Maxim Eremenko
email: maxim.eremenko@lisa.u-pec.fr

Luca Sgheri

Marco Ridolfi

Juan Cuesta

Lorenzo Costantino

Pasquale Sellitto

Gaelle Dufour

Eremenko Maxim

Sgheri Luca

Ridolfi Marco

Cuesta Juan

Costantino Lorenzo

Sellitto Pasquale

Dufour Gaëlle

Tropospheric ozone retrieval from thermal infrared nadir satellite measurements: Towards more adaptability of the constraint using a self-adapting regularization

Keywords: Atmospheric composition, Air pollution monitoring, Ozone, Passive remote sensing, Inverse problems

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Tropospheric ozone is a key species for tropospheric chemistry, air quality and radiative balance. Its monitoring is essential to better understand sources, transport, chemical transformation and sinks of atmospheric pollutants. Accurate data are required to characterize the evolution of atmospheric composition.

Space-borne observations are very promising for these concerns. Several studies using the last generation satellite instruments, in particular those operating in the thermal infrared, like the Infrared Atmospheric Sounding Interferometer (IASI) [START_REF] Hilton | Hyperspectral earth observation from iasi: Five years of accomplishments[END_REF], have shown capabilities to detect and monitor pollution events [2, [START_REF] Dufour | IASI observations of seasonal and day-to-day variations of tropospheric ozone over three highly populated areas of China: Beijing, Shanghai, and Hong Kong[END_REF][START_REF] Dufour | Springtime daily variations in lower-tropospheric ozone over east Asia: the role of cyclonic activity and pollution as observed from space with IASI[END_REF] and to identify the influence of the meteorology, horizontal transport and exchange with the stratosphere on tropospheric ozone variability and trends [START_REF] Dufour | Springtime daily variations in lower-tropospheric ozone over east Asia: the role of cyclonic activity and pollution as observed from space with IASI[END_REF][START_REF] Kalabokas | An investigation on the origin of regional springtime ozone episodes in the western Mediterranean[END_REF][START_REF] Doche | Summertime tropospheric-ozone variability over the Mediterranean basin observed with IASI[END_REF][START_REF] Dufour | Lower tropospheric ozone over the North China Plain: variability and trends revealed by IASI satellite observations for 2008-2016[END_REF][START_REF] Wespes | O3 variability in the troposphere as observed by IASI over 2008-2016: Contribution of atmospheric chemistry and dynamics[END_REF][START_REF] Safieddine | Summertime tropospheric ozone assessment over the Mediterranean region using the thermal infrared IASI/MetOp sounder and the WRF-Chem model[END_REF]. Even so, lower tropospheric ozone retrievals from nadir sounders remain challenging due to the lack of sensitivity of the measurements to the composition of near-surface layers. Even if improvements have been made especially combining the information provided by multiple spectral regions and instruments, as done by synergism of infrared and ultraviolet observations [START_REF] Cuesta | Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe[END_REF][START_REF] Cuesta | Transboundary ozone pollution across East Asia: daily evolution and photochemical production analysed by IASI + GOME2 multispectral satellite observations and models[END_REF], it is still important to explore possibilities to improve the adaptability of the retrieval algorithms and their capability to exploit the information provided by any kind of satellite measurements. The ozone retrieval from nadir satellite observations, in fact, is an ill-conditioned problem which requires regularization using constraints and a priori information [12]. Up to now, most of the retrieval algorithms rely on a fixed constraint and a single a priori profile. The most widely used method is the optimal estimation approach [START_REF] Boynard | Measurements of total and tropospheric ozone from IASI: comparison with correlative satellite, ground-based and ozonesonde observations[END_REF][START_REF] Barret | The detection of post-monsoon tropospheric ozone variability over south Asia using IASI data[END_REF] based on a priori ozone climatological constraint. There are some other attempts to develop infrared nadir retrievals such as the physical retrieval methods [START_REF] Carissimo | The Physical Retrieval Methodology for IASI: The delta-IASI Code[END_REF][START_REF] Liuzzi | Physical inversion of the full IASI spectra: Assessment of atmospheric parameters retrievals, consistency of spectroscopy and forward modelling[END_REF], where the a priori covariance constraint has an additional regularization parameter: scalar coefficient that may be adjusted to tune the trade off between data and a priori constraint. Another class of retrieval methods uses an analytical constraint based on Tikhonov-Twomey type regularization ( [START_REF] Tikhonov | On the regularization of ill-posed problems[END_REF][START_REF] Twomey | On the numerical solution of Fredholm integral equations of the first kind by the inversion of the linear system produced by quadrature[END_REF][START_REF] Phillips | A technique for the numerical solution of certain integral equations of the first kind[END_REF]) in place of the a priori covariance [START_REF] Kulawik | Calculation of altitude-dependent tikhonov constraints for TES nadir retrievals[END_REF]2]. The regularization strength in these retrieval methods is adjusted by the minimisation scheme based on the Levenberg-Marquardt method [START_REF] Levenberg | A Method for the Solution of Certain Problems in Least Squares[END_REF][START_REF] Marquardt | An Algorithm for Least-Squares Estimation of Nonlinear Parameters[END_REF]. A number of validation studies including some of these retrieval schemes shows that all these approaches provide globally comparable, though not identical, results in terms of the accuracy of the retrieval [START_REF] Keim | Tropospheric ozone from IASI: comparison of different inversion algorithms and validation with ozone sondes in the northern middle latitudes[END_REF][START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF]. In the following, one of these algorithms used by the authors in previous studies for IASI retrievals [2] will be referred to as Fixed Constraint Retrieval Schemes (FCRS). This approach, as well as the optimal estimation technique, works satisfactorily for most retrieval scenes, but in some situations both methods may lead to difficulties in reproducing the natural variability of ozone and in correctly describing its vertical distribution. Independent validation studies of different retrieval methods mention these difficulties for the IASI [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF] and the TES [START_REF] Nassar | Validation of Tropospheric Emission Spectrometer (TES) nadir ozone profiles using ozonesonde measurements[END_REF][START_REF] Worden | Comparisons of Tropospheric Emission Spectrometer (TES) ozone profiles to ozonesondes: Methods and initial results[END_REF] ozone profile retrievals. Larger biases are found, e.g. in higher latitudes that are usually attributed to the reduced sensitivity of the infrared measurements in these regions due to unfavourable thermal conditions (low thermal contrast and cold tropospheric temperatures). Larger biases are also showed in the upper troposphere -lower stratosphere (UTLS), for some tropical scenes, that may possibly be explained by the limited vertical resolution of the retrieval in the infrared, spectroscopic uncertainties, or the use of inadequate a priori information [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF]. These profiles retrieval difficulties are illustrated for the case of TES instrument in figure 2 of [START_REF] Nassar | Validation of Tropospheric Emission Spectrometer (TES) nadir ozone profiles using ozonesonde measurements[END_REF] and figure 4 of [START_REF] Worden | Comparisons of Tropospheric Emission Spectrometer (TES) ozone profiles to ozonesondes: Methods and initial results[END_REF], and for the IASI instrument in figure 14 of [START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF] and figure 13 of [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF]. Figure 10 of the present study shows similar retrieval problems for the case of FCRS. One of the motivations for the SACRS development was the necessity to tackle these retrieval limitations. In the present study, we propose to go a step further by developing a Self-Adapting Constraint Retrieval Scheme (SACRS) in which the constraint, dependent on altitude, is derived for each individual measurement while using a large choice of multiple a priori profiles. The objective is to better exploit the information provided by satellite measurements, adapting the retrieval to the atmospheric scenario, and to finally derive more accurate ozone vertical profiles.

We evaluate the potential of this new approach for the future satellite instrument IASI-NG (IASI Next Generation), which is expected to provide more spectral information on the ozone vertical profile than IASI [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF][START_REF] Sellitto | Analysis of the potential of one possible instrumental configuration of the next generation of IASI instruments to monitor lower tropospheric ozone[END_REF]. This instrument is expected to be launched in 2022 as part of the EPS-SG (EUMETSAT Polar System-Second Generation) mission. Its spectral and radiometric performances are twice better than those of the IASI instruments [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF], with an apodized spectral resolution of 0.25 cm -1 and radiometric noise of 10 nW/(cm 2 srcm -1 ) in the 10 µm ozone band. Studies using FCRS retrieval algorithms like those used for IASI show that the improved instrumental performances of IASI-NG will lead to an improved vertical resolution both over land and over sea [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF][START_REF] Sellitto | Analysis of the potential of one possible instrumental configuration of the next generation of IASI instruments to monitor lower tropospheric ozone[END_REF].

The SACRS algorithm has been tested on synthetic observations matching the future IASI-NG satellite instrumental characteristics and compared to results obtained with the FCRS algorithm previously developed for IASI [2] and applied to IASI-NG. For both retrieval schemes, as for the IASI retrievals, the seven spectral windows have been used for O 3 retrieval. In the case of these spectral regions and IASI-NG spectral resolution, the measurement vector is composed of 559 spectral radiances. Within this simulation exercise the surface temperatures and the atmospheric temperature profiles are considered as known. The developed multiple a priori and SACRS approach are described in section 2. The pseudo-observation setup and the correlative database as well as the simulation experiments are summarized in section 3. Section 4 discusses the results of the simulation experiments performed from the model simulations and the ozonesondes measurements. Conclusions are given in section 5.

SACRS approach

The new SACRS approach is the combination of two previously developed algorithms: a retrieval method of lower tropospheric ozone from the IASI nadir sounder, originally implemented as a FCRS and used for different studies:

[2, [START_REF] Dufour | IASI observations of seasonal and day-to-day variations of tropospheric ozone over three highly populated areas of China: Beijing, Shanghai, and Hong Kong[END_REF][START_REF] Dufour | Springtime daily variations in lower-tropospheric ozone over east Asia: the role of cyclonic activity and pollution as observed from space with IASI[END_REF][START_REF] Kalabokas | An investigation on the origin of regional springtime ozone episodes in the western Mediterranean[END_REF][START_REF] Doche | Summertime tropospheric-ozone variability over the Mediterranean basin observed with IASI[END_REF][START_REF] Dufour | Lower tropospheric ozone over the North China Plain: variability and trends revealed by IASI satellite observations for 2008-2016[END_REF], and the Iterative Variable Strength (IVS) technique [START_REF] Ridolfi | A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals[END_REF][START_REF] Ridolfi | Iterative approach to self-adapting and altitudedependent regularization for atmospheric profile retrievals[END_REF],

initially conceived as a self-adapting approach for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) limb sensor and now the operative choice for the MIPAS retrievals made by the European Space Agency (ESA) [START_REF] Raspollini | Ten years of MIPAS measurements with ESA Level 2 processor V6 Part 1: Retrieval algorithm and diagnostics of the products[END_REF].

