Environmental Pollution 148 (2007) 73e82
www.elsevier.com/locate/envpol
Modelling PCB bioaccumulation in a Baltic food web
Erick Nfon, Ian T. Cousins*
Department of Applied Environmental Science (ITM), Unit for Environmental Toxicology and Environmental Chemistry,
Frescativägen 50, Stockholm University, SE 10691, Stockholm, Sweden
Received 31 March 2006; received in revised form 19 October 2006; accepted 1 November 2006
The bioaccumulation behaviour of PCB congeners in a Baltic food web is studied using a novel mechanistic model.
Abstract
A steady state model is developed to describe the bioaccumulation of organic contaminants by 14 species in a Baltic food web including
pelagic and benthic aquatic organisms. The model is used to study the bioaccumulation of five PCB congeners of different chlorination levels.
The model predictions are evaluated against monitoring data for five of the species in the food web. Predicted concentrations are on average
within a factor of two of measured concentrations. The model shows that all PCB congeners were biomagnified in the food web, which is consistent with observations. Sensitivity analysis reveals that the single most sensitive parameter is log KOW. The most sensitive environmental parameter is the annual average temperature. Although not identified amongst the most sensitive input parameters, the dissolved concentration in
water is believed to be important because of the uncertainty in its determination. The most sensitive organism-specific input parameters are the
fractional respiration of species from the water column and sediment pore water, which are also difficult to determine. Parameters such as feeding rate, growth rate and lipid content of organism are only important at higher trophic levels.
Ó 2007 Elsevier Ltd. All rights reserved.
Keywords: Food web; Model; PCBs; Baltic Sea; Bioaccumulation
1. Introduction
The overall behaviour of pollutants released into the
environment may be assessed by structuring the environment
into different compartments, developing mathematical
relationships to describe the fate and behaviour in a compartment and transport from one compartment to another (Mackay,
2001). Similarly, food web models have been developed to describe the uptake and bioaccumulation of organic pollutants by
single organisms and in aquatic food webs (Neely et al., 1974;
Clark et al., 1990; Clark and Mackay, 1991; Thomann et al.,
1992; Gobas, 1993; Morrison et al., 1996; Campfens and
Mackay, 1997; Morrison et al., 1997; Endicott et al., 1998;
Fraser et al., 2002; Czub and Mclachlan, 2004). The primary
concern in food web models is the phenomenon by which
* Corresponding author. Tel.: þ46 8 16 4012; fax: þ46 8 674 7638.
E-mail address: [email protected] (I.T. Cousins).
0269-7491/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envpol.2006.11.033
pollutants present at low concentrations in water become
concentrated by many orders of magnitude in fish, birds and
humans who consume fish (Mackay, 2001; Kelly et al.,
2004). The uptake of pollutants by aquatic organisms occurs
via water (by gills, epidermis) or diet, however, dietary exposure is usually the dominant pathway of uptake for organisms
at higher trophic levels in aquatic and terrestrial food webs
(Thomann and Connolly, 1984; Clark et al., 1990; Gobas
et al., 1993; Sharpe and Mackay, 2000).
The polychlorinated biphenyls (PCBs) have emerged as important pollutants of concern because of their ubiquitous
character (Kjeller and Rappe, 1995; Roots and Talvari, 1997;
Bignert et al., 1998; Nyman et al., 2002) the tendency to
bioaccumulate within food webs from water and sediment to
aquatic invertebrates (Koistinen et al., 1995; Strandberg
et al., 1998; Kiviranta et al., 2003) and their relative toxicity
(Konat and Kowalewska, 2001). The Baltic Sea is particularly
vulnerable to contamination by organic contaminants due to
the low diversity of species and slow water exchange with
74
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
the open ocean. Many studies have revealed the presence of
PCBs in air, water and sediment samples collected from the
Baltic region (Kjeller and Rappe, 1995; Bignert et al., 1998;
Jönsson and Carman, 2000; Kiviranta et al., 2003).
The objectives of this study were: (a) to develop a steady
state food web model with a capability to predict PCB levels
and assess the importance of the different uptake and elimination processes by 14 organisms in a Baltic food web; (b) to
evaluate the model performance by comparing model
predicted concentrations in organisms to measured concentrations; and (c) identify by sensitivity analysis the input
parameters that significantly influenced the variance in the predicted concentrations. Previous papers on the concentrations
and bioaccumulation of organic pollutants in the Baltic have
focussed on the short food chains characteristic of the Baltic
(Rolff et al., 1995; Strandberg et al., 1998; Burreau et al.,
2004). Furthermore, the only known mechanistic food web
model available for the Baltic consists of a short marine system that includes three pelagic species namely zooplankton,
planktivorous and piscivorous fish and considers the different
life stages of these species as the same organism (Czub and
Mclachlan, 2004). This study presents a novel approach in
Baltic food web modelling due to the following reasons. First,
to the best of our knowledge, this is the most extensive mechanistic food web model developed for the Baltic and includes
several trophic levels comprising pelagic and benthic aquatic
organisms. Secondly, the model treats the different life stages
of one species as different organisms, which was deemed
appropriate since the different life stages of the same species
show variability in their dependence on a particular prey and
diverse physiological characteristics (Harvey et al., 2003;
Gorokhova et al., 2004). For example, the diet of juvenile herring consists of 90% mezozooplankon, 6% pelagic macrofauna
and 3% benthic macro fauna while the diet of adult herring
consists of 70% mezozooplankon, 6% pelagic macrofauna,
8% juvenile sprat and 11% juvenile cod (see Table 3).
