Mario Burbano

Sian Nadin

Dario Marrocchelli

Mathieu Salanne

Graeme W Watson

Ceria co-doping: synergistic or average effect?

INTRODUCTION

Distributed Generation of electricity has been touted by national and international agencies, such as, the Organisation for Economic Co-operation and Development (OECD) [1][START_REF]IEA, Distributed Generation in Liberalised Electricity Markets[END_REF][START_REF]IEA, Combined Heat and Power[END_REF], as a possible means to incorporate nascent technologies into the energy market. Some of these technologies rely on renewable sources, e.g. wind and solar, but they are still severely limited by high production costs. For this reason, more established technologies continue to attract significant attention. One such technology is called Combined Heat and Power (CHP) where the excess heat generated in electricity production is recycled to improve the overall efficiency of the process. CHP systems have traditionally made use of steam turbines, gas turbines, reciprocating engines and, more recently, Solid Oxide Fuel Cells (SOFCs) [START_REF] Ormerod | [END_REF]. The latter are particularly interesting given their high efficiency (up to 85%), low pollutant emissions and fuel flexibility (they can utilize hydrogen, natural gas, landfill gas, gasified coal, etc [5]); thus, SOFCs have the potential to play a key role in the energy conversion landscape for the medium and long term future [6][7][8][9]. Commercial applications of SOFCs could range from domestic units to small power stations. Nonetheless, widespread use of SOFCs has been held back by their high operating temperature, which requires the use of expensive materials and affects the long-term performance of these devices. Substantial research efforts have been devoted to lowering their operating regime to between 500 to 750 C • , known as the intermediate temperature (IT) range [10]. There are two processes which limit significantly the performance of IT-SOFCs, namely the Oxygen Reduction Reaction (ORR) at the cathode and the ionic conductivity of the electrolyte. This article focuses on the latter effect as a means to improve the performance of SOFC electrolytes, in particular those based on doped ceria (CeO 2 ).

Fluorite-structured materials, such as CeO 2 , ZrO 2 and δ-Bi 2 O 3 are among the best oxide ion conductors, which makes them ideal candidates for use as electrolytes in IT-SOFCs. The conduction mechanism in these ceramics is known to occur by means of vacancy migration in the anion sublattice [11,12]. Oxygen vacancies are introduced by doping these materials with lower valent cations [START_REF]δ-Bi2O3 actually behaves differently[END_REF]. For example, CeO 2 is usually doped with Rare Earth cations (RE = lanthanides + Sc and Y) which leads to the formation of one vacancy (V •• O ) for each pair of cations, as illustrated by Equation 1, in Kröger-Vink notation [START_REF] Sun | [END_REF]:

RE 2 O 3 + 2Ce x Ce + O x O → 2RE ′ Ce + V •• O + 2CeO 2 (1)
The formation of oxygen vacancies has a beneficial effect on the conductivity, which shows, at first, a marked increase as more vacancies are added. This increase, however, does not display a monotonic behaviour, but rather, a sharp drop in conductivity is observed beyond a critical concentration. In the case of ceria, this value varies between 2.5 and 5 % vacancy concentration, depending on the dopant cation species, the temperature and other factors. There is a general consensus that this drop in conductivity with increased number of vacancies is caused by defect-defect interactions [15][16][17][18][19][20][21][22][23][24]. The interactions between these defects can be broadly classified into cation-vacancy, vacancy-vacancy and cation-cation [15][16][17][18][19][20][21][22]. In practice, these interactions can prove deleterious to the ionic conductivity because defect association leads to fewer mobile vacancies being available [23], thus the need for high operating temperatures. This process is generally summarised by the following formula for the conductivity, σ:

σT = σ 0 exp - E a k B T , ( 2
)
where T is the temperature, σ 0 the compositiondependent pre-exponential factor, E a the activation energy and k B the Boltzmann constant. The activation energy is given by the sum of a migration enthalpy, ∆H m , and a defect association enthalpy ∆H ass . The higher the association enthalpy, the lower the conductivity.