The SACRS algorithm consists of two steps, both containing a self-adapting feature. The first step enhances the choice of a priori profiles made for the FCRS algorithm, by modifying the fixed a priori error profiles in order to obtain a less constrained retrieval. The retrieval is then carried out with this weak constraint, in order to maximize the number of degrees of freedom of the solution. This also decreases the bias introduced by the a priori. The result is an oscillating profile with large error bars. In the second and final step, the IVS method adds an a posteriori regularization with an altitude dependent strength determined primarily by the measurement errors and the sensitivity of the actual measurements to the retrieved profile. The result is a smooth profile with a reduced bias with respect to the original FCRS method.

Enhanced choice of multiple a priori profiles

The choice of a priori profiles in the FCRS used in this study is based on averaged ozone profiles derived from climatology for three geographical zones (polar, midlatitudes and tropics) [2]. The terms 'polar', 'midlatitudes' and 'tropics' define here the type of the air masses according to the tropopause height and not the geographical coordinates. For example, the polar-type air masses are characterised by a low tropopause height (TPH) even when transported off the polar region.The chosen discriminant parameter is TPH with polar conditions defined as observed scenes with TPH lower than 10 km, midlatitudes conditions: TPH between 10 km and 14 km, and tropical conditions: TPH greater than 14 km. This is a first approach for adapting the retrieval for the measuring conditions which shows reliable validation results [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Dufour | Springtime daily variations in lower-tropospheric ozone over east Asia: the role of cyclonic activity and pollution as observed from space with IASI[END_REF]. However, some ozone profile shapes, especially in the upper troposphere, remain penalized and lead to spurious oscillations in the retrieved profiles, especially for tropical-like atmosphere [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF]. As the vertical resolution (the altitude range of the independent pieces of information) of nadir sounders like IASI or IASI-NG is limited (5-7 km), retrieving the strong vertical ozone variability in the upper troposphere -lower stratosphere is challenging. Introducing reliable a priori information may help to partly overcome the problem. As the first step of the SACRS approach, we developed a new set of a priori ozone profiles. We moved from a set of three a priori profiles to a set of eleven a priori profiles covering the TPH range between 7 km to 17 km. The new set of a priori profiles has been built using the ozone soundings provided by the networks of sounding stations such as World Ozone and Ultraviolet Radiation Data Centre (WOUDC), Southern Hemisphere Additional Ozonesondes (SHADOZ) and Global Monitoring Division (GMD).

Over 10,000 ozone soundings measured between 2004 and 2007 were used to calculate the average ozone profiles grouped on the basis of TPH. Fig. 1 shows these ozone profiles and the three climatological a priori used for the FCRS.

The comparison of these two sets of a priori profiles and their influence on the quality of the retrieval was made using both sets for the FCRS retrievals (see section 4). The TPH-dependent a priori profiles are used as the first guess in all simulated retrievals presented in this paper.

Retrieval bases and the FCRS

The details of the FCRS method used in this study in order to compare with the new SACRS approach are described in [2]. For FCRS, the optimal solution x F is defined as

x F = x k + K T k S -1 y K k + R -1 K T k S -1 y (y -F(x k )) + R(x a -x k ) , (1)
where k is the iteration counter, y is the m-dimensional vector of the spectral observations with radiometric noise covariance matrix S y . The function F is the radiative transfer model, depending on the n-dimensional atmospheric state vector x given in Volume Mixing Ratio (VMR), where (x) i = x(z i ) for a chosen altitude grid z i . The matrix K k is the Jacobian of F evaluated at iteration k.

In the FCRS approach R is a fixed altitude-dependent matrix constraining the solution towards the a priori estimate x a , and defined to fulfil a chosen criterion.

Different methods to determine the criterion are described in [START_REF] Steck | Methods for determining regularization for atmospheric retrieval problems[END_REF]. The criterion used for FCRS in this study is based on a compromise between the minimization of the retrieved profile error and the maximization of the degrees of freedom (DOF) of the solution in the lower troposphere. In addition to the strength and shape of the constraint (diagonal elements of the R matrix) used to regularize the values and the general shape of the retrieved profile, the first or the second derivative operators (off-diagonal elements of the R matrix) are necessary to establish the inter-layer correlations and thus to limit the oscillations of the

retrieved vertical profile [2].
The Averaging Kernel (AvK) of the FCRS solution is given by:

A F = K T k S -1 y K k + R -1 K T k S -1 y K k , (2)
and the a posteriori covariance matrix of the parameters due to the mapping of the measurement noise into the solution is:

S F,m = K T k S -1 y K k + R -1 K T k S -1 y K k K T k S -1 y K k + R -1 . (3)
If S a is a covariance matrix representing the atmospheric variability of the profile

x, the covariance matrix of the smoothing error [12] of the solution is given by:

S F,s = (I -A F )S a (I -A F ) T , ( 4
)
where I is the identity matrix.

The covariance matrix of the total error for the FCRS solution is:

S F,tot = S F,m + S F,s . (5)

SACRS

As mentioned above, in FCRS approaches, the strength and the off-diagonal components of the regularization matrix R are similar for all the measurements.

This can lead to under-or over-constraining the solution, depending if the thermal conditions of the measurement are favorable or not to the retrieval. As a consequence, the retrieval may have difficulties to reproduce correctly the natural ozone variability. To overcome this problem and ensure an efficient use of the satellite instrument capabilities, we propose the new SACRS approach which is a two-step retrieval scheme.

The rationale behind the SACRS approach is to couple the FCRS regularization with variable constraint and a priori, with the IVS a posteriori self-adapting regularization method. The IVS is not meant to be used during the iterations because the target function to minimize would be modified by the change of the regularization matrix R. This would introduce instabilities in the retrieval iterations, and slow down the convergence, as explained in [START_REF] Ridolfi | A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals[END_REF]. Thus, the IVS should be used as an a posteriori regularization. However, in order to make the application of the IVS method possible and effective, we need a converging retrieval, with the weakest possible constraint R. This will minimize the bias introduced by the smoothing, whilst the IVS will take care of dumping the oscillations in the altitude regions where the sensitivity of the measurement is weak.

Weak constraint solution

The definition of the weak constraint is based on the diagonal elements of the R matrix developed for the IASI retrievals [2]. This diagonal is shifted, scaled and/or stretched to build the diagonal of the weak constraint matrix R.

Let f (z) be a generic function of the altitude z to be scaled, shifted or stretched.

The scaled function is written:

f scale (z) = af (z) (6)
with a the strength coefficient of the scaling.

The shifted function is written with its shift coefficient b b > 0

   f shift (z) = f (z + b), z ∈ [0, n -b] f shift (z) = f (n), z ∈ ]n -b, n] b < 0    f shift (z) = f (z + b), z ∈ [-b, n] f shift (z) = f (0), z ∈ [0, -b[
The stretched function is written:

f stretch (z) = f c (z) ( 7
)
where c is the stretching coefficient.

These resulting transformations are illustrated by the Fig. 2.

The function combining scaling, shifting and stretching is written:

f (z) = af c shif t (z) (8)
Applying this function to the retrieval altitude grid z ⊂ [0, n], the constraint vector fv is obtained.

The weak constraint matrix is then: to estimate to which altitudes the measured partial column is most sensitive [START_REF] Cuesta | Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe[END_REF].

ϕ = c 1 * 1 N ex + c 2 * RM S + c 3 * 1 √ DOF 6 + c 4 * H max 6 (9)
The parameter is chosen to control the altitude range of the AvK sensitivity in the lower troposphere.

The oscillating solution that is obtained at this stage is meant to have as many DOFs as possible. By maximizing the number of oscillations N ex and keeping acceptable the RM S of the fit, we ensure that these multiple oscillations are taking place around the real atmospheric profile. The limit of 6 km for the is done using a linear approximation for calculating the radiance spectrum. This linear approximation of the spectrum is then used to calculate the spectral RMS difference with respect to measurements (which then allows the choice of the best parameters a, b, c). The linear approximation of the spectrum y linear is calculated using a single call of the forward model as follows

y linear = y a + K a ( x(a, b, c) -x a ) (10)
where:

x a is the a priori state vector, -y a is the spectrum calculated with the forward model (KOPRA) for the a priori state vector x a , -K a is the Jacobian matrix calculated for x a , -x(a, b, c) is the state vector obtained from inverse calculation (KOPRAFIT) using the R matrix which depends on the combination of the parameters (a, b, c).

The ozone profile is then retrieved using the chosen weak regularization constraint. Subsequently the IVS approach is applied.

The IVS regularization technique

The IVS technique adapts the regularization strengths in the constraint matrix so as to regularize as strongly as possible, provided some pre-defined conditions are met by the regularized profile. The standard two conditions employed by the IVS technique are (i) At each altitude z i the difference between the regularized and the oscillating profile should not exceed some fraction w e (z i ) of the retrieval error of the oscillating profile.

(ii) At each altitude z i the vertical resolution of the regularized profile should not be degraded more than w r (z i ) times the vertical resolution of the unregularized profile.

We then start with a large regularization strength at all altitudes, and decrease it until conditions (i-ii) above are met, or the regularization strength is lower than a prescribed bound.