2. Methods
2.1. Model development
The Baltic food web model developed in this study comprises 14 target
‘‘organisms’’ representing different trophic levels and guilds in the Baltic
Sea, namely: bacteria, phytoplankton (e.g. diatoms cyanobacteria and dinoflagellates), microzooplankton (e.g. Acartia spp.), mezozooplankton (e.g. Eurytemora spp.), pelagic macro fauna (Mysis sp.), benthic meiofauna (e.g. ostracods
and harpacticoid copepods), benthic macro fauna (e.g. amphipod e Monoporeia affinis and isopod e Saduria entomon), juvenile sprat (Sprattus sprattus),
juvenile herring (Clupea harengus), juvenile cod (Gadus morhua), adult sprat,
adult herring, adult cod and salmon (Salmo salar). Defining guilds rather than
specific organisms was preferred at lower trophic levels because there are
multiple species within these guilds.
The model is based on the approach of Campfens and Mackay (1997) and
describes the uptake (by respiration and dietary transfer) of PCBs by
organisms in a Baltic food web and uses the fugacity concept to express these
distributions. Since the formulation of the model is based on a previously
developed model we do not include full descriptions of model equations in
the text. We refer readers interested in the model details to the supplementary
information available for this article. A brief description is given below.
The steady state fugacity, fF (Pa), of each organism in the food web is
expressed by Eq. (1) (Campfens and Mackay, 1997)
fF ¼ fW W þ fA A
ð1Þ
where W and A are fugacity factors for respiration or diffusive uptake from
water and food, fW (Pa) is the fugacity of water, and fA (Pa) is the fugacity
of food. Pelagic species respire only in the water column, while benthic organisms respire both in the water column and sediment pore water. Eq. (1) is thus
modified as follows
fF ¼ WðxW fW þ xS fS Þ þ AfA
ð2Þ
The parameters xW and xS are the fractional respiration from water and
pore water, respectively.
Following the approach of Campfens and Mackay (1997), a mass balance
equation was written for each organism in the food web and then these equations linked together to describe transport and transformation in the whole
food web. The only exception was for estimation of concentrations in bacteria
and phytoplankton which were calculated using an equilibrium partitioning
equation (Morrison et al., 1997)
CF ¼ CW KOC fOC
ð3Þ
where CW (g/l) is the freely dissolved water concentration, KOC is the organic
carbon water partitioning coefficient and fOC (g/g) is the organic carbon content of bacteria and phytoplankton.
Following the approach of Campfens and Mackay (1997) and Sharpe and
Mackay (2000), the general expression describing the total uptake (by respiration and through the diet) for the 14 organisms was written as
Af ¼ E
ð4Þ
where A is a (14 14) biomagnification matrix, f is a vector of organism fugacities ( f1 to f14) and E is a respiration vector characterizing the fugacity induced
in the organism from its abiotic environment with elements of the form
Wi ðxWi fW þ xSi fS Þ For i ¼ 1e14
ð5Þ
A steady state solution for this equation was generated as described in
Campfens and Mackay (1997) and Sharpe and Mackay (2000). Steady state
in this context refers to the following assumptions generally used in steady
state modelling approaches (Mackay and Fraser, 2000), i.e. (i) growth rates
of invertebrates and fish species are linear and all processes are first order
with respect to chemical concentration; and (ii) the organism has maintained
constant D-values for a period of time which is long relative to the clearance
time of the contaminant. Furthermore, the model is based on the assumption
that internal equilibrium exists within organisms and predicted concentrations
are for whole body weight. The use of a steady state introduces a simplifying
assumption since there are temporal differences in contaminant levels (Nfon
and Cousins, 2006) and thus exposure. Since the steady state approach has
been shown to be consistently successful for a range of other aquatic ecosystems (e.g. Campfens and Mackay, 1997; Morrison et al., 1997) it appears that
changes in PCB levels are sufficiently slow so that steady state is a reasonable
approximation. It is important, however, that input abiotic concentrations are
from the same time period as the biotic concentrations used for model
evaluation.
2.2. Model parameterisation
2.2.1. Physicalechemical properties and metabolism rates of PCBs
Selecting reliable physicalechemical properties is extremely important in
environmental modelling due to the relative significance of physicalechemical
properties with respect to partitioning and mass transfer of chemicals (for example, Beyer et al., 2002; Li et al., 2003 for a review). Five PCB congeners
(PCB 28, PCB 101, PCB 138, PCB 153 and PCB 180) covering a range of
chlorination were selected for modelling. Physicalechemical properties of
PCBs used as model inputs were taken from a consistent data set in Mackay
et al. (1992) and Li et al. (2003). These sources were regarded as reliable since
the physicalechemical properties were generated from a larger data set that
had undergone the required adjustments and quality checks for consistency.
