Chemical Contamination Baseline in the Western Basin of the Mediterranean Sea Based on Transplanted...

Chemical Contamination Baseline in the Western Basinof the Mediterranean Sea Based on Transplanted Mussels

Bruno Andral • Francois Galgani • Corinne Tomasino • Marc Bouchoucha •

Charlotte Blottiere • Alfonso Scarpato • Jose Benedicto • Salud Deudero •

Monica Calvo • Alexandro Cento • Samir Benbrahim • Moustapha Boulahdid •

Cherif Sammari

Received: 19 March 2010 / Accepted: 23 August 2010 / Published online: 23 September 2010

� Springer Science+Business Media, LLC 2010

Abstract The MYTILOS project aimed at drawing up a

preliminary report on coastal chemical contamination at the

scale of the Western Mediterranean (continental coasts of the

Balearic Islands, Sicily, Sardinia, Corsica and Maghreb)

based on a transplanted mussels methodology validated

along the French coasts since 1996 by Ifremer and the Rhone

Mediterranee & Corsica water board. MYTILOS is backed

up by the INTERREG III B/MEDOC programme, the

PNUE/PAM-MEDPOL and Rhone Mediterranee & Corsica

water board. Three cruises (2004, 2005, 2006) have taken

place to assess the first state of chemical contamination along

the Western Mediterranean shores with the same method-

ology. Approximately 120 days were spent at sea deploying

and retrieving 123 mussel bags. The results obtained for all

studied contaminants were equivalent to those obtained

along the French coast according the RINBIO network.

These similarities relate to both the highest measured levels

and background levels throughout the 123 stations. The areas

of greatest impact were mainly urban and industrial centers

and the outlets of major rivers, with a far higher midsea

impact on the dilution of organic compounds than on metals.

Metal levels measured in midsea zones were found to be

similar to those in natural shellfish populations living along

the coast. On a global scale we can observe that the con-

taminants levels in the Mediterranean Sea are in the same

range as in other areas worldwide. Overall, the research

demonstrates the reliability of this methodology for marine

pollution monitoring, especially in the Mediterranean sea.

Marine mussels (Mytilus spp.) are known to be good can-

didates for quantitative biomonitoring of chemical

B. Andral (&) � F. Galgani � C. Tomasino � M. Bouchoucha

IFREMER, Laboratoire Environnement Ressource Provence

Azur Corse, BP 330-83507 La Seyne sur Mer Cedex, France

e-mail: [email protected]

C. Blottiere

TVT, Maison des Technologies, Place G. Pompidou,

83 000 Toulon, France

A. Scarpato

ISPRA, Via di Casalotti, 300 Rome, Italy

J. Benedicto

IEO/Murcia, Calle Varadero no. 1,

30740 San pedro de Pinatar, Spain

S. Deudero

Univ Balears/IMEDEA, Cra. de Valldemossa, Palma 07122,

Illes Balears

M. Calvo

CSIC-ACA, Jordi Girona 18-26, 08034 Barcelona, Spain

A. Cento

PSTS, Zona Industriale Blocco Palma 1,

S G. Agnelli Angolo, 95030 Catania, Sicily

S. Benbrahim

INRH, rue de Tiznit, Casablanca 01, Morocco

M. Boulahdid

ESSMAL, bois des cars, BP 19, 16320 Alger, Algeria

C. Sammari

INSTM, 28 rue du 02 mars 1934, 2025 Salammbo, Tunisia

123

Arch Environ Contam Toxicol (2011) 61:261–271

DOI 10.1007/s00244-010-9599-x

contaminants in coastal waters (Cossa 1989; Goldberg 1975;

Phillips and Rainbow 1993). They concentrate chemical

contaminants in their soft tissue up to 105 times more than the

concentration in water and they have a large geographical

distribution and a high tolerance to stress. They have already

been widely used in the so-called ‘‘passive’’ or ‘‘active’’

Mussel Watch Programmes (Honkoop et al. 2003; Kramer

1994; Martin et al. 1984; Sericano et al. 1995; Widdows et al.

1995). The previous studies such as the US Mussel Watch

and the French RNO (Claisse 1989) used indigenous mus-

sels. The latter studies used transplanted mollusks as in the

French RINBIO network (http://www.ifremer.fr/envlit/)

(Andral et al. 1998; Cossa 1989; De Kock and Van Het

Groenewoud 1985; Riget et al. 1997).

