Francis Claret

Christophe Tournassat

Catherine Crouzet

Eric C Gaucher

Thorsten Schäfer

Gilles Braibant

Dominique Guyonnet

Metal speciation in landfill leachates with a focus on the influence of organic matter

This study characterises the heavy-metal content in leachates collected from eight landfills in France. In order to identify heavy metal occurrence in the different size fractions of leachates, a cascade filtration protocol was applied directly in the field, under a nitrogen gas atmosphere to avoid metal oxidation. The results of analyses performed on the leachates suggest that most of the metals are concentrated in the < 30 kD fraction, while lead, copper and cadmium show an association with larger particles. Initial speciation calculations, without considering metal association with organic matter, suggest that leachate concentrations in lead, copper, nickel and zinc are super-saturated with respect to sulphur phases. Speciation calculations that account for metal complexation with organic matter, considered as fulvic acids based on C1(s) NEXAFS spectroscopy, show that this mechanism is not sufficient to explain such deviation from equilibrium conditions. It is therefore hypothesized that the deviation results also from the influence of biological activity on the kinetics of mineral phase precipitation and dissolution, thus providing a dynamic system. The results of chemical analyses of sampled fluids are compared with speciation calculations and some implications for the assessment of metal mobility and natural attenuation in a context of landfill risk assessment are discussed.

Introduction

Public concern regarding the impacts of landfills is often focused on the long term fate of heavy metals contained in the wastes. Several heavy metals or metalloids, that may be present in landfill leachates, are considered as priority pollutants for groundwater resources (for example lead, cadmium, nickel; (OJEC, 2001). However, in the subsurface, different processes such as precipitation or co-precipitation participate in the natural attenuation of landfill leachate and help explain why heavy metals are rarely detected in groundwater at significant distances from landfills (Christensen et al., 2000b;[START_REF] Christensen | Attenuation of landfill leachate pollutants in aquifers[END_REF][START_REF] Christensen | Biogeochemistry of landfill leachate plumes[END_REF]. In a 15-year old sanitary landfill (Sarnia, Ontario) overlying a 30 m thick natural clay deposit, Cu, Zn and Pb migration was detected only up to 10 cm (Yanful et al., 1988a;Yanful et al., 1988b). Based on selective chemical dissolution analyses data and thermodynamic calculations, these authors suggest the occurrence of heavy-metal precipitation as carbonate minerals. In Horn, located in the Austrian part of the bohemian massif, the mineralogical and geochemical investigation of a 10-year old base sealing from a municipal waste deposit [START_REF] Kugler | Retention of inorganic pollutants in clayey base sealings of municipal landfills[END_REF] highlights a very efficient retention of heavy metals. Structural incorporation during iron (hydrated) oxides and probably carbonate precipitation were considered to be mainly responsible for the retention of heavy metals, in addition to retention on clay mineral surfaces. Other illustrations of the natural attenuation of heavy metals can be found in [START_REF] Essington | The soil mineralogy of lead at Horace's Villa[END_REF][START_REF] Howari | Heavy metal speciation and mobility assessment of arid soils in the vicinity of Al Ain landfill, United Arab Emirates[END_REF][START_REF] Johnson | Hydrological and geochemical factors affecting leachate composition in municipal solid waste incinerator bottom ash Part II. The geochemistry of leachate from Landfill Lostorf, Switzerland[END_REF].

Further to the documented occurrence of heavy metal natural attenuation mechanisms in the subsurface, monitored natural attenuation has been proposed as an eligible management strategy for landfills during their post-closure phase [START_REF] Baun | Natural attenuation of xenobiotic organic compounds in a landfill leachate plume (Vejen, Denmark)[END_REF]Christensen et al., 2000b;[START_REF] Christensen | Attenuation of landfill leachate pollutants in aquifers[END_REF][START_REF] Hubé | Investigations of natural attenuation in groundwater near a landfill and implications for landfill post-closure[END_REF][START_REF] Tuxen | Identification of a reactive degradation zone at a landfill leachate plume fringe using high resolution sampling and incubation techniques[END_REF][START_REF] Van Vossen | New and practical approach for sustainable emission reduction of closed landfills based on natural biochemical processes: results of a full scale bioreactor Project at the landfill Vlagheide in Shijndel (NL)[END_REF]. There still remains, however, a relative paucity of documented field data regarding to landfill leachate composition, that take into account the complexing behaviour of heavy metals. Such complexing behaviour has a major influence on the potential occurrence of attenuation mechanisms, since it directly influences mobility and saturation indices. A particular reason for this paucity is the sensitivity of heavy metals to oxidation conditions, which hampers sampling in the field. Detailed data regarding the speciation of heavy metals in landfill leachate are provided for example by [START_REF] Li | Size fractionation of organic matter and heavy metals in raw and treated leachate[END_REF], [START_REF] Ostman | Leachability and metal-binding capacity in ageing landfill material[END_REF], [START_REF] Oygard | Size charge fractionation of metals in municipal solid waste landfill leachate[END_REF], [START_REF] Ponthieu | Speciation analysis of arsenic in landfill leachate[END_REF], [START_REF] Ward | Heavy metal binding capacity (HMBC) of municipal solid waste landfill leachates[END_REF], Jensen et al. (1999), Jensen andChristensen (1999). Extensive reviews of the composition of landfill leachates are proposed by [START_REF] Kjeldsen | Present and long-term composition of MSW landfill leachate: A review[END_REF] and [START_REF] Christensen | Biogeochemistry of landfill leachate plumes[END_REF]. Reliable data regarding heavy metal content of landfill leachate are particularly important in a context of landfill post-closure, in particular to define the so-called "source term" [START_REF] Cen | EN 12920, Methodology Guideline for the Determination of the Leaching Behaviour of Waste under Specified Conditions[END_REF]. If this term is known with some confidence, it can be fed as input to models that couple transport and chemical reactions in the subsurface (e.g. PHAST, [START_REF] Parkhurst | PHAST-A program for simulating ground-water flow, solute transport, and multicomponent geochemical reactions[END_REF], in order to assess the potential impacts of residual leakage from landfills on groundwater resources. This paper presents the results of a study on leachates representative of landfills in France. The aim of the study was (i) to apply a sequential filtration protocol in order to identify the effect of filter pore size on dissolved metal concentrations, (ii) to perform speciation calculations based on analyses results, to explore potential equilibrium/disequilibrium with respect to the solid phase and

(iii) to propose some implications regarding landfill risk assessments.