Traditionally, ionic conductivity optimization has been approached from a compositional perspective. This has meant that, in order to improve the conductivity of ceria-based electrolytes, researchers have mostly focused on parameters such as the ionic radius of the dopant cation and its concentration,

x in Ce 1-x RE x O 2-x/2 .
The chief aim has been to minimize defect interactions, especially those between dopants and vacancies [16,[25][26][27][28][29]. These studies have variously identified a number of RE elements, such as Gd 3+ , Y 3+ , Sm 3+ and Pm 3+ as the best candidate dopants, given that their radius mismatch with the host cation (Ce 4+ ) balances the competing electrostatic and elastic components of the defect interactions which control their association [30]. Nevertheless, recent studies have shown that in the limit where cation-vacancy interactions are reduced to a minimum, it is vacancy-vacancy association which ultimately determines the ionic conductivity drop as a function of dopant concentration in fluorite-structured materials, such as, Yttria Doped Ceria (YDC) [22], Yttria Stabilized Zirconia (YSZ) and Scandia Stabilized Zirconia (ScSZ) [21]. This means that different optimization strategies must be sought if IT-SOFCs are to realize their potential in commercial applications [6,7,31,32].

An interesting route for improving the ionic conductivity of fluorite-structured electrolytes is co-doping, i.e. doping these materials with more than one cation species. This approach was employed on ZrO 2 by Politova and Irvine [33] with two different cation species, each playing a different role. In this case, the material was doped with two different cation species, where each of which played a different role. Sc was added to improve the ionic conductivity, since its radius is very close to that of Zr, thus minimizing cation-vacancy interactions; while Y, on the other hand, was introduced because its larger ionic radius fully stabilizes the fluorite structure and removes a phase transition to a lower-symmetry phase observed in pure Sc-doped ZrO 2 . It is important to note that Y addition to Sc-doped ZrO 2 lowers the conductivity of this material. Therefore, a compromise exists between stability and ionic conductivity in this co-doped zirconia system. In the case of Politova and Irvine, the authors found that very small concentrations of Y are necessary to stabilize the cubic fluorite structure and that this has a small effect on the conductivity.

It has been suggested that co-doping can also be used to improve the ionic conductivity of ceria based electrolytes [34][35][36][37][38][39][40][41][42][43][44][45][46]. However, contrary to the stabilizing role it plays in zirconia, co-doping in ceria has been used in order to either reproduce the ionic radius of an ideal dopant, or the lattice constant of ceria doped with said dopant. Co-doping with two or more different cations aims to obtain an average or "effective" cation radius that is very close to that of Ce 4+ , hence the average strain introduced by the dopant cations is minimized. This is substantiated by different interpretations of how a dopant with a critical radius (r C ) is likely to affect defect-defect interactions. For example, in 1989 Kim suggested that the ideal dopant would not change the volume of the host lattice upon its introduction, thus minimizing the elastic strain, and identified r C = 1.038 Å [47]. More recently, researchers have had access to a more detailed view of the interplay between strain and electrostatics with the use of ab initio methods, which in 2006 lead Andersson et al. [29] to ascertain that r C should be that which maximizes oxygen vacancy disorder. Their simulations showed that when ceria is doped with relatively small cations, vacancies prefer to sit in a nearest neighbour (NN) position with respect to the dopant cation, whereas, for larger cations, vacancies prefer to sit in a next nearest neighbour (NNN) position. The crossover between these two tendencies was observed at Pm 3+ , for which the NN and NNN positions have the same energy, which was rationalized in terms of a perfect balance between the elastic (related to the dopant's radius) and Coulombic interactions between Pm 3+ and a vacancy. This finding implies that Pm 3+ is the ideal dopant for ceria because it increases the configurational entropy and should display the highest ionic conductivity. Unfortunately, Pm 3+ is radioactive, so the authors suggested, instead, to try a mixture of Sm/Nd which have slightly smaller/larger ionic radii than Pm 3+ .[48] Based on these ideas, multiple research groups have carried out experiments in order to test several co-doping schemes for ceria, e.g. Y/Sm codoping [39], La/Y co-doping [40], Lu/Nd co-doping [49] and Sm/Nd co-doping [38], of which the latter two were specifically aimed at reproducing the r C values predicted by Kim and by Andersson, respectively. In general, co-doping studies have pointed to increases in the ionic conductivity with respect to singly doped systems, which has lead to the conclusion that there exists a "co-doping" effect in ceria.

