×105 a
6.26
infinite
-
HCB
118-74-1
284.79
0.005
0.0023
2.2×105 a
5.73
1530
-
a
b
estimated from KOW using the Karickhoff (1981) correlation.
Mackay and Stiver, 1991.
20
Table 2: Soil properties used in BASL4 simulations. All values are estimates based on Mackay (2001), except where otherwise noted. In all
cases: Field Area = 1 ha, Air boundary layer thickness = 4.75 mm, Molecular diffusivity – air = 0.018 m2/h, Molecular diffusivity – water = 1.8 ×
10-6 m2/h, OM density = 1000 kg/m3, MM density = 2500 kg/m3.
Illustrative b Sanchez cJianlong dMcLachlan e Wild
Ochten Gelderse Artificial
et al
et al
et al
et al
Poort
Soil
Leaching rate, mm/day
5 negligible
5
1
5
5
5
0.1
a
Bioturbation rate, cm/y
0.3
0.3
0.3
0.3
0
0.3
0.3
0.3
Mass fraction OC in OM
0.56
0.57
0.56
0.56
0.56
0.56
0.56
0.59g
Fast-degrading OC half-life, days
60
60
60
N/A
60
N/A
N/A
N/A
Slow-degrading OC half-life, days
360
360
360
N/A
360
N/A
N/A
N/A
Surface layer depth, m
0.1
0.1
0.1
0.05
0.2
0.1
0.1
0.1
Surface layer diffusion distance, m
0.05
0.05
0.05
0.025
0.1
0.05
0.05
0.05
2nd layer depth, m
0.4
0.1
0.1
0.1
0.2
0.4
0.4
0.4
2nd layer diffusion distance, m
0.25
0.05
0.05
0.05
0.2
0.25
0.25
0.25
Volume fraction of air
0.2
0.2
0.2
0.2
0.25
0.2
0.2
0.2
Volume fraction of water
0.3
0.0643b
0.3
0.3
0.25
0.3
0.35
0.345
Mass fraction of OC (fOC), g/g DW
0.02
0.004b
0.0114c
0.02d 0.0121e
0.028f
0.05f
0.059g,h
Fast-degrading fraction of OC
0.5
0.5
0
0
0
0
0
(see text)
Slow-degrading fraction of OC
0.5
0.5
0
0
0
0
0
Non-degrading fraction of OC
0
0
1
1
1
1
1
a
McLachlan et al, 2002
b
Sanchez et al., 2004
c
Jianlong et al, 2004
d
MacLachlan et al, 1996
e
Wild et al, 1992
f
Calculated from organic matter fractions (fOM) reported in Hendriks et al, 1995
g
Estimated by Jager, 2004
h
Belfroid et al, 1994
21
Table 3 shows the distribution of the chemicals between the four phases initially, at 30
days, and at the end of the one-year simulation. It also reports the relative importance of
different loss processes on the fates of the chemicals.
Table 3: BALS4 Results – Comparison of partitioning and fates of some chemicals
Chemical Distribution Between Phases
(%)
Air
Water
OM
MM
DDT
Day 0
Day 30
Day 365
Benzene
Day 0
Day 30
Day 365
2,4-D
Day 0
Day 30
Day 365
Chemical Losses (%)
Reacted
Leached
Volatilized
Remaining
< 0.1
< 0.1
<0.1
< 0.1
< 0.1
< 0.1
> 99.9
> 99.9
> 99.9
< 0.1
< 0.1
< 0.1
0.5
6.4
< 0.1
0.1
< 0.1
< 0.1
99.5
93.5
1.3
1.4
2.1
8.7
9.5
14.3
56.9
52.3
25.5
33.1
36.8
58.1
28.0
55.3
7.0
15.5
19.3
29.2
45.7
< 0.1
< 0.1
< 0.1
< 0.1
15.6
16.1
18.2
24.7
21.4
7.8
59.6
62.5
74.0
70.8
88.9
8.54
11.1
< 0.1
< 0.1
20.6
< 0.1
DDT, the least soluble and second least volatile of the chemicals, sorbs strongly to the
organic matter in the soil. It is very persistent with a half-life of 3837 days (Mackay and
Stiver, 1991). It is not present in any significant quantity in the pore air or water of the
soil. After one year of OM degradation DDT remains in the sorbed phase. Volatilization
and leaching are not important loss processes in this scenario.
Benzene is volatile, and as expected is removed from the soil primarily by degrading
reactions and volatilization. It sorbs preferentially to the organic matter in the soil, but
much less strongly than does DDT, and therefore as OM decays there is some
redistribution of benzene to the pore water and pore air phases.
2,4-D is soluble in water, very involatile, and it reacts quite rapidly with a half-life of 15
days. It is lost from the system largely by leaching and degrading reaction. As OM
decays, it is found in larger quantities in the pore water of the soil and is more likely to
leave the system by leaching into groundwater.
4.2.2
Long Term Fate
Figures 4a-c and 5a-c show the fates of DDT in soil without and with organic matter
degradation respectively. Figures 4d and 5d show the mass fractions of OM over the
simulation time. In both simulations, the initial concentrations are identical in the two
layers (0.1 mg/kg dw). There is no addition of chemical and the layers are not ploughed.
Predictably, the effects of the OM degradation on the fugacity of DDT are dramatic
because DDT has a much longer degradation half-life in soil than either the fast- or slowdegrading types of OM, and, being relatively involatile and sparingly water soluble is not
lost by evaporation or leaching. There is a slow loss of chemical, thus the amount of DDT
in the soil declines, but the more rapid decrease in OM (and therefore in the soil's
sorptive capacity) causes the fugacity to increase. There is also an initial increase in dry
22
weight concentration (Figure 5a) between 0 and 900 hours (approximately the first
month) during which the mass of fast-degrading OM declines much more quickly than
the mass of the chemical (Figure 5d). This increase is very small as can be seen by
examining the range of concentrations displayed in Figure 5a.
Figure 6 shows the results of the same simulation as depicted in Figure 5 but with no
initial concentration in the second layer and a single plough event occurring at
approximately 2 months into the simulation. Ploughing causes both layers to have the
same concentration and fugacity. The small, short-term rise in the soil concentration seen
in Figure 5a is not noticeable here due to the scale of Figure 6a. Figure 6d tracks the
changes in OM mass fractions throughout the simulation. These do no differ between
layers as both were defined to have equal fractions of fast- and slow-degrading OM.