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
Physicalechemical properties were corrected to reflect the typical
conditions of the Baltic assuming an average annual temperature of 7 C for
the Baltic (Sinkkonen and Paasivirta, 2000). The KOW was corrected using
the Van’t Hoff expression; for the sake of consistency, the Henry’s law constant
H was calculated from the KOW and the KOA (both corrected for temperature,
see Table 1 for details). The KOC was determined as 0.41 KOW (Mackay,
2001), and the change of KOC was thus proportional to change in KOW. The effect of salinity on the tendency of PCBs to partition into organic phases was
accounted for via the Setschenow constant ðKis Þ assuming a salinity of 6&
for the Baltic Sea (Rodhe and Winsor, 2002). A mean value of 0.035 M1
for Kis was determined for PCB congeners using literature citations (Schwarzenbach et al., 2003).
Generally, the metabolism rate constant for PCB congeners by aquatic
species is expected to be both congener and species dependent, hence the
complete parameterisation for the present model would require data for the
metabolism of five PCB congeners by 14 species. Due to the lack of empirical
data on metabolic transformation in PCBs as has been reported by other authors (for example, Arnot and Gobas, 2003), we assumed an infinite halflife (t1/2) for PCB congers. This makes the metabolism rate constant kM
(kM ¼ ln 2/t1/2) negligible and the D-value for metabolism insignificant. The
implication of this assumption is that it might lead to an overestimation of predicted concentrations in species were metabolism is significant.
2.2.2. Environmental parameters for the Baltic
Eight input parameters were used to describe the environmental characteristics of the Baltic. Included were suspended particulate matter concentration,
organic carbon fraction of suspended matter and sediment particulates, the
volume fraction of sediment particulates and the density of particulates in
the water column. Additional model inputs were the dissolved water concentration of each PCB congener, the chemical sediment concentration and temperature (discussed above); input values for these parameters were taken from
the literature and are listed in Table 2.
The water and sediment concentrations in Table 2 were converted to
fugacities by the model. The fugacity in water is derived from measured
dissolved water concentrations. These concentrations may not be the truly
dissolved concentration as there may be some partitioning to dissolved
organic matter (DOM) or colloidal material (Meylan et al., 1999; Konat and
Kowalewska, 2001; Borgå et al., 2004). We decided not to use estimation
techniques that account for DOM partitioning to estimate the truly dissolved
concentration (Gschwend and Wu, 1985). The sensitivity of the dissolved water
concentrations on model predicted concentrations in organisms is discussed
later.
2.2.3. Parameters describing food web species
The weight of fish species were determined from generalized fish data
(ICES, 2001). Masses were not required for bacteria and phytoplankton as
equilibrium partitioning was assumed (Eq. (3)). The precise mass and volume
of small organisms such as zooplankton is uncertain and is only useful in determining the time required to attain equilibrium, which we believe is short.
75
Table 2
Environmental input parameters for the Baltic
Input parameter
Unit
Input value Reference
Annual average water
C
temperature
Suspended particulate
g/m3
matter concentration
Organic carbon fraction
of suspended matter
Organic carbon fraction
of sediment particles
Volume fraction of
sediment particles
Density of particles
kg/m3
in water column
Input concentrations
of PCB congeners
PCB 28
PCB 101
PCB 138
PCB 153
PCB 180
7
4
Sinkkonen and
Paasivirta (2000)
Granskog (1999)
0.05
Håkanson et al. (2004)
0.05
Hüttig and Oehme (2005)
0.3
Hüttig and Oehme (2005)
2500
Mackay (2001)
CW (ng/l) CS (ng/g)
0.0009
0.0020
0.0006
0.0005
0.0001
5.2
8.6
2.6
3.1
2.2
CW, Input concentration in water; CS, input sediment concentration.
We have therefore input an arbitrary low mass for zooplankton (Table 2). Sensitivity analysis later revealed that variation of this mass had negligible impact
on predicted concentrations.
The feeding rates and growth rates are from Hansson (Personal communication). The dietary preference of 13 of the 14 organisms were taken from
a previously developed ecosystem model for the Baltic (Harvey et al.,
2003). The different prey items in the diet of salmon were derived directly
from data reported in Hansson et al. (2001). The dietary preferences of the
organisms are listed in Table 3 and all other organism-specific inputs are listed
in Table 4. The mathematical expressions used to determine specific organism
parameters are presented in the supplementary information.
The values of xW and xS in Eq. (2) were set as one and zero, respectively,
for all fish species respiring exclusively in the water column. It proved difficult
to estimate xW and xS for the benthic species since they show a variation in
habitat depending on, for example, diurnal conditions and the presence or
absence of a predator (Hill, 1991; Ejdung, 1998; Stevenson, 2003). Benthic
organisms bioturbate sediments to enhance water circulation and oxygenation,
however, due to the anoxic nature of bottom sediments, sediment dwelling organisms that need oxygen either live close to the surface or maintain a burrow
to allow water circulation and oxygenation (Snelgrove, 1999). A review of the
literature revealed that various authors had used different approximations to
describe the fraction of pore water respiration of benthic organisms. For
example, Campfens and Mackay (1997) assumed 100% pore water respiration
Table 1
Physicalechemical properties of PCBs that constitute model input
MW (g/mol)
log KOW
log KOA
H (Pa m3/mol)
log KOWa
log KOAa
DOW KJ/mol
DOA KJ/mol
DAW KJ/mol
PCB 28
PCB 101
PCB 138
PCB 153
PCB 180
References
257.54
5.67
7.85
30.5
5.47
7.85
26.3
78.5
52.3
326.4
6.3
8.73
24.1
6.14
8.73
23.8
83.5
59.7
360.9
7.21
9.66
30.1
7.03
9.66
25.0
86.3
61.3
360.9
6.87
9.44
19.8
6.68
9.44
31.1
93.9
62.8
395.32
7.16
10.16
8.1
6.97
10.16
29.1
92.8
63.6
Mackay et al. (1992)
Li et al. (2003)
Li et al. (2003)
Li et al. (2003)
Li et al. (2003)
Li et al. (2003)
MW is the molecular weight, H is the Henry’s law constant, KOW and KOA are the octanolewater, octanoleair and the airewater partition coefficients, respectively.