Materials and methods

Landfill sites and collection points

Seven non-hazardous waste landfills (primarily municipal solid waste) and one hazardous waste landfill were sampled. All landfill waste cells that contributed the collected leachates were less than ten years old and were in stabilized methanogenic conditions. The leachate samples were collected at various sampling points, depending on possibilities offered by the specific landfill designs. Some landfills were equipped with leachate collection systems that could be sampled directly at an outlet, before discharge into a leachate collection pond. This situation is the most favourable in terms of geochemical representativeness of the leachate samples. On other landfills, however, the outlet was not accessible and therefore samples were collected from the leachate collection pond. Table 1 summarizes sampling conditions. In accordance with the industrial partners of the project, the locations of the landfills are not provided for reasons of confidentiality.

Sample collection protocol

A leachate sampling protocol was adapted from [START_REF] Baun | Speciation of heavy metals in landfill leachate: a review[END_REF] and [START_REF] Beaucaire | Regional fluid characterisation and modelling of water-rock equilibria in the Boom clay Formation and in the Rupelian aquifer at Mol, Belgium[END_REF].

For each sample, three levels of sequential filtration were performed directly in the field: 5.0 μm, 0.45 μm and 0.1 μm. Cascade filtration was performed using a polycarbonate Sartorius system to which was applied a pressure of nitrogen gas (1.5 bar). An additional level of ultrafiltration was performed under a slightly over-pressured azote atmosphere in the laboratory using 30 KDa membranes. Due to the high suspended particulate matter content, the filters had to be frequently replaced during filtration in the field. This was considered to be necessary both from the viewpoint of filtration duration (otherwise the flow of filtered leachate rapidly decreased to a few drops per minute), but also to avoid over-filtration related to the presence of the filter cake. All the filters were preserved for analysis in order to perform mass balances and quantify the proportions of heavy metals associated with particles of different sizes. Sub-samples of leachate were conditioned for the various analyses in the laboratory (cation sub-samples were acidified with nitric acid to pH 2, total organic carbon samples were acidified to pH 2 with sulfuric acid, etc.).

Parameters such as pH, electrical conductivity, dissolved oxygen, redox potential and temperature were analysed directly on site with, respectively, a MP 120 Mettler Toledo pH meter, a Jenway 9200 portable dissolved oxygen Meter, a WTW pH 320 voltmeter and a Jenway 4200 portable conductivity meter. Temperature was measured alongside pH and electrical conductivity.

Redox-sensitive species such as ferrous iron, nitrite, sulphite and chromate, were also measured on site using field spectrophotometric kits (Spectroquant references) and a field spectrophotometer (Merck -Nova60).

Laboratory Measurements

One implication of investigating the effects of speciation on metal attenuation is that sample analyses must necessarily include all the chemical elements that might influence speciation, including in particular major ions. Major cations and anions were analysed in the laboratory using a Dionex DX600 ion chromatographer. A control on cation analyses was performed using an inductively-coupled plasma atomic emission spectrometer (ICP-AES, Jobin Yvon Ultima2), following the standard NF EN ISO 11885-T90-136 of the international organization for standardization. For K, Na, Ca and Mg, detection limits were 0.5 mg kg -1 . Total organic carbon (TOC) in the raw leachate was analysed following acid oxidation with sodium persulfate (standard NF EN 1484). The organic carbon, converted into CO 2 (g), was detected and measured by an infrared cell (OI Analytical 1010 Bioritech). The estimated quantification limit based on the XPT90-210 standard is 0.5mg kg -1 of carbon. Dissolved organic and inorganic carbon (DOC and DIC) were measured on a Shimadzu 5000 TOC analyser. Total dissolved carbon (TDC) is measured first, followed by DIC, while DOC is obtained as the difference between TDC and DIC. Detection limits are also around 0.5 mg kg -1 C. Trace elements were analysed by inductively-coupled plasma mass spectrometry with a PQ3 Thermoelectron apparatus. The aqueous samples were digested with nitric acid (70%) in a closed vessel using pressure and temperature controlled microwave heating (SW 846 Method 3015 from CEM). Indium was used as an internal standard. Detection limits are in the µg kg -1 range. The measurement accuracy was checked by comparison with certified reference materials (TMDA62 and NIST1643) following the laboratory's COFRAC accreditation rules.

Carbon K-edge NEXAFS spectra were measured at the Scanning Transmission X-ray Microscopy (STXM) beamline X1A1 (NSLS), operated by the State University of New York at Stony Brook. The principle of the method is described in detail elsewhere [START_REF] Jacobsen | Diffraction-limited imaging in a scanning transmission x-ray microscope[END_REF].

The absorption by different carbon structures follows the Beer-Lambert Law; i.e., the absorption is directly proportional to the mass absorption coefficients of different carbon functional groups, depending on the X-ray wavelength. The optical density (OD) is equal to the product of sample thickness d, the sample density ρ and the mass absorption coefficient µ(E), which is related to the ratio between the incident flux on the sample I 0 (E) and the flux detected behind the sample I(E) via the following equation:

d ⋅ ⋅ = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ - = ρ μ(E) (E) I I(E) ln OD 0 (1)
STXM sample preparation was performed by drying a 1 µL droplet of resuspended lyophilised fraction on a Si 3 N 4 window (100 nm thick). The spectra were extracted from images taken at different energies across the absorption edge and were aligned using cross-correlation to acquire image stacks [START_REF] Jacobsen | Soft x-ray microscopy from sub-100nm regions[END_REF]. Energy calibration of the spherical grating monochromator was achieved using the photon energy of the CO 2 gas adsorption band at 290.74 eV [START_REF] Hitchcock | Bibliography of atomic and molecular inner-shell excitation studies[END_REF].

3.

Numerical methods

Speciation calculations were performed to provide information about possible controls on heavy metal concentrations in the leachates, based on concentrations of heavy metal and major dissolved species, pH and temperature values. These speciation calculations rely on the equilibrium constants for solute complexes, with an underlying hypothesis of thermodynamic equilibrium. Therefore a shortcoming of these calculations is the fact that the various redox couples in the system may be in disequilibrium (e.g. sulphate/sulphide vs. nitrate/ammonium; cf. Christensen et al., 2000a). Most of the investigated heavy metals exist in a single (or primary) redox state in low temperature solutions (e.g., Pb(+2), Zn(+2), Ni(+2) and to a lesser extent Cu(+2) and Co(+2)). For these elements, knowledge of the redox potential is not mandatory to compute their solute speciation, provided that concentrations of their solute complexing ions are known (e.g. carbonates, sulphides, sulfates).