Figure 7 shows the original simulation with 2.0 × 105 kg/ha biosolid amendment added at
2 months to the second layer of soil (by injection). The soil layers have equal initial
concentrations. The biosolid is made up of 50% fast-, 50% slow-degrading OM and no
non-degrading OM, and has a DDT concentration of 0.3 mg/kg. No ploughing occurs. At
1440 hours, the amount of chemical in the second layer is increased by the contaminated
biosolid addition. The fugacity of the second layer drops after the amendment, due to the
increase in organic matter. Both fugacities continue to rise slowly for the remainder of
the simulation as OM degrades. The concentration of the second layer rises with the
biosolid addition. Both layers show the slight increase in concentration as seen in Figure
5a during the time of maximum rate of decrease of the mass fraction of fast-degrading
OM.
Figure 8 shows the original simulation with biosolid amendment added at 1 month, and
ploughing occurring at 5 months. As in the previous simulation, Figure 8a shows the
temporary increase in the chemical concentration shortly after the biosolid addition.
Figure 8b shows the decrease in the fugacity of layer 2 resulting from the addition of OM
and Figure 7d depicts the increase in OM. At 5 months (3600 hours), the layers are mixed
by ploughing resulting in equal layer fugacities and identical soil composition.
Consequently, there is a small increase in OM in the first layer and a decrease of OM in
the second.
23
4a. Concentration (mg/kg dw)
4b. Fugacity (Pa)
0.1005
1.60E-07
0.1
1.40E-07
0.0995
1.20E-07
0.099
1.00E-07
0.0985
8.00E-08
0.098
6.00E-08
0.0975
0.097
4.00E-08
0.0965
2.00E-08
0.096
0.00E+00
0
1000
2000
3000
4000
5000
0
1000
2000
Time (h)
4c. Amount (mol)
kg OM / kg soil dw
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1000
2000
3000
Time (h)
4000
5000
4000
5000
4d. Mass Fractions of OM
1.6
0
3000
Time (h)
4000
5000
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
0
1000
2000
3000
Time (h)
Figure 4: BASL4 results for DDT in soil with no organic matter degradation. Both layers have identical initial concentrations. Note the small range of
concentrations in (a). The upper layer is represented by a solid line and the lower layer by a dashed line .
5a. Concentration (mg/kg dw)
5b. Fugacity (Pa)
0.1002
3.50E-07
0.1
3.00E-07
0.0998
2.50E-07
0.0996
0.0994
2.00E-07
0.0992
1.50E-07
0.099
1.00E-07
0.0988
5.00E-08
0.0986
0.00E+00
0.0984
0
1000
2000
3000
4000
0
5000
1000
2000
3000
4000
5000
4000
5000
Time (h)
Time (h)
5d. Mass Fractions of OM
5c. Amount (mol)
kg OM / kg soil dw
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
0
0
0
1000
2000
3000
Time (h)
4000
5000
1000
2000
3000
Time (h)
Fast-Degrading
Slow-Degrading
Figure 5: BASL4 results for DDT in soil with organic matter degradation. Both layers have identical initial concentrations. The initial rise in
concentration is due to the degradation of the fast-degrading OM. The upper layer is represented by a solid line and the lower layer by a dashed line.
6a. Concentration (mg/kg dw)
6b. Fugacity (Pa)
0.12
2.50E-07
0.1
2.00E-07
0.08
1.50E-07
0.06
1.00E-07
0.04
5.00E-08
0.02
0
0.00E+00
0
1000
2000
3000
4000
5000
0
1000
2000
Time (h)
3000
4000
5000
4000
5000
Time (h)
6d. Mass Fractions of OM
6c. Amount (mol)
0.02
0.35
0.018
kg OM / kg soil dw
0.4
0.3
0.25
0.2
0.15
0.1
0.05
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
0
0
1000
2000
3000
Time (h)
4000
5000
0
1000
2000
3000
Time (h)
Fast-Degrading
Slow-Degrading
Figure 6: BASL4 results for DDT in soil with degrading organic matter. There is an initial concentration of DDT in the surface layer only. A single plough
event occurred at approximately 2 months into the simulation. Both layers have the same fractions of fast- and slow-degrading OM, thus (d) is identical to
Figure 3(d). The upper layer is represented by a solid line and the lower layer by a dashed line.
7a. Concentration (mg/kg dw)
7b. Fugacity (Pa)
0.11
3.50E-07
0.108
3.00E-07
2.50E-07
0.106
2.00E-07
0.104
1.50E-07
0.102
1.00E-07
0.1
5.00E-08
0.00E+00
0.098
0
1000
2000
3000
4000
0
5000
1000
2000
3000
4000
5000
4000
5000
Time (h)
Time (h)
7c. Amount (mol)
7d. Mass Fractions of OM
1.6
0.04
1.4
0.035
kg OM / kg soil dw
1.2
1
0.8
0.6
0.4
0.2
0.03
0.025
0.02
0.015
0.01
0.005
0
0
0
1000
2000
3000
Time (h)
4000
5000
0
1000
2000
3000
Time (h)
Layer 1, Fast-Deg.
Layer 1, Slow-Deg.
Layer 2, Fast-Deg.
Layer 2, Slow-Deg.
Figure 7: BASL4 results for DDT in soil with degrading organic matter, both layers having identical initial concentrations. Biosolid amendment is
added to the second layer (by injection) 2 months into the simulation at a rate of 2.0 x 10 5 kg/ha. The biosolid is 50% fast- and 50% slow-degrading
OM, and has a DDT concentration of 0.3 mg/kg. The upper layer is represented by a solid line and the lower layer by a dashed line.
8a. Concentration (mg/kg dw)
8b. Fugacity (Pa)
0.1015
3.00E-07
0.101
2.50E-07
0.1005
2.00E-07
0.1
1.50E-07
0.0995
1.00E-07
0.099
5.00E-08
0.00E+00
0.0985
0
1000
2000
3000
4000
0
5000
1000
2000
3000
4000
5000
4000
5000
Time (h)
Time (h)
8c. Amount (mol)
8d. Mass Fractions of OM
0.04
1.4
0.035
1.2
0.03
kg OM / kg soil dw
1.6
1
0.8
0.6
0.4
0.2
0.025
0.02
0.015
0.01
0.005
0
0
1000
2000
3000
Time (h)
4000
5000
0
0
1000
2000
3000
Time (h)
Layer 1, Fast-Deg.
