Project no.
003956
Project acronym
NOMIRACLE
Project title
Novel Methods for Integrated Risk Assessment of Cumulative
Stressors in Europe
Instrument
IP
Thematic Priority
1.1.6.3, ‘Global Change and Ecosystems’
Topic VII.1.1.a, ‘Development of risk assessment methodologies’
Deliverable reference number and title:
D.2.4.10 Report on the indication of the spatial detail for bioaccumulation of Polycyclic Aromatic
Hydrocarbons
Due date of deliverable: May 1, 2008 Actual submission date: November 21st, 2008
Start date of project: November 01, 2004
Duration: 5 years
Organisation name of lead contractor for this milestone: RU
Revision [draft, 1, 2, …]: final
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
Dissemination Level
Public
PU
Restricted to other programme participants (including the Commission Services)
PP
Restricted to a group specified by the consortium (including the Commission Services)
RE
CO Confidential, only for members of the consortium (including the Commission Services)
PU
Authors and their organization:
Mara Hauck, Mark A. J Huijbregts, Karin Veltman, A. Jan Hendriks
Radboud University Nijmegen,
Albert A. Koelmans, Caroline T. A. Moermond
Wageningen University
Martine J. Van den Heuvel-Greve, A. Dick Vethaak
National Institute for Coastal and Marine Management
Deliverable
Nature:
DisseminaDate of delivery:
no: D2.4.10
R
tion level: PP April, 21st 2008
Status: Final
Date of publishing:
Reviewed by (name and period):
Ad Ragas, Radboud University
2
Contents
Contents .................................................................................................................................................... 3
Summary ................................................................................................................................................... 4
1 Introduction ............................................................................................................................................ 5
2 Methods .................................................................................................................................................. 7
3 Results and discussion ......................................................................................................................... 15
5 Implications .......................................................................................................................................... 22
References ............................................................................................................................................... 23
Appendix ................................................................................................................................................. 31
3
Summary
Variability and uncertainty are two different characteristics of a model that can lead to variation in
model predictions. Because variability and uncertainty can have different implications for decision
making, it can be useful to consider them separately in an analysis. Concentrations dissolved in water
and internal concentrations are considered a more accurate indication for risk to organisms compared to
bulk concentrations because only the dissolved fraction is generally available for uptake by biota.
Within workpackage 2.4 of the NOMIRACLE project, it was investigated, how the effect of spatial
variability relates to uncertainty for dissolved Benzo[a]pyrene concentrations in Europe.
This deliverable studies variability and uncertainty in more detail for modeling uptake by organisms.
Model estimations of bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) have been higher
than field or laboratory data. This has been explained by strong sorption to black carbon (BC). In this
report, eight previously published bioaccumulation datasets are reinterpreted in terms of additional BC
sorption. Biota-Solids Accumulation Factors (BSAFs) of PAHs typically decrease by one to two orders
of magnitude and are better in line with field data in marine, fresh water and terrestrial ecosystems.
Probabilistic BC-inclusive modeling showed that if BC content is not accurately known, uncertainty in
BSAFs is two to three orders of magnitude (90 percentile confidence interval) due to uncertainty in the
BC sorption term. When BC contents are measured, the deviation between model estimations and field
measurements reduces to about a factor of 3. This implies that including routine measurements of BC
contents is crucial in improving risk estimations of PAHs.
4
1 Introduction
Ecological risk assessment involves use of reliable models to estimate accumulation of hazardous
substances in organisms. The main goal of the NOMIRACLE project is to improve both human and
environmental risk assessment procedures by addressing a series of major shortcomings that exist within the current approaches. Concentrations of polycyclic aromatic hydrocarbons (PAHs) in biota derived
from field-contaminated solids, are often substantially lower than estimations by bioaccumulation
models using standard solids-water equilibrium partitioning (1, 2). Their low accumulation levels have
been attributed to strong sorption of PAHs to black carbon (BC; i.e. soot and chars), kerogen and coal
in the solids. This sorption to BC reduces availability of hydrophobic organic compounds as PAHs for
partitioning to water (1, 3, 4). Reduced uptake from solids containing BC has been shown in experiments with marine invertebrates (5-8) and fresh water invertebrates (9). Field measurements on marine
and fresh water invertebrates confirmed the experimental observations (9, 10). Including sorption to
BC into equilibrium partitioning, as suggested by Gustafsson et al. (11) and Accardi-Dey & Gschwend
(12) results in lower predicted concentrations in biota (6, 9, 13, 14). However, BC-inclusive models do
not predict all field data very accurately yet (9, 14). It can be hypothesized that especially uncertainties
in sedimentary BC contents and in the PAH-BC association parameters may limit the accuracy of model predictions. Accordingly, it is crucial to quantify the relative importance of these two sources of uncertainty. This will increase our understanding of which parameters contribute dominantly to variation
in PAH concentration in biota.
The model OMEGA estimates bioaccumulation for species from four trophic levels and aquatic as
well as terrestrial food chains (15, 16). It uses a one-compartment first order kinetic approach similar to
earlier bioaccumulation models (17-20). These models predict accumulation levels on the basis of
modeling of uptake and elimination kinetics determined by advective flow, diffusion through water,
penetration through lipid membrane, each with an own resistance or delay.
5
The aim of this study is twofold: (i) to improve model estimations of PAH accumulation by incorporating BC in current state of the art food chain models like OMEGA and to compare these estimations
with field measurements; and (ii) to determine the range of variation in modeled bioaccumulation as a
result of the newly included sorption term.
6
2 Methods
2.1 Model equations.
The accumulation of neutral organic compounds in biota can be expressed as the ratio between the
concentration in the organism (Ci) and the concentration in the abiotic compartment. As most empirical
data refer to total concentrations in sediment, suspended solids or soil (Cs) this ratio is calculated as the
Biota-Solids-Accumulation Factor:
BSAF 
Ci f lipid
C s f OC
(1)
OMEGA calculates steady-state chemical residues in invertebrates as the sum of influx via water (absorption) divided by the total of elimination processes kex (17-20). The dissolved chemical fraction in
water is available for uptake by biota. The concentration in water for a given concentration in solids
depends on the solids-water-partition-coefficient (Ksw). The BSAF can be calculated from the description of the solids-water partitioning and the uptake rate constants as in equation 2. Symbols are explained in Table 1 and a more thorough description of OMEGA is given in the Appendix.
BSAF 
k w ,in
Ksw  flipid  foc  k ex
(2)
In the traditional approach, partitioning between solids and (pore) water is assumed to be at equilibrium (25). As organic chemicals have a strong affinity for organic matter, sorption to solids is determined by sorption to the organic carbon fraction (fOC). The Ksw can be expressed by using the octanolwater-partition coefficient (Kow) of a substance and an octanol equivalent fraction in the organic solids
(flso) (26):
K sw  f OC  K OC  f OC  f lso K ow
(3)
This partition coefficient describes the linear sorption of organic compounds to ‘amorphous’ organic
carbon. The release of these compounds from this organic carbon type is considered to occur with typi7
cal half-lives of hours to days (fast desorbing PAH fraction related to BC-exclusive organic carbon).
