Ste ´phane Mounier

Huiyu Zhao
email: huiyu_zhao@hotmail.fr

Ce ´dric Garnier

Roland Redon

S Mounier

Á H Zhao

Á C Garnier

Copper complexing properties of dissolved organic matter: PARAFAC treatment of fluorescence quenching

Keywords: Natural organic matter, Fluorescence quenching, Excitation emission matrix of fluorescence, PARAFAC, Copper complex

Fluorescence quenching of the fluorescence is a useful method to determine copper complexing properties of the dissolved organic matter. This technique provides express results often by choosing one or two excitation-emission fluorescence measurement for a straight range of concentration. That why it is not representative of the whole sample. On another hand, using total luminescence spectra gives lots of information that is difficult to manipulate in term of complexing properties. This work focus on a fluorescence quenching experiment carried out on a filtered black water from Rio Negro from north Brazil (Sao Gabriel da Cachoeira) using excitation emission matrix of fluorescence (EEMF) with a large range of copper concentration. Indeed, metal additions were performed using logarithmic increments of the total copper concentration to cover a wide range of concentration from 1.7 9 10 -9 to 10 -3 mol l -1 . These data were treated by two different ways for comparison: on the one hand, data treatment were computed, as usually, at different fluorescence intensity positions using multi-response wavelength method with two ligand, and, on the another hand, a statistical method, parallel factor analysis (PARAFAC), was applied to extract fluorescent component. Then a fitting was done on these PARAFAC components with one or two complexing sites each one. Results show that PARAFAC enables quantitative evaluation of complexing parameters for copper as good as certain multi-response methods: L 1T 16.2 9 10 -6 mol l -1 (5.0 \ log(K 1 ) \ 5.8) and L 2T 3.0 9 10 -4 mol l -1 (2.8 \ log(K 2 ) \ 3.9). PARAFAC confirms that for this natural sample only two fluorescent ligands are present with two types of site for each component. No residual fluorescence was detected by the statistical treatment. Wall surface interferences were pointed-out as phenomenon to be solve to overcome the limiting efficiency of the total copper concentration range.

Introduction

Natural organic matter (NOM) is a complex mixture of naturally occurring organic material resulting from the decay of plant and animal residues. The widespread occurrence of NOM, particularly its most soluble fractions, fulvic acids and humic acids, in soils, sediments and waters, and the existence in their molecules of chemical reactive groups, such as carboxylic and hydroxylic structures, are responsible for their important environmental role. For example, they function as acid-base buffers and bind to metal ions (Esteves da [START_REF] Da Silva | Evolving factor analysis of synchronous fluorescence spectra of humic substances in the presence of Cu(II)[END_REF][START_REF] Smith | Fluorescence analysis for multisite aluminium binding to natural organic matter[END_REF]. Their complexing properties versus cations were studied a lot due to importance in trace metal cycle and pH-buffer properties [START_REF] Cabaniss | Synchronous fluorescence spectra of metal-fulvic acid complexes[END_REF][START_REF] Da Silva | Evolving factor analysis of synchronous fluorescence spectra of humic substances in the presence of Cu(II)[END_REF]Esteves da Silva et al. 1996). A famous way to measure complexing properties of the NOM is using its fluorescence properties [START_REF] Cabaniss | Synchronous fluorescence spectra of metal-fulvic acid complexes[END_REF]. Since [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF] fluorescent dissolved organic matter (FDOM) was studied by using fluorescence quenching. As this method is easy to use, lots of measurements were done on FDOM from several types of samples: seawater (Esteves da Silva and Machado 1996), river (Mounier et al. 1999), complex mixture [START_REF] Smith | Multi-site aluminum speciation with natural organic matter using multiresponse fluorescence data[END_REF] or extracted organic matter [START_REF] Cabaniss | Synchronous fluorescence spectra of metal-fulvic acid complexes[END_REF][START_REF] Da Silva | Fluorescence quenching of anthropogenic fulvic acids by Cu(II), Fe(III) and UO 2 2?[END_REF][START_REF] Smith | Fluorescence analysis for multisite aluminium binding to natural organic matter[END_REF][START_REF] Dudal | Rapid screening of DOM's metal-binding ability using a fluorescence-based microplate assay[END_REF][START_REF] Manciulea | A fluorescence quenching study of the interaction of Suwannee River fulvic acid with iron oxide nanoparticles[END_REF]). Even if it is common in use, the fluorescence quenching suffers serious drawbacks: (1) It exclusively concerns the fluorescent functions, and the result of a quenching experiment is only a part of the DOM response [START_REF] Smith | Multisite metal binding to fulvic acid determined using multiresponse fluorescence[END_REF], (2) The choice of the observation wavelength is determinant. If the emission wavelength around 450 nm has generally been used at the beginning of fluorescence quenching history, many authors preferred using either single peak intensity (Esteves da [START_REF] Da Silva | Fluorescence quenching of anthropogenic fulvic acids by Cu(II), Fe(III) and UO 2 2?[END_REF], or either a multi-peak treatment on a fluorescence matrix [START_REF] Luster | Multi-wavelength molecular fluorescence spectrometry for quantitative characterization of copper(II) and aluminium(III) complexation by dissolved organic matter[END_REF] or a synchronous spectra [START_REF] Cabaniss | Synchronous fluorescence spectra of metal-fulvic acid complexes[END_REF]; Esteves da Silva and Machado 1995), (3) The covered range of metal concentration is generally distributing among 10 -6 and 10 -3 mol l -1 which is higher than the natural concentration of trace metals, (4) Fluorescence is pH dependent (Esteves da Silva et al. 1996).

Since [START_REF] Coble | Characterization of marine and terrestrial DOM in sea water using excitation-emission matrix spectroscopy[END_REF], FDOM is observed by measuring excitation and emission matrix of fluorescence (EEMF) and by locating peaks of fluorescence on the map to detect fluorescent compounds in the sample. Hence, EEMF could be used during fluorescence quenching experiment to detect FDOM behaviour in presence of metal. As the whole fluorescence of the sample is observed, several methods were developed to calculated complexing parameters. Multi-response treatments of EEMF have been done using multi-peak intensities independently [START_REF] Luster | Multi-wavelength molecular fluorescence spectrometry for quantitative characterization of copper(II) and aluminium(III) complexation by dissolved organic matter[END_REF] or coupled (Smith andKramer 1998, 2000). Moreover, during fluorescence quenching experiments several problems occur: maximum peak position could move due to inner filter effect, residual fluorescence from unquenched fluorescent species could be present, etc.. Hence, the choice of a fixed intensity for a couple wavelength k ex , k em could lead to errors in complexing properties determination. To avoid misinterpretation, several works have used all the information present in EEMF to extract fluorescent compounds before data treatment of the fluorescence quenching experiment. This decomposition of EEMF set has first been done by principal component analysis (PCA) on synchronous spectra (Esteves da Silva and Machado 1995), or on fluorescence matrices using SIMPLISMA software (Smith andKramer. 1999, 2000). More recently chemometric methods have been used in order to extract useful information like the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) [START_REF] Antunes | MCR of the quenching of the EEM of fluorescence of dissolved organic matter by metal ions[END_REF] or the statistical PARAFAC method [START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF][START_REF] Luciani | Tracing of dissolved organic matter from the SEPETIBA Bay (Brazil) by PARAFAC analysis of total luminescence matrices[END_REF]. These statistical methods enable the decomposition using the multiple EEMF of the fluorescence quenching experiment itself.

Fluorescence quenching of ligands is defined by the decreasing, or enhancement, of the fluorescence intensity in presence of cations or anions. The association of FDOM and the ion species lead to the formation of a metal-ligand complex species. This compound generally has a fluorescence quantum yield lower than the free ligand, except for some cations like Al 3? (Esteves da Silva and Machado 1996;[START_REF] Smith | Fluorescence analysis for multisite aluminium binding to natural organic matter[END_REF]. When there is only one ligand present in a solution, the relation between the free ligand (L), the complexed ligand (ML) and the free metal (M) concentration is characterised by a second degree equation which is dependant on the total concentration of ligand (L T ), metal (M T ) and the conditional equilibrium constant (K ML ) [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF]. In this case determination of K ML and L T , when M T is known, could be obtained by a simple treatment [START_REF] Dudal | Rapid screening of DOM's metal-binding ability using a fluorescence-based microplate assay[END_REF]), a quadratic calculus [START_REF] Cabaniss | Copper binding by dissolved organic matter: I. Suwannee River fulvic acid equilibria[END_REF][START_REF] Da Silva | Fluorescence quenching of anthropogenic fulvic acids by Cu(II), Fe(III) and UO 2 2?[END_REF] or by a numerical determination of K ML and L T [START_REF] Smith | Multi-site aluminum speciation with natural organic matter using multiresponse fluorescence data[END_REF], 1999[START_REF] Smith | Multisite metal binding to fulvic acid determined using multiresponse fluorescence[END_REF]. When N ligands are present (N [ 2) the number of equation increases and the solution is obtained by numerical optimisation [START_REF] Smith | Multisite metal binding to fulvic acid determined using multiresponse fluorescence[END_REF][START_REF] Garnier | Influence of the type of titration and of data treatment methods on metal complexing parameters determination of single-and multi-ligand systems measured by stripping voltametry[END_REF]. In this work the software PROCESE (Programme d'Optimisation et de Spe ´ciation Chimique dans l'Environnement) is used as new simultaneous multi-response approach that enables the evaluation of the N ligands concentrations and their stability constants [START_REF] Garnier | Influence of the type of titration and of data treatment methods on metal complexing parameters determination of single-and multi-ligand systems measured by stripping voltametry[END_REF][START_REF] Louis | Characterisation and modelling of marine dissolved organic matter interactions with major and trace cations[END_REF].