Mollusks transplantation from a clean reference site to a

contaminated area can be an effective strategy for biomon-

itoring the effects of environmental changes in coastal zones

(Andral and Stanisiere 1999; Andral et al. 2001; Buestel

1997; De Kock 1983; De Kock and Van Het Groenewoud

1985; Odzac et al. 2000). The caging technique compensates

for the scarcity of natural shellfish stocks in many parts of the

coast. Bioaccumulation results from a pseudoequilibrium

between the concentration of the micropollutant in the

organism and levels in the environment (Borchard 1983;

Cossa 1989). It is based on the processes of absorption,

excretion, and accumulation. The amount of bioaccumulated

contaminants is closely linked to the mussel’s life cycle,

particularly the individual’s age and sexual maturity. Char-

acteristics of the immersion site, such as salinity and food

availability, influence pollutant bioavailability and specia-

tion as well as the metabolism and tissue growth of the

mussel, in which the pollutant is diluted (Cossa 1989; Kra-

mer 1994; NAS 1980; Phillips 1976). The transplantation

method makes it possible to control the source, age, and stage

of sexual maturity of the samples. However, implementing it

on a large geographic scale introduces factors such as vari-

ations in physiochemical characteristics and food availabil-

ity in the immersion zones. Although the concentrations

measured in the tissue are an indicator of bioavailable pol-

lutant levels, the bioaccumulation factor depends on mussel

growth in relation to the primary food production, or trophic

capacity, of the environment. Comparison of raw data on

tissue concentration between sectors of different trophic

potential might be misleading.

The Condition Index (CI), defined as the ratio of the soft

tissues dry weight over shell weight (Andral et al. 2004), is

a good indicator of the physiological state and growth

resulting from the environmental effect. The results

obtained by the RINBIO network show a strong correlation

between the CI and contaminant concentration in all geo-

graphical zones for certain contaminants, especially heavy

metals. It is therefore possible to define a correction model

for these contaminants and to obtain a concentration,

independent from the environmental effect, that can be

considered representative of bioavailable contaminant

concentration in the environment. On a large spatial scale,

this model enables the results to be adjusted for standard

mussels, and this makes result comparisons easier.

This original technique, validated along the French coast,

has been deployed in the West Mediterranean basin in our

studies in the context of the MYTILOS European project.

The purpose of MYTILOS is to obtain with the same

methodology the first map of coastal chemical contamina-

tion along the Western Mediterranean coast, including the

coast of Tunisia, Algeria, Morocco, Spain, France, Italy, the

Balearic Islands, Sicily, Sardinia, and Corsica.

MYTILOS is supported by the INTERREG III B/

MEDOC program, steered by Ifremer and backed by

Toulon Var Technologies, in cooperation with the ISPRA

(Italy), IEO (Spain), PSTS (Sicily), IMEDEA (Balearic

Islands), CSIC and the Agence Catalane de l’Eau (Cata-

lonia), INSTM (Tunisia), ISMAL (Algeria), INRH, DSPR,

and University of Agadir (Morocco). MYTILOS is also

backed by the PNUE/PAM–MEDPOL program and Rhone

Mediterranee & Corsica water board.

Materials and Methods

Transplantation

The species used was Mytilus galloprovincialis. The mus-

sels came from a firm in Languedoc-Roussillon that har-

vests contamination-free mussels from the open sea. The

batch was made up of adult mussels 18–24 months old, of

about 50 mm in dimension. The 3-kg samples were stored

in conchylicultural pouches mounted on PVC tubing and

reimmersed for 5 days so they could recluster prior to

transplantation.

Subsurface anchorages, consisting of a mussel bag

attached to a 30-kg weight, were kept in open water at a

depth of 15 m using an 11-l float. To compensate for

accidental trawl fishing losses, the subsurface anchorage

was doubled or even tripled at most stations to increase

chances of retrieval.

The stations were placed for 3 months at various depths

and distances from the coast. Generally, the depth was

20–50 m according to coastal configuration, and the bags

were attached at depths of 8–10 m. The aim was to locate

each station in an equivalent continental input dilution

volume to avoid being under the direct influence of one

contamination source.

A total of 149 stations were deployed during three

sample deployment periods. The deployment was con-

ducted using the R/V l0 Europe, which was able to carry the

3.5-m3 tanks used to transported the mussel bags plus all

262 Arch Environ Contam Toxicol (2011) 61:261–271

123

http://www.ifremer.fr/envlit/

anchorage equipment. The deployments were conducted

during the mussels’ period of sexual dormancy. The

deployments dates and regions were as follows: Mytilos I

(March 20–April 15, 2004) extended from Cartagena

(Spain) to Orbelleto (Italy); Mytilos II (March 21–April 19,

2005) covered southern Italy and part of southern Spain as

well as the Balearic Islands, Sardinia, and Sicily; Mytilos

III (May 16–June 3, 2006) covered Corsica, the extreme

southern coasts of Spain, and the Maghreb coasts (Mor-

occo, Algeria, and Tunisia).

