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: jose.zaldivar-comenges@jrc.it 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.

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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

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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.

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