Moreover, in case of natural sample, it is difficult to determine previously the number of ligands (N) which is often empirically determinated based on spectroscopy deduction [START_REF] Smith | Multisite metal binding to fulvic acid determined using multiresponse fluorescence[END_REF] or using the usage of the lowest number of parameter to fit the experimental data well (Esteves da [START_REF] Da Silva | Evolving factor analysis of synchronous fluorescence spectra of humic substances in the presence of Cu(II)[END_REF]. This number N could vary from 1 [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF][START_REF] Da Silva | Fluorescence quenching of anthropogenic fulvic acids by Cu(II), Fe(III) and UO 2 2?[END_REF] to 4 ligands [START_REF] Antunes | MCR of the quenching of the EEM of fluorescence of dissolved organic matter by metal ions[END_REF]) depending on the measurement method, the titration range, and the number of independent measured data. Moreover, during quenching experiment, it is very difficult to determine the quantum yield of each species because pure species solutions do not exist. Particularly, in the natural samples, the initial total concentration of cations is intrinsic, therefore, the initial state of FDOM is unknown.

This work proposes a fluorescence quenching experiment base on a logarithmic metal addition that allows a wide range of total metal concentration between 1.7 9 10 -9 to 10 -3 mol l -1 . Usual data treatment of [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF] method is compared with a PARAFAC treatment to directly deal with component entities. Instead of a partial and systematic determination of the number of fluorescent ligands for fluorescence quenching modelling this experiment leads to an optimum exploitation of the entire EEMF data.

Instrumentation and experiments

Fluorescence quenching has been done on a surface water sample from an Amazonian tropical river (Rio Negro at Sao Gabriel da Cachoeira, Brazil). Two litres were filtered on site with a burned glass filter, poisoned with sodium azide (final concentration of 0.5 9 10 -3 mol l -1 ) and conserved in a glass flask at 4°C in dark. This sample presents a concentration of dissolved organic carbon 20 mg C l -1 , an important fluorescent response and an initial concentration of copper of 1.7 9 10 -9 mol l -1 measured by differential pulse anodic stripping voltametry after UV irradiation (pH = 2) by a low pressure mercury lamp to eliminate any complexation with organic matter [START_REF] Louis | Characterisation and modelling of marine dissolved organic matter interactions with major and trace cations[END_REF]. In order to avoid the Inner Filter Effect [START_REF] Luciani | A simple correction method of inner filter effects affecting FEEM and its application to the PARAFAC decomposition[END_REF], a 8 times dilution with MQ water has been applied to the sample before the fluorescence quenching experiment. A 29.2 ml volume of diluted sample was spiked by 50 additions of microvolume of four copper nitrate standard solutions. The different concentrations (10 -6 , 10 -5 , 9.10 -4 and 9.10 -3 mol l -1 , Cu(NO 3 ) 2 analytical grade in MQ water) were used to cover a wide range of total copper concentrations, from the initial concentration to 10 -3 mol l -1 , by constant increments of the logarithm of the total copper concentration (Fig. 1). These logarithmic increases improved accuracy of the method and allowed to obtain a better determination of the complexing properties for the lowest concentrations as well as for the highest ones [START_REF] Garnier | Influence of the type of titration and of data treatment methods on metal complexing parameters determination of single-and multi-ligand systems measured by stripping voltametry[END_REF]).

Additions were done in an acid cleaned Teflon bottle (Nalgene). Previous experiments show that for all additions, 30 min of stabilisation under agitation is sufficient to reach the fluorescence stabilisation [START_REF] Louis | Characterisation and modelling of marine dissolved organic matter interactions with major and trace cations[END_REF]). The pH and 3D fluorescence matrices were measured (T = 22°C) in a 3 ml Suprasil standard quartz cell.

All measured spectra were obtained with a fluorometer Hitachi F4500. EEMF of the solutions were recorded at 2,400 nm/min scan speed from 250 to 600 nm in emission by step of 5 nm and for an excitation wavelength from 200 to 500 nm by step of 5 nm. Excitation and emission bandwidth were 10 nm. Fluorescence intensity was corrected from detector response using manufacturer's setting. Data for EEMF treatment was extracted by FLWinLab software for emission and excitation range stepped every 5 nm. Rayleigh and Raman physical diffusion of light were numerically removed by the method proposed by [START_REF] Zepp | Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks in excitation-emission matrices[END_REF]. During the experiment, the pH is maintained at natural level, around 6.0 ± 0.1 and nitrogen bubbling purges the experiment from dissolved oxygen. The quenching dilution factor was under 1.2, and dilution correction of intensity was systematically done during the data treatment of fluorescence intensity.

Once all the EEMF were collected, two data treatments were done: PARAFAC and classical. For the PARAFAC treatment, all the measured EEMF of the experiment were treated to obtain the fluorescent components present in solution and their relative contribution to total fluorescence [START_REF] Luciani | Tracing of dissolved organic matter from the SEPETIBA Bay (Brazil) by PARAFAC analysis of total luminescence matrices[END_REF][START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF]. Using these results an equilibrium optimization was done considering the component's contribution as a proportional proxy of the concentration of ligand in solution. For the classical treatment, multi-wavelength fluorescence intensity quenching was done on (excitation/emission) peak position (332/450 nm), (260/450 nm), (230/340 nm) and (240/420 nm) respective positions of humic-like (peak C), fulvic-like (peak A), protein moieties (peak T) and a PARAFAC component (M) as referenced by [START_REF] Coble | Characterization of marine and terrestrial DOM in sea water using excitation-emission matrix spectroscopy[END_REF]. All intensity measurements were expressed in percentage of the first intensity (Fig. 2). Once the contribution (PARAFAC) or the intensities (for classical) data are obtained, curves were modelled by the equilibrium optimization program PROCESE [START_REF] Garnier | Influence of the type of titration and of data treatment methods on metal complexing parameters determination of single-and multi-ligand systems measured by stripping voltametry[END_REF]) for simultaneous multi-ligands optimization according to the method of [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF].

Results and discussion

The initial EEMF of the sample shows the maximal fluorescence for humic-like region (332/450 nm), with a less important peak at (260/450 nm) representing the fulvic-like compounds. Protein-like region is masked by the higher intensity of the other two peak. These EEMF are typical of black water body from tropical region (Mounier et al. 1999b).

The results of the classical approach for the three observed domains during the quenching experiment are represented in Figs. 2 and3. Quenching curve for the (332/450 nm) peak shows a well decreasing curve defined from 100% to 20% of the initial fluorescence, with low noise interference. This curve is similar to those obtained by [START_REF] Smith | Multisite metal binding to fulvic acid determined using multiresponse fluorescence[END_REF] for a Suwanee River Fulvic Acid solution. The fulviclike region gives a noisier curve at the beginning. Intensity appears to be higher than the initial value until 140%, which is due to the excitation Xenon lamp low energy in UV area. After 2.0 9 10 -4 mol l -1 of total cooper concentration a quasi linear decrease is observed until 30%. For protein-like components, noise is considerable, giving intensity around 230% of the initial value. For proteins, the value is under 100% until 4.0 9 10 -4 mol l -1 , and decreases down to 60% at the end of experiment. As regards the initial logarithmic additions, the good signal stability is a fine point. The signal degradation occurs after 10 -7 mol l -1 of total copper concentration as shows by the logarithmic representation of the quenching curves (Fig. 3). At the beginning of the experiment from 1.7 9 10 -9 to 3.16 9 10 -7 mol l -1 of total copper concentration, the phenomenon obviously corresponds to an adsorption or a complexation of the copper onto the system surface (Teflon flask, glass and quartz wall). When modelling all the points, it is necessary to introduce a very strong constant corresponding to this loading effect (data not shown). This forbids the study of the complexation upper 3.16 9 10 -7 mol l -1 of total copper concentration, but allows the study of the complexing properties of the DOM above this limit [START_REF] Luster | Multi-wavelength molecular fluorescence spectrometry for quantitative characterization of copper(II) and aluminium(III) complexation by dissolved organic matter[END_REF]. It is worth noting that this wall effect is detected for the first time in a fluorescence quenching experiment at such a wide range of copper concentration. All the results of this work will be treated for concentration above this limit (3.16 9 10 -7 mol l -1 ) since it does not change the results for NOM complexing properties, at the exception of the effective copper concentration analytical window.