Recovery

The samples were hauled with two vessels (‘‘Tethys II’’

and ‘‘Europe’’) on different occasions. Oceanographic

vessels provided vital logistical support (diving logistics,

sample processing, preparation), including a zodiac

equipped with detection instruments (sweep sonar, vertical

echo sounder) for station retrieval operations. Retrieval

was mainly done by diving. Operations were fast and safe

and because the bags resided at depths of 8–10 m, no

decompression was needed.

The retrieval dates were July 2–21, 2004, June 17–26,

2005, and August 10–27, 2006. After retrieval, the mussels

were separated and rinsed in seawater. At each station, the

length of the shell was recorded, the mussels were opened

raw, and the flesh was scraped out of the shell with a

stainless-steel scalpel. For each sample, pooled shells were

dried at 60�C in an oven for 48 h and then weighed. Pooled

flesh was weighed after freeze-drying. The ratio of dry

flesh weight to dry shell weight (FW/SW) was used to

determine the CI for each sample.

Analysis

Before the chemical analysis, mussel tissues were dried and

homogenized. The quality of analytical data was checked

by the analysis of certified reference material. Analyses

were performed under the Quasimeme assurance quality

program. The following techniques were used to test for

contaminants.

For heavy metals, 0.5 g of freeze-dried sample was

mineralized by an HNO3 solution at 90�C.

Quantification of lead, cadmium, and nickel was

performed by atomic adsorption spectrometry with a

Zeeman-effect graphite furnace [detection limit: 0.1 mg/

kg dry weight (dw)].

Mercury was determined by atomic fluorescence after

reduction with an SnCl2 solution (detection limit:

0.01 mg/kg dw).

DORM2 certified mussel reference materials was used to

control analytical reliability.

For dichlorodiphenyltrichlorethane and its metabolites

(DDT ? DDD ? DDE), hexachlorocyclohexane (aHCH,

cHCH), and polychlorobiphenyls (PCBs), 5 g of freeze-

dried sample were extracted by hexane/acetone solution

and cleaned up with sulfuric acid.

PCB 205 and PCB 207 as internal standards were added

for recovery estimates. Analyses were performed by

capillary gas chromatography coupled with an electron

capture detector (detection limit: 1 lg/kg dw).

For polycyclic aromatic hydrocarbons (PAHs), 5 g of

freeze-dried sample were extracted by hexane/acetone

solution and cleaned up with a silica gel cartridge.

Fluorene D10 and Perylene D12 as internal standards

were added for recovery estimates. Analyses were

performed by capillary gas chromatography coupled with

mass spectrometry (detection limit: 1–10 lg/kg dw).

For dioxins, 5 g of freeze-dried sample were extracted by

dichloromethane/acetone solution and cleaned up with a

silica gel cartridge, an alumin column, a florisil column,

active carbon, and sulfuric acid. The 16 congener of

dioxins and furans marked by 13C as the internal standard

was added for recovery estimates. Analyses were per-

formed by high-performance chromatography combined

with mass spectrometry (detection limit: 1 lg/kg dw).

For nonionic detergents such as nonylphenols [4-(para)-

nonylphenol] and octylphenols (para-tert-octylphenol),

5 g of freeze-dried sample were extracted by hexane/

acetone solution and cleaned up with a fluorisil column.

Phenanthrene D10 as the internal standard was added for

recovery estimates. Analyses were performed by gas

chromatography coupled with mass spectrometry (detec-

tion limit: 10 lg/kg dw).

For brominated diphenyl ethers, 5 g of freeze-dried

sample were extracted by hexane/acetone solution and

cleaned up with a fluorisil column. PCB 209 as the

internal standard was added for recovery estimates.

Analyses were performed by gas chromatography com-

bined with mass spectrometry–CI negative (detection

limit: 1 lg/kg dw).

All of the above compounds were analyzed for each

station, except dioxins, brominated diphenyl ethers, and

nonionic detergents, which were measured at around one

out of five stations. Considering the cost of analysis, these

compounds were measured on a reduced number of sam-

ples that were situated in front of urban and industrial

centers and in front of major rivers.

All results are expressed in micrograms, nanograms, or

picograms of contaminants per gram of dry mussel flesh.

For dioxins, results are expressed according to the potential

toxicity of the measured elements compared to the most

toxic compound: 2,3,7,8-TCDD (tetrachloro-dibenzo-p-

dioxin). The potential quantity of associated dioxins and

Arch Environ Contam Toxicol (2011) 61:261–271 263

123

furans is described using the toxic equivalent quantity

(TEQ), expressed in equivalent dioxin (Van den Berg et al.