For As and Cr, on the other hand, experimental determination of their speciation (As(3+) vs.As(+5) and Cr(+3) vs. Cr(+6) should allow the calculation of their solute speciation, for each redox state. However, the analysis of solution speciation of As and Cr after leachate collection is not trivial as it is very sensitive to changes accurring during sample handling. For this reason, such calculations were not performed for As and Cr.

In a first approach, solute speciation and saturation index calculations were performed using total concentrations of heavy metals in the soluble fraction. While dissolved organic matter is well known to be a strong complexing agent for metals, the distribution between "free" metal in solution and metal complexed with organic matter was not determined experimentally. Considering the high concentrations of organic matter in the dissolved fraction, speciation calculation results using this first approach are therefore potentially erroneous. As a consequence, in a second approach, speciation calculations were performed while considering generic organic matter complexation models, according to the methods described in [START_REF] Appelo | Geochemistry, Groundwater and Pollution[END_REF]. Speciation calculations were performed using the code PHREEQC2 v2.12 [START_REF] Parkhurst | User's guide to PHREEQC (version 2) -a computer program for speciation, reaction-path, 1D-transport, and inverse geochemical calculations[END_REF] and the LLNL thermodynamic data base (llnl.dat 85 2005-02-02 Lawrence Livermore National Laboratory).

Results

Physico-chemical parameters measured on-site on untreated leachate samples

Results of analyses performed on site are presented in Tables 2 and3. As seen in Table 2, the values of pH are neutral to basic. Redox potentials measured in the field with the platinum electrode vary from negative to positive values and dissolved oxygen measurements suggest anoxic conditions in most samples but these both measurements are not in agreement from a thermodynamic point of view: redox potentials calculated on the basis of oxygen measurement are always higher than the potential given by the electrode. Based both on in situ spectrophotometric measurements and laboratory analyses for redox sensitive species, speciation calculations show that the other redox couples potential values are not in mutual agreement (see Table 2). Redox sensitive species are not in thermodynamic equilibrium in the leachate samples, as can be expected for such dynamic systems (Christensen et al., 2000a).

Tabulated values of major ion analyses are given in Table 4. The primary anions in these leachates are chloride and bicarbonate. Bicarbonate concentrations were computed based on dissolved inorganic carbon, pH and ionic strength (a method considered to be more reliable than alkalinity measurements due to the possible presence of organic acids such as acetate or inorganic sulphur ions that would have contributed to the measured alkalinity values). Cation concentrations are dominated by the presence of sodium, potassium and ammonium. Ionic balances are good (< 8%) suggesting that no major anion or cation was omitted or measured erroneously.

Trace elements analysis and size distribution

Results of trace element analyses are shown in Table 5 while Figure 1 compares trace element concentrations measured in samples filtered at 0.1 µm with those measured in ultrafiltered samples (< 30 kDa). The same comparison is given in Table 6 for organic carbon. Results generally show little difference between the two collected fractions, suggesting that elements in the < 0.1 µm fraction are either truly dissolved, or else associated with very small organic colloids/particles (e.g. fulvic or humic acids). It is worth noticing that significant fractions of heavy metals have been found in colloidal fractions with a cut off 100 times lower than the smallest one used in this study (Jensen and Christensen, 1999;Jensen et al., 1999). In the following, the <0.1 µm fraction is termed "colloidal fraction". The metal complexation by small organic colloids/molecules in this fraction is taken into account below in the speciation calculations.

Particles collected on the various filters were analysed and mass balances performed (Figure 1). Four different fractions were determined: (i) larger than 5 µm, (ii) between 0.45 and 5 µm, (iii) between 0.45 and 0.1 µm and (iv) below 0.1 µm. Arsenic, cadmium, chromium, nickel and zinc are found mainly in solution and thus, do not appear to be associated with particles (except in rare occasions). Cadmium, copper and lead, behave differently, displaying the associations with the largest particle fraction.

Organic matter characterisation

Carbon edge XANES spectroscopy measurements were performed on the fraction below 0.1 µm for samples S6, S8 and S9 as well on a raw (without filtration) sample S8 in order to identify the organic compounds in the leachates. The XANES spectra are shown on Figure 2. The spectrum of the Suwannee river, United States, fulvic acids (FA), recorded during the same run, is also reported for the purpose of comparison. Indeed, several studies have shown that leachate organic matter is composed mainly of humic and fulvic acids [START_REF] Artiolafortuny | Humic substances in landfill leachates .1. Humic-acid extraction and identification[END_REF]Christensen et al., 1998a;[START_REF] Christensen | Biogeochemistry of landfill leachate plumes[END_REF][START_REF] Frimmel | Aging effects of high-molecular-weight organic-acids which can be isolated from landfill leachates[END_REF][START_REF] Weis | Humic-like substances from landfill leachates -characterization and comparison with terrestrial and aquatic humic substances[END_REF]. The spectra were normalized with the peak at 295 eV. For all spectra, three main peaks located respectively at 285.2 286.6 and 288.6 eV (corresponding to the C(1s)→π* transitions) can be identified (Figure 2). The 285.2 eV band corresponds to aromatic C-C or C-H bonds [START_REF] Cody | Inner-Shell spectroscopy and imaging of a subbituminous coal: In-situ analysis of organic and inorganic microstructure using C(1s)-, Ca(2p)-, and Cl(2s)-NEXAFS[END_REF]. The 286.6 eV transition can be assigned to aromatic C-O (phenols) or C-X (halogen) bonds [START_REF] Cody | Inner-Shell spectroscopy and imaging of a subbituminous coal: In-situ analysis of organic and inorganic microstructure using C(1s)-, Ca(2p)-, and Cl(2s)-NEXAFS[END_REF].The most intense band around 288.6 eV is related to the oscillation strength of the C=O bonds of carboxylic groups [START_REF] Hitchcock | Inner-shell excitation of formaldehyde, acetaldehyde and acetone studied by electron impact[END_REF][START_REF] Urquhart | Trends in the carbonyl core (C 1s, O 1s) -> pi*c=o transition in the near-edge X-ray absorption fine structure spectra of organic molecules[END_REF]. This latter peak is clearly asymmetric, suggesting that both the 289.5 eV transition (assigned to the O-alkyl group of alcohols/carbohydrates), and the mixed Rydberg valence band around 287.4 eV (generally assigned to aliphatic groups), are masked [START_REF] Ade | Chemical contrast in X-Ray microscopy and spatially resolved Xanes Spectroscopy of organic specimens[END_REF].