H was estimated as KAW RT (KAW was calculated from KOW/KOA). DOW, DOA and DAW are the internal energies of phase transfer.
a
Corrected for temperature and salinity.
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
76
Table 3
Dietary preference matrix and trophic positions (TP) for organisms
Prey
TP
Predator
De
1
1
2
2.25
2.6
3.5
2.8
3.2
3.3
3.6
3.2
4.1
4.2
4.3
Ba
Phyt
Mi.Z
Me.Z
P.Ma
B.Me
B.Ma
J.sprat
J.He
J.Cod
A.sprat
A.He
A.Cod
Sa
1
Ba
Phyt
Mi.Z
0.79
0.21
0.75
0.5
0.25
Me.Z
P.Ma
B.Me
B.Ma
J.sprat
J.He
J.Cod
A.sprat
A.He
0.14
0.02
0.166
0.13
0.079
0.03
A.Cod
Sa
0.5
1
0.67
0.04
0.999
0.9
0
0.999
0.87
0.06
0.1
0.0009
0.03
0.47
0.03
0.219
0.14
0.0009
0.102
0.14
0.59
0.0001
0.230
0.296
0.25
0.001
0.138
0.07
De, Detritus; Ba, bacteria; Phyt, phytoplankton; Mi.Z, mizozooplankton; Me.Z, mezozooplankton; P.Ma, pelagic macrofauna; B.Me benthic meiofauna; B.Ma,
benthic macrofauna; J.sprat, juvenile sprat; J.He, juvenile herring; J.Cod, juvenile cod; A.sprat, adult sprat; A.He, adult herring; A.Cod, adult cod; Sa, salmon.
for Lake Ontario benthic species, Winsor et al. (1990) assumed a 4% pore
water respiration for Macoma nasuta while Arnot and Gobas (2004) approximated a 5% pore water respiration for benthic species in Lake Erie and Lake
St. Claire. For ‘‘benthic’’ organisms included in the present model, we
assumed a 10% sediment pore water respiration (i.e. xS ¼ 0.1 and xW ¼ 0.9)
after consultation with scientists who study benthic organisms (Prof. Dag
Broman and Assoc. Prof. Brita Sundelin, ITM, Stockholm University, personal
communication). We admit that this value is very uncertain, but the fraction
used is our best estimate. It will be replaced if better data become available.
The sensitivity of the model to this input parameter is examined in the
sensitivity analysis presented later in this paper.
2.3. Sorting organisms into trophic position
In order to aid presentation and interpretation of model predictions,
organisms were sorted into approximate tropic position following an approach
outlined in Mackintosh et al. (2004). The estimated trophic positions of the
organisms are listed in Table 3. Abiotic media (air and water) together with
phytoplankton were assigned a default trophic position of one and sediment
was assigned a value of 2.5. Using the derived trophic levels, we classified
the organisms in the food web into four trophic levels as follows; level 1 (phytoplankton, bacteria); level 2 (microzooplankton, mezozooplankton, pelagic
macrofauna, benthic macrofauna) level 3 (juvenile sprat, adult sprat, juvenile
Table 4
Organism characteristics for model parameterisation
Species
Bacteria
Phytoplankton
Microzooplankton
Mezozooplankton
Pelagic macrofauna
Benthic meiofauna
Benthic macrofauna
Juvenile sprat
Juvenile herring
Juvenile cod
Adult sprat
Adult herring
Adult cod
Salmon
M (g)
e
e
e
e
0.012a
0.0052b
0.01b
7.9c
17.6c
160c
12.3c
33.2c
3944c
4700c
LF
d
0.25
0.25d
0.015e
0.015e
0.07e
0.52f
0.52f
0.04e
0.04e
0.05e
0.04e
0.044e
0.055e
0.16e
GIPV (g/gd)
GRRD (g/gd)
e
e
0.678
1.487e
0.822e
0.068e
0.085e
0.036e
0.058e
0.040e
0.007e
0.028e
0.022e
0.005e
0.032e
0.391
0.192e
0.587e
0.226e
0.021e
0.017e
0.001e
0.002e
0.001e
0.001e
0.002e
0.001e
0.003e
0.002e
xW
1
1
1
1
1
0.90
0.90
1
1
1
1
1
1
1
xS
QD
h
0.10g
0.10g
3
3h
3h
3h
3h
3h
3h
3h
3h
3h
3h
3h
3h
3h
GV (L/d)
i
0.002
0.002i
0.002i
0.002i
0.002i
0.5i
0.5i
EW
0.9i
0.9i
0.9i
0.9i
0.9i
0.9i
0.9i
M, Mass of organism in grams; LF, lipid fraction; GIPV, feeding rate as percent of body per day; GRRD, growth rate as fraction of volume per day; xW, fractional
respiration from water; xS, fractional respiration from pore water; EA, gut absorption efficiency; QD, maximum biomagnification factor; QW, water transport parameter (L/d); GV, gill uptake rate (L/d); EW, gill uptake efficiency.