1998). It corresponds to the sum, for all measured toxic

congeners, of concentrations weighted by the TEF (toxic

equivalent factor).

Data Standardization

The use of linear regression analysis for each contaminant

statistically infers that tissue concentration under steady-

state conditions can be a function of the CI. Depending on

the contaminant, a few stations that were apparently located

in contaminated areas did not obey this rule: They stand out

from linear models due to their consistently higher results.

For each contaminant, these outlying values were

removed and the parameters of the model describing the

effect of mussel physiology bioaccumulation were calcu-

lated. These regression lines allow one to normalize the

measured concentrations to a reference CI, and the con-

taminant levels are then comparable at a large spatial scale,

independently of the trophic conditions prevailing around

the sampling sites (Andral et al. 2004).

Data were regrouped for each of the regional subbasins

proposed by the MEDPOL program (UNEP/MAR/WMO

2001): Alboran (Alb), North Western (NW), South Wes-

tern (SW), and Tyrrhenian (Tyr) and descriptive statistics

were calculated. Significant differences between each

subbasin or country were calculated and analyzed in more

detail by the family of contaminants in published (Galgani

et al. 2010; Scarpato et al. 2009) or submitted (Benedicto

et al. 2010) articles.

Results

A total of 123 stations was retrieved from the 149 stations

originally deployed (82.5%) (Fig. 1).

Biometric Parameters

The analysis of shell height distribution showed that the

collected samples were well calibrated (Table 1). How-

ever, the analysis of flesh dry weight showed very marked

trophic variations between study sites. Growth conditions

in Alb (1.282 ? 0.343 g) and NW (1.049 ? 0.432 g) were

generally better than the SW (0.735 ? 0.223 g) and Tyr

(0.724 ? 0.222 g) basins. We also observed more favor-

able growing conditions in southern Spain, in the Ebre and

Rhone rivers input zones, and, generally speaking, in areas

adjacent to major cities and ports (Palma, Barcelona, and

Marseille). This observation was confirmed by the distri-

bution of individual flesh dry weight and by the condition

index.

Raw Contaminant Concentrations

The raw concentration results show that growth has a major

impact on result distribution, especially with regard to

heavy metals. Some trace metals (Cd, Hg, and Ni) showed

systematically higher levels in the most oligotrophic zones,

in relation to the distribution of the mussel condition index.

For example, the raw values of Cd measured in the Balearic

Islands were two to three times higher than those measured

at the mouth of the Ebre and on the Spanish coast (Fig. 2).

Concentrations of the vast majority of PCB congeners

were lower than the detection limits for the study. PCB 153

and PCB138 were the most reliable markers and were

present in all samples; their distribution is similar to that of

the sum of the 10 congeners.

Regarding PAHs, out of the 16 compounds analyzed, a

large majority did not exceed the analytical limit of

detection. At the scale of the three cruises, no compound

showed a distribution profile identical to that of the sum of

the 16 compounds. Compound distribution between sta-

tions was widely heterogeneous in comparison, for exam-

ple, to that of PCBs. aHCH, bHCH, and cHCH did not in

any case exceed the limit of detection in coastal zones.

Finally, metabolites of DDT were the most commonly

found elements.

Analysis of brominated diphenyl ethers and nonionic

detergents all showed results below the analytical limit of

detection. Regarding values that were lower than the ana-

lytical limit of detection, half of the detection limit value

was retained for data processing purposes for the other

contaminants.

Models

For each contaminant, adjustment parameters calculated on

the basis of the raw data from this campaign are presented

in Table 2. Models were significant (p values \ 0.05) for

most contaminants, with the exception of PAHs, which

showed no correlation between CI and tissue concentration.

The highest growth effect was observed for Cd, Hg, and

Nil, with a variation of more than 50% in results explained

by sample growth.

Adjusted Data

The value of the reference condition index is 0.11, which

corresponds to the mean of the CIs obtained from each

sample. Raw data were adjusted using this value and the

parameters for each model. However, data on PAH were

not processed according to this method.

For each contaminant, descriptive data statistics from

this campaign are presented in Table 3 by regional subba-

sin. At the scale of the studies, the distribution adjusted data


123

for Pb was relatively homogenous, with an average value of

1.41 lg/g and a median of 1.17 lg/g. However, two sites

were pinpointed as being particularly impacted by lead: the

Portoscuso industrial site (subbasin SW), with a maximum

of 8.25 lg/g and the zone extending from Portman to El

Portus (Alb subbasin) from 5.3 to 6.25 lg/g, which was

home to a thriving mining industry with the dumping of

50 9 106 tons of waste-mining during the intensive

extractive activities carried out during the 1960–1990 per-

iod. The maximum levels observed in the NW subbasin are

in the area of Barcelona (2.79 lg/g) and in the Tyr subbasin

in Porto Ferrario on Elba Island (3.05 lg/g).

