Experimental and modelling studies to identify...
Transcript of Experimental and modelling studies to identify...
Annual average of log BCF [(ng/kg)/(ng/m3)]
3
3.2
3.4
3.6
3.8
4
TCDD
PeC
DD
HxC
DD
HpC
DD
OCDD
TCDF
PeC
DF
HxC
DF
HpC
DF
OCDF
logBCF phytopl
logBCF bact
logBCF zoopl
EUR 23266 EN
Experimental and modelling studies to identify bioavailable contaminant concentrations and bioavailability
S. Dueri, D. Marinov, R. Carafa, B. Avery, B. Boutier, D. Cossa, J. L. Gonzalez, J. Knoery, D. Muranon, J. Tronczynski and J. M. Zaldívar
Towards the definition of a general scale for thresholds calculation
2
The mission of the Institute for Environment and Sustainability is to provide scientific-technical support to the European Union’s Policies for the protection and sustainable development of the European and global environment. European Commission Joint Research Centre Institute for Environment and Sustainability Contact information Address: TP272 E-mail: [email protected] Tel.: +39-0332-789202 Fax: +39-0332-785807 http://ies.jrc.ec.europa.eu/ http://www.jrc.ec.europa.eu/ Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication.
Europe Direct is a service to help you find answers
to your questions about the European Union
Freephone number (*):
00 800 6 7 8 9 10 11
(*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed.
A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/ JRC 43702 EUR 23266 EN ISBN 978-92-79-08494-2 ISSN 1018-5593 DOI 10.2788/6944 Luxembourg: Office for Official Publications of the European Communities © European Communities, 2008 Reproduction is authorised provided the source is acknowledged Printed in Italy
Table of Contents
1. INTRODUCTION 4
2. PARTITIONING BEHAVIOUR OF CONTAMINANTS IN BIOTA 4
2.1. Bioconcentration 4
2.2. Biomagnification 5
2.3. Bioaccumulation 5
2.4. Biotransformation 6
3. BIOAVAILABILITY OF PERSISTENT ORGANIC POLLUTANTS 6
3.1. Modelling the bioavailability of POPs 6
3.1.1.Bioconcentration in phytoplankton and bacteria 7
3.1.2.Bioaccumulation in zooplankton 10
3.2. Bioavailability of PAHs 13
3.3. Bioavailability of PCBs 15
3.4. Bioavailability of PCCD/Fs 19
3.5. Experimental data on Thau Lagoon 22
4. BIOAVAILABILITY OF METALS 25
4.1. Metals speciation and bioavailability 25
4.2. Evaluation of Cadmium speciation in the Thau lagoon (France) 27
4.2.1. Introduction 27
4.2.2. Sampling and conditioning of samples 27
4.2.3. Analysis 28
4.2.4. Results 29
4.2.5. Discussion 33
4.2.6. Conclusions 34
4.3. Distribution of Mercury species in the waters of Thau lagoon: Consequences for the
bioaccumulation factor calculations fro marine mussels 34
4.3.1. Introduction 35
4.3.2. Studied site and sampling collection 35
4.3.3. Sampling and analytical techniques 36
4.3.4. Results and discussion 36
4.3.5. Conclusions on bioavailability and in situ bioaccumulation factor (BF) within mussel
39
5. CONCLUSIONS 40
6. REFERENCES 41
4
List of Tables Table 3.1: Uptake (m
3.kg
-1.d
-1) and depuration (d
-1) constants for PAHs used in the model. 9
Table 3.2: Uptake (m3.
kg-1.
d-1
) and depuration (d-1
) constants for PCBs used in the model. 9
Table 3.3: Uptake (m3.
kg-1.
d-1
) and depuration (d-1
) constants for PCDD/Fs used in the model. 10
Table 3.4: Uptake (m3.
kg-1.
d-1
), depuration (d-1
), grazing (m3.
kg-1.
d-1
), egestion (d-1
) and metabolism(d-1
)
constants for PAHs used in the model (determined experimentally by Berrojalbiz et al., 2006). 10
Table 3.5: Uptake (m3.
kg-1.
d-1
), depuration (d-1
), egestion (d-1
) and metabolism (d-1
) constants for PCBs
used in the model. 11
Table 3.6: Uptake (m3.
kg-1.
d-1
), depuration (d-1
), egestion (d-1
) and metabolism (d-1
) constants for
PCDD/Fs used in the model. 11
Table 3.7: Bioavailability (dissolved phase concentration in the water column) of PAHs at Finokalia
remote station of the eastern Mediterranean Sea. 14
Table 3.8: Bioaccumulation of Pyrene in different biotic compartments. 14
Table 3.9: BCF of PAHs compounds into different biotic compartments. 15
Table 3.10: Bioavailability (dissolved phase in the water column) of PAHs at Finokalia remote station
of the eastern Mediterranean Sea. 18
Table 3.11: Bioaccumulation of PCBs in zooplankton under open sea conditions of remote site of
Finokalia station (Create) in Eastern Mediterranean. 18
Table 3.12: BCF of PCBs under open sea conditions of remote site of Finokalia station (Create) in
Eastern Mediterranean. 19
Table 4.1:Surface salinities in the Thau lagoon. 29
Table 4.2:Total dissolved Cd concentrations (nM/l) in February and September2006. 29
Table 4.3:Surface particulate Cd and Cr concentrations (µg/g). 30
Table 4.4:Electroactive Cd determination on C4 February 2006. 30
Table 4.5:Electroactive Cd determination. C4. September 2006. 31
Table 4.6:Electroactive Cd determination on station C5S in February 2006. 31
Table 4.7:Electroactive Cd determination. Station C5S September 2006. 32
Table 4.8:Electroactive Cd determination. Station T12. February 2006. 32
Table 4.9:Electroactive Cd determination Station T12 September 2006. 33
Table 4.10:Total labile and free dissolved Cd concentrations in the Thau lagoon. Percentages of
electroactive (labile) Cd in the dissolved phase. 33
Table 4.11: Summary statistics on mercury species concentrations (pM) measured in the water column
of the Thau Lagoon in February, April and September 2006. Mean ± standard deviation (number of
determinations). 37
List of Figures Figure 3.1. Picture of main phytoplankton species considered for the Adriatic Sea: Prorocentrum
minimum (on the left) and Skeletonema costatum (on the top). 8
Figure 3.2. Simulated bioacccummulation of PAHs compounds: fluorene, phenanthrene, pyrene and
fluoranthene in phytoplankton compartments during two-year period. 14
Figure 3.3. Correlations between BCF factors and KOW of PAHs for different biotic elements. 15
Figure 3.4. Annual, seasonal and daily variability of PCBs (TeCB-52 and HxCB-138) in terms of total
concentration and dissolved, particulate and DOC phases into surface water layer, complemented by
the atmospheric-water gas exchange and dry and wet deposition fluxes and a comparison with
measurements (dissolved phase) taken from Schultz-Bull et al., (1997); Mandalakis and Stephanou,
(2004) and Mandalakis et al. (2005). 17
Figure 3.5. Correlations of BCF factors and KOW for PCBs and different biotic compartments. 18
Figure 3.6. Distribution of HxCDD between the dissolved, particulate and DOC fraction, results from
simulation. 19
5
Figure 3.7. Dynamics of bioaccumulation of different PCDD/Fs congeners in phytoplankton (results
form model simulation over 2 year period). 20
Figure 3.8. Annual average, minimum and maximum of the dissolved concentration of different
PCDD/Fs congeners (model results). 20
Figure 3.9. Bioconcentration factor of different PCDD/Fs congeners in phytoplankton, bacteria and
zooplankton as a function of KOW. 21
Figure 3.10. Bioconcentration factor of different PCDD/Fs congeners in phytoplankton, bacteria and
zooplankton 22
Figure 3.11. Example of PAHs concentrations (µg kg-1
) found in mussels at Thau lagoon at two
sampling stations: Thau1 (blue), Thau4 (pink). 23
Figure 3.12. Example of PCB congeners concentrations (µg kg-1
) found in mussels at Thau lagoon at
two sampling stations: Thau1 (blue), Thau4 (pink). 23
Figure 4.1.Sampling stations C4, C5 and T12 in the Thau lagoon. 28
Figure 4.2.Electroactive Cd determination on C4 February 2006. 30
Figure 4.3.Electroactive Cd determination. C4. September 2006. 31
Figure 4.4.Electroactive Cd determination on station C5S in February 2006. 31
Figure 4.5.Electroactive Cd determination. station C5S September 2006. 32
Figure 4.6.Electroactive Cd determination. Station T12. February 2006. 32
Figure 4.7.Electroactive Cd determination Station T12 September 2006. 33
Figure 4.8. Mercury species and cycling. 35
Figure 4.9. A/ Vertical profiles for HgTD in the water column of the Thau Lagoon.B/ Vertical profiles
for MeHgD in the water column of the Thau Lagoon. 37
Figure 4.10. MeHgD versus PO4 relationship in the water column of the Thau Lagoon. 39
6
1. INTRODUCTION
Contaminants produced by industrial and urban activities are continuously released to the atmosphere
and to the water. The aquatic environment is often the ultimate sink for these compounds, and
organisms may suffer from consequences related to the exposure to contaminated water and sediments.
However, only a fraction of the bulk amount of the chemical present in sediment and water is available
to be taken up into the organism’s tissue. This fraction is called the bioavailable concentration.
The degree of bioavailability depends on several factors, like the sediment characteristics (particle size,
OM content, OM composition), the residence time of the compound in the sediment (longer residence
usually decrease bioavailability), the interaction with dissolved organic matter DOM as well as
physico-chemical characteristics of the compound and its partitioning in the system. Also the vector of
contamination plays an important role in determine the bioavailability. For example, dioxins introduced
into the environment by the atmospheric deposition of black carbon remain quasi irreversibly particle
bound and therefore their bioavailability is very low.
Recently, there have been some progresses in the development of techniques for the assessment of
bioavailable pollutant concentration in sediment and water. Semipermeable membrane devices
(SPMDs) and Diffusive Gradient in Thin Films (DGT) have been successfully used for the assessment
of bioavailable concentration of organic pollutants and metals, respectively in the Seine River
(Tusseau-Vuillemin et al. 2007). A review on passive sampling devices and their application for
organic chemicals can be found in (Stuer-Lauridsen, 2005).
The definition of the maximal concentrations of contaminants that can be tolerated in a specific
environmental compartment should consider only the bioavailable part of contamination, not the total
concentration. In fact, only the part of the concentration that can be taken up by the biota represents a
real risk for the ecosystem and the human health. Therefore, environmental legislation should be based
on the concept of bioavailability; this will provide effective health protection and avoid unnecessary
economic pressures.
2. PARTITIONING BEHAVIOUR OF CONTAMINANTS IN BIOTA
2.1. Bioconcentration
The bioconcentration factor (BCF) of a compound is defined as the ratio of concentration of the
chemical in the organism and in water at equilibrium.
w
i
C
CBCF = (1)
7
The uptake of a chemical from water is a passive diffusion process across the skin or gill membrane,
similar to oxygen uptake. Several factors affect this uptake, such as the physicochemical characteristics
of the compound, the characteristics of the receptor and the environmental conditions. For example,
(Boese 1984) demonstrated that decreasing oxygen level in the water accelerated the accumulation of
contaminants in the body of clams. Moreover, bioconcentration is related to the octanol-water partition
coefficient of the compound and the lipid fraction in tissues of the organism (Van der Oost et al.,
2003).
Several log-linear correlations exist between the logarithm of the octanol-water partition coefficient
and the BCF (e.g.: Devillers et al., 1996; Hawker and Connel, 1985). Furthermore, experiments have
been carried out to measure the time required to reach equilibrium between water and fish
concentration. For rainbow trout Vigano et al. (1994) measured a time range between 15 and 256 days
to reach equilibrium after exposure to different concentrations of PCBs, while for OCPs Galassi et al.
(1996) measured a range between 56 and 275.
2.2. Biomagnification
The biomagnification factor is defined as the ratio between the uptake of a contaminant from food and
its removal (Sijm et al., 1992),
metaecxdep
food
KKK
KBMF
++= (2)
The uptake from food can be also defined as:
FFfood effFK ⋅= (3)
where FF is the quantity of food ingested per unit mass per unit time and effF is the efficiency of uptake
of the chemical from food.
Russell et al. (1999) demonstrated that significant biomagnification is not observed for values of log
Kow lower than 5.5. Moreover, Fisk et al. (1998) observed a high potential to accumulate along aquatic
food webs for chemicals with log Kow ≈ 7.
Laboratory experiments demonstrated that digestibility and absorption of food are critical parameters
controlling the BCFs in fish (Gobas et al. 1999). Furthermore, Opperhuizen (1991) found that
biomagnification accounts for a more important fraction of accumulation of chemicals for larger fish
than for smaller fish, which is probably due to a decrease in gill ventilation volume while the relative
feeding rate is almost the same.
2.3. Bioaccumulation
Transfer mechanisms of persistent hydrophobic contaminants in aquatic organisms are essentially two:
8
the first one is the direct uptake of dissolved phase from water trough skin or gills, named
bioconcentration, the second one is the indirect uptake of bound contaminants to suspended particular
matter and through consumption of contaminated food (biomagnification).
The bioaccumulation of pollutants may be an important source of hazard for the ecosystem, due to
adverse effect not quickly evident (e.g. acute or chronic toxicity) but that became manifested after
years in the higher levels of the trophic food web or in a later stage of life of organisms or after several
generations (Van der Oost et al., 2003).
The mass balance of a contaminant (A) in the tissue of an aquatic organism can be defined as (adapted
from Thomann, 1989 and Thomann et al., 1992):
iGimetaidepfoodfood
diss
Aupt
i CkCkCkCkCkdt
dC−−−+= (4)
where the first two terms indicate the uptake of contaminant from water and predation, respectively,
and the third, fourth and fifth terms indicate losses of contaminants through depuration (release from
gill membranes or excretion through feces), metabolism and dilution effect of growth, respectively.
