An integrated approach for bioaccumulation assessment in mussels: Towards the development of Environmental Quality Standards for biota
J.M. Zaldívara , D. Marinovb, S. Dueric, J. Castro-Jiménezd, C. Michelettia and A.P. Wortha
Abstract
The possible use of chemical concentrations measured in mussels (Mytillus galloprovincialis) for compliance checking against Environmental Quality Standards (EQS) established for biota is analyzed with the help of an integrated model. The model consists of a 3D planktonic module that provides biomasses in the different compartments, i.e., phytoplankton, zooplankton and bacteria; a 3D fate module that provides the concentrations of contaminants in the water column and in the sediments; and a 3D bioaccumulation module that calculates internal concentrations in relevant biotic compartments. These modules feed a 0D growth and bioaccumulation module for mussels, based on the Dynamic Energy Budget (DEB) approach. The integrated model has been applied to study the bioaccumulation of persistent organic pollutants (POPs) in the Thau lagoon (France). The model correctly predicts the concentrations of polychlorinated biphenyls (PCBs) and polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) in mussels as a function of the concentrations in the water column and in phytoplankton. It also sheds light on the origin of the complexity associated with the use of EQS for biota and their conversion to water column concentrations. The integrated model is potentially useful for regulatory purposes, for example in the context of the European Water Framework (WFD) and Marine Strategy Framework Directives (MSFD).
Keywords: Bioaccumulation modeling; Dynamic Energy Budget; Mussels; PCBs; PCDD/Fs; WFD; MSFD; Environmental Quality Standard; EQS
1. Introduction
Mussels are frequently employed in contaminant monitoring programmes in transitional, coastal and marine waters. For example, the US Mussel Watch program (Kimbrough et al., 2008) has been monitoring trace metals and organic contaminants in mussels at several estuaries and coastal sites in the US since 1986, and now it covers approximately 140 chemicals. The OSPAR (OSPAR, 1999), HELCOM and Barcelona Conventions have included mussels among the species analyzed in the assessment of chemical contamination in coastal ecosystems (Roose and Brinkman, 2005). In addition, there are several European monitoring programmes in the Member States, e.g. RINBIO (Réseaux Intégrateurs Biologiques, France).
Monitoring contaminant concentrations in mussels has some advantages compared with the measurement of the total contaminant concentration in the water column, especially in the case of hydrophobic compounds that bioaccumulate in the food web and which are found at very low levels. The accumulation of organic contaminants in the tissues of mussels (or other biota) is a time-integrated indicator of pollutant occurrence, bioavailability and its distribution in aquatic ecosystems ([Goldberg, 1986] and [Pereira et al., 1996]). In particular, molluscs have been used as bioindicators of pollution in coastal ecosystems because of their feeding behavior and their limited mobility, which make them particularly exposed to contamination both via the water column and sediment, either directly or after resuspension. Mussels are farmed for human consumption and therefore, if contaminated, pose a potential risk for human health.
Although seafood represents a significant means of contamination in the human diet, to date few legal thresholds have been established to protect human health from toxic compounds and their mixtures. The European Commission (EC) introduced several laws to regulate the water quality of bivalves farming zones (EC, 1991) and to restrict farming, transport and purchase ([European Commission, 1979] and [European Commission, 1989]). However, these rules refer only to microbiological contamination. More recently, several pieces of EC legislation have been put in place to regulate several families of organic contaminants such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), dioxin-like polychlorinated biphenyls (DL-PCBs), polycyclic aromatic hydrocarbons (PAHs) (EC, 2006), and metals such as lead, cadmium, mercury (EC, 2005) for several aquatic species such as fish, molluscs, crustaceans and cephalopods. To supplement the Water Framework Directive (EC, 2000), a new directive has been approved (EC, 2008a) to establish Environmental Quality Standard (EQS) limits for 33 priority substances and 8 priority hazardous substances in surface waters, but also, for some of these compounds, in sediment and biota. For example EQS of 10 and 55 μg/kg (wet weight in prey tissue), have been established for hexachlorobenzene and hexachlorobutadiene, respectively.