Modern simulation techniques have become a mainstay within the materials science community, not only because they afford researchers information which is complementary to their experiments, but also because they can serve as predictive tools [50]. Hence, the implementation of reliable computer simulations can be used to clarify the role of particular effects present in physical experiments in a targeted and controlled manner. To this end, the interaction potentials reported here are shown to perform with the accuracy of state-ofthe-art first-principles calculations, i.e. hybrid Density Functional Therory (DFT), but at the computational cost of classical (polarizable) molecular dynamics. This approach allows us to study systems with realistic doped/co-doped defect concentrations within the temperatures of interest for SOFC applications (600-1000 C • ), and to accumulate sufficiently long trajectories to calculate the conductivity. This is in contrast to most of the previous computational work on doped ceria which has typically used static DFT calculations or emprirical potentials fitted to equilibrium properties [25,51]. The use of computer simulations allows us to focus on the bulk behaviour of this material, excluding factors like grain size and boundaries, sintering conditions, impurity levels, etc, which are known to also (negatively) affect the conductivity of these materials [52,53]. We show that co-doping does not significantly improve the conductivity of these materials, but rather, we find that the conductivity of the co-doped systems lies within the range spanned by the singly doped systems, i.e. it is an average of the two. The reason for this is that introducing two cation species with radii which are bigger or smaller than that of a given r C affects the local structure of ceria and results in deep traps for the vacancies.

METHODS

Interionic Potential

The highly correlated nature of the f -electrons found in lanthanide elements makes necessary the use of high levels of theory in order to correctly describe their electronic structure. Such demands have been found to be satisfied by the inclusion of a fraction of non-local Hartree-Fock exchange within the framework of Density Funcional Theory (DFT), which gives rise to hybrid functionals (h-DFT) [54][55][56]. Alternative DFT functionals are also available, namely, those which include a Hubbard parameter U (DFT+U ). They represent a viable alternative to h-DFT and their ability to describe the properties of ceria has been widely documented [57][58][59][60][61]. Nonetheless, h-DFT provides a better agreement with experimental lattice constants and does not require fitting a +U value for the f -electron systems. In either case, however, DFT calculations are prohibitively expensive from a computational point of view for this type of study regardless of the functional; this is because of the long Molecular Dynamics (MD) simulation times and large systems required to study the ionic conductivity of doped ceria. For this reason interionic potentials (IP) implemented in an in-house MD code (PIMAIM) [62] and derived from static h-DFT calculations were used in this work, as they accurately reproduce the structural ab initio data at a fraction of the computational cost. This approach has been successfully used for a series of related oxides [20,21,[63][64][65][66][67], including Y-doped ceria [22,68], as well as a variety of ionic systems [69][70][71]. The RE dopant cations studied in this article included La, Nd, Sm and Gd, as well as, Sc. A crucial feature of this potential set is that they were fitted with a common O -O term, which made it possible to perform simulations with several dopant cations within the same cell, i.e. to co-dope ceria. Details on the interionic potential used (DIPPIM -DIPole Polarizable Ionic Model), its parameterization and the parameters used are found in Appendix and Appendix , respectively.