2.4. Biotransformation
Removal of chemicals in an aquatic organism is realized essentially through two main pathways: the
contaminant is either eliminated by depuration/excretion in the original chemical form (parent
molecule) or bio-transformed by the organism. The latter process leads in general to the formation of
more hydrophilic compounds. In this case the metabolites are rapidly excreted after a detoxification
reaction. These compounds are normally less harmful than the parent compound. However, in some
cases the parent compound can be “bioactivated” through metabolic reactions and lead to formation of
a metabolite more toxic than the former molecule (Van der Oost, et al., 2003).
The velocity and efficiency of metabolic clearance have been demonstrated to be a function of several
species-specific characteristics: presence of enzymes, feeding status, stage of life, spawning period
(Van der Oost et al., 2003).
3. BIOAVAILABILITY OF PERSISTENT ORGANIC POLLUTANTS (POPs)
3.1. Modelling the bioavailability of POPs
In order to calculate the bioavailability of POPs and to assess the bioaccumulation of these compounds,
we assume that their concentrations in the dissolved phase are calculated using the fate model
developed in Marinov et al. (2007) and validated for PAHs and coupled with a simple ecological model
in Zaldívar et al. (2007). In this case, after solving the mass balance equations that gives the total
9
concentration in the water column we split the contaminant as dissolved, attached to dissolved organic
carbon and to particulate organic matter. The bioavailable concentration for the organism is only the
dissolved phase concentration (Schwarzenbach et al., 2003).
3.1.1. Bioconcentration in phytoplankton and bacteria
Bioconcentration of contaminants by phytoplankton can be calculated assuming constant uptake and
depuration rates and by modelling the water-phytoplankton exchange as shown by Del Vento and
Dachs (2002).
The concentration of a chemical in the two phytoplankton groups (CPd ,CPf) and for bacteria (CB) over
time can be expressed using Eq. (4), assuming there is no biomagnification (kfood= 0), a self-sustained
phytoplankton community (kG= 0), and a metabolism rate much lower than the depuration rate. Under
these assumptions Eq. (4) becomes:
Pd
Pd
dep
dis
PAH
Pd
upt
Pd CkCkdt
dC⋅−⋅= (5)
Pf
Pf
dep
dis
PAH
Pf
upt
PfCkCk
dt
dC⋅−⋅= (6)
B
B
dep
dis
PAH
B
uptB CkCk
dt
dC⋅−⋅= (7)
where kupt (m3 ng
-1 h
-1) and kdep (h
-1) are the uptake and depuration rates constants. Bacteria feed on
detritus. However, it is assumed that there is no egestion and therefore we do not consider the
concentration in the particulate phase (Detritus).Uptake and depuration constants can be parameterized
as function of bioconcentration factors of the chemical, permeability (P, m/h) of the cell membrane and
specific surface area (Sp, m2/kg) (Del Vento and Dachs, 2002):
PSk
BCF
PSk
pupt
p
dep
⋅=
⋅=
(8)
The specific surface area of phytoplankton has been estimated by assuming oblate ellipsoid shape for
flagellates and cylinder shape for diatoms, taking into account the shapes of the dominant species of
each class in the Adriatic Sea: (Prorocentrum minimum (Fig. 3.1) for flagellates and Skeletonema
costatum for diatoms)(Regione Emilia Romagna, 2002). In particular the volume (Vf) and surface area
(Af) of flagellates are given by:
10
ppppppp
f
f
cbcabaA
abcV
/1
34
3
4
++⋅=
=
π
π
(9)
where a, b and c are the lengths of the three semi-axes, determining the shape of the ellipsoid and p ≈
1.6075 (Knud Thomsen’s formula).The lengths of a, b and c have been set equal to 18, 12.5 and 12.5
µm (http://www.nmnh.si.edu/botany/projects/dinoflag/Taxa/Pminimum.htm). Diameters of diatoms
cells and pervalvaraxis are taken as of 11.5 µm of diameter and 31.5 µm of height respectively
(http://elvire.antajan.chez-alice.fr/Diatoms/Skeletonema.html). The density of phytoplankton (ρphyto) is
taken as of 1025 Kg/m3 (Del Vento and Dachs, 2002). This gives a volume of 1.18
.10
-14 m
3 and
3.27.10
-15 m
3, areas of 2.56
.10
-9 and 1.35
.10
-9 m
2, and specific surface areas (Sp) of 211.57 and 401.29
m2 kg
-1, respectively.
Figure 3.1. Picture of main phytoplankton species considered for the Adriatic
Sea: Prorocentrum minimum (on the left) and Skeletonema costatum (on the
top).
Bioconcentration of contaminants in bacteria has been calculated in the same way and the specific
surface area (Sp) has been calculated assuming a diameter of 1 µm, spherical shape and density (ρbac)
equal to 1080 kg m-3
(Hailiang et al., 2002), which gives 2777.78 m2 kg
-1.
In order to predict uptake and depuration rates it is necessary to know values for BCF and P. Since
estimations of BCF and P exist only for a few number of compounds (e.g. Skoglund et al., 1996;
Wallberg and Andersson, 1999; Swackhamer and Skoglund. 1993), these parameter has been
calculated using empirical approximation based on the physical-chemical properties of the
contaminant.
It has been demonstrated (Swackhamer and Skoglund, 1993; Stange and Swackhamer, 1994) that, for
11
many organic compounds, the logarithm of the bioconcentration factor plotted against the logarithm of
the octanol/water partition coefficient gives two linear correlations (with a plateau in correspondance to
log Kow ≈ 6.5, that can be fitted by least squares and may be represented by the following log linear
equations (Del Vento and Dachs, 2002):
log BCF= 1.085 log Kow – 3.770 for log Kow < 6.4 (10)
log BCF= 0.343 log Kow + 0.913 for log Kow ≥ 6.4 (11)
The same considerations can be made for the estimation of permeability of cell membrane and similar
regressions have been proposed (Del Vento and Dachs, 2002):
log P= 1.340 log Kow – 8.433 for log Kow < 6.4 (12)
log P= 0.078 for log Kow ≥ 6.4 (13)
Table 3.1-3.3 summarizes the uptake and depuration constants used in Eqs. (5)-(7) to calculate the
concentrations of PAHs, PCBs and PCCD/Fs in diatoms, flagellates and bacteria.
Table 3.1. Uptake (m3.
kg-1.
d-1
) and depuration (d-1
) constants for PAHs used in the model.
Compound (PAHs) log Kow Diatoms (Pd) Flagellates (Pf) Bacteria (B)
kupt kdep kupt kdep kupt kdep
Naphthalene 3.37 0.0486 0.0631 0.0256 0.0333 0.336 0.436
Fluorene 4.12 0.491 0.0979 0.259 0.0517 3.400 0.678
Antracene 4.54 1.795 0.125 0.946 0.0661 12.425 0.868
Phenanthrene 4.57 1.969 0.128 1.038 0.0673 13.630 0.883
Pyrene 5.17 12.539 0.181 6.611 0.0957 86.796 1.256
Fluoranthene 5.22 14.630 0.187 7.714 0.0985 101.274 1.294
Benzo[a]anthrecene 5.84 99.097 0.269 52.246 0.142 685.96 1.862
Chrysene 5.84 99.097 0.269 52.246 0.142 685.96 1.862
Benzo [a]pyrene 6.04 183.679 0.302 96.840 0.159 1271.446 2.094
Benzo[b]fluoranthene 6.44 480.240 0.363 253.194 0.191 3324.282 2.511
Benzo[k]fluoranthene 6.44 480.240 0.363 253.194 0.191 3324.282 2.511
Indeno[1,2,3-cd]pyrene 6.58 480.240 0.325 253.194 0.171 3324.282 2.248
Benzo[ghi]perylene 6.90 480.240 0.252 253.194 0.133 3324.282 1.746
Table 3.2. Uptake (m3.
kg-1.
d-1
) and depuration (d-1
) constants for PCBs used in the model.
Compound (PCBs) log Kow Diatoms (Pd) Flagellates (Pf) Bacteria (B)
kupt kdep kupt kdep kupt kdep
PCB28 5.67 58.649 0.243 30.921 0.128 405.974 1.685
PCB52 5.80 87.591 0.263 46.180 0.139 606.314 1.818
PCB101 6.40 480.240 0.374 253.194 0.197 3324.282 2.591
PCB118 6.70 480.240 0.295 253.194 0.156 3324.282 2.045
PCB138 6.83 480.240 0.267 253.194 0.141 3324.282 1.845
PCB153 6.92 480.240 0.248 253.194 0.131 3324.282 1.718
PCB180 7.40 480.240 0.170 253.194 0.090 3324.282 1.176
12
Table 3.3. Uptake (m3.
kg-1.
d-1
) and depuration (d-1
) constants for PCDD/Fs used in the model. Compound (PCDD/Fs) log Kow Diatoms (Pd) Flagellates (Pf) Bacteria (B)
kupt kdep kupt kdep kupt kdep
TCDD 6.9 480.240 0.252 253.194 0.133 3324.282 1.746
PeCDD 7.4 480.240 0.170 253.194 0.090 3324.282 1.176
HxCDD 7.8 480.240 0.124 253.194 0.065 3324.282 0.858
HpCDD 8.0 480.240 0.106 253.194 0.056 3324.282 0.732
OCDD 8.2 480.240 0.090 253.194 0.048 3324.282 0.625
TCDF 7.7 480.240 0.134 253.194 0.071 3324.282 0.928
PeCDF 7.6 480.240 0.145 253.194 0.076 3324.282 1.004
HxCDF 7.7 480.240 0.134 253.194 0.071 3324.282 0.928
HpCDF 7.5 480.240 0.157 253.194 0.083 3324.282 1.087
OCDF 7.6 480.240 0.145 253.194 0.076 3324.282 1.004
3.1.2. Bioaccumulation in zooplankton
In the case of zooplankton, we have also to consider the intake due to food consumption as well as the
egestion and metabolization (Berrojalbiz et al., 2006). In this case the concentration of a chemical in
the two zooplankton groups (CZs ,CZl) over time can be expressed as:
Zs
Zs
mZs
Zs
eZs
Zs
dp
Zs
g
dis
PAH
Zs
u
Zs CkCkCkCkCkdt
dC⋅−⋅−⋅−⋅+⋅= (14)
Zl
Zl
mZl
Zl
eZl
Zl
dp
Zl
g
dis
PAH
Zl
u
Zl CkCkCkCkCkdt
dC⋅−⋅−⋅−⋅+⋅= (15)
where kg (m3.
kg-1.
d-1
) , ke (d-1
) and km (d-1
) are the grazing, egestion and metabolization rate constants.
For the case of several PAHs these constants have been obtained experimentally by Berrojalbiz et al.
(2006). Their values are reported in Table 3.4 and they have been introduced in the model.
Table 3.4. Uptake (m3.
kg-1.
d-1
), depuration (d-1
), grazing (m3.
kg-1.
d-1
), egestion (d-1
) and metabolism (d-
1) constants for PAHs used in the model (determined experimentally by Berrojalbiz et al., 2006).
Compound (PAHs) Zuk
Zdk
Zgk
Zek
Zmk
Fluorene 1.23 69.38 1.21 9.95 0.56
Phenanthrene 23.04 167.56 5.41 10.37 0.39
Pyrene 113.27 371.78 128.06 17.86 1.03
Fluoranthene 117.230 519.87 115.87 19.64 0.99
For the case of PCBs and PCCD/Fs, we did not have experimental results within the Tresholds project.
Therefore, values for the constants have been taken from existing correlations in literature.
Following Farley et al. (1999) the uptake constant for aquatic species can be expressed as:
][ 2
2
O
Rk
O
u β= (16)
where β is a transfer efficiency constant- between 0.5 and 0.33-, RO2 is the respiration rate, and [O2] is
the dissolved oxygen concentration, which in our case is assumed constant and equal to 8.0 g m-3
. The
respiration rate in g O2 gww-1
d-1
can be calculated according with Thomann (1989) as:
13
RfaaR drywtdrywtcarboncarbonoxygenO ⋅⋅⋅= −−2
(17)
where for respiration rates in oxygen equivalents aoxygen-carbon, acarbon-dry wt , and fdry wt are taken as 2.67,
0.4 and 0.2, respectively (Farley et al., 1999) and R for zooplankton is calculated as (Farley et al.,
1999):
TeR
⋅⋅= 06293.001249.0 (18)
The depuration constant indicates the chemical losses from gill and skin, and they can be expressed as
(Farley et al., 1999):
owlipid
ud
Kf
kk
⋅= (19)
where flipid is the fraction lipid weight (kg (lp)/kg(ww)), which in zooplankton is 0.06 (Farley et al.,
1999).This equation assumes that the same transport mechanisms responsible of chemical uptake from
water are active as well in the transport out of lipidic cell membranes.
The excretion constant (ke) was taken from Van der Linde et al. (2001) and set constant to 0.05 d-1
,
which is the average value for chlorinated dioxins, furans and PCBs
The contaminant metabolic rate (km) is strictly related to specific chemical-physical properties of the
compound and to the particular metabolic processes and enzymes of the organism. For the case of
PCBs and PCDD/Fs is normally assumed negligible (Farley et al., 1999).
Table 3.5. Uptake (m3.
kg-1.
d-1
), depuration (d-1
), egestion (d-1
) and metabolism (d-1
) constants for PCBs
used in the model.
Compound (PCBs) Zuk
Zdk
Zek
Zmk
PCB28 0.429 1.53
.10
-5 0.05 0
PCB52 0.386 1.02.10
-5 0.05 0
PCB101 0.386 2.56.10
-6 0.05 0
PCB118 0.386 1.28.10
-6 0.05 0
PCB138 0.282 6.95.10
-7 0.05 0
PCB153 0.282 5.65.10
-7 0.05 0
PCB180 0.282 1.87.10
-7 0.05 0
Table 3.6. Uptake (m3.
kg-1.
d-1
), depuration (d-1
),grazing (m3.
kg-1.
d-1
), egestion (d-1
) and metabolism (d-1
)
constants for PCDD/Fsused in the model.
