J Rebufa

J Kister

N Artaud

Dupuy

O Galtier

O Abbas

Y Le Dréau

C Rebufa

J Artaud

N Dupuy

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

Keywords:

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Introduction

Pure pattern recognition techniques are oriented in discriminated way among different groups of samples and operate by dividing the hyperspace in as many regions as the number of groups. So, if a sample is represented in the region of space corresponding to a particular category, it is classified as belonging to that category. In this case, each sample is always assigned to one and only one group [START_REF] Vandeginste | Handbook of Chemometrics and Qualimetrics: Part B, Elsevier[END_REF]. These methods include Linear Discriminant Analysis (LDA) [START_REF] Mclachlan | Discriminant Analysis and Statistical Pattern Recognition[END_REF] and Partial Least-Squares Discriminant Analysis (PLS-DA) [START_REF] Wold | Food Research and Data Analysis[END_REF]. The principle of PLS-DA consists in a classical PLS regression where the response variable is binary and expresses a class membership. Therefore, PLS-DA does not allow attributing a sample to other groups than the ones first defined. As a consequence, all measured variables play the same role with respect to the class assignment. Actually, PLS latent variables are built to find a proper compromise between two purposes: describing the set of explanatory variables and predicting the response ones. A PLS-DA classification should well benefit from such a property in the direction of building typologies with an intrinsic prediction power.

Another group of class-modeling techniques represents a different approach to pattern recognition, as it focuses on modeling the analogies among the elements of a class rather than on discriminating among the different categories. In these methods, each category is modeled separately. The objects in agreement with the model are considered as a member of the class, while objects not in agreement are rejected as non-members. When more than one class is modeled, three different situations can be encountered: each sample can be assigned to a single category, or represented by several categories or not be included in any category. In comparison with pure pattern recognition techniques, class-modeling tools offer at least two main advantages: it is in principle possible to recover samples which are not represented in any of the examined categories and which, as a consequence, can be either simply outlying observations or members of a new class not considered during the modeling stage. Moreover, as each category is modeled separately, any additional class can be added without recalculating the already existing class models. The most commonly used chemometric class-modeling technique is SIMCA (Soft Independent Modeling of Class Analogy) [START_REF] Wold | [END_REF][START_REF] Wold | Chemometrics, Theory and Application[END_REF].

The range of study by supervised pattern recognition techniques is wide. Some recent reviews about applications of these chemometric techniques have been published: general reviews on geographical origin of foods [START_REF] Galtier | [END_REF]7] or reviews on source rock origin [8], wines [9], and honeys [10].

There have been many reports in the literature comparing the performance of different pattern recognition techniques on spectroscopic data [11][12][13][14]. The increase of pattern recognition techniques and pattern recognition applications led us to search the best method to be used.

The aim of this study was to compare the results obtained with SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two applications, after optimization on the basis of spectral variance analysis. The first application concerns a classification of crude petroleum oils according to their geographical origins using midinfrared (MIR) spectroscopy. The second application is about the classification of French virgin olive oils in five registered designation of origin (RDOs) by near-infrared (NIR) spectroscopy.

Experimental

Samples

Crude petroleum oils

Crude petroleum oils (36 samples), from different fields, were analyzed by MIR spectroscopy to identify their geographical origins: Algeria (ALG), n = 11; South America (S.A.), n = 7; Equator (EQU), n = 11; and Venezuela (VEN), n = 7. Five MIR spectra were recorded for each sample and models were performed on these five spectra per sample. Replicates have been collected following. Spectra have not been recording origin by origin to not bias chemometric treatments. Hence, a total of 180 spectra which were divided in two subgroups: the calibration (n = 23 × 5 = 115) set samples which were chosen to take into account all possible variations because of natural variations; and the prediction set samples (n = 13 × 5 = 65) which were randomly selected for each geographic origin. All the replicates of the same sample were used in the same set.

Virgin olive oils

Commercial virgin olive oils (317 samples) were obtained from the French Inter-Professional Olive Oil Association (AFIDOL, Aixen-Provence, France) and from Service Commun des Laboratoires du ministère des finances franc ¸ais (SCL, Marseille, France). Samples were obtained from four successive crops (from 2003/2004 to 2006/2007). They came from five French RDOs: Aix-en-Provence (AP) n = 97, Haute-Provence (HP) n = 46, Nice (NI) n = 46, Nyons (NY) n = 41, and Vallée des Baux de Provence (VB) n = 87. Samples were analyzed by NIR spectroscopy. Spectra have not been recorded origin by origin, not to bias chemometric treatments.

The calibration set was made up of 225 samples chosen in order to take into account all possible variations because of natural variations among fatty acid and triacylglycerol compositions. The determination of fatty acid and triacylglycerol rates in these samples have been described in previous work [7]; fifty samples having the highest and the lowest fatty acid and triacylglycerol concentrations were chosen in calibration. The other samples were randomly selected. The prediction set was made up of 92 samples which were not selected in the calibration set. The years of harvest are not used as a criterion and all of them could be found in calibration and prediction sets.

Spectroscopic techniques

Mid-infrared spectroscopy

MIR spectra of each crude petroleum oils were recorded five times from 3400 to 700 cm -1 , with 4 cm -1 resolution and 100 scans on a Nicolet Avatar spectrometer equipped with a DTGS detector, an Ever-Glo source and a KBr/Germanium beam splitter. The spectrometer was placed in an air-conditioned room (21 • C). Samples were deposited without preparation on a single bounce attenuated total reflection (ATR) cell provided with a diamond crystal. Air was taken as reference for the background spectrum before each sample. Between each spectrum, the ATR plate was cleaned in situ by scrubbing with ethanol solution and dried. Cleanliness was verified by a comparison between a new background spectrum and the previous background spectrum. The recorded spectra have been normalized after correction of the baseline by the instrument software OMNIC 4.1b (Thermo Nicolet). Each spectrum is constituted of 1402 points.

Near-infrared spectroscopy

NIR spectra of each virgin olive oil were recorded with a Nicolet Antaris spectrometer interfaced to a personal computer using the software result integration 2.1 Thermo Nicolet 2.1. Virgin olive oil samples were filled into a 2 mm pathlength quartz cell directly sampled from the bottle without any chemical treatment. Spectra were recorded between 4500 and 10,000 cm -1 at 4 cm -1 resolution by co-adding 10 scans using double sided interferograms and an empty cell as a reference. The recorded spectra have been normalized by the UNSCRAMBLER software before chemometric applications. Each spectrum is constituted of 2853 points.

Unsupervised pattern recognition

Principal Component Analysis (PCA) is an unsupervised pattern recognition and it is often the first step of exploratory data analysis to detect groups in the measured data. PCA models the directions of maximum variations in a data set by projecting as a swarm of points in a space defined by principal components (PCs). PCs describe, in decreasing order, the higher variations among the objects, and because they are calculated to be orthogonal to another one, each PC can be interpreted independently. That permits an overview of the data structure by revealing relationships between the objects as well as the detection of deviating objects. To find these sources of variations, the original data matrix is decomposed into the object space, the variable space, and the error matrix. The error matrix represents the variations not explained by the previously extracted PCs and is dependent on the problem definition [START_REF] Eriksson | Introduction to Multi-and MegaVariate Data Analysis using Projection Methods (PCA and PLS)[END_REF][START_REF] Esbensen | MultiVariate Data Analysis-In Practise[END_REF]. The PCA algorithm is used with mean centered data.

Supervised pattern recognition

Soft Independent Modeling of Class Analogy Classification (SIMCA)

SIMCA is the most used of the class-modeling techniques. The SIMCA classification is a method based on disjoint PCA modeling realized for each class in the calibration set. Unknown samples are then compared to the class models and assigned to classes according to their analogy with the calibration samples. A new sample will be recognized as a member of a class if it is enough similar to the other members; else it will be rejected. Each class is modeled using separate PCA models. A model distance limit S max is used for classifying new samples and S max is calculated for the class model m as follows (Eq. ( 1)):

S max (m) = S 0 (m) F c (1)
where S 0 is the average distance within the model, and F c (Fisher criterion) is the critical value provided by the Fisher-Snedecor tables. The F c value depends on the percentage of risk, generally set to 5% [START_REF] Bauer | [END_REF]. Class membership is defined at a significance level of 2.5% of S max . Mean centering is applied before modeling.

Partial least squares regression (PLS)

PLS [18,19] was initially built for quantitative analysis, but now it is also used for pattern recognition. This supervised analysis is based on the relation between spectral intensity and sample characteristics [20]. Interference and overlapping of the spectral information may be overcome using powerful multicomponent analysis such as PLS regression. The ability of this algorithm is to mathematically correlate spectral data to a property matrix (relative rate or geographical origin) [21]. Mean centering is applied before modeling. The number of latent variables selected for the PLS model was obtained by cross validation on the calibration set.

When several dependent data are available for calibration, two approaches can be used in PLS regression: either properties are calibrated for one at a time (PLS1), or properties are calibrated at once (PLS2). In PLS1 model, the Y response consists of a single variable. When there is more than one Y response a separated model must be constructed for each Y response. In PLS2 model, responses are multivariate. PLS1 and PLS2 models provide different prediction set and PLS2 regression give better results than PLS1 regression only if Y variables are strongly correlated [START_REF] Vandeginste | Handbook of Chemometrics and Qualimetrics: Part B, Elsevier[END_REF]11,[START_REF] Martens | Multivariate Calibration[END_REF]. In the other case, PLS1 models are generally more robust [START_REF] Martens | Multivariate Calibration[END_REF][START_REF] Pedro | [END_REF].

PLS regression can be adapted for pattern recognition, giving rise to the PLS-DA method. PLS-DA is performed using an exclusive binary coding. During the calibration process, the PLS-DA method is trained to compute the "membership values", one for each class; the sample is then assigned to one class when the value is above a specific prediction threshold [24]. This method, adapted from PLS1 or PLS2 regressions, uses M spectral variables as predictors and q variables (0 or 1) as variables response [START_REF] Sjöström | PLS discriminant plots[END_REF][START_REF] Stahle | [END_REF][27][28].

The predicted origins seldom lead to a binary result not exactly equal 0 or 1 but to a result near 0 or 1, which is justified by the natural variability of the sample constituents. In front of the difficulty of calibrating and predicting origin with binary variables, it is necessary to discriminate the results between the initial values 0 or 1. Samples with values lower than 0.5 and higher than 1.5 were identified as outside the defined origin and samples with values between 0.5 and 1.5 were identified as belonging to the defined origin.