Besides this fraction, a ‘slow desorbing’ PAH fraction related to BC has release half-lives of years to
decades (13, 14).
Adding a term for calculating the concentration of organic compounds adsorbed to BC in solids under
equilibrium
conditions
(11,
13,
K sw 
Cs
n
1
 f OC  K OC  f BC  K f , BC  C 0 wf , BC
C 0,w
14,
27)
results
in
a
new
Ksw
in
OMEGA:
(4)
By adding this second term, the concentration dissolved in water (C0,w) is no longer linearly related to
the concentration in solids (Cs).
TABLE 1. Explanation of symbols
Symbol Description
BSAF
Ci a
Unit
Source
Biota-Solids-Accumulation-
µg·kg-1 lipid weight / Equation 1
Factor
µg·kg-1 organic carbon
Concentration in biota of trophic µg·kg-1 wet weight
Equation 2, references 28-
level i
34 (see Supporting Information)
flipid
Cs
Lipid fraction in organism
Concentration in solids
kg lipid weight / kg wet references
28-34
(see
weight
Supporting Information)
µg·kg-1 total dry weight
references
28-34
(see
Supporting Information)
8
fOC
C0,w
Black carbon exclusive organic kg organic carbon / kg Reference 15e
carbon fraction of solids
total dry weight
Concentration dissolved in water
µg·l-1
= fTOC - fBC
Fitted according to equation 4, see Supporting Information
l·kg-1 wet weight d-1
kw,in
Rate constant for absorption
kex
Rate constant for total elimination kg·kg-1·d-1
Ksw
Solids-water-partition-coefficient
KOC
Coefficient for partitioning to or- l·kg-1 organic carbon
l·kg-1 total dry weight
Reference 15
Reference 15
Equation 3; Equation 4
= flso . Kow
ganic carbon
flso
Octanol-equivalent fraction in or- Kg octanol equivalent / kg Reference 15d
ganic carbon
Kow
Octanol-water
organic carbon
partition
coeffi- [-]
References 21, 22
cient
9
fom
Organic matter fraction
kg organic matter / kg to- = 2 . fTOC for aquatic ecotal dry weight
systems (23)
= 1.7 . fTOC for terrestrial
ecosystems (24)
reference 15; references
28-34 (see Supporting Information)
fTOC
Black carbon inclusive organic kg organic carbon / kg reference 15; references
carbon fraction of solids
total dry weight
28-34 (see Supporting Information)
fBC
Black carbon fraction of solids
kg BC / kg total dry references
weight
28-34
(see
Supporting Information);
Table 2
Kf,BC
Freundlich constant for sorption μg kg-1 BC / (μg l-1)n
Table 2
to black carbon
nf,BC
Freundlich coefficient for sorption [-]
Table 2
to black carbon
a: for invebrates i = 2 in OMEGA; d: in OMEGA a default value of 36% is included; e: in OMEGA a
default value of 6% for soil and of 8% for suspended solids and sediments is included. These defaults
agree reasonably with the datasets.
10
2.2 Data sources
Sampling locations, dates and variables measured are summarized in the Appendix. More detailed
descriptions of sampling locations and analytical methods can be found in the references cited there.
For comparison with model results, concentrations measured in biota and solids were converted to
µg·kg-1 lipid weight and to µg·kg-1 organic carbon respectively.
Data on marine semi-field experiments were obtained from Vethaak et al. (28). They measured the
concentration of 13 PAHs in sediment, suspended solids and two herbi-detrivores (the lugworm Arenicola marina and the blue mussel Mytilus edulis) in large scale mesocosms using a relatively clean, an
indirectly polluted and a directly polluted system. Field measurements on marine ecosystems were obtained from research programs in the Western Scheldt estuary carried out by National Institute for
Coastal and Marine Management (RWS-RIKZ) (29, 30). They cover measurements in sediment as well
as in suspended solids and in Arenicola marina, Cerastoderma edule and Nereis diversicolor. Chemical
analyses were performed using validated and accredited (ISO 17025) methods. Field measurements on
fresh water systems were obtained from monitoring programs carried out by the Institute for Inland
Water Management and Waste Water Treatment (RIZA) covering measurements in the mussel Dreissena polymorpha and suspended solids and in juvenile chironomids and sediment (31-33). Terrestrial
data were taken from Van Brummelen (34), who measured concentrations of 8 PAHs in forest soil, and
in earthworms.
2.3 Probabilistic modelling
A Monte Carlo simulation was carried out to assess the variation in BSAF estimations related to the
BC sorption term (equation 4). Each simulation consisted of 10,000 iterations. The model OMEGA
was adapted to include the Monte Carlo simulation using Crystal Ball 7.1.2 (35). According to Cornelissen et al. (13) and Koelmans et al. (14) the BC term in equation 4 is dominant under typical environ11
mental conditions and PAH concentrations of interest for risk assessment. A preliminary sensitivity
analyses confirmed dominance of the BC term in the Ksw in BSAF estimation. Two sources of variation
are distinguished: (i) variability and (ii) uncertainty (36). Variable parameters can be measured, but
vary inherently in the environment, such as PAH concentrations, organic carbon fractions and BC fractions in solids. The variability in these parameters can be due to variation in space, such as different
organic carbon contents in different soils, and due to variation in time, such as different PAH concentrations caused by differences in emissions. Uncertainty refers to the fact that parameter values are not
perfectly known, for instance due to the lack of data or uncertain measurements. In our analysis this
refers to the Freundlich parameter nf,BC and the Kf,BC – estimates from linear regression. Uncertainties
due to measurement techniques are not taken into account.
Variability and uncertainty both contribute to the variation in modeled BSAFs. The total variation is
quantified in a generic assessment to give a range of variation as comprehensive as possible. Table 2
summarizes the probability distributions for the input variables and the uncertain parameters for this
generic assessment. For the concentrations in solids (Cs), probability distributions were fitted to the
measured field data (references 28-34). These field data cover various Dutch environmental conditions,
variability in these data can be temporal as well as spatial. Values for the black carbon fraction (fBC)
were taken from the literature reviewed by Cornelissen et al. (13), where values for the BC fraction
were explicitly reported or could be calculated (Appendix). Following Moermond et al. (9), AccardiDey et al. (12), Cornelissen et al. (13), Koelmans et al. (14) and Lohman et al. (37) the Freundlich parameter nf,BC was approximated by a triangular distribution with extremes reported in the literature of
about 0.5 – 0.9 and an average of 0.7. The Kf,BC-Kow relation based on an empirical linear regression
was taken from Koelmans et al. (14). The uncertainty in the regression equation was included in the
Monte Carlo simulation using statistics for linear regression analysis (38; Appendix).
12
To assess uncertainty only, a second Monte Carlo simulation was conducted for the dataset that included location specific BC values (30). In this simulation location specific data from the Westernscheldt in 2005 were used for the variables black carbon fraction (fBC), concentration in sediments
(Cs) and lipid fraction and the distributions from Table 2 were used for the Freundlich parameters for
sorption to BC (Kf,BC, nf,bc).