Layer 1, Slow-Deg
Layer 2, Fast-Deg
Layer 2, Slow-Deg
Figure 8: BASL4 results for DDT in soil with degrading organic matter, an initial concentration of 0.1 mg/kg dw in both layers, 2.0 x 10 5 kg/ha biosolid
amendment added at 1 month, and ploughing occuring at 5 months. The biosolid is 50% fast- and 50% slow-degrading OM, and has a DDT
concentration of 0.3 mg/kg. Note the small range of concentrations. The upper layer is represented by a solid line and the lower layer by a dashed
line.
4.3
Comparison With Reported Soil Data
The model was applied to soil experiments described in four published reports.
Regrettably none of the studies provided full details of soil properties, however, typical
properties were assumed and it is believed that the simulation results obtained can be
compared with the empirical data thus obtaining an indication of validity. Rigorous and
full validation is impossible because conditions can always be found in which the model
is invalid and the results are, therefore, not applicable.
Chemical property data are given in Table 1 and soil property data in Table 2.
4.3.1
Sanchez et al (2004)
In this study the degradation of three pesticides was followed in soil with two types of
sludge application, one from an urban treatment plant and the other from a food
processing plant. Sterilized and non-sterilized soils were tested. Only the non-sterilized
soil results are considered here. Sludge application rates were not specified, so only the
pesticide degradation in the unamended control soil was simulated.
The pesticides were fenitrothion, diazonin, and dimethoate. An important quantity is the
degradation half-life in the soil. Recommended Handobook values are based on a
number of experiments, not all of which accurately reflect these experimental conditions.
Also given are “fitted” half-lives as discussed later. No other properties were fitted or
changed.
Figure 9a shows the degradation of fenitrothion. The Handbook half-life of 4 days clearly
over-estimates the degradation rate and a value of 20 days proves to give an excellent fit.
Figure 9b for diazinon shows that the Handbook value of 70 days is too long, a value of
30 days being fitted. Figure 1c for dimethoate shows that the Handbook value of 10 days
gives an excellent fit.
It is concluded that caution must be exercised when using Handbook values of half-lives
or those from QSARs since they may be in error by a factor of up to 5 and possibly more.
Presumably the soil conditions and especially the nature and numbers of microflora have
a profound effect on the degradation rate.
The model showed that for all three chemicals, the major loss process was degradation.
Volatilization contributed only very minor losses, less than 1% for all three substances.
Leaching was set to a minimal rate in the model to simulate the laboratory conditions of
no leaching. It was not possible to adjust the parameterization of BASL4 to simulate the
darkness maintained in the laboratory.
29
% Remaining
Fenitrothion
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
Time, (d)
% remaining in soil
Diazinon
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
60
70
80
90
100
Time, days
% remaining in soil
Dimethoate
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
Time, days
Figure 9: The loss of fenitrothion, diazinon, and dimethoate from
non-sterlized soil as a function of time. The crosses are the
experimental results of Sanchez et al (2004). The heavy lines are
the model results using the fitted half-lives, and the thin lines are the
model results using half-lives taken from the handbook of Mackay
et al (2000).
4.3.2
Jianlong et al (2004)
This is a study of the degradation of four phthalate esters (PEs) in soil augmented with
acclimated activated sludge. The chemicals were di-methyl phthalate (DMP), di-ethyl
phthalate (DEP), di-n-butyl phthalate (DBP), and di-n-octyl phthalate (DnOP). There is
uncertainty about the reported degradation half-lives of the octyl phthalates in the
literature because of ambiguity about the identity of the specific isomer.
Figure 10 shows the experimental degradation of the esters and the concentrations
corresponding to the fitted values. Clearly degradation is first order in nature and the
half-lives vary from about 3 to 30 days. Again there are discrepancies between the
literature and fitted values of up to a factor of 7 for DMP. The losses by volatilization and
leaching are relatively small for all the PEs.
100
90
80
% remaining in soil
DMP
70
DEP
60
DBP
DnOP
50
DMP
DBP
40
DEP
30
DnOP
20
10
0
0
5
10
15
20
25
30
Time, days
Figure 10: The loss of DMP, DEP, DBP, and DnOP in sludge-augmented soil. The
model results are shown as solid lines and the experimental results as points.
4.3.3 McLachlan et al (1996)
In this study a number of dioxins and furans were studied over a period of 20 years in a
sludge-amended soil. The authors concluded that the degradation half-lives for the set of
PCDD/Fs measured "were of the order of 20 years", but may be longer. In 1976 between
67 and 88% (mean 76%) of the chemicals present in 1972 remained in the amended plots.
By 1990 it was between 50 and 74% (mean 59%). The loss from 1972 to 1976 of 24%
corresponds to a half-life of 10 years, and the loss from 1976 to 1990 of a further 17%
corresponds to a half-life of 38 years as shown below.
31
Calculation
Year:
Amount remaining:
1972
100%
Loss half-life for
1976
76%
1972-1976:
ln(C2/C1) = ln(0.76) = - kt
t = 4 years; k = 0.0686
τ1/2 = ln 2 / k = 0.693/k
= 10.1 years
1990
59%
1976-1990:
ln(C2/C1) = ln(59/76) = -0.253
t = 14 years; k = 0.018
τ1/2 = 38.8 years
Clearly these substances are highly persistent with half-lives probably lying in the range
from 10 to 40 years.
In Figure 11, model results for OCDD are compared to the field data assuming a half-life
of 2300 days (6.3 years), suggested by Mackay et al, and a half-life of 5000 days (13.7
years). For the experimental data shown in Figure 11, the concentration of OCDD in the
control plot was used to correct for OCDD gained through atmospheric deposition,
thought to be a contributor nationally in the UK.
The model was run again for half-lives of 10, 20, and 40 years giving the results in Figure
12 which extends over a 50 year period. Bioturbation becomes an important long-term
transport mechanism between the soil layers in the model, however, the losses by
volatilization and leaching are small i.e., less than 1%.
The model results are consistent with the experimental data but the persistent nature of
these substances precludes determination of an accurate half-life.