For PLS1-DA, one regression for each class has been build. For PLS2-DA, all the classes are included in one regression. For PLS2-DA-SIMCA, the SIMCA classification is performed on the PLS2-DA scores. The number of latent variables selected for the PLS model was obtained by cross validation on the calibration set.

Data processing 2.5.1. Comparison of the methods

The percentage of correct classification (%CC) is the criterion used to compare classification results obtained with chemometric

%CC = N c N c + N ic × 100 (2)
where N c is the number of correct classifications and N ic is the number of incorrect classifications [29].

Selection of variables with variance method

The total sample variance (S 2 ) of a sample set is explained by the intra-spectral variance (IntraSP) and by the inter-spectral variance (InterSP). IntraSP represents the variance of all spectra into one class (j spectra) calculated at each wavenumber calculated as follows (Eq. ( 3)):

IntraSP = j i=1 (a ix -a x ) 2 j -1 (3)
where a ix is the absorbance of the spectrum i at the wavenumber x and a x is the mean absorbance of all the j spectra into the class considered at the wavenumber x.

InterSP represents variance of all spectra (n spectra) into the sample set as follows (Eq. ( 4)): where A kx is the absorbance of the spectrum k at the wavenumber x and A x is the mean absorbance of all the n mean spectra of each class considered at the wavenumber x.

InterSP = n k=1 (A kx -A x ) 2 n -1 (4)
Both intra-and extra-spectral variance have been calculated using the Bessel's correction, n -1, where n is the number of spectra. InterSP and IntraSP are estimators of the distribution of spectral variances. The Fisher-Snedecor test [START_REF] Massart | Chemometrics: A Textbook[END_REF] enables to determine whether a variance is significantly higher than another one by the calculation of the Fisher-Snedecor variable (F) according to Eq. ( 5):

F = InterSP IntraSP (5)
IntraSP is an estimator of the intra-group variance 2 1 , InterSP is an estimator of the inter-group variance 2 2 . The null hypothesis H 0 ( 2 1 = 2 2 ) is tested against the alternative hypothesis H 1 ( 2 1 = 2 2 ). If the ratio (j -1)IntraSP/ 2 1 follows a 2 j-1 law and if the ratio (n -1)IntraSP/ 2 2 follows a 2 n-1 law, so that, under the null hypothesis 2 1 = 2 2 , F is distributed as

[ 2 n-1 /j -1]/[ 2 m-1 /n -1]
which does not depend on the common variance 2 of the two normal distributions, and can therefore be used as a test statistic.

The value F is compared to the Fisher criterion (F c ). F c is the critical value provided by the Fisher-Snedecor tables obtained by freedom degree number of interSP and by freedom degree number of IntraSP. F c depends on the percentage of risk, generally set to 5% [START_REF] Bauer | [END_REF]. If F < F c , the null hypothesis H 0 is verified and the classification is impossible. If F > F c , the null hypothesis H 0 is not verified and the classification is possible. In this case, IntraSP is significantly lower than the InterSP. Thus, the corresponding spectral range will be selected for model construction. Discrimination power of variable selection procedure is estimated from the number of correctly classified samples.

Software

The chemometric applications were performed by the UNSCRAMBLER software version 9.6 from CAMO (Computer Aided Modelling, Trondheim, Norway).

Results

Classification of crude petroleum oils according to their geographical origins

Crude petroleum oils extracted from different petroleum fields [Algeria (ALG), South America (S.A.), Equator (EQU), and Venezuela

Table 1

Percentage of well classified petroleum oils for each statistical treatment in the 700-3400 cm -1 spectral range. (VEN)] were analyzed by MIR spectroscopy. In spite of their different locations, these samples have similar MIR spectra (Fig. 1) and display characteristic bands of aliphatic hydrocarbons: as CH 3 : 2952 cm -1 , as CH 2 : 2922 cm -1 , s CH 2 : 2853 cm -1 , ı as C-H in CH 3 and CH 2 groups: 1456 cm -1 , ı s C-H in CH 3 group: 1376 cm -1 , ıCH 2 in -(CH 2 ) n (n > 3): 721 cm -1 . Absorption bands describing aromatic compounds occur at 3052 cm -1 ( C-H ), 1602 cm -1 ( C C ) and in the range 900-746 cm -1 ( C-H ) characteristic of the number of adjacent hydrogen atoms on the aromatic ring. Fig. 2 shows the PCA performed on the full MIR spectra (1402 variables) of crude petroleum oils that constitute four perfectly distinct groups. Prediction samples are highlighted in the score plot projection. The score plots are projected in the PC1-PC2 plane. These PCs explain respectively 53% and 32% of the spectral variance. Three groups are narrowed, and another group (EQU) is farthest. In the light of this PCA overview (85% of the spectral variance is explained with only 2 PCs), a good classification of crude petroleum oils was expected. In our previous work [8], the first principal component was attributed to the aliphatic part of the oil and the second one to oxidised and aromatic compounds.

The samples belonging to ALG, S.A. and VEN groups are very closed to each other unlike the EQU samples which have a large dispersion. This could be explained by an American Petroleum Industry (API) degree and chemical compositions more disparate in the EQU group than in the other ones [START_REF] Abbas | Vieillissement simulé ou naturel de la matière organique. Apport du traitement chimiométrique des données spectroscopiques[END_REF].

The data were treated by different chemometric methods in order to predict their geographic origins. Table 1 shows the best classification results obtained in prediction by each method to predict crude petroleum oil groups according to their geographical origin (ALG, S.A., EQU and VEN). These results were obtained on normalized spectra; spectral treatments as Multiplicative Signal Correction (MSC) or Standard Normal Variate (SNV) have not improved the results. The results of each classification model were 100% satisfactory with all methods. The number of principal components (PCs) and latent variables (LVs) were compared according to the percentage of correct classification. The PLS2-DA method used more LVs than PLS1-DA. The number of LVs in PLS1-DA is lower than the number of PCs used in SIMCA analysis except for S.A. group. Model built on SIMCA analysis of PLS2-DA results was good only with one principal component. Whatever the model, the number of variables (PCs or LVs) allowing to obtain good results is smaller than 5 and almost the same for each.

Fig. 3a shows the percentage of correct classification (%CC) as a function of the number of PCs used in the SIMCA model for each origin. When one principal component is used for all the models, 100% of good classifications are obtained for two origins (EQU and S.A.), 92% of good classification is obtained for ALG and 67% for VEN. The increase of PCs number conduced to 100% of good classification for all the origins. The VEN model obtained with 2 PCs provided to 92% of good classified samples, that represents only 4 misclassified spectra of the 65 analyzed. One hypothesis is that to get good results, it is necessary to have more PCs for models for near groups (ALG and VEN, according to the projection on PC1, Fig. 2) than for other groups that are more distant. Fig. 3b shows the %CC as a function of the number of latent variables (LVs) used in the PLS2-DA model. The best model contained 5 LVs even if only 1 LV was necessary for S.A. and EQU origins to well predict all samples, because in PLS2 the same number of LVs must be chosen for all the origins. Fig. 3c shows the %CC as a function of the number of LVs used in the PLS1-DA model. The number of LVs necessary to obtain the best model for PLS1-DA method was lower than the number of LVs used in PLS2-DA method, and comparable to the number of PCs used in SIMCA analysis. When the number of LVs for the PLS1-DA models increases the results of prediction are altered because of over fitting.

In this case, the variable selection was not necessary because all the petroleum crude oils are correctly classified.

Classification of virgin olive oils according to their geographical origins

NIR spectra obtained for all virgin olive oil samples seem to be similar (Fig. 4). Band assessments were realized according to the literature [START_REF] Hourant | [END_REF][START_REF] Riaublanc | La spectroscopie infrarouge et ses applications analytiques[END_REF]. Bands A (4500-4800 cm -1 ) are attributed to combination of CH stretching vibrations with other vibrational modes, bands B (5300-6100 cm -1 ) are attributed to first overtone of CH 2 stretching vibrations (methyl and methylene groups), bands C (6700-7450 cm -1 ) are attributed to combination of CH stretching vibrations, and bands D (7900-9000 cm -1 ) are attributed to second overtone of CH stretching vibrations (D: methyl and methylene groups).

Fig. 5 shows the PCA performed on the full NIR spectra (2853 variables) of the virgin olive oils, which constitute five groups with high overlapping because of the very close RDO origins of the samples. The score plot which allow obtaining the best RDO group separation, are projected in the plane PC1, PC3 and explain 48% and 16% of the spectral variance. Even though the other PCs explain 35% of the spectral variance, plots of PC1 vs. PC2 (19%), PC2 vs. PC3, etc., do not allow a better group separation. The difficulty of obtaining five perfectly distinct groups comes from some similarities of the compositions of these virgin olive oil RDOs. For instance, Aglandau is one of principal cultivars in AP, HP and VB RDOs and Salonenque is the second principal cultivar in AP and VB RDOs with, however, different ratios. NI and NY RDOs are mono-varietal oils constituted respectively by Cailletier and Tanche cultivars. Table 2 shows the best classification results obtained by each method to predict virgin olive oil groups according to the RDO origin (AP, HP, VB, NI and NY). Best result was obtained with PLS1-DA method, significantly better than SIMCA classification and PLS2-DA method. The results obtained using SIMCA classifications were poor for two origins (AP and VB); the percentage of correct classifications (%CC) was never more than 58% for VB and 68% for AP. The results obtained with PLS2-SIMCA are in the same order than the ones obtained with PLS1-DA, for VB and HP only one spectrum lead to bad classification. Fig. 6a shows the %CC as a function of the number of PCs used in SIMCA model for each origin. Four components were enough to obtain the best %CC for VB prediction, but 7 PCs were used for the prediction of AP, NY and HP and 8 PCs were used for the prediction of NI. The high numbers of PCs necessary to predict origin can be explained by the overlapping showed in the PCA score plots.

Fig. 6b shows the %CC as a function of the number of LVs used in the PLS2-DA model. The best model contained at least 8 LVs. More LVs did not significantly increase the %CC.

Fig. 6c shows the %CC as a function of the number of LVs used in the PLS1-DA model. The number of LVs necessary to obtain the best model for PLS1-DA method was between 5 and 9 according to the RDO.

For all the models, prediction of the AP and VB origins were the most difficult, because these oils came from the same cultivars with different rates. All the methods based on PLS-DA analysis give good results and the use of SIMCA classification performed on the scores obtained with PLS2-DA method do not improve the classification results significantly.