In addition, an uncertainty importance analysis was performed to identify the parameters or variables
that contribute most to variation in BSAF. This analysis consisted of a Monte Carlo simulation in combination with a Rank correlation (expressed as percentage of total variance).
TABLE 2. Characteristics of the probability distributions used for the BC-sorption term in equation
4.
Name
Cs
fBC
Unit
µg·kg-1 total dry weight
kg BC / kg total dry
Distribution
Lognormal
Lognormal
weight
nf,BC
-
Triangular
Aquatic eco-
Terrestrial eco- Origin
systems
systems
Median: 229
Median: 122
Coefficient of
Coefficient of
variation: 2.5
variation: 1.4
Median: 0.002
Median: 0.007
Coefficient of
Coefficient of
variation: 2.5
variation: 6.3
Most likely: 0.7
Minimum: 0.5
Variable
Reference
References
28-34
Variable
References
in 13
Uncertain 9, 12, 13,
14, 37
Maximum: 0.9
13
Kf,BC
μg kg-1 BC / (μg l-1)n
Non-central
Degrees of freedom: 11
t distribution
Standard error: 0.07 – 0.16
Uncertain 13
mean:
log KBC  0.7  log Kow  2.8
14
3 Results and discussion
3.1 Reduction in modeled BSAFs
The 5th-, 50th-, and 95th-percentiles of estimated BSAFs corrected with BC sorption are shown in
Figure 1. For comparison, the BSAF calculated without BC correction is included as well. Typically,
BSAF estimations for PAHs with BC are reduced by one order of magnitude compared to estimations
without BC (Figure 1). However, the reduction of BSAFs ranges from half an order of magnitude for
aquatic invertebrates up to four orders of magnitude for terrestrial invertebrates due to uncertainty in
the BC sorption term (90 percentile confidence interval). This difference in uncertainty between aquatic
and terrestrial data can be explained by a larger variability in the black carbon fraction (fBC) for terrestrial data (Table 2). The modeled typical BSAF reduction observed for the terrestrial data is larger than
for the aquatic data, which can be explained by a systematically higher black carbon fraction (fBC) employed in the Monte Carlo Simulation for soil compared to aquatic solids (Table 2). Koelmans et al.
(13) and Cornelissen et al. (14) report reductions in BSAFs up to three orders of magnitude. These values are comparable to our modeled estimations. This range is comparable with the variation in measured organic carbon-water partition coefficients (KOC-) values reported by Hawthorne et al. (39) and
b
1E+01
1E+00
1E-01
1E-02
1E-03
1E-04
1E-05
1E+03
1E+04
1E+05
1E+06
1E+07
Octanol-w ater partition ratio Kow [-]
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
a
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
ascribed to differences in sediment characteristics.
1E+01
1E+00
1E-01
1E-02
1E-03
1E-04
1E-05
1E+03
1E+04
1E+05
1E+06
1E+07
Octanol-w ater partition ratio Kow [-]
15
d
e
1E+01
1E+01
1E+00
1E-01
1E-02
1E-03
1E-04
1E+03
1E+04
1E+05
1E+06
1E+07
Octanol-w ater partition ratio Kow [-]
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
c
1E+00
1E-01
1E-02
1E-03
1E-04
1E-05
1E+03
1E+04
1E+05
1E+06
1E+07
Octanol-w ater partition ratio Kow [-]
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
1E+01
1E+00
1E-01
1E-02
1E-03
1E-04
1E+03
1E+04
1E+05
1E+06
1E+07
Octanol-w ater partion ratio Kow [-]
FIGURE 1. OMEGA estimation of Biota-Solids Accumulation Factors for PAHs
3 and
_____
____
using equation
50th (thick line), ___ __ __ 95th, and - - - - 5th percentile values using equation 4 versus Kow, -
compared to a) Arenicola marina to sediment concentration ratios (■ ref 28; ● ref 29) b) Mytilus edulis–sediment concentration ratios (■ ref 28), and Nereis diversicolor-sediment concentration ratios (●
ref 29), c) Cerastoderma edule-suspended solids concentration ratios (+ ref 30) and Nereis diversicolor
-suspended solids concentration ratios (○ ref 29), d) Dreissena polymorpha-suspended solids concentration ratios (▲ref 31; ∆ ref 32) and juvenile Chironomidae-sediment concentration ratios (♦ ref 33),
e) Lumbricus rubellus-soil concentration ratios; (ref 34) including standard deviations of the measurements where available. Panel a – c) show marine species, panel d) shows freshwater species and panel
e) terrestrial species.
16
4.2 Sensitivity analyses
The uncertainty importance analysis showed that the variation in the black carbon fraction (fBC) contributes dominantly to the variation in BSAFs of PAHs for the whole range of octanol-water partition
coefficients (Kow). Table 3 shows an representative example for a Kow of 3.5∙105. This implies that the
variation in model estimations can be substantially reduced when measured fBC values are used. It also
indicates that spatial variability is probably more relevant than temporal variability. Others (1, 3, 40)
also observe an important influence of the BC content on bioaccumulation. Overestimation of solidswater partitioning might be attributed to competitive sorption by other organic compounds (8, 13, 14).
The Kf,BC’s in the regression analysis were derived from in-situ partitioning data from different locations, which means that this attenuation effect is already accounted for in the BSAF estimations and
will not lead to additional overestimation or uncertainty.
TABLE 3. Contribution to variance in BSAF estimation for parameters included in the soot sorption
term for a Kow of 3.5.1005.
Sediment / suspended
solids
Soil
Black carbon fraction fBC
82%
88%
Freundlich nf,BC
8%
9%
Concentration in solids Cs
8%
2%
Freundlich Kf, BC
2%
1%
17
4.3 Field BSAFs
Accumulation ratios for Dreissena polymorpha in the Rhine – Meuse delta show levels similar to the
data found for marine polychaetes and bivalves (Figure 1a-e). Accumulation levels for juvenile chironomids are lower (Figure 1e). This deviation is discussed by Reinhold et al. (33), but no general explanation is given. BSAFs for the earthworm Lumbricus rubellus are slightly higher (Figure 1f) than for
the aquatic data. Within the marine species the measured bioaccumulation in Arenicola marina (Figure
1a, b) reaches higher levels than in Cerastoderma edule, Mytilus edulis and Nereis diversicolor (Figure
1 c, d). Penry et al. (41) mention feeding behavior and digestive physiology to affect PAH bioaccumulation, which might explain these higher levels in sediment feeders. Others, however, report no such
differences in bioaccumulation (42).
4.4 Estimated BSAFs and field data
The majority (93%) of the BSAFs of PAHs in Figure 1 are between 0.002 and 0.7, whereas OMEGA
without BC correction estimates a BSAF of PAHs of about 3. The measured BSAFs lie within the
range calculated in the Monte Carlo simulation with BC. However, 95% of the field data lie below the
50th- percentile of estimations. Some substances are above the 50th-percentile line of model estimations
for high Kow’s where model lines show a decline in BSAF that is not reflected in the field data. The
variation in the field data, however, is high. No clear pattern can be observed in the differences between substances, this may be overlain by the difference between individual biota in the measurements.