In the risk management of hazardous chemicals, the prediction of bioconcentration and bioaccumulation factors in aquatic organisms has become a very important element in the assessment of environmental and human health effects. A quantitative assessment of uptake, metabolism, excretion, and depuration processes is needed to predict the fate and bioaccumulation of contaminants in the food web (Moriarty and Walker, 1987). These processes are related to specific physiological characteristics, feeding behavior and metabolism of the aquatic organism, to the physico-chemical characteristics of the compound, and to the environmental conditions of the aquatic system in which the organisms resides. Due to the complex influence of these factors, it is difficult to make comparisons between different field studies and results may appear contradictory.
Consequently, it is desirable to have a general modeling tool able to simulate field and laboratory toxicological experiments and integrate all the results into a predictive assessment of the ecotoxicological behavior of a certain substance. In addition, models may complement field studies and monitoring activities, and help to understand the transport and fate of contaminants ([Carafa et al., 2006], [Jurado et al., 2007], [Dueri et al., 2009b] and [Dueri et al., 2010]) and their impacts on communities and ecosystems ([Carafa et al., 2009], [Bacelar et al., 2009], [Dueri et al., 2009a] and [Marinov et al., 2009]).
The overall objective of this work was to investigate the problems in developing EQS for biota (concentrations that should not be exceeded to protect human health and the environment) and in using these values for compliance checking in the context of the Water Framework Directive (WFD) and the Marine Strategy Framework Directive, MSFD (EC, 2008b). The development of EQS values for biota is particularly important for certain hydrophobic compounds for which bioaccumulation and biomagnification in the food chain may occur. To investigate these issues, we have developed an integrated modeling approach for calculating the contamination level in mussels (Mytilus galloprovincialis) from the pollutant concentration values in the water column. This modeling framework has been applied to the Thau lagoon (France) to predict the tissue concentration of selected PCB (28, 52, 101, 118, 138, 153 and 180) and PCDD/F (PeCDD, OCDD, TCDF, PeCDF and HxCDF) congeners. The modeling framework, consisting of 3D hydrodynamics, fate, planktonic and bioaccumulation modules, provides: (a) temperatures and chemical concentrations in the water column, (b) biomasses in the different compartments, i.e., phytoplankton, zooplankton and bacteria ([Marinov et al., 2009] and [Dueri et al., 2010]) and (c) internal concentrations of contaminant in the planktonic compartments. These values are used to feed a 0D growth and bioaccumulation model for mussels based on the Dynamic Energy Budget approach (DEB) ([Kooijman, 2000], [Kooijman and van Haren, 1990] and [Van Haren et al., 1994]).
The Thau lagoon was selected because, besides its ecological interest as a recruitment zone for various species of sea fish, it is of notable economic importance due to shellfish cultivation (about 15 000 tons per year, amongst the highest in the Mediterranean Sea) and it has been extensively studied during the last 20 years ([Amanieu et al., 1989], [Picot et al., 1990] and [Plus et al., 2006] and references therein). Even though various numerical models have been developed, focusing on the lagoon hydrodynamics (Lazure, 1992), the nitrogen and oxygen cycles ([Chapelle, 1995] and [Chapelle et al., 2001]), the plankton ecosystem (Chapelle et al., 2000), the impact of shellfish farming ([Bacher et al., 1997], [Gangery et al., 2004a] and [Gangery et al., 2004b]) and the macrophytes ([Plus et al., 2003a] and [Plus et al., 2003b]); no model has been developed to support the study of the fate and effects of organic contaminants.
Furthermore, for over 20 years IFREMER has been coordinating a monitoring program (RNO, Réseau National d'Observation de la qualité du milieu marin) aimed at evaluating the levels and trends of chemical contaminants in the marine environment (http://www.ifremer.fr/envlit/surveillance/rno.htm). As a consequence, there is a database containing historical and present contaminant levels, as well as the geographical contaminant distribution in the French coastal waters for PAHs, PCBs PBDEs, OCPs and PCDD/Fs in mussels and sediments during the last decades ([Johansson et al., 2006] and [Munschy et al., 2008]).