Filtration of sample S8 led to a slight decrease of aromatic and phenolic functional groups, whereas carboxylic groups content remained constant (Figure 2). Chemical analyses show that both sulfur and acetate concentrations in S9 are higher in comparison to other leachates (Tables 3 and4).

NEXAFS spectroscopy indicates that carboxylic functional group content is slightly lower than the ones measured in S6 and S8, while aromatic and phenolic functional group contents are higher. In comparison to the Suwannee river sample, the leachates seem to have a lower aromatic and phenolic functional group content and a higher carboxylic group content. These differences could either be related to true chemical differences or to extraction procedures. Indeed, it is worth reminding that Suwannee river fulvic acid is purified following a standardized procedure on a column, whereas the leachates were only filtered.

Discussion

Scope of speciation calculations

Speciation calculations allow the computation of saturation indices for many mineral phases that could potentially control heavy metal maximum concentration levels. For instance, metal sulphide phases such as PbS or ZnS could form as a consequence of the increase of the leachate sulphide concentration, consecutive to bacterial sulphate reduction. Intense bacterial activity is suggested by the measured sulphide concentration as well as the high calculated partial pressure of CO 2 in the leachate. Calcite is strongly oversaturated for all samples, due to this continuous bacterial activity and bicarbonate production (Figure 3). In the context of municipal waste storage, scavenging of heavy metals by carbonate phases has been described by several authors (Ettler et al., 2006a;[START_REF] Ettler | Temporal variation of trace elements in waters polluted by municipal solid waste landfill leachate[END_REF]Ettler et al., 2006b;[START_REF] Kugler | Retention of inorganic pollutants in clayey base sealings of municipal landfills[END_REF][START_REF] Manning | Calcite precipitation in landfills: an essential product of waste stabilization[END_REF][START_REF] Strnad | Determination of trace elements in calcite using solution and laser ablation ICP-MS: Calibration to NIST SRM glass and USGS MACS carbonate, and application to real landfill calcite[END_REF]Yanful et al., 1988b). [START_REF] Curti | Coprecipitation of radionuclides with calcite: estimation of partition coefficients based on a review of laboratory investigations and geochemical data[END_REF] has shown that metals are incorporated into calcite following well-defined and predictable geochemical rules.

Calculations without consideration of organic matter complexation

As a first approach, saturation index were computed on the basis of total metal concentrations in the <0.1 µm fraction. Redox potential was considered to be at the value of the redox couple S(-2)/S(+6), because of the potential effect of sulphide phase precipitation on metal solubility [START_REF] Weber | Multi-metal contaminant dynamics in temporarily flooded soil under sulfate limitation[END_REF]. For lead, speciation calculations (Figure 4) suggest leachate super-saturation with respect to galena (PbS). In addition to lead, for all our speciation calculations (figures not shown) similar conclusions ensue for other heavy metals (Zn, Cu, Cd, Co, Ni) where sulphide phases (sphalerite ZnS, covellite CuS, greenockite CoS, millerite NiS, …) are above saturation in all leachate samples. The question then arises as to the reason of such a deviation from equilibrium conditions; i.e. why do the sulphide phases not precipitate ?

The predominance of sulphate reducing bacteria in landfill leachate has been shown by several authors. Yunes et al. (2009) identified sulphate-reducing bacteria (SRB) as one of the main microbial populations in leachate from landfills in Argentina. Zhou et al. (2008) investigated biogeochemical processes occurring in a column filled with sandy soil and fed with leachate and observed a predominance of sulphate-reducing bacteria. Formation of metal sulphides as a result of SRB activity was investigated also by [START_REF] Ehrlich | Microbes as geologic agents: Their role in mineral formation[END_REF]. Galena precipitation has been observed in a bioreactor where sulphate reduction occurred [START_REF] Schroder-Wolthoorn | Application of sulfate reduction for the biological conversion of anglesite (PbSO 4 ) to galena (PbS)[END_REF]. Gallegos-Garcia et al. (2009) recovered metal sulphides (pyrite FeS 2 , sphalerite ZnS and greenockite CdS), separated from the biomass in a one-stage sulphate-reducing process. [START_REF] Labrenz | Formation of sphalerite (ZnS) deposits in natural biofilms of sulfate-reducing bacteria[END_REF] have also reported low-temperature, biologically mediated sphalerite precipitation from dilute natural solutions. [START_REF] Moller | Precipitation of heavy metals from landfill leachates by microbially-produced sulphide[END_REF] studied the impact of sulphate-reducing bacteria for the removal of heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn from four leachates at two landfills in Sweden. The experiments showed that Cd and Cu were most efficiently removed, while Cr was the most difficult to precipitate. In the batch experiments, precipitation of the metals was found to be a relatively fast process.

Such evidence of metal sulphide precipitation via biogeochemical processes raises the question as to why calculations presented here show saturation indices above zero. This discrepancy can be explained either by the validity of the available thermodynamic data or to kinetic effects induced by the microbiological activity. But the fact that metal complexation by organic matter is not taken into account in the latter calculations could also explain the positive saturation indices. This latter hypothesis is tested in the following section. With respect to thermodynamic data, solubility data need to be carefully evaluated. For example, in the case of millerite (NiS), the most likely Ni sulphide mineral to form, widely used solubility product constants turn out to be based only on crude estimates and accurate solubility data are missing [START_REF] Thoenen | Pitfalls in the use of solubility limits for radioactive waste disposal: The case of nickel in sulfidic groundwaters[END_REF]. In the case of greenockite, [START_REF] Clever | The solubility of some sparingly soluble salts of zinc and cadmium in water and in aqueous-electrolyte solutions[END_REF] have suggested a solubility constant of -15.89 log units, emphasizing that the uncertainty on this value is high but without quantifying it. [START_REF] Wang | Cadmium complexation with bisulfide[END_REF] have experimentally determined a value of 14.82 log units. Nevertheless, even assuming an error of 1 or 2 log unit for the solubility constant of these sulphide minerals, calculations will still show them as over-saturated. Kinetic effects are likely to play a major role in this apparent supersaturation [START_REF] Moller | Precipitation of heavy metals from landfill leachates by microbially-produced sulphide[END_REF].