Classification optimization by spectral selection

According to literature [START_REF] Pierna | [END_REF], the variable selection conduces to improved results for quantitative analysis. The variable selection could be done on the basis of the study of spectral variance, which may directly influence classification. Two points are important, the first one is spectral variance in one group (IntraSP), the second one is spectral variance for all data (InterSP). The Fisher-Snedecor test is used to compare the two variances. Fig. 7 shows ratios of InterSP/IntraSP as function of the wavenumbers calculated for each class of crude petroleum oils, in this case according to the different degrees of freedom F c = 1.8. Ratios were higher to the F c threshold for three origins ALG, S.A., VEN (Fig. 7a, b andd), which explain good results obtained for the classification of crude petroleum oils. The spectral ranges where interSP/intraSP ratios are higher than F c correspond to the regions with significant bands, except for the water absorption bands at the spectral ranges (3300-3100 cm -1 , 1700-1600 cm -1 , 750-700 cm -1 ). Some crude petroleum oil samples contained a small amount of water which explained the high variance in these spectral ranges. The zones where the F c is close to noise do not present absorption bands. For EQU (Fig. 7c), ratios were higher than F c only in the 900-1500 cm -1 and 2700-3000 cm -1 spectral ranges. This observation is in good agreement with Fig. 2 where PCA shows high dispersion. The spectra used in this study are recorded in a spectrometer without any purge system, so it is not surprising to see some bands due to water vapor and carbon dioxide in the ratio. Fig. 8 shows ratios of InterSP/IntraSP as a function of wavenumbers, calculated for each class of virgin olive oils in this case according to the different degrees of freedom F c = 1.5. The ratios were not always higher than the F c threshold, which is why the SIMCA classification gives poor results. In order to confirm these results, the virgin olive oils have been classified in the 6500-5500 cm -1 spectral range, where InterSP versus IntraSP is higher than F c for all virgin olive oil classes. The selected region must be the same for all the origins in order to perform PLS2 analysis. So the 6500-5500 cm -1 (518 points) spectral range is used for all the analyses.

Table 3 shows the best classification results obtained by each method except for PLS1-DA (100% of good predictions in the previous results) to predict virgin olive oil groups according to the RDO origin (AP, HP, VB, NI and NY). Hence, the percentage of correct classified samples increased. In the 6500-5500 cm -1 spectral range, SIMCA analysis performance was increased. The %CC varied between 68% and 99%. There is a clear improvement with regards to the number of PCs used in each model. The decrease of PCs used proves that models are now more robust. Both PLS2-DA and PLS2-DA-SIMCA analysis performed a little better even if the results in Table 2 are good enough.

For AP models, Fig. 9 shows the first regression coefficient obtained by PLS1-DA (Fig. 9a), PLS2-DA (Fig. 9b) and the first principal component obtained by SIMCA analysis (Fig. 9c). It shows some variations on the spectral information used in each case. As a matter of fact, the first regression coefficient obtained by PLS1-DA (Fig. 9a) is similar to the one obtained by PLS2-DA (Fig. 9b) in the 6500-5500 cm -1 spectral range but it is very different in the 5500-4500 cm -1 spectral range. The principal component (Fig. 9c) presents high intensity in the 10,000-7000 cm -1 spectral range, which is correlated to instrumental deviation (baseline) and not really to chemical information. After the variable selection, the first regression coefficient obtained in PLS2-DA model (Fig. 9d) and the principal component obtained in the AP group for SIMCA analysis (Fig. 9e) are very close to the first regression coefficient obtained in PLS1-DA model (Fig. 9a).

The main difference between SIMCA and PLS-DA is the criterion used to build models. While PCA submodels are computed in SIMCA with the goal of capturing variations within each class, PLS-DA identifies directions in the data space that discriminate classes directly. Therefore, for these applications, SIMCA classification always provides worse results than methods based on PLS analysis. The difference between PLS1-DA and PLS2-DA results is significant. PLS2-DA is a version of the PLS-DA method in which several Y-variables are modeled simultaneously. Thus, PLS2-DA takes advantage of possible correlations or collinearties between Y-variables. In both cases, virgin olive oils and crude petroleum oils, the classes are independent, so the Y variables are less pertinent than in a PLS1-DA analysis where one model is calculated independently from the others.

Conclusion

In these two different applications -classification of crude petroleum oils and virgin olive oils according to their origin -PLS-DA analysis provides better results than the SIMCA method. SIMCA performance may be improved when the spectral region is reduced to the spectral range where the ratio of InterSP/IntraSP at each wavenumber is higher to the critical value of the Fisher-Snedecor test (F c ). In this case SIMCA is performed with a reduced number of principal components. PLS-DA methods are more efficient and PLS1-DA always gives better results than PLS2-DA. Therefore, the ratio InterSP/IntraSP compared to F c is a good criterion for choosing the best infrared spectral range and the most suitable classification method for spectral analysis.

Fig. 1 .

 1 Fig. 1. MIR normalized spectra of crude petroleum oils. Absorption bands: C-H : 3052 cm -1 ; as CH3: 2952 cm -1 ; as CH2: 2922 cm -1 ; s CH2: 2853 cm -1 ; C C : 1602 cm -1 ; ıas C-H in CH3 and CH2 groups:1456 cm -1 ; ıs C-H in CH3 group: 1376 cm -1 ; ıCH2 in -(CH2)n, n > 3 : 721 cm -1 .

Fig. 2 .

 2 Fig.2. PCA of the crude petroleum oil spectra on PC1 (53%) and PC2 (32%). ALG: Algeria; S.A.: South America; EQU: Equator; VEN: Venezuela.

Fig. 3 .

 3 Fig. 3. Percentage of correct classification (%CC) for each petroleum oil origin as function of: (a) the number of principal component used in the SIMCA model; (b) the number of latent variable used in the PLS2-DA model; (c) the number of latent variable used in the PLS1-DA model.

Fig. 4 .

 4 Fig. 4. NIR normalized spectra of virgin olive oil samples. (A) (4500-4800 cm -1 ) combination of CH stretching vibrations with other vibrational modes; (B) (5300-6100 cm -1 ) first overtone of CH2 stretching vibrations (methyl and methylene groups groups); (C) (6700-7450 and 7900-9000 cm -1 ) combination of CH stretching vibrations, and (D) (cm -1 ) second overtone of CH stretching vibrations (D: methyl and methylene groups groups).

Fig. 5 .

 5 Fig. 5. PCA of the virgin olive oil spectra on PC1 (48%) and PC3 (17%). AP: Aix-en-Provence; HP: Haute-Provence; NI: Nice; NY: Nyons; VB: Vallée des Baux.

  classification percentage; PC: principal component; LV: latent variable.

Fig. 6 .

 6 Fig. 6. Percentage of correct classification (%CC) for each virgin olive oil origin as function of: (a) the number of principal component used in the SIMCA model; (b) the number of latent variable used in the PLS2-DA model; (c) the number of latent variable used in the PLS1-DA model.

Fig. 7 .

 7 Fig. 7. Ratio of InterSP/IntraSP of each class of petroleum oils. (a) Algerian; (b) South American; (c) Equator; (d) Venezuelan and (e) superposition with the mean spectra of each class and Fc.

Fig. 8 .

 8 Fig. 8. Ratio of InterSP/IntraSP of each class of virgin olive oil. (a) Aix-en-Provence; (b) Haute-Provence; (c) Nice; (d) Nyons; (e) Vallée des Baux, and superposition with the mean spectra of each class and Fc.

Fig. 9 .

 9 Fig. 9. First regression coefficients obtained in the 10,000-4500 cm -1 spectral range for AP models by (a) PLS1-DA and (b) PLS2-DA. (c) First principal component obtained for the AP group by SIMCA analysis in the 10,000-4500 cm -1 spectral range, (d) first regression coefficient obtained by PLS2-DA in the 6500-5500 cm -1 spectral range and (e) first principal component obtained for AP model by SIMCA analysis in the 6500-5500 cm -1 spectral range.

Table 2

 2 Percentage of well classified virgin olive oils in the prediction set for each statistical treatment in the 4500-10,000 cm -1 spectral range.

	Origins	SIMCA		PLS2-DA		PLS2-DA-SIMCA		PLS1-DA
		%CC	PC	%CC	LV	%CC	LV-PC	%CC	LV
	Aix-en-Provence (AP)	68	7	93	8	97	8-1	100	9
	Haute-Provence (HP)	91	7	100	8	99	8-2	100	6
	Nice (NI)	89	8	96	8	100	8-1	100	6
	Nyons (NY)	89	7	100	8	100	8-1	100	2
	Vallée des Baux (VB)	58	4	96	8	99	8-3	100	8

%CC: correct classification percentage; PC: principal component; LV: latent variable.

Table 3

 3 Percentage of well classified virgin olive oils in the prediction set for each statistical treatment in the 5500-6500 cm -1 spectral range.

	Origins	SIMCA		PLS2-DA		PLS2-DA-SIMCA
		%CC	PC	%CC	LV	%CC	LV-PC
	Aix-en-Provence (AP)	70	5	100	8	96
	Haute-Provence (HP)	99	5	100	8	100	8-1
	Nice (NI)	92	2	100	8	100	8-1
	Nyons (NY)	95	5	100	8	100	8-1
	Vallée des Baux (VB)	68	2	96	8	96	8-1

%CC: correct classification percentage; PC: principal component; LV: latent variable.

Acknowledgements

The authors are grateful to Dr Denis Ollivier (SCL) and Christian Pinatel (AFIDOL) for supplying the samples of olive oils and to Pr Albert Permanyer (University of Barcelona) for supplying the samples of crude petroleum oils.


M Abdou

M.-L Tercier-Waeber

L Dutruch

C Bossy

F Pougnet

A Coynel

E Bakker

G Blanc

J Schäfer

Estuarine dissolved speciation and partitioning of trace metals: a

Keywords: Lead, Cadmium, Copper, Priority Substances, Potentially Bioavailable Dissolved Fraction, Suspended Particles

HAL is

Introduction

Trace metals in aquatic systems are distributed as numerous chemical species, comprising free hydrated ions, their complexes with various inorganic and organic ligands and colloids (of 1 nm to 0.2 µm size) and other suspended particles of size > 0.2 µm [START_REF] Tercier Waeber | A Novel Voltammetric In-Situ Profiling System for ContinuousReal-Time Monitoring of Trace Elements in Natural Waters[END_REF].

Transformations among the various metal species are usually reversible, with the important consequence that the speciation of a metal is a function of the bio-physicochemical conditions of the medium. Any variation of the speciation of a metal will influence its transport, reactivity and potential ecotoxicological impact. A comprehensive study of trace metal biogeochemical cycles must therefore include the determination of specific metal species or the group of homologous metal species (i.e. metal speciation). In addition, the partitioning between dissolved and particulate phases changes in response to the surrounding environment, particularly along estuarine gradients.