2. Methods and approach
2.1. Study area
The Thau lagoon is 25 km long, 5 km wide and on average 4 m deep. The lagoon is located on the French Mediterranean coast (Fig. 1) and is sheltered by two narrow sea mouths. The catchment area is small (280 km2) and drained by numerous small streams with intermittent flows. The climate imposes a wide range of water temperatures and salinities with minima of 5 °C in February and salinity near 27‰, and maxima of 29 °C in August and a salinity of 40‰. Precipitation also shows large interannual variation (from 200 to 1000 mm per year). The wind is often strong, particularly when it blows from the Northwest (the so called “Tramontane”). The Thau lagoon hydrodynamics are heavily influenced by wind and precipitation (Lazure, 1992). During the summer, it frequently undergoes anoxia that can lead to important economic losses.
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Fig. 1.
The Thau lagoon and its watershed. Connections with the Mediterranean Sea are located at the extremities: in the Sète city and near Marseillan village.
In terms of experimental measurements, there are temporal time series for polycyclic aromatic hydrocarbons (PAHs), PCBs, polybrominated diphenyl ethers (PBDEs), organochlorine pesticides (OCPs) and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in mussels and sediments during the past few decades ([Munschy et al., 2008] and [Castro-Jiménez et al., 2008]). All these contaminants show a decreasing trend with the exception of PBDEs. This decreasing trend has significantly slower rates in sediments than in mussels, for example the half-lives (t1/2) for ΣPCBs are 8 and 32 years in mussels and sediments, respectively (Munschy et al., 2008). As an example, Fig. 2 shows some of the observed trends at two stations (in Zones A and C, see Fig. 1) for PCB 153.
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Fig. 2.
Concentrations of PCB153 found in mussels at Thau lagoon at two sampling stations in Zone C (o) and in Zone A (
), see Fig. 1, respectively (data from IFREMER, http://wwz.ifremer.fr/envlit/).
2.2. Bioaccumulation
Bioconcentration refers to the accumulation of a substance dissolved in water by an aquatic organism. The bioconcentration factor (BCF) of a compound is defined as the ratio of concentrations of the chemical in the organism (Cb) and in water (Cw) at equilibrium; normally Cw is the dissolved water concentration:
The existence of equilibrium between the concentration of the chemical in the organism and the concentration in the water is not easy to assess. For example, 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.
Biomagnification refers to the accumulation of substances via the food chain. It may be defined as an increase in the (fat-adjusted) internal concentration of a substance in organisms at successive trophic levels in a food chain. The biomagnification factor (BMF) can be expressed as the ratio of the concentration in the organism (predator) to the concentration in the diet (prey):
where Co is the steady-state chemical concentration in the organism (mg kg−1) and Cd is the steady-state chemical concentration in the diet (mg kg−1).
The term bioaccumulation refers to uptake from all environmental sources including water, food and sediment. The bioaccumulation factor (BAF) can be expressed for simplicity as the steady-state (equilibrium) ratio of the substance concentration in an organism to the concentration in the surrounding medium (e.g. water). Normally, it is evaluated using a multiplicative approach. Therefore, the BAF may be calculated as
where the number of biomagnifications factors depends on the trophic level or position of the organism in the food web (EC, 2003).
In EC (2003), the criteria for bioaccumulative (B) or very-bioaccumulative (vB) potential were established as
• BCF>2000 l kg−1 and BCF<5000>−1→B
• BCF>5000 l kg−1→vB
It has been demonstrated that, for many organic compounds, the logarithm of the BCF plotted against the logarithm of the octanol/water partition coefficient, Kow, gives two linear correlations, with a plateau in correspondence to log Kow≈6.5 ([Swackhamer and Skoglund, 1993] and [Stange and Swackhamer, 1994]). However, Jonker and van der Heijden (2007) explained the existence of this plateau as being due to nonequilibrium conditions and the binding to dissolved organic carbon (DOC) that produces an overestimation of the water concentration for highly hydrophobic compounds.
Geyer et al. (1982) obtained the following correlation between BCF and Kow (between 1.5 and 7) for mussels (Mytilus edulis):
A similar approach has been proposed by Booij et al. (2006) where they developed two correlations for calculating the uptake constant (via water and food, kuf) and the depuration plus metabolism plus growth constant (kdmg) as a linear function of Kow values. They found that the uptake constant for M. edulis could be modeled by the following linear correlation:
whereas the bioaccumulation factor, BAF=kuf/kdmg, could be correlated with Kow as follows:
The fact that the WFD allows the definition of EQS in water, sediment and biota has raised a considerable interest in the use of biota monitoring, and in the conversion of these values to dissolved concentrations in the water column using BCF or BAF estimated values in the absence of experimental data. One of the objectives of this work was to assess the problems and uncertainties associated with this approach.