Within the group of PAHs partitioning to water is often observed to decrease with increasing molecular
surface area and Kow (3). Few substances in Figure 1, however, exceed a Kow of 106 and bioaccumulation is expected to be reduced above this value (18, 43).
The tendency for overestimation is in line with observations by Moermond et al. (9), who, with BC
contents comparable to the median in Table 2, found that their BC-inclusive model perfectly fitted pol18
ychlorinated biphenyl data but still overestimated BSAFs for PAHs by a factor of 3 to 9. They mention
that part of the deviation could be attributable to metabolic transformation. Other factors could also
lead to additional reduction in BSAFs or represent an alternative explanation as discussed below.
4.5 Metabolic transformation
Metabolic transformation of pyrene and benzo(a)pyrene has been reported for some invertebrates, including for example for Nereis diversicolor (44-47), but is considered to play a limited role in elimination kinetics of oligochaetes and bivalves (48-51). As metabolic transformation is not explicitly modeled in this study, it might still have a lowering effect on the BC-exclusive BSAFs. Metabolic transformation leads to lower BSAFs, particularly for higher molecular weight substances (52). Van der
Linde et al. (53) estimate rates of metabolic transformation of PAHs by annelids around 1 d-1. Such a
transformation rate would lead to a reduction of BSAFs between a factor of 2 (lower weight PAHs) and
a factor of 200 (higher weight PAHs) as modeled using OMEGA. This doesn’t seem sufficient to explain the lower field data over the whole range of octanol-water partition coefficients (Kow’s).
4.6 Model assumptions
Concentrations in biota are calculated assuming steady state (equation 2). Non-steady state situations
might lead to an overestimation of BSAFs by the model as well. Steady state in aquatic invertebrates is
reported to be reached after 4-20 days for compounds with Kow around 1005 – 1006 by several studies
(42, 45, 47, 54). It is expected that field exposure exceeds this period. Hendriks (31) and Reinhold (33)
show that field conditions can be described by steady state. Results are therefore not affected by the
steady state assumption.
Another assumption is that the influence of other carbonaceous geosorbents, such as coal and kerogen, were not included in the uncertainty analysis. Cornelissen et al. (55) showed that ignoring these
19
sorbents into the Freundlich partitioning can lead to overestimations of the Kf,BC up to a factor of 5.
However, in this case this will not yield significant bias because (a) kerogen and coal levels will be minor in comparison to BC, and (b) any systematic underestimation due to ignoring kerogen or coal phases is counteracted by the overestimation in BC normalized Kf,BC values from the sediments used in the
regression analysis (14).
4.7 Remaining uncertainty
Figure 2 compares BSAFs calculated with location-specific values for concentrations in solids (Cs),
organic carbon fractions (fOC), black carbon fractions (fBC) and lipid content measured by Van den
Heuvel-Greve et al. (30) to corresponding field BSAFs. These fBC values were measured with the
CTO375 method (11), which ascertains consistency with the BC sorption parameters provided by Koelmans et al. (14) and applied in the present study, which were also derived from CTO375 based BC
measurements. The variation in predicted BC-inclusive BSAFs (5th-, 50th-, and 95th-percentile lines in
Figure 2) is entirely caused by uncertainty in the Freundlich parameters Kf,BC and nf,BC, as variability
has been excluded by using location-specific measurements. These uncertainties, as assessed in this
paper, can only be reduced by an improved estimation of the parameters, particularly the Freundlich
coefficient. Figure 2 shows that the uncertainty is reduced to two orders of magnitude (the 90thpercentile confidence interval). The deviation between measured BSAFs and the 50 th-percentile values
of BC-inclusive modeled BSAFs is reduced to a factor 3 on average.
20
Organism-solids concentration ratio Ci/C0h
[μg kg-1 lipid / μg kg-1 organic carbon]
1E+01
1E+00
1E-01
1E-02
1E-03
1E-04
1E+03
1E+04
1E+05
1E+06
1E+07
octanol-w ater partition ratio Kow [-]
FIGURE 2. Field A. marina-sediment concentration ratios (+) and field C. edule (x)-suspended solids
concentration ratios PAHs versus Kow (ref 30). Lines represent the _____ 50th , ___ __ __ 95th, and - - - - 5th
percentile values of estimated BSAFs including the uncertainty in the Freundlich parameters for the
same data.
21
5 Implications
It was found that including sorption of PAHs to BC in the estimation of BSAFs results in model estimations that are better in line with field measurements. This indicates that BC sorption should be included in future risk assessments of PAHs. However, the uncertainty in the modeled BSAFs due to the
uncertainty in sorption to BC spans 1.5 (for known BC contents) to 3 orders of magnitude (90th percentile confidence interval), which shows that further research is needed to better quantify reduced accumulation of PAHs. The BC fraction most strongly influences predicted BSAFs, implying that BC contents are particularly important in estimating bioaccumulation ratios and should be measured routinely,
particularly as this does not involve substantial additional cost or difficulties.
22
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30
Appendix
Abbreviations used in the tables:
Ac
Acenaphtene
Ant
Antracene
BaA
Benzo(a)anthracene
BaP
Benzo(a)pyrene
BeP
Benzo(e)pyrene
BbF
Benzo(b)fluoranthene
BkF
Benzo(k)fluoranthene
BghiPe
benzo(g,h,i)perylene
Chr
Chrysene
dBahA
Dibenzo(ah)anthracene
Fle
Fluorene
Flu
Fluoranthene
InP
indeno(1,2,3-c,d)pyrene
Phen
Phenanthrene
Pyr
Pyrene
31
Description of the model OMEGA
The model OMEGA (Optimal Modeling for EcotoxicoloGical Applications) estimates accumulation of
neutral organic compounds and metals in aquatic and terrestrial food chains. Validation studies showed
that the model OMEGA is able to predict field or laboratory BSAFs within a factor of 5 for various
substances, such as brominated flame retardants and organochlorines, including polychlorinated biphenyls (1-3).
Here, a brief explanation of main processes and equations on accumulation of organic substances is
given. More detailed information can be found in Hendriks et al. (1) and Hendriks and Heikens (3).
Food chains in OMEGA consist of four trophic levels. This research focuses on two trophic levels: 1.
food: detritus/algae, 2. detritivores. The mass of organisms in such food chains results from four basic
flows:
1) absorption and excretion of water
2) ingestion and egestion of food
3) (re)production of mass
4) mortality of tissues
Each of these flows may carry a toxicant into and out of an organism. OMEGA calculates steady-state
chemical residues in biota as the sum of influx via water (absorption) and uptake of food (assimilation)
divided by the total elimination rate (equation S1). Symbols are explained in Table S1. Different routes
of elimination or dilution exist: efflux via water, food and biomass (growth dilution), respiration and
metabolic transformation.