4.3.4
Petersen et al (2003)
In this study the degradation and uptake by plants of polycyclic aromatic hydrocarbons
(PAH), di(2-ethylhexyl)phthalate (DEHP), nonylphenol (NP), and ethoxylates (NP+NPE)
and linear alkylbenzene sulfonates (LAS) were studied. The focus of the study was on
plant uptake and the effect on soil fertility and microflora, and not on chemical
degradation kinetics.
In the three-year trial, levels of LAS and NP+NPE fell below detection limits thus no
kinetic parameters could be determined, except that the half-life is presumably less than a
year. Concentrations of DEHP ranged from below the detection limit of 0.05 mg/kg to
0.103 mg/kg and the total PAH concentration ranged from 0.125 to 0.209 mg/kg at the
end of the 3-year trial. The reported concentrations of total PAHs in the soil exceed the
calculated inputs by about an order of magnitude. This may be due to atmospheric
deposition.
32
100
% of 1972 amount remaining
90
80
70
60
50
40
30
20
10
0
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
Year
Figure 11: The loss of OCDD during a field study from 1972 to 1990. The crosses indicate experimental data, the
thin line is model results assuming the 2300-day half-life as suggested by Mackay et al (2000) and the thick line a
half-life of 5000 days.
100
90
80
% remaining
70
10yr half-life
20yr half-life
40yr half-life
Expt
5yr half-life
60
50
40
30
20
10
0
0
10
20
30
40
50
Time, years
Figure 12: The loss of OCDD during an 18-year field study from 1972 to 1990. The crosses indicate experimental
data, the four lines indicate model results for OCDD degradation half-lives of 40, 20, 10 and 5 years.
4.4
Screening-Level Biota Models Evaluation
4.4.1
EQP Plant Model
The BASL4 EQP leafy tuber model results were compared to an experiment that
monitored PAH uptake by carrots in sludge-amended soil (Wild et al, 1992). In the
experiment, carrots were grown from seeds in pots of soil collected from a control plot
adjacent to the Woburn Market Garden Experiment in Rothamsted, UK. Four different
soil treatments were prepared: one control soil which was not amended and three other
soils which were mixed with different amounts of PAH contaminated sludge at the
beginning of the experiment. Soil concentrations were recorded at the beginning and end
of the 82-day experiment, and carrot root cores, peels, and carrot tops were analyzed for a
variety of PAHs.
Four PAHs of different molar mass and KOW values were chosen for this comparison as
listed in Table 1.
For the model simulation the soil was defined as shown in Table 2. The amendments
were treated as amendment and ploughing events on day one of the simulation.
Application rates and mean chemical concentrations in the sludge are reported in Tables
4 and 5.
Table 4: Application rates (Wild et al, 1992).
Treatment
Control
Rate 1
Rate 2
Rate 3
Application Rate, kg dw/ha
0
18000
55000
180000
Table 5: Mean concentrations of chemical in sewage sludge (Wild et al, 1992).
Chemical
Mean concentration in sludge
Initial concentrations in soil
mg/kg dw
(calculated) µg/kg dw
Naphthalene
0.86
1.16
Phenanthrene
3.00
9.92
Pyrene
2.00
28.9
Benzo[a]pyrene
0.82
15.3
The soil from the control plot was not initially pristine. The initial ΣPAH concentration of
the control soil was given as 214 ± 49 µg/kg (dry weight), which is typical of soil
concentrations found in the natural environment. Initial concentrations of the individual
compounds in the soil were not reported, but were required for the modelling exercise.
The average soil concentrations of the individual PAHs were estimated from the reported
dry weight concentrations and the plant-soil concentration ratios (PSRs) of the roots and
peels of the carrots. These were used as initial concentrations in the simulation, and
theoretical PSRs were compared with experimental values rather than comparing plant
concentrations directly.
34
PAHs in the soil and sludge amendment were not likely fully bioavailable as the
compounds were present in both soil and sludge for a prolonged period of time. A
bioavaible fraction (B) of 15% was chosen to fit experimental results (see TLEQ carrot
root model results).
The model results include predictions for all three guilds for illustrative purposes, but
only the leafy tubers were used in the comparison. The lipid and water fractions of the
roots and foliage of each guild are tabulated below (Table 6).
Table 6: Lipid and water volume fractions of BASL4 plants, estimated from Fryer et al, (2003)
unless otherwise indicated.
Root
Foliage
Plant guild
lipid content
water content
lipid content
water content
0.89a
0.01
0.70
Leafy tuber
0.025a
Grass/sedge
0.01
0.88
0.01
0.88
Conifer
0.01
0.94
0.064
0.58
a
Trapp, 2001.
Experimental and model results for carrot roots are reported in Table 7. Wild et al (1992)
state that peel concentrations, while constituting only 33 to 43% of the total carrot
weight, generally contained a disproportionately large fraction of the PAHs in the carrots.
This has also been seen in other plant uptake experiments, but is not treated by the
screening level calculations in BASL4. Experimental whole carrot concentrations were
therefore estimated using a weighted average of core and peel concentrations assuming
that the peel makes up 38% of a carrot’s mass. Concentration ratios with respect to the
soil are also calculated as weighted averages of the peel and core values determined
experimentally.
Experimental PSRs were calculated using concentrations (µg/kg dry weight) of cores and
peels harvested after 82 days. The soil concentrations (µg/kg soil solids) used in these
calculations are averages of the appropriate initial and final values measured. The
BASL4 plant model assumes instantaneous equifugacity, which means that theoretical
PSRs are time independent:
PSR =
C m ,n (mol m 3 )
3
C1 (mol m )
=
Z m ,n f m ,n
Z1 f1
=
Z m ,n
Z1
(69)
where C1, Z1, and f1 are respectively the concentration, Z value and fugacity of the top
layer of soil. The soil’s Z value is constant in the absence of OM degradation.
The concentration of naphthalene in the carrot peels of all but the control soil were
indeterminable due to instrumental limitations. It is likely that concentrations in the peel
were higher than in the core of the carrot, but for comparison purposes the whole carrot
concentration was assumed to be equal to the concentration of the core. The
35
experimentally determined PSRs reported here are therefore likely lower than would have
been measured.
Table 7: EQP predicted and measured
dw) (Wild et al, 1992).