a
Broman et al. (1992).
b
Breitholtz et al. (2001).
c
ICES (2001).
d
Granskog (1999).
e
From Hansson (unpublished data).
f
Strandberg et al. (1998).
g
Estimated (personal communication, see text).
h
Campfens and Mackay (1997).
i
Gobas and Wilcockson (2003).
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
herring and benthic meiofauna); level 4 (juvenile cod, adult herring, adult cod,
salmon). Trophic positions differed within a trophic level, for example within
trophic level 3; juvenile herring was at a higher trophic position than juvenile
sprat whereas within level 4, salmon was at a higher position than adult cod.
77
predicted
measured
1
A
0.01
0.001
ph
yt
op
la
nk
to
n
zo
op
la
nk
to
pe
n
la
gi
cm
ac
ro
be
fa
nt
un
hi
a
cm
ac
ro
fa
ju
un
ve
a
ni
le
he
rri
ng
ju
ve
ni
le
co
d
ad
ul
th
er
rin
g
ad
ul
tc
od
0.0001
B
1
0.1
ELP,g/m3
0.01
0.001
ul
ad
ul
ju
ad
ju
tc
rin
er
th
ni
ve
le
ni
ve
od
g
d
le
rri
he
fa
ro
ac
cm
co
ng
a
un
a
un
fa
ro
od
ad
ul
tc
g
rin
th
ul
ad
ni
ve
ju
er
co
le
rri
he
le
ni
ju
ve
d
ng
a
fa
ro
ac
cm
hi
be
pe
nt
la
gi
zo
cm
ac
op
la
ro
fa
nk
un
a
un
to
to
n
n
0.0001
nk
The model was evaluated by comparing model predicted
ELP concentrations to measured ELP concentrations from
a previously developed database for the Baltic (Fig. 1). The
hi
0.001
la
3.1. Comparison between model predictions and
monitoring data
ac
0.01
op
3. Results and discussion
be
0.1
yt
The influence of input parameters on model predictions was assessed by
sensitivity analysis. A total of 165 input parameters were randomly varied
within 10% using a Monte Carlo analysis technique. A uniform distribution was selected for the sampling of values within this range. A uniform
distribution ensures that all values are equally likely to occur within the
range and the Monte Carlo analysis technique randomly selects values
within the range without applying any weighting factors. The lipid equivalent concentrations of PCB congeners for each food web species were selected as the output to be monitored. One thousand simulation trials were
run using the Crystal BallÒ software package for Microsoft ExcelÒ (Crystal
Ball, 2002).
nt
gi
la
pe
1
C
ph
2.5. Evaluation of model sensitivity to input parameters
cm
yt
zo
op
op
la
la
nk
nk
to
to
n
n
0.0001
ph
The model development and evaluation exercise required two monitoring
data sets of PCB congener concentrations, an abiotic data set for model
simulation and a biotic (evaluation) data set for evaluation of the model
predictions. The abiotic data set included the dissolved PCB concentration in
water and sediments and the biotic (evaluation) data set contained monitoring
data of PCB concentrations in food web species.
Both the abiotic and biotic data sets were taken from a previously developed database of organic pollutants for the Baltic Sea. Nfon and Cousins
(2006) described this database in detail so it is only briefly outlined here.
Concentrations of persistent organic pollutants, including PCBs, in abiotic
and biotic samples from a variety of data sources were compiled in a database.
The database covered samples collected from multiple locations in the Baltic,
representing background sites over a 32 years period (1970e2002). However,
the concentrations used as model input and model evaluation were a subset of
data for the period 1990e2000.
Abiotic samples were collected at different locations, depths and reported
in a variety of different units. In some cases abiotic data were reported as total
concentrations, in others dissolved and particulate concentrations and in some
cases the speciation was unspecified. Biotic samples including benthic invertebrates and fish species of different sizes and ages were also included in
the database.
In generating our simulation and evaluation data sets, the following criteria were used to eliminate any sources of variability and errors: (a) the concentrations used as model input and the data used to compare the model
predictions represent data for 1990e2000; (b) only water concentrations reported as dissolved were used; and (c) care was taken to include only data of
congener concentrations in fish species that could be separated into juveniles
or adults. A general criterion of the database of Nfon and Cousins (2006)
was that only data from background sites, away from obvious point sources,
be included.
Predicted and measured biota concentrations were lipid normalized by
dividing the reported concentration by the lipid weight of the species. In
many cases biota concentrations were already reported on a lipid basis. Units
were equilibrium lipid (ELP) concentrations in g/m3.