Compound (PCCD/Fs) Zuk
Zdk
Zek
Zmk
TCDD 0.386 8.10
.10
-7 0.05 0
PeCDD 0.386 2.56.10
-7 0.05 0
HxCDD 0.282 7.45.10
-8 0.05 0
HpCDD 0.282 4.70.10
-8 0.05 0
OCDD 0.282 2.97.10
-8 0.05 0
TCDF 0.386 1.28.10
-7 0.05 0
PeCDF 0.386 1.62.10
-7 0.05 0
14
HxCDF 0.282 9.38.10
-8 0.05 0
HpCDF 0.282 1.49.10
-7 0.05 0
OCDF 0.282 1.18.10
-7 0.05 0
Concerning grazing, the model use the values provided by the ecological model developed in Zaldívar
et al. (2007), taking into account the diets of micro- and macro-zooplankton. In this case, following
Oguz et al. (1999), we define the total food availability for each zooplankton group as:
BbPdbPfbF BPdPfZs ⋅+⋅+⋅= and ZsaPdaPfaF ZsPdPfZl ⋅+⋅+⋅= (20)
where aPf, aPd, aZs (0.3,0.8,0.7) and bPf, bPd, bB (0.7,0.2,0.5) are the food preference coefficients for
flagellates, diatoms, bacteria and micro-zooplankton, respectively. Grazing rates of microzooplankton
are then defined as:
ZsG
PdZsZs
PdFK
Pdbggrazing
+
⋅= max (21)
ZsG
PfZsZs
PfFK
Pfbggrazing
+
⋅= max (22)
ZsG
BZs
maz
Zs
BFK
Bbggrazing
+
⋅= (23)
where KG is an apparent half saturation constant (KG =0.5 mmol N m-3
) and Zsgmax is the maximum
grazing rate which is defined as a function of temperature as:
−−=
2
'
max expwidth
opt
Zs
Zs
T
TTgg (24)
with Topt and Twidth being the optimal temperature and the range of suitable temperatures, respectively.
In this case for microzooplankton:Topt=23.0 ºC and Twidth=8.0 ºC. The maximum specific grazing rate
for microzooplankton is gZs’= 0.036 h-1
.
The grazing rates of mesozooplankton are then defined as:
ZlG
PdZlZl
PdFK
Pdaggrazing
+
⋅= max (25)
ZlG
PfZlZl
PfFK
Pfaggrazing
+
⋅= max (26)
ZlG
ZsZlZl
ZsFK
Zsaggrazing
+
⋅= max (27)
where KG is an apparent half saturation constant (KG =0.5 mmol N m-3
) and Zlgmax is the maximum
grazing rate which is defined as a function of temperature as:
15
−−=
2
'
max expwidth
opt
Zl
Zl
T
TTgg (28)
with Topt and Twidth being the optimal temperature and the range of suitable temperatures respectively.
In this case for mesozooplankton: Topt=23.0 ºC and Twidth=8 ºC. The maximum specific grazing rate for
mesozooplankton is gZs’=0.033 h-1
.
In order to calculate the amount of ingested contaminant by food, the grazing is multiplied by the
assimilation coefficients ( effP, effZs and effB ) which are equal to 0.75 and by the concentration in each
biological compartment. In this way, the grazing constant, obtained experimentally for PAHs, is now a
variable.
In conclusion, the developed integrated hydrodynamic-ecological-contaminant model (Zaldívar et al.,
2007) was foreseen not only to calculate the fate of pollutants in aquatic environment but also to
simulate the bioaccumulation and biomagnification of POPs into different biotic compartments, starting
with the primary producers (phytoplankton) and continuing with higher trophic levels (zooplankton)
and bacteria. For that reason during the current application we have been able to simulate the
concentrations in these ecological compartments for several POPs families as PAHs, PCBs and
PCDD/Fs assuming open sea conditions and not counting the toxic effects of POPs on the biota
(missing relevant dose-response curves). That is why we run the model as 1D vertical application using
forcing data for the remote site of Finokalia station in Eastern Mediterranean - 35º19' N, 25º40' E,
Island of Crete, Greece (Tsapakis and Stephanou, 2005 and Tsapakis et al., 2006). Besides, the model
set up and parameters - vertical grid, meteorological data, boundary and initial conditions, etc. - in
connection with the present model application were similar to those used during the integrated model
validation and then could be found in the reports Marinov et al. (2007) and Zaldívar et al. (2007).
3.2. Biovailability of PAHs
The bioavailability of PAHs has been studied for seven selected congeners fluorene, antharacene,
phenanthrene, pyrene, fluoranthene, benzo[a] antharacene and chrysene while the PAHs
bioaccumulation was investigated only for those congeners for which experimentally estimated uptake,
depuration, grazing, egestion and metabolization rate constants for biotic compartments were available
(Berrojalbiz et al., 2006). The calculated bioavailable (dissolved phase in the water column)
concentrations of PAHs at Finokalia remote station of the eastern Mediterranean Sea are presented in
Table 3.7 in terms of minimum, maximum and annual average values.
16
Table 3.7. Bioavailability (dissolved phase concentration in the water column) of PAHs at Finokalia
remote station of the eastern Mediterranean Sea. PAHs (dissolved phase) [ng/m³] Min. value Max. value Annual average
Fluorene 28 306 137
Anthracene 16 180 82.5
Phenanthrene 55 1170 611
Pyrene 36 221 117
Fluoranthene 43 320 169
Benzo[a]anthracene 9 67 38
Chrysene 16 159 52
Concerning the bioaccumulation of PAHs (see Fig. 3.2) it was found that, for example, the
concentrations in phytoplankton follow the dynamics of the dissolved concentrations in the water
column and ecosystem seasonal/annual cycling (Zaldívar et al., 2007). This explains the lower
bioaccumulation in the phytoplankton during the winter period (for example monthly average for
pyrene is 3000ng/kg) which grows in the spring and reach the annual maximum in May-June (about
13200ng/kg for pyrene) and then gradually declines in the summer followed by a little autumn increase
(the second phytoplankton bloom) and finally is stabilized to the usual winter values. More details
about Pyrene bioaccumulation into the other throphic levels were given in Table 3.8. The other biotic
compartments (bacteria and zooplankton) demonstrate equivalent seasonal/annual bioaccumulation
dynamics but with higher daily oscillations. Moreover, the bacteria undertake similar amounts of
PAHs, while the zooplankton, since its specific metabolic/depuration processes, has actually very little
PAHs bioaccumulation and the concentrations found are more than two orders of magnitude lower
when compared with the planktonic compartments.
Table 3.8. Bioaccumulation of Pyrene in different biotic compartments.
Bioaccumulation of Pyrene in biotic compartments [ng/kg] Min. value Max. value Annual average
Phytoplankton 51 15600 7822
Bacteria 610 16880 8536
Zooplankton 10 74 37
PAHs in phytoplankton: [ng/kg]
0.00E+00
5.00E+03
1.00E+04
1.50E+04
2.00E+04
2.50E+04
3.00E+04
01/0
1/0
1
01/0
3/0
1
01/0
5/0
1
01/0
7/0
1
01/0
9/0
1
01/1
1/0
1
01/0
1/0
2
01/0
3/0
2
01/0
5/0
2
01/0
7/0
2
01/0
9/0
2
01/1
1/0
2
01/0
1/0
3
Time [month]
fluorene
phenanthrene
pyrene
fluoranthene
Figure 3.2. Simulated bioacccummulation of PAHs compounds: fluorene, phenanthrene, pyrene and
fluoranthene in phytoplankton compartments during two-year period.
17
In addition the Bioconcentration Factor (BCF) for the PAHs compounds into different biotic
compartments has been determined and for instance the average during the considered two-year period
BCF of Pyrene in phytoplankton is 66.85 while for zooplankton it is 211.5 times smaller. This is due to
the fact that we have used the metabolization rates defined by Berrojalbiz et al. (2006).
Table 3.9. BCF of PAHs compounds into different biotic compartments. BCF of PAHs [ng/kg / ng/m³] Phytoplankton Bacteria Zooplankton KOW
Fluorene 4.07 5.00 0.017 1.32.10
4
Phenanthrene 13.75 13.71 0.133 1.72.10
4
Pyrene 66.85 72.95 0.316 1.51.10
5
Fluoranthene 72.39 79.11 0.230 1.66.10
5
The simulations evidence (see Fig.3.2) that the low KOW PAHs are considerably less bioaccumulated
with almost constant values during the year (for example for fluorene ca. 560ng/kg in phytoplankton).
Furthermore, good correlations between BCF factors and KOW of PAHs for phytoplankton and bacteria
as well as for zooplankton have been found (see Fig. 3.3). The lower R2 values found for zooplankton
are due to the fact that in the model metabolization constants are different and therefore the correlation
with KOW is less evident in this case.
BCF of PAHs [ ng/kg / ng/m3]
y = 1E-06x + 0.0564
R2 = 0.7626
y = 0.0004x + 2.34
R2 = 0.9882
0.01
0.1
1
10
100
1000
1.E+03 1.E+04 1.E+05 1.E+06
Kow [-]
BC
F
phyto&bac-pln
zooplankton
Figure 3.3. Correlations between BCF factors and KOW of PAHs for different biotic elements.
3.3. Bioavailability of PCBs
The integrated hydrodynamic-ecological-contaminant model to calculate the fate and bioaccumulation
of contaminants in aquatic environment has been tested and verified for PAHs family (Zaldívar et al.,
2007) under open sea conditions of the remote site of Finokalia station (Crete) in Eastern
Mediterranean (Tsapakis and Stephanou, 2005 and Tsapakis et al., 2006). Aiming to undertake further
18
model verification in this part we checked the model applicability for another group of POPs - those of
PCBs. In parallel the bioavailability and bioaccumulation of PCBs in different species has been also
investigated.
Similarly to the PAHs case study, remote open sea conditions have been supposed again and the
corresponding forcing and PCBs measurements (atmospheric gas and particulate phase and fractions
into the water column) were taken from Schultz-Bull et al. (1997), Mandalakis and Stephanou (2004)
and Mandalakis et al. (2005). During this exercise intending to see the impact of chlorination on the
fate of the biphenyl compounds seven PCBs isomers have been considered – TriCB-28, TeCB-52,
PeCB-101, PeCB-118, HxCB-138, HxCB153 and HpCB-180. The results evidenced that in general the
model reproduced the observations with 50% error level. The specific details about annual, seasonal
and daily variability of PCBs in terms of total concentration and dissolved, particulate and DOC phases
into surface water layer, complemented by the atmospheric-water gas exchange and dry and wet
deposition fluxes and comparison with measurements taken from Schultz-Bull et al. (1997);
Mandalakis and Stephanou (2004) and Mandalakis et al. (2005) are presented only for TeCB-52 and
HxCB-138 in Figure 3.4. Obviously the increased chlorination of PCBs diminished dissolved but
expanded particulate and DOC phases and led to reduction of air-water exchange.
19
J F M A M J J A S O N D J F M A M J J A S O N D0
0.5
1
1.5
2
2.5
3
3.5PCB52: total, dissolve, particulate and DOC [ng/m3]
time [month]
total conc.
diss. phase
part. phase
DOC phase
diss. phase
J F M A M J J A S O N D J F M A M J J A S O N D-0.5
0
0.5
1
1.5
2PCB52: air-water fluxes [ng/m2/d]
time [month]
AW gas
wet dep.
dry dep.
vol abs w-par w-gas w-r.w. dry total measured-2
-1
0
1
2
3
4x 10
-4 PCB52: annual air-water fluxes [mg/m2/y]
J F M A M J J A S O N D J F M A M J J A S O N D0
0.5
1
1.5
2
2.5
3
3.5PCB138: total, dissolve, particulate and DOC [ng/m3]
time [month]
total conc.
diss. phase
part. phase
DOC phase
dissoled phase
J F M A M J J A S O N D J F M A M J J A S O N D-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4PCB138: air-water fluxes [ng/m2/d]
time [month]
AW gas
wet dep.
dry dep.
vol abs w-par w-gas w-r.w. dry total measured-4
-2
0
2
4
6
8
10x 10
-5 PCB138: annual air-water fluxes [mg/m2/y]
Figure 3.4. Annual, seasonal and daily variability of PCBs (TeCB-52 and HxCB-138) in terms of total
concentration and dissolved, particulate and DOC phases into surface water layer, complemented by
the atmospheric-water gas exchange and dry and wet deposition fluxes and a comparison with
measurements (dissolved phase) taken from Schultz-Bull et al., (1997); Mandalakis and Stephanou
(2004) and Mandalakis et al. (2005).
Furthermore, a summary of the bioavailability of PCBs for aquatic biota and PCBs bioaccumulation
(only for zooplankton) under open sea conditions of remote site of Finokalia station (Create) can be
found on Tables 3.10 and 3.11, respectively. It was found that the maximum bioavailability of Pe-CBs
and Hx-CBs and their physical-chemical properties imposed higher bioaccumulation of these isomers
in lower food-chain levels compared to the others PCB congeners.
20
Table 3.10. Bioavailability of PCBs under open sea conditions of remote site of Finokalia station
(Create) in Eastern Mediterranean. PCBs (dissolved phase) [ng/m³] Min value Max value Annual average
PCB28 0.14 1.96 0.53
PCB52 0.53 1.40 0.77
PCB101 0.69 1.14 0.86
PCB118 0.54 1.06 0.86
PCB138 0.38 0.92 0.63
PCB153 0.41 1.33 0.82
PCB180 0.14 0.52 0.29
Table 3.11. Bioaccumulation of PCBs in zooplankton under open sea conditions of remote site of
Finokalia station (Create) in Eastern Mediterranean. PCBs bioaccumulation in zooplankton [ng/kg] Min value Max value Annual average
PCB28 6 635 160
PCB52 21 1020 289
PCB101 62 4260 1270
PCB118 85 5400 1610
PCB138 57 5290 1360
PCB153 75 8460 1940
PCB180 36 4650 1020
In addition a comparison of calculated BCF of PCBs under open sea conditions for phytoplankton,
bacteria and zooplankton are given in Table 3.12 depending on the chlorine number of atoms or KOW.
The results clearly indicate a higher BCF for more chlorinated PCB compounds (or those with higher
KOW) for all biotic compounds and for instance the differences between TriCB-28 and HpCB-180
could reach more than one order magnitude difference. Besides, a gradual increase of BCF factors
throughout the food-chain levels for single PCB congeners has been observed.