The bias errors between experimental and calculated fluorescence is given by bias which is equal to the sum of the absolute value of the difference between the logarithm of the experimental fluorescence intensity and the logarithm of the calculated fluorescence intensity. Results are shown in Table 1. Results using [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF] model with one ligand (Table 1), generally lead to a bad modelling (bias form 2.56-4.06) of the experimental data (L T = 1.7 9 10 -5 -5.7 9 10 -5 mol l -1 and conditional constant K L = 10 4.0 -10 4.4 ). The one ligand fitting on Fig. 4 (dotted lines), gives evidence that model 1:1 is not sufficient. This implies that it is necessary to have more than one ligand. For the four fluorescence responses, using a `2 ligands model, it gives a bias from 0.38 to 3.03 which is better for all fluorescence position (Fig. 4, plain lines). The strongest ligands present conditional constant from 10 4.7 to 10 5.7 instead of the weaker from 10 3.3 to 10 3.9 . The lowest concentration is found for the peak M intensity (L 1,240/420 = 6.6 9 10 -6 mol l -1 , K 1,240/420 = 10 4.8 ) whereas the higher corresponds to the weak ligand at the fluorescence position of the peak A (L 2,250/ 450 = 4.2 9 10 -4 , K 2,250/450 = 10 3.9 ). For all fluorescence positions the stronger ligand appears to be the less concentrated whatever its position. Like previous studies on copper fluorescence quenching experiments undertaken with FDOM [START_REF] Grimm | Continuous multiligand distribution model used to predict the stability constant of Cu(II) metal complexation with humic material from fluorescence quenching data[END_REF][START_REF] Seritti | Fluorescence emission characteristics of naturally occurring organic matter in relation to metal complexation studies[END_REF][START_REF] Luster | Multi-wavelength molecular fluorescence spectrometry for quantitative characterization of copper(II) and aluminium(III) complexation by dissolved organic matter[END_REF][START_REF] Antunes | MCR of the quenching of the EEM of fluorescence of dissolved organic matter by metal ions[END_REF], our results on strong ligands confirm the trend for the conditional constant around 10 5 with ligand concentration around 10 -6 mol l -1 . Results for the weak ligands (around 10 3.6 ) are lower than those found in literature with a very high concentration around 10 -4 mol l -1 .

PARAFAC treatment gives optimum results for two components using decision tools described in [START_REF] Luciani | Tracing of dissolved organic matter from the SEPETIBA Bay (Brazil) by PARAFAC analysis of total luminescence matrices[END_REF]. Component 1 presents an excitation wavelength at 230 nm and an emission maximum at 420-425 nm. Component 2 shows two excitation wavelength at 255 and 350 nm and a unique emission maximum at 475 nm (Fig. 5). These two components are sufficient to explain all the EEMF during the quenching titration and this result corresponds to a unique statistical solution [START_REF] Luciani | Tracing of dissolved organic matter from the SEPETIBA Bay (Brazil) by PARAFAC analysis of total luminescence matrices[END_REF]. Both components 1 and 2 are consistent with UV (component 1 and part of the component 2) and visible (component 2) humic-like compounds [START_REF] Coble | Characterization of marine and terrestrial DOM in sea water using excitation-emission matrix spectroscopy[END_REF][START_REF] Smith | Fluorescence analysis for multisite aluminium binding to natural organic matter[END_REF]. They are often present in NOM's EEMF [START_REF] Murphy | Optimized parameters for fluorescence-based verification of ballast water exchange by ships[END_REF][START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF]. Component 2 shows slight red shift for emission wavelength. For the two components, a variation is observed during the fluorescence quenching experiment (Fig. 6) meaning that no unquenched component was detected.

Once these components extracted, the contribution of each component to each EEMF measurement is calculated (Fig. 6). The participation of the two components are used in a multi-response treatment where the complexing parameters are calculated. Each component represents normally a unique independent ligand which in contribution respects the [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF] conditions, i.e. quenching fluorescence has to be proportional to the complex concentration. This contribution to total fluorescence enables to calculate the complexing properties of each component (Fig. 6, left graph). The sigmoid shape is for both components consistent with the 1:1 complexation model [START_REF] Grimm | Continuous multiligand distribution model used to predict the stability constant of Cu(II) metal complexation with humic material from fluorescence quenching data[END_REF]. This decreasing contribution to fluorescence is linked with the formation of a complex with metal that modifies the ligand's quantum yield of fluorescence. Consequently, this modifies the EEMF of the solution. The site concentrations calculated for the component 1 and 2 give respectively (L 1/comp1 = 12 9 10 -6 mol l -1 , K 1/comp1 = 10 4.3 ) and (L 1/comp2 = 4.1 9 10 -6 mol l -1 , K 1/comp2 = 10 4.6 ). The bias for this fitting model give a value of 5.21. Since this bias is calculated from the two contribution curves, it is not possible to compare it with the cases of two ligands in Table 1. However, by observing the errors distribution (Fig. 6, left graph) it is obvious that two independent ligands are not sufficient to fit the experimental points. To obtain a better modelling of the PARAFAC components, it is possible to consider that two complexing sites on each component exist. This gives results for four simultaneous ligands model (Fig. 6, right graph) with a lower bias (3.03) and a better error distribution.

On one hand, the new parameters give component 1 a strong ligand (L 1/comp1 = 7.0 9 10 -6 and K 1/ comp1 = 10 5.0 ) and a weak one (L 2/comp1 = 1.3 9 10 -4 and K 1/comp2 = 10 2.8 ) and on another hand for component 2 a strong ligand (L 2/comp1 = 9.2 9 10 -6 and K 2/comp1 = 10 5.8 ) and a weak one (L 2/comp2 = 1.7 9 10 -4 and K 2/comp2 = 10 3.9 ) (Table 2). Since independent components 1 and 2 represent the whole fluorescence response variation, results give the parameters of the fluorescent complexing entities present in the sample that can be studied by a fluorescence quenching experiment.

PARAFAC decomposition confirms that there is only 2 fluorescent sites that contribute to the metal binding process for this metal concentration range. Both K 1/comp1 and K 1/comp2 fit around the usual conditional constant value found for copper (10 5 ). As these components represent the entire fluorescence response, we can consider they represent all the fluorescent complexing sites and by summing them up one obtains the complexing properties of sample: For strong ligands L 1T = 16.2 9 10 -6 mol l -1 and for weak ligands L 2T = 3.0 9 10 -4 mol l -1 . The PARAFAC approach gives a higher concentration than the classical approach (6.0-8.1 9 10 -6 mol l -1 ) with conditional constant ranging around 10 5 .

It is important to note that the bias for component 1 and 2 (respectively 5.21 or 3.03) is higher than the bias obtained for the peak C (0.38) modelled with two independent ligands. This is understandable because all the data fluorescence information is used by PARAFAC to obtain results for comp1 and comp2. This method also integrates the statistical variability of the fluorescence response, whereas peak C domain specifically focuses on fluorescence signals with low variability, neglecting a part of the phenomenon. Moreover, PARAFAC treatment of the fluorescence quenching experiment allows us to manipulate these parameters like components, which is not possible with the classical method. Hence, based on the same fluorescence quenching experiment of the NOM of the Rio Negro at Sao Gabriel da Cachoeira, results of both methods may appear to be different but they actually depend on data treatment. Assuming that PARAFAC treatment extracts unique components, the complexing parameters of this sample are described (Table 2).

Conclusion

Three dimensional fluorescence allows a rapid approach of the metal-NOM complex formation by using fluorescence quenching. PARAFAC pretreatment enables us to reduce the system to a well defined number of fluorescent ligands that could be manipulate in respect of the [START_REF] Ryan | Fluorescence quenching titration for determination of complexing capacities and stability constant of fulvic acid[END_REF] assumption.

Results even show that each component is unique; it gives better results when considering two complexing sites on each component. No unaffected ligand was present in the studied sample, all the components, 1 and 2, are quenched by the copper addition. However, the results show that if we consider one complexing site for one component it is not sufficient. Thanks to a trace metal addition experiment, fluorescence quenching turn out to be effective in detecting a surface adsorption problem for this type of experiment, limiting the total copper concentration useful range at a lower frontier to 3.16 9 10 -7 mol l -1 in this case.

If researchers want to study the strongest trace fluorescent sites of FDOM, it is important for further fluorescence quenching experiments with trace metal concentration titration to take into account the wall absorption. As PARAFAC gives a unique solution, inspite of its noisier results, we support this EEMF quenching data treatment to gives results that represent a unique solution and a more realistic evaluation of the complexing parameters.