ELP,g/m3
2.4. Monitoring data used for model evaluation
ELP,g/m3
0.1
Fig. 1. (AeC) Comparison between predicted and measured equilibrium lipid
(ELP) concentrations: (A) PCB 28; (B) PCB 101; and (C) PCB 180. Species
are listed in order of estimated trophic position. The error bars represent the
min/max range of measured concentrations.
model predictions showed a general increase in ELP concentrations for all PCB congeners from the base of the food
web to organisms at higher trophic positions, with the highest
levels predicted in cod and salmon. A difference in bioaccumulation behaviour of individual congeners was observed
with PCB 138 and PCB 153 emerging as the most biomagnified congeners.
A quantitative model evaluation was performed by calculating the model bias (Eqs. (6) and (7)) as described in Gobas and
Wilcockson (2003) and Arnot and Gobas (2004).
Pn ½logðPELP;i =MELP;i Þ
MBj ¼ 10
i¼1
n
ð6Þ
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
78
2
6 Pm
4 j¼1
Pn
i¼1
½logðPELPi; j =MELPi; j Þ
3
n
m
3.2. Role of uptake and loss processes
7
5
ð7Þ
MB ¼ 10
where MBj is the combined model bias for all PCB congeners
in a selected species j, MB is the overall model bias of all PCB
congeners in all species, PELP is the predicted ELP concentrations (g/m3), and MELP is the measured ELP concentrations
(g/m3), i refers to each PCB congener for which PELP and
MELP were available, n is the number of chemicals (n ¼ 5),
m is the number of species included in the model evaluation
(m ¼ 5; phytoplankton, mizozooplankton, adult sprat, adult
cod and salmon). The MB is a measure of the systematic
over-prediction (MB > 1) or under prediction (MB < 1) of
the model. The results of the analysis are presented in Table
5. The data presented in Table 5 indicate that the model
over predicted phytoplankton concentrations with an average
MBj of 1.8 for the five selected congeners, although model
predictions exceeded measured values by a factor of approximately one for PCB 28, PCB 138 and PCB 153 and a factor of
two for PCB 101 and PCB 180. Predicted concentrations for
mezozooplankton were close to measured values with an
MBj ¼ 0.22, an under prediction of a factor of less than two.
For the fish species, predictions for adult sprat and salmon
were within a factor of less than two of measured values
(MBj 0.03 and 0.12, respectively). Predictions for adult cod
were less accurate with an average MBj of 0.73 representing
a difference of a factor of five between predicted and measured concentrations. The results for individual congeners
showed an over-prediction of a factor of two and three for
PCB 28 and PCB 101, respectively, and a factor of one for
PCB 138, PCB 153 and PCB 180. Generally, the evaluation
showed overestimations and underestimations of measured
lipid equivalent concentrations within reasonable limits. The
overall estimated model bias (MB) for five species and five
PCB congeners was 0.21 indicating an average model underestimation of less than a factor of two. The 95% confidence
intervals of MB (0.06 and 5.4) express the variability in the accuracy of the model predictions among the PCB congeners. It
can thus be concluded that 95% of the observed congener specific data lie between PELP/18 and PELP 6.0.
The relationship between concentration and trophic level
has previously been shown to be complicated by the different
water and sediment fugacities. For example, in the model developed by Campfens and Mackay (1997), benthic organisms
tended to be relatively more contaminated than pelagic organism of a similar trophic status. This is because benthic organisms in the Campfens and Mackay (1997) model were
assumed to dwell in the sediment and respire 100% sediment
pore water, which has a higher fugacity than water. In the
model developed in this study, the sediment fugacity (i.e. sediment pore water respiration) does not have such a large effect
on the contamination of so called ‘‘benthic’’ organisms; rather
uptake by respiration in water dominated the total uptake by
respiration in benthic meiofauna and benthic macrofauna for
all PCB congeners. This is a result of the assumed fraction
of water respired in the water column (90%) compared to
the fraction of sediment pore water respired (10%).
For the fish species, food was the dominant uptake pathway. A decrease in the percentage contribution of uptake
by respiration with increasing log KOW was observed for
the same species and with trophic levels. For example, uptake by respiration in sprat was 84% of the total uptake of
PCB 28 (log KOW of 5.7), 15% of the total uptake of PCB
138 (log KOW of 6.8) and 17% of the total uptake of PCB
180 (log KOW of 7.3). Uptake by respiration for cod and
salmon at higher trophic levels were 33 and 22%, respectively, PCB 28, 3% and less than 2%, respectively, PCB
180. The net chemical uptake through the diet expressed as
a fraction of the total uptake was on the average between
1 and 8% for the benthic invertebrates and between 60 and
99% for the fish species. Dietary uptake was more significant
for the heavier congeners than for the lighter congeners. For
example, dietary uptake contributed more than 93% of the
total uptake of the heavier congeners in herring, 95% in
cod and 99% in salmon.
Loss of contaminants by respiration was the dominant loss
process at lower trophic levels contributing more than 99% of
the total loss of contaminants in phytoplankton and zooplankton. For the fish species at intermediate trophic positions, the
most significant loss processes was loss by excretion while at
Table 5
Results from quantitative model evaluation
log (PELP/MELP)
Phytoplankton
Mezozooplankton
Adult sprat
Adult cod
Salmon
PCB 28
PCB 101
PCB 138
PCB 153
PCB 180
SDa
MBjb
CIc
0.65
1.25
1.66
0.48
0.61
0.34
0.44
1.30
0.15
0.65
0.36
0.53
1.34
0.45
0.97
0.05
0.95
1.89
0.65
1.37
0.18
1.09
1.66
0.20
0.97
0.42
0.35
0.25
0.45
0.31
1.09
0.14
0.03
0.73
0.12
0.36
0.31
0.22
0.40
0.27
Overall model bias (MB)d ¼ 0.21.
a
Standard deviation of the ratio of predicted and measured lipid equivalent concentrations.
b
Model bias for species j combining the results of all PCBs in species j.
c
The 95% confidence intervals of the geometric mean of the ratio of the predicted and measured lipid equivalent concentrations.
d
Overall model bias combining the results of all PCBs in five species included in the model performance evaluation.