Adjusted levels of Cd were globally homogenous

throughout the stations, with an average of 1.32 lg/g and a

median of 1.28 lg/g. A few stations showed relative peaks

of around 2 lg/g: Filicudi and Ustica stations in the

Tyr subbasin (Sicily) and Aguilas and Adra in Spain

(Alb subbasin).

Several sites impacted by Hg were recorded: first and

foremost the Portoscuso site in Sardinia (SW subbasin),

with a maximum level of 0.31 lg/g, witnessing significant

contamination generated by a large industrial complex. To

a slightly lesser degree, high levels are recorded in the SW

subbasin in Skida (0.19 lg/g) and in the Tyr subbasin,

especially in Palermo (0.22 lg/g).

Average adjusted concentrations of Ni were around

1.1 lg/g, with a median of 0.94 lg/g. Extreme values were

found in some sampling sites in SW subbasin, especially in

Tunisia (Tabarka, 3.18 lg/g), Algeria (Oued Zhor,2.89 lg/g;

Oran,2.47 lg/g), Morocco (Nador, 2.72 lg/g), and the south

of Spain (Fuengirola, 2.44 lg/g). Median values between

each subbasin are homogeneous.

The average value of the sum of DDT compounds was

3.93 ng/g, with a median of 3 ng/g at the scale of the study.

For this compound, median values between each subbasin are

also similar. Significant peaks were recorded in the NW and

Tyr subbasins, especially in front of Marseille (15.47 ng/g),

Barcelone (15.17 ng/g), and Napoli (15.34 ng/g). In the SW

subbasin, Algiers also showed a high level (10.23 ng/g). The

level recorded at the Algiers station was equivalent to the

overall levels recorded at stations off the coast of the

following rivers and streams: Ebre, Rhone, and, to slightly

lesser levels, Tet, Aude, Herault (NW subbasin), and Tevere

(Tyr subbasin).

Fig. 1 Location of the artificial mussel stations deployed for the MYTILOS project


123

Regarding the sum of the 10 congeners of PCBs and the

PCB153, the distribution showed a similar profile. The

average value of the sum of PCBs compounds was

14.58 ng/g, with a median of 8.98 ng/g at the scale of the

studies. The results indicate the presence of sites impacted

by PCBs, in the NW sub basin(Barcelona, 63.87 ng/g;

Marseille, 103.52 ng/g), the Tyr subbasin (Naples.

91.48 ng/g), and SW subbasin (Algiers, 51.13 ng/g).

This characteristic presence of PCBs in front of major

urban centers is further confirmed by values obtained in the

Tyr subbasin at La Maddalena (58.49 ng/g), adjacent to a

major naval base. To a lesser degree, inputs by the Ebre

(20.37 ng/g) and Rhone rivers (37.80 ng/g) can also be

pinpointed.

Results concerning the sum of the 16 dosed molecules

for PAHs are expressed in raw values. Large data hetero-

geneity was observed at the scale of the study, with an

average value of 46.51 ng/g and a median of 44.4 ng/g.

Two peaks were identified in the NW and Tyr subbasins

situated in Marseille (105.5 ng/g) in France and Piombino

in Italy (80.8 ng/g), adjacent to a large industrial complex.

Regarding fluoranthene—considered by the RINBIO

and RNO networks as the best representative of PAHs—we

also observed large data heterogeneity, with the highest

levels at the Bagnoli station (15.93 ng/g) in the Naples bay.

However, the peaks identified using the sum of 16 PAHs at

Cortiou and Piombino were not found for this compound.

Table 1 Statistical distribution of biometric parameters

N SH SW FW FW/N CI Fat

Alboran

Mean 11.7 237.000 102.867 14.457 1.282 0.143 6.266

Standard

dev.

1.7 15.152 9.900 2.350 0.343 0.032 1.735

SW

Mean 16.9 237.737 138.282 11.919 0.735 0.091 7.350

Standard

dev.

2.7 19.119 26.881 1.895 0.223 0.027 2.824

TYR

Mean 18.8 236.717 149.215 13.263 0.724 0.089 5.971

Standard

dev.

6.4 19.314 39.544 5.696 0.220 0.024 2.822

NW

Mean 24.3 243.420 197.857 25.810 1.049 0.126 5.881

Standard

dev.