The SACRS solution x Λ is defined as

x Λ = x k + K T k S -1 y K k + R + L T ΛL -1 (11)
• K T k S -1 y (y -F(x k )) + ( R + L T ΛL)(x a -x k ) ,
where Λ is a diagonal semi-definite positive matrix, and L is a matrix approximating some derivative of the vertical profile. We chose L to be the (n -1) × n matrix approximating the first derivative, so that

(Lx) i ≈ d dz x | (z i +z i+1 )/2 . ( 12
)
Hence we can interpret Λ as the sampling of a vertical profile λ(z). Let zi =

(z i + z i+1 )/2, then (Λ) ii ≡ λ i = λ(z i ).
Following [START_REF] Ridolfi | Iterative approach to self-adapting and altitudedependent regularization for atmospheric profile retrievals[END_REF] we can connect x F and

x Λ as

x Λ = K T k S -1 y K k + R + L T ΛL -1 • K T k S -1 y K k + R x F + L T ΛLx a . ( 13
)
Also, the AvKs and the a posteriori covariance matrix of the IVS solution are connected to those of the FCRS solution by

A Λ = DA F , (14)
S Λ,m = DS F,m D T , (15)
where

D = K T k S -1 y K k + R + L T ΛL -1 K T k S -1 y K k + R . ( 16
)
The smoothing error of the SACRS solution is given by:

S Λ,s = (I -A Λ )S a (I -A Λ ) T . (17)
The covariance matrix of the total error is:

S Λ,tot = S Λ,m + S Λ,s . (18)
Finally, to calculate the vertical resolution of the x Λ solution we use

ν i = n j=1 (A Λ ) ij (z j-1 -z j+1 ) 2|A Λ | ii , (19)
see [START_REF] Ridolfi | Iterative approach to self-adapting and altitudedependent regularization for atmospheric profile retrievals[END_REF] for the motivations.

We start with a constant profile λ (0) (z) = λ max and iterate the calculation of x Λ with decreasing profiles λ (j+1) (z) ≤ λ (j) (z).

Given the thresholds w e (z) and w r (z) we check the following two sets of

conditions (i) |(x Λ ) i -(x F ) i | ≤ w e (z i ) (S F ) ii , (ii) ν i ≤ w r (z i )∆z i ,
and collect the altitudes Q (j) = {z i1 , . . . , z im } where these conditions are not met. In the MIPAS limb measurements [START_REF] Ridolfi | A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals[END_REF][START_REF] Ridolfi | Iterative approach to self-adapting and altitudedependent regularization for atmospheric profile retrievals[END_REF] the averaging kernels are mostly diagonal. Hence, to obtain a variation of the regularized profile at a given altitude it is sufficient to alter the regularization profile in a neighborhood of that altitude. Due to the different observation geometry this is not the case of the nadir measurements in which, for example, the most relevant information on ozone originates from the free troposphere and the lower stratosphere. Therefore we calculate the sensitivity coefficients s ij = ∂(x Λ ) i /∂λ j , i = 1, . . . , n, j = 1, . . . , n -1 in order to determine in which region the regularization profile has to be decreased to obtain a localized effect on the regularized profile. Let l such that |s l l| = max j {|s lj |}. Then for each altitude z l ∈ Q (j) we decrease λ(z) in a neighborhood of z l. At each step j the new regularization profile λ (j+1) (z)

is the result of the combined application of the decreasing λ(z) relative to the altitudes in Q (j) . For details on how the reduction is performed we refer to [START_REF] Ridolfi | Iterative approach to self-adapting and altitudedependent regularization for atmospheric profile retrievals[END_REF].

We then repeat the calculation of x Λ and iterate the procedure until (i) Q (j) is empty, or (ii) λ(z) < λ min , or (iii) a maximal number of iterations is reached.

Note that the calculation of the sensitivity coefficients s ij is equivalent to performing n iterations of the IVS, so they are updated only every 10 cycles.

In the present implementation we use the following numerical values for the parameters driving the IVS algorithm: w e (z) = 1, w r (z) = 1.5, λ min = 10 -6 , λ max = 10, maximal number of iterations equal to 1000. With these settings the typical running time of the IVS algorithm is a few seconds on an average CPU.

A retrieval example for a synthetic observation is shown in Fig. 3. The oscillating profile (red), the final SACRS solution (blue), and the FCRS solution (green), are compared with the true ozone (orange) and the a-priori (grey) profiles without any smoothing by the AvK. In this case, the FCRS profile is close to the a priori and underestimates the true profile in the lowest 3-4 km.

It overestimates the true profile above this height range and fails to reproduce the ozone gradient in the UTLS. As expected, the weakly constrained profile oscillates around the true profile. Once the a posteriori IVS regularization is applied, the SACRS profile matches well the true profile up to 9-10 km. In the UTLS, the strong oscillation introduced by the FCRS retrieval is largely reduced by the SACRS retrieval, despite remaining difficulties to retrieve small vertical features in this region due to the limited vertical resolution of the measurement.

The AvK in the bottom panel of Fig. 3 show that the weak constraint producing the oscillating solution is characterized by a larger number of DOF i.e. higher AvK values at tropospheric altitudes (red and blue AvK curves) and also in the lower UTLS altitudes (green curves). The IVS regularization applied to the oscillating solution provides a slightly stronger constraint: tropospheric AvK become smaller than those of the oscillating solution, but still larger than those of the FCRS solution. The corresponding DOFs are shown in the legend.

Simulation of the ozone pseudo-observations (PO)

The pseudo-observations or synthetic observations presented in this section are the simulated measurements based on a known atmospheric state and the corresponding forward radiative transfer calculation for the chosen instrumental configuration. In order to evaluate the performance of the SACRS algorithm, two sets of pseudo-observations have been generated: one using a Chemistry and Transport Model (CTM) as true ozone, referred to as CTM-pseudoobservations (CTM-PO), and one using the ozonesondes, referred to as sondepseudo-observations(S-PO). The true ozone profiles (from CTM or sondes) are used as the inputs of the KOPRA/KOPRAFIT radiative transfer model [START_REF] Stiller | Karlsruhe optimized and precise radiative transfer algorithm: I. Requirements, justification, and model error estimation[END_REF] to simulate IASI-NG-like radiances. The radiances are simulated including the instrumental specifications and performances of IASI-NG in terms of radiometric noise in the 10 µm spectral region (10 nW/(cm 2 srcm -1 ) and the apodized spectral resolution (0.25 cm -1 ) [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF]. As for the measurement geometry, we use the IASI geometry based on the location of the pixels actually sampled by IASI for the simulated dates. For the CTM-PO, we used the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Échelle) model [START_REF] Josse | Radon global simulations with the multiscale chemistry and transport model MOCAGE[END_REF]. This model provides vertical profiles of atmospheric state and composition variables for 47 levels up to 5 hPa (approximately 35 km altitude) with a vertical resolution that increases from 150 m (lower troposphere) to approximately 1 km (stratosphere) and a horizontal resolution of 0.2 • × 0.2 • . The same set of data was used by [START_REF] Costantino | Potential of multispectral synergism for observing ozone pollution by combining IASI-NG and UVNS measurements from the EPS-SG satellite[END_REF] to study multispectral ozone retrievals. We present here one day of simulations over Europe (9 July 2010). Over 18,000 measurements have been simulated. No cloud information was taken into account for these simulations.

Ozonesondes signal over the atmosphere up to 60 km of altitude corresponding to 51 layers of the chosen vertical retrieval grid. For the case of S-PO only the zero degrees inclination nadir geometry is considered. We use, for the simulations, the soundings available after 2007 in order to avoid using the same soundings used to define the a priori profiles of the retrieval (section 2.1). Over 4,000 ozone soundings have been used to simulate IASI-NG-like observations. We consider the surface temperatures and the temperature profiles from ECMWF to define the associated temperatures.

Table 1 summarizes the five retrieval sets used in the present study. The three retrievals using the CTM-PO are performed in order to study the effect of taking into account different sets of a priori profiles in the FCRS and compare them to the SACRS retrievals using the spatially resolved ozone plumes as the known atmospheric state. The two S-PO retrievals were made in order to evaluate the retrievals using better vertically resolved true ozone profiles as the known atmospheric state. The retrieval results are evaluated against the true ozone in terms of bias, root mean square error (RMSE), and correlation. The error estimate resulting from the two algorithms is evaluated using the RMSE that represents the total error, which includes both measurement and smoothing errors.

Results and discussion

First, we evaluate the impact of using the three climatological profiles or the set of eleven TPH-dependent a priori profiles (see Fig. 1) in the CTM-PO 380 retrievals. The retrievals are made with the FCRS algorithm used in previous studies [2, 24, 4] and which includes different sets of a priori (Table 1, retrieval sets 1 and 2). Left and center panels of Fig. 4 show the results. The evaluation is done on the lower tropospheric ozone columns (surface-6 km) calculated in Dobson Units (DU). In order to estimate the fraction of con-385 tamination of the lower tropospheric column by higher altitudes, we calculated the ratio between the integral of the AvK of the lower tropospheric column from 6 to 60 km and the integral from the surface to 60 km. The average made for all retrievals made for CTM-PO shows that higher atmospheric layers contribute to about 20% of the lower-tropospheric column.

Using the enhanced set of a priori for FCRS improves the correlation by about 9% of the retrievals but not the bias. The remaining tail on the scatterplot (values < 25 DU) shows that using the enhanced a priori set does not improve the FCRS retrieval. This tail disappears on the right panel when SACRS (Table 1, retrieval set 3) is used. This tail corresponds to the higher latitudes and strong negative biases that can be seen on the map of the differences between the retrieved and the true ozone for the case of FCRS (Fig. 5, left top panel).

The bias and the RMSE are reduced by 40% and 15% respectively when using the SACRS approach (right panel in comparison with the left panel).

The correlation is also improved by about 10%. The retrieval sensitivity is also improved using the SACRS approach with DOF 6km increased from 0.75 to 0.83 in the lower troposphere.

The true ozone distribution and the differences between the true and the retrieved ozone distribution are shown in Fig. 5. The figure presents the results obtained using the FCRS (Table 1, retrieval set 1) and SACRS (Table 1, retrieval set 3) approaches (first row). The spatial distribution of the DOFs for 0-6 km is also plotted for the two approaches (second row). Pixels for which at least one of the two retrievals did not converge are displayed in white. The main spatial features of the true ozone distribution are reproduced by both retrieval schemes. Nevertheless, some overestimations and underestimations introduced by the FCRS and already seen on the scatterplots (Fig. 4) are improved with the SACRS, especially over the northwestern area. The lower tropospheric DOF are increased on average using SACRS. They are more spatially homogeneous being less dependent of the sampled atmospheric conditions (TPH, surface temperature, thermal contrast, etc).