Control
Compound
exp.
model
Naphthalene
5.6
7.0
Phenanthrene
0.58
7.6
Pyrene
0.072
7.7
Benzo[a]pyrene
0.094
7.7
plant-soil concentration ratios (kg soil solids/kg of plant
Rate 1
exp.
model
5.2
6.2
0.27
6.5
0.37
6.6
0.098
6.6
Rate 2
exp.
model
2.2
4.8
0.32
5.1
0.32
5.1
0.077
5.1
Rate 3
exp.
Model
0.65
2.9
0.11
2.9
0.19
2.9
0.021
2.9
BASL4 overestimated the chemical concentrations in the plant with respect to the soil in
all scenarios. Good agreement between theoretical and experimental results was
achieved for naphthalene, the most water-soluble of the four PAHs modelled (see Table
1). Model results overestimated experimental results by less than a factor of 4 for this
compound.
Chemicals with higher KOW values were not accurately described by the EQP plant
model. Calculated PSRs overestimated actual concentrations by 1 to 2 orders of
magnitude. This is likely due to the chemicals’ tendency to resist diffusion deep into
plant tissues as they are more hydrophobic than naphthalene. The EQP model assumes
that the chemicals have access to all of the lipid in the whole carrot root and therefore is
expected to overestimate concentrations of high KOW chemicals.
Carrot foliage results are presented in Table 8 for illustrative purposes only, as the study
concluded that foliar uptake of PAHs is independent of soil concentration, whereas the
model assumes otherwise. Further characterization of the soil-air-foliage pathway,
including vapour phase and sorbed phase chemical deposition is recommended for aboveground plant modelling. The importance of air sources must also be evaluated, as these
may be the dominant contributor to foliar contamination for some chemicals.
Table 8: Illustrative results of the EQP model. Plant concentrations are in µg/kg dry weight.
CSOIL,
Leafy tuber
Grass/sedge
Conifer
Compound
root
foliage
root
foliage
root
Foliage
µg/kg dw
Naphthalene
1.19
8.20
2.17
3.77
3.77
6.45
5.81
Phenanthrene
9.92
75.9
20.0
34.5
34.5
58.5
54.3
Pyrene
28.9
222
58.6
101
101
171
159
Benzo[a]pyrene
15.3
118
31.0
53.3
53.3
90.5
84.1
4.4.2 TLEQ Carrot Root Model
Table 9 lists theoretical characteristic times for the uptake of naphthalene, phenanthrene,
pyrene and benzo[a]pyrene based on the KOW values of the chemicals, an assumed root
volume of 1 × 10-4 m3 (100 cm3), and the tuber lipid and water fractions given in Table 6.
36
Table 9: Calculated uptake characteristic times for selected chemicals.
Chemical
Naphthalene
Phenanthrene
Pyrene
Benzo[a]pyrene
tchar, h
158
2.48 × 103
1.01 × 104
7.30 × 104
tchar, d
7
103
420
3040
According to this model, naphthalene is the only chemical expected to have approached
thermodynamic equilibrium between plant tissues and soil by the end of the 82-day
experiment. Equation 6 was used to estimate carrot concentrations under the same
conditions as described in the previous section, and the resulting carrot-soil concentration
ratios are reported in Table 10.
The dynamic carrot root model produces results typically an order of magnitude greater
than what was observed for phenanthrene. This could be the result of phenanthrene
degradation. The model performed well when predicting the uptake of pyrene and
benzo[a]pyrene. This simple single-process model may be very useful for screening
organic chemicals of varying KOW for their potential to be taken up into carrot roots, and
with further transpiration rate data other plant models can be developed and tested
similarly.
Table 10: TLEQ carrot model fugacity ratios (α, see Section 3.2.3) and predicted and measured
plant-soil concentration ratios (kg soil solids/kg of plant dw) after 82 days (Wild et al, 1992).
Compound
Naphthalene
Phenanthrene
Pyrene
Benzo[a]pyrene
4.4.3
α
1.0
0.55
0.18
0.027
Control
exp.
TLEQ
5.6
7.1
0.58
4.2
0.072
1.4
0.094
0.20
Rate 1
exp.
TLEQ
5.2
6.1
0.27
3.6
0.37
1.2
0.098
0.17
Rate 2
exp.
TLEQ
2.2
4.8
0.32
2.8
0.32
0.9
0.077
0.14
Rate 3
exp.
TLEQ
0.65
2.9
0.11
1.6
0.19
0.52
0.021
0.078
Soil Fauna Models
4.4.3.1 Model Parameterization
The EQP and SSB sub-models were tested using the results of a field study in which the
uptake of organic chemicals in worms and shrews was measured (Hendriks et al, 1995).
In this study worms were collected from two sites in a contaminated flood plain in the
Netherlands, purged of ingested soil and analyzed for different PCBs. Shrews were
gathered from the same sites and their livers were analyzed. The BASL4 sub-models
were tested against the results of the field study for PCB-99, PCB-110, HCB and DDT
(including its derivatives). The physical chemical properties used in the simulation are
given in Table 1.
The SSB models were tested indirectly by having the dynamic model simulate a 180-day
period to ensure that the model worm and shrew concentrations were allowed to achieve
near steady-state. Observed worm concentrations, biota-soil accumulation factors
(BSAFs) and shrew biomagnification factors (BMFs) were compared to the theoretical
37
steady-state model results. Since the field study does not report changes in soil OM
content, all soil OC was considered non-degrading. It was assumed that any OC degraded
at the flood plain sites is continuously replenished by natural processes including run-off
from neighbouring sites, biotic fecal egestion and vegetation death and decay. Due to the
negligible degradation rates and high KOC values of the chemicals surveyed, it was also
assumed that worms and shrews were subject to near steady-state soil concentrations.
The soil parameters used in this simulation are listed in Table 2. The solid matter contents
of the soils at the Ochten and Gelderse Poort sites were reported as 0.81 ± 0.07 and 0.73
± 0.03 kg/kg bulk soil respectively. This was interpreted to correspond with an air
volume fraction of 0.2 for both sites and water volume fractions of 0.3 and 0.35 at Ochten
and Gelderse Poort respectively. The worm and shrew parameters are listed in Table 11.
Table 11: BASL4 dynamic worm and shrew sub-model parameter values (Armitage et al, 2004).