Fig. 2

 2 Fig. 2 Relative fluorescence quenching intensity at (336/450 nm), (260/450 nm) and (230/ 340 nm) of the Rio Negro sample from Sao Gabriel da Cachoeira (Brasil) (pH = 6.0, T = 22°C)

Fig. 3

 3 Fig. 3 Logarithmic scale for fluorescence quenching intensity at (336/450 nm), (260/450 nm) and (230/ 340 nm) of the Rio Negro sample from Sao Gabriel da Cachoeira (Brasil) (N = 51) (pH = 6.0, T = 22°C)

Fig. 4

 4 Fig. 4 PROSECE fitting of multi-response fluorescence quenching (diamond) at k ex /k em wavelengths 332/450 nm (upper left: peak C), 260/450 nm (upper right: peak A), 240/420 nm (down left: peak M), 230/340 nm (down right: peak P), using one ligand (dotted line) and two ligands (full line) systems. Inserted graph: error (in %) of calculated intensities versus total copper concentration for one ligand (filled square) and two ligands (open square) systems

Fig. 5

 5 Fig. 5 PARAFAC component from the 51 experimental fluorescence quenching

Table 1

 1 Binding parameters obtained by PROSECE fitting of multi-response fluorescence quenching of peak C, A, M, P, using one ligand and two ligands systems

	Model Peak	L 1	Log(K 1 ) L 2	Log(K 2 ) Bias
			(mol/l)		(mol/l)
	1 L	Peak A 1.7E-5 4.4	2.56
		Peak C 4.6E-5 4.0	2.78
		Peak P 2.9E-5 4.3	3.13
		Peak M 5.7E-5 4.2	4.06
	2 L	Peak A 8.1E-6 5.5	4.2E-4 3.9	1.94
		Peak C 7.6E-6 5.7	1.6E-4 3.8	0.38
		Peak P 7.1E-6 4.7	1.5E-4 3.3	2.95
		Peak M 6.0E-6 4.8	2.9E-4 3.4	3.03

Table 2

 2 Binding parameters obtained by PROSECE fitting of simultaneous multi-response fluorescence quenching of components 1 and 2, using one ligand and two ligands model

	Model	PARAFAC component	L 1 (mol l -1 )	Log(K 1 )	L 2 (mol l -1 )	Log(K 2 )	Bias
	1 L & 1 L	Component 1	1.2E-5	4.3			5.21
		Component 2	4.1E-6	4.6
	2 L & 2 L	Component 1	7.0E-6	5.0	1.3E-4	2.8	3.03
		Component 2	9.2E-6	5.8	1.7E-4	3.9

 Biogeochemistry (2011) 106:107-116

Biogeochemistry (2011) 106:107-116

Acknowledgement This research was partly supported by the ARCUS 2006 Projet Provence-Alpes-Co ˆte-d'Azur/Bre ´sil, « Construction d'un espace scientifique et technologique PACA/Bre ´sil ».


Jean-Marie Garnier
email: garnier@cerege.fr

Hurel Charlotte

Jérémy Garnier

Véronique Lenoble

Cédric Garnier

K M Ahmed

Jérôme Rose

J.-M Garnier

C Hurel

J Garnier

Strong chemical evidence for high Fe

Introduction

High As contents in shallow groundwater have long been a human health issue for millions of people in certain regions of southern Asia [START_REF] Smedley | A review of the source, behaviour and distribution of arsenic in natural waters[END_REF]. Moreover, As in soil inherited from increasing irrigation practices during the dry season with As-rich groundwater becomes of interest because of its potential transfer to plants such as rice [START_REF] Meharg | Arsenic contamination of Bangladesh paddy field soils: implications for rice contribution to arsenic consumption[END_REF]. It is well recognized that high Fe and As concentrations observed in groundwater in Bangladesh result from reductive dissolution of Fe (oxyhydr)-oxides due to bacterially-induced anoxic conditions [START_REF] Van Geen | Decoupling of As and Fe release to Bangladesh groundwater under reducing conditions. Part II: evidence from sediment incubation[END_REF][START_REF] Mladenov | Dissolved organic matter sources and consequences for iron and arsenic mobilization in Bangladesh aquifers[END_REF]. For paddy fields irrigated with As-enriched groundwater, it is worth noting that the As content of the rice-submerging water and in soil water obviously depends on the As content of the irrigation water [START_REF] Norra | Impact of irrigation with As rich groundwater on soil and crops: a geochemical case study in West Bengal Delta Plain[END_REF][START_REF] Van Geen | Impact of irrigation with groundwater elevated in arsenic on rice paddies in Bangladesh[END_REF][START_REF] Roberts | Arsenic release from paddy soils during monsoon flooding[END_REF]) but no direct relationship has been observed [START_REF] Garnier | Temporal variations in arsenic uptake by rice plants: the role of iron plaque in paddy fields of Bangladesh irrigated with groundwater[END_REF]. As far as As speciation and its availability in natural solution are concerned, they mainly depend on As and Fe redox status (As(III) or As(V), Fe(II) or Fe(III)) in solution but also on the impact of other component of natural systems such as PO 4 , silica, Mn or organic matter. Furthermore, more than dissolved species, it is now well accepted that natural colloids must be taken into account for accurate predictive modelling of the metal(loid)s speciation and then to determine their bioavailability. Arsenic mobility has been shown to be particularly controlled by interaction with Fe oxyhydroxide-colloids because of their strong ability to adsorb As at circum neutral pH [START_REF] Dixit | Comparison of arsenic(V) and arsenic(III) sorption onto iron oxide minerals: implications for arsenic mobility[END_REF]. However, interactions of As with Fe mineral phases may be indirectly and strongly modified by the presence of PO 4 , silica, Mn or organic matter that can act upon the Fe (oxyhydr)-oxides) formation and structure [START_REF] Rose | Nucleation and growth mechanisms of Fe oxyhydroxide in the presence of PO 4 ions. 1. Fe Kedge EXAFS study[END_REF][START_REF] Doelsch | Chemistry and structure of colloids obtained by hydrolysis of Fe(III) in the presence of SiO 4 ligands[END_REF][START_REF] Wolthoorn | Colloid formation in groundwater: effect of phosphate, manganese, silicate and dissolved organic matter on the dynamic heterogeneous oxidation of ferrous iron[END_REF], and fixation of As on/in Fe minerals [START_REF] Davis | Implications of aqueous silica sorption to iron hydroxide: mobilization of iron colloids and interference with sorption of arsenate and humic substances[END_REF][START_REF] Liu | Effect of pH, phosphate and oxalate on the adsorption/desorption of arsenate on/from goethite[END_REF][START_REF] Thoral | XAS study of the evolution of iron and arsenic speciation during Fe II oxidation in presence of As III[END_REF][START_REF] Sahai | Role of Fe(II) and phosphate in arsenic uptake by coprecipitation[END_REF][START_REF] Ciardelli | Role of Fe(II), phosphate, silicate, sulfate, and carbonate in arsenic uptake by coprecipitation in synthetic and natural groundwater[END_REF][START_REF] Bauer | Arsenic distribution in the dissolved, colloidal and particulate size fraction of experimental solutions rich in dissolved organic matter and ferric iron[END_REF].

Aquifers and paddy fields in Asian deltaic areas have anoxic conditions. It has to be pointed out that Fe speciation in such an anoxic natural environment has received little attention, likely because anoxic conditions are difficult to preserve during and after sampling, for further investigations such as size fractionation [START_REF] Hassellöv | Application of cross-flow ultrafiltration for the determination of colloidal abundances in suboxic ferrousrich ground waters[END_REF]. In addition, very little is known about the presence of Fe(II) in colloidal fractions under anoxic conditions; as this has only been assessed in the two studies mentioned here. First, in the colloidal fraction of a stream in a catchment which includes some Fe-rich shale bands partly overlain with peat [START_REF] Gaffney | Oxidation state and size of fe controlled by organic matter in natural waters[END_REF]. Second, in suboxic groundwater from a sandy coastal aquifer where 4% of the Fe was associated with the colloidal fraction, but the authors did not indicate whether this small fraction was composed of Fe(II) or Fe(III) [START_REF] Hassellöv | Application of cross-flow ultrafiltration for the determination of colloidal abundances in suboxic ferrousrich ground waters[END_REF].

Whether and how colloids influence As mobility and speciation under natural anoxic conditions is of great scientific interest due to the impact on the water and food supply of the local population. Indeed, in the case of rice, As complexation by colloidal particles may significantly reduce its bioavailability for the plant by competitive effects between colloidal reactive sites and Asspecific sites (or Fe plaque) on the cell membrane of roots [START_REF] Liu | Do phosphorus nutrition and iron plaque alter arsenate (As) uptake by rice in hydroponic culture?[END_REF][START_REF] Guo | Effect of silicate on the growth and arsenate uptake by rice (Oryza sativa L.) seedlings in solution culture[END_REF]. Therefore, in the present paper, the leading role of such colloids was investigated through the occurrence of colloidal species and the impact of components such as Fe, Mn, PO 3À 4 and SiO 4À 4 on colloid formation. For this purpose, a size fractionation procedure was carried out (under controlled anoxic conditions) with decreasing pore size cut-offs in order to allow colloid isolation from the so-called ''solute'' dissolved forms. Moreover, the identification of processes controlling As mobility and As speciation in soil water being a key point when considering As uptake by rice plants in flooded paddy fields from As-affected areas, the investigations were performed on Bangladesh groundwaters and soil waters from paddy fields irrigated with As-rich groundwaters.

Material and methods

Water sampling

Twelve groundwaters used for irrigation during the dry season were sampled in the Araihazar sub-district (Bangladesh) between 2006 and 2008 from tube wells equipped with electrical pumps. Groundwater sampling points were selected according to their Fe and As concentrations in order to cover a large As/Fe ratio (see results below), with two samples W-F1 et W-F2 (see below nomenclature) collected at the same site 11 days apart. Nine interstitial water samples coming from three flooded paddy fields (irrigated with water pumped from three of the above selected tube wells) were sampled at different periods of time of irrigation during the dry season. Sampling at different places and periods of the rice growth also allowed covering different As/Fe ratios in relation to the great difference in observed geochemical conditions such as redox potential [START_REF] Garnier | Temporal variations in arsenic uptake by rice plants: the role of iron plaque in paddy fields of Bangladesh irrigated with groundwater[END_REF]. One sample corresponding to the water layer submerging the rice field was also collected. Soil water was extracted by squeezing soil cores (Top 6 cm of the soil) giving between 40 and 60 mL of water for each soil sample. Concerning irrigation groundwater, a 2 L bottle was filled by overflow until the O 2 concentration was stabilized to the lowest recorded value (generally a few%). It has to be noted that, due to the volume needed for dissolved organic C (DOC) analysis in soil solution, a second core was sampled for this purpose in addition to the one used for the other analyses.