Calculations with consideration of organic matter complexation

As most of the measured pollutants are divalent metals (Cd, Zn, Ni, Co, Cu and Pb), which tend to form complexes with organic matter, calcium measured in high concentrations in the leachates can compete with divalent cations for complexation with organic matter. The different pH values, between 7.25 and 8.65, as well as measured sulphate (< 65 mg L -1 ) or calcium (< 152 mg L -1 ) concentrations, suggest that these leachates are characteristic of methanogenic conditions, implying that fatty acids produced during the acidic stage are not representative of the organic matter in these leachates. XANES results confirm this hypothesis and shows that fulvic acid (FA) is representative of the leachate organic matter. Organic matter complexation was taken into account using the WHAM model [START_REF] Tipping | Wham -a chemical-equilibrium model and computer code for waters, sediments, and soils incorporating a discrete site electrostatic model of ionbinding by humic substances[END_REF][START_REF] Tipping | Humic ion-binding model VI: An improved description of the interactions of protons and metal ions with humic substances[END_REF] that can be implemented in the speciation code PHREEQC [START_REF] Appelo | Geochemistry, Groundwater and Pollution[END_REF]. As shown previously with NEXAFS spectroscopy, the organic matter can be considered, in a first approximation, as humic substances (fulvic and humic acids).

Inputs for the quantity of the four carboxylic sites, the four phenolic sites and the twelve bidentate sites are provided in Table 7. The latter sites were calculated by multiplying the charge in meq g -1 of the different sites by the DOC mass content. The different charges were calculated using the algorithm and the formalism defined in [START_REF] Tipping | A unifying model of cation binding by humic substances[END_REF]. The pK used in the WHAM model for the metal dissociation was taken from [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]1998b) and [START_REF] Lofts | Solid-solution metal partitioning in the Humber rivers: application of WHAM and SCAMP[END_REF]. According to the speciation calculations, Pb, Cd, Ni, Cu and Zn are strongly complexed with OM, except for cadmium in the case of S1 and S2 (Figure 5). This is related both to a lower affinity of cadmium for OM and to a lower concentration of OM in these two samples.

Association of theses metals with colloidal OM is not sufficient to decrease metal concentrations below the metal sulphide solubility limit (Figure 6). As an example, this leads to a two log units decrease of galena saturation index, in agreement with a hundred-fold reduction of lead concentration in the solution (Figure 4).

Implications for landfill risk assessments

As shown in our study and by other researchers, there are strong solubility controls on the fate of heavy metals in the vicinity of landfills. However, the quantitative assessment of these controls is hampered by the occurrence of at least two mechanisms discussed in this paper: (i) kinetic effects related to microbial activity leading to a deviation from equilibrium conditions and (ii) the association between heavy metals and organic matter in the form of very small colloids associated with the < 30 kD fraction. However, in the area of landfill risk assessment, the assessment of heavy metal potential impact still often relies on the concept of equilibrium partitioning between the dissolved and solid (adsorbed) phases (i.e., the "K d " approach; e.g., Slack et al., 2006). While limitations of this approach have been well documented (e.g., Reardon, 1981), its relative simplicity makes it attractive, particularly in a context of consulting studies. The K d is expressed as:

LS eq s d R C C K × = (2)
Where C s is concentration on the solid surface (mol L -1 ), C eq is dissolved concentration at equilibrium (mol L -1 ) and R LS is the liquid to solid ratio (L kg -1 ). The strict domain of validity of the K d modelling approach is underlain by three strong hypotheses:

(i) The reactions that lead to the partition between the liquid and the solid phases are fast and reversible (which excludes for example mineral precipitation),

(ii) The isotherm representing the sorbed pollutant quantity on the solid as a function of its concentration in solution is linear, (iii) The K d value should be independent of physico-chemical conditions of the media (pH, ionic strength, alkalinity). This last point can be neglected if the solution containing the pollutant does not disturb the chemical conditions of the medium and if the K d is measured in the same conditions.

In the advection-dispersion transport equation [START_REF] Freeze | Groundwater[END_REF], the K d enters the calculation of the retardation factor R, provided that the three conditions mentioned above are fulfilled and that a solid phase (e.g. colloidal transport) does not participate in the transport of the element of interest:

Kd R ε ρ + = 1 (3)
Where ρ is the dry density of the solid media (kgdm -3 ) and ε is the porosity of the media (unitless).

Drawbacks of this approach in a context of landfill risk assessment are, on the one hand, that the underlying hypotheses are rarely, if ever, met and, on the other hand, that it usually leads to an overly conservative estimation of landfill potential impact. Regarding the first drawback, among the three criteria presented above that underlie the concept of K d , the third one is particularly questionable in a context of leachate migration in the vicinity of a landfill, as shown by several authors (e.g., [START_REF] Rolle | A kinetic approach for simulating redoxcontrolled fringe and core biodegradation processes in groundwater: model development and application to a landfill site in Piedmont, Italy[END_REF]van Breukelen and Griffioen, 2004;van Breukelen et al. 2004)).

Regarding the second drawback, the K d approach accounts for retardation of pollutant migration, but does not yield an effective attenuation of long-term groundwater concentrations, compared to the situation without retardation, because attenuation mechanisms such as pollutant co-precipitation with mineral phases are not taken into account.

Process-oriented modelling approaches (e.g., [START_REF] Dijkstra | A consistent geochemical modelling approach for the leaching and reactive transport of major and trace elements in MSWI bottom ash[END_REF] are required in order to account for the mechanisms involved in a true attenuation of emissions from landfills.

Conclusions

The results of analyses performed on leachates collected from eight landfills in France suggest that most of the metals (or metalloids) are concentrated in the < 30 kD fraction. For lead, copper and cadmium data show their association with larger particles. Intial speciation calculations suggest that the leachate concentrations of lead, copper, nickel and zinc are super-saturated with respect to sulphur mineral phases. Additional speciation calculations taking into account complexation of these metals with organic matter considered as fulvic acids, based on C1(s) NEXAFS spectroscopy, shows that this mechanism is not sufficient to explain this deviation from equilibrium conditions. It is therefore hypothesized that such deviation results from the influence of biological activity on mineral phase precipitation and dissolution kinetics, the controlling parameters of which are largely unknown.