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
higher trophic levels, growth dilution was the dominant loss
process. Metabolism was insignificant since we assumed an infinite half-life for PCB congeners in all food web species.
As a means of identifying the abiotic media making the
most significant contribution to the final fugacity in the food
web species, we calculated the contribution of PCB levels in
water and sediment to the total fugacity in food web species
using the approach in Sharpe and Mackay (2000). Water
was the most significant contributor to the total fugacity of
all the species in the food web for all five congeners. PCB
levels in sediment were only significant to the total fugacity
in the benthic species and the fish species that interacted
with the benthic species. The result for PCB 180 is shown in
Fig. 2, which also provides a way of identifying the contribution of direct exposure (by respiration) and indirect exposure
(through food web interactions) to the total fugacity of food
web species. It can be observed that 90% of the fugacity of
the benthic species is due to PCB levels in water and about
10% is due to PCB levels in sediments. For herring, 95% of
the total fugacity is due to direct uptake by respiration in water
and 5% is obtained indirectly from sediment through interaction with benthic species. In cod, between 90 and 92% of the
total fugacity is due to direct effects of respiration in water
while between 8 and 10% of the total fugacity in cod is from
sediment. Finally, 99% of the total fugacity in salmon is due
to respiration in water and 1% is due to PCB 180 in sediments.
3.3. Key input parameters identified by sensitivity
analysis
Only parameters contributing more than 5% to the variance
in the predicted concentrations for each species in the food
web are included in Table 6. The species are listed according
to trophic levels; each sensitive parameter is indicated
followed by the percentage contribution to the variance in
water
on
lm
sa
g
A
.C
od
e
od
er
rin
A
.h
J.C
J.H
a
M
B.
M
e
B.
M
a
J.s
pr
at
P.
o
zo
ph
yt
contribution to fugacity
sediment
Fig. 2. Contribution of PCB 180 levels in abiotic media to total fugacity in
food web species. Species are listed in order of estimated trophic position.
Abbreviations used on the x-axis are as follows. Phyt, phytoplankton; zoo,
zooplankton; P.Ma, pelagic macrofauna; B.Me, benthic meiofauna; B.Ma, benthic macrofauna; J.sprat, juvenile sprat; J.Cod, juvenile cod; A.sprat, adult
sprat; A.He, adult herring; A.Cod, adult cod; Sa, salmon.
79
Table 6
Key parameters from sensitivity analysis
Trophic level
Species
Significant parameters
1
Phytoplankton
log KOW (50e70), Temp
(30e40)
log KOW (50e70), Temp
(20e40)
Bacteria
2
Zooplankton
Pelagic macrofauna
xW (45e70), log KOW
(15e65), Temp (10e30)
log KOW (40e60), Temp
(5e40), xW (15e50)
2 and 3
Benthic macrofauna
Benthic meiofauna
xW (10e85), log KOW
(10e60), Temp (5e30)
3
Sprat
xW (5e70), log KOW
(20e60), and Temp (10e30)
3 and 4
Herring
log KOW (15e65), Temp
(5e30), xW of He (5e45),
xS of B.Ma (5e35)
4
Cod
xW of Me.Z (20e70),
log KOW (5e45), Temp
(5e20), FR (10) GR (5)
xW of Me.Z (25e80),
log KOW (5e40), Temp
(5e15), xW of sprat (5e25),
FR (10) GR (5), LF (5)
Salmon
xW is the fractional respiration from water, xS is the fractional respiration from
pore water, FR is the feeding rate, GR and LF is the lipid fraction. All other
parameters are defined in the text. The cut-off criterion for inclusion was
that the parameter contributed at least 5% to the output variance.
the predicted concentration in parentheses, with the most
sensitive first. The percentages indicated are the averages for
all five PCB congeners in the model simulation.
Generally, the log KOW and the annual average temperature
dominated the sensitivity analyses with log KOW emerging as
the single most important parameter. Other important parameters were the fractional respiration from water and the
fractional respiration from sediment pore water. Feeding rate,
growth rate and lipid fraction of fish became important for
higher trophic levels (i.e. cod and salmon), but contributed
only about 5e10% of the variance in predicted concentrations.
Furthermore, parameters related to the prey of the species at
higher trophic levels together contributed between 5 and 80%
of the sensitivity data in the fish species. For example, the fractional respiration of benthic organisms, sprat and zooplankton
made significant contributions to the variance in predicted concentrations in herring, cod and salmon. It is noteworthy that the
fractional respiration in pore water of benthic species and
sediment related input parameters (organic carbon fraction of
sediment particulates) were particularly important for herring
and cod that feed on benthic invertebrates.