Two typical cases of the polar air mass retrievals are shown in Fig. 6 and relatively high thermal contrast (2.43 K) if compared to typical values for the polar air masses (the average thermal contrast for the simulated polar air mass cases is 0.01 K) . The second retrieval example (Fig. 7) is taken from the strong positive bias plume shown in Fig. 5 around (59.52 N, -9.24 E). These polar air mass situations are characterized by low TPH (9 km in this case) and a thermal contrast closer to zero (1.06 K in this case). The differences in the retrieved profiles can be explained by the retrieval AvK which, in the SACRS case, are better separated between the lower tropospheric part (0 km to 6 km) and the polar air mass UTLS region (7 km to 12 km). The FCRS AvK for the first polar air mass case (Fig. 6) show that almost the entire polar troposphere is correlated in this retrieval and the ozone overestimation in the lower stratosphere is compensated by the underestimation in the lower tropospheric part. In the case of the SACRS retrieval, the adapted constraint allows to better separate these two altitude regions. The AvK diagonal shows that the SACRS retrieval is less sensitive than the FCRS to the lower polar stratosphere and more sensitive 20 to the lower tropospheric altitudes. The two maxima show the altitudes for which the partial columns are less inter-dependent. In the second polar air mass case (Fig. 7), FCRS AvK show again that almost the entire polar troposphere is correlated in this retrieval. This explains why the ozone underestimation in the lower stratosphere is compensated by the overestimation in the lower tropospheric part. The SACRS retrieval adapts the strength of the constraint so that the lower tropospheric part of the profile is regularized more strongly.

The AvK diagonal shows that, as compared to the FCRS, the SACRS retrieval is less sensitive in the lower polar stratosphere but less correlated to the polar UTLS altitudes.

The typical case for a tropical air mass retrieval is shown in Fig. 8. This tropical air mass situation is characterized by high TPH (14 km). Here again the difference between the FCRS and SACRS retrieved profiles can be explained by the differences in retrieval AvK. The self-adapting regularization of the SACRS approach constrains more effectively the profiles in the tropical UTLS region.

Contrary to the FCRS retrieval, the SACRS constraint does not allow the so-450 lution vector to have the strong variations in the UTLS. In the FCRS retrieval, although the tropospheric profile is well reproduced, the ozone gradient in the UTLS region is not represented correctly.

We also evaluate the performances of the two retrieval approaches on six different partial columns in the troposphere and UTLS. We compare (Fig. 9) the 455 correlation coefficient, the mean bias and the RMSE obtained for the different partial columns calculated between the surface and the altitude of 21 km. This evaluation is based on the CTM-PO. We see that with the SACRS approach the three statistical indicators are improved for almost all these partial columns.

We use the ozone soundings (Table 1, retrieval sets 4 and 5) to evaluate the retrieved profiles because they provide a sampling of a larger range of atmospheric conditions (tropical, midlatitudes and polar). In Fig. 10, the mean ozonesondes profile (averaged over the entire sounding dataset) is compared to the mean a priori profile, and the mean profiles retrieved with the FCRS and the SACRS approaches. The ozone profiles are not smoothed using the retrieval AvK for this comparison. The differences between the ozonesonde profiles and the retrieved profiles are also displayed as well as the mean diagonal of the AvK matrix which is an estimate of the sensitivity of the retrieval with respect to variations in the atmospheric true state. On average (top row), the vertical sensitivity as seen by the AvK is increased below 5 km and decreased in the UTLS with the SACRS approach. The bias is more constant (about 10%) in the troposphere, up to 10 km. The bias compensation in the UTLS is strongly reduced.

Considering the validation issues reported in [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF], we also explore the two atmospheric situations for which the FCRS approach failed more systematically:

the polar and the tropical latitudes (second and third rows of Fig. 10). Polar 475 situations are usually less favorable to the retrieval due to thermal conditions decreasing the sensitivity of infrared sounders. The SACRS approach adapts the constraint to these situations, reducing the sensitivity of the retrieval in the troposphere and then avoiding the over-interpretation of the observation. The final agreement with the sondes is better with a very small bias in the troposphere. The tropospheric ozone column between 0 and 9 km calculated for these polar situations shows a bias of 8.6% with FCRS and 0.5% with SACRS. The correlation is close for both retrieval schemes: 0.87 for FCRS, 0.88 for SACRS.

The underestimation of ozone in the UTLS remains. Concerning the tropics, previous studies underline the difficulties to retrieve the strong ozone gradient in the UTLS region, especially due to the S-shape of the ozone profile in this region (e.g. [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF]). The SACRS approach strongly improves the retrieval in the UTLS (Fig. 10 last row). The upper tropospheric ozone column between 12 and 18 km calculated for these profiles shows a bias of 24.4% with FCRS and 10.1% with SACRS. The correlation is also improved: 0.88 for FCRS and 0.93 for SACRS.

We also evaluate the ability of the FCRS and SACRS algorithms to prescribe realistic error estimates for both the profiles and the partial columns. We use the S-PO and the CTM-PO and compare the mean estimated error profiles, which is the diagonal of the total error matrix (Eq. 5 for the FCRS and Eq. 18

for the SACRS) to the RMSE profile, which gives an estimate of the actual error. Results are provided in Fig. 11. In the case of the S-PO, the FCRS error estimation (green) is in good agreement with the actual error (RMSE)

for the lower troposphere (below 7 km) and shows a strong underestimation of the error at higher altitudes. The error estimation derived from the SACRS algorithm (blue) shows a slight overestimation in the lower troposphere and a good agreement for the altitudes above 8 km. Considering the CTM-PO, the SACRS error estimation is in good agreement with the actual error, whereas the FCRS error is strongly underestimated in the entire atmosphere.

The same behavior of the error can be seen in terms of partial columns for the case of CTM-PO: Fig. 12 presents the differences between the errors calculated as the RMSE (presented also in the Fig. 9, bottom panel) and the estimated total errors (Eq. 5 for the FCRS, and Eqs. 18 for the SACRS). These differences are shown for different retrieved partial columns between 0 and 21 km. The errors estimates from the FCRS approach are usually underestimated 510 compared to the RMSE leading to a difference ranging between 6 and 14%. The SACRS reduces this difference by about 5% with a better error estimation.

Conclusions

The SACRS has better retrieval performances in terms of bias reduction (40%), correlation improvement (10%) and RMSE reduction (15%) with respect to the FCRS. The error estimate is also in better agreement with the actual error derived from the comparison with the true ozone. The major improvements are seen for the cases of polar and tropical retrievals. Indeed, the self-adapting approach succeeds in better constraining the polar retrievals where the infrared nadir measurements usually have lower brightness temperatures and hence less information in the signal [START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF]. The SACRS approach performs better in the tropical cases with higher TPH where the shape of the ozone profile favors spurious oscillations in the retrieved profiles and the retrieval often may be biased in the UTLS region [START_REF] Dufour | Validation of three different scientific ozone products retrieved from IASI spectra using ozonesondes[END_REF][START_REF] Boynard | Seven years of IASI ozone retrievals from FORLI: validation with independent total column and vertical profile measurements[END_REF]. The spatially resolved retrievals show that the lower tropospheric DOFs are increased on average using SACRS. They are more spatially homogeneous being less dependent of the sampled atmospheric conditions. The execution time for one SACRS retrieval is about 280 seconds, while for the FCRS this time is about 25 seconds. The calculation time is of course not comparable for these two retrieval schemes. The current study presents the principles of the new method to define an adjustable retrieval constraint. This retrieval scheme is not meant to be used operationally in its current state. For that purpose, an optimization of the code will be required. The new retrieval scheme was tested for instrumental specifications of the future IASI-NG instrument. Nevertheless, it may be applied to any other type of measurements. It may be particularly interesting to test this retrieval scheme with the multispectral retrievals [START_REF] Cuesta | Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe[END_REF][START_REF] Cuesta | Transboundary ozone pollution across East Asia: daily evolution and photochemical production analysed by IASI + GOME2 multispectral satellite observations and models[END_REF] where the sensitivity of the measurement to different altitudes may be highly variable due to the nature of different spectral components of the measurement and their behaviour in different atmospheric conditions. This regularization scheme may be applied to other species retrieval. The initial shape of the constraint function that is adjusted during the retrieval of the oscillating solution may be adapted to each particular species.

Figure 1 :

 1 Figure 1: A priori ozone profiles as the function of TPH (left panel) used in SACRS, a priori ozone profiles as the function of the climatological zone (right panel) used in [2] .

Figure 2 :

 2 Figure 2: Scaling, shifting and stretching applied to the constraint function in order to find the weak constraint adapted to a particular measurement.

where 205 -

 205 N ex is the number of extremal (maximum or minimum) points of the retrieved profile under 20 km. This parameter is chosen in order to control the oscillation in the tropospheric/stratospheric part of the retrieved profile, -RM S is the root mean square of the spectral fit . This parameter is used to control the quality of the spectral fit, 210 -DOF 6 is the DOF between the surface and 6 km of altitude. This parameter allows maximizing the AvK values in the lower troposphere, 10 -H max 6 is the height of maximum of sensitivity to ozone between the surface and 6 km. The value of H max 6 is calculated as the altitude of the maximum of the function obtained by the sum of the first 6 rows of the AvK matrix. This allows

DOF 6

 6 and H max 6 is chosen taking into account the expected vertical resolution of IASI-NG in the lower troposphere [32]. The 6 km limit is used to optimise the retrieval of this lower tropospheric quantity. A more flexible definition of the criterion ϕ is also feasible. A different definition of the targeted partial column would imply different values for the coefficients used to compute ϕ, however the structure used for the ϕ function would not change. The values of coefficients c 1,2,3,4 are chosen so that the four terms of ϕ have similar magnitudes. These coefficients do not change during the minimization of the target function. The weak regularization is obtained at the first iteration of the retrieval by testing the combinations of a,b and c in order to minimize ϕ. In order to avoid calling numerous times the forward model, the choice of the parameters (a, b, c)

Figure 3 :

 3 Figure 3: : The retrieval example with the corresponding AvK. The orange profile is the model used for the measurement simulation (true ozone), the green is the FCRS solution, the red profile is the oscillating solution found with the criterion of Eq. 9, the blue profile is the final result after the IVS regularization. The bottom panel shows three groups of retrieval AvK with the corresponding DOFs.