Future work will be aimed at, on the one hand, sampling solid phase precipitated at the base of the landfill cells from which the sampled leachates originate and, on the other hand, investigating, in the laboratory precipitation and dissolution kinetics of sulphur phases in the presence of organic matter and sulphate reducing bacteria representative of the sampled leachates.

Acknowledgements

The LISPEX project was supported by the French Agency for Environmental and Occupational Health Safety (AFSSET). We are particularly grateful to Arnaud Budka and Carole Bloquet of Suez-Environnement, for providing access to the sampling sites. Use of the National Synchrotron Light Source, Brookhaven National Laboratory, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-98CH10886. We also thank the anonymous reviewers who helped us to improve our manuscript.


Mathieu Delalandre

Pierre Héroux

Sébastien Adam

Eric Trupin

Jean-Marc Ogier

A statistical and structural approach for symbol recognition, using XML modelling

This paper deals with the problem of symbol recognition in technical document interpretation. We present a system using a statistical and structural approach. This system uses two interpretation levels. In a first level, the system extracts and recognizes the loops of symbols. In the second level, it relies on proximity relations between the loops in order to rebuild loop graphs, and then to recognize the complete symbols. Our aim is to build a generic device, so we have tried to outsource models descriptions and tools parameters. Data manipulated by our system are modelling in XML. This gives the system the ability to interface tools using different communication data structures, and to create graphic representation of process results.

Introduction

The current improvements of intranet structures allow large companies to develop internal communications between services. The representation of the heritage of huge companies like a network managers firm is often represented through paper documents, which can be either graphic or textual. As a consequence, the sharing of these kind of information will stay very difficult as long as the storage format will not be digital. This explains the current development of studies concerning the automatic analysis of cartographic or engineering documents, which comes as a result of the growing needs of industries and local groups in the development and use of maps and charts. The aim of the interpretation of technical maps is to make the production of documents easier by proposing a set of steps to transform the paper map into interpreted numerical storage [START_REF] Boatto | An interpretation system for land register maps[END_REF][2][3] [START_REF] Vaxivière | CELESTIN : CAD conversion of Mechanical Drawings[END_REF]. An important step of this conversion process consists in the recognition of symbols, which often appear on technical documents. We present in this document a symbol recognition system. It is based on a statistical and structural approach combination. In the second section, we will briefly describe the classical approaches for symbol recognition. Then, we will present our approach. Finally, we will give conclusions and propose some perspectives for our future works.

Symbols constitute an important informative source on technical documents (geographical maps, utility maps, architectural drawings…). Good states of the art dealing with such a problem can be found in [START_REF] Chhabra | Graphic Symbol Recognition: An Overview[END_REF] [START_REF] Lladós | Symbol recognition: current advances an perspectives[END_REF]. These surveys show that structural approaches are generally chosen for symbol recognition. Such approaches begin with a graphical primitives extraction step. These primitives can be either structural [START_REF] Pasternak | Adaptable drawing interpretation using object oriented and constrained-based graphic specification[END_REF][8] [START_REF] Myers | Verification-based approach for automated text and feature extraction from raster-scanned maps[END_REF] or statistical-structural [START_REF] Lee | Recognized hand-drawn electrical circuit symbols with attributed graph matching[END_REF]. After this first step, primitives and their relations are then represented into a graph, which is used in a process of sub-graph matching in a reference graph. Nowadays, such structural symbol recognition systems are generally efficient for specific applications but cannot be generalized. Only the works of Messmer [START_REF] Messmer | Automatic learning and recognition of graphical symbols in engineering drawing[END_REF], Schettini [START_REF] Schettini | A general purpose procedure for complex graphic Symbols Recognition[END_REF] and Pasternak [START_REF] Pasternak | Adaptable drawing interpretation using object oriented and constrained-based graphic specification[END_REF] can be considered as generic approaches. Indeed, these authors propose generic symbol description tools. Symbols are described by different primitives obtained through the use of low-level operators, and by the association relations between these primitives. However, some problems are not solved in these systems:

• Limitations appear when symbols integrate important variability, or when they are represented by elements, which are closed but not connected.

• Very few works propose a correction step of the primitives extracted by low-level operators. Yet, this point is important, essentially in the case of damaged documents, for which low-level operators can be disrupted by noise.

• Symbol representations are generally dedicated to existing tool libraries and to specific applications. A system allowing a more global representation of symbols does not exist.

3 Our statistical and structural approach for symbol recognition

Introduction

Our approach may be decomposed into 3 steps:

• Extraction of loops • Extraction of orientation invariant features and statistical recognition of loops • Reconstruction of loop graphs, and structural symbol recognition

The system relies on proximity relations between the loops in order to recognize the symbols. Our aim is to build a generic device. So, we have tried to outsource from algorithms model descriptions and tool parameters. In this way, this system is evolutionary and can be used in practice for different applications. Until now, it has been exploited only for symbol recognition on France telecom (a French telecommunication operator) utility maps, and for meteorological symbol recognition. The France Telecom symbols represent technical equipments permitting connections on telephonic network: Concentration Points "Point de Concentration" and room "chambre". They are composed of a variable number of loops belonging to 5 different classes. The In the following, we will present successively each of the 3 processing steps. We will present succinctly the two first steps (loop extraction and classification) to develop more extensively the structural recognition step. Then, we will present in subsection 3.5 the strategy used for the application and the obtained results. Finally, we will present in subsection 3.6 XML use in our system.

Extraction of loops from symbols

An image of loops is obtained through the application of a classical connected components extraction on image. The Fig. 2.a and 2.b show a part of France Telecom utility map as well as the loop extraction result.

Extraction of orientation invariant features and statistical recognition of loops

This processing step constitutes the statistical approach of our system. A feature vector is extracted for each loop on the image, using three outstanding and complementary tools: the Zernike moments, the Fourier-Mellin invariants, and the circular probes [START_REF] Adam | Combination of invariant pattern recognition primitive on technical documents, Graphic Recognition -Recent Advances[END_REF]. These features enable to constitute a vector set describing loops. This description is invariant to scale and orientation changes. We have constituted a test and a training set using France Télécom utility map loops, with a size of fifty loops each. Then, with the help of the k nearest neighbors (knn, with k=1) classifier, using Euclidian distance, we got recognition results that are presented in the Fig. 3 for each feature extraction tool.