BCF of PCBs [(ng/kg) / (ng/m3)]
y = 802.62Ln(x) - 10351
R2 = 0.9768
y = 634.33Ln(x) - 8135.4
R2 = 0.9849
0
500
1000
1500
2000
2500
3000
3500
4000
1.E+05 1.E+06 1.E+07 1.E+08
Kow [-]
BC
F
phyto&bac-pln
zooplankton
Figure 3.5 Correlations of BCF factors and KOW for PCBs and different biotic compartments.
21
Table 3.12. BCF of PCBs under open sea conditions of remote site of Finokalia station (Create) in
Eastern Mediterranean. BCF of PCBs [ng/kg / ng/m³] Phytoplankton Bacteria Zooplankton KOW
PCB28 224 236 302 4.67e5
PCB52 334 335 375 6.92e5
PCB101 1267 1279 1477 2.39e6
PCB118 1604 1616 1872 5.49e6
PCB138 1746 1793 2159 6.76e6
PCB153 1841 1902 2366 8.31e6
PCB180 2706 2827 3517 2.29e7
Good correlations between BCF factors for phytoplankton and bacteria, considered together because
possess identical BCF, and for zooplankton (considered individually) with the octanol-water partition
coefficient (KOW) of PCBs have been found (see Fig. 3.5).
3.4. BIOAVAILABILITY OF PCDD/Fs
Polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are two groups
of almost planar tricyclic aromatic compounds with a number of chlorine atoms that can vary between
1 and 8. These compounds have low solubilities and are highly lipophilic, thus they tend to accumulate
in organisms and to adsorb to particles. In the environment PCDD/F are most likely associated to
particulate matter or dissolved organic carbon. (Muir et al. 1992). This trend is illustrated in Figure
3.6, which shows the distribution of HxCDD between different fractions (dissolved, particulate and
DOC) obtained from model simulations.
Surface water layer concentration of HxCDD
[ng/m3]
00.0050.01
0.0150.02
0.0250.03
0.0350.04
0.045
1/1
/01
3/1
/01
5/1
/01
7/1
/01
9/1
/01
11/1
/01
1/1
/02
3/1
/02
5/1
/02
7/1
/02
9/1
/02
11/1
/02
Time
Ctot
Cdiss
Cpart
Cdoc
Figure 3.6. Distribution of HxCDD between the dissolved, particulate and DOC fraction, results from
simulation.
Hydrophobic compounds tend to associate with organic matter and the consequence is a decrease in
bioavailability. In fact, Loonen et al. (1994a,b) observed that the presence of sediment particles
reduced the accumulation of PCDD/F in fish and that the effect of the sediment became more
22
important with increasing hydrophobicity of the compound.
The bioavailability of 10 congeners (TCDD, PeCDD, HxCDD, HpCDD, OCDD, TCDF, PeCDF,
HxCDF, HpCDF, OCDF) has been studied by calculating the bioaccumulation in different
compartments (phytoplankton, zooplankton, bacteria). In agreement with the previously reported
results for PAHs and PCBs, the bioaccumulation in the phytoplankton compartment shows a seasonal
trend corresponding to the dissolved water concentration (Figure 3.7). The enrichement of a congener
in the phytoplankton compartment is proportional to the dissolved concentration of that congener in
water (see Figure 3.8), confirming that there is a direct link between the dissolved concentration and
what is preferentially taken up by the phytoplankton.
Bioaccumulation of PCDD/F in phytoplankton [ng/kg]
0
20
40
60
80
100
120
140
1/1
/01
3/1
/01
5/1
/01
7/1
/01
9/1
/01
11/1
/01
1/1
/02
3/1
/02
5/1
/02
7/1
/02
9/1
/02
11/1
/02
Time [days]
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Figure 3.7. Dynamics of bioaccumulation of different PCDD/Fs congeners in phytoplankton (results
form model simulation over 2 year period).
Annual average of diss conc [ng/m3]
1.00E-04
1.00E-03
1.00E-02
1.00E-01
TCDD
PeC
DD
HxC
DD
HpC
DD
OCDD
TCDF
PeC
DF
HxC
DF
HpC
DF
OCDF
Figure 3.8. Annual average, minimum and maximum of the dissolved concentration of different
PCDD/Fs congeners (model results).
23
The annual average of the bioconcentration factor BCF has been calculated for phytoplankton,
zooplankton and bacteria and plotted against the octanol-water partition coefficient KOW (Figure 3.9)
The results show that, as expected, there is a linear relationship between the KOW and the BCF.
Moreover, the BCF is higher for zooplankton than for phytoplankton and bacteria, which means that
there is higher accumulation towards higher trophic level. This is the expected outcome since there is
no metabolisation of PCDD/Fs in zooplankton and the depuration and egestion constants are low
compared to the ingestion constant (Table 3.6). However, since the uptake of PCDD/Fs in zooplankton
is mainly driven by ingestion of phytoplankton the trend can be reversed depending on the food
availability. If we consider a year with low phytoplankton productivity there will be less
bioaccumulation of dioxines and furans in zooplankton and the bioconcentration factor will be lower
than for phytoplankton (results not shown). This results could explain why some studies report that the
bioaccumulation of PCDD/Fs decrease with increasing trophic level (Ruus et al. 2006, Wan et al.
2005), while other studies report an increase of PCDD with the trophic level (Cooper et al. 1992).
y = 0.3121x + 1.1234
R2 = 0.9335
y = 0.3406x + 0.9756
R2 = 0.9885
3.2
3.3
3.4
3.5
3.6
3.7
3.8
6.5 7 7.5 8 8.5
log Kow
log
BC
F [
ng
/kg
/ n
g/m
³]
phytopl. + bact
zoopl
Figure 3.9. Bioconcentration factor of different PCDD/Fs congeners in phytoplankton, bacteria and
zooplankton as a function of KOW.
Our model considers only phytoplankton, bacteria and zooplankton, but there is evidence that
ingestion is a main pathway for the accumulation of PCDD/Fs in oligochaetes (Loonen et al. 1997) as
well as in fishes (Rifkin and LaKind 1991).
Figure 3.10 shows the annual average bioconcentration factor for the ten different PCDD/Fs
congeners, as calculated by the model. The graph illustrate that the BCF of polychlorinated
dibenzodioxin increases with increasing chlorination, while this trend does not appear for the furans.
These results are directly linked to the depuration constants (Table 3.6), which decrease towards higher
chlorinated dioxins, while they are in a similar range for furans.
24
Annual average of log BCF [(ng/kg)/(ng/m3)]
3
3.2
3.4
3.6
3.8
4
TCDD
PeC
DD
HxC
DD
HpC
DD
OCDD
TCDF
PeC
DF
HxC
DF
HpC
DF
OCDF
logBCF phytopl
logBCF bact
logBCF zoopl
Figure 3.10: Bioconcentration factor of different PCDD/Fs congeners in phytoplankton, bacteria and
zooplankton
A recent study on the factors affecting the bioavailability of sediment-associated PCDD/Fs
(Lyytikaeinen et al. 2003) has shown that the uptake by biota is not only affected by lipophilicity, but
also by contaminant molecular size and conformation (planarity) as well as sediment characteristics
(particle size and aromaticity of organic carbon). Moreover, a study by Loonen et al. (1997) showed
that bioavailability of PCDD declines after prolonged contact time with sediment.
3.5. Experimental data on Thau Lagoon (France)
IFREMER has been coordinating a monitoring programme (RNO, Réseau National d'Observation de
la qualité du milieu marin) with the objective of evaluating the levels and trends of chemical
contaminants in the marine environment (http://www.ifremer.fr/envlit/surveillance/rno.htm).
Concerning Thau lagoon, there are temporal time series for PAHs and PCBs in mussels and oysters
during the last decades. Figures 3.11 - 3.12 show some of the observed trends at two stations (Thau 1
and Thau 4) for several PAHs and PCBs.
Our next objective is to validate the fate model in a 3D version for predicting the environmental
concentrations of selected POPs at the Etang de Thau (France) and, afterwards, include the
bioaccumulation model to predict experimental data concerning concentrations of these compounds
found in mussels and oysters.
25
Fluorene
0
2
4
6
8
10
12
14
01
/11
/19
94
01
/03
/19
95
01
/07
/19
95
01
/11
/19
95
01
/03
/19
96
01
/07
/19
96
01
/11
/19
96
01
/03
/19
97
01
/07
/19
97
01
/11
/19
97
01
/03
/19
98
01
/07
/19
98
01
/11
/19
98
01
/03
/19
99
01
/07
/19
99
01
/11
/19
99
01
/03
/20
00
01
/07
/20
00
01
/11
/20
00
Co
nc
en
tra
tio
ns
Phenanthrene
0
5
10
15
20
25
30
35
40
45
50
01
/11
/19
94
01
/03
/19
95
01
/07
/19
95
01
/11
/19
95
01
/03
/19
96
01
/07
/19
96
01
/11
/19
96
01
/03
/19
97
01
/07
/19
97
01
/11
/19
97
01
/03
/19
98
01
/07
/19
98
01
/11
/19
98
01
/03
/19
99
01
/07
/19
99
01
/11
/19
99
01
/03
/20
00
01
/07
/20
00
01
/11
/20
00
Co
nc
en
tra
tio
ns
Pyrene
0
5
10
15
20
25
30
35
40
15
/ 06
/19
94
28
/10
/19
95
11
/03
/19
97
24
/ 07
/19
98
06
/ 12
/19
99
19
/04
/20
01
Co
nc
en
tra
tio
ns
Fluoranthene
0
5
10
15
20
25
30
35
40
01
/11
/19
94
01
/03
/19
95
01
/07
/19
95
01
/11
/19
95
01
/03
/19
96
01
/07
/19
96
01
/11
/19
96
01
/03
/19
97
01
/07
/19
97
01
/11
/19
97
01
/03
/19
98
01
/07
/19
98
01
/11
/19
98
01
/03
/19
99
01
/07
/19
99
01
/11
/19
99
01
/03
/20
00
01
/07
/20
00
01
/11
/20
00
Co
nc
en
tra
tio
ns
Figure 3.11. Example of PAHs concentrations (µg kg-1
) found in mussels at Thau lagoon at two
sampling stations: Thau1 (blue), Thau4 (pink).
PCB28
0
0.5
1
1.5
2
2.5
3
23/0
2/9
3
23/0
8/9
3
23/0
2/9
4
23/0
8/9
4
23/0
2/9
5
23/0
8/9
5
23/0
2/9
6
23/0
8/9
6
23/0
2/9
7
23/0
8/9
7
23/0
2/9
8
23/0
8/9
8
23/0
2/9
9
23/0
8/9
9
23/0
2/0
0
23/0
8/0
0
23/0
2/0
1
Co
ncen
trati
on
PCB52
0
2
4
6
8
10
12
14
19/0
9/1
991
31/0
1/1
993
15/0
6/1
994
28/1
0/1
995
11/0
3/1
997
24/0
7/1
998
06/1
2/1
999
19/0
4/2
001
01/0
9/2
002
Co
ncen
trati
on
PCB101
0
2
4
6
8
10
12
19/0
9/9
1
31/0
1/9
3
15/0
6/9
4
28/1
0/9
5
11/0
3/9
7
24/0
7/9
8
06/1
2/9
9
19/0
4/0
1
01/0
9/0
2
Co
ncen
trati
on
s
PCB118
0
2
4
6
8
10
12
19/0
9/1
991
31/0
1/1
993
15/0
6/1
994
28/1
0/1
995
11/0
3/1
997
24/0
7/1
998
06/1
2/1
999
19/0
4/2
001
01/0
9/2
002
Co
ncen
trati
on
s
26
PCB138
0
5
10
15
20
25
30
35
40
11/0
5/9
2
11/0
5/9
3
11/0
5/9
4
11/0
5/9
5
11/0
5/9
6
11/0
5/9
7
11/0
5/9
8
11/0
5/9
9
11/0
5/0
0
11/0
5/0
1
Co
ncen
trati
on
s
PCB180
0
0.5
1
1.5
2
2.5
3
3.5
19/0
9/9
1
31/0
1/9
3
15/0
6/9
4
28/1
0/9
5
11/0
3/9
7
24/0
7/9
8
06/1
2/9
9
19/0
4/0
1
01/0
9/0
2
Co
ncen
trati
on
s
Figure 3.12. Example of PCB congeners concentrations (µg kg-1
) found in mussels at Thau lagoon at
two sampling stations: Thau1 (blue), Thau4 (pink).
27
4. BIOAVAILABILITY OF METALS
4.1. Metal Speciation and Bioavailability
Metals are naturally constituents of the environment and they have always been present at different
concentration depending on the geographical and geological characteristics of a certain site. In addition
they are necessary for a considerable number of biological functions. However, fluxes of
anthropogenic metals to the environment presently exceed natural inputs by 10 to 100-fold, and thus
are greatly increasing metal concentrations in many bodies of water (Nriagu and Pacyna 1988). For an
example, levels of metal (including cadmium) are enriched enough in freshwater and in the coastal
ocean that they have measurable impacts on the marine indigenous biota (e.g. Couillard et al. 1993).
Trace metals that can have adverse (toxic) effects on marine phytoplankton growth include Cd, Hg
(also Ag, Pb, Sn and Cr) while other metals exhibit the properties of limiting nutrients (Fe, Zn, Mn,
Cu, Co, Mo and Ni).
Phytoplankton is the basic element of the food web and hence the most important entry point of metals
in the different organisms that make up the food web. Furthermore, and in terms of ecosystem
description, changes in floristic composition could occur if phytoplankton species were to exhibit
different sensitivity to exposure of these compounds.
To examine whether thresholds of contaminants could exist and affect ecosystems through the
phytoplankton, a first step was to appraise the aquatic chemistry of selected metals in terms of metal
concentrations and the variations in their chemical species, which is the scope of this deliverable.