6.4 13.528 50.827 11.721 0.432 0.045 2.728

N number of individuals per batch, SH shell height (mm), SW shell

weight (g), FW flesh weight (g), FW/N individual flesh weight (g), CIcondition index (FW/SW), fat content (%)

Fig. 2 Raw levels of cadmium

in lg/gof dry flesh (filled circle)

along the Spanish

Mediterranean coast and the

Balearic Islands against the CI

(open triangle)

Table 2 CI/contaminant level regression models

Contaminant Model R2 Cp

Cadmium Cd = 0.124[1/CI] ? 0.01 84.65 \0.0001

Lead Pb = 0.021[1/CI ] ? 0.932 6.57 0.01

Mercury Hg = 0.008 [1/CI ] ? 0.007 81.71 \0.0001

Nickel Ni = 0.049[1/CI ] ? 0.41 50.25 \0.0001

Sum of DDTs DDTs = 15.398CI ? 1.328 19.44 \0.0001

Sum of PCBs PCBs = 39.236CI ? 5.174 14.78 \0.0001

Sum of 16 PAHs PAHs = –36.277CI ? 43.223 1.41 0.21

Dioxins DEF = 3.069CI ? 0.145 47.47 \0.0001

R2: regression coefficient; Cp: critical probability


123

Regarding the results for the dioxines compounds, the

median distribution is situated at *0.7 pg/g (TEQ).

Medians for the Tyr and the NW subbasins are higher. One

peak recorded in the Marseille area reveals the existence of

significant inputs of these compounds (2.66 pg/g). It also

confirms the peak measured for PCBs, which belong to the

same compound family. On the project scale, we observed a

similar distribution to that of PCB congeners, with highest

values at Barcelona, La Maddalena, Napoli, and Algiers.

Discussion

The MYTILOS project confirmed the operational viability

of the RINBIO methodology. The project’s logistics,

anchorage structures, and deployment/retrieval techniques

allowed cost minimization, plus a highly satisfactory

retrieval rate taking into account the shape and diversity of

the studied coasts.

The CI distribution is indicative of the trophic hetero-

geneity of the Mediterranean coastal waters. Overall, the

waters are richer in the NW subbasin, due to the nutrients

contributed by the Rhone (Minas and Monas 1989) and the

Ebre rivers. In the Alb subbasin, this high productivity is

due to a nutriment enrichment, which has an Atlantic or a

Mediterranean origin in relation with upwelling activities

(Minas et al. 1984).

The CI distribution also provides clues as to the levels of

chemical contamination (except PAH), especially in the

case of trace metals. These findings can be explained by

greater tissue growth in areas where concentrations in the

waters are the highest and thus require adjustment with

reference to a standard CI. Tissue growth is especially

likely to mask or dilute the levels measured in the case of

metals like Cd, which is essentially present in seawater in a

dissolved form. Its uptake and bioaccumulation via feeding

(filtered particles) is negligible (Borchardt 1985; Riisgaard

et al. 1987).

In a more general way, the most highly impacted zones

were mainly situated adjacent to urban and industrial

centers and the outlets of major rivers.

During this study, all measured contaminants showed

equivalent levels to those recorded by the RINBIO network

(Andral and Tomasino 2004, 2007). This similarity related

to both highest levels and the background noise recorded at

the 123 study stations.

This method is also valuable in that it makes it pos-

sible to confront the adjusted data to those available on

Table 3 Statistical distribution of adjusted data (Pb, Cd, Hg, Ni, DDTs, CB 153, PCBs, dioxins) and raw data (fluoranthene, PAHs) in dry

weight

Pb

(lg g-1)

Cd

(lg g-1)

Hg

(lg g-1)

Ni

(lg g-1)

Sum of DDTs

(ng g-1)

CB 153

(ng g-1)

Sum of PCBs

(ng g-1)

Fluo

(ng g-1)

Sum of PAHs

(ng g-1)

Sum of dioxins

(pg g-1)