2.3. Model development
In this work an existing 3D contaminant fate model (Dueri et al., 2010) was extended by adding a Dynamic Energy Budget (DEB) 0D growth, reproduction (Kooijman, 2000) and bioaccumulation modules (Kooijman and van Haren, 1990). Fig. 3 illustrates the model structure and the results from one module that serve as input for other modules. The 3D hydrodynamic model forced by meteorological data provides current, temperature and salinity fields that serve as forcing to the fate and to the planktonic model. The last model is used to calculate Particulate Organic Matter (POM) and Dissolved Organic Carbon (DOC) that control the partitioning of the chemicals in the water column between purely dissolved, bound to DOC or attached to POM. The DEB module is then run for each zone in which mussels were analysed using temperature and phytoplankton biomasses. Two bioaccumulation modules have been considered one for the planktonic ecosystem which provides the internal contaminant concentrations for phytoplankton, zooplankton and bacteria and the other for the bioaccumulation in mussels, following Kooijman and van Haren (1990), which uses dissolved concentrations from the fate model and particle bounded concentrations for the planktonic bioaccumulation module to calculate internal concentrations in mussels.
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Fig. 3.
General structure of the integrated model incorporated into COHERENS (Luyten et al., 1999). In gray forcing parameters, light blue the 3D modules and light green the 0D modules for both zones. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The DEB theory (Kooijman, 2000) uses three state variables to describe organisms: the energy reserves, structural volume and the energy allocated to development and reproduction. A fixed fraction of the energy, κ, is allocated to somatic maintenance and growth while the remainder, (1−κ), is used for maturation and reproduction. The parameters of the model used here for M. galloprovincialis are those reported by Casas and Bacher (2006), and also evaluated by van der Veer et al. (2006) with the exception of the half saturation coefficient, KX (μg l−1), which is used in the functional response of assimilation to food concentration, X, given by a type II Holling function: X/(X+KX), the energy content of the reserves, μE (J g−1), and the shape coefficient, δm. These three parameters were evaluated experimentally by Casas and Bacher (2006) for the Thau lagoon. In addition, in the DEB model developed for mussels spawning occurs when the water temperature is sufficiently high and enough energy is accumulated in the reproduction buffer. We have used here the experimental observation in Casas and Bacher (2006) that spawning occurs around November.
The contaminant fate model, developed to simulate the pollutant concentrations in shallow water bodies by taking into account the exchange of contaminant with the atmosphere and the sediment, was originally implemented for the simulation of plant protection products in the Sacca di Goro lagoon (Italy) (Carafa et al., 2006) and it has been progressively adapted for a variety of case studies involving 1D water column simulation of PCBs ([Jurado et al., 2007] and [Dueri et al., 2009b]) and PAHs (Marinov et al., 2009) and 3D simulation of PCDD/Fs (Dueri et al., 2010). In addition, the fate model was coupled, through a bioaccumulation model, with a planktonic model that considers the biomass variation of two phytoplankton populations, two zooplankton populations, bacteria and detritus and which was used to assess the combined influence of nutrients and contaminants on planktonic ecosystems (Dueri et al., 2009a). Therefore, the model is capable of simulating the fate and distribution of contaminants by considering physical, chemical and biological processes on several spatio-temporal scales.
Finally, the bioaccumulation model in mussels is based on the model developed by Kooijman and van Haren (1990) and Van Haren et al. (1994). In this approach the chemicals, once taken up by the organism, partition instantaneously over four compartments (Kooijman and van Haren, 1990): one aqueous fraction and three non-aqueous fractions: the structural component of the body, the stored energy reserves and the energy reserves set apart for reproduction. In addition, it is assumed that the uptake and elimination rates are proportional to the surface area, V2/3, of the organism. A detailed description of the assumptions, equations and parameters used is provided in Supplementary Information.