Size fractionation procedure

In both cases (aquifers and flooded paddy fields), the water was under reducing conditions before sampling and the challenge was to maintain these conditions during the sampling and the size fractionation procedure. This was realised by performing the different filtrations on site, a few minutes after sampling and under a N 2 atmosphere using a glove bag. Size fractionation was performed at the nominal cut-off of 200 nm, 25 nm and 10 kDa for the groundwater samples and only at the nominal cut-off of 200 nm and 10 kDa (due to the small amount of water available) for the soil water samples. The size fractioning was carried out using a cascade filtration procedure. The 200 nm filtration was performed using a syringe holder filter for the soil water samples and with a filtration set holder under N 2 pressure using 200 nm Nuclepore Ò membranes (0.47 mm diameter) in the case of groundwater. The 25 nm and 10 kDa filtrations were carried out under N 2 pressure with acetate membranes and polyethersulfone Millipore Ò membranes, respectively, using a 47 mm filter holder. According to the available water volume, the size fractioning took between 1 and 5 min for the 200 nm filtrations and between 5 and 15 min for the 10 kDa filtrations, for soil waters and groundwaters, respectively. Even if membrane clogging cannot be ignored, Nuclepore Ò and Millipore Ò membranes are known to be little affected by this process [START_REF] Buffle | The use of filtration and ultrafiltration for size fractionation of aquatic particles, colloids, and macromolecules[END_REF] and no reddish colour was observed on the membranes after filtration. More details on the water sampling and the size fraction procedure of groundwaters and soil waters are given in Supporting information.

Sample nomenclature

Samples coming from wells (groundwater) or soils (soil water from flooded paddy fields) were named by ''W'' and ''SW'', respectively, followed by letters and/or numbers identifying the location and by a final letter (A-C) corresponding to the fraction passing through 200 nm, 25 nm and 10 kDa membranes, respectively. The sample labelled SS corresponds to the water layer on a flooded paddy field.

In the present study, any element encountered in the fraction lower than 10 kDa will be considered as ''solute'' using the nomenclature proposed by [START_REF] Buffle | The use of filtration and ultrafiltration for size fractionation of aquatic particles, colloids, and macromolecules[END_REF]. Even if a 25 nm filtration was performed in the case of groundwaters, only one colloidal fraction was considered in this paper, corresponding to the size fraction isolated between 200 nm and 10 kDa membranes. The percentage (with respect to the total) of an element in this operationally-defined colloidal fraction was calculated by subtracting the concentration in the 10 kDa fraction to the concentration passing through the 200 nm filter. It has to be noted that some negative values can be observed when calculating the colloidal fraction because of closed concentrations in fractions A and C falling within the range of analytical error.

Fe speciation in solution

Only filtered solutions were used for determination of Fe speciation in solution. Therefore, Fe potentially adsorbed on macro organic matter components was not taken into account in this study. Because of a probable rapid change of Fe speciation in the case of modification of the initial reducing conditions of the samples, Fe 3+ and Fe 2+ concentrations in filtered samples were determined in the field, immediately after sampling. This was carried out by colorimetric measurements on a 12 V spectrophotometer (Dr. Lange/Hach) using registered analytical kits (LCK 320) based first, on Fe(II) complexation with phenanthroline and in a second step, after reduction of Fe(III) to Fe(II). Therefore, Fe(III) concentration in the filtered solution was calculated by difference between total Fe measured after reduction and Fe(II) concentration. The total Fe in the filtered solution (Fe(Tot)) refers to Fe measured in the laboratory or to the sum of Fe(III)+Fe(II) measured in the field. It should be noted that any change in the Fe(Tot)/Fe(II) ratio between two successive filtrations indicates an oxidation process during the filtration procedure. Therefore, determination of the Fe speciation in the field also allowed checking if the water samples had maintained their initial anoxic conditions during the different size fractionation procedures.

Water analysis

Filtered water samples were acidified with 1% supra pure HCl in the field. Arsenic and Al concentrations were measured by Inductively Coupled Plasma-Mass Spectrometry (ICP MS -Elan DRC II -Perkin Elmer Sciex), and Fe, P, Mn, Si and K were measured by Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP AES Optima 2000 DV Perkin Elmer). The analytical conditions used for both techniques are described in Supporting information (Table SI-1). The analytical error for both techniques was below 5% and the results are given in lmolL À1 , with the relative standard deviation (%RSD) obtained on five replicates.

Fe(II) speciation modelling

Various models have been developed to describe the interactions of natural organic matter (NOM) with proton or metals, using discrete or continuous distribution of binding sites (e.g. [START_REF] Tipping | Humic Ion Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF][START_REF] Kinniburgh | Ion binding to natural organic matter: competition, heterogeneity, stoichiometry and thermodynamic consistency[END_REF][START_REF] Dudal | Accounting for natural organic matter in aqueous chemical equilibrium models: a review of the theories and applications[END_REF][START_REF] Lenoble | Combination of 13C/113Cd NMR, potentiometry, and voltammetry in characterizing the interactions between Cd and two models of the main components of soil organic matter[END_REF]. In this study, to calculate Fe(II) speciation, and particularly its interactions with the dissolved organic matter, the Win-HumicV [START_REF] Gustafsson | WinHumicV For Win95/98/NT[END_REF] model has been used. It is based on the Humic Ion-Binding Model V, assuming a discrete distribution of binding sites which form mono-or bi-dentate complexes with proton and metal, as described in detail elsewhere [START_REF] Tipping | Humic Ion Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. To depict the metal-humic binding, the following parameters have to be defined: organic matter content (in g L À1 ), percentage of reactive humic substances and percentage as fulvic acid. It is generally assumed that the organic matter is composed of 50% C. Default values for the other two parameters are both 100%, which has been used by several authors in comparable studies (e.g. [START_REF] Christensen | Complexation of Cu and Pb by DOC in polluted groundwater: a comparison of experimental data and predictions by computer speciation models (WHAM and MINTEQA2)[END_REF][START_REF] Jansen | Organic complexation of Al and Fe in acidic soil solutions. Comparison of diffusive gradients in thin films analyses with Models V and VI predictions[END_REF][START_REF] Ge | Modeling of Cd and Pb speciation in soil solutions by WinHumicV and NICA-Donnan model[END_REF], even if some authors have tested other conditions, for instance 50% of reactive humic substances (HS) with 100% or 90% of fulvic acid (FA) [START_REF] Unsworth | Model predictions of metal speciation in freshwaters compared to measurements by in situ techniques[END_REF]. In this study, as it was not possible to speculate on these parameters, it was decided to test the following organic matter models: 50% or 100% of reactive HS with a proportion of 50% or 100% of FA. Considering the available data, Fe(II) speciation calculation was performed separately for groundwater and soil water samples, studying three different cases: (I -unfavourable) minimal pH and DOC values with maximum Fe(II) content; (IIfavourable) maximum pH and DOC with minimum Fe(II); and (III -average) average values of pH, DOC and Fe(II).

Results

The groundwater and the soil water pH, respectively, varied from 6.3 to 7.4 and 6.5 to 7.5. Only seven values of dissolved organic C are available for the A fraction (fraction below 200 nm) (three groundwaters and four soil waters) and the values ranged between 2.8 and 4.2 mg L À1 for groundwaters and between 14.2 and 62.4 mg L À1 for soil solutions. Arsenic, Fe, Mn, SiO 4À 4 and PO 3À 4 (when available) concentrations in A, B and C size fractions for all the studied samples are given in Tables 1a and1b and as stacked histograms for easier comparison of the data in Figures SI-1a and SI-1b for groundwater and SI-1c for soil waters in Supporting information.

Determination of Fe speciation in the field was not possible for all samples (Tables 2a and2b) because of the small volume available for the isolated fractions of some soil solutions. For high Fe concentrations, the total Fe (i.e. 200 nm-filtered Fe) concentrations measured in the field (when available) in all the different isolated fractions were in good agreement with the concentrations measured in laboratory (Fig. 1 and Tables 1a and1b). However, for low Fe concentrations, the ratio between the concentration measured in the field and in the laboratory can deviate by up to 50%. It is worth noting that (for all the size fractions) Fe was mainly in the Fe(II) form as shown by the Fe(II) percentages as a function of total Fe concentration in groundwater and soil water samples measured in the field (Tables 2a and2b and Fig. 1).