Alboran

Median 1.097 1.542 0.087 0.837 3.145 2.114 7.870 1.568 38.400 0.386

Mean 1.498 1.554 0.087 1.099 3.688 2.445 8.826 1.691 43.067 0.386

Standard Dev. 1.379 0.278 0.021 0.627 2.073 1.948 4.423 0.845 17.087 0.003

Max 6.254 2.114 0.152 2.722 7.514 6.821 18.749 4.080 84.600 0.388

SW

Median 1.260 1.399 0.101 0.900 2.411 1.837 7.859 1.683 42.800 0.403

Mean 2.184 1.386 0.113 1.072 3.188 3.212 11.436 2.244 46.774 0.534

Standard dev. 2.125 0.180 0.057 0.300 1.957 4.627 10.448 1.782 14.434 0.276

Max 8.249 1.684 0.309 2.908 10.215 19.555 51.135 7.426 79.600 1.022

TYR

Median 1.020 1.224 0.098 0.962 3.242 2.316 9.438 1.988 47.700 0.530

Mean 1.106 1.262 0.102 1.127 3.951 4.668 15.221 2.931 48.310 0.653

Standard dev. 0.405 0.231 0.026 0.519 2.584 6.257 16.072 3.016 14.138 0.306

Max 3.057 2.045 0.216 3.198 15.333 28.046 91.486 15.934 80.400 1.493

NW

Median 1.297 1.273 0.084 0.926 3.060 2.552 9.997 1.553 41.900 0.494

Mean 1.357 1.292 0.087 0.979 4.429 4.965 17.122 1.827 45.900 0.668

Standard dev. 0.416 0.214 0.012 0.275 3.509 7.914 19.232 1.794 18.351 0.540

Max 2.790 1.968 0.123 1.845 15.445 40.707 103.524 10.819 105.500 2.667


123

Ta

ble

4B

asel

ine

for

the

MY

TIL

OS

pro

ject

,R

INB

IO,

RN

On

etw

ork

and

wo

rld

wid

ed

ata

(in

mg

orl

g/k

gd

ryw

eig

ht)

Loca

tion

Yea

rS

pec

ies

Dat

aP

b

(lg

g-

1)

Cd

(lg

g-

1)

Hg

(lg

g-

1)

Ni

(lg

g-

1)

DD

T

(ng

g-

1)

DD

Ts

(ng

g-

1)

CB

15

3

(ng

g-

1)

PC

Bs

(ng

g-

1)

Flu

o

(ng

g-

1)

PA

Hs

(ng

g-

1)

Ref

eren

ces

Wes

tM

edit

erra

nea

nco

ast

2004–2006

Tra

nsp

lante

d

Mu

ssel

Med

ian

1.1

71

.29

0.0

90

.94

0.5

32

.28

.98

1.7

54

4.4

Th

isst

ud

y

Min

0.6

0.8

60

.05

0.6

20

.51

.50

.51

.83

0.5

21

.9

Max

8.2

52

.11

0.3

3.1

86

15

.54

1.3

10

3.5

16

10

5.5

Fre

nch

Med

iter

ranea

nco

ast

2006

Tra

nsp

lante

d

Mu

ssel

Med

ian

1.0

70

.88

0.0

70

.93

0.5

06

.60

4.0

41

2.4

62

.10

33

.05

An

dra

lan

d

To

mas

ino

(20

07)

Min

0.2

80

.40

.02

0.4

70

.50

1.5

00

.50

1.4

70

.50

18

.50

Max

8.4

42

.67

0.2

32

.48

5.4

07

0.8

b4

4.3

01

36

.74

12

.30

82

.30

Fre

nch

Med

iter

ranea

nco

ast

1995–1999

Muss

elM

ean

1.8

0.7

20.1

21.4

815.1

18.7

13.2

RN

O(2

00

6)

Min

0.1

0.2

0.0

40

.47

1.8

71

.57

2.1

9

Max

10

0.6

88

.41

83

.2b

59

32

43

Fre

nch

atla

nti

c/ch

anel

coas

t1995–1999

Muss

elM

ean

1.4

0.6

0.1

21.5

55.3

19.4

21.4

RN

O(2

00

6)

Min

0.4

0.1

70

.03

0.4

50

.63

0.1

64

Max

9.6

3.0

30

.53

63

6.6

49

52

45

No

rth

Sea

19

93

Mu

ssel

Med

ian

2.5

50

.66

0.2

14

48

56

Ber

gm

an(1

99

3)

Min

1.8

0.3

0.1

31

22

0

Max

6.3

7.6

90

.39

10

19

9.1

57

4

Cel

tic

Sea

a1

99

8M

uss

elM

ean

0.7

50

.75

0.1

10

–70

\1

35

0O

SP

AR

(20

00)

Bal

tic

Sea

19

97

Mu

ssel

Mea

n2

2.1

30

.13

11

.8S

zefe

r(2

00

2)

US

A2

00

3M

uss

els

and

Oy

ster

Med

ian

0.9

35

3.3

70

.08

22

.16

0.4

99

3.0

28

10

.35

NO

AA

Nat

ion

al

stat

us

Lo

wle

vel

\0

.58

\2

.156

\0

.055

\1

.39

\0

.14

\0

.88

\1

.775

Hig

hle

vel

[1

.903

5[

6.3

36

[0

.166

[4

.70

[7

.18

[3

5.3

0[

19

0.6

9

Was

hin

gto

n2

00

4–

200

5M

uss

elM

in0

.55

2.1

0.0

70

.88

1.6

12

13

4K

imb

roug

het

al.