Figure 4 :

 4 Figure 4: Scatterplots of the retrieved versus simulated ozone partial columns 0-6 km for the CTM-PO. Left panel -FCRS retrieval with the set of 3 a priori. Middle panel -FCRS retrieval with the set of 11 a priori. Right panel -SACRS retrieval with the set of 11 a priori.

Fig. 7 .

 7 Fig. 7. The first retrieval example (Fig. 6) is taken from the Scandinavian plume with the strong negative bias in the FCRS results shown on the maps of Fig. 5. These polar air mass situations are characterized by low TPH (10 km) and a

Figure 5 :

 5 Figure 5: Distribution of the differences between the retrieved and true ozone (CTM-PO) partial columns 0-6 km (first row), retrieved DOFs for 0-6 km (second row).

Figure 6 :

 6 Figure 6: Example of the retrieval profiles for the polar air mass (68.56N, 21.52E).

Figure 7 :

 7 Figure 7: Example of the retrieval profiles for the polar air mass (59.52N, -9.24E).

Figure 8 :

 8 Figure 8: Example of the retrieval profiles for the tropical air mass (41.71N, 18,24E).

Figure 9 :

 9 Figure 9: The 6km partial columns correlations (top), bias (middle), RMSE (bottom) for the two retrieval schemes (CTM-PO).

Figure 10 :

 10 Figure 10: Simulated versus retrieved profiles from the S-PO. First row -average of the 4125profiles (all latitudes, all stations), Second row -average of the particular cases in the polar latitudes where the DOF is decreased by self-adapting. Third row -average of the particular cases in the tropical latitudes where the retrieval of the 'S-shape' profiles is improved.

Figure 11 :

 11 Figure 11: Estimated and actual error profiles for the S-PO retrievals (left) and for the CTM-PO retrievals (right).

Figure 12 :

 12 Figure 12: Differences between the estimated and the actual errors for the 6km ozone partial columns obtained by the two retrieval schemes (CTM-PO).

  used for this study were obtained from different archives: WOUDC (http://www.woudc.org/), NOAA-CMDL (http://www.esrl.noaa.gov/gmd/dv/ftpdata.html), and SHADOZ (http://croc.gsfc.nasa.gov/shadoz/). These ozonesondes provide in situ measurements of vertically-resolved ozone concentrations at different stations around the world. The measured vertical profiles of ozone have a vertical resolution of ∼ 150 m and reach up to 30-35 km. For the altitudes over 30 km, the ozone profiles in both cases (CTM-PO and S-PO) are completed with climatological values in order to calculate the radiative transfer and the satellite

Table 1 :

 1 Description of the simulated retrievals.

	Retrieval set Algorithm	A priori	PO-type Simulated period Number of obs
	1	FCRS	3 a priori CTM-PO	09.07.2010	18540
	2	FCRS	11 a priori CTM-PO	09.07.2010	18540
	3	SACRS	11 a priori CTM-PO	09.07.2010	18540
	4	FCRS	3 a priori	S-PO	2007-2012	4125
	5	SACRS	11 a priori	S-PO	2007-2012	4125

Acknowledgments

This study was supported by the French Space Agency CNES (project "IASI-NG-TOSCA"). Financial support of the GDRI (Groupement de Recherche International) HiResMir of the CNRS is gratefully acknowledged . The HiResMir GDRI is for "High resolution microwave, infrared and Raman molecular spectroscopy for atmospheric, planetological and astrophysical applications". We thank also Agnes Perrin (agnes.perrin@lmd.polytechnique.fr) for valuable scientific discussions during the course of this work. The ozonesonde data used in this study were provided by the World Ozone and Ultraviolet Data Centre (WOUDC), the Southern Hemisphere Additional Ozonesondes (SHADOZ), and the Global Monitoring Division (GMD) of NOAA's Earth System Research Laboratory and are publicly available (see http://www.woudc.org, http://croc.gsfc.nasa.gov/shadoz, http://www.esrl.noaa.gov/gmd). The authors thank all those responsible for the WOUDC, SHADOZ, and GMD measurements and archives for making the ozonesonde data available.


Marine Breitwieser
email: m.breitwieser@healthymind.fr

Marine Barbarin
email: m.barbarin@larochelle.port.fr

Christine Plumejeaud-Perreau

Emmanuel Dubillot
email: lienssemmanuel.dubillot@univ-lr.fr

Thierry Guyot
email: thierry.guyot@univ-lr.fr

Valérie Huet
email: lienssvalerie.huet@univ-lr.fr

Carine Churlaud

Thibaut Coulombier

Isabelle Brenon
email: lienssisabelle.brenon@univ-lr.fr

Denis Fichet
email: denis.fichet@univ-lr.fr

# Marine Barbarin

Thibaut Coulombier

Nathalie Imbert
email: nathalie.imbert@univ-lr.fr

Hélène Thomas

Christine Plumejeaud- Perreau

Thierry Guyot Lienss

Thibault Coulombier
email: lienssthibault.coulombier@univ-lr.fr

Nathalie Imbert Lienss

Biomonitoring of Mimachlamys varia transplanted to areas impacted by human activities (La Rochelle Marina, France)

Keywords: biomarkers, environmental quality, heavy metals, port area

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Over the years, concerns have been raised about heavy metal contaminations, particularly in port areas and their surroundings. The human activities and downtown locations, which have direct access to the sea, could generate different types of wastes released in the environment as dissolved particles, macro-wastes and liquid effluent. These sea wastewaters reveal the presence of a mixture of chemical contaminants in significant amount, including heavy metals, polycyclic aromatic hydrocarbons (PAHs), pesticides, polychlorobiphenyl (PCBs) and other chemicals [START_REF] Dorris | Identification of Toxic Components in Oil Refinery Wastewaters and Determination of Their Effect upon the Aquatic Biota[END_REF]. Most of them are toxic, even at low concentrations [START_REF] Long | Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments[END_REF]. Therefore, environmental contamination is a major threat to marine ecosystems and human health (Fasulo et al., 2012a;[START_REF] Sureda | Biochemical responses of Mytilus galloprovincialis as biomarkers of acute environmental pollution caused by the Don Pedro oil spill (Eivissa Island, Spain)[END_REF], particularly near harbours and cities.

One of the largest harbour sites in Europe is the marina of La Rochelle. The harbour stretches from the marina to 15 km of docks along the coastline of La Rochelle (France). This harbour, settled in the 1970s, has today 5,500 spaces and welcomes 160 companies of the nautical sector, as a result of urban and economic developments.

Previous reports published by CREOCEAN (2016) and recent studies performed in Bay of Biscay [START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF][START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF] revealed that Cu, Cd, As and trace elements are found in high concentrations, exceeding the standard limit reported by national and international sediment quality guidelines (Low n° 1881/2006). Moreover, different projects documented the key role played by cities and harbours in the contamination of the coastline. The Charente-Maritime area has acknowledged the importance of environmental state of health and was recognised as an outstanding functional set on the Atlantic coast by the French Environmental Protection Ministry in 2006. Indeed, this marine region is a mudflat ecosystem full of various commercialised organisms such as bivalve species. Bivalve molluscs -in particular mussels, oysters and variegated scallops -are widely used as bio-indicators in environmental monitoring programmes (Mussel Watch Program, IFREMER networks) due to their large geographical distribution. In addition, these filter-feeding organisms have a sedentary lifestyle, which enables them to bioaccumulate and tolerate great amounts of pollutants in their tissues [START_REF] Lacroix | Active and passive biomonitoring suggest metabolic adaptation in blue mussels (Mytilus spp.) chronically exposed to a moderate contamination in Brest harbor (France)[END_REF][START_REF] Sureda | Biochemical responses of Mytilus galloprovincialis as biomarkers of acute environmental pollution caused by the Don Pedro oil spill (Eivissa Island, Spain)[END_REF][START_REF] Luna-Acosta | First evidence of laccase activity in the Pacific oyster Crassostrea gigas[END_REF][START_REF] Bustamante | Evaluation of the variegated scallop Chlamys varia as a biomonitor of temporal trends of Cd, Cu, and Zn in the field[END_REF]. In addition, as mussels, black scallops Mimachlamys varia are suitable to conduct caging experiments in port areas (active biomonitoring). Mimachlamys varia is located in water/sediment interface, which is a highly relevant area to assess sediment quality. Our approach allows a reduction of physiological variables by using variegated scallops from a single hatchery population, more controlled over the experiment, and an accurate evaluation of the effects of polymetallic contamination in natural environments (Cappello et al., 2013a(Cappello et al., , 2013b[START_REF] Cappello | Neurotoxicological effects on marine mussel Mytilus galloprovincialis caged at petrochemical contaminated areas (eastern Sicily, Italy): ¹H NMR and immunohistochemical assays[END_REF]Fasulo et al., 2012b;[START_REF] Lacroix | Active and passive biomonitoring suggest metabolic adaptation in blue mussels (Mytilus spp.) chronically exposed to a moderate contamination in Brest harbor (France)[END_REF][START_REF] Marigomez | Combined use of native and caged mussels to assess biological effects of pollution through the integrative biomarker approach[END_REF][START_REF] Jimeno-Romero | Bioaccumulation, tissue and cell distribution, biomarkers and toxicopathic effects of CdS quantum dots in mussels, Mytilus galloprovincialis[END_REF].

This work presents some of the results from the DYPOMAR research project. A multibiomarker approach is performed on the digestive glands of scallops to assess the potential adverse effects of polymetallic contamination on black scallops caged in the La Rochelle marina. The bivalve digestive glands are involved in digestion, bioaccumulation and are one of the first organs to be affected by environmental stressors. Therefore, they are frequently used as a model system in ecotoxicological studies [START_REF] Bustamante | Evaluation of the variegated scallop Chlamys varia as a biomonitor of temporal trends of Cd, Cu, and Zn in the field[END_REF][START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF][START_REF] Frantzen | Biological effects of mechanically and chemically dispersed oil on the Icelandic scallop (Chlamys islandica)[END_REF][START_REF] Breitwieser | Short-term and long-term biological effects of chronic chemical contamination on natural populations of a marine bivalve[END_REF][START_REF] Luna-Acosta | Recent findings on Phenoloxidases in Bivalves[END_REF]. Further studies describe that after pollutants exposure, bivalve's digestive tissues are involved in immune defence, detoxification processes and in homeostatic regulation (Marigomez et al., 2002;Moore and Allen, 2002), and histopathological alterations (Garmendia et al., 2011); so, digestive gland could correspond to a standardised tool for monitoring the general state of health of organisms [START_REF] Amiard-Triquet | Ecological Biomarkers: Indicators of Ecotoxicological Effects[END_REF] is assessed.