2.4. Model forcing data
The hydrodynamic module of the model was forced using meteorological data provided by Meteo France (http://www.meteo.fr) for Sète station, namely: air temperature at 2 m, wind speed and direction at 10 m, precipitation rate, cloud cover and relative humidity.
The model boundary conditions consisted of the watershed inputs and the exchanges with the open sea. The fresh river water discharges and nutrients, supply in the form of nitrate and ammonium, were specified on a daily basis, using a previously developed model (Plus et al., 2006). The open sea boundary conditions for temperature and salinity were obtained by linear interpolation between measured data sets, whereas the nutrients at the connection with Mediterranean Sea were specified according to Plus et al. (2003a) as constant annual values and the hydrodynamic-physical module was initialized homogeneously with null current velocities, constant vertical profiles for temperature (6 °C) and salinity (35‰).
The fate model was forced with experimental data obtained during several sampling campaigns in the Thau lagoon. The PCB and PCDD/F concentrations in the lagoon and river water and in the sediment were obtained from a one-week sampling campaign (Castro-Jiménez et al., 2008) whereas air concentrations were obtained from a 1-year sampling campaign Castro-Jiménez et al., submitted for publication. To deal with measured sediments inhomogeneities, i.e., higher concentrations in the eastern part close to the harbor and the city of Sète, linear interpolation between measured points was carried out, see Supplementary Information.
The data to force the DEB growth and bioaccumulation model for mussels were provided by the hydrodynamic (temperature), the planktonic (Chlorophyll-a) and the fate model (concentrations of PCBs and PCDD/Fs in the water column and in the phytoplankton compartments).
2.5. Model computation
The different modules have been incorporated into the program structure of the COHERENS model (COupled Hydrodynamical Ecological model for REgioNal Shelf seas), a 3D finite-difference multi-purpose model dedicated to coastal and shelf seas (Luyten et al., 1999). The hydrodynamic/physical part was kept unchanged while numerical solvers of the original model code were used for the new modules. COHERENS was selected for this work because it allows different sub-models to be coupled and provides an opportunity for further sub-modules development. Moreover, COHERENS is freely available for scientific purposes, is well documented (Luyten et al., 1999) and has been extensively verified, especially for test cases such as advection processes, turbulence closure schemes, plumes, river fronts, marine biological problems, etc. The complete model documentation and the program code can be requested via the internet (http://www.mumm.ac.be/EN/Models/Coherens/index.php).
The Thau lagoon model was built on a homogeneous spherical grid corresponding to horizontal 100 m×100 m of cell size in a Cartesian co-ordinate system. The model vertical spatial resolution has 7 σ-vertical layers. The model time step, restricted by the spatial resolution and barotropic mode, was set to 4 s. Under these conditions, a typical simulation run of 1 year, including all the modules, takes around 10 days of CPU time on a Mac Pro1.1 with dual core Intel Xeon processors of 2.66 GHz each.
3. Results and discussion
3.1. Model assessment
The assessment of the results concerning the hydrodynamic part of the 3D model for the Thau lagoon have already been presented in Aliaume et al. (2006) for current speeds and in Dueri et al. (2010) for temperature and salinity. Concerning current speeds, simulation results were compared with the results obtained from an existing model, MARS3D (Lazure and Jegou, 1998). The root mean squared error had a mean value close to 0.03 ms−1. Simulated surface water layer temperatures and salinities were compared with experimental data for 2 years (2004 and 2005) for three stations in the lagoon: Marseilan, Bouzigues and Crique (close to Vène river mouth, see Fig. 1). The model showed a good agreement with the data, with maximum deviations of 3–4 °C found during the winter period in the shallow part of the Thau lagoon (Dueri et al., 2010). The comparison of water column salinity calculated by the model against field measurements in the Thau lagoon also gave good results with a mean deviation range of ±5‰. The max. deviations of 6–7‰ were found during lower salinity levels corresponding to periods of intensive fresh water discharge (Dueri et al., 2010).