Worm
Shrew
3
-6
Volume, m
1.0 × 10
1.0 × 10-5
Density, kg/m3
1000
1000
Lipid volume fraction
0.012
0.07
Water volume fraction
0.80
0.70
Water absorption efficiency (from diet)
0.85
Lipid digestion efficiency
0.98
NLOM digestion efficiency
0.75
Chemical uptake efficiency from air
0.70
0.70
Chemical uptake efficiency from diet
0.10
0.70
OM digestion efficiency
0.10
0
Air respiration rate, m3/d
0.0343
1.20 × 10-6
Water turnover rate, m3/d
1.00 × 10-4
3
Water intake rate, m /d
5.00 × 10-7
Worm tissue ingestion rate, m3/d
9.15 × 10-6
Soil solid ingestion rate, m3/d
1.02 × 10-6
4.18 × 10-6
-1
Metabolic rate constant, d
0
0
0.0015
0
Reproductive loss rate constant, d-1
Growth dilution rate constant, d-1
0.005
0.001
The results of a laboratory experiment (Belfroid et al, 1994) were used to evaluate the
dynamic worm sub-model’s ability to predict the worm concentration and overall rate of
uptake of HCBs under lab conditions. In the experiment, it was found that adult compost
worms reached steady-state concentrations approximately seven days after being
introduced to contaminated artificial soil.
The worm sub-model simulated a period of 60 days, with an initial soil concentration of
13.2 mg/kg dry weight (Belfroid et al., 1994). The soil properties used in the simulation
are listed in Table 2 (“Artificial Soil”). The leaching rate was reduced to a negligible
amount as the soil and worms were kept in jars. Since the soil used was artificially
created out of a combination of peat, clay, quartz sand and calcium carbonate, all organic
matter was considered to be non-degrading. Assuming the jars were 20 cm deep, both soil
layer depths were set to 10 cm with diffusion distances of 5 cm in the top layer and 10 cm
(the distance between the depth midpoints of the two layers) in the bottom layer. The
38
organic carbon content of the soil was set to 0.059 after assuming that the organic carbon
content of the peat (the source of OM in the artificial soil) was 0.59 (Jager, 2004). The
water content was given as 35% wet weight, which was translated to a bulk soil water
volume fraction of 0.345 and an air volume fraction of 0.2. As the chemicals in the
laboratory experiment were only allowed one week to reach equilibrium within the soil
before the worms were introduced, the bioavailability factor was set to 1.
4.4.3.2 D Values
Illustrative uptake and elimination D values are presented in Tables 12, 13 and Figure 13
for worms and shrews at the Ochten site. PCB-99 (logKOW = 6.4) and naphthalene
(logKOW = 3.37) were chosen to show how different processes dominate depending on the
physical properties of the chemical.
Uptake of PCB-99 in worms appears to be dominated by OM intake, whereas the more
soluble naphthalene is taken up and released predominantly through water. The OM
ingestion and fecal egestion D values are similar for both chemicals. Chemical
elimination rates are therefore likely to be only slightly greater than uptake rates
depending on the effects of growth dilution and reproductive losses.
Table 12: Selected D values for worms at Ochten (mol Pa-1 h-1).
Process description
PCB-99
Air respiration
DUA-O, DEA-O
1.41 × 10-11
Water turnover
DUW-O, DEW-O
3.68 × 10-8
Soil ingestion
DUD-O
4.66 × 10-6
Fecal egestion
DEF-O
4.19 × 10-6
Metabolism
DM-O
0
Growth
DG-O
1.59 × 10-7
Reproduction
DR-O
4.77 × 10-8
Naphthalene
1.41 × 10-11
5.06 × 10-8
6.18 × 10-9
5.57 × 10-9
0
2.15 × 10-10
6.45 × 10-11
Table 13: Selected D values for shrews at Ochten (mol Pa-1 h-1).
Process description
PCB-99
Respiration
DUA-M, DEA-M
4.04×10-7
Water intake/ urination
DUW-M, DEW-M
2.38×10-10
Worm ingestion
DUD-M
2.57×10-4
Soil ingestion
DUS-M
1.35×10-8
Fecal egestion
DEF-M + DES-M
2.95×10-5
Metabolism
DM-M
0
Growth
DG-M
1.33×10-6
Reproduction
DR-M
0
Naphthalene
4.04×10-7
3.39×10-10
3.52×10-7
1.82×10-11
4.02×10-8
0
1.78×10-9
0
Pore air respiration is the least important process for the worm, but plays a bigger role for
the shrew. Worm tissue intake is the driving process for uptake in shrews according to
this model, and the rate of intake increases with increasing KOW. Fecal egestion D values
are approximately an order of magnitude lower than dietary intake D values for both
chemicals, suggesting that elimination kinetics for the shrew are slower than uptake
kinetics.
39
W
ro
w
th
R
ep
ro
du
ct
io
n
G
R
es
at
pi
ra
er
tio
in
n
ta
ke
/u
rin
at
W
io
n
or
m
in
ge
st
io
n
So
il
in
ge
st
io
Fe
n
ca
le
ge
st
io
n
M
et
ab
ol
is
m
at
in
ep
ol
n
n
m
io
io
is
st
st
du
ct
io
n
G
ro
w
th
ab
ro
et
ge
ge
le
M
ca
R
Fe
il
n
er
tio
ov
ra
rn
pi
tu
es
er
rr
So
W
Ai
a)
10-7
10-8
10-9
PCB-99
Naphthalene
10-10
10-11
b)
10-5
10-6
10-7
10-8
PCB-99
Naphthalene
10-9
10-10
10-11
Figure 13: Selected D values (mol Pa-1 h-1) for a) worms and b) shrews at Ochten.
4.4.3.3 EQP and SSB Model Results
The results of the worm sub-models and the field study are reported in Tables 14 and 15.
The concentration of chemical in the soil, CS, is given in units of mg/kg (µg/g) dry weight
and the worm concentration is given as µg/g lipid, as reported in Hendriks et al (1995).
Both layers of soil were assumed to have equal concentrations of chemical. The
experimental and theoretical BSAFs where
BSAF(g dw/g lipid) = CO (µg/g lipid) / CS (mg/kg dw)
(70)
are also included.
The EQP model results were all within an order of magnitude of the observed results.