Ci 
k w,in  C 0, w  k n ,in  C i 1
k w,ex  k n ,ex  k p  k m
S1
32
The concentration in the organism is determined by a species-specific combination of all routes of
uptake and elimination/dilution described in equation 2. Invertebrates predominantly accumulate organic substances via absorption, and intake via food is generally less important. Normally, metabolic
transformation is not explicitly accounted for, but rate constants can easily be added to the model, if
available.
Values for the rate constants in equation 1 are derived by Hendriks et al. (1). Rate constants for influx
and efflux are predicted based on species-weight following allometric relationships. Additionally, these
constants are inversely proportional to resistances, substances encounter in water and lipid layers and
flow delays. The rate constants for inflow via water (kw,in) and uptake from food (kn,in) are calculated as
in equations S2 and S3.
k w , in 
k n,in 
1
S2
 CH 2 ,0
w
 H 2 O,0  w  
w 
K ow
w
p1
1


1  p1 p CH 2 , food  K ow  1  1
1

 H 2O ,1  w 
 CH 2 , food
K ow
w
w 
p CH 2 , food  K ow  1  p 1    n
S3

Equations S4 to S7 show the calculation of the rate constants for reduction:
For excretion via water: k w,ex 
p
1



K
CH 2 ,i
ow  1  1
1

CH 2 ,0  w 
H O,0  w  
w 
2
Kow
w
S4
For egestion with faeces:
k n,ex 
1

pCH ,i  Kow  1  1
2
1
S5

w
CH 2 ,food


H2O,1  w 
w 
Kow
pCH 2 ,food  Kow  1  p1   n
33
For dilution of biomass
kp 
p
S6
w
TABLE S 1: Factors used in equations with typical values for parameters (1;3)
Symbol Description
Unit
Ci
µg·kg-1
Concentration in biota of trophic
Typical value
level i
C0,w
Concentration dissolved in water
µg·l-1
kw,in
Rate constant for absorption
l·kg-1·d-1
kn,in
Rate constant for assimilation
l·kg-1·d-1
Ci-1
Concentration in food (detritus/algae µg·kg-1·d-1
for i = 1)
kw,ex
Rate constant for excretion via water kg·kg-1·d-1= d-1
kn,ex
Rate constant for egestion with fae-
kg·kg-1·d-1 = d-1
ces
kp
Rate constant for dilution of biomass kg·kg-1·d-1= d-1
by reproduction or growth
kr
Rate constant for respiration
kg·kg-1·d-1= d-1
km
Rate constant for metabolic trans-
kg·kg-1·d-1= d-1
formation
w
Water absorption – excretion coeffi-
kgd-1
200
cient
p
Biomass (re)production coefficient
kgd-1
0.0006
n
Food ingestion coefficient
kgd-1
0.005

Rate exponent
[-]
0.25
34
pCH2,i
Lipid fraction of organism (i)
kg·kg-1
0.03·w-0.04
P1
Fraction of food assimilated
%
0.20
CH2,0
Lipid layer resistance from / to water dkg-
1103
CH2,food Lipid layer resistance from / to food
dkg-
68
H2O,0
dkg-
6.0
H2O,food Water layer resistance from / to food dkg-
0.3
w
1.7 10-3 - 5.4 10-2
Water layer resistance from / to water
Species weight
kg
Data Sources
TABLE S 2. Sampling locations, periods, compartments and variables measured
Sampling location
Sampling
Solids
Measured variables
References
period
Mesocosms,
1990-1993
Sediment
Wadden Sea
Concentration in sediment (Cs),
4
BC inclusive organic carbon
fraction (fTOC), concentration in
biota (Ci), lipid fraction of biota
Western Scheldt
1987
Sediment,
Concentration in sediment (Cs), 5
suspended
organic matter fraction (fOM),
solids
concentration in biota (Ci), lipid
fraction of biota
Western Scheldt
2005
Sediment,
Concentration in sediment (Cs), 6
35
suspended
BC inclusive organic carbon
solids
fraction (fTOC), concentration in
biota (Ci), lipid fraction of biota
Rhine-Meuse Del- 1990
Suspended
Concentration in sediment (Cs), 3
ta
solids
organic matter fraction of solids
fOM, concentration in biota (Ci),
lipid fraction of biota
Rhine-Meuse Del- 1994
Suspended
Concentration in sediment (Cs), 7
ta
solids
organic matter fraction (fOM),
concentration in biota (Ci), lipid
fraction of biota
Biesbosch
1993
Sediment
Concentration in sediment (Cs), 8
organic matter fraction (fOM),
concentration in biota (Ci), lipid
fraction of biota
IJmuiden
1992
Soil
Concentration in sediment (Cs), 9
organic matter fraction (fOM),
concentration in biota (Ci), lipid
fraction of biota
36
TABLE S 3: PAH concentrations and organic carbon fractions in sediment measured by Vethaak et al. (4) in experimental tanks near Texel.
Organic
Date
carbon
Ant
BaA
BaP
BbF
BeP
BghiPe
BkF
Chr
dBahA
Flu
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
%
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
7.4
391
1274
1966
2483
1684
2075
1027
1621
284
3665
2446
2281
2545
5.6
1412
3374
4846
6060
4199
4418
2558
4248
497
9108
4752
5585
6356
4.9
562
1826
3020
3773
2685
3127
1679
2483
397
5061
4319
2937
3462
5.2
1157
3370
4761
6167
4337
4510
2740
4290
617
8989
5274
5587
6098
5.6
470
1336
1648
1931
1683
1773
1037
1451
183
4009
1782
2637
2947
4.9
2064
3542
3844
5531
4178
4178
2399
3674
430
8603
3727
6771
6394
5.2
6542
10603
10093
14534
11151
9767
6397
8477
1016
20847
10351
12877
16485
6.0
486
1478
1790
2736
1920
1864
1191
1683
247
3954
2367
3013
3004
6.0
2190
4869
5578
8177
5692
170
3247
5360
620
11661
6992
7913
8226
07-051992
07-051992
10-111990
10-111990
04-111992
03-111992
05-111992
28-041993
27-041993
TABLE S 4: PAH concentrations and organic and black carbon fractions measured by Van den Heuvel-Greve et al. (5).
organic black
Location carbon carbon Ac
Ant
BaA
BaP
BbF
BeP
BghiPe BkF
Chr
dBahA Fle
Flu
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
organic organic organic organic organic organic organic organic organic organic organic organic organic organic
%
%a
carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon
sediment
Terneuzen 1
0.2
1667
206
4468
4640
9966
6014
4983
2921
4640
2234
2921
11169 3093
6186
9279
0.6
427
701
3251
3570
6183
1339
3378
1976
3506
892
1020
6374
3570
5163
suspended
solids
Terneuzen 4.2
2295
a: according to the CTO 375 method (Gustafsson et al., 1997)
38
TABLE S 5: PAH concentrations in suspended solids measured by Stronkhorst (6).