Fig. 4 shows the comparison between the numerical results for seven PCBs congeners (PCB 28, 52, 101, 118, 138, 153 and 180) and three PCDD/Fs (PeCDD, OCDD and HxCDF) vs. field measurements at four observation stations in the Thau lagoon water column. As can be seen the model produced reasonable results (the correlation coefficient between experimental concentrations and simulated values is R2=0.8 with p<0.05).>−3), see PCB 52 and PCB 101 in Fig. 4. A possible explanation might be the use of constant river and sea concentration values over the entire simulation period, but the magnitude of the errors is in line with environmental fate models (Marinov et al., 2009).
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Fig. 4.
Comparison between the numerical results for individual PCBs and PCDD/Fs vs. field measurements in the water column at four observation stations in Thau lagoon (R2=0.8, p<0.05).
Experimental data, concerning mussel concentrations on PCDD/Fs and PCBs have recently been summarized for Thau lagoon in Castro-Jiménez et al. (2008) and in Munschy et al. (2008). In the present work, we have tried to simulate the experimental conditions to reproduce their results. Even though the 3D model provides the forcing values, there are some uncertainties on the initial conditions of the state variables of the DEB and bioaccumulation models, i.e., structural volume, V, energy reserves, E, energy allocated to development and reproduction, R, and tissue concentration, Cb. According to Munschy et al. (2008) all mussels spent at least 6 months on site before collection and each composite sample contained at least 50 mussels of homogeneous size: 45–55 mm shell length. Since the data from Castro-Jiménez et al. (2008) for mussels, summarized in Table 1, refer to the month of May, we have run the model for 1 year and then until May of the second year. By doing that, we have avoided transients due to errors in the initial conditions and the simulated shell lengths also correspond to the defined values in Munschy et al. (2008), i.e., 50.5 mm at Zone A and 54 mm at Zone C. In addition, the bioaccumulation model needs the evaluation of the uptake and elimination rates for each compound which we have estimated by non-linear optimization of the difference between the errors in the zones A and C, see Supplementary Information. The results obtained have been compared with experimental data in Table 1, the correlation coefficient between experimental concentrations and simulated values is R2=0.8 with p<0.001.
Table 1. Measured and simulated individual PCB and PCDD/Fs concentrations in mussels (pg g−1 dw). Experimental data from Castro-Jiménez et al. (2008) and Munschy et al. (2008).
| Compounds | Measured concentrations (pg g−1 dw) | Simulated concentrations (pg g−1 dw) | ||
|---|---|---|---|---|
|
| Zone A | Zone C | Zone A | Zone C |
| PCB 28 | 173 | 118 | 147 | 138 |
| PCB 52 | 254 | 92 | 256 | 108 |
| PCB 101 | 3543 | 955 | 3066 | 1806 |
| PCB 118 | 3327 | 802 | 2356 | 1925 |
| PCB 138 | 12229 | 2305 | 8446 | 7150 |
| PCB 153 | 18670 | 5242 | 13620 | 11010 |
| PCB 180 | 503 | 322 | 456 | 364 |
| PeCDD | – | 0.24 | 0.08 | 0.19 |
| OCDD | 6.4 | 10.44 | 6.14 | 11.28 |
| TCDF | 4.0 | 2.7 | 2.52 | 3.30 |
| PeCDF | 0.9 | 1.0 | 0.72 | 0.97 |
| HxCDF | 0.3 | 0.6 | 0.29 | 0.60 |
3.2. Implications for biota monitoring in the WFD and MSFD
The problem of monitoring contaminant environmental concentrations in mussels for EQS compliance checking and/or obtain equivalenting concentrations in water, assuming the latter can be calculated using Eqs. (1) or (3), can be analyzed first with the use of experimental data and second by the use of the modeling results.
For the first case, we have calculated BAF, for three different Stations in Thau lagoon, based on measured concentrations data on the water column (dissolved+particulate) from Castro-Jiménez et al. (2008) and mussels measurements from Munschy et al. (2008) following the approach proposed in Arnot and Gobas (2006). Fig. 5 summarizes the results obtained for PCBs compared also with proposed correlations, Eqs. (4) and (6). For PCDD/Fs the obtained values refer only to the particulate phase and therefore the BAF could not be calculated. The discrepancy between experimental values and existing correlations for certain compounds may be due to the fact that the reported measurements from Castro-Jiménez et al. (2008) and Munschy et al. (2008) refer to different periods: whereas mussels’ measurements were performed in May 2004, water measurements were carried out in November 2005. Despite this and the fact that the values for some compounds are close to the predicted ones using existing correlations, i.e., Eqs. (4) (6), it is also clear that there exists a high variability in the calculation of BAF as a function of experimental measurements (
1.7 log in the worse case).