The bioavailability of dissolved trace metals in the water column depends on their speciation (Tessier
and Turner, 1995), ie. in which forms are they present in the environment, which in turn depends on
several physico-chemical parameters. In addition to the temperature, pH, redox potential and ionic
strength of the water; the presence of ligands and major cations (Ca2+
, Mg2+
) has an important
influence on their distribution between several forms. Therefore speciation characteristics are essential
to establish how metals will enter into the aquatic food webs, i.e. their bioavailability. However, no
general tool is available to evaluate trace metal bioavailability to aquatic organisms (Fairbrother et al.,
2007).
Trace metals exist in natural waters in a variety of chemical species, strongly influencing their
availability to phytoplankton. Most exist as cations that are complexed to varying degrees by inorganic
and organic ligands or are adsorbed on or bound within particles. In addition, many biologically active
metals that include Hg can have different oxidation states. Thus, both complexation and redox cycling
affect the bioavailability of these metals in aquatic systems because of the large differences in the
reactivity, kinetic lability, solubility (or volatility in the case of Hg), of their individual species.
The complexation of trace metals by inorganic ligands in the ocean, estuaries, and freshwater systems
has been characterized through the use of thermodynamic models, and through the chemical
28
characterization of their different species.
For seawater, these models show that the majority of Ni, Mn, Zn, Co, and Fe (II) are present as free
aquo metal ions, while other metals are heavily complexed by inorganic anions like chloride, including
Cd, and Hg(II),(Byrne et al., 1988). Seawater generally has a relatively constant pH and major ion
composition, and thus inorganic speciation of trace metals varies little over most of the ocean’s
surface. However, large variations in chloride concentration, alkalinity and pH exist in fresh and
estuarine waters that produce substantial variations in inorganic complexation in these systems. In
estuaries, the large salinity gradients result in large variations in the extent of chloride complexation of
notably Cd and Hg.
Less is known about organic complexation of trace metals, but this situation is rapidly changing with
the recent development of a number of sensitive and chemically selective metal speciation techniques.
Complexation of Cu II has been most extensively studied. Research with a variety of methods has
revealed that ≈99% of this metal is complexed to organic ligands in virtually all aquatic systems with
the notable exception of deep aphotic ocean water (Coale and Bruland, 1988). The copper is largely
bound by unidentified organic ligands which are present at low concentrations and possess extremely
high conditional stability constants log K ≈13 in seawater (Coale and Bruland, 1990). More recent
determinations with a ligand-competition, cathodic stripping voltammetric method indicates that
Fe(III) and Cu(II) is 99% complexed in near-surface seawater by as yet unidentified organic ligands
(Gledhill and van den Berg, 1994); a similar (electrochemical determination) analytical technique
indicate that for zinc 98-99% is organically complexed in oceanic waters (Bruland, 1989) while only
50-99% in estuarine and fresh waters (Van den Berg et al., 1987). In Narragansett Bay, a polluted
coastal environment, similar percentages of dissolved Zn (51-97%), Pb (67-94%) and Cd (73-83%)
were present as organic complexes (Wells et al., 1998).
The redox state of a metal (mercury, but also chromium and silver) also has a major impact on their
biological uptake and toxicity. For an example, the thermodynamically stable and biologically avail
able redox form of chromium, chromate, is an oxyanion with the same charge and virtually the same
stereochemistry as sulfate. Like iron, chromate can be photochemically or biologically reduced to
Cr(III), a redox form that is biologically much less available due to its very slow kinetics of
coordinatio. Photochemical and biological reduction of thermodynamically stable Hg(II) also leads to
substantial decreases in biological uptake and toxicity. Hg(II) and silver (I) which strongly bind to
biological ligands like sulfhydryls are reduced to their elemental forms, Hg0 and Ag
0 which do not
form complexes but exhibit different properties : notably, Hg0 is volatile and can be detoxified by
outgasing as such.
The work described in the present deliverable assessed the bioavailablity of the selected model metals
(Cd and Hg) relative to the chemical nature and the distinct reactivities of their species. The
29
bioavailability of metals, pertaining to the passage through cell membranes, is either resulting form
passive diffusion (uncharged molecules) or cell-mediated transport for charged molecules. Complexes
(and labile ion pairs) can have either forms in the marine environment.
The selected different metals possess distinct speciations. Dissolved cadmium is thought to be
bioavailable when complexes of its ions are is either labile or non-existent. To quantify this, the Cd
speciation study examined the distribution of Cd in its different species using a electrochemical
analysis technique. The question of mercury speciation and bioavailability is rather complex. The
present report summarizes the biogeochemical behavior and cycling of mercury by proposing a
bioaccumulation factor at the filter-feeder level of the food-web.
Even though bioaccumulation and transfer of metals through the food web occurs, biomagnifications is
normally not common, with several exceptions, between them methyl mercury. For these reasons, Cd
and Hg were chosen to investigate and compare their bioavailaty and their transfer potential through
the aquatic food web, using Thau lagoon as a case study. The results obtained through this first phase
to understand their speciation will allow to analyse and evaluate their bioaccumulative potential.
4.2. Evaluation of Cadmium speciation in the Thau Lagoon (France)
4.2.1. Introduction
It has long been recognized that dissolved and particulate elements do not present the same availability
to marine living organisms. For example Borchardt (1983) showed that dissolved Cd was more
available to mussels than particulate Cd.
Yet, the total dissolved metal concentration is not a pertinent information for assessing the
bioavailability and potential effects on the biomass of the presence of metals in the marine
environment. Free hydrated ions, hydroxides, and inorganic complexes are the most readily available
species for living organisms (Morel et al.1991,Morel & Hering,1993), besides neutral organic
complexes (Phinney and Bruland 1994)
In this study we determine on three surface points of the lagoon the total dissolved Cd concentrations,
the particulate Cd, and the “electrochemically labile “Cd concentrations. Samples for this study were
taken in 20-22nd
February and 19-21st September 2006.
4.2.2. Sampling and conditioning of samples
Three sampling stations were visited twice: C4 (43°24.018N, 03°36.703E), T12 (43°25.425N; 03°
41.283E) and C5 (43°25.994N, 03°39.657E ), see Figure 4.1.
Samples were collected using a pump (ASTI®
Teflon pump, polyethylene tubing). In-line filtration
was performed in February using a nuclepore®
polycarbonate membrane filter (47 mm in diameter,
0.4-µm pore size. In September the samples were taken unfiltered to the laboratory and filtered within
4 hours under a clean bench. Samples devoted to total dissolved analysis were acidified under clean
30
conditions, and put in two plastic bags.
Samples devoted to speciation studies were filtered, put in two plastic bags and immediately frozen.
Filters with SPM were put in clean polystyrene Petri dishes and immediately frozen.
Figure 4.1. Sampling stations C4, C5 and T12 in the Thau lagoon.
4.2.3. Analysis
Particles were dissolved in HCl, HNO3 and HF. Cadmium was then analysed by atomic absorption
spectrophotometry.
After liquid- liquid extraction in freon as described by Danielsson et al (1982) total dissolved cadmium
was analysed by atomic absorption spectrophotometry for the February cruise and by ICP-MS for the
September 06 campaign.
Cadmium speciation was studied using anodic stripping voltammetry. This method allows the
determination of free ions and labile complexes that constitute most of the “bioavailable” cadmium
(Kozelka & Bruland,1998). Neutral organic complexes, which are directly available to
phytoplanktonic cells are not detected by ASV.
The raw filtered sample is divided into several parts; one is left natural, the others are spiked with
increasing quantities of cadmium and left overnight in the refrigerator to equilibrate. When enough
cadmium has been added to saturate the ligands, the signal obtained in ASV increases linearly as a
function of Cd spike augmentation (Morel & Hering 1993, Ruzic 1982).Then the response is the same
T
31
as if there was no ligand in the sample, and the increase rate of the peak vs spike may be used to
calculate the initial concentration of cadmium which caused the peak obtained without any spike. This
is the “electrochemically labile “or “electroactive “cadmium which we consider as representative of
“labile” or “available” cadmium
Sampling and analytical methods have been described in detail in Boutier et al. (2007).
4.2.4. Results
- Salinity
Surface salinities are much higher in September than in February (Table 4.1). This points out at an
important freshwater runoff in winter.
Table 4.1. Surface salinities in the Thau lagoon. February September
T12 32 37.6
C4 32.2 39
C5 31.5 38
- Total dissolved concentrations
Total dissolved Cadmium concentrations have been measured by AAS for the February 06 campaign
(Table 4.2).
Table 4.2. Total dissolved Cd concentrations (nM/l) in February and September2006. Feb. 06 Sep.06
C4surf 0.19 0.05
C4 3m 0.19 0.05
C4 bottom 0.17 0.06
C5 surf 0.16 0.07
C5 3m 0.19 0.07
C5 6m 0.18 0.05
T12 surf 0.19 0.08
T12 2.5m 0.19 0.08
T12 5m 0.18 0.09
In February total dissolved Cd concentrations are very homogenous (0.18±0.01 nM/l) and no clear
spatial trend could be pointed out. They are higher than those observed in a previous cruise in may
2004 (mean=0.12nM, sd = 0.02; n=20) and far higher than those of September 2006 (mean = 0.067
nM; sd = 0.015, Table.4.2). Salinities are much higher in September, which is in accordance with the
fact that heavy rain and floods in winter cause important fresh water runoff to the lagoon and may
cause dissolved Cd enhancement.
- Particulate concentrations
The origin of the particles may be traced by chromium concentrations. This element is abundant in the
earth crust (Chiffoleau, 1994) and a higher concentration of Cr in suspended particles indicates a
bigger mineral fraction.
32
Table 4.3. Surface particulate Cd and Cr concentrations (µg/g).
Cr(µg/g) Cd (µg/g)
Feb.2006 Sep.2006 Feb. 2006 Sep.2006
C4s 36 <13 0.67 0.18
C5s 17 25 0.34 0.24
T12s 26 25 .039 0.27
On C4 and T12 Particulate Cr concentrations are higher in February, than in September. This suggests
a more terrigenous origin of the particles in winter. This trend is slightly inverted on station C5.
Particulate Cd and Cr concentrations vary in the same way on stations C4 and T12 (Table 4.3). This
suggests that in winter heavy rain and floods bring contaminated terrigeneous material. to the lagoon.
Station C5, situated among the oyster farming installations behaves differently.
- Speciation study
Station C4 (February 2006):
Table 4.4 and Figure 4.2 show the response of Cd peaks in ASV upon growing spikes of ionic Cd.
Concentration augmentation (Mole/l) 0 1.8 E-10 5.4 E-10 9E-10 1.8E-9 2.7E-9
Asv signal (peak height, nA) 8.1 27.4 70 93 180 302
y = 1E+11x + 5.855
R2 = 0.9918
0
50
100
150
200
250
300
350
0 5E-10 1E-09 2E-09 2E-09 3E-09 3E-09
Added concentration (M)
Pe
ak h
eig
ht
(nA
)
Added concentration (M/l)
Table 4.4 Figure 4.2. Electroactive Cd determination on C4 February 2006.
The quasi perfect linear regression line (R2=0.99) shows no complexation of the spikes. This linear set
of data allows calculation of the electroactive concentration by dividing the peak height for the natural
sample 8.1 nA by the slope of the regression line 1011
nA/(M/l). This leads to a concentration of 8.10-11
M/l, or 0.08nM/l. This represents 40% of the total dissolved cadmium measured by AAS (0.19 nM,
Table 4.2). This asks a question: The linearity of the relation between added Cd and the peak
increments can be interpreted as a sign of absence of complexation. Therefore, the ASV value of Cd
concentration should be equal to the total AAS concentration, which is not. The answer to this question
may be that all the ligands present in the sample are saturated by the Cd initially present in the sample,
then allowing a linear response to the spikes.
Station C4 (September 2006) :
In September the situation is similar. The peak response to the Cd spikes is perfectly linear, including
the point with no spike (Figure 4.3) Nevertheless, the calculated Cd concentration by dividing the peak
height (2.2nA) for spike 0 by the slope of the regression line (8.1010
nA/(M/l)) is 2.75 10-11
M/l.
33
Cd spike M/l 0 5.7E-11 1.2E-10 2.4E-10 7.6E-10 4.8E-10 4.8E-10 1.2E-09 1.9E-09 2.0E-09 4.2E-09 6.7E-09 9.9E-09 2.3E-08 3.4E-08
Peak height nA 2.2 14.8 28 31 31 61 52 112 159 161 333 459 799 1900 2720
y = 8E+10x + 2.9513
R2 = 0.9984
0
500
1000
1500
2000
2500
3000
0.E+00 1.E-08 2.E-08 3.E-08 4.E-08
Added Cd (M/l)
pe
ak h
eig
ht
(nA
)
Table 4.5 Figure 4.3.Electroactive Cd determination. C4. September 2006.
This represents 55% of the total dissolved Cd (5.10-11
M/l). It seems that in this sample the same
phenomenon as in February occurs. The ligands that are present in this sample complex 45% of the
dissolved cadmium and are totally saturated, leaving the spikes free for ASV measuring.
Station C5 (February 2006):
The same technique as for C4 S leads to a slight curvature of the graph in the low added concentrations
part of the graph (Table 4.6, Fig.4.4). This is a mark of the partial complexation of the spikes, until
8.9*10-10
M/l Cd have been added. From this added concentration, higher spikes lead to a linear
increase, the slope of which (9.1010
nA/(M/l))is used to calculate the initial electroactive cadmium as
before.
Cd spike (M) 0 1,80E-10 3,60E-10 5,40E-10 7,10E-10 8,90E-10 1,30E-09 1,80E-09 2,20E-09 2,70E-09
Peak height (nA) 4 7,3 19,8 30,7 38,2 63 99 116 181 218
y = 9E+10x - 21.4
R2 = 0.9698
0
50
100
150
200
250
0 5E-10 1E-09 1.5E-09 2E-09 2.5E-09 3E-09
Added concentration (M)
iCd
(n
A
Table 4.6 Figure 4.4. Electroactive Cd determination on station C5S in February 2006.
This results in a labile concentration of 4.4 10-11
M, which represents 28% of the total dissolved Cd
(0.16nM, Table 4.2).