Characteristics extraction tools

Zernike moments

Fourier-Mellin invariants

Reconstruction of loop graphs, then structural symbol recognition

This processing step constitutes the structural approach of our system. It may be divided into two steps.

The first step is a model reconstruction step in the sequential processing between the statistical classifier and the structural classifier showed in the Fig. 4.

Models reconstruction

Structural classifier Statistical classifier

Fig. 4. Sequential processing of statistical and structural classification

The models reconstruction tool we used rebuilds some graphs under connection and/or distance constraints. This reconstruction uses the results of loop statistical recognition. The distance constraint permits to control inter-connection of the graphs corresponding to the symbols. It is thus possible to create a graph in which image loops are completely inter-connected, or to isolate each of the symbols of the image. These connections constraints are defined according to the nature of loops. The maximal connections number is specified for each loop class. The symbol description is a priori considered in model reconstruction strategy. The Fig. 5.a shows an example of France Telecom utility map. Model reconstruction of this map is achieved by using constraints concerning connection and distance constraints. The distance constraint permits to detect all the 4 graphs of loops. The Fig. 5.b shows the graphic representation of the model reconstruction of the symbol located below on the right of the map. This graphic representation uses the information obtained from the statistical classification, from the model reconstruction, from a step of contours detection and polygonisation of the loop image. Here, we have a 4-connections constraint for the loop "triangle", and a 1-connection constraint for the loops "ellipse" and for the loop "chambre". The second step is the structural recognition of symbols. It consists in submitting the graphs obtained from the model reconstruction phase to the structural classifier. Our graph matching tool [START_REF] Héroux | A structural classification for retrospective conversion of document[END_REF] allows a graph edges and nodes typing possibility (integer, float, character, string, object). It permits to compute similarity criterion between graphs, based on the overlap between a candidate graph and a model graph. This overlap corresponds to their common sub-graph. This common sub-graph is searched in three times by matching candidate graph and model graph. In a first time, a filtering step aims at suppressing in the two graphs the nodes and their edges unable to be matched. This concerns the nodes whose label is not common in the two graphs nodes lists. This step has for purpose to reduce the algorithm temporal complexity. In a second time, a research of matching edges is done. The edges are matched if they are equal and if their extremities are equal. The nodes corresponding to the edges extremities are also matched during the edge matching. In a third time, the remaining nodes are matched. Two similarity criteria can finally be computed according to the number of common elements, either on the nodes (1), either on the edges (2). In these equations, n1, n2, and nc and e1, e2, and ec respectively represent the number of nodes and edges of the graph 1, the graph 2, and the common sub-graph.

1 2 1 ) 2 , 1 ( 2 - × = nc n n g g n δ (1) 1 2 1 ) 2 , 1 ( 2 - × = ec e e g g e δ (2)
It is possible to control the combination of the results obtained out of ( 1) and ( 2) by average or variance computation. The similarity criterion computation tool is also parameterized in order to take or not into account the types and their attributes. It can be done on the nodes and/or on the edges. For example, it is possible to compute the graph matching taking into account the graph topology, or the graph types. Finally, it is possible to combine the whole similarity criteria obtained by the computation of weighted average, in order to get global similarity criterion. The Fig. 6 is a graphic representation of a model extraction (under connection and distance constraints) and a structural recognition, the loop graphs have been submitted to the structural classifier with a similarity computation taking into account the types and their attributes based on an average of (1) and of (2). The matching graph tool used does not allow the localization and therefore the manipulation of sub-graphs in a candidate graph. Thus, it is impossible to exploit a candidate graph representing the whole image loops. For that reason, it is impossible to distinguish the symbols "chambre" when they are connected to the symbols "PC paires sur appui" and "PC paires sur bornes". Indeed the "chambre" are closely connected to this PC (Fig. 5.a and Fig. 2.a), and a too strict distance constraint could split up the PC symbol in several symbols. To overcome this problem, we have considered the symbol "PC paires sur support + chambre" and "PC paires sur appui + chambre" as complete symbols. We didn't have this problem with meteorological symbol because distance between loops is sufficiently long.

Strategy and results

In the setting of our application, we have tested several symbol recognition strategies.

The most efficient strategy uses the models only taking into account the distance constraint. Indeed, recognition errors in statistical classification inevitably generate some errors in the models reconstruction, if one takes into account connection constraints. With this strategy, we obtained completely inter-connected graph for every symbol. The matching tool is then parameterized to only take into account the global similarity criterion between nodes (Fig. 7.b). This global criterion is a weighted average of two similarity criteria, the first on topology graphs, and the second on exact graphs. The weighted average is computed with a more important coefficient for similarity criterion on graphs topology.

In term of results, we have tested this approach on 29 symbols, constituted of about hundred loops, and distributed on 9 plan extracts. We have constituted a training set of feature vectors describing the loops. This training set has been used as test set, in order to obtain a 100% statistical recognition results. We obtained 100% of recognition on symbols. Obviously, we are interested to test the correction ability of structural recognition step on statistical recognition results. We voluntary altered statistical training set of France Telecom symbols loops in order to reduce the loops recognition rate. Tests carried out on 22 symbols composed of 74 loops give a loops statistical recognition rate of 55.4% and a symbol structural recognition rate of 86.86%. Among these symbols we have up to 75% of a wrong statistical recognition loops. These results prove the ability of structural recognition step to correct the statistical recognition step. But, taking into account similarity criterion on topology graphs increases these results. Indeed, only the node number used alone permits to distinguish France Telecom symbols (1 for "chambre", 2 for the "PC paires sur appui", 3 for the "PC paires sur appui + chambre", 4 for the "PC paires sur bornes", 5 for the "PC paires sur bornes + chambre"). We realized similar tests on the meteorological symbols in order to prove the importance of similarity criterion on topology graphs in symbol recognition. Tests on 56 symbols composed of 96 loops extracted from the same image rotated into 8 different directions gives 44% for loops statistical recognition and 55% symbol structural recognition. Indeed, only symbol "ciel obscurci" (Fig. 2) can be recognized by its loops number. Nevertheless, we have corrected 33% of symbol in which loops were badly recognized by statistical step.