Simulation Details

All MD simulations on the singly doped Ce 1-x RE x O 2-x/2 and co-doped systems were performed using 6 × 6 × 6 supercells (∼ 2592 atoms, depending on the dopant concentration). Two different co-doped systems formed part of this study. Firstly, Ce 1-x Nd 0.5x Sm 0.5x O 2-x/2 , which has been previously studied experimentally [38] because the average radius of both dopants matches that of Pm. Similarly, Ce 1-x Sc 0.22x La 0.78x O 2-x/2 was included given that this ratio of La and Sc also reproduces the ionic radius of Pm. Three different supercells were set up for each dopant concentration, x = 0.05, 0.10, 0.15, 0.20, 0.25 and the values reported here, such as, ionic conductivities, lattice constants and activation energies were obtained from the averages of these three configurations. Each calculation was set up by randomly distributing the dopants over the cation sublattice and the oxygen vacancies over the anion sublattice.

The supercells were initially equilibrated at a temperature of 1673 K for 40 ps; the temperature was then scaled down to room temperature at a rate of 2 K ps -1 . The diffusion coefficients were calculated for temperatures between 873 K and 1473 K from simulations that were up to 3 ns long in the case of the lowest temperature. The 300 K lattice constants were obtained from 10 ps long runs. All simulations were performed at constant temperature and pressure (NPT ensemble), as described by Martyna et al. [72] using a time step of 1 fs. The Coulombic and dispersion interactions were summed using Ewald summations [73], while the short-range part of the potential was truncated at 12.96 Å.

RESULTS AND DISCUSSION

The reliability of the models used in the computer simulations presented throughout this work is assessed in Section by means of comparison against experimental and computational results for singly doped ceria. We also note that this approach has already been successfully used to model yttria-doped ceria, as reported in ref. [68]. Section builds upon these results to determine whether co-doping is likely to improve the ionic conductivity in these solid electrolytes. The remainder of this article abbreviates the various RE-Doped Ceria systems (Ce 1-x RE x O 2-x/2 ) under study to ScDC, GdDC, SmDC, NdDC and LaDC. In a similar fashion, the codoped systems are referred to as Sc:LaDC and Nd:SmDC.

Potential Assessment

Lattice constants of singly doped ceria

The DIPPIM simulated lattice constants for Ce 0.90 RE 0.10 O 1.95 at 300 K are shown in Figure 1 as a solid black line. Their associated errors are represented by the standard deviation of the values from the three simulations carried out for each system. The dashed red line in Figure 1 corresponds to the values for the same systems predicted by Hong and Virkar [74], who derived an empirical expression for the relationship between the ionic radius of the dopant and the lattice constant of RE-doped ceria. Figure 1 also presents experimental (open symbols) and computational (closed symbols) lattice constant values for the same compositions (10% cation doped) of ScDC, GdDC, SmDC, NdDC and LaDC.

As shown in a previous study by the authors [22], DIP-PIM simulations are expected to perform as well as the DFT functional from which they were parameterized. In this case, the use of hybrid DFT functionals means that errors in the calculated value of lattice constants with respect to experiment for doped ceria should be in the order of 0.20% [54]. This is borne out by the results presented in Figure 1, which show an excellent agreement with the range of experimental data available in the literature and also with the values calculated using Hong and Virkar's equation. In the case of ScDC, the simulations provide a better estimation of the lattice constant than that obtained from Hong and Virkar with respect to experiment, however both models underestimate the value of a 0 . For DIPPIM simulations of ScDC, the largest error is 0.45% compared to the value from Grover et al. [75]. Despite this being a relatively small error, it is likely that this discrepancy arises from sources other than the DFT functional employed in this work. In fact, of the three experimental sources cited, Grover et al. (highest value for a 0 ), as well as, Gerhardt-Anderson and Nowick [77] (lowest value for a 0 ) predict low solubilities for scandia (Sc 2 O 3 ) and thus, formation of C-type phases in Ce 0.90 Sc 0.10 O 1.95 which are characteristic of sesquioxides that crystallize in the cubic bixbyite structure, such as scandia. This phase separation in Ce 0.90 Sc 0.10 O 1.95 was recently demonstrated by a series of elegant simulations that coupled DFT+U and Monte Carlo simulations [84].