Station C5 (September 2006):
34
Cd spike (M/l) 0 3.1E-11 8.4E-11 2.3E-10 1.8E-10 3.8E-10 4.6E-10 6.1E-10 1.0E-09 1.8E-09 2.6E-09 3.8E-09 6.5E-09
Peak height (nA) 1.2 2.3 3.85 4.25 4.81 10.2 17 18 29 54.5 96.9 143 318
y = 6E+10x - 54.159
R2 = 0.9916
0
50
100
150
200
250
300
350
0 1E-09 2E-09 3E-09 4E-09 5E-09 6E-09 7E-09
Cd spike (M/l)peak h
eig
ht (n
A)
Table 4.7 Figure 4.5 - Electroactive Cd determination. station C5S September 2006.
The peak height vs Cd spike curve also shows a slight curvature, between 0 and 1.8nM/l spike. This is
the mark of partial complexation of the spikes until 1.8nM/l (Table 4.7, Figure 4.5). For higher spike
values, the relation between the peak height and the spike is a quasi perfect straight line (R2=0.99).
This means that for this part of the titration, all the ligands are saturated with cadmium. Then we can
calculate the elecroactive Cd concentration in the same way as before: this gives 1.2/6.10-10
=2 10-11
M/l which represents100* 2 10-11
/7.10-11
= 28 per cent of the total dissolved Cd
Station T12 (February 2006):
Between the spikes 8.9 10-11
and1.10-9
the ASV signal grows linearly (R2= 0.97) with the dissolved Cd
concentration enhancement. (Table 4.8 – Fig. 4.6). This is an indication that complexing agents are
saturated as soon as the first spike.
Cd spike (M) 0 8,90E-11 3,60E-10 5,40E-10 7,10E-10 1,10E-09
iCd (nA) 20 26 78 138 165 238
y = 2E+11x + 5.707
R2 = 0.997
0
50
100
150
200
250
0 5E-10 1E-09 1.5E-09 2E-09
iCd
(n
A)
Added Cd (M)
Table 4.8 Figure 4.6. Electroactive Cd determination. Station T12. February 2006.
Following the same method as before, we estimate the elecro-labile Cd to be 20 / 2*1011
=10-10
M/l.
This represents 53% of the total dissolved cadmium (0.19µM/l).
Station T12 (September 2006):
The response to the spikes shows that until 2.21*10-10
Mol/l, all the added Cd is complexed in an
electroinactive form. (Peak height is of course 0nA for the zero spike, Table 4.9, Figure 4.7). This
35
means that on Station T12 no electroactive Cd was detected in September 2006.
Cd spike (M) 0.00 8.90E-11 2.21E-10 4.67E-10 7.34E-10 9.98E-10 1.69E-09 3.29E-09 5.75E-09 8.22E-09 1.16E-08 2.29E-08 2.93E-08 4.38E-08
Peak height (A) 0 0 0 12 23 37 77 170 332 527 820 1700 2250 3770
y = 9E+10x - 234.37
R2 = 0.9952
0
500
1000
1500
2000
2500
3000
3500
4000
0.00E+00 1.00E-08 2.00E-08 3.00E-08 4.00E-08 5.00E-08
Added Cd (M)P
ea
k h
eig
ht
(nA
)
Table 4.9 Figure 4.7. Electroactive Cd determination Station T12 September 2006.
4.2.5. Discussion
Winter concentrations are far higher than summer concentrations (Table 4.10). Heavy rain and floods,
causing high continental runoff to the lagoon may partially explain this situation. An argument for this
is the higher particulate Cd concentrations in winter. The higher Cr concentrations on C4 and C5 in
February than in September (Table 4.2) also contribute to denote a terrigeneous origin of the particles
in winter on these stations. In September, total dissolved concentrations were much less than the
winter concentrations (Table 4.10).
Table 4.10. Total labile and free dissolved Cd concentrations in the Thau lagoon. Percentages of
electroactive (labile) Cd in the dissolved phase. February 2006 September 2006
Total Dissolved (nMol/l)
Electro-Labile (nMol/l)
[Cd 2+
] pM
%labile Total Dissolved
Electro- labile
[Cd 2+
]
pMol/l %labile
C4 0.19 0.08 2.7 42 0.05 0.028 0.9 56
C5 0.16 0.044 1.5 28 0.07 0.02 0.6 28
T12 0.19 0.1 3.30 53 0.08 0.0 00 0
Biological uptake which takes place during spring and summer blooms may explain this situation.
Unfortunately we did not measure the chlorophyll-a concentrations during our campaigns, but the
French phytoplankton and phycotoxins monitoring network (REPHY) indicates the main trends of this
parameter in the lagoon.
From the 30/1 to the 13/2/2006, the concentrations were growing from 0.6 to3.2 µg/l and lowering
later. From the 03/07 to the 11/ 09, a few days before the September campaign, the concentrations
were between 3.3 and 9.7 µg/l. This shows that the phytoplankton populations were much more
important in the Thau lagoon in September than during the February cruise. The uptake of dissolved
Cd by phytoplankton (Morel et al., 2003) could explain the big differences between winter and
36
summer total dissolved Cd concentrations. The phytoplanktonic cells may absorb dissolved metals and
convey them to the lower layers, in the same way as in the open sea. In the shallow waters of the
lagoon; degradation cannot take place in the water column. So it happens on the bottom sediment,
which is anoxic in summer (Metzger et al 2007) Sulphides present in these conditions may trap the Cd
coming from phytoplankton degradation. This could explain the surface water Cd impoverishment.
Applying an inorganic side reaction coefficient of 30 (Kozelka and Bruland, 1998); the free Cd activity
can be calculated from the electroactive Cd concentrations (Table 4.10). The [Cd2+
] values obtained
are considered as the bioavailable fraction (Morel,1983). All values are below 4pM/l. Brand et al.
(1986) showed that an activity of 10-9.5
M reduces by a factor two the reproduction rate of
cyanobacteria which are the most sensitive marine microorganisms to cadmium. This value (300 pM)
is far above the values we have found in the Thau lagoon (Table 4.10)
Cd activities are lower in summer than in winter (Table 4.10). This may also be explained by the
action of phytoplankton, as these organisms produce dissolved ligands that complex metals in surface
oceanic waters (Bruland, 1992).
4.2.6. Conclusions
These results give a coherent sight on the spatial and temporal repartition of electroactive cadmium in
the Thau lagoon. In winter terrigenous inputs make the concentrations in dissolved and bioavailable
Cd higher (Table 4.10)
In summer a drastic decrease of the total dissolved Cd concentration is observed. This causes at the
same time, an important decrease of the electroactive and bioavailable species. This seasonal influence
seems to occur through the primary production, the influence of which seems very important on the
removal of total dissolved, electroactive, and bioavailable Cd.
Free Cd concentrations are very low and seem far from the toxicity limits. Nevertheless, a large panel
of Cd toxicity studies on different organisms of the lagoon would be useful to assess its real situation
vis à vis cadmium contamination consequences
4.3. Distribution of Mercury species in the waters of Thau Lagoon: Consequences for the
bioaccumulation factor calculations for marine mussels
Total mercury (HgT), methylmercury (MeHg) and dissolved gaseous mercury (DGHg) have been
measured in the water column at three stations of the Thau Lagoon (N-W Mediterranean Sea) where
mussel farming is present, in February, April and September 2006. The concentrations of total
dissolved mercury (HgTD) in the water of the Thau Lagoon ranged from 0.30 to 4.84 pM, with an
overall mean 0.99 ± 0.75 pM for 37 determinations. MeHg concentrations varied from 0.03 to 0.32
pM, with an overall mean of 0.09 ± 0.06 pM for 45 determinations. Methylated species accounted for
10 ± 3 % of the HgT, ranging from 3 to 18 %. A statistically significant relationship was found
37
between PO4 and MeHgD, which suggests a connection between methylmercury production and the
organic matter mineralisation. Comparison of these results with speciation measurements within the
marine mussel (Mytilus galloprovincialis) allows the calculation of in situ bioaccumulation factors
(BF) of 30 000 and 300 000 for HgT and MeHg respectively. These values are higher than those
usually obtained with laboratory experiments.
4.3.1. Introduction
Knowledge of the mercury (Hg) speciation in water is required to predict bioavailability of this metal
for the aquatic food webs. It has long been recognised that methylmercury (MeHg) is the most
bioavailable, biomagnificable and toxic mercury molecule for aquatic ecosystems. In this context, we
determined mercury speciation in the water column of the Thau Lagoon located along the seashore of
the North-Western Mediterranean Sea where shellfish farming is well developed. The analytical
speciation scheme consisted of total mercury in unfiltered samples (HgTUNF), dissolved total mercury
(HgTD), methylmercury (MeHgUNF and MeHgD), and dissolved gaseous mercury (DGHg), which
mainly consists of dissolved element mercury (Hg0). Our data are thought to be helpful for the mercury
the calculation of NQE in the risk assessment by proposing in situ bioaccumulation factor for MeHg.
Figure 4.8. Mercury species and cycling.
4.3.2. Studied site and sampling collection
Located along the Gulf of Lions (North-Western Mediterranean) French coast, the Thau Lagoon is a
human-impacted lagoon with a surface area of about 75 km², the mean depth 3.5 m, and the maximum
depth of 11 m. The salinity varies between 31 and 39. Twenty percent of the surface is occupied by
shellfish farming, mainly oysters Crassostera gigas, and mussels Mytilus galloprovincialis.
38
Three stations have been studied (C4, C5 and T12, Fig. 4.1). The sampling site C4 is centrally located
in the lagoon (43°24.018N, 03°36.703
E). At this point, the depth is about 8 m. Station C5 (43°25.994
N,
03°39.657E) is located close to “mussel tables” (the mussel farming devices), where organic carbon
fluxes are higher. At station C5 the water column is 9 m deep. Station T12 (43°25.425N; 03° 41.283
E)
is located in the vicinity of the industrial and harbour zones of the city of Sête. The water depth at T12
station is 5 m. Three (3) cruises were conducted since the beginning of the Thresholds program in
order to indicate seasonal trends: the first was performed in February, the second in April 2006, and
the third in September 2006. Depending on the station, Four (4) to seven (7) water column depths were
sampled.
4.3.3. Sampling and analytical techniques
The ultra clean sampling techniques and analytical methods applied for water analyses are those
presented and discussed in detail by Cossa et al. (2003). In short, water column samples were collected
by pneumatic pumping (an all Teflon double bellows ASTI pump) using acid-cleaned polyethylene
tubing. Samples were stored in acid-clean Teflon (FEP) bottles. Filtrations were performed using
Sterivex-HV cartridges (Millipore®
0.45 µm hydrophilic PVDF membranes) and polypropylene
syringes. All water samples were acidified to 0.4 % (v/v) with Suprapur®
HCl, double bagged and
stored at +4°C in dark conditions until analyses were performed.
All mercury species in water samples were detected by cold vapor atomic fluorescence spectrometry
(AFS, Tekran, Model 2500). DGHg was measured by bubbling the water samples with Hg-free Ar and
concentrating the Hg0 evolved on a gold sand trap. The trap was then heated and the Hg vapor
quantified by AFS. Dissolved HgTD was determined by the formation of volatile elemental Hg
(released by SnCl2 reduction, after acidic BrCl oxidation, and its preconcentration on a gold trap). The
detection limits for HgTD, defined as 3.3 times the standard deviation of the blanks, were 0.01 pM. The
reproducibility (the coefficient of variation in percentage of five replicate samples) was lower than 10
%. The accuracy for HgTD determinations was regularly checked using the reference material (ORMS-
3) from the National Council of Canada as certified reference material (CRM). MeHgD was determined
using the method initially proposed by Tseng et al. (1998) and modified by Cossa et al. (2003).
Detection limit were 0.005 pM for a 60 mL water sample. Precision was better than 10 % for all
analyses. No CRM is available for MeHg in water. The detailed procedure is given by Cossa et al.
(2003). Ancillary parameters (temperature, salinity, and nutrients) were measured using standard
methods (Grasshoff et al., 1983).
4.3.4. Results and discussion
Summary statistics for the three campaigns are given in Table 4.11 and vertical profiles for HgT and
MeHg shown on Figure 4.9.
39
Table 4.11. Summary statistics on mercury species concentrations (pM) measured in the water column
of the Thau Lagoon in February, April and September 2006. Mean ± standard deviation (number of
determinations). Station Campaign HgTUNF HgTD MMHgUNF MMHgD DGHg
February 0.87 ± 0.27 (6) 0.57 ± 0.45 (5) 0.14 ± 0.16 (6) 0.12 ± 0.14 (6) 0.20 (1)
April 2.38 ± 0.29 (6) 1.91 ± 0.10 (6) 0.16 ± 0.04 (6) 0.08 ± 0.02 (6) 0.17 (1)
C4
September - 0.65 ± 0.26 (4) 0.11 ± 0.02 (4) 0.07 ± 0.02 (4) 0.18 (1)
February - 0.85 ± 0.13 (6) - 0.12 ± 0.11 (6) -
April 2.62 ± 0.17 (7) - 0.07 ± 0.02 (7) 0.06 ± 0.02 (7) -
C5
September - 0.97 ± 0.56 (6) 0.16 ± 0.08 (6) 0.09 ± 0.05 (6) -
February - 2.14 ± 2.34 (3) - 0.04 ± 0.02 (3) -
April 3.05 ± 0.41 (4) 2.36 ± 0.29 (4) 0.16 ± 0.03 (4) 0.10 ± 0.03 (4) -
T12
September - 1.33 ± 0.48 (3) 0.19 ± 0.02 (3) 0.12 ± 0.01 (3) -
0
1
2
3
4
5
6
7
8
0.0 1.0 2.0 3.0 4.0 5.0
HgTD (pM)
Depth
(m
)
C4 Feb 2006
C4 Apr 2006
C4 Sept 2006
C5 Feb 2006
C5 Apr 2006
C5 Sept 2006
T12 Feb 2006
T12 Apr 2006
T12 Sept 2006
0
1
2
3
4
5
6
7
8
0.00 0.10 0.20 0.30 0.40 0.50
MeHgD (pM)
Depth
(m
)
C4 Feb 2006
C4 Apr 2006
C4 Sept 2006
C5 Feb 2006
C5 Apr 2006
C5 Sept 2006
T12 Feb 2006
T12 Apr 2006
T12 Sept 2006
Figure 4.9. A/ Vertical profiles for HgTD in the water column of the Thau Lagoon.B/ Vertical profiles
for MeHgD in the water column of the Thau Lagoon.