XML modelling

Data manipulated by our system are modelled in XML [START_REF]World Wide Web Consortium, eXtensible Markup Language (XML) 1[END_REF]. The use of this data description language offers several advantages. First, XML seems to become a reference concerning data description languages. This guarantees in the future a durability of our tools and especially a possibility to exploit tools provided by the scientific community. Secondly, the properties of XML can be used in a recognition system. Among these properties, we used the data transformation and the specialized sublanguages. Data transformation tools: XML permits to use of XSLT processors (for example, the Xalan processor [START_REF]Apache XML projects[END_REF]). These processors transform a XML data flow with the help of a XSLT script program [START_REF]World Wide Web Consortium, eXtensible Style-sheet Language Transformation (XSLT) 1[END_REF], which permits an easy data transformation. If data have a XML type flag format, we can have two tools using different communication data structures. In the same way, it is also possible to merge data stemming from several tools. The specialized sub-languages: XML is described as a meta-language because it is a root language, which permits to define specialized sub-languages. For example the SVG [START_REF]World Wide Web Consortium, Scalable Vector Graphic (SVG)[END_REF] language permits a data graphic description. We used this language in order to rebuild a graphic representation of all our steps processing (Fig. 5.b, 6). Moreover, we can superpose the image with the graphic representation of our process result (Fig. 7.b). With the help of the XSLT processor, we merged and transformed information of our different tools in SVG format. We used tools provided by computer science community (SVG viewer [START_REF]Adobe[END_REF], Batik [START_REF]Batik SVG toolkit 1.1[END_REF]) for the recognition visualization.

Conclusions and perspectives

In conclusion, we have presented in this document a symbol recognition system combining statistical and structural approaches. We have exploited these approaches in order to recognize some technical symbols composed of loops. These symbols have proximity relations between theirs loops. We exploited statistical approach in a first interpretation level in order to recognize loops found in symbols. In a second interpretation level, we exploited proximity relations between these loops by a structural approach, in order to recognize the complete symbols. The first results are encouraging. On one hand, a perfect statistical recognition gives a perfect symbol structural recognition. On the other hand, the statistical recognition results can be corrected by structural recognition step. The efficiency of this correction is a function of similarity between symbols (differences in topology symbols, share of loops classes between symbols). The model set and the configuration information of tools have been outsourced from algorithms. This confers to the system a generic aspect. Data manipulated by our system are modelled in XML. This gives to system the ability to interface tools using different data format, and to create graphic representation of each treatment step. Our first perspective is to extend the statistical/structural serial combination in a parallel combination. Indeed, we hope will integrate structural model extraction tools for compare with statistical model extraction tolls actually used. These structural tools allow extracting connected component structure (contrary to statistical model extraction tolls). This property gives ability to extract part of a connected component. Among these structural model extraction tools we hope will integrate:

• squeletonization method -skeleton structuring -and mathematics approximations [START_REF] Hillaire | improving the accuracy of skeleton-based vectorisation[END_REF] • follow line method -structuring method of follow line [START_REF] Ogier | Extraction of roads from digitized maps[END_REF] • line adjacency graph method [START_REF] Di Zenzo | Run-based algorithms for binary image analysis and processing[END_REF] The second perspective consists in improving our structural classifier. In a first time we plan to realize localization and manipulation of sub-graphs in a candidate graph, in order to exploit loops graph representing several symbols on image. It will permit to treat the neighbor symbols that can't be isolated by simple distance constraint (as it's the case of the France Télécom symbol "chambre"). In a second time we hope will compute inexact graph matching in order to allow a tolerance between node and edge values of candidate and model graphs during matching process. It would permit for example to take into account the distances between loops given by reconstruction models tool. Finally, in a third time, we wish to exploit the confusion matrix of our statistical classifier. The goal is to weight similarity computation between graphs according to confidence degrees of node labels (provided by the statistical classifier). Finally, the third perspective is to integrate our tools in a knowledge-based approach. In a first time, the aim is to create a common knowledge set in XML, for the whole tools of our system. A simultaneous use of XSLT and XML-QL [START_REF]World Wide Web Consortium, XQuery 1.0 an XML query language[END_REF] (norm for the XML data set management) will permit management and adaptation of this knowledge set for the whole tools of our system. It will be necessary to define a representation formalism of a generic model for the entire recognition tools. In a second time we wish to develop a supervision program of our recognition system, permitting the combination of our different tools (classifiers and models extraction tools). The aim is to control the whole processing from the image processing to the classifier combination. We wish to control our recognition system by a processing scenario, in order to adapt easily and quickly the system to a new recognition objective.

Fig. 1 .

 1 a shows these symbols. On the top one can see from left to right: « chambre, PC paires sur bornes, PC paires sur appui ». Just below one can see the 5 loops classes named from left to right: « chambre, ellipse, triangle, cercle, portion ». The meteorological symbols represent cloud cover rate. They are composed of a variable number of loops belonging to 4 different classes. These symbols are shown on Fig.1.b. On the top one can see from left to right: « aucun nuage, 2/10 à 3/10 du ciel couvert, 4/10 du ciel couvert, 5/10 du ciel couvert, 6/10 du ciel couvert, 6/10 à 7/10 du ciel couvert, ciel obscurci ». Just below one can see the 4 loops classes named from left to right: « cercle_25, cercle_50, cercle_75, cercle_100 ».

Fig. 1 .Fig. 1 .

 11 Fig. 1.a) France Telecom utility map symbols and their loops

Fig. 2 .Fig. 2 .

 22 Fig. 2.a) A part of France Telecom utility map

Fig. 5 .Fig. 5 .

 55 Fig. 5.b) Graphic representation of the model reconstruction of the right low symbol

Fig. 6 .Fig. 7 .

 67 Fig. 6. Graphic representation of treatment steps of the Fig. 5.a Fig. 7.a shows a meteorological symbol image. The Fig 7.b shows a graphic representation (of model extraction "only under distance constraint" and a structural recognition) superposition with meteorological symbol image. The loop graphs have been submitted to the structural classifier with a similarity computation taking into account the types and their attributes only based on (1).

Fig. 7

 7 Fig. 7. a) Example of meteorological symbol image

The authors would like to thank Joël Gardes (France telecom R&D) for his contribution to this work.