The degree of metal sorption and desorption depends on the type of particulate matter present in the water column [START_REF] Sanders | The role of suspended sediments and phytoplankton in the partitioning and transport of silver in estuaries[END_REF].

Most trace metals released into aquatic systems originate from land-based human activities and estuaries often represent recipient waters of riverine pollution at the whole watershed scale [START_REF] Tercier Waeber | FP7-OCEAN-2013 -SCHeMA: Integrated in situ chemical mapping probes[END_REF]. Estuaries are complex systems where the mixing of fresh-and seawaters from the upper tidal zone to the outer estuary generate gradients of salinity and turbidity. These systems undergo major biogeochemical transformations that influence the export of contaminants to the ocean (e.g. [START_REF] Larrose | Assessing the current state of the Gironde Estuary by mapping priority contaminant distribution and risk potential in surface sediment[END_REF]. The Gironde Estuary is a major European estuary considered as a model for physical, hydrological, and geochemical studies, and especially for estuarine trace element transport and reactivity. Intense past and present agricultural activity, wastewater discharge and historical metal pollution originating from former industrial/metallurgic activities in its watershed have strongly affected the environmental quality of the estuary and the adjacent coast [START_REF] Coynel | Assessment of metal contamination in a small mining-and smeltingaffected watershed: high resolution monitoring coupled with spatial analysis by GIS[END_REF][START_REF] Deycard | Inputs, dynamics and potential impacts of silver (Ag) from urban wastewater to a highly turbid estuary (SW France)[END_REF][START_REF] Larrose | Assessing the current state of the Gironde Estuary by mapping priority contaminant distribution and risk potential in surface sediment[END_REF][START_REF] Masson | Geochemical signals and source contributions to heavy metal (Cd, Zn, Pb, Cu) fluxes into the Gironde Estuary via its major tributaries[END_REF][START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF]Schäfer et al., 2002;[START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF]. Trace metal contamination and biogeochemical behavior along this fluvial-estuarine continuum have been thoroughly studied since decades [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF][START_REF] Michel | Net Fluxes of Dissolved Arsenic, Cadmium, Copper, Zinc, Nitrogen and Phosphorus from the Gironde Estuary (France): Seasonal Variations and Trends[END_REF][START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF]. However, a lack of knowledge still exists for various biogeochemical processes. This includes the importance of small colloids (< 0.02 µm) for trace metal distribution and transport, the bioavailability of trace metals in such complex systems, and the influence of primary producers on trace metal speciation, especially in the coastal productive zone. Indeed, the literature reports that effects of metals on phytoplankton are reciprocal and that biota and particularly phytoplankton may have profound effects on trace metal chemistry through sorption processes [START_REF] Sunda | Trace Metal Interactions with Marine Phytoplankton[END_REF]. In particular, lead (Pb) and cadmium (Cd), identified as priority substances by European regulations, as well as copper (Cu) have, over the past decades, presented anomalously high concentrations in various tributaries within the Gironde Estuary watershed [START_REF] Masson | Geochemical signals and source contributions to heavy metal (Cd, Zn, Pb, Cu) fluxes into the Gironde Estuary via its major tributaries[END_REF]. These trace metals are considered essential and/or toxic for aquatic organisms that can bioaccumulate them and in turn alter their chemical forms (e.g. [START_REF] Sunda | Trace Metal Interactions with Marine Phytoplankton[END_REF]. We therefore require precise monitoring of these trace elements at high spatial resolution that allows for the accurate quantification of specific metal fractions, especially the potentially bioavailable dissolved forms. Voltammetric sensors represent promising tools for direct determination of metallic pollutants in the aqueous phase (e.g. [START_REF] Nezamzadeh-Ejhieh | Modification of clinoptilolite nano-particles with hexadecylpyridynium bromide surfactant as an active component of Cr(VI) selective electrode[END_REF]. In particular, recent analytical developments based on in situ or on-board electrochemical measurements of the so-called dynamic dissolved metal fractions using antifouling gel-integrated sensors allow for highly sensitive, reliable, and frequent data acquisition bringing new information for the monitoring and understanding of trace metal biogeochemical processes in marine systems [START_REF] Penezić | Spatial variability of arsenic speciation in the Gironde Estuary: Emphasis on dynamic (potentially bioavailable) inorganic arsenite and arsenate fractions[END_REF]Tercier-Waeber et al., 2021b, 2021a).

The objectives of the present work are therefore to perform the first assessment of the dynamic fraction of Pb, Cd, and Cu along the salinity gradient of the Gironde Estuary at high spatial resolution, and to use the data from the innovative sensing approach together with classical methodologies to better understand the biogeochemical behavior of trace metal species in terms of dissolved speciation and partitioning in this representative system.

Material and methods

Sampling area and strategy

The Gironde Estuary (~ 170 km length) is impacted by multi-metal pollution (including Cd, Cu, Zn, and Pb) which originates from a common point source that is a former mining and smelting industry and affects the fluvial-estuarine continuum since decades [START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF]. This industry, located in Decazeville and active from 1842 to 1987, has led to metal concentration enrichment of the riverine system [START_REF] Audry | Fifty-year sedimentary record of heavy metal pollution (Cd, Zn, Cu, Pb) in the Lot River reservoirs (France)[END_REF]. The Gironde Estuary is formed from the meeting of the Garonne (its major tributary) and the Dordogne River (total watershed ~ 80,000 km 2 ; Figure 1). It hosts important urban sectors along its riverine areas, mainly the Garonne River with the Bordeaux conurbation (~ 1 million inhabitant equivalents; [START_REF] Deycard | Contributions and potential impacts of seven priority substances (As, Cd, Cu, Cr, Ni, Pb, and Zn) to a major European Estuary (Gironde Estuary, France) from urban wastewater[END_REF]"Insee.fr"). Hydrodynamic characteristics of the Gironde Estuary have been thoroughly described in the literature (e.g. [START_REF] Jalón-Rojas | Turbidity in the fluvial Gironde Estuary (southwest France) based on 10-year continuous monitoring: sensitivity to hydrological conditions[END_REF]. This is a partially mixed to well-mixed estuary, characterized by a semi-diurnal meso-to macro-tidal regime and a maximum turbidity zone (MTZ) where concentrations of Suspended Particulate Matter (SPM) exceed 1 g.L -1 in surface water. Water residence time was estimated to ~ 20 -90 days for high and low water discharge periods, respectively, while estimated particle residence time is 1 -2 years [START_REF] Jouanneau | Origin and pathways of Cadmium Contamination in the Gironde estuary, Garonne river and tributaries[END_REF]. The field campaign was carried out in June 2017 (16 th -21 st ) on board the R/V Thalia and consisted in a longitudinal profile along the estuarine salinity gradient (practical salinity "S"; S=0.10 to S=34.00; Figure 1) with four sampling sites in the Garonne River Branch (1-4), the following twelve down the Gironde Estuary (5-16), and four samples in the north channel of the outer estuary (17)(18)(19)(20). Four samples were collected along the south channel of the outer estuary (21)(22)(23)(24). Sampling was carried out during low river discharge conditions (Q ~ 235 m 3 .s -1 ; Garonne and Dordogne) and spring tide regime (tidal coefficient and range of 50 -75 and 2.5 -3.6 m, respectively).

Sample collection and analytical methods

Sampling. Subsurface water samples were collected with a pre-cleaned Niskin bottle at ~1m depth and transferred into acid-cleaned polypropylene (PP) bottles that were previously rinsed three times with subsurface water from the site. Suspended particles were retrieved using a peristaltic pump with PP tubing and collected by centrifugation (12,000 g, Westfalia®; Schäfer and Blanc, 2002).

Samples were collected during daytime, except for one sample at site 16, S=28 (Figures 1 and2c).

Dynamic metal fraction (Medyn).

Quantification of Medyn was performed by subtractive square wave stripping voltammetry (SWASV) on two gel-integrated microelectrode arrays (GIME) sensors implemented in the TracMetal sensing probe (channels 1 and 2). The GIME used in this application consisted of on-chip 190 interconnected iridium-(Ir)-based microdisk arrays of 5 µm in radius [START_REF] Fighera | Three-Electrode on-Chip Sensors for Voltammetric Detection of Trace Metals in Natural Waters[END_REF], electroplated with mercury (Hg) micro-hemispheres and covered with an antifouling agarose gel [START_REF] Buffle | Voltammetric environmental trace-metal analysis and speciation: from laboratory to in situ measurements[END_REF]. A detailed description of the TracMetal, a three-channel submersible probe designed for simultaneous in situ measurements of a range of (priority) hazardous trace metals using various GIME sensors, can be found elsewhere (Tercier-Waeber et al., 2021b). Because of time constraints related to the cruise trajectory, the TracMetal was not deployed in situ. The Medyn quantification was performed on-board applying the TracMetal after placing 1 L of sub-surface water sample freshly collected in an acid-cleaned PP bottle.

Sensors were calibrated before and after field monitoring in the on-board laboratory. Calibrations were performed in dinitrogen-(N2)-purged sodium nitrate (0.1 M NaNO3; Suprapur Merk®) solutions spiked with Cd(II), Pb(II), and Cu(II) in ranges of 0.1 to 0.6 μg.L -1 , 0.2 to 1.2 μg.L -1 and 0.2 to 1 μg.L -1 , respectively. Individual stock solutions of Cu, Pb, and Cd used for the calibration were prepared by appropriate dilution of a 1 g.L -1 atomic absorption standard solution (Aldrich®) in HNO3 and acidified to pH 2 with HNO3 (14 M Suprapur, Merck®). The subtractive SWASV protocol and conditions used were as follows: after a cleaning phase at 70 (S<10) or -50 mV (S>10)

for 1 min, metals were pre-concentrated at -1200 mV for a given time (typically 10 min for calibration; 20 min for on-board measurements). The potential was then scanned from -1200 to -50 or 70 mV (stripping step) with a pulse amplitude of 25 mV, a step amplitude of 8 mV, and a frequency of 200 Hz. After each measurement, a background scan was recorded, using the same parameters, but with shortened pre-concentration time (5 s). The background scan was subtracted from the analytical scan to obtain a corrected final scan [START_REF] Tercier Waeber | A Novel Voltammetric In-Situ Profiling System for ContinuousReal-Time Monitoring of Trace Elements in Natural Waters[END_REF]. Standard deviations of the calibration slopes obtained before and after field measurements were < 10 % for the three metals, demonstrating the excellent stability of the Hg hemispheres and the absence of fouling during the 6 days of on-board monitoring. The Medyn concentrations were determined from the temperature effect corrected subtractive SWASV signals [START_REF] Tercier-Waeber | Submersible voltammetric probes for in situ real-time trace element measurements in surface water, groundwater and sediment-water interface[END_REF] bottles and acidified with HNO3 (1/1000 v/v; Ultrapure J.T. Baker ®, 14 M). Samples were then stored at 4 °C in the dark pending analysis. Field blanks using Milli-Q water were performed for all sampled parameters and processed in the same way as natural water samples. The Me0.2 and Me0.02 were quantified through Inductively Coupled Plasma-Tandem Mass Spectrometry (ICP-MS/MS; Thermo Scientific®; iCAP TQ) using Argon Gas Dilution (AGD) allowing direct analysis of estuarine waters without any prior dilution [START_REF] Dutruch | Direct analysis of trace elements in estuarine waters using triple-quadrupole inductively coupled plasma-mass spectrometry[END_REF]. Considering relatively important changes in the signal response according to sample salinity, dissolved concentrations were quantified using standard addition method adapted to three different salinity ranges (0.1 to 10, 10 to 30, > 30).