There is excellent agreement between theoretical and observed results for PCB-110 and
HCB at the Ochten site. Worm concentrations were generally overestimated with the
exceptions being PCB-99 and DDT at Ochten, which were underestimated by 43% and
28% respectively. The largest discrepancy between theoretical EQP and observed values
occurred in the case of DDT at Gelderse Poort, where the model overestimated
concentrations by a factor of 7.2.
Observed BSAFs at Gelderse Poort were smaller for these chemicals than at Ochten.
Gelderse Poort soil was reported as having a higher fOC (see Table 2). A greater fraction
of organic carbon in the soil is expected to reduce the availability of the chemical to the
worm. The higher fOC would result in a higher Z value and a correspondingly smaller
fugacity for the Gelderse Poort soil. Results from the EQP worm model appear to reflect
this phenomenon adequately. BSAF values for each chemical reported by the model
differ between the two sites by approximately the same factor that the fOCs differ. The
predicted BSAFs at Gelderse Poort, however, were all greater than observed by at least
36%. The predicted concentration of DDT at the Gelderse Poort site was significantly
higher than measured, while the model more accurately predicted the amount of DDT
found at Ochten.
The dynamic BASL4 sub-model produced steady-state concentrations nearly identical to
those produced by the EQP model. Estimates of worm concentration were generally not
improved by the added parameters, except in the case of PCB-99 at Ochten where the
improvement was insignificant.
Table 14: Observed worm and soil concentrations at Ochten (Hendriks, 1995), EQP and SSB
worm model results. Observed concentrations are geometric means.
CO, µg/g lipid
BSAF
CS, mg/kg
Chemical
dw
Observed
EQP
SSB
Observed
EQP
SSB
PCB-99
0.0047
0.11
0.063
0.067
23
13
14
PCB-110
0.011
0.13
0.15
0.16
12
14
14
HCB
0.018
0.20
0.24
0.25
11
13
14
ΣDDT +
0.015
0.75
0.54
0.52
50
36
35
derivatives
41
Table 15: Observed worm and soil concentrations at Gelderse Poort (Hendriks, 1995), EQP and
SSB worm model results. Observed concentrations are geometric means.
CO, µg/g lipid
BSAF
CS, mg/kg
Chemical
dw
Observed
EQP
SSB
Observed
EQP
SSB
PCB-99
0.019
0.097
0.14
0.15
5.1
7.4
7.9
PCB-110
0.048
0.20
0.36
0.39
4.2
7.5
8.1
HCB
0.080
0.44
0.60
0.64
5.5
7.5
8.0
ΣDDT +
0.092
0.25
1.8
1.9
2.7
20
21
derivatives
The effect of soil OC content on chemical uptake by worms indicates a need for accurate
KOC information. While the Karickhoff approximation (KOC= 0.41KOW) yields reasonable
results for some chemicals, greater confidence in model results may be achieved with
more accurate sorption coefficients.
It is important to note that a bioavailability factor, B, of 0.1 was used for every chemical
simulated. This factor must be reassessed for different scenarios, for example, in a
laboratory experiment where there is little time between the introduction of chemical to
the soil and its subsequent uptake by an organism. It is not known whether this value
would apply to chemicals with much smaller KOC values.
Shrew BMFs predicted by the EQP and SSB models are reported in Tables 16 and 18,
along with those calculated from the geometric means of the observed values. The
observed BMFs are presented here with some reservations:
1) The BASL4 sub-model is designed to predict total body concentrations
whereas only the shrew livers were analyzed for organochlorines in the study.
Armitage (2004) suggests that since some PCB congeners have been shown to
be more quickly eliminated from liver tissue then from adipose tissue, total
body shrew BMFs determined using liver concentrations and assuming livertissue equilibrium may underestimate actual total body BMFs.
2) The age and gender of the shrews are not reported in the study. Adult male
shrews are likely the worst-case scenario indicators, as they do not lose
chemical through growth, parturition and lactation.
3) Shrews are very mobile and likely had been exposed to food from other sites.
This is likely not problematic in this case as nearby sites had been analyzed
previously and showed soil concentrations within a factor of 3.3 of those
observed at the Ochten and Gelderse Poort sites.
4) Shrews were collected 2-5 weeks after worm and soil samples were collected.
Again, this is not likely to have resulted in any misestimation as soil and
worm concentrations were likely constant during that time.
42
The field study found that lipid-normalized concentrations of organochlorines in shrew
livers varied from 1.0 to 13 times the lipid-normalized concentrations found in worms in
the same area.
The EQP model underestimates the shrew BMFs by almost an order of magnitude. The
calculated steady-state BMFs are very similar for all four chemicals, ranging from 5.0 to
6.0. Predicted BMFs for HCB at both Ochten and Gelderse Poort were slightly lower than
for the other substances because the chemical is expected to degrade in the soil and be
eliminated by the worm before the shrew achieves steady-state. The agreement between
calculated and observed BMFs for PCB-99 at Ochten is very good; however the
calculated steady-state worm concentration used by the shrew model underestimated the
mean observed concentration (see Table 14).
Metabolism likely plays an important role in lowering steady-state shrew concentrations
of metabolically susceptible chemicals (Armitage, 2004) and needs to be quantified in
order to be included in this model.
Table 16: BASL4 shrew sub-model results for Ochten. Observed BMFs calculated from
geometric mean concentrations (Hendriks et al, 1995).
CO (worm), µg/g
CM (shrew), µg/g
BMFs
Chemical
lipid
lipid
EQP
SSB
EQP
SSB
EQP
SSB
Observed
PCB-99
0.063
0.067
0.046
0.39
0.73
5.8
5.8
PCB-110
0.15
0.16
0.11
0.92
0.73
5.8
2.3
HCB
0.24
0.25
0.17
1.3
0.71
5.2
-ΣDDT +
0.54
0.52
0.38
3.0
0.70
5.8
1.9
derivatives
Table 17: BASL4 shrew sub-model results for Gelderse Poort. Observed BMFs calculated from
geometric mean concentrations (Hendriks et al, 1995).