Ionic conductivity of singly doped ceria

The ionic conductivities calculated for all the singly doped ceria systems under study are presented in Figure 2. The conductivities from simulations at 1273 K are indicated by the dashed lines, those at 1073 K by dotted lines and solid lines for those at 873 K. These lines are to be interpreted only as a guide to the eye, as they connect the individual values from the calculations, each of which has an associated error indicated by the standard deviation from three measurements. The colours differentiate the concentration of the dopant cations as a percentage, thus 5% is shown in black, 10% in blue, 15% in orange, 20% in green and 25% in magenta. In agreement with the literature for singly doped ceria, the best dopants were found to be Gd and Sm [25,81,85]. In particular, GdDC was found to have the highest conductivity at all temperatures. The results also show that the concentration which gives the highest conductivity varies from one doped system to another for a particular temperature. However 10% GdDC is consistently among the systems that display the highest conductivity. In addition, the errors associated with each measurement become larger at lower temperatures because of the slower diffusion. As was mentioned above, Sc is soluble in ceria only in small amounts, hence the results presented here for a fluorite structure with randomly distributed cations correspond to an idealized description of ScDC. The dashed red line (vertical) shown in Figure 2 represents Andersson's critical ionic radius [29] (r c ) for the ideal dopant cation.

Figure 3 focuses on the DIPPIM simulated ionic conductivities at 873 K for the systems already presented and puts them in the context of a range of conductivity values from other studies for the same singly doped ceria (Ce 0.90 RE 0.10 O 1.95 ) systems at the same temperature. The value for Sc is not included in this plot as there are no data available in the literature for the selected concentration and temperature. Individual literature values are labelled a) to m), with open symbols indicating experimental values and filled symbols those from other computational studies. This figure shows that there is a significant spread in the experimental data set, as exemplified by GdDC whose conductivities vary by a factor of 2.5 (from 0.01 S/cm [86] to 0.025 S/cm [85]). Such fluctuations are typically ascribed to different fabrication methods, sintering times and temperatures, grain sizes and impurities [85,86], all of which are excluded from the our computational bulk models. Nonetheless, what is clear from Figure 3 lie in the lower end of the range of the experimental ones, but constitute a good predictor of the overall tendencies in doped ceria.

The calculated E a values for these systems are shown in Figure 4 (filled black circles) as a function of dopant ionic radius and were obtained from simulations between 1473 K and 873 K. The plots of ln(σT ) vs 1/T within this range of temperatures were found to be linear for all dopants. This is important as it indicates that there is no extensive clustering of dopants and vacancies, which would occur if the simulations spanned both sides of the critical temperature (T * ) below which nucleation centres form around the dopants leading to progressive trapping of the vacancies into such clusters as the temperature decreases [52,78]. If the calculations had been carried out at sufficiently low temperatures (T * ≈ 856 K for GdDC [78]), then two different E a values would have been obtained for the T > T * and T < T * temperature regimes. For this reason, the literature data presented in Figure 4 corresponds to the T > T * region only. Experimental values are distinguished with the same open symbols from Figure 3 and labelled a) to o). Just as was found to be the case with ionic conductivities, Figure 4 illustrates that the self-consistent DIPPIM potentials used in this work are able to predict the activation energies for the various RE dopants used in this study. This is particularly impressive since no experimental data were used at any stage of the potential parameterization. We can therefore proceed to study co-doping in this material, with the confidence that the employed simulation technique can reliably predict the properties of these materi-als. The predicted E a for Ce 0.90 Sc 0.10 O 1.95 is 0.675 eV. [81] at 300 K for Ce 1-x RE x O 2-x/2 , where x = 0.05, 0.10, 0.15, 0.20, 0.25 and RE = Sm, Nd, Nd:SmDC and Sc:LaDC. The codoped systems shown correspond to an effective dopant cation radius of 1.093 Å for the stoichiometries specified in Section . It is evident from the figure that the calculations predict the correct lattice constant for the singly doped systems over the entire composition range, and that this carries over to the co-doped cerias under study. The agreement is particularly good for the systems that are the focus of this study, namely Ce 0.90 RE 0.10 O 1. [START_REF] Lang | Solid Oxide Fuel Cells (SOFC VI): Proceedings of the Sixth International Symposium[END_REF] . The results show that co-doping can be successfully used to reproduce the lattice constant of a cation with a critical dopant radius, r C .