Analyses of Certified

Particulate metal fraction (Mepart).

The particulate samples obtained after centrifugation of suspended particles were dried (50 °C, 48 h), powdered and homogenized with an agate mortar.

Samples were stored in sealed PP containers and kept in the dark at room temperature pending digestion and analysis. Representative subsamples (~ 30 mg) of particulate material, CRM and method blanks were acid digested. This step was performed in closed PP tubes (DigiTUBEs®, SCP SCIENCE) in a heating block (2 h at 110 ºC) using 1.5 mL hydrochloric acid (HCl; 10 M Suprapur, Merck®), 750 μL HNO3 (14 M Suprapur, Merck®) and 2.5 mL hydrofluoric acid (HF; 29 M Suprapur, Fisher®), as described elsewhere [START_REF] Gil-Díaz | Antimony in the Lot-Garonne river system: a 14-year record of solid-liquid partitioning and fluxes[END_REF][START_REF] Lerat-Hardy | Impacts of Highway Runoff on Metal Contamination Including Rare Earth Elements in a Small Urban Watershed: Case Study of Bordeaux Metropole (SW France)[END_REF]. After evaporation to dryness, we performed re-dissolution with 250 μL HNO3 (14 M) at 110 °C and, after cooling, the samples were brought to 10 mL using Milli-Q water. The Mepart were quantified by ICP-MS (XSeries 2, Thermo Scientific®) applying external calibration. Stream and estuarine sediments CRM (NCS DC 73307 and BCR®-277R, respectively) were analyzed for quality control. They provided recoveries > 90% and precision of ~ 5 % (n = 3; vertical error bars in the graphics).

Ancillary measurements. Particulate Organic Carbon (POC) concentrations in surface water were determined by filtration of precise water volumes through pre-combusted and pre-weighed filters (Whatman® GF/F, 0.7 µm). The filters were rinsed with MilliQ water to remove salts and were then oven dried to constant weight at 50 °C and weighed again. The weight difference provided the SPM concentrations. Filters were then kept in the dark at room temperature pending analysis. They

were analyzed with a LECO© CS-125 after carbonate elimination with 2M HCl as described elsewhere [START_REF] Etcheber | Particulate organic carbon in the estuarine turbidity maxima of the Gironde, Loire and Seine estuaries: origin and lability[END_REF]. Analytical quality was checked by measuring CRMs (e.g. LECO 501-503) providing recovery of ~ 95 %. Physical-chemical parameters including practical salinity (S), temperature, oxygen saturation level (O2 sat), and pH were measured in collected samples with portable TetraCon 325 and Sentix 41 probes (PROFILINE®, WTW). Finally, we retrieved information on solar radiation from "Meteo France", tidal information from the website "maree.info", and river discharges from the National Hydrographic Agencies (Banque Hydro database).

Data treatment

Normalization of particulate metal concentration.

It is essential to consider lithogenic trace metal variability before evaluating the influence of anthropogenic inputs [START_REF] Loring | Manual for the geochemical analyses of marine sediments and suspended particulate matter[END_REF].

Also, in most aquatic systems, hydro-sedimentary processes fractionate particles according to parameters such a grain size, density and shape inducing mineralogical (chemical) bias due to the common observation that trace metal concentrations tend to increase with decreasing sediment grain sizes. Thorium (Th), which must be lithogenically controlled [START_REF] Marmolejo-Rodríguez | Thorium accumulation in the sedimentary environment of the Vigo Ria (NW Iberian Peninsula)[END_REF] and which does not originate from any anthropogenic inputs in our study area, served as a proxy to normalize variations from lithogenic sources. It is also useful to correct potential variations due to grain size effects as already applied in previous studies in the Gironde fluvialestuarine system (e.g. Coynel et al., 2007;[START_REF] Larrose | Assessing the current state of the Gironde Estuary by mapping priority contaminant distribution and risk potential in surface sediment[END_REF].

Partitioning. Trace metal particle/water distribution coefficients i.e. partition ratio, P, were calculated following the Eq 1 (McKinley and Alexander, 1992):

P = Mepart / Me0.2 (Eq1)
Values are shown in Supporting Information (Figure S1).

Proportion of the dissolved metal fractions. Three specific homologous metal fractions were defined, based on their size and complex lability, as follows: (1) The Medyn obtained by direct GIME-SWASV measurements. This fraction includes the free metal ions and the small sufficiently mobile (size ≤ 4 nm) and labile (fast dissociation rates) metal complexes that are potentially bioavailable [START_REF] Buffle | Voltammetric environmental trace-metal analysis and speciation: from laboratory to in situ measurements[END_REF]. ( 2) The small colloidal metal species (Mecol<0.02) obtained by subtracting Medyn from Me0.02 and including the metals adsorbed on small inorganic colloids or forming inert complexes with organic ligands.

(3) The inorganic and organic colloidal metal species (Me0.02<col<0.2) obtained by subtracting Me0.02 from Me0.2. Graphical representation of the different fractions using interpolations were performed with IGOR PRO (WaveMetrics®;

Figure S2).

Effective solar radiation.

Variations of solar radiation intensity on the water column may induce various changes of trace metal speciation through photochemical reactions (e.g. photolysis of dissolved organic matter, photo-induced reduction/oxidation of inorganic colloids) and/or biotic sorption (e.g. following phytoplankton development). Yet, ambient SPM concentrations found in the estuarine gradient may limit both processes. Normalization of instantaneous solar radiation by SPM concentration (rad/SPM) should account for light attenuation by SPM and thus be related with the effective light abundance available for photo-redox mechanisms [START_REF] Castelle | Gaseous mercury at the air-water interface of a highly turbid estuary (Gironde Estuary, France)[END_REF].

Correlations of the different trace metal fractions with rad/SPM (except for sites 21-24 with relatively very low SPM < 15 mg.L -1 ) are given in Table S1.

Results

Hydrological conditions and ancillary parameters

Salinity ranged from 0.10 to 34.0 (Figure 1). Temperature as well as pH values followed the salinity gradient with increasing pH and decreasing temperatures from the freshwater endmember towards the coastal Atlantic Ocean (7.62 to 8.19, 25.1°C to 19.0°C, respectively; Figure 2a). After maximum SPM values of 1,600 mg.L -1 (Figure 2b), decreasing SPM trend was observed for low salinities with values reaching 91.35 mg.L -1 at S=4.3. Thereafter, SPM levels showed a slight increase between S=5 and S=15 followed by rather constant values in the mid-estuary (123 ± 6 mg.L -1 ). Estuary mouth concentrations were slightly lower (63 ± 7 mg.L -1 ) and a peak value (~ 1000 mg.L -1 ) was observed at S=28.1. Coastal SPM concentrations were drastically lower at 8 ± 1 mg.L -1 in the south channel. The oxygen saturation levels (O2 sat) displayed first a decrease from 96.1 % at S=0.1 to ~ 80 % at S=0.2 and S=0.6 (Figure 2c). An increasing trend followed this pattern, with values over 100 % in the estuary mouth. Particulate Organic Carbon/SPM ratio (POC%) showed a decreasing trend in the riverine branch going from ~ 2 % to 1 % (Figure 2d)

and an increasing pattern along the salinity gradient with relatively high values of 15 % reached at external sites. After a decreasing trend in the riverine branch (Fe0.2 from 0.86 to 0.61 µg.L -1 ; Fe0.02 from 0.82 to 0.51 µg.L -1 ), Fe0.2 varied from 0.75 to 1.3 µg.L -1 , and from 0.49 to 0.69 µg.L -1 for Fe0.02, showing an increasing trend seaward (Figure 2e). The Mn0.2 and Mn0.02 were very similar and ranged between ~ 0.07 and 7 µg.L -1 with a clear increasing trend along the salinity gradient (Figure 2f).

Trace metal longitudinal profiles

Lead

In the fluvial estuary (S=0.1 to S=1.1), a decreasing trend was observed for Pb0.2 and Pb0.02 (0.04-0.03 µg.L -1 respectively, to 0.02 µg.L -1 ; Figure 3a). Along the estuary, the slightly decreasing trend in Pb0.02 continued, reaching a minimum of 0.006 µg.L -1 at S=32.7. In contrast, Pb0.2 did not show a well-defined spatial trend, with highest values for salinities ranging from S=22.3 to S=28.1 and slightly lower concentrations in the lower estuary compared to the upper estuary. In the coastal waters, the concentrations were similar to those in the lower estuary for Pb0.2 (0.016 to 0.022 µg.L -1 ) and slightly higher for Pb0.02 (0.011 to 0.017 µg.L -1 ). The Pbdyn showed a slightly decreasing trend from 0.002 to 0.001 µg.L -1 in the riverine part of the estuary and then a linear increase (0.002 to 0.005 µg.L -1 ; Pearson r = 0.83) along the salinity gradient (Figure 3a). Two small Pbdyn peaks were observed first from S=9 to S=15, then from S=22 to S=28. The four southern coastal sites displayed higher Pbdyn (0.012 to 0.014 µg.L -1 ) reaching concentration levels similar to Pb0.02.

The Pbpart suggested a decreasing trend in the Garonne Branch (S≤1.1; from 64 to 41 mg.kg -1 ) as well as in the lower estuary (S≥27; from 83 to 56 mg.kg -1 ) and southern coastal sites (from 71 to 44 mg.kg -1 ), while a rather constant value close to 60 mg.kg -1 was observed for 3 <S<27 (Figure 3b).