CM (shrew), µg/g
CO (worm), µg/g
BMFs
Chemical
lipid
lipid
EQP
SSB
EQP
SSB
EQP
SSB
Observed
PCB-99
0.14
0.15
0.10
0.90
0.74
6.0
2.4
PCB-110
0.36
0.39
0.26
2.3
0.74
5.9
-HCB
0.60
0.64
0.44
3.2
0.73
5.0
-ΣDDT +
1.8
1.9
1.3
3.0
0.73
5.8
2.7
derivatives
4.4.3.4 DYB Model Results
Figure 14 shows the results of the laboratory experiment (Belfroid et al, 1994) and the
DYB worm model simulation. The concentrations in the worm are reported in µg/g lipid,
and although the lipid fractions of the worms were measured, they were not reported.
While the BASL4 sub-model adequately predicted the amount of time taken for the
worms to reach steady-state using the parameters suggested (Armitage, 2004), it
underestimated the steady-state concentrations of the worms in the artificial soil by a
factor of about three. This discrepancy is likely due to there being insufficient time
43
allowed for the different phases of the chemical in the laboratory soil to reach
thermodynamic equilibrium, resulting in higher-than-equilibrium concentrations of freely
dissolved chemical in the pore water phase. It has been proposed (Belfroid et al, 1996)
that although the importance of soil solid ingestion as a route for chemical uptake in
worms increases with increasing chemical KOW, the most significant uptake route is from
interstitial water.
concentration, µg/g lipid
3500
3000
2500
2000
1500
1000
500
0
0
10
20
30
40
50
60
time, d
Figure 14: Observed (Belfroid et al, 1994) and predicted worm concentrations.
Figures 15 and 16 show the DYB worm and shrew concentrations resulting from the
simulation of the Ochten and Gelderse Poort sites (Hendriks, 1995). Worm and shrews
are assumed to have zero chemical concentration at time zero. Worms achieve near
steady-state concentrations after approximately one week to one month of exposure to the
contaminated soil. This response time is shown to be much smaller for the worm than for
the shrew, which takes between 4 to 6 months to achieve steady-state.
44
PCB-110
0.45
1
0.4
0.9
0.35
0.8
concentration (µ g/g lipid)
concentration (µ g/g lipid)
PCB-99
0.3
0.25
0.2
0.15
0.1
0.05
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0
50
100
150
0
200
50
150
200
150
200
DDT
HCB
1.4
3.5
1.2
3
concentration, µ g/g lipid
concentration (µ g/g lipid)
100
time, d
time, d
1
0.8
0.6
0.4
2.5
2
1.5
1
0.5
0.2
0
0
0
50
100
time, d
150
200
0
50
100
time, d
Figure 15: Predicted worm (solid line) and shrew (dashed line) concentrations at Ochten.
PCB-99
PCB-110
2.5
1
0.8
concentration (µ
µ g/g lipid)
concentration (µ
µ g/g lipid)
0.9
0.7
0.6
0.5
0.4
0.3
0.2
2
1.5
1
0.5
0.1
0
0
0
50
100
150
0
200
50
150
200
150
200
DDT
HCB
3.5
12
3
10
concentration (µ
µ g/g lipid)
concentration (µ
µ g/g lipid)
100
time, d
time, d
2.5
2
1.5
1
0.5
0
8
6
4
2
0
0
50
100
time, d
150
200
0
50
100
time, d
Figure 16: Predicted worm (solid line) and shrew (dashed line) concentrations at Gelderse Poort.
5
Discussion
Initial validation efforts indicate that BASL4 will be a valuable tool for estimating the
fate of substances in sludge-amended soils under a wide range of conditions. More field
data, including more thorough soil characterization, organic matter degradation data,
sludge application rates and long-term soil concentration monitoring are needed to better
validate the model. The role of atmospheric deposition on agricultural soil loadings is
currently being examined and may be accounted for in future versions of BASL4.
The BASL4 sub-models offer useful screening-level estimates of biotic uptake of
chemicals in soil. Because the models are sensitive to the organic matter content of the
soil, it is expected that they will also perform well in situations where biosolid
amendments are concerned. Further experimental data is required in order to better
calibrate these models.
The EQP plant models offer screening-level estimates of plant uptake, and can be used to
predict worst case scenarios. It provides reasonable estimates of the uptake of low KOW
chemicals (logKOW < ~ 4). The dynamic carrot root model has been shown to give
reasonable predictions of chemical uptake in carrot roots for a range of KOW values, and is
therefore recommended for screening of hydrophobic chemicals. For predicting plant
uptake in field conditions, a bioavailability factor may need to be included, and further
validation is required for field situations. Foliar uptake of chemicals present in soils and
growth dilution factors need to be better characterized.
The DYB model characterization of worm response times indicate that both EQP and
SSB worm models yield satisfying results when a bioavailability factor is included in the
model calculations. Because worms reach steady-state conditions relatively rapidly
(typically within a week to a month), DYB models may be unnecessarily complicated for
worm modelling purposes. Worm uptake and elimination processes are featured in the
SSB model, and the additional information of the dynamic model (i.e., overall response
times to changing conditions) may not be useful in more realistic long term scenarios
involving soil conditions that do not change drastically over time.
The dynamic shrew model, however, offers valuable insight into the response times of
shrews, and is a good candidate for situations where soil conditions change over a period
of months. Overall, the SSB model performs well when compared to the lipid-normalized
BMFs reported in Hendriks et al (1995). These results indicate that these models may be
a useful tool in predicting at least order-of-magnitude concentrations of organochlorines
in small soil-dwelling insectivorous mammals. The EQP model is not ideal, as it does not
detail the processes responsible for chemical uptake and release and it produces
inadequate results based on problematic assumptions.
The treatment of bioavailability by the BASL4 sub-models is regarded as being
excessively simplistic. More rigorous treatment of this very important subject is
desirable. Plant, worm and shrew metabolism likely has an important role to play in the
47
breakdown of some organic chemicals, and quantification of this process should be
considered. Because these models are dynamic and deal with changing environmental
conditions (i.e., degradation of soil OM) and unsteady-state concentrations, further
examination of predicted biotic uptake rates is needed. Despite these concerns, the
preliminary BASL4 sub-model results indicate that they will be valuable tools for
examining maximum loading rates of organic chemicals in soils provided that the
relevant physical chemical, soil, and biosolid properties are available.
48
References
Armitage, J.M. 2004. Development and Evaluation of a Terrestrial Food Web Bioaccumulation
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