Organic
Location
carbon
%
Westerschelde
Ac
Ant
BaA
BaP
BbF
BeP
BghiP
BkF
Chry
DBahA
Fle
Flu
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
organic
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
3600
1560
3560
3560
5879
3820
3440
2700
3260
416
7660
2020
3863
7200
7100
4245
1571
3714
4460
5879
3820
3566
2696
3948
346
8256
2696
3863
11737
6877
2229
1218
3560
3714
5200
3269
3031
2377
3417
416
7191
1337
3655
5438
5497
5
East
Westerschelde
4.7
Central
Westerschelde
West
3.4
39
TABLE S 6: PAH concentrations in sediment measured by Stronkhorst (6).
Organic
Location
car-
bon
BaP
%
u/kg
carbon
BbF
organic
u/kg
carbon
BghiP
organic
u/kg
carbon
BkF
organic
u/kg
carbon
Flu
organic
u/kg
InP
organic
u/kg
carbon
carbon
4000
0
Westerschelde East 1,1
0.3
Westerschelde East 2,1
0.3
8000
20000
16000
8000
28000
16000
Westerschelde Central 1,1
2.4
2500
6250
3750
2500
8333
4583
Westerschelde Central 3,1
0.3
4000
8000
4000
0
12000
4000
Westerschelde West 1,1
0.3
4000
8000
4000
4000
12000
4000
Westerschelde West 3,1
1.0
9000
12000
10000
4000
17000
8000
organic
40
TABLE S 7: PAH concentrations in suspended solids measured by Hendriks et al. (3).
Organic
Location
carbon
Ant
BaA
BaP
BbF
%
BghiPe
BkF
Chr
dBahA
Flu
InP
Phen
Pyr
ug/kg
or-ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or- ug/kg
or-
ganic
car-ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car- ganic
car-
bon
bon
bon
bon
bon
bon
bon
bon
bon
bon
bon
bon
Rijn, Lobith
10
3272
9146
10448
13532
8319
5758
9233
2056
20634
8026
14055
16426
Maas, Eijsden
10
1748
8933
9344
15367
9860
5899
9827
1547
18578
9991
8489
18364
IJsselmeer
10
523
649
799
1051
836
587
733
bd
1412
776
1172
1193
Hollands Diep
10
2468
6892
8160
12161
7971
5267
7018
1698
15046
72359
9659
12832
bd = below detection limit
41
TABLE S 8: PAH concentrations in suspended solids measured by Hendriks (7).
Organic
Location
carbon
Ant
BaA
BaP
BbF
BghiPe
BkF
Chr
Flu
InP
Phen
%
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
or-
organic
organic
organic
organic
organic
organic
organic
organic
organic
ganic
car-
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
carbon
bon
Haringvliet-West 10
6000
6000
8000
12000
8000
5000
7000
15000
7000
29000
Markermeer
10
2000
1000
1000
2000
1000
1000
1000
2000
1000
2000
Rijn, Lobith
10
12000
10000
13000
17000
11000
7000
11000
27000
9000
51000
Haringvliet-East 10
12000
10000
13000
17000
11000
7000
11000
27000
9000
51000
Hollands Diep
10
12000
10000
13000
17000
11000
7000
11000
27000
9000
51000
Ketelmeer
10
12000
10000
13000
17000
11000
7000
11000
27000
9000
51000
Nieuwe Merwede 10
12000
10000
13000
17000
11000
7000
11000
27000
9000
51000
42
TABLE S 9: PAH concentrations in sediment measured by Reinhold et al. (8).
Organic
Location
carbon
Ant
%
ug/kg organ-ug/kg organ- ug/kg organ- ug/kg organ- ug/kg organ- ug/kg organ- ug/kg organ- ug/kg organ- ug/kg organ-
Rijn, Biesbosch 5
BaA
BaP
BbF
BghiPe
BkF
Chr
Flu
Pyr
ic carbon
ic carbon
ic carbon
ic carbon
ic carbon
ic carbon
ic carbon
ic carbon
ic carbon
4860
11800
13300
20900
0
7810
15100
22500
20000
43
TABLE S 10: PAH concentrations (averages) in soil measured by Van Brummelen (7).
Loca-
Organic
tion
carbon
Ant
ug/kg
BaA
or-
ug/kg
BaP
or-
ug/kg
BbF
or-
ug/kg
BkF
or-
ug/kg
Chr
or-
ug/kg
Fle
or-
ug/kg
Flu
or-
ug/kg
Phen
or-
ug/kg
Pyr
or-
ug/kg
or-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
ganic car-
[%]
bon
bon
bon
bon
bon
bon
bon
bon
bon
bon
1
3.1
3099
12031
13240
26252
9415
13960
2870
22852
15987
14221
2
3.3
2242
9267
9688
18143
5265
10110
2221
17512
11584
11343
3
3.8
1139
6563
6774
13653
3664
5614
920
13732
8118
9357
4
3.7
947
4869
5059
5685
2965
6446
952
9819
6446
6501
5
4.4
565
2792
2927
6890
1777
3873
574
5854
3648
5314
6
4.3
512
3214
3260
7219
2000
4122
603
6614
3889
4052
7
3.8
422
2549
1202
7143
1845
4191
424
5667
3743
3532
8
4.7
336
2110
0
5971
1515
3485
1252
4951
2933
4314
9
3.5
244
1638
0
5667
1343
3173
989
4533
2751
3627
10
6.1
129
855
989
3367
843
320
820
2558
1408
1683
44
TABLE S 11: PAH concentrations in Mytilus edulis measured by Vethaak et al. (3).
Date
Ant
BaA
BaP
BbF
BeP
BghiPe
BkF
Chr
dBahA
Flu
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
%
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
8
27
80
38
164
159
69
59
274
1
974
522
505
555
9
36
386
109
509
849
169
153
793
13
2426
467
827
1658
10
12
26
17
60
71
36
22
99
2
509
387
390
209
11
27
180
62
308
566
106
98
322
6
1343
354
443
897
8
31
127
84
349
296
158
98
440
9
910
115
578
558
9
75
315
172
556
834
234
168
241
27
1759
133
587
1595
9
189
1159
662
1534
1946
540
511
594
24
3225
376
715
3434
8
7
45
30
127
151
79
45
153
1
547
55
335
256
8
24
153
63
301
454
79
104
113
9
854
59
360
676
lipid
21-111990
20-111990
07-051992
05-051992
04-111992
03-111992
05-111992
28-041993
26-041993
45
TABLE S 12: PAH concentrations in Arenicola marina measured by Vethaak et al. (3)
lipid
Date
Ant
BaA
BaP
BbF
BeP
BghiPe
BkF
Chr
dBahA
Flu
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
weight
%
weight
weight
weight
weight
weight
weight
weight
weight
weight
6
21
156
185
507
427
227
248
1108
16
6
15
167
157
906
717
265
218
789
7
13
72
166
557
456
157
149
5
34
302
503
1369
1174
381
4
9
56
128
363
287
5
35
260
348
1212
7
12
53
66
6
23
257
5
34
302
weight
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
weight
weight
weight
738
699
395
1250
27
1187
675
511
1253
321
11
947
326
359
870
391
901
30
1408
811
485
1529
126
92
170
11
498
74
305
491
999
352
349
372
30
1175
247
361
1186
549
418
152
114
164
9
711
38
195
592
320
1451
1194
388
432
239
29
1252
250
270
1221
503
1369
1174
381
391
901
30
1408
811
485
1529
lipid
21-111990
20-111990
07-051992
06-051992
04-111992
03-111992
28-041993
27-041993
06-051992
46
TABLE S 13: PAH concentrations in Cerastoderma edule and Arenicola marina measured by Van den Heuvel-Greve et al. (4).