40
- The total mercury: The concentrations of total dissolved mercury (HgTD) in the water of the Thau
Lagoon ranged from 0.30 to 4.84 pM, with an overall mean 0.99 ± 0.75 pM for 37 determinations.
They are in the upper range of the Mediterranean open seawaters and very similar to levels found in
the coastal area of the adjacent Gulf of Lions. Cossa and Coquery (2005) estimate that HgT
concentrations in the Levantine Intermediate water and Western Mediterranean Deep waters range
from 0.7 to 1.2 pM, while previous studies detected HgT concentrations up to 2.2 pM for the open
North-Western Mediterranean sea (Cossa et al., 1997). In the Gulf of Lions coastal waters 80 % of the
HgT concentrations ranged from 0.61 to 3.50 pM, with mean ± standard deviation of 1.64 ± 0.78 pM
(Bioprhofi cruise May 2006,unpublished results, http://www.insu.cnrs.fr/web/article/art.php?art=1783)
. The highest HgT concentrations in the Thau Lagoon were, as expected, found for unfiltered samples.
The particulate mercury accounted for 18 to 34 % of the mercury in the unfiltered sample (HgTUNF)
depending on the station. Highest HgTD was found at station T12, which is the station located in the
“Petit-étang”, which is surrounded by land used for industrial and harbour activities of the city of Sête.
A tendency for higher HgTD (i.e. filetered) concentrations in the surface and bottom waters is visible
on Figure 4.9A; this feature may be related both to truly dissolved and colloidal mercury, since these
increases are also notice on the turbidity profiles (not shown). There is no correlation between mercury
and salinity, which would have indicated a significant freshwater source. The profiles rather suggest
that atmosphere and sediment are the major mercury sources for the water column.
- The bioavailable methylmercury: The range of dissolved methylated mercury (MeHgD)
concentrations, which is the most bioavailable and the bioamplified mercury molecule in food webs, is
one order of magnitude, varying from 0.03 to 0.32 pM, with an overall mean of 0.09 ± 0.06 pM for 45
determinations. However, MeHgUNF concentrations (total) were quite uniform/homogeneous regardless
of the station or sampling period (Table 4.11). Methylated species accounted for 10 ± 3 % of the HgT,
ranging from 3 to 18 %. This proportion is within the range currently observed in marine surface
waters; yet lower than the proportions calculated for the intermediate and deep waters in the North-
Western Mediterranean Sea (Cossa and Coquery, 2005). Contrarily to HgTD the “Petit étang” (T12),
MeHgD concentrations were not higher than at C4 or C5, except in September (Table 4.11). The
MeHgD vertical distribution (Fig. 4.9B) depicts higher concentrations near the bottom confirming the
sediment and the nepheloid layer as the MeHg main source (Muresan et al., 2007). Noteworthy are the
similar vertical distribution patterns of PO4 and MeHgD (Fig. 4.10). This relationship, which has been
already found in the open Mediterranean waters (Cossa et al., 2008), suggests a connection between
methylmercury distribution or production and the organic matter mineralisation. Consequently, the
conditions, which favour organic matter decomposition (especially suboxic ones according to
literature, e.g., Benoit et al., 2003), are governing factors for the mercury bioavailability. This aspect
41
has to be explored further on.
MeHgD (pM) = 0.245 PO
4 (µM) + 0.043
R2 = 0.423
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.00 0.10 0.20 0.30 0.40 0.50
PO4 (µM)
MeH
gD (
pM
)
Figure 4.10. MeHgD versus PO4 relationship in the water column of the Thau Lagoon.
- Dissolved gaseous mercury and air-sea exchange: The concentration of DGHg has been determined
only in the surface waters at station C4. This volatile species represented 20 to 35 % of the HgTD.
(Table 4.11). The DGHg concentrations correspond to a supersaturation of the water when compared
to the concentrations of mercuy in the atmosphere. Indeed, total gaseous mercury was measured on the
north shore of the Lagoon within the framework of E.U. Mercyms project. Concentrations varied
between 1.9 and 3.2 ng/m3
(Amouroux et al., personal communication). Using a simple Henry’s law
diffusion model it can be inferred from these results that the surface waters of the Lagoon are a source
for the mercury for the atmosphere. This pathway competes for the in situ methylation of divalent
mercury.
4.3.5. Conclusions on bioavailabity and in situ bioaccumulation factor (BF) within mussel
Mercury concentrations in the mussel (Mytilus galloprovincialis) have been monitored at a quarterly to
yearly frequency within the French Mussel Watch Program (Claisse, 1989). The median value for the
last five years is 70 nmole kg-1
(wet weight). Claisse et al. (2001) found that 64 % of the mercury was
present in the mussel soft tissues of the Lagoon as MeHg. This, compared to the 7-12 % in the waters
of the Lagoon, illustrates the favoured bioaccumulation of MeHg compared to inorganic mercury
compounds. Combining the data for mussel soft tissues (expressed per unit of wet weight) with the
present concentrations found in unfiltered waters, we arrive with in situ bioconcentration factor (BF)
of around 300 000 and 30 000 for MeHg and HgT respectively. These BF values, which confirm the
findings of the field approach by Casas et al. (2008), show that BF obtained with in situ measurements
are higher than those obtained with laboratory approaches (e.g. Gagnon and Fisher, 1997; Hédouin,
2006). This last observation is useful for the risk assessment models.
42
5. CONCLUSIONS
Chemicals present in the water column or sediments are not totally bioavailable to the aquatic
organisms. Chemical compounds are distributed among all phases in environmental systems and,
therefore, they can be buried in a media that does not allow direct transfer to the organism. In this
situation, the total concentration is normally not the right value for assessing which will be the risks of
a certain pollutant. In fact, only the part of the concentration that can be taken up by the biota
represents a real risk for the ecosystem and the human health. Therefore, environmental legislation
should be based on the concept of bioavailability; this will provide effective health protection and
avoid unnecessary economic pressures.
In this report, simulated results as well as experimental results from several campaigns carried out
within the Thresholds project have been summarized with the aim of providing a comprehensive study
on bioavality for POPs and metals. A comprehensive modeling approach has been developed to assess
bioconcentration factors for several families of POPs. This approach is quite general and aims at
developing a scale between total concentration in the water column and bioavailable concentration as a
function of physicochemical properties of a compound. However, in order to develop further the
approach, the experimental data obtained from Thresholds-1 and Thresholds-2 Mediterranean
campaigns should be included in the study as well as the results provided by the Thau lagoon study.
With this information, validation of the model and assessment of the preliminary results obtained in
this deliverable will be possible. These open questions will be explored in a companion document
when the Thau modeling case study will be developed and experimental data on POPs concentrations
in biota would be available.
The bioavailability of dissolved trace metals in the water column depends on their speciation, which in
turn depends on several physico-chemical parameters (e.g. T, pH, redox, etc.). However, up to know
no general tool is available to evaluate trace metal bioavailability to aquatic organisms. We expect that
the experimental measurements carried out at Thau lagoon for Cd and Hg will help in the development
of such as tool.
43
6. REFERENCES
Amouroux, D. LCABIE-CNRS, Hélioparc, Université de Pau, France
Benoit, J. M., C. C., Gilmour, A., Heyes, R. P., Mason, and C. L.,Miller, 2003. Geochemical and
Biological Controls over Mercury Production and Degradation in Aquatic Systems. In:
Biogeochemistry of Environmentally Important Trace Elements. Y. Cai and O.C. Braids Eds. ACS
Symp. Series 835, 262-297.
Berrojalbiz, N., Lacorte, S., Barata, C., Calbet, A. and Dachs, J. 2006. Accumulation of low MW
polycyclic aromatic hydrocarbons in marine phytoplankton, zooplankton and fecal pellets.
Thresholds IP, Stream 4 Meeting, 2nd
-3rd
May, Barcelona.
Boese, B. 1984. The uptake efficiency of the gills of English sole (Parophrys vetulus) for four
phthalate esters. Marine Environmental Research14:515.
Borchardt, T. 1983 .The influence of food quantity on the kinetics of Cd uptake and loss via food and
sea water in Mytilus edulis.Marine Biology 76, 67.
Boutier, B. , Cossa, D., Munaron, D., Auger, D., Knoery, J., Averty, B., Sanjuan, J., Gonzalez, J. ,
Guiot, N., Heas-Moisan, K., Leaute, F., Munschy, C., Tixier, C., Tronczynski, J., Agusti, S.,
Echeveste, P., Berrojalbiz, N., Lacorte, S., Dachs, J., Castro Jimenez, J, Ghiani, M., Deviller, G.,
Mariani, G., Skejo, H., Umlauf, G. and Zaldívar, J.M. 2007. Experimental results and population
response for selected chemicals. EUR 22648 EN.
Brand, L.E., Sunda, W.G. and Guillard, R.R.L 1986 Reduction of marine phytoplankton reproduction
rates.by copper and cadmium. J. Exp. Mar. Biol. Ecol. 96, 225-250.
Bruland KW 1989 Complexation of zinc by natural organic ligands in the central North Pacific. Limnol
Oceanogr 34,269-285.
Byrne RH, Kump LR, Cantrell KJ 1988 The influence of temperature and pH on trace metal speciation
in seawater. Mar Chem 25,163-181.
Casas, S., J.L. Gonzalez, Andral, B. and D. Cossa. 2008. Discriminating Physiological Influences from
Element Bioavailability in Water on the Hg, Pb, Cd and Cu Content of Marine Mussels during
Transplantation Experiments. Environ. Toxicol.Chem., in press.
Chiffoleau, J. F. 1994.Le chrome en milieu marin. Repères Ocean N°8. Editions Ifremer Centre de
Brest BP 70 29280 PLOUZANE France.
Claisse, D. 1989. Chemical contamination of the French coasts: the results of a ten years Mussel
Watch. Mar. Pollut. Bull. 20, 523-528.
Claisse, D., D. Cossa et J. Bretaudeau-Sanjuan 2001. Methylmercury in molluscs along the French
coast. Mar. Pollut. Bull. 42, 329-332.
Coale KH, Bruland KW 1988 Copper complexation in the northeast Pacific. Limnol Oceanogr 33,
1084-1101.
44
Coale KH, Bruland KW 1990 Spatial and temporal variability in copper complexation in the North
Pacific. Deep-Sea Res 34, 317-336.
Cooper, K. R., Cristini, A., Bangqvist, P. and Rappe, C. 1992. Bioavailability and bioconcentration of
polychlorinated dioxins (PCDD) and furans (PCDF) to organisms inhabiting a heavily
contaminated estuarine ecosystem. Chemosphere 25, 25-28.
Cossa, D. et Coquery, M. 2005. The Mediterranean mercury anomaly, a geochemical or a biological
issue. pp 177-208. In : The Mediterranean Sea. Handbook of Environmental Chemistry, Vol 5.
Saliot, A. editeur. Springer, 413 p.
Cossa, D., B. Averty, J. Bretaudeau and A.-S. Senard. 2003. Spéciation du mercure dissous dans les
eaux marines. Méthodes d'analyse en milieu marin. Co-édition Ifremer et Ministère de l'Ecologie et
du Développement Durable. 27 pp. ISBN 2-84433-125-4.
Cossa, D., B. Averty, R. Kérouel and N. Pirrone. 2008. The Nutrient Type Distribution of
Methylmercury in the Mediterranean Waters: Relationships with Phosphate. Ocean Sciences
Meeting (ASLO-AGU-OS-ERASLO Meeting). Orlando, USA, 3-7 March 2008.
Cossa, D., J.M. Martin, K. Takayanagi and J. Sanjuan. 1997. The Distribution and Cycling of Mercury
in the Western Mediterranean. Deep Sea Res. 44,721-740.
Couillard Y, Campbell, P. G. C., & Tessier, A. 1993. Response of metallothionein concentrations in a
freshwater bivalve (Anodonta grandis) along an environmental cadmium gradient. Limnol.
Oceanogr. 38, 299–313.
Danielsson, L.G., Magnusson, B., Westerlund, S. and Zhang, K. 1982 Trace metal determination in
estuarine waters by electrothermal atomic absorption spectrometry after extraction of
dithiocarbamate complexes into freon. Analytica Chimica Acta 114, 183-188.
Del Vento, S. and Dachs, J., 2002. Prediction of uptake dynamics of persistent organic pollutants by
bacteria and phytoplankton. Environmental Toxicology and Chemistry 21, 2099-2107.
Devillers, J., Bintein, S. and Domine, D., 1996. Comparison of BCF models based on log P.
Chemosphere 33, 1047-1065.
Fairbrother, A., Wenstel, R., Sappington, K. and Wood, W. 2007. Framework for metals risk
assessment. Exotoxicology and Environmental Safety 68. 145-227.
Farley, K.J., Thomann, R. V., Cooney, T. F., Damiani, D. R. and Wands, J.R. 1999. An integrated
model of organic chemical fate and bioaccumulation in the Hudson river estuary. Hudson River
Foundation. pp. 170.
Fisk, A.T., Norstrom, R.J., Cymbalisty, C.D. and Muir, D.C.G., 1998. Dietary accumulation and
depuration of hydrophobic organochlorines: bioaccumulation parameters and their relationship
with the octanol/water partition coefficient. Environ. Toxicol. Chem. 17, 951-961.
Gagnon, C. and N.S. Fisher. 1997. Bioavailability of sediment-bound Methyl and Inorganic Mercury
45
to Marine Bivalve. Environ. Sci. Technol. 31, 993-998.
Galassi, S., Vigano, L. and Sanna, M. ,1996. Bioconcentration of organochlorine pesticides in rainbow
trout caged in the River Po. Chemosphere 32:1729-1739.