Cadmium

The concentrations of the different Cd fractions measured in the water column are reported in Figure 3c showing similar Cd0.2 and Cd0.02 concentrations. From S=0.10 to S=17.95 i.e. to the midestuary (site 11, Figure 1) concentrations increased from 0.01 to 0.07 µg.L -1 then decreased towards coastal waters, reaching again concentrations of ~ 0.01 µg.L -1 . The Cddyn ranged from 0.003 to 0.007 µg.L -1 in the fluvial estuary (S≤1.1), increased up to 0.07 µg.L -1 at S≤22.3, then decreased to 0.009 µg.L -1 near the estuary mouth.

The Cdpart showed two similar decreasing trends from ~ 0.50 to 0.35 mg.kg -1 both in the riverine branch (S=0.1 to S=0.6) and along the estuarine gradient (S=3 to S=32.7). The four southern coastal sites displayed an increasing trend with Pbpart varying from 0.42 to 0.94 (S=29.9 to S=32.75).

Copper

Both Cu0.2 and Cu0.02 showed similar concentrations and spatial trends (Figure 3e). Maxima were recorded at S=0.1 with Cu0.2 and Cu0.02 of 2.0 and 1.3 µg.L -1 , respectively. Then in the upper estuary, a slight increase in both, Cu0.2 and Cu0.02 occurred from 1.2 to 1.5 µg.L -1 and 1.1 to 1.4 µg.L -1 , respectively (S=0.2 to S=4.3). This is followed by a subsequent seaward decrease down to ~ 0.3 µg.L -1 for both fractions. The Cudyn, represented a minor proportion and showed a different profile with highest values in the riverine part, reaching 0.27 µg.L -1 and thereafter decreasing to a minimal, constant value of ~ 0.01 µg.L -1 from S=15 to the outer estuary (Figure 3e).

The Cupart showed a decreasing trend for S=0.1 to S=1.1 with values varying from 40 to 30 mg.kg -1 (Figure 3f). This is followed by an increasing trend with southern coastal values of up to 315 mg.kg -1 .

Discussion

Behavior of trace metal species

Lead

The similar Pb0.2 and Pb0.02 coupled to the lower Pbdyn along the longitudinal profile suggests that colloids in the size range 0.2-0.02 µm are negligible or have only a minor influence on Pbdiss variations. On the other hand, small colloids (< 0.02 µm) may play an important role in the transport and fate of Pb and especially on the proportion of its potentially bioavailable fraction.

Estuarine Pb reactivity includes rapid adsorption onto suspended particles, influencing Pbdiss removal [START_REF] Chiffoleau | Trace metal distribution, partition and fluxes in the Seine estuary (France) in low discharge regime[END_REF]. The interaction energy between colloids and associated trace metals results essentially from long range electrostatic interactions and short range attractive van der Waals forces that are predominant in marine and estuarine waters owing to increasing ionic strength [START_REF] Tercier Waeber | A Novel Voltammetric In-Situ Profiling System for ContinuousReal-Time Monitoring of Trace Elements in Natural Waters[END_REF]). An increasing fraction of colloidal trace metals, organic matter (OM), and dissolved organic metal complexes is therefore expected throughout the salinity gradient [START_REF] Seijo | Modeling the Adsorption and Coagulation of Fulvic Acids on Colloids by Brownian Dynamics Simulations[END_REF], while colloid aggregation is also promoted [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF][START_REF] Tercier Waeber | A Novel Voltammetric In-Situ Profiling System for ContinuousReal-Time Monitoring of Trace Elements in Natural Waters[END_REF]. Colloid coagulation may therefore partly explain Pbdiss removal with increasing salinity along the riverine branch. In addition, increase of Pbdyn along the salinity gradient may reflect a Pb mobilization towards its potentially bioavailable form. The opposite trend observed between Pbdyn and Pb0.02 suggests Pb desorption from small colloids throughout the salinity gradient and subsequent gradual increase of its dynamic forms (Figure S2a).

In the southern coastal sites, the observed Pb0.02 displayed some values lower than Pbdyn, suggesting a potential analytical bias and an under-estimation of Pb0.02. A preconcentration of colloids and/or OM at the membrane surface by promoting Pb adsorption/complexation could explain this bias, as this would decrease Pb in the dissolved phase. The nature of colloids in the coastal zone might differ from the one along the estuary, with a likely dominance of organic colloids complexing dissolved Pb. Indeed, literature reports that a non-negligible part of Pb is complexed with organic ligands (e.g. [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF]. This assumption is also supported by the longitudinal profile of Pbpart. Accordingly, while the Pbpart decrease for low salinities was associated to lithogenic effects (with only a slight decrease of Th-normalized Pbpart), the normalized-Pbpart showed an increasing trend from S > 25 up to the estuary mouth (Figure 3b), along with a parallel POC increase from 5 to 15 % (Figure 2d). This pattern might reflect Pb adsorption on POC and suggest an organic origin of Pb ligation that could possibly be biogenic (algal cell debris and/or their exudates; e.g. [START_REF] Illuminati | In-situ trace metal (Cd, Pb, Cu) speciation along the Po River plume (Northern Adriatic Sea) using submersible systems[END_REF]. Previous studies on Pbpart speciation through sequential extractions have shown that the carbonate-bound and exchangeable Pbpart decreased over the salinity gradient while bound Fe-Mn oxides and organic Pbpart increased [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF], supporting our observations. Similar Pbpart concentration levels and longitudinal trend were recorded in 1982[START_REF] Chiffoleau | Trace metal distribution, partition and fluxes in the Seine estuary (France) in low discharge regime[END_REF]studies (Elbaz-Poulichet et al., 1984;[START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. Following this hypothesis, maximum Pbdyn recorded outside the estuary may correspond to Pb release due to photoreduction/oxidation of inorganic/organic ligands. In fact, Pb remobilization from Pb-bearing complexes [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF][START_REF] Pinedo-Gonzalez | Diel Changes in Trace Metal Concentration and Distribution in Coastal Waters: Catalina Island As a Study Case[END_REF] and a subsequent increase of Pbdyn in the water column may occur in coastal waters with very low SPM and significant solar radiation. Such processes might also contribute to the two relatively high Pbdyn values along the gradient (S~12 and 25) that occurred at peaks of solar irradiation (Figures 2c and3a) as further discussed in Section 4.2.

Overall, the slight decreasing trend characterizing Pb0.2 and Pb0.02 along the salinity gradient suggests that the estuary is a source of Pbdiss to the coastal waters. Previous studies on Pbdiss variations in this system confirmed a general decrease of the concentrations from the river endmember to the sea (< 0.45 µm; [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. However, reported Pbdiss levels of the Gironde Estuary were about 10 to 20 times higher in 1982 [START_REF] Elbaz-Poulichet | Lead cycling in estuaries, illustrated by the Gironde estuary, France[END_REF] than the present data, while the concentrations at low salinity were about twice higher in 1994 [START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. This suggests a decrease of anthropogenic inputs of Pbdiss to the system over time as observed for Cd [START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF] with the remediation works in the Decazeville watershed and potentially the elimination of Pb in gasoline (e.g. [START_REF] Boyle | Anthropogenic Lead Emissions in the Ocean: The Evolving Global Experiment[END_REF]. This is in line with ongoing resilience of the historical metal contamination in the watershed [START_REF] Schäfer | Impact of metallurgy tailings in a major European fluvialestuarine system: Trajectories and resilience over seven decades[END_REF].

Cadmium

The spatial variation of the Cd0.2 and Cd0.02 fractions showed the typical "bell-shape" trend observed also earlier for dissolved Cd in the Gironde Estuary (e.g. [START_REF] Dabrin | Improving estuarine net flux estimates for dissolved cadmium export at the annual timescale: Application to the Gironde Estuary[END_REF][START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF] as well as in other estuarine environments, e.g. the Mississippi (USA; [START_REF] Shiller | Trace Elements in the Mississippi River Delta Outflow Region: Behavior at High Discharge[END_REF] and the Seine Estuary (France; [START_REF] Chiffoleau | Trace metal distribution, partition and fluxes in the Seine estuary (France) in low discharge regime[END_REF]. The non-conservative behavior (addition) observed from S=0.1 to mid-salinity range (S=15-20) is explained by an efficient Cd remobilization from the particulate phase due to increasing chloride-complexation [START_REF] Bourg | Trace metal adsorption modelling and particle-water interactions in estuarine environments[END_REF][START_REF] Waeles | Seasonal variations of cadmium speciation in the Penzé estuary, NW France[END_REF] implying a strong decrease in the partition coefficient PCd (Figure S2c). From the mid-salinity range towards the sea, dilution by Cd-poor oceanic water becomes dominant due to the absence of important net mobilization from particles to the dissolved phase (e.g. [START_REF] Dabrin | Mécanismes de transfert des éléments traces métalliques (ETM) et réactivité estuarienne : cas des systèmes Gironde, Charente, Seudre et Baie de Marennes Oléron[END_REF]. The fact that Cd0.2 and Cd0.02 are almost identical all over the salinity gradient clearly indicates that coarse colloids (>0.02 µm) play a minor role in dissolved Cd speciation. The Cddyn variations also mainly displayed a bell-shape with similar concentrations than other Cddiss and represented up to 80 % for S=22. . Two lower values of Cddyn (S=15.35 and S=17.95; sites 10 and 11) suggest that locally very active complexing agents may fix part of the Cddiss in a non-dynamic form. Various hydrodynamic processes including tidal shear stress and subsequent particles remobilization (potential ligands of Cddyn) may also be involved but not visible in SPM data. These sampling sites are yet located in one of the area where sediments dredged elsewhere for navigational purposes are re-deposited (SMIDDEST, 2016). Mid-salinity maximum values of Cd0.2 were half than those reported in the literature for high discharge conditions (Q ~ 2,400 m 3 .s -1 ; probably due to Cd-rich bottom sediment resuspension; [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF] and close to the values observed for low discharge conditions (0.1 µg.L -1 ; Q ~ 570 m 3 .s -1 ; [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF] as occurring during our study (0.08 µg.L -1 ; Q ~ 235 m 3 .s -1 ). The literature also reports that this Cd0.2 behavior is not seasonally dependent but equally observed throughout the year.

The range of Cdpart obtained from the present work was similar to that reported in previous studies [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Boutier | Influence of the Gironde estuary outputs on cadmium concentrations in the waters: consequences on the Marennes-Oléron bay (France)[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF] reported a sharp decrease of Cdpart concentrations between the freshwater endmember and the saline estuary (from 4 to 0.6 mg.kg -1 ) where concentrations were rather constant, varying between 0.3 and 0.7 mg.kg -1 .