It is important that values of the most sensitive input
parameters are of the highest quality in order to limit model uncertainty. The estimated KOW for different PCBs vary widely,
depending on the method of estimation. A variation of over
a range of about 0.3 log units or more between measured and
estimated log KOW values of PCB congeners has been reported
(Güsten et al., 1991). As previously discussed, the most reliable
80
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
KOW values were selected from the best sources currently
available so model error due to using unreliable KOW has
been limited as much as possible. The high sensitivity of temperature is related to its effect on physicalechemical properties. Changes in temperature alter the values of the Henry’s
law constant and KOW. Since Baltic water temperatures are
well known, model error should be low. The largest model error
may well be associated with the fractional respiration from
water and the fractional respiration from sediment pore water,
since as previously discussed, the values used for model inputs
were uncertain.
The concentration in water, although only identified to contribute less that 5% of the variance in predicted concentrations,
is worthy of some further discussion because estimating this
value presents special difficulties (Konat and Kowalewska,
2001). PCB concentrations in the sub- and low pg/dm3 range
have been reported for the Baltic (Schulz-Bull et al., 1995;
Sobek et al., 2002, 2004). Quantifying such low level concentrations with certainty is challenging. Secondly, the linear
relationships used in the present model as well as in other
food web models to describe partitioning, bioconcentration
and bioaccumulation are only valid if the truly dissolved
PCB concentrations are used. Using total concentrations or
concentrations determined in the presence of colloids or dissolved organic matter could introduce non-linearity in the bioaccumulation relationships. For example, Borgå et al. (2005)
attributed the variability in predicted BAFs in copepods collected from the North American great Lakes to analytical constraints in measuring very low concentrations of contaminants
in the water column. Analytical methodology has been improving over the years, for example stainless steel equipment
has been replaced by HDPE or silicon tubing to limit sorption
to sampling equipment, and sample handling procedures are
currently being improved (Sobek, 2005). In spite of these improvements, the extensiveness of the analytical process implies that errors might occur at different stages and it is
therefore possible that our input water concentrations are
a source of model error.
The present model also assumes that sorption is principally
by dissolution to the organic carbon fraction of sediments and
particulate matter. However, it has been suggested that such
equilibrium partitioning approaches are too simplistic because
they do not account for the slow sorption/desorption kinetics
of some organic compounds (Pignatello and Xing, 1996).
Furthermore, organic carbon may not be the only part of a benthic or suspended particulate that can strongly sorb organic
compounds. It has emerged over the last decade that soot carbon, carbonaceous organic materials, black-carbon, coal and
kerogen may have a stronger sorption capacity than organic
carbon and may be of significance in the adsorption of hydrophobic organic chemicals in the environment (Bärring et al.,
2002; Cornelissen et al., 2005). Equations have been derived
for estimating the sorption of organic compounds to soot
carbon and to date has only been validated experimentally
for the polycyclic aromatic hydrocarbons and are not yet
widely applicable to other organic chemicals (Qiu and Davis,
2004; Cornelissen et al., 2005). In this study we assumed that
soot sorption is not important for dilute marine systems
(Cornelissen and Gustafsson, 2004), such as the open Baltic,
and that equilibrium sorption to particulate organic carbon is
an appropriate simplification.
4. Concluding remarks
The modelling exercise presented here provided a mechanistic and quantitative understanding of the uptake and loss
processes affecting PCB levels in a large Baltic food web.
Key model input parameters for different species were also
identified from the sensitivity analysis. It should be noted,
however, that the model applies simplifying assumptions and
therefore has limitations. For example, the steady state assumption in the current model version does not account for
the effect of temporal differences in contaminant levels (and
thus exposure) due to changing chemical emissions. Another
simplifying assumption was that the model input exposure
concentrations for water and sediment were averages for the
entire Baltic. A possible model improvement could consider
the division of the Baltic into different basins as has been
done in the POPCYCLING-Baltic Model (Wania et al.,
2000). This would allow different water and sediment concentrations to be defined for each basin. A disadvantage of this
approach is that it would no longer be possible to pool
biomonitoring data from different Baltic basins for model
evaluation.
The results of the model sensitivity analysis provide a guide
to model improvements. Little can be currently done to
improve the input values of the two most sensitive model input
parameters, namely KOW and temperature, since best available
values were used. We suggest that the focus of future efforts
should be on better describing the fractional respiration from
water and pore water, which, as discussed, is currently a highly
uncertain input parameter.
Finally, since the model is mechanistic, it can potentially be
used to estimate the food web bioaccumulation of a wide
range of other non-ionic organic contaminants that are present
in the Baltic Sea. We therefore recommend further model evaluation using other compound classes as more data become
available. The model could be a useful tool for estimating
wildlife exposure to organic contaminants as well as human
exposure from consumption of fish.
Acknowledgements
This study was financially supported by FORMAS (the
Swedish Research Council for Environment, Agricultural
Sciences and Spatial Planning) through grant number 21.
0/2003-0206. The authors thank Costas Prevedouros for
providing valuable comments and suggestions.
Appendix A. Supplementary data
Supplementary data associated with this article can be found,
in the online version, at doi:10.1016/j.envpol.2006.11.033.
E. Nfon, I.T. Cousins / Environmental Pollution 148 (2007) 73e82
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