Gledhill M, van den Berg CMG, 1994 Determination of complexation of iron III with natural organic
complexing ligands in seawater using cathodic stripping voltammetry. Mar Chem 47, 41-54.
Gobas, F.A.P.C., Wilcockson, J.B., Russell, R.W., Haffner, G.D., 1999. Mechanism of
biomagnification in fish under laboratory and field conditions. Environ. Sci. Technol. 33, 133-141.
Grasshoff, K, M. Ehrardt and K. Kremling. 1983. Methods of Sea water Analysis. Verlag-Chemie
Hawker, D.W., Connel, D.W., 1985. Relationships between partition coefficient, uptake rate constant,
clearance rate constant, and time to equilibrium for bioaccumulation. Chemosphere 14, 1205-1219.
Hédouin, L; 2006. Caractérisation d'espèces bioindicatrices pour la surveillance des activités
minières et la gestion de l'environnement en milieu récifal et lagunaire: application au lagon de
Nouvelle-Calédonie. Thèse de doctorat, Université de La Rochelle, France, 327 pages.
Kozelka, P.B. and Bruland, K.W. 1998 Chemical speciation of dissolved Cu, Zn, Cd, Pb in
Narragansett Bay, Rhode Island. Marine Chemistry 60, 267-282.
Loonen, H., Muir, D. C. G., Parsons, J. R., Govers, H. A. J. 1997. Bioaccumulation of polychlorinated
dibenzo-p-dioxins in sediment by oligochaetes: Influence of exposure pathway and contact time.
Environmental Toxicology and Chemistry. 16 (7): 1518-1525.
Loonen, H., Parsons, J. R., Govers, H. A. J., 1994. Effect of sediment on the bioaccumulation of a
complex mixture of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated
dibenzofurans (PCDFs) by fish. Chemosphere. 28, 1433-1446.
Lyytikäinen, M., Hirva, P., Minkkinen, P., Hämäläinen, H., Rantalainen, A. L., Mikkelson, P.,
Paasivirta, J. and Kukkonen, J. V. K., 2003. Bioavailability of sediment-associated PCDD/Fs and
PCDEs: Relative importance of contaminant and sediment characteristics and biological factors.
Environmental Science and Technology 37, 3926-3934.
Mandalakis, M. and Stephanou, E. G., 2004. Wet Deposition of Polychlorinated Biphenyls in the
Eastern Mediterranean. Environ. Sci. Technol. 38, 3011-3018.
Mandalakis, M., Apostolaki, M., Stephanou, E. G., 2005. Mass budget and dynamics of
polychlorinated biphenils in the Eastern Mediterranean Sea, Global Biogeochemical cycles 19,
GB3018, 1-16.
Marinov, D., Dueri, S., Puillat, I., Zaldívar, J.M., Jurado, E. and Dachs, J. 2007- Description of
contaminant fate model structure, functions, input data, forcing functions and physicochemical
properties data for selected contaminants (PCBs, PAHs, PBDEs, PCDD/Fs) - EUR Report n 22627
EN.
Metzger, E. Simonucci, C., Viollier, E., Sarazin G., Prevot R, F, Elbaz-Poulichet, F., Seidel J.-L.,
46
Jezequel, D. 2007. Influence of diagenetic processes in the Thau lagoon on cadmium behaviour
and benthic fluxes. Estuarine Coastal and Shelf Science 72, 497-510
Morel, F.M.M 1983 Principles of aquatic chemistry. John Wiley and sons.
Morel, F.M.M., Hudson, R.J.M.,and Price, N.M. 1991. Limitation of productivity by trace metals in
the sea. Limnol. Oceanogr. 36, 1742-1755.
Morel, F.M.M., Milligan A.J. and Saito, M.A. 2003. Marine bioinorganic chemistry: The role of trace
metals in the oceanic cycles of major nutrients. Treatise on geochemistry Vol 6 pp113-143
Elsevier.
Morel, F.M.M.and Hering J.G. 1993. Principles and applications of aquatic chemistry. Wiley
Interscience . John wiley &sons Inc.
Muir, D. C. G., Lawrence, S., Holoka, M., Fairchild, W. L., Segstro, M. D., Webster, G. R. B. and
Servos, M. R. 1992. Partitioning of polychlorinated dioxins and furans between water, sediments
and biota in lake mesocosms. Chemosphere 25,119-124.
Muresan, B., Cossa, D., Jézéquel, D., Prévot, F. et Kerbellec, S. 2007. The biogeochemistry of
mercury at the sediment water interface in the Thau lagoon. 1. Partition and speciation. Est. Cstl.
Shelf Sci. 72, 472-484.
Nriagu JO & Pacyna, JM 1988 Quantitative assessment of worldwide contamination of air, water and
soils with trace metals. Nature 333, 134–139.
Oguz , T., Ducklow, H. W., Malanotte-Rizzoli, P., Murray, J. W., Shushkina, E. A., Vedernikov, V. I.
and Unluata, U. 1999. A physical-biochemical model of plankton productivity and nitrogen cycling
in the Black Sea. Deep-Sea Res. I 46, 597-636.
Opperhuizen, A., 1991. Bioconcentration and biomagnification: is a distinction necessary. In: Nagel,
R., Loskill, R. (Eds.), Bioaccumulation in Aquatic Systems. VCH Publishers, Weinheim, pp. 67-
80.
Phinney, J.T., and Bruland, K.W. 1994 Uptake of lipophilicOrganic Cu, Cd, and Pb complexes in the
coastal diatom Thalassiosira weissflogii. Environmental Science and Technology 28, 1781-1790.
Rifkin, E. and LaKind, J.,1991. Dioxin bioaccumulation: Key to a sound risk assessment methodology.
Journal of Toxicology and Environmental Health. 33,103-112.
Russell, R.W, Gobas, F.A.P.C., Haffner, G.D., 1999. Role of chemical and ecological factors in
trophic transfer of organic chemicals in aquatic food webs. Environ. Toxicol. Chem. 18, 1250-
1257.
Ruus, A., Berge, J. A., Bergstad, O. A., Knutsen, J. A., Hylland, K., 2006. Disposition of
polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) in two
Norwegian epibenthic marine food webs. Chemosphere 62,1856-1868
Ruzic 1982. Theoretical aspects of the direct titration of natural waters and its information yield for
47
trace metal speciation. Analytica Chimica Acta 140, 99-113.
Schulz-Bull, D.E., G. Petric, H. Johannsen, J.C. Duinker, 1997. Chlorinated biphenyls and p,p’-DDE
in Mediterranean surface waters. Croatica Chemica Acta 70, 309-321.
Schwarzenbach, R. P., Gschwend, P. M., Imboden, D. M., 2003, Environmental Organic Chemistry,
2nd Edition, Wiley Interscience, New York.
Sijm, D.T.H.M., Seinen, W., Opperhuizen, A., 1992. Life-cycle biomagnification study in fish.
Environ. Sci. Technol. 26, 2162-2174.
Skoglund, R.S. K. Stange, and D.L. Swackhamer. 1996. A kinetics model for predicting the
accumulation of PCBs in phytoplankton. Environ Sci Technol 30, 2113–2120.
Stange, K. and Swackhamer, D. L. 1994. Factors affecting phytoplankton species-specific differences
in accumulation of 40 polychlorinated biphenyls (PCBs). Environ. Toxicol. Chem. 11, 1849-1860.
Stuer-Lauridsen, F., 2005. Review of passive accumulation devices for monitoring organic
micropollutants in the aquatic environment. Environmental Pollution. 136: 503-524.
Swackhamer, D.L. and R.S. Skoglund. 1993. Bioaccumulation of PCBs by phytoplankton: kinetics vs.
equilibrium. Environ Toxicol Chem 12, 831-838.
Tessier, A. and Turner, D.R. (Eds.) 1995. Metal speciation and bioavailability in aquatic systems.
Wiley.
Thomann R.V., 1989. Bioaccumulation model of organic chemical distribution in aquatic food chains.
Environ. Sci.Technol. 23, 699–707.
Thomann, R.V., Conolly, J.P., Parkerton, T.F., 1992. An equilibrium model of organic chemical
accumulation in aquatic food webs with sediment interaction. Environ. Toxicol. Chem. 11, 615-
629.
Tsapakis, M. and Stephanou, E. G., 2005. Polycyclic Aromatic Hydrocarbons in the Atmosphere of the
Eastern Mediterranean. Environ. Sci. Technol.; 39, 6584-6590.
Tsapakis, M., Apostolaki, M., Eisenreich, S., Stephanou, E. G., 2006. Atmospheric Deposition and
Marine Sedimentation Fluxes of Polycyclic Aromatic Hydrocarbons in the Eastern Mediterranean
Basin. Environ. Sci. Technol., 40, 4922-4927.
Tseng, C.M., A. de Diego, H. Pinaly, D. Amouroux, O.F.X. Donard. 1998. Cryofocusing coupled to
atomic absorption spectrometry for rapid and simple mercury speciation in environmental matrices.
J. Anal. Atom. Spectro. 13, 755-764.
Tusseau-Vuillemin, M. H., Gourlay, C., Lorgeoux, C., Mouchel, J. M., Buzier, R., Gilbin, R., Seidel, J.
L. and Elbaz-Poulichet, F. 2007. Dissolved and bioavailable contaminants in the Seine river basin.
Science of the Total Environment 375: 244-256.
Van den Berg CMG, Merks AGA, Dursma EK 1987 Organic complexation and its control of the
dissolved concentration of copper and zinc in the Scheldt estuary. Estuar Coast Shelf Sci 24, 785-
48
797.
Van der Linde, A., Jan Hendriks, A., Sijm, D. T.H.M. 2001. Estimating biotransformation rate
constants of organic chemicals from modeled and measured elimination rates. Chemosphere 44,
423-435.
Van der Oost R., J. Beyer , N. P.E. Vermeulen, 2003. Fish bioaccumulation and biomarkers in
environmental risk assessment: a review. Environmental Toxicology and Pharmacology 13,
57/149.
Vigano, L., S. Galassi and A. Arillo, 1994. Bioconcentration of polychlorinated biphenyls (PCBs) in
rainbow trout caged in the river Po. Ecotoxicol. Environ. Safe. 28, 287-297.
Wallberg, P. and Andersson, A. 1999. Determination of adsorbed and absorbed polychlorinated
biphenyls (PCBs) in seawater microorganisms. Marine Chemistry 64, 287-299 .
Wan, Y., Hu, J., Yang, M., An, L., An, W., Jin, X., Hattori, T.and Itoh, M. 2005. Characterization of
trophic transfer for polychlorinated dibenzo-p-dioxins, dibenzofurans, non- and mono-ortho
polychlorinated biphenyls in the marine food web of Bohai Bay, North China. Environmental
Science and Technology 39, 2417-2425.
Wells ML, Kozelka, PB and Bruland, KW 1998 The complexation of ‘dissolved’ Cu, Zn, Cd and Pb
by soluble and colloidal organic matter in Narragansett Bay, RI, Mar. Chem. 62, 203–217.
Wilkinson KJ, and Buffle J 2004 Critical Evaluation of Physicochemical Parameters and Processes for
Modelling the Biological Uptake of Trace Metals in Environmental (Aquatic) Systems, Chapt 10 in
Physicochemical Kinetics and Transport at Biointerfaces, edited by H. P. van Leeuwen and W.
Koester, John Wiley & Sons, Ltd, pp 446-533.
Zaldívar, J.M., Marinov, D., Dueri, S., Puillat,I., Carafa, R., Berrojalbiz, N., Lacorte, S., Jurado, E. and
Dachs, J. 2007. Integrated Modeling of Fate and Effects of Persistent Organic Pollutants in Marine
Ecosystems. EUR 22882 EN.
European Commission EUR 23266 EN – Joint Research Centre – Institute for Environment and Sustainability Title: Experimental and modelling studies to identify bioavailable contaminant concentrations and bioavailability Author(s): S. Dueri, D. Marinov, R. Carafa, B. Avery, B. Boutier, D. Cossa, J. L. Gonzalez, J. Knoery, D. Muranon, J. Tronczynski and J. M. Zaldívar Luxembourg: Office for Official Publications of the European Communities 2008 – 51 pp. – 21 x 29.7 cm EUR – Scientific and Technical Research series – ISSN 1018-5593 ISBN 978-92-79-08494-2 DOI 10.2788/6944 Abstract The increasing worldwide contamination of aquatic ecosystems with thousands of industrial chemical compounds is one of the key environmental problems today. However, chemicals present in the water column or sediments are not totally bioavailable to the aquatic organisms. Chemical compounds are distributed among all phases in environmental systems and, therefore, they can be buried in a media that does not allow direct transfer to the organism. In this situation, the total concentration is normally not the right value for assessing which will be the risks of a certain pollutant. In fact, only the part of the concentration that can be taken up by the biota represents a real risk for the ecosystem and the human health. Therefore, environmental legislation should be based on the concept of bioavailability; this will provide effective health protection and avoid unnecessary economic pressures. In this report, simulated results as well as experimental campaigns have been summarized with the aim of providing a comprehensive study on bioavality for POPs and metals. A comprehensive modeling approach has been developed to assess bioconcentration factors for several families of POPs. This approach is quite general and aims at developing a scale between total concentration in the water column and bioavailable concentration as a function of physicochemical properties of a compound. However, in order to develop further the approach, the experimental data obtained from Thresholds-1 and Thresholds-2 Mediterranean campaigns should be included in the study as well as the results provided by the Thau lagoon study. With this information, validation of the model and assessment of the preliminary results obtained in this deliverable will be possible. These open questions will be explored in a companion report when the Thau modeling case study will be developed and experimental data on POPs concentrations in biota would be available. The bioavailability of dissolved trace metals in the water column depends on their speciation, which in turn depends on several physico-chemical parameters (e.g. T, pH, redox, etc.). However, up to know no general tool is available to evaluate trace metal bioavailability to aquatic organisms. We expect that the experimental measurements carried out at Thau lagoon for Cd and Hg will help in the development of such as tool.
50
How to obtain EU publications Our priced publications are available from EU Bookshop (http://bookshop.europa.eu), where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352) 29 29-42758.
51
The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national.
LB
-NA
-232
66-E
N-C