Several biomarkers (defence and damage) were used during the study to highlight the different reactions that can be induced after contamination. The defence biomarkers used are superoxide dismutase (SOD), a marker of oxidative stress, glutathione S-transferase (GST), involved in the process of detoxification of xenobiotics, and Laccase with a role in the modulation of immune response. The biomarker of damage, the malondialdehyde (MDA), is involved in lipid peroxidation process. Indeed, aquatic invertebrates may suffer from impairment in the enzymatic systems after being exposed to toxic compounds [START_REF] Amiard-Triquet | Ecological Biomarkers: Indicators of Ecotoxicological Effects[END_REF]. The concentrations of heavy metals in the digestive glands are also measured to carry out a qualitative correlative approach. The quality brings together several biometric parameters (length, thickness and width), using the following equation:

= ℎ + ℎ ℎ
This measure of quality is found in particular within the framework of the Red Label program (quality index) or biomonitoring measures to evaluate the health status of bivalves.

In addition, metallothionein concentrations were measured in other organisms at similar sampling dates to collect additional information on metal contamination at port sites (fairing, rainwater).

Metallothionein is a defence biomarker that can be overexpressed in tissues in the presence of high metal concentrations [START_REF] Damiens | Metal bioaccumulation and metallothioneine concentrations in larvae of Crassostrea gigas[END_REF].

Material and methods

Sampling sites and caging transplantation procedure

Two sampling sites located in the marina of La Rochelle (Nouvelle-Aquitaine region) and exposed to diffuse trace elements contamination are chosen. Sampling sites are named as follow: the careening area, exposed to contamination due to boat handling (antifouling paint residues, sacrificial anodes), and the rainwaters site, located near one of the biggest rainwaters outlets, which is characterised by a lack of filtration system (Figure 1). Previous studies revealed that the marina of La Rochelle contained high levels of heavy metals due to the port area being semi-closed [START_REF] Breitwieser | Short-term and long-term biological effects of chronic chemical contamination on natural populations of a marine bivalve[END_REF][START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF]. The third study site is a closed marsh of the laboratory, considered as the reference site, located a few kilometres away from the marina. In spring 2016, a batch of variegated intertidal scallop (Mimachlamys varia) from the Tinduff hatchery (Brittany Region) was caged in the marsh area for fifteen days in order to acclimate. Scallops were maintained one week in aerated seawater and then transplanted in selected sites in the two intra-harbour sites and the marsh. At each experiment day (D0, D07, D21), surviving species in the caging are counted and compared to the initial batch number (Fasulo et al., 2012b, Cappello et al. 2013a). A high mortality rate is observed during the acclimation period, likely due to the stress of transportation from Brittany to La Rochelle (Maguire et al., 1999[START_REF] Sarkis | A simple transport procedure for juvenile calico scallops, Argopecten gibbus (Liannaeus, 1758)[END_REF]. In contrast, a low mortality rate (23%) is recorded after transplantation in the harbour sites. Ten scallops per site at two experiment dates (D07 and D21) are sampled and prepared on the spot for biochemical analysis and heavy metals quantification. Ten scallops are sampled as representative of D0 at the marsh site (Table 1). The dissections and the conservation in the fridge on site prevent from biological degradation.

Water physicochemical assessments

From 17 June 2016 to 14 July 2017, temperature, salinity, turbidity, chlorophyll and dissolved oxygen are measured at 5min intervals using a multi-parameter probe (YSI 6600V2). Three monitoring stations, composed of cages settled on floating structures moored to the dock, are set up at three locations: the rainwaters site, the fairway area and the marsh. The rainwaters and careening sites floated around 0.8 meters under water surface. The marsh probe is set on a pole at 1 meter under the water surface. These three stations are set up at less than 1 meter from the scallops (Figure 2).

Salinity and turbidity are laboratory calibrated using 12880µs.cm -1 and 100 and 1000 NTU standard. Calibration constants are checked to be within the acceptable range mentioned in the user manual. Laboratory calibrations of turbidity sensors for SSC (Suspend Sediment Concentration) are performed in a 40L black bucket using wet sediments collected under the instruments. SSC are validated by filtration, using GF/C filters on field samples. Times-series data are processed using Matlab routines. Inter-calibration of probes are checked. No significant differences are noticed (paired T-student test with simultaneous field-measurements, p<0.01).

The ROX Optical Dissolved Oxygen sensors (6150 ROX probe) are calibrated using the one point « saturated water » method, mentioned in the « Calibration, Maintenance and Troubleshooting Tips » manual.

We carry out a post-deployment calibration on chlorophyll measurement using field samplings. Filters are analysed using a fluorometric method with acetone 90% (Fluorometer Turner TD 700), as proposed by [START_REF] Lorenz | Determination of chlorophyll and pheopigments: Spectrophotometric equations[END_REF].

Trace element assessments

Trace elements analyses are carried out according to previous studies [START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF][START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF]. Analyses of 14 trace elements (Ag, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, Sn, V, Zn) are performed using two measuring instruments: a Varian Vista-Pro ICP-OES and a

Thermofisher Scientific XSeries 2 ICP-MS.

Biomarker assessments

All the biochemical assays (GST, Laccase, SOD, MDA) are performed using the same instrument: SAFAS Flx-xenius at the specific absorbance for each biomarker kind. The assays were carried out on the digestive glands of M. varia at the reference site (marsh) and the contaminated sites (outlet rainwaters and careening areas) at D0, 7 and 21 (D0, D7, D21, n = 10 per site and per date).

Before statistical analysis, row data for GST, SOD and Laccase are expressed in U/mf of proteins.

MDA row data concentrations are expressed in µM/mg of protein. Total protein concentrations are determined using the BCA Kit methodology (Bicinchononique Acid Kit, Sigma Aldrich). The BCA kit contains bovine serum albumin (BSA) as a standard and involves the reduction of alkaline Cu 2 + by proteins [START_REF] Smith | Measurement of protein using bicinchoninic acid[END_REF] at absorbance 562 nm.

Moreover, another method to assess metallic pollution is used as additional data (Table S1).

According to [START_REF] Mouneyrac | Effects of natural factors (salinity and body weight) on cadmium, copper, zinc and metallothionein-like protein levels in resident populations of oysters Crassostrea gigas from a polluted estuary[END_REF], metallothionein concentrations are determined in the digestive glands of scallops sampled at the same dates (D0, D07 and D21).

Glutathion S-transferase (GST) assay

Glutathion S-transferase (GST) specific activity plays an important role in xenobiotic detoxification. Total activity of GST (GST being a group of enzymes) is determined according to the kit method of Sigma (CS0410-1KT) [START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF].

Superoxide Dismutase (SOD) assay

Superoxide dismutase (SOD) activity plays a key role in antioxidant response [START_REF] Paoletti | A sensitive spectrophoto-metric method for the determination of superoxide dismutase activity in tissue extracts[END_REF].

Malondialdehyde (MDA) assay

Oxidative stress is assessed through lipid peroxidation by quantifying malondialdehyde (MDA), a chemical metabolite of cell lipid degradation. MDA concentration is measured using a commercially available MDA assay kit (Oxis International) [START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF].

Laccase assay

The phenoloxidase (PO) activity (Laccase) reveals bivalve's immune system alteration. Its enzymatic activity is contaminant sensitive [START_REF] Luna-Acosta | First evidence of laccase activity in the Pacific oyster Crassostrea gigas[END_REF] and plays a crucial role in the immune defence mechanism of marine invertebrates.

Statistical analysis

Statistical analysis is performed using R software (R core team, 3.2.2 version). The survey is structured according to the dates (3 dates), the sites (3 sites), and the size of the samples for each subgroup (10 specimens only).

For all variables, homogeneity of variances is assessed using Fligner-Killeen tests. Then, the multivariate response of individuals at our three sampling sites and D0, D07, D21 is measured using a Multivariate Factorial Analysis. Indeed, the sample group is of 10 specimens. Each sample distribution reveals great heterogeneity for each variable. Therefore, it is unlikely to conduct a MANOVA, considering the number of trace elements ( 14), the number of morphological traits (3) and the number of biomarkers (4). In order to overcome this issue, all variables are divided into two sections: low and high, whether the value of the sample is under or above the median value of the 70 individuals. This should also prevent from further issues due to abnormal values and non-normal distribution of variables inside each group of individuals. Normality is tested using a Shapiro-Wilk test. The normality hypothesis inside sub-groups (of 10 specimens) is rejected most of the time when examining the 21 variables. At D0 only 10 specimens are sampled, whereas 30 specimens are collected both at D7 and D21. Therefore, values of DO individuals are weighted by 3 to balance the results between the 3 periods. Thus, all variables are recorded as being high (1) or low (0) for each individual. Then, a qualitative multivariate analysis is conducted to underline relationships between variables (site, date, heavy metals, biomarkers and biometry parameters).

Results

Water physical parameters

The water physical parameters are presented in Table 2. We perform daily averages to understand the general trends of the environmental conditions. The temperature trends range from 16.8°C to 19.7°C and are representative of current weather conditions. In contrast, salinity and turbidity vary depending on the climatic conditions ranging from 27.61 to 33.19 PSU, and 0.004 to 0.037 g/L. These measures are mainly due to rainfall bringing fresh water and sediment. This phenomenon is observed at the rainwater station at D21. Chlorophyll trends also differ very slightly at sites and dates, likely due to sunstroke. Dissolved oxygen rarely drops below 80% in surface water.

100% saturations at the surface water occur during renewal of water bodies and in presence of wind.

Levels of trace elements

Table 3 presents the results of the analyses on heavy metals performed in the digestive glands of the black scallop M. varia. These results are presented in µg/g of dry weight.