Arsenic concentrations in groundwater A fractions varied by an order of magnitude between samples (from 0.40 to 4.20 lmole L À1 ). Compared to groundwaters, higher As concentrations were found in soil waters (0.40-8.9 lmole L À1 ). The Fe concentrations in groundwater A fractions also varied by an order of magnitude between samples (from 30 to 290 lmole L À1 ) but, contrary to what was observed for As, Fe concentrations decreased sharply in the B and C fractions. Very little variation was observed for Mn and SiO 4À 4 concentrations between groundwater or soil water different size fractions. Yet, for Mn, higher concentrations were measured in soil waters (a maximum of 10 lmole L À1 for groundwaters, and 170 lmole L À1 for soil waters). Conversely, SiO 4À 4 exhibited much higher concentration in groundwaters (around 700 lmole L À1 against 380 lmole L À1 maximum in soil waters). Concerning PO 3À 4 , as for Fe, concentrations exhibited decreasing values from A to C size fraction for both groundwaters and soil waters.

Discussion

Arsenic speciation (arsenite As(III) and/or arsenate As(V)) in the different size fractions was not determined in the present study but previous studies on the same groundwaters and soil water from the same flooded paddy fields have shown that of the As in the <200 nm fraction was between 95% and 98% As(III) [START_REF] Garnier | Temporal variations in arsenic uptake by rice plants: the role of iron plaque in paddy fields of Bangladesh irrigated with groundwater[END_REF][START_REF] Van Geen | Impact of irrigation with groundwater elevated in arsenic on rice paddies in Bangladesh[END_REF]. It is then assumed that As speciation in the present samples is similar to that observed in the previous studies, even if this assumption could be less justifiable for soil water for which redox conditions are changing [START_REF] Takahashi | Arsenic behavior in paddy fields during the cycle of flooded and non-flooded periods[END_REF].

Lower concentrations of a given element observed between two different size fractions indicate the presence of a colloidal form of this element. This finding, as well as the variation of the ratio between some elements in the different size fractions can be used to determine the impact of the different components on As, Si, Mn and Fe-colloid formation, if any.

The total Fe (i.e. 200 nm-filtered Fe) concentrations observed in both groundwater and soil water are very high and obviously such high concentrations are found only because of the presence of Fe(II) species and not because of the presence of Fe(III). Therefore, for soil waters, even when the Fe(Tot)/Fe(II) ratio (Tables 2a and2b) was not available and because of the similar environmental conditions for all the sample sites (paddy fields flooded with groundwater), it is reasonable to assume hereafter that Fe concentration mainly corresponds to Fe(II) species. In addition, and more surprisingly, it was observed that the Fe(II) concentration decreased with decreasing size of the isolated fractions (Tables 1a and1b). Indeed, the Author's personal copy colloidal fraction (200 nm-10kD) represents between 12% and 98% of the total Fe for soil waters and between 40% and 95% for groundwater with an average of 67.2% and 65.4% for soil water and groundwater, respectively. Therefore, the two above findings clearly indicate the presence of Fe(II)-colloids in the colloidal fraction (200 nm-10 kDa) in anoxic groundwater and soil water of flooded paddy fields. Due to the initial redox conditions encountered in the sampling sites, Fe(III) species were unexpected. However, even if the formation of Fe(II) due to the Fe(III) photoreduction during Fe measurement in the field is conceivable, this can be ignored because the kinetics of such a reduction is slow (a few hours; [START_REF] Chen | The roles of natural organic matter in chemical and microbial reduction of ferric iron[END_REF] compared to the filtration time scale of the study and Author's personal copy no significant artefact was observed in the presence of high concentration of NOM [START_REF] Viollier | The ferrozine method revisited/ Fe(II)/Fe(III) determination in natural waters[END_REF]. As mentioned in the introduction, to the best of the authors' knowledge, this study is the first where results clearly showed the presence of a high proportion of Fe(II) in the colloidal fraction in the case of groundwater and soil water under anoxic conditions. Concerning the formation and the nature of Fe(II)-colloids, physical-chemical conditions of the waters, as well as Fe concentrations do not favour the precipitation of Fe(OH) 2 hydroxides (under saturation). However, in the organic matter-rich natural systems, the analogy with results obtained for Fe(III) in the presence of simple organic compound can be proposed.

In addition, knowing that NOM is able to abiotically reduce Fe(III) [START_REF] Chen | The roles of natural organic matter in chemical and microbial reduction of ferric iron[END_REF], the occurrence of Fe(II) associated with NOM is enhanced. If so, it is postulated that Fe(II) may be linked (whether adsorbed or complexed) with organic matter and can exist as monomeric or small oligomeric polycations (2-20 Fe atoms). Indeed, in the case of Fe(III), it has been shown that for the 20-300 kDa colloidal fraction, Fe(III) was complexed by carboxylic groups leading to a strong hindrance of Fe polymerizationprecipitation since Fe has been found to form dimers or trimers [START_REF] Rose | Iron speciation in natural organic matter colloids[END_REF]. It is difficult to determine the structure of the Fe(II) oligomers complexed by carboxylic ligands but it is supposed that the octahedral Fe(II) shares edge forming small clusters of the Fe(OH) 2 structure. It has been shown in the case of Fe(II) polymerisation under anoxic conditions that such structures exist [START_REF] Doelsch | Hydrolysis of Iron(II) chloride in reducing conditions and influence of SiO 4 ligands[END_REF].

Results of Fe(II) speciation calculation performed using the WinHumicV model for the different studied cases (depending on pH, and Fe(II) and DOC concentrations) are summarized in the Table 3. These results show that Fe(II) could be strongly bound by organic matter, especially for soil water samples due to higher DOC contents. For groundwater samples, the proportion of organic Fe(II) is predominant only in the case of low Fe(II) total concentrations (i.e. case II -favourable), almost independently of the parameters (i.e. percentage of reactive HS and FA proportion) used to describe the organic matter reactivity. This modelling confirms the hypothesis that Fe(II) speciation could predominantly be dominated by organic complexes of colloidal size, as experimentally observed by size fractionation.

In contrast to what was observed for Fe, the variation of As concentrations, between different size fractions for any given sample, most of the time were weak for both groundwater and soil water (Tables 1a and1b). Most of the time, less than 10% ±5 of the total As was associated with the 200 nm-10 kDa fraction (Fig. 2).

Table 2a

Percentage of Fe(II) compared to the total Fe measured in the field, ratio of the total Fe measured in the laboratory to that measured in the field and molar Fe/As, Mn/As, Si/ As and P/As ratios in groundwater. Therefore, the relatively constant As concentration in the different fractions suggests that As is mainly present in the solute fraction. However, when As-colloids are observed (% As-coll values above 10-15% on Fig. 2), it must be noted that the percentage of As-colloids is relatively well correlated with the total Fe concentration in solution. Therefore, combining As and Fe results allowed the conclusion that, where present, As-colloids could be in the form of As(III)-bearing Fe(II)-colloids. In addition, where interaction between As and Fe is concerned, the decrease of the Fe/As ratio (Tables 2a and2b) with size fraction in the case where As concentrations remain relatively constant (Tables 1a and1b) implies relatively independent behaviour of these two elements. The low As bearing Fe-colloids as the colloid size decreased is not in agreement with the results of [START_REF] Mayo | The effect of nanocrystalline magnetite size on arsenic removal[END_REF] who, for synthetic systems, showed that as the particle size of magnetite (Fe 3 O 4 ) decreased from 300 to 12 nm, the adsorption capacities for both As(III) and As(V) increased nearly 200 times. Similarly, the present results are not in good agreement with the results of [START_REF] Thoral | XAS study of the evolution of iron and arsenic speciation during Fe II oxidation in presence of As III[END_REF] that detailed the strong affinity between As(III)-Fe(II) colloids for synthetic systems during the oxidation of Fe(II)-As(III) solutions. So, the very low affinity between Fe(II)-colloids and As(III) that was observed for natural systems may arise from the effect of other(s) component(s) that could act as competitors for sorption of As to (or coprecipitation on) Fe (oxyhydr)-oxides. In fact, [START_REF] Gaffney | Oxidation state and size of fe controlled by organic matter in natural waters[END_REF], have clearly demonstrated that Fe(II) even if weakly bound to colloidal organic matter is subject to modification of its behaviour. In addition, dissolved organic matter in Bangladesh aquifers has been found to act as a labile substrate for Fe-and humic-reducing bacteria [START_REF] Mladenov | Dissolved organic matter sources and consequences for iron and arsenic mobilization in Bangladesh aquifers[END_REF]. Some of the components present in natural ecosystem are known to affect colloid formation, it is then of interest to compare the field results to those obtained in the laboratory, most of the time under controlled conditions. The presence of co-occurring oxyanions such as PO 3À 4 and SiO 4À 4 that are effective competitors for As adsorption on Fe (oxyhydr)-oxides may significantly influence As mobility, according to its speciation [START_REF] Dixit | Comparison of arsenic(V) and arsenic(III) sorption onto iron oxide minerals: implications for arsenic mobility[END_REF][START_REF] Sahai | Role of Fe(II) and phosphate in arsenic uptake by coprecipitation[END_REF][START_REF] Ciardelli | Role of Fe(II), phosphate, silicate, sulfate, and carbonate in arsenic uptake by coprecipitation in synthetic and natural groundwater[END_REF]. However, most of the literature concerning the affinity between oxyanions and Fe (oxyhydr)-oxides has dealt with Fe(III). Because the majority of Fe was in the Fe(II) form in the samples, a direct comparison with previous studies was not possible. However, comparing PO 3À 4 , As and Fe variations in the different size fractions allowed drawing some specific conclusions regarding As behaviour in the systems. Phophorus and Fe concentrations were found to decrease (Tables 1a and1b) in the B and C size fractions. To assess whether P is associated with the Fe colloids, the % of colloidal P was compared to the initial Fe/P ratio, i.e. the Fe/P ratio in the 200 nm fraction. For the groundwater samples (W), the fraction of colloidal P linearly increased with increasing Fe/P ratios (Fig. 3), but not in the case of soil water (SW) a weak control of P on Fe-colloid formation is suggested. Similarly, the comparison between P/As ratio and the% of colloidal As did not show any relationship neither for groundwater nor for soil water (see Fig. SI-2 in Supporting information).