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Fig. 5.
(a) Experimental log BAFww (L kg−1) as a function of log Kow for PCBs in mussels from the Thau lagoon using data from Castro-Jiménez et al. (2008). Continuous green line Eq. (4), Continuous blue line Eq. (6) with standard deviation. The error bars refer to the standard deviation between the three measurements sites in the Thau lagoon. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
In order to evaluate the effect of the spatio-temporal variability, we have applied the same approach using simulated data, but instead of using a single measurement at several stations, we have employed an annual basis, i.e., a 1-year simulation at the sampling stations. The results are represented in Fig. 6. In this case the points refer to annual average whereas the error bars represent the combined effects of the variability in the standard deviation of the concentration in biota, sdb, and the concentration in the water column, sdw, during a whole year. According to the Gaussian error propagation law when two variables are divided, we obtain
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Fig. 6.
Simulated (annual mean and standard deviation) log BAFww (L kg−1) for PCBs for Zone A (squares) and Zone C (diamonds). Simulated standard deviation was calculated as a function of the standard deviations of concentrations in the water column and in the mussels (see text for details).
This high variability has a physico-chemical and physiological component: the spatio-temporal variability of the contaminant’s concentrations in the water column, and the variability of their concentration in the organism that considers the strong decrease after release of gametes during reproduction simulated by the DEB model as well as observed experimentally for molluscs ([Casas and Bacher, 2006], [Bacher and Gangnery, 2006] and [Pouvreau et al., 2006]). To illustrate these aspects, Fig. 7 shows the simulated temporal annual concentration of PCB101 at the water surface, Fig. 8 shows the simulated spatio-temporal variation of the concentrations of PCB101 and, finally, Fig. 9 depicts the simulated concentrations of mussels PCB180 in two zones in the lagoon (Zones A and C, Fig. 1).
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Fig. 7.
Simulated temporal annual patterns of PCB101 in the surface water layer in Zones A and C. Experimental values, square for Zone A and diamond for Zone C, from Castro-Jiménez et al. (2008) and Munschy et al. (2008).
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Fig. 8.
Spatial distribution of PCB101 concentrations (ng m−3) at the surface water layer in Thau lagoon during the simulated year for selected months: February, May, August and November. Black dots indicate selected observation stations (3 and 11 correspond to Zones C and A, respectively), whereas red arrows indicate riverine inputs (VE: Vène, LA: Lauze, Ai: Aiguilles, Jo: Joncas, Pa: Pallas, Ay: Aygues-Vaques, Ne: Nègue-Vaques, Ma: Mayroual, So: Soupié, Fo: Fontanilles) and exchanges with the Mediterranean Sea (MS).
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Fig. 9.
Simulated annual evolution of PCB180 in mussels for both zones (the sharp decrease around November is due to reproduction). Experimental values, square for Zone A and diamond for Zone C, from Castro-Jiménez et al. (2008) and Munschy et al. (2008).
Fig. 7 represents an example of the temporal variability in the total concentrations of these compounds in the surface layer. As in the case of PCDD/Fs (Dueri et al., 2010), the comparison of the different fluxes for PCBs highlighted the domination of the atmospheric fluxes (combination of diffusive exchange, wet and dry deposition) over the sediments and watershed inputs on an annual scale, with decreasing importance of diffusive exchange for the more chlorinated congeners. In addition to the temporal variability, there is a spatial variability as illustrated in Fig. 8 for PCB101 where the effects of atmospheric forcing and historical concentrations in the sediment can be observed (see Supplementary Information). Furthermore, the distribution of these hydrophobic compounds is also controlled by the amount of particulate and organic matter which also shows spatio-temporal variability. Finally, in Fig. 9 the temporal evolution of internal concentrations in mussels is represented for PCB180. In this case, there is a difference in concentrations between the Zone A and Zone C in the lagoon due to differences in temperature and nutrient availability. The sharp decrease in concentration in mussels, Fig. 9, corresponds to the spawning period that normally occurs around November (Casas and Bacher, 2006). In fact, after spawning, individuals may lose up to 50% of their soft tissue weight (OSPAR, 1999). This would certainly increase the uncertainty when comparing concentrations in biota and in the water column for EQS assessment.