By comparing these 14 heavy metal values with the ones from previous studies in pectinids (Metian et al., 2008;[START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF][START_REF] Viarengo | Role of metallothionein against oxidative stress in the mussel Mytilus galloprovincialis[END_REF][START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF], results reveal a lower contamination (in italic in the table 3) by Pb at D7 (-30%) at all sampling sites in comparison with the marsh (2.3 ± 0.1 µg/g of dry weight). In addition, there is a lower contamination (in italic in the Table 3) by Cd (-18%), Co (-28%), Mn (-30%), Pb (26.2%), Se (-23%), Zn (-14%),

and Ag (-20%) at the rainwaters and careening sites at D21, in comparison to the reference site. In contrast, there is a higher contamination (in bold in the Table 3) by As (+7.5%), Cd (+14%), Cr (+125%), Cu (+423%), Fe (+ 18%), Mn (+18%), V (+62%) and Zn (+117%) at the rainwater or/and careening sites at D7 as well as a higher contamination (in bold in the table 3) by As (+12%), Cr, Cu (+54%)and Fe (+52%) at the same sites at D21.

Biomarker responses

The enzymatic assays presented in Figure 3 underline the state of health of marine bivalves.

These are performed in order to target the capacities of detoxification of xenobiotics (GST) and to assess the struggle (SOD) against reactive oxygen species (ROS), the integrity of cell membranes, the involvement of lipid peroxidation (MDA), and the modulations of the immune system (Laccase).

The scatter plot in Figure 3 displays an easy-to-read, descriptive and multi-criteria analysis that compares results depending on the sampling dates and the caging sites of variegated scallops. All pollution biomarkers show significant modulations of their responses at various dates and the two intra-harbour sites.

The plot in Figure 3 presents GST distributions. There are no significant differences. Only trends of mean specific activities seem to increase over time and are higher at the marsh site (0.75 ± 0.2 UI/mg of proteins at D07, 1 ± 0.2 UI/mg of proteins at D21) than at the rainwater (0.66 ± 0.1 UI/mg of proteins at D07, 0.85 ± 0.1 UI/mg of proteins at D21), or the careening areas (0.7 ± 0.1 UI/mg of proteins at D07, 0.94 ± 0.1 UI/mg of proteins at D21). The non-parametric test of Fligner-Killeen for homogeneity of variance with a p-value of 0.5817 confirms that we can compare both dates and sites for GST.

In contrast, MDA homogeneity of variance test fails either for date (p-value = 0.0388), site (pvalue = 0.0013), and both date and site (p-value = 0.0002, < 0.05). In fact, at the rainwater site at D21, the large distribution is characterised by a high heterogeneity of MDA concentration (16.1 ± 13.4

µM/mg of proteins), which is not influenced by dates or sites.

The analysis of the Laccase activity at the three sites and sampling dates reveals discrimination at all sites at D21. The non-parametric test of Fligner-Killeen for homogeneity of variance confirms (p-value = 0.4522) the analysis. Whereas the behaviour of the Laccase enzyme is similar at D0 and D7, and stable for all sites (average of 4.5 ± 1 UI/mg of proteins), there is an overall increase at D21. However, at D21, Laccase is lower at the careening site (7.5 ± 1.1 UI/mg of proteins) than at the other sites (10.3 ± 2 UI/mg of proteins).

Superoxide Dismutase activity is compared for both sites and dates, as shown by the nonparametric test of Fligner-Killeen for homogeneity of variance (p-value = 0.2022). The rainwaters and careening sites have similar patterns: they increase slowly between D7 and D21, although they remain lower than the levels at the marsh site at D0 (57 ± 8 UI/mg of proteins) and D7 (72 ± 12 UI/mg of proteins). The marsh site highlights an irregular pattern: starting with high levels at the beginning (D0 and D7), they increase drastically before dropping at D21 (23 ± 11 UI/mg of proteins). The activity levels at D21 are lower than the ones of the rainwaters (40 ± 21 UI/mg of proteins) and careening sites (30 ± 11 UI/mg of proteins).

Moreover, metallothionein concentrations presented in Table S1 show a significantly higher response and a specific inorganic contamination at the careening and rainwaters areas, in comparison to the marsh site at D7.

Factor analysis to underline relationships between biomarker responses and trace element assessments

Figure 4 displays an easy-to-read, descriptive and multi-criteria analysis that compares results of the qualitative analysis used ("low signal" or "high signal"), the sampling dates and the various sites.

The analysis focuses on the two first components, since the others do not bring further details (Figure S1). The first two axes explain 36.92% of the variance, which is enough to perform a qualitative factor analysis.

Looking at axis 1 (horizontal dimension), variables above the 0.5 contribution value (Figure S2 Looking at axis 2 (vertical dimension), variables above the 0.5 value shows the antagonistic effect of low concentration (for high.0) of Ni, Co, Pb, Mn elements against the effect of high concentration (for high.1) of Pb, Fe, Se, Ni, Zn, Co, and Mn.

Thus, individuals at the right of axis 1 have a strong SOD activity but a low Laccase activity. They seem to be less exposed to heavy metal elements than those on the left side of axis 1. Axis 1 reveals the effect of Cu, Sn and As on enzymatic activities (SOD, Laccase and GST), as a major interaction of the state of health of scallops. Furthermore, GST and SOD seem to have antagonist effects. Axis 2 underlines a separation between high concentrations of heavy metals such as Mn, Co, Zn, Ni, Se, Fe, and Pb and low concentrations of Mn, Ni, Co, Pb. It highlights the preponderant concentration of Mn, Ni and Pb in scallops (individuals having a high concentration in heavy metals are shown at the bottom of the Figure 4).

In order to check whether the site and date factors (temporal dimension) influence the specimen's repartition on the plan defined by axes 1 and 2, we have drawn confidence ellipses on the plot of Figure 4. A label and a distinct colour are assigned for each group or individual. Groups are divided as followed: D0, D7 and D21 at the marsh, D0, D7 and D21 at the careening area, and D0, D7 and D21 at the rainwaters site. Harbour sites (careening and rainwaters) have similar evolution for each date (D7 and D21), in comparison to the marsh groups, which are merely located at the right of axis 1. Over time (from D0 to D21), a movement towards the bottom of the axis 2 is common to all groups, which is symptomatic of an increasing concentration of Pb, Fe, Se, Ni, Zn, Co, and Mn trace elements. Equally, a movement of all groups towards the left of the axis 1 can be observed, due to a higher concentration of Fe, As, Sn, Mn, Cr, Cu trace elements conjointly with stronger GST and Laccase activities, and a lower SOD signal.

Discussion

Study method

So far, few caging multi-biomarker studies (biomarkers of effect and exposure) have been carried out in France using other molluscs such as the freshwater mussel Unio tumidus [START_REF] Charissou | Relationship between two oxidative stress biomarkers, malondialdehyde and 8-oxo-7,8-dihydro-2'-deoxyguanosine, in the freshwater bivalve Unio tumidus[END_REF], and the marine mussel Mytilus spp. [START_REF] Lacroix | Seasonal monitoring of blue mussel (Mytilus spp.) populations in a harbor area: A focus on responses to environmental factors and chronic contamination[END_REF]. Caged bivalves could be successfully used as sentinel animals of contamination events in marine ecosystems. Some data show that the uptake and elimination of metals were evaluated in different tissues of the mussel Pinna nobilis L. transplanted to harbour's area of Tunisia [START_REF] Jebali | Comparative study of the bioaccumulation and elimination of trace metals (Cd, Pb, Zn, Mn and Fe) in the digestive gland, gills and muscle of bivalve Pinna nobilis during a field transplant experiment[END_REF]. Moreover, mussels of the specie Mytilus galloprovincialis could also reveal high levels of contaminants in bioactive monitoring experiments conducted in Spain (De los Rios et al., 2012;[START_REF] Marigomez | Combined use of native and caged mussels to assess biological effects of pollution through the integrative biomarker approach[END_REF]De los Rios et al., 2018).

The black scallop M. varia is located in the subtidal zone, unlike oysters and mussels that live higher in the intertidal zone. Unlike the last two species, the black scallop and its digestive glands are used as tools revealing the environmental quality of the port area [START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF].

One of the goals of this study is to assess the feasibility of a caging experiment on marine bivalves of similar backgrounds in order to compare the environmental quality between intra-harbour sites (potentially contaminated environments by various chemical compounds) and a less contaminated area that had been rarely done before on variegated scallop (M. varia).

To this end, various precautions are taken: the choice of a calm and safe zone for the installation of the cages, the types of cages (dimensions and meshes adapted to the species) and the choice of the species, adapted to the condition's environmental aspects of the study areas. The shellfish from the Tinduff Hatchery are 18 months old and already acclimated to caging experiment conditions. In addition, the dissection of living bivalves is carried out on site to prevent from disruptions of the enzymatic activities.

Moreover, even if each individual is considered as a replicate of the study, we duplicate cages, fixed to a floating structure on each site, to accentuate the contact with trapped pollutants in the sediment. The cages are risen to the surface at each sampling date -D0, D07, D21 -(as exposure time is necessary for the bioaccumulation of bivalves). Our experimental design inspired of the study of [START_REF] Charissou | Relationship between two oxidative stress biomarkers, malondialdehyde and 8-oxo-7,8-dihydro-2'-deoxyguanosine, in the freshwater bivalve Unio tumidus[END_REF] is agreeing with the recent work which aimed to study impacts of dredging operations on biomarkers responses of caged bivalves [START_REF] Moreira | Impacts of dredging on biomarkers responses of caged bivalves in a semi-arid region (Ceará State, NE Brazil)[END_REF].

Sensitivity and relevance of parameters

The chemical composition of the water samples does not enable to classify the sites as mentioned by [START_REF] Charissou | Relationship between two oxidative stress biomarkers, malondialdehyde and 8-oxo-7,8-dihydro-2'-deoxyguanosine, in the freshwater bivalve Unio tumidus[END_REF]. All the parameters suggest normal water quality based on values measured at each harbour site at D0.

Biomonitoring of variegated scallops allows putting forward a global classification of the three sites. In decreasing order of the biological quality, the classification is: careening<rainwaters<marsh.