Our data suggested a decrease of Cdpart from 0.5 to 0.3 mg.kg -1 from S=0.1 to S=1.1 which seems to be related to lithogenic effects since the Th-normalized Cdpart profile is constant along the salinity gradient until S=30.45, except for a single greater value at S=4.3. This outlier is also slightly observed in Th-normalized Pbpart and could be associated to a local contamination in this other zone where sediment are deposited after dredging (SMIDDEST, 2016). Clear maxima were observed in the southern coastal estuary with values reaching ~ 1 mg.kg -1 and confirmed by Thnormalized profile. Such processes might be related to Cd adsorption or uptake by primary producers [START_REF] Boutier | Influence of the Gironde estuary outputs on cadmium concentrations in the waters: consequences on the Marennes-Oléron bay (France)[END_REF][START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF]. This hypothesis is supported by the POC% data that increase towards the coastal zone.

Copper

Biotic processes may partly explain Cudiss fluvial maxima, but one cannot exclude anthropogenic source of Cudiss, which is especially true for the fluvial estuary in the Bordeaux conurbation and its wastewater treatment plant effluents. Previous studies performed during a low discharge period similar to this sampling campaign estimated an anthropogenic source contribution of 10 to 20 % to the observed Cudiss [START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF]. This addition of dissolved Cu occurs most likely to a nonnegligible part (~ 20 %, Figure S2c) in its dynamic, i.e. potentially bioavailable form. For Cu0.2 a sharp decrease (from 2 to 1.2 µg.L -1 ) was then recorded from S=0.1 to S=0.2 and might be related to changes in coarse colloid interactions. Accordingly, a previous study in the riverine endmember (S < 5) performed in June 2001 reports a decreasing trend of similar amplitude for Cu0.2 (1.5 to 0.7 µg.L -1 ; [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF]. This Cudiss removal may be attributed to flocculation of organometal complexes, or colloid adsorption and/or coagulation as already reported in the Gironde Estuary [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF] or in the Scheldt Estuary [START_REF] Zwolsman | Geochemistry of dissolved trace metals (cadmium, copper, zinc) in the Scheldt estuary, southwestern Netherlands: Impact of seasonal variability[END_REF]. Thereafter, both Cu0.2 and Cu0.02 showed similar concentrations and a non-conservative (addition) behavior in the low salinity range (S=0.2 to S=4.3), followed by steady dilution with seawater as previously observed [START_REF] Dabrin | Mécanismes de transfert des éléments traces métalliques (ETM) et réactivité estuarienne : cas des systèmes Gironde, Charente, Seudre et Baie de Marennes Oléron[END_REF]. Budget estimations reported in [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF] suggested that Cudiss in the riverine endmember are higher than predicted, revealing Cudiss addition in the fluvial estuary.

The Cudiss reported in this study were close to values measured in samples from 2001 and 2007 [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Dabrin | Mécanismes de transfert des éléments traces métalliques (ETM) et réactivité estuarienne : cas des systèmes Gironde, Charente, Seudre et Baie de Marennes Oléron[END_REF]. Yet, the low proportion of Cudyn displayed interesting features with riverine endmember levels varying from 0.06 µg.L -1 (S=0.1) to ~ 0.25 µg.L -1 (S=0.2 and S=1.1) that may be associated with biotic processes and/or anthropogenic inputs as discussed in Section 3.3.

Previous studies reported the sharpest Cupart decrease with values ranging from 75 mg.kg -1 for the freshwater endmember to 30 mg.kg -1 [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. Particulate Cu concentrations in the riverine part, in June 2017, were roughly twice lower than levels reported in June 2001. However, Th-normalized values suggested that these variations were rather grain-size related since the decrease was no longer observed after normalization. These previous studies reported a constant Cupart throughout the salinity gradient (~ 30 mg.kg -1 ), while our observations showed an increasing trend from S=25 with Cupart, reaching their highest values of 300 mg.kg -1 in the southern coastal estuary. This trend was confirmed by the Th-normalized data. As the coastal waters showed the highest POC levels, these observations were attributed to an enrichment of Cupart associated to marine phytoplankton sorption [START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF] as discussed below.

New insights from the dynamic fraction monitoring in combination with other fractions

Photo-redox processes

The analysis of the three different dissolved metal fractions, revealed changes in the dissolved Pb speciation along the salinity gradient. For low salinities (S<10), Pbdyn represented less than 10 % of the dissolved fractions while in the outer estuary it encompassed ~ 80 % of the Pbdiss (Figure S2a), with concentrations of ~ 0.015 µg.L -1 for the four southern coastal sites (Figure 3a).

A small increase in Pbdyn around S=10 was concomitant with lower Pb0.2 and Pb0.02 values and a SPM peak which may be related to sediment remobilization processes (Figure 2b and Figure 3a).

Yet, another similar Pbdyn increase also occurred at S=25. Previous studies on biogeochemical cycles of Pbdiss showed that photolysis of colloidal OM and the photo-redox cycling of colloidal metal(hydrous)oxides cause a daytime decrease of colloidal Pb (0.2 -0.02 µm) in coastal systems [START_REF] Pinedo-Gonzalez | Diel Changes in Trace Metal Concentration and Distribution in Coastal Waters: Catalina Island As a Study Case[END_REF]. A study of Pbdiss measured by ASV in the Krka Estuary showed that, despite an enhanced formation of electroactive Pb chloro-complexes through increasing salinity, ~ 30 to 40 % of total Pb bound to strong complexes may be degraded after solar radiation [START_REF] Omanović | Trace metal detection in Šibenik Bay, Croatia: Cadmium, lead and copper with anodic stripping voltammetry and manganese via sonoelectrochemistry. A case study[END_REF]. Such photo-redox processes may contribute to intra-estuarine increases of Pbdyn which is supported by the significantly positive correlation of this fraction variations with rad/SPM (r = 0.614; Table S1). [START_REF] Tercier-Waeber | In Situ Monitoring of the Diurnal Cycling of Dynamic Metal Species in a Stream under Contrasting Photobenthic Biofilm Activity and Hydrological Conditions[END_REF] also observed daytime increases of Pbdyn and Cddyn that were induced by photoreduction/reoxidation of colloidal metal oxides, especially small (< 0.02 µm) colloidal Mn oxides in rivers. Variations of Fediss and Mndiss along the estuarine gradient do not allow one to observe Fe-Mn colloid influence on dynamic trace metal fraction while the POC increase seawards suggests an increasing importance of organic particles as trace metal carrier phase. In the Gironde coastal waters, very low SPM (in comparison with intra-estuarine levels) may therefore favor the relatively high Pbdyn with Pb-bearing organic colloids photolysis releasing dynamic Pb species. The nature of this colloidal fraction remains to be determined but may be biogenic, originating from small algal cell debris and/or phytoplankton exudates (extracellular organic polymeric matter). Monitoring of the Pbdyn performed with the TracMetal supports previous assumptions by Pinedo-Gonzalez et al. (2014, and references therein) that soluble metals generated by both colloid photolysis/photoreduction processes in seawater can be significantly biologically active and play an important role enhancing trace metal availability.

Stronger biological interactions (sorption processes) with Cd than with Pb (e.g. Tercier-Waeber et al., 2009) may explain the observed lower proportion of Cddyn (~ 30 % of Cddiss) in the southern coastal sites compared to Pbdyn (~ 60 % of Pbdiss).

Biotic processes

The literature reports that the distribution of Cudiss along the Gironde Estuary salinity gradient displays strong seasonal variability [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF]. When entering the MTZ, with SPM > 1,500 mg.L -1 (Figure 2b), in the very low salinity range, riverine phytoplankton and bacteria experience turbidity and salinity stress which cause cell lysis [START_REF] Fisher | Dissolved and particulate organic carbon in Chesapeake Bay[END_REF][START_REF] Goosen | Bacterial and phytoplankton production in the maximum turbidity zone of three European estuaries: the Elbe, Westerschelde and Gironde[END_REF]. Subsequent release of bioaccumulated/adsorbed Cu supports the notion that microbially-driven OM degradation explains Cudiss addition in the turbidity gradient [START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF]. The typical rapid drop of POC% from 2 % at S=0.1 to 1.1 % at S=1.1 (Figure 2d) has been attributed to the mineralization of the organic-rich riverine SPM (heterotrophic activity) and parallel dilution with organic-poor particles in the MTZ with this zone showing constant POC% values of ~ 1.5 % [START_REF] Abril | Behaviour of Organic Carbon in Nine Contrasting European Estuaries[END_REF][START_REF] Etcheber | Particulate organic carbon in the estuarine turbidity maxima of the Gironde, Loire and Seine estuaries: origin and lability[END_REF][START_REF] Masson | Copper addition by organic matter degradation in the freshwater reaches of a turbid estuary[END_REF][START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF].

These processes may cause Cu addition since this element exhibits a strong affinity to OM (> 95 % of Cu in seawater is complexed with organic ligands; [START_REF] Sunda | The use of chemiluminescence and ligand competition with EDTA to measure copper concentration and speciation in seawater[END_REF]. Our study showed an increasing trend in POC% levels along the salinity gradient from 2 % at S>6 to 15 % at S=31.85. Assuming that in the low turbidity, high salinity range near the estuary mouth, POC is a proxy for algal cell presence, biological sorption by phytoplankton during the bloom period might therefore also contribute to continuous Cudiss removal, which is supported by the continuous Cupart and PCu increase along the gradient observed in our study (Figures 3f andS2d). Significant negative correlations were observed between rad/SPM and Cu0.2 as well as Cu0.02 (r = 0.740 and 0.663, respectively. Table S1) suggesting the increasing phytoplankton sorption of dissolved Cu with increasing effective radiation along the estuary. Previous studies report ~ 50 % removal of Cudiss in the Atlantic Ocean surface waters due to biological activity during spring [START_REF] Kremling | Studies on the spatial and seasonal variability of dissolved cadmium, copper and nickel in northeast atlantic surface waters[END_REF]. Pearson r correlation between Mepart and POC% were of -0.1 for Pb, 0.4 for Cd, and 0.7 for Cu, while a typical lithogenic trace element such as Th gave a correlation of -0.9 (data not shown).

Such values support the increasing biotic removal (subtraction) of dissolved Cu through cell sorption together with increasing phytoplankton load, especially in the outer estuary where algal accumulation likely represents a seasonal net sink of Cu, and Cd [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Boutier | Influence of the Gironde estuary outputs on cadmium concentrations in the waters: consequences on the Marennes-Oléron bay (France)[END_REF][START_REF] Dabrin | Origin of suspended matter and sediment inferred from the residual metal fraction: Application to the Marennes Oleron Bay, France[END_REF][START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF].