The combination of all these abiotic and biotic parameters contribute to the high variability observed in the calculated BAF values and make it difficult to compare concentrations in the water column with concentrations in biota.
Even though spatio-temporal variability cannot be modified, an important factor in this variability, especially for hydrophobic compounds, is the different amount of lipid content during the life cycle of molluscs and therefore measured concentrations should be corrected with a factor as function of lipid content before being assessed in a monitoring program for compliance checking.
3.3. Assessing the B and vB potential using a DEB bioaccumulation model
In principle, bioaccumulation assessment should be based preferably on the measurement of the bioconcentration factor (BCF) in aquatic species (normally fish or molluscs) and/or the BMF (biomagnification factors), see Eq. (3) and EC (2003). However, due to the high number of substances to be assessed, it would be interesting to develop more realistic screening procedures than those based only on lipophilicity, i.e., log Kow.
The use of the Dynamic Energy Budget approach in toxicology with DEBtox (Kooijman and Bedaux, 1996) has been recently included in ISO and OECD guidance documents (ISO (International Organization for Standardization), 2006 and OECD (Organization for Economic Cooperation and Development), 2006) as an alternative to standard dose response analysis. However, it has been discussed (Billoir et al., 2008) that a fundamental problem for their use within a regulatory context is the estimation of the model’s parameters as well as their confidence intervals. A similar problem concerns the use of the DEB model for estimating the bioaccumulative (B) and very-bioaccumulative (vB) potential of chemicals. Even though the model is able to predict the concentrations of contaminants in mussels, a better experimental database is needed to support the development of correlations that will allow the estimation of the bioaccumulation parameters (uptake and depuration rates) as a function of the physico-chemical properties of the substance. This is needed to run the model in a more systematic way and with the possibility of assessing the confidence in the model predictions.
The use of this type of modeling approach will provide a better assessment of B and vB potential compared with existing approaches based simply on lipophilicity (Daginnus et al., submitted for publication). This approach could combine environmental forcing with the dynamics of bioaccumulation. In fact, it has been shown (Andersen et al., 2008) that the major determinants of bioaccumulation are the depuration–excretion–metabolism rates, rather than high lipophilicity, which is only important in determining the speed at which equilibrium between the internal and external concentrations will be reached.
4. Conclusions
An integrated model able to predict the concentration in mussels (M. galloprovincialis), based on the concentration of contaminants in the water column and in phytoplankton, has been implemented and calibrated for PCBs and PCDD/Fs using experimental data from the Thau lagoon (France). The 0D Dynamic Energy Budget (DEB) growth and bioaccumulation model uses input data from a 3D model that provides temperature, concentrations of the contaminant in the water column, relevant biomasses and concentrations of contaminants in the food. The bioaccumulation model correctly predicts the concentrations of several PCBs and PCDD/Fs congeners in mussels.
The combined simulation results allowed an assessment of the spatio-temporal variability of the contaminants in the water column as well as in the mussels. Both contribute to the variability in the simulated BAFs with the related problems for EQS derivation and monitoring as requested by the WFD. For highly hydrophobic compounds, standardization as a function of lipid content will probably eliminate a high portion of this variability, which is typical in the life cycle of molluscs thereby decreasing the problems associated with the use of mussels for WFD compliance checking and/or for a reliable conversion into concentrations in water.
DEB bioaccumulation models for mussels represent a promising and more realistic screening tool for bioaccumulative (B) and very-bioaccumulative (vB) behavior of chemicals. However, statistical or Quantitative Structure–Activity Relationships (QSAR)-based correlations between model parameters and physico-chemical properties need to be developed beforehand and, for this, uptake and depuration experimental data are necessary.
Disclaimer
The content of this manuscript does not necessarily reflect the views and policies of the European Commission.
Acknowledgements
This research has been partially supported by the THRESHOLDS (FP6 Integrated Project Thresholds of Environmental Sustainability, Contract no. 003933).
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