Co-doped ceria

Lattice constants of co-doped ceria

Ionic conductivity of co-doped ceria

Thus far the results presented for the ionic conductivity of singly doped ceria have shown only progressive changes in both the bulk ionic conductivities and activation energies as a function of dopant ionic radius. Hence, the question of whether co-doping is a viable alternative for substantially improving these properties becomes: Do co-doped systems show a marked increase (decrease) in ionic conductivity (activation energy) or is this property simply the average of the singly doped systems?

To answer this question it is necessary to compare directly the ionic conductivity of the co-doped systems with that of their "parent" singly doped ceria compounds. Hence if there exists a co-doping effect it would be expected that such systems display ionic conductivities that are higher than the average of the values obtained for singly doped ceria. These data are shown in Figure 6 (a) for the Sc:LaDC system and Figure 6 (b) for Nd:SmDC. The data in these plots is presented as a ratio of the calculated conductivities for the co-doped systems (σ CD ) with respect to the weighted average for the singly doped parent compounds with the same total number of dopant cations (σ WA ). The weighting factors are given by the ratio of each co-dopant as specified in Section , namely 0.50 for both Nd and Sm in Nd:SmDC, as well as, 0.22 and 0.78 for Sc and La, respectively in Sc:LaDC. The solid red line indicates a linear correspondence between both data sets (co-doped vs weighted average of singly doped), i.e. no co-doping effect. It is clear from these plots that any deviations in σ CD away from the σ WA values are within the margin of error of these measurements (standard deviation). This indicates that the conductivities of co-doped ceria can be predicted by simply calculating the average of the two parent singly doped cerias for all temperatures and dopant concentrations. Accordingly, it is expected that the corresponding activation energies for the ionic conductivity of co-doped ceria display the same averaging effect. This is confirmed in Figure 7, which depicts the DIPPIM E a (eV) for Ce 0.90 RE 0.10 O 1.95 from Figure 4 (filled black circles), along with those for the co-doped systems, Nd:SmDC (filled blue triangle) and Sc:LaDC (filled black triangle). Experimental values from a) Omar et al. [37] (open squares) and b) Kasse and Nino [46] (open circles), with green for SmDC, maroon for Nd:SmDC and orange for NdDC. Both data sets show that despite having the same effective r C value of 1.093 Åand taking into account the errors intrinsic to these calculations, the codoped Sc:LaDC system has a higher activation energy than Nd:SmDC; that is, the DIPPIM simulations, as well as, the experimental data for these co-doped systems show changes in bulk ionic conductivities and activation energies that are in line with an averaging effect with respect to the singly doped "parent" oxides, with small deviations from this behaviour likely due to sampling error. The simple, yet often overlooked, explanation for these patterns is found by analyzing the local structure around the dopants in these systems as shown in the next section.