The absence of a clear correlation between Fe and P and the absence of correlation between P and As tends to confirm the impact of other competitors (knowing that the chemistry of P and As is similar) that inhibited As (and to a lesser extent for P) association with Fe-colloids.

As mentioned above, Si concentrations in the soil water were drastically lower than in groundwater used for irrigation (Tables 1a and1b) of the paddy fields. The lower Si concentration in soil water likely reflected Si uptake by rice plants [START_REF] Guo | Effect of silicate on the growth and arsenate uptake by rice (Oryza sativa L.) seedlings in solution culture[END_REF]. Author's personal copy Such a decrease for P (also needed for the growing of rice) between groundwater and soil water was not observed because of P addition as fertilizer by farmers. Here again, the constant Si concentration in the different size fractions (Tables 1a and1b) confirms the absence of colloidal Si phase in the 200 nm-10 kDa fraction. Therefore, a direct impact of As-bearing Si-colloid formation on As mobility cannot be invoked in the Si-rich natural waters. An indirect impact of silica on As mobility could be due to its impact on Fe (oxyhydr)-oxides formation. However, in the case of Fe(II) (oxyhydr)-oxide formation, [START_REF] Doelsch | Hydrolysis of Iron(II) chloride in reducing conditions and influence of SiO 4 ligands[END_REF] have demonstrated, in controlled systems, that the role of silica during the formation of Fe(OH) 2 is weak, leading to a limited hindrance of the formation of hydroxides. Therefore, in the present study, taking into account the absence of Si-colloids, the similar Si/As ratio in the different fractions (Tables 2a and2b) and the limited observations of the presence of As-bearing Fe-colloids, it can be concluded that Si affected neither As sorption on, nor complexation with, Fe(II)colloids.

As for Fe, the high Mn concentrations in groundwater and soil waters under flooded paddy fields resulted from the reductive dissolution of Mn oxides or Mn associated with Fe (oxyhydr)-oxides. For groundwaters, and for any given sample, the absence of variation of Mn concentrations between the different size fractions (Tables 1a and1b) indicated the absence of Mn-colloids (Mn (oxyhydr)-oxides) in the 200 nm-10 kDa fraction. For soil waters, the higher Mn concentrations in the A fraction than in the C fraction implies Mn-colloid formation. The correlation (R 2 = 0.76; Fig. in Supporting information) between the amount of Fe in solution and the fraction of Mn-colloids tends to suggest Mn coprecipitation with Fe-colloids. Yet, the Mn/As ratio is significantly higher in the C fraction than in the A fraction (Tables 2a and2b) thus suggesting that As is not associated with Mn-colloids, which is in agreement with the net negative charge of Mn-oxides at circumneutral pH, not favourable for As sorption [START_REF] Sadiq | Arsenic chemistry in soils: an overview of thermo dynamic predictions and field investigations[END_REF].

The results observed here under anoxic conditions are quite different from those obtained for synthetic systems under oxic conditions with As, dissolved organic matter and Fe(III) where relationships between these components (depending on Fe/C ratios) and the amount of As-bearing colloids have been demonstrated [START_REF] Bauer | Arsenic distribution in the dissolved, colloidal and particulate size fraction of experimental solutions rich in dissolved organic matter and ferric iron[END_REF]. This is likely due to the fact that, under oxic conditions, Fe is mostly insoluble and, therefore, has more potential to control the As speciation. Further investigations are needed to check if there is any link between organic matter and the occurrence of Fe(II)-colloids under the field anoxic conditions. In fact, the results presented were obtained in the field, therefore, the size-fractionation protocol was obviously not as efficient for the metal(loid) fractionation as for Carbon isolation (not the same type of filters, pore cut-offs, etc.). Future studies dedicated to As speciation under anoxic conditions should take into account Carbon isolation to better define the presence of Fe(II) colloids and to determine why some of the Fe(II) and As became effectively ''insoluble'' in some of the samples.

Because As-colloids might contribute to limit availability and bioavailability of this toxic element in waters, the evidence that a large amount of As(III) is not linked to Fe(II)-colloids but is present in dissolved ''solute'' form is of relevance when studying As mobility and transfer in soil/water/plant systems under anoxic conditions such as in flooded paddy fields [START_REF] Liu | Do phosphorus nutrition and iron plaque alter arsenate (As) uptake by rice in hydroponic culture?[END_REF][START_REF] Chen | Direct evidence showing the effect of root surface iron coating on arsenite and arsenate uptake into rice (Oryza sativa) roots[END_REF][START_REF] Bravin | Oxygen input controls the spatial and temporal dynamics of arsenic in soil and rhizosphere of lowland rice (Oryza sativa L)[END_REF][START_REF] Garnier | Temporal variations in arsenic uptake by rice plants: the role of iron plaque in paddy fields of Bangladesh irrigated with groundwater[END_REF].

Fig. 1 .

 1 Fig.1. Relationship between the total Fe concentration (i.e. 200 nm-filtered Fe) measured in field (Fe(Tot)Field) and in the laboratory (Fe(Tot) lab) in groundwaters (grey diamonds), and for soil waters (black diamonds) and the relationship between the percentage of Fe(II) (in all the different size fractions) and the total Fe concentration measured in field (Fe(Tot) field) in groundwater and soil water samples (grey and black circles symbols, respectively).

Fig. 2 .

 2 Fig. 2. .Relationship between the total Fe (i.e. 200 nm-filtered Fe) concentration (Fe(Tot) and the percentage of As-bearing colloids (% As coll) in soil waters (black circles), groundwaters (grey circles) and flooded surface water (cross).

Fig. 3 .

 3 Fig. 3. Relationship between the Fe/P ratio and the percentage of P-bearing colloids (% P coll) in soil waters (black circles), groundwaters (grey circles) and flooded surface water (Cross). The secondary Y axis is the Fe/P ratio for groundwaters and flooded surface water.

Table 1a

 1a Concentrations (lmole L À1 ) measured in the different size fractions of groundwater.