(20

08)

Max

7.6

11

1.3

44

29

14

46

96

2

Cal

iforn

ia2004–2005

Muss

elM

in0.4

60.5

90.0

40.5

42

4.4

63

Kim

bro

ugh

etal

.

(20

08)

Max

5.5

8.4

0.3

49

.25

20

64

24

43

4

Ven

ezuel

a1991

Oyst

erM

in0.4

0.3

311

0.5

20.4

0.8

7Ja

ffe

etal

.(1

99

8)

Max

0.7

10

.91

18

1.1

1.4

2.6

Gulf

of

Aden

2005

Per

nap

erna

Min

2.6

0.1

0.5

Most

afa

etal

.(2

00

9)

Max

22

.63

.62

.5

India

2002

Per

nap

ernaa

Mea

n1

.25

6.3

1.2

Sas

iku

mar

etal

.(2

00

6)

Chin

a2

00

1M

uss

elM

in0

.46

0.4

81

.35

.77

14

1.3

41

0.3

Fu

ng

etal

.(2

00

4)

Max

2.9

35

.31

4.7

83

30

64

01

33

52

.4


123

M. galloprovincialis and M. edulis while respecting

equivalent biometric criteria. Comparison of these is also

possible with data from the National Observation Network

of the water quality on French coasts (RNO 2006).

As for the RINBIO network, the tendency for shellfish to

uptake metals in dissolved form (Andral et al. 2004;

Borchardt 1985; Riisgaard et al. 1987; Wang et al. 1997)

and homogeneous levels in the water column seem to have

little impact on variations in levels between marine stations

(Table 4). Under these conditions, levels of metals mea-

sured in natural populations sampled on the coast are

nearly identical to those obtained from transplants of

mussels immersed in the open sea.

However, the open-sea dilution effect is greater with

organic compounds. Levels measured in mussels in artifi-

cial stations are much lower than values observed in

organisms sampled directly on the coast. This phenomenon

might be related to the uptake kinetics of these molecules,

which are ingested as food, adsorbed on suspended parti-

cles (Herbes 1977). In an oligotrophic sector, or at great

distances from the shoreline, the scarcity of particulate

matter from watersheds results in low concentrations in the

mussels.

Concerning dioxin compounds, the distribution median

for MYTILOS samples is situated around 0.7 pg/g (TEQ),

which is relatively low in comparison to the data obtained

by an RNO study (RNO 2002). RNO average concentra-

tions measured throughout the French coasts were of

8.8 pg/g (TEQ), with lower concentrations on the Medi-

terranean coast than on other coasts.

Results from different studies involving bivalve samples

from various marine environments worldwide are also

summarized in Table 4. The comparison of data from

different studies, however, is generally complicated by

substantial changes that have been made in the analytical

methods, the seasonality of sampling, and the number of

congener for the organic compounds and must be exercised

with caution.

At the global scale, we can observe that the levels in the

Mediterranean Sea are in the same range as in other areas

worldwide. The main differences concern the maximum

values that are related to local high contaminations like for

Pb in the Gulf of Aden or Ni in Washington state. As for

French coasts, natural populations are more affected by

organic compounds than transplanted organisms.

Acknowledgments The authors wish to underline the great expe-

rience-sharing through which all project partners were able to judge

the easy implementation of this methodology and familiarize them-

selves with its main concepts through active participation in all

operations. The authors also wish to acknowledge the support by EEC

(Interreg /Medocc IIIC) and the Rhone-Mediterranean-Corsica Water

Agency and the support of UNEP MEDPOL for Morocco Algeria and

Tunisia.

Ta

ble

4co

nti

nu

ed

Loca

tion

Yea

rS

pec

ies

Dat

aP

b

(lg

g-

1)

Cd

(lg

g-

1)

Hg

(lg

g-

1)

Ni

(lg

g-

1)

DD

T

(ng

g-

1)

DD

Ts

(ng

g-

1)

CB

15

3

(ng

g-

1)

PC

Bs

(ng

g-

1)

Flu

o

(ng

g-

1)

PA

Hs

(ng

g-

1)

Ref

eren

ces

Au

stra

lia

19

93

Mu

ssel

Mea

n1

.13

0.0

7H

ayn

esan

d

To

ohey

(19

95)

Min

0.7

0.0

2

Max

1.6

20

.18

Arc

tic

Oce

an1

99

3B

ival

ves

Mea

n1

.42

.70

.08

6.4

26

21

57

0S

eric

ano

etal

.(2

00

1)

aD

ata

tran

sform

edin

dry

wet

(fac

tor

5)

bD

ata

ob

tain

edw

ith

lag

oo

nst

atio

n


123

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