Species
lipid
Ac
Ant
BaA
BaP
BbF
BeP
BghiPe BkF
Chr
dBahA Fle
Flu
InP
Phen
Pyr
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
%
weight weight weight weight weight weight weight weight weight weight weight weight weight weight weight
1.8
22
17
72
250
889
833
167
106
328
67
722
44
89
233
833
Cerastoderma edule 1.1
27
4
136
118
391
336
<5
118
273
55
55
745
64
482
436
Arenicola marina
47
TABLE S 14: PAH concentrations in Arenicola marina measured by Stronkhorst (5).
Location
Westerschelde
lipid
Ac
Ant
BaA
BaP
BbF
BeP
BghiP
BkF
Chry
DBahA
Fle
Flu
InP
Phen
Pyr
%
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
6.3
1.4
39.2
7.2
137
165
30
44
154
5
9.7
177
13
38.2
266
7.4
1.4
27.8
7.7
101.6
115.3
24
33.9
100.2
5.2
7.9
113
14.2
40
177.5
8.1
2.1
30.3
7.3
138
146.5
35.1
50.5
128.8
5.1
11.1
167.3
18.2
48.2
224.4
2.6
15.6
8
74.1
69.8
19.6
24.2
51.8
7.7
102.9
11.2
34.9
93.6
0.7
8
2.2
64.9
44
12.5
17.6
43
6.4
89.7
7.9
30.9
65.5
7.3
138
35.1
50.5
167.3
18.2
8
74.1
19.6
24.2
102.9
11.2
2.2
64.9
12.5
17.6
89.7
7.9
6.8
East 3
Westerschelde
5.1
East 4
Westerschelde
6.2
Central 1
Westerschelde
3.5
West 1
Westerschelde
4.1
West 2
Westerschelde
6.2
Central 1,1a
Westerschelde
3.5
Central 1,1b
Westerschelde
West 2,1
4.2
4.1
48
TABLE S 15: PAH concentrations in Nereis diversicolor measured by Stronkhorst (5).
Location
Westerschelde
lipid
Ant
BaA
BaP
BbF
BeP
BghiP
BkF
Chry
Fle
Flu
Phen
Pyr
%
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
0.7
3.8
2.2
11.3
18.9
2.1
2.3
9.3
4.8
30.8
25.2
47.3
0.6
3.6
2.1
7.8
10.5
2
2.1
0
3.5
21.6
17.8
35.5
0.7
7.7
3
16.7
20.2
4.9
3.9
14.8
3.7
52.1
26.2
50.8
0.7
1.6
0.5
4
3.9
2.1
1
5.5
3.2
24.2
19.1
14.7
1.2
4.7
1.3
14
14.6
3.9
3
11.6
6.9
51.4
38
32.3
4.2
East 1
Westerschelde
3.0
East 2
Westerschelde
5.8
Central 1
Westerschelde
2.9
West 1
Westerschelde
5.7
West 3
Westerschelde
4.2
2.1
East 1,1
Westerschelde
3.0
East 2,1
Westerschelde
2.1
7.8
2
2.1
21.6
3
16.7
4.9
3.9
52.1
5.8
Central 1,1
Westerschelde
30.8
2.9
West 1,1
Westerschelde
5.7
West 3,1
49
TABLE S 16: PAH concentrations in Dreissena polymorpha measured by Hendriks et al. (3).
Location
lipid
Ant
BaA
BaP
BbF
BghiP
BkF
Chry
dBahA
Flu
InP
Phen
Pyr
%
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
1.8
56
1100
330
1100
170
220
28
1800
56
280
670
Eijsden
1.9
1100
13000
790
3300
26
840
3400
26
14000
110
6200
6400
IJsselmeer
09
43
74
210
640
210
210
430
53
850
53
210
320
Rijn, Lobith
Maas,
50
TABLE S 17: PAH concentrations in Dreissena polymorpha measured by Hendriks, (7).
Location
Haringvliet-
lipid
Ant
BaA
BaP
BbF
BghiP
BkF
Chry
Flu
InP
Phen
%
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg
ug/kg l
ug/kg
ug/kg
ug/kg
lipid
lipid
lipid
lipid
lipid
lipid
ipid
lipid
lipid
lipid
weight
weight
weight
weight
weight
weight
weight
weight
weight
weight
bd
1222
278
1667
167
278
944
1667
bd
278
127
5076
812
3198
457
1168
1777
10812
305
1472
80
959
878
1939
1347
796
2245
2858
1408
1286
120
640
320
680
120
320
1600
2040
320
440
1.8
West
Haringvliet-
2.0
East
Hollands
Di-
2.0
ep
Ketelmeer
1.3
TABLE S 18: PAH concentrations in juvenile chironomids measured by Reinhold et al. (8).
Location
Ant
ug/kg
Rijn, Biesbos
BaA
lipid
ug/kg
BaP
lipid
ug/kg
BbF
lipid
ug/kg
BkF
lipid
ug/kg
Chry
lipid
ug/kg
Flu
lipid
ug/kg
Pyr
lipid
ug/kg
weight
weight
weight
weight
weight
weight
weight
weight
22
125
53
323
103
338
2930
2013
lipid
51
TABLE S 19: Biota-Solids Accumulation Factor (BSAF) in Lumbricus rubellus reported by Van Brummelen (9).