		As	%RSD	Fe	%RSD	Mn	%RSD	Si	%RSD	P	%RSD
	W-K1-A	4.20	0.2	206.8	0.3	1.4	0.6	553	0.7	33.5	0.6
	W-K1-B	3.96	0.3	162.9	0.2	1.4	0.5	544	0.8	22.0	0.7
	W-K1-C	3.88	0.2	91.7	0.2	1.3	0.6	517	0.9	3.7	0.8
	W-K2-A	2.49	0.5	156.1	0.6	4.4	0.4	586	0.6	29.5	0.2
	W-K2-B	2.39	0.6	114.1	0.2	4.4	0.3	602	0.7	14.5	0.3
	W-K2-C	1.88	0.5	33.0	0.4	4.1	0.2	568	0.8	2.9	0.7
	W-C1-A	1.65	0.4	55.9	0.5	3.3	0.1	616	0.3	24.2	0.2
	W-C1-B	1.62	0.5	48.9	0.4	3.2	0.2	611	0.6	21.4	0.2
	W-C1-C	1.45	0.3	16.5	0.5	3.1	0.1	607	0.4	8.9	0.4
	W-C2-A	1.48	0.7	59.5	0.4	2.9	0.4	470	0.4	21.5	0.4
	W-C2-B	1.47	0.6	52.7	0.6	2.9	0.4	478	0.8	18.6	0.6
	W-C2-C	1.38	0.4	23.7	0.4	2.8	0.6	472	0.6	4.8	0.7
	W-G1-A	0.24	0.6	11.4	0.7	1.8	0.7	449	0.7	7.3	0.6
	W-G1-B	0.26	0.7	8.0	0.9	1.8	0.7	451	0.9	6.0	0.6
	W-G1-C	0.24	0.6	2.9	0.7	1.8	0.6	445	0.8	2.8	0.8
	W-C3-A	1.79	0.6	57.0	0.23	3.1	0.51	569	0.65
	W-C3-B	1.73	0.7	45.0	0.59	3.1	0.72	577	0.88
	W-C3-C	1.55	0.7	11.3	0.03	2.9	0.8	544	2.53
	W-D1-A	2.52	0.4	32.3	0.1	2.7	1.26	527	0.34
	W-D1-B	2.41	0.3	22.4	0.24	2.7	0.72	537	2.14
	W-D1-C	2.35	0.3	1.8	0.26	2.6	0.87	541	0.94
	W-H1-A	0.64	0.2	30.6	0.35	4.6	0.04	708	1.7
	W-H1-B	0.51	0.6	24.0	0.37	4.0	1.92	566	0.6
	W-H1-C	0.65	0.7	21.0	0.34	4.6	2.5	708	0.54
	W-M1-A	1.13	0.4	117.4	1.59	10.1	0.11	726	0.45
	W-M1-B	1.11	0.3	108.2	1.4	10.1	1.37	719	0.3
	W-M1-C	0.98	0.4	57.7	0.24	9.9	0.71	712	0.11
	W-F1-A	1.13	0.2	57.2	0.3	2.2	0.3
	W-F1-B	1.19	0.3	44.1	0.5	2.1	0.2
	W-F1-C	1.08	2	17.3	2.2	2.0	1.3
	W-F2-A	1.18	0.4	45.9	0.6	2.2	0.8
	W-F2-B	1.15	0.3	32.0	0.7	2.1	0.2
	W-F2-C	1.21	1	28.6	0.2	2.1	0.4
	W-S1-A	0.44	0.6	288.5	2.55	4.5	0.65	569	1.22
	W-S1-B	0.44	0.4	270.6	0.07	4.6	1.06	559	2.07
	W-S1-C	0.27	0.4	57.0	0.34	4.2	0.36	548	0.13
	Table 1b
	Concentrations (lmole L À1 ) measured in the different size fractions of soil water and surface water.
		As	%RSD	Fe	%RSD	Mn	%RSD	Si	%RSD	P	%RSD
	SW-1-A	3.71	0.7	258.6	0.8	93.8	0.5	171	0.3	13.9	0.8
	SW-1-C	1.82	0.9	6.3	1.1	80.0	0.3	153	0.3	6.1	0.9
	SW-2-A	1.82	0.8	156.5	0.5	111.1	0.6	64	0.3	11.3	0.9
	SW-2-C	1.67	0.9	138.0	0.5	100.7	0.7	63	0.5	10.0	0.9
	SW-3-A	8.91	0.4	141.9	0.4	72.1	0.2	298	0.5	9.4	1.1
	SW-3-C	5.45	0.6	12.6	0.7	62.4	0.2	249	0.4	4.9	1.2
	SW-4-A	4.60	0.2	297.1	0.5	168.5	0.4	182	0.5	13.2	1.3
	SW-4-C	2.46	1.7	7.7	0.8	131.5	0.3	142	0.6	11.3	1.2
	SW-6-A	8.41	0.7	43.9	0.6	171.4	0.1	372	0.5	3.9	1.1
	SW-6-C	7.33	1.1	33.9	0.6	164.3	0.3	362	0.5	3.2	1.2
	SW-7-A	0.40	1.6	6.5	1.6	36.1	0.6			1.4	1.6
	SW-7-C	0.34	0.5	4.3	0.4	36.0	0.4			1.2	2.1
	SW-8-A	0.57	0.9	7.7	0.7	29.8	0.2			0.8	7.4
	SW-8-C	0.53	1.1	2.8	0.5	28.1	0.2			0.8	1
	SW-9-A	1.86	0.3	188.5	0.5	85.2	0.4	382	0.7	5.0	0.4
	SW-9-C	1.64	0.2	132.9	0.4	77.7	0.5	366	0.6	2.8	0.4
	SW-10-A	1.46	0.5	14.1	0.4	25.4	0.2			12.6	0.7
	SW-10-B	1.51	0.3	5.6	0.3	25.1	0.1			5.9	0.4
	SW-10-C	1.52	0.2	3.2	0.6	24.4	0.1			4.8	0.6
	SS-A	5.65	0.4	27.9	0.5	129.1	0.2	656	0.6	8.5	0.4
	SS-B	5.20	0.3	1.2	0.6	122.1	0.1	638	0.5	8.5	0.2
	SS-C	5.32	0.4	0.5	0.3	119.3	0.2	634	0.4	6.2	0.4

  Table 2bPercentage of Fe(II) compared to the total Fe measured in the field, ratio of the total Fe measured in the laboratory to that measured in the field and molar Fe/As, Mn/As, Si/ As and P/As ratios in soil water and surface water.

		% Fe(II)	Fe(Lalb)/Fe(field)	Fe/As	Mn/As	Si/As	P/As
	W-K1-A	97	0.96	49.3	3.4	131.7	8.0
	W-K1-B	85	0.84	41.2	3.5	137.6	5.6
	W-K1-C	87	0.84	23.7	3.5	133.4	0.9
	W-K2-A	99	1.02	62.8	17.6	235.9	11.9
	W-K2-B	99	0.95	47.7	18.3	251.8	6.1
	W-K2-C	96	0.47	17.5	21.5	301.2	1.5
	W-C1-A	96	0.50	33.9	19.9	373.9	14.7
	W-C1-B	97	0.47	30.2	19.7	377.5	13.2
	W-C1-C	93	0.97	11.4	21.6	418.1	6.1
	W-C2-A	99	1.03	40.2	19.6	317.7	14.5
	W-C2-B	99	1.03	35.8	19.9	324.6	12.6
	W-C2-C	99	0.70	17.2	20.1	341.8	3.5
	W-G1-A	95	0.83	46.8	74.0	1841.7	29.8
	W-G1-B		0.83	31.1	68.6	1749.8	23.3
	W-G1-C		0.86	12.0	72.2	1820.7	11.4
	W-C3-A	96	1.10	31.9	17.4	318.8
	W-C3-B	79	1.05	26.0	18.1	332.7
	W-C3-C	94	0.90	7.3	18.6	351.0
	W-D1-A	99	1.00	12.8	10.7	209.1
	W-D1-B	98	0.96	9.3	11.3	223.1
	W-D1-C	95	0.49	0.7	10.9	229.9
	W-H1-A	99	1.06	47.7	0.0	0.0
	W-H1-B	99	0.91	47.5	80.0	1119.1
	W-H1-C	89	1.16	32.4	70.9	1094.1
	W-M1-A	98	0.97	103.8	89.6	642.1
	W-M1-B	98	1.00	97.8	91.0	649.5
	W-M1-C	99	1.03	58.9	100.9	726.1
	W-F1-A		1.35	50.4	19.0
	W-F1-B		1.01	36.9	17.6
	W-F1-C		0.76	16.0	18.3
	W-F2-A	98	0.88	39.0	18.3
	W-F2-B	82	0.79	27.7	18.6
	W-F2-C	97	1.34	23.6	17.1
	W-S1-A	99	1.05	658.1	103.0	1298.7
	W-S1-B	100	0.75	617.4	103.9	1274.7
	W-S1-C	88	1.22	215.0	157.4	2067.9
		% Fe(II)	Fe(Lalb)/Fe(field)	Fe/As	Mn/As	Si/As	P/As
	SW-1-A	90	0.93	69.7	25.3	46.1	3.7
	SW-1-C			3.4	43.9	84.1	3.4
	SW-2-A	90	1.06	86.1	61.2	35.5	6.2
	SW-2-C			82.6	60.3	37.7	6.0
	SW-3-A	93	0.77	15.9	8.1	33.4	1.1
	SW-3-C	99	0.78	2.3	11.4	45.7	0.9
	SW-4-A	85	1.01	64.6	36.6	39.6	2.9
	SW-4-C	71	0.61	3.1	53.6	58.0	4.6
	SW-6-A	90	0.84	5.2	20.4	44.2	0.5
	SW-6-C	91	0.82	4.6	22.4	49.3	0.4
	SW-7-A			16.1	89.4		1.0
	SW-7-C			12.4	104.6		3.6
	SW-8-A			13.5	52.1		1.4
	SW-8-C			5.3	53.2		1.7
	SW-9-A			101.2	45.7	204.9	2.7
	SW-9-C			81.2	47.5	223.5	1.7
	SW-10-A			9.6	17.4		8.6
	SW-10-B			3.7	16.6		3.9
	SW-10-C			2.1	16.0		3.1
	SS-A		0.96	4.9	22.8	116.0	1.5
	SS-B		0.44	0.2	23.5	122.7	1.6
	SS-C			0.1	22.4	119.3	1.2

Table 3

 3 Results of Fe(II) speciation calculation using the WinHumicV model for the different studied cases (in bold -results with the default values of parameters used to describe the organic matter reactivity, see text for explanations).

	Sample	Groundwater
	Studied case	I -unfavourable			II -favourable			III -favourable
	Reactive HS (%)	50		100		50		100		50		100
	FA proportion (%)	50	100	50	100	50	100	50	100	50	100	50	100
	Calculated organic Fe(II) (%)	2.4	2.6	4.7	5.1	92.0	97.6	100.0	100.0	5.9	6.3	11.7	12.6
		Soil water
	Reactive HS (%)	50		50		50		50		50		50
	FA proportion (%)	50	100	50	100	50	100	50	100	50	100	50	100
	Calculated organic Fe(II) (%)	13.8	14.9	27.5	29.7	100.0	100.0	100.0	100.0	63.0	67.7	100.0	100.0

J.-M.Garnier et al. / Applied Geochemistry 26 (2011) 1665-1672

J.-M.Garnier et al. / Applied Geochemistry 26 (2011) 1665-1672

Acknowledgments

This research was funded by the ACI ''Ecodyn'' from the French ministry of research and sponsored by EDF -R&D (Electricité de France, Recherche et Développement).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.apgeochem.2011.04.023.