The marsh is considered as the best site, as only a few heavy metals are significantly higher, when compared to the other sites.

Many scientific studies underline that higher heavy metals values in bivalve organisms reveal an exposition to metal compounds and, thus, a risk of having toxic effects [START_REF] Bouchard | Le mal du dehors. L'influence de l'environnement sur la santé par Rémy Slama. Éditions Quae[END_REF]. At D21, our results show higher concentrations of As, Cr, Cu, Fe and Sn at the careening area, when compared to the marsh. Moreover, As, Cu, Fe concentrations are higher at the rainwaters area than at the marsh.

Our results support the hypothesis of the World Health Organization and the potential ecological risk of environmental pollutants. It states that ambient environmental contamination is caused by a combination of agents from public road, roofing panels, metal adhesives, pigments, metal industry, and antifouling paint [START_REF] Pourabadehei | Effect of the resuspension technique on distribution of the heavy metals in sediment and suspended particulate matter[END_REF][START_REF] Jafarabadi | Spatial distribution, ecological and health risk assessment of heavy metals in marine surface sediments and coastal seawaters of fringing coral reefs of the Persian Gulf, Iran[END_REF][START_REF] Chen | SeaNine 211 as antifouling biocide: A coastal pollutant of emerging concern[END_REF][START_REF] Jahan | Comparison of pollution indices for the assessment of heavy metals in the sediments of seaports of NSW, Australia[END_REF][START_REF] Gargouri | Use of sediment quality indicators for heavy metals contamination and ecological risk assessment in urbanized coastal zones[END_REF][START_REF] Kim | Critical evaluation of different methods to calculate the Geoaccumulation Index for environmental studies: a new approach for Baixada Santista e southeastern Brazil[END_REF][START_REF] Tunca | An ecological risk investigation of marine sediment from the northern Mediterranean coasts (Aegean Sea) using multiple methods of pollution determination[END_REF].

After 21 days, concentrations of bioaccumulation in in situ scallops are similar to previous studies performed at the same location. Moreover, the biomarker response of transplanted bivalves matched the results of in situ bivalves obtained from a previous study [START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF].

Overall, this research revealed a SOD decrease, Laccase and GST stimulations, and higher concentrations in Cu, Fe, As, Co, Mn, Zn, Sn in the digestive glands. This is an interesting criterion to consider for future studies using the specie M. varia. Nonetheless, this finding does not explain the toxicology of heavy metals, which depends on molecular shapes. New methods for the analysis of toxicology induced by heavy metals could be used, such as the quantum dot method used for cadmium [START_REF] Jimeno-Romero | Bioaccumulation, tissue and cell distribution, biomarkers and toxicopathic effects of CdS quantum dots in mussels, Mytilus galloprovincialis[END_REF] where the solubility of particles would be partly the cause toxicity generated in bivalves.

In addition to heavy metal assessments, it will be relevant to write down all the contaminants of the harbour of La Rochelle. This list could include pesticides already identified in coastal waters, as biocides (diuron, triazine) used in antifouling paint of ship and small boat, pharmaceutical residues and other micro-pollutants (micro-plastics, phtalates), which are all potentially dangerous for the health of organisms, and, thus, to consumers at the end of the food chain.

Multi-criteria analysis (Figure 4) is carried out. We compare the inorganic contaminant, molecular biomarker and quality qualitative analyses ("low signal=high.0" or "high signal=high.1") as well as the sampling dates and the various sites. Pollutant qualitative results (Pb, Fe, Se, Ni, Zn, Co, Mn, Cr, Sn, As) in the digestive gland of this species are positively correlated with biomarker of responses (GST and Laccase) and negatively correlated with the SOD biomarker. The stability of the MDA results reveals the efficacy of anti-oxidants and lipid peroxidation in variegated scallops, as already found in juvenile bivalves [START_REF] Roméo | Utilisation des biomarqueurs dans les stades embryo-larvaires de bivalves marins comme outils rapides et sensibles d'évaluation et de gestion du risque chimique. Essai de validation in situ[END_REF]. Moreover, several natural abiotic factors (turbidity, chlorophyll a, oxygen level, temperature, salinity) could also explain the modulation of oxidative stress, although no significant modulation is observed. Thus, the digestive glands of the variegated scallop are relevant organs to assess the environmental quality of the harbour (Triebskorn et al., 2001) as biomarker results reveal similar modulations to previous studies using this specie [START_REF] Milinkovitch | In situ evaluation of oxidative stress and immunological parameters as ecotoxicological biomarkers in a novel sentinel species (Mimachlamys varia)[END_REF][START_REF] Breitwieser | Short-term and long-term biological effects of chronic chemical contamination on natural populations of a marine bivalve[END_REF][START_REF] Breitwieser | First data on three bivalve species exposed to an intra-harbour polymetallic contamination (La Rochelle, France)[END_REF]. Indeed, three biomarkers of exposure (Laccase, SOD and metallothioneins) show significant responses and the GST biomarker highlight an activity trend that displays environmental stress. These results suggest that the oxidative stress and reactive oxygen species neutralisation could be due to a mixture of contaminants (e.g pesticides, hydrocarbons, heavy metals) from wastes of watershed and urban activities (Luschak, 2011).

These molecular biomarkers of exposure are relevant and efficient tools to use for the harbour authorities. Overall the study demonstrated that differences in chemical bioaccumulation and biomarker responses, and confirm results of [START_REF] Brooks | Differences in copper bioaccumulation and biological responses in three Mytilus species[END_REF] which explain the importance of identifying the pertinent species when using bivalve in biological effects studies. In addition, new easy, rapid and inexpensive methods could be implemented. For instance, energetic metabolism with ATP assay could be linked to behavioural parameters (reproduction, growth). The study of energy allocation mechanisms at the biomolecular level up to the cellular level could bring a better knowledge of the responses of M. varia. Indeed, the energy cost of environmental stress may decrease the amount of energy available to maintain homeostasis and, therefore, reduce the fitness of individuals as highlighted in sea fish and mussel species (Lauwrence et al., 2003;[START_REF] Donaghy | Assessment of the fitness of the mussel Mytilus galloprovincialis two years after the Hebei Spirit oil spill[END_REF]. The majority of energy would be allocated to the development of defences, which could lead to the increase in tolerance of marine species to environmental chemical contamination. Energy metabolism therefore remains a relevant pathway to investigate in order to understand how populations maintain themselves in the environment, particularly with abundance fluctuations between both species. It could be complementary to plan also to consider current perspectives and future directions of three main omics (transcriptomics, proteomics and metabolomics) with regards to identify, characterize and determine functional aspects across biological processes to better understand bivalve physiology, especially with regards to immune defences to environmental stresses (Fasulo et al., 2012b;[START_REF] Gouveia | Ecotoxicoproteomics: A decade of progress in our understanding of T anthropogenic impact on the environment[END_REF][START_REF] Nguyen | Applications of omics to investigate responses of bivalve haemocytes to pathogen infections and environmental stress[END_REF]. Finally, it seems to be urge (1) to compare the concentration of trace elements found in bivalve M. varia collected from the La Rochelle Marina with the xenobiotics data determined with those reported in other French port's areas and (2) to participate to the enrichment of the data available on the potential risks to human health associated with the consumption of marine bivalve [START_REF] Marengo | Comparison of elemental composition in two wild and cultured marine fish and potential risks to human health[END_REF].

Conclusion

This field study attests that scallops, particularly the digestive glands, are effective tools to assess environmental port's quality. Biochemical parameters could represent reliable biomarkers for the assessment of environmental pollution on M. varia as biological biomonitoring model. In addition, for the first time, the research reveals a rapid bioaccumulation of heavy metals in this specie as well as a similar toxicity to previous studies performed in situ in the marina of La Rochelle. Further investigations are needed to evaluate the environmental impacts of environmental (biotic and abiotic) factors using multi-biomarker approaches to confirm such point of view.

Coastal waters collect storm waters containing substances, such as heavy metals and pesticides, which have negative impacts on the water quality. It could be interesting to explore some differences in histological microbiological and biomarker responses of scallops were site-dependent.

In addition, dilution caused by tides, currents and rainfall have adverse effects on water. Thus, it is essential to develop low-cost and transposable approaches for monitoring the water quality in similar areas. Finally, we propose the use of scallops as indicator species for monitoring of diffuse pollution in the marine environment. We could consider the formation of European workgroup of marine chemical contamination in bivalve to develop an internationally accepted protocol to conduct harbour's active biomonitoring and collect preliminary data comparing coastal scallops from around the European port's area. Prineau from LIENSs (the University of La Rochelle) and J. Pigeot.

Bibliography

Articles:

  in supplementary data) indicate a high concentration of Pb and Ag, and a low concentration of Cu, Sn and As linked to strong SOD and low Laccase activities. Variables under the -0.5-contribution value show a high concentration of Cu, Cr, Mn, Sn, and As linked to a strong Laccase and GST activities, but a low SOD activity.

  received a doctoral grant (Communauté d'Agglomération de La Rochelle) and financial support from the marina of La Rochelle while working with the La Rochelle University. This study is a contribution to the project ECONAT funded by the Contrat de Plan Etat-Région and the CNRS and the European Regional Development Fund. The LIENSs laboratory would like to thank the general management of the marina of La Rochelle for giving access to the harbour. The authors also thank the people working at the logistic platforms of the LIENSs laboratory (Analyses Élémentaires and Données: Intégration, Structuration et Analyse). The authors would like to thank Oriane Titeca for proofreading this paper. We are deeply indebted to A. Combaud, T. Guyot, V. Becquet, A. Viricel, M.

Figure 1 :Figure 2 :

 12 Figure 1: Map of France including the locations of the study sites. The marsh is the reference site (less-contaminated). The two impacted areas are the careening and the rainwaters sites, located in the harbour of La Rochelle.

Figure 3 :Figure 4 :

 34 Figure3: Boxplots showing biomarker results in the digestive glands of M. varia at the reference site (marsh) and the contaminated sites (outlet rainwaters and careening areas) at D0, 07 and 21 (D0, D07, D21, n = 10 per site and per date). Significant differences between impacted sites and the marsh (control site) for each date are indicated using asterisks: *p<0.05; ***p<0.001.

AFCM for periods and sites

(axis 1/2)