BSAF
average
BaA
BaP
BbF
BkF
Chr
Flu
Phen
Pyr
ug/kg lipid
ug/kg lipid
ug/kg lipid
ug/kg lipid
ug/kg lipid
ug/kg lipid
ug/kg lipid
ug/kg lipid
weight
weight
weight
weight
weight
weight
weight
weight
0.2
0.4
0.5
0.4
0.7
0.2
0.3
0.7
0.7
0.7
0.9
0.7
0.8
0.8
0.8
1.3
standard deviation
52
TABLE S 20: Black carbon fractions (fBC) for sediments taken from literature reviewed by Cornelissen et al. (10)
ref 11
ref 12
ref 13
ref 14
ref 15
ref 16
ref 17
ref18
ref 19
ref 20
ref 21
ref 22
ref 23
ref 24
ref 25
1.7E-03
6.9E-03
4.0E-04
7.0E-03
3.2E-03
3.9E-04
2.4E-04
2.4E -04
5.3E-04
2.6E-02
6.6E-03
9.0E-05
3.8E-03
1.5E-03
8.0E-03
2.5E-03
9.0E-04
2.0E-04
8.4E-03
3.1E-03
7.5E-04
8.4E-04
2.5E-03
6.7E-04
1.5E-02
2.7E-03
1.7E-04
4.2E-03
2.7E-03
9.3E-03
1.9E-03
6.6E-03
3.6E-03
1.9E-04
1.5E-03
2.3E-03
3.4E-04
5.0E-03
1.8E-03
6.0E-05
5.5E-03
8.5E-04
6.6E-03
5.0E-04
1.2E-02
5.7E-04
1.1E-03
1.3E-03
3.9E-04
3.7E-03
5.0E-05
7.8E-03
1.2E-03
3.7E-03
7.0E-04
8.4E-03
1.3E-03
7.2E-03
1.3E-02
4.5E-04
6.9E-03
3.0E-05
4.7E-04
1.3E-03
7.5E-03
7.0E-04
8.6E-03
1.5E-04
2.1E-02
9.0E-04
7.2E-04
1.3E-04
7.6E-04
1.8E-03
5.4E-03
3.8E-03
6.7E-03
5.2E-04
1.0E-02
7.0E-04
7.1E-04
1.7E-04
8.6E-04
1.0E-02
2.5E-03
9.6E-03
6.8E-04
4.8E-03
3.6E-04
3.4E-04
3.1E-03
3.7E-03
1.7E-02
9.0E-04
2.7E-03
1.1E-03
9.1E-03
1.6E-03
2.9E-04
4.0E-05
4.7E-04
1.9E-02
5.0E-04
2.9E-03
1.4E-03
4.8E-03
1.4E-03
3.2E-04
6.0E-05
4.3E-04
3.0E-03
1.0E-03
2.6E-03
2.5E-03
1.2E-03
2.3E-03
4.2E-04
1.0E-04
4.0E-03
9.5E-04
1.4E-01
1.9E-03
1.1E-03
7.0E-05
4.4E-03
1.1E-03
2.4E-03
1.1E-04
2.1E-03
2.4E-04
3.2E-03
7.5E-04
1.8E-03
8.4E-04
2.7E-03
1.7E-03
2.2E-03
1.5E-03
3.8E-03
1.2E-03
2.4E-03
1.1E-03
8.0E-03
1.4E-03
1.8E-03
7.2E-03
8.9E-03
3.7E-03
2.3E-03
3.6E-03
1.1E-03
4.5E-03
5.0E-04
3.9E-03
53
1.5E-03
1.0E-03
2.0E-03
3.7E-04
5.0E-04
9.3E-04
3.4E-03
3.4E-04
1.6E-02
7.4E-04
9.0E-04
9.0E-04
5.0E-04
1.2E-03
3.0E-04
2.4E-03
2.8E-03
1.5E-03
8.6E-04
6.6E-03
TABLE S 21: Blackc carbon fractions (fBC) for soil taken from literature reviewed by Cornelissen et al. (10)
ref 11
7.3E-04
1.5E-03
3.8E-03
5.2E-03
1.5E-03
ref 14
3.0E-04
3.0E-04
ref 18
4.0E-04
4.0E-04
5.0E-04
1.0E-03
2.9E-03
7.0E-04
3.1E-03
ref 20
9.3E-03
ref 26
1.7E-03
2.5E-03
5.2E-04
1.3E-02
2.6E-03
1.6E-03
1.5E-02
7.3E-03
2.8E-03
3.9E-02
5.4E-02
1.3E-02
7.6E-03
ref 27
7.6E-04
2.5E-04
ref 28
1.5E-01
2.7E-01
4.5E-02
1.4E-01
8.3E-02
1.1E-01
1.4E-01
1.7E-01
4.3E-01
1.7E-01
2.7E-01
8.0E-02
4.7E-02
ref 29
4.9E-03
8.0E-03
1.3E-02
1.6E-03
3.6E-03
1.1E-02
8.8E-03
2.4E-02
4.0E-02
2.9E-03
3.7E-03
7.3E-03
3.1E-02
54
4.3E-04
6.0E-03
2.7E-03
7.4E-03
7.2E-02
2.3E-02
3.4E-03
6.0E-03
1.1E-02
3.1E-03
3.0E-02
5.3E-02
4.4E-02
5.9E-02
1.1E-02
1.6E-02
2.9E-02
2.2E-03
1.7E-03
2.4E-02
1.4E-03
5.9E-03
1.0E-02
55
Derivation of the concentration in water C0,w
The difference between the measured concentration in the solids (Cs,measured) and the Cs calculated from equation 4 is fitted to become zero by changing the concentration in water C 0,w (other
parameters are known or derived from the frequency distributions in the Monte Carlo Analysis
(Table 2)):
Cs,measured - f OC  f lso  K ow  C0,w  f BC  K BC  C0n,w  0
S7
Assessment of the uncertainty in the Freundlich constant Kf,BC
The Freundlich constant Kf, BC is related to the octanol-water partition coefficient (Kow) by regression analyses according to
log K f , BC  a  b  log K ow
S8
The values of the regression parameters and the regression coefficients in this relation are represented in TABLE S 22 and in TABLE S 23.
TABLE S 22: Regression parameters for the Freundlich constant Kf,BC (30).
Log Kow
Log Kf
phenanthrene
4.6
6.0
anthracene
4.6
6.1
pyrene
5.2
6.3
benzo(b)fluoranthene
5.8
6.5
fluoranthene
5.2
6.6
benzo(a)anthracene
5.9
7.0
benzo(e)pyrene
6.4
7.0
56
chrysene
5.8
7.1
benzo(k)fluoranthene
6.2
7.1
benzo(a)pyrene
6.0
7.2
dibenzo(ah)anthracene
7.0
7.5
benzo(ghi)perylene
6.9
8.0
indeno(123cd)pyrene
7.0
8.1
TABLE S 23: Regression coefficients for the Freundlich constant Kf,BC (30).
na
a
b
r2
13
2.6
0.7
0.9
a: n in this case indicates the number of measurement and not the Freundlich parameter
nf,BC
The confidence interval for the expected value of Kf,BC can be described as follows (31):
K f , BC  a  b  K ow   t n  2  s yˆ
S9
In which:
tn-2
central Student t-distribution with n-2 degrees of freedom, where n is the
number of measurements and 2 refers to the number of parameters of the
fitted regression line
sŷ
standard error of the mean for the expected values of Kf,BC, calculated as
 1 (x p  x)2 
s yˆ  s   

S xx 
 n
2
in which xp the value of Kow for which the expected value of Kf,BC is de-
57
termined;
s2 
SSE
n2
SSE  S yy 
( S xy ) 2
S xx
;
n
n
n
( yi ) 2
i 1
i 1
n
S yy   ( yi  y ) 2  yi2 
i 1
;
n
n
n
i 1
n
S xy   ( xi  x )( yi  y )   xi yi 
i 1
n
( xi )( yi )
i 1
i 1
;
n
n
n
( xi ) 2
i 1
i 1
n
S xx   ( xi  x ) 2  xi2 
i 1
.
58
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63