Ecotoxicological implications

For all trace metals, various biogeochemical processes that lead to Medyn release may be of concern for wild living organisms. In the outer estuary, the dynamic fraction represents only 40-50% of Cddiss (Figure S2b). A lower proportion of Cddyn (compared to intra-estuarine sites) suggests the importance of small colloids in distribution of Cd in the outer estuary with stronger complexation by marine OM (algae as supported by POC%) that may transform dynamic Cd into non-dynamic forms. The dominance of Cddyn fraction along the salinity gradient followed by important Cddyn complexation by algal cells in the estuary mouth is of major ecotoxicological concern. Indeed, historical Cd contamination of the Gironde Estuary has resulted in oyster farming being prohibited in this system, while the Marennes-Oléron Bay, located further north, hosts the largest oyster production in France and even Europe [START_REF] Michel | Net Fluxes of Dissolved Arsenic, Cadmium, Copper, Zinc, Nitrogen and Phosphorus from the Gironde Estuary (France): Seasonal Variations and Trends[END_REF][START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF]. Because the Marennes-Oléron Bay is influenced by the polymetallic (especially Cd) pollution of the Gironde Estuary (e.g. [START_REF] Boutier | Influence of the Gironde estuary outputs on cadmium concentrations in the waters: consequences on the Marennes-Oléron bay (France)[END_REF][START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF], the understanding of Cd dynamics, speciation and associated oyster contamination is crucial. This contamination may occur from the likely dominant direct pathway i.e. from the dissolved phase (e.g. [START_REF] Lekhi | Role of dissolved and particulate cadmium in the accumulation of cadmium in cultured oysters (Crassostrea gigas)[END_REF], or through the trophic pathway (phytoplankton consumption and/or ingested fine particles e.g. [START_REF] Ramteke | Geochemical fractionation study in combination with equilibrium based chemical speciation modelling of Cd in finer sediments provide a better description of Cd bioavailability in tropical estuarine systems[END_REF]. In the Gironde system, Cd bioaccumulation by oysters most likely occurs through both contamination pathways [START_REF] Strady | Roles of regional hydrodynamic and trophic contamination in cadmium bioaccumulation by Pacific oysters in the Marennes-Oléron Bay (France)[END_REF], yet this element has a strong affinity for the dissolved phase, (relatively low PCd values compared to the other studied metals; Figure S1c; [START_REF] Chiffoleau | Trace metal distribution, partition and fluxes in the Seine estuary (France) in low discharge regime[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. Therefore, considering the dominance of Cddyn along the gradient, potential algal sorption in the coastal waters observed in this study may reduce the availability of Cd to marine oysters (through uptake from the dissolved phase). The seasonality of algal blooms may influence the availability of Cddyn to filter feeders and requires further study under different seasonal and hydrological conditions. The TracMetal is therefore an efficient sensing and sentinel tool for freshly mobilized, dynamic metal species to observe processes that are only partly visible or not monitored with only conventional dissolved fractions.

Overall, contamination levels recorded for trace metals in this study were at least twice lower than ~ 20 years ago [START_REF] Audry | Budgets of Mn, Cd and Cu in the macrotidal Gironde estuary (SW France)[END_REF][START_REF] Kraepiel | Geochemistry of trace metals in the Gironde estuary[END_REF]. Estuarine dissolved Cd fluxes exported to the ocean were shown to be eight times lower than ~ 40 years ago and imply a general decrease of Cd pollution in Gironde wild oysters [START_REF] Lanceleur | Long-term records of cadmium and silver contamination in sediments and oysters from the Gironde fluvial-estuarine continuum -Evidence of changing silver sources[END_REF][START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF]. These observations may be related to the decreased anthropogenic trace metal emissions in this region combined with the modification of the hydrodynamic regime of the system with, in particular, a decreasing trend in river discharge [START_REF] Boyle | Anthropogenic Lead Emissions in the Ocean: The Evolving Global Experiment[END_REF][START_REF] Jalón-Rojas | Turbidity in the fluvial Gironde Estuary (southwest France) based on 10-year continuous monitoring: sensitivity to hydrological conditions[END_REF][START_REF] Pougnet | Nouveau modèle analytique pour une meilleure estimation des flux nets annuels en métaux dissous[END_REF].

Conclusions and Perspectives

The speciation and partitioning of trace metals in the Gironde Estuary has been studied for decades but this study completes its understanding by performing the first assessment of the dynamic fraction of the trace elements related to key biogeochemical processes. In the riverine branch, biogeochemical reactivity of Cu (release by OM mineralization in the MTZ; [START_REF] Petit | Anthropogenic sources and biogeochemical reactivity of particulate and dissolved Cu isotopes in the turbidity gradient of the Garonne River (France)[END_REF] may explain the important proportion of the dynamic fraction in the dissolved phase. For Cd, the dynamic fraction may represent up to 90% of the dissolved phase along the salinity gradient. This observation is of major concern considering the potential bioavailability of trace metal dynamic fractions. The dissolved phase < 0.02 µm is conventionally referred to as the "truly" dissolved fractions. This is the most bioavailable fraction containing the free metal ions and therefore the potentially the most toxic (e.g. [START_REF] Masson | Copper addition by organic matter degradation in the freshwater reaches of a turbid estuary[END_REF]. Monitoring of the TracMetal in the Gironde Estuary has shown that Cudyn represented only a minor fraction of dissolved Cu < 0.02 µm, which points to the importance of small colloids in estuarine dissolved Cu speciation. Similar observations were recently reported for arsenic (As) in the same system [START_REF] Penezić | Spatial variability of arsenic speciation in the Gironde Estuary: Emphasis on dynamic (potentially bioavailable) inorganic arsenite and arsenate fractions[END_REF] highlighting the need to reconsider the "truly" dissolved fraction from an ecotoxicological point of view. The application of the TracMetal also shed light on photo-redox processes by detecting an increasing dynamic species concentration especially for Pbdyn along the estuary and near the Gironde Estuary mouth. This is where low SPM and intense light penetration occurred in the water column, probably impacting biogenic colloids. In coastal waters, phytoplankton influences the distribution of trace metals and may act as efficient scavenger to trap trace metals in the particulate phase, possibly explaining the decrease in the proportion of the Cddyn fraction coupled to the increase of Cdpart. All these new finding support the need for a closer monitoring of processes occurring in productive coastal zones. Considering the wide range of potential metal carrying phases, the application of the TracMetal state-of-the-art monitoring device at high temporal and spatial scales allows one to observe and interpret biogeochemical processes that are not accessible by conventional methods.

Bioavailability-oriented methods should therefore be considered in ecotoxicological risk assessment of metals and regulations [START_REF] Merrington | Accounting for metal bioavailability in assessing water quality: A step change?[END_REF][START_REF] Verweij | Speciation of copper in relation to its bioavailability[END_REF].

Figure 1 :

 1 Figure 1: Map of the study area with sampling sites and corresponding salinity. Left: site locations along the salinity gradient (1 to 20) and the four coastal sites in the south channel (21 to 24); Right: the practical salinity (S) determined at each site.

  Reference Material (CRM) for estuarine waters (SLEW-3, NRC) provided recoveries of ~ 100 % for Mn and Fe, ~ 110 % for Cu and Cd and of ~ 130 % for Pb. Precision (RSD%) was of ~ 5 % for Mn, Fe, Cu and Pb and ~ 10 % for Cd (n = 5; vertical error bars in the graphics). Analyses of CRM for coastal waters (CASS-6, NRC) provided recoveries of ~ 80 % for Fe, ~ 90 % for Mn, ~ 110 % for Pb, Cd, and Cu and RSD% of 5 % for Mn, Fe, and Cu and ~ 15 % for Cd and Pb (n = 5; vertical error bars in the graphics). The longitudinal profile of Fediss and Mndiss was previously presented in Penezić et al. (2020).

Figure 2 :

 2 Figure 2: Longitudinal profiles of major physical-chemical parameters and dissolved Fe and Mn concentrations. a: pH and temperature; b: Suspended Particulate Matter (SPM); c: solar radiation and oxygen saturation level (O2 sat); d: Particulate Organic Carbon / SPM ratio (POC%); dissolved concentrations in 0.2 and 0.02 fractions of Fe and Mn (e, f; Penezić et al., 2020). Half-black symbols correspond to coastal sites in the south channel. Values out of the range (presumed outliers) are presented in parentheses.

Figure 3 :

 3 Figure 3: Longitudinal profiles of trace metal concentrations in the different dissolved and particulate fractions. Metal dissolved 0.2 µm (Me0.2), 0.02 µm (Me0.02), and dynamic (Medyn) fractions for Pb, Cd, and Cu (a, c, and e, respectively, µg.L -1 ); metal particulate concentrations (Mepart, mg.kg -1 ) and Th-normalized Mepart (Mepart/Thpart) for Pb, Cd, and Cu (b, d, and f, respectively). Half-black symbols correspond to coastal sites in the south channel. Values out of the range are presented in parentheses.

Dissolved 0.2 and 0.02 µm metal fraction (Me0.2 and Me0.02). Water

	samples were immediately
	filtered on-site through 0.2 µm cellulose acetate filters (Me0.2; Minisart, Sartorius®) and through
	0.02 µm alumina-based filters (Me0.02; Whatman®). Filtrates were placed into acid-cleaned PP
	and the
	average values of the calibration slopes, both normalized by the pre-concentration time. The lower
	detection limits, calculated on the basis of 3.3 x the standard deviation of the regression lines
	divided by the slopes, were 0.7, 1 and 6.6 ng.L -1 for Cd(II), Pb(II), and Cu(II), respectively. Vertical
	error bars in the graphics of the Medyn longitudinal profiles represent the maximum Relative
	Standard Deviation (RSD%) determined from the two replicate measurements performed with the
	two GIME in each sample (i.e. n = 4): 6 % for Cd(II) and 8 % for Pb(II) and Cu(II). The accuracy
	of the GIME was demonstrated by inter-comparison exercises performed by EU laboratories
	(Braungardt et al., 2009).

Acknowledgements

This work has benefited from financial supports by the EU FP7 Ocean 2013.2 Project SCHeMA (Project-Grant Agreement 614002), the Regional Water Agency (Agence de l'Eau Adour-Garonne) the FEDER Aquitaine-1999-Z0061, and the Portuguese FCT (M. Abdou contract, CEECIND/01777/2018), which are gratefully acknowledged. Authors also gratefully acknowledge the help of the R/V Thalia Crew (TGIR FOF); Dr. A. Penezić, Dr. T. Gil-Díaz, Dr. C. Catrouillet, Dr. A. Lerat, L. Troi, Dr. C. Charbonnier, D. Poirier, A. Charrier, and C. Pereto, for sampling and assistance during field campaigns, ancillary parameter analyses, as well as scientific support; Dr. K. Fauquembergue and Dr. P. Maroni for support in data presentation.