Local structure of co-doped systems

A configurational analysis of the MD simulations for 10% cation doped LaDC, ScDC and Sc:LaDC is illustrated in Figure 8 in the form of the cation-vacancy partial radial distribution functions (g Cat-Vac (r)) obtained from the average of the three configurations used for each system. Appendix details the process of oxygen vacancy identification and subsequent calculation of g(r). The av-erage values are presented in order to eliminate any possible configuration-dependent ordering of the cations. The La/Sc doubly and singly doped systems are illustrated given that the large radius mismatch between the dopant cations with the host facilitates visualization of the small changes undergone, however the conclusions were confirmed for the other co-doped systems. The top panel shows the g(r)s for La -Vac in Sc:LaDC (dashed orange line) vs La -Vac in LaDC (dotted turquoise line); the bottom panel contains the g(r)s for Sc -Vac in Sc:LaDC (dashed green line) vs Sc -Vac in ScDC (dotted magenta line). The solid black lines in both panels correspond to the Ce -O g(r) in bulk ceria at the same temperature, which exemplifies a random vacancy distribution, but with a slightly different lattice constant due to the absence of dopants. The number of vacancies coordinated to the cations in these systems were obtained by integrating the peaks in Figure 8 and are reported in Table I, with the addition of the values for GdDC of the same concentration which are included for comparison as it is the best single dopant system. These results show that Sc acts as a vacancy scavenger in ScDC, with the vacancies ordering in the first coordination shell of this cation. This is a well known effect which has been documented by experiments [77,[START_REF] Li | [END_REF] and simulations [25,97]. In fact, dopant cations that are smaller in radius than Ce 4+ are generally expected to have vacancies in Nearest Neighbour (NN) positions, while those that are larger are expected to have the vacancies in the Next Nearest Neighbour (NNN) position. The latter effect is observed in the case of the larger La and Gd (Table I) cations as indicated by the pronounced second peak. The results in Figure 8 and Table I 4 along with those for the co-doped systems, Nd:SmDC (filled blue triangle) and Sc:LaDC (filled black triangle). Experimental values from a) Omar et al. [37] (open squares) and b) Kasse and Nino [46] (open circles) with green for SmDC, maroon for Nd:SmDC and orange for NdDC.

tems to those with more than one dopant species.

Previous studies have shown that concomitant with cation-vacancy ordering, there also exist inherent vacancy-vacancy ordering interactions in fluoritestructured materials [21,22,98,99]. Figure 9 presents the three configuration average vacancy-vacancy partial radial distribution functions (g Vac-Vac (r)) at 873 K for Sc:LaDC (solid black line), LaDC (dot-dashed turquoise line), ScDC (dotted magenta line), GdDC (dot-dot-dashed green line) as well as a random vacancy distribution, which is simply the O -O g(r) from CeO 2 at the same temperature. The <100>, <110> and <111>, <210>, etc labels indicate different directions along the simple cubic anion sublattice. The values obtained upon integration of these peaks are reported in Table II. The results show that for this dopant concentration vacancies display some degree of long range ordering as evidenced by the sharp peaks in the <210> and <211> positions, while the positions that are at shorter distance in the simple cubic lattice are underpopulated with respect to a random vacancy distribution. This effect arises from the Coulomb repulsions between the vacancies. Common to all the doped ceria systems is also a deleterious redistribution of the vacancies which favours short range occupancy along the <111> direction with respect to an idealized random system. This effect is larger in inferior conductors, like LaDC and ScDC than in the better ones like GdDC, for example; in the case of Sc:LaDC, co-doping is shown to enhance this ordering, because the small Sc 3+ cations trap the vacancies in the NN positions, while the large La 3+ cations repel them towards NNN but also increase their migration barrier [97], which leads to an overall increase in the vacancy ordering of co-doped systems. This indicates that the "synergistic" effect on bulk ionic conductivity from co-coping is not realized.

CERIA CO-DOPING IN PERSPECTIVE

Despite the significant improvements in the ionic conductivity of co-doped ceria reported by several previous studies [38-42, 44, 49, 100, 101] this work found that this property is simply an average of the singly doped materials. This is in accordance with an early experimental/computational study by Yoshida et al. [102,103], as well as, recent experimental data reported Figure 7 for Nd:SmDC [37,46]. Similar results were reported by Ralph et al. for Yb:LaDC and Sm:YDC, and by Li et al. [START_REF] Li | [END_REF] for Sc:GdDC, who observed a worsening in these properties for co-doped cerias with substantially mismatched dopant cations. The interpretation provided in both cases was that the localized nature of the strains caused by each dopant species does not change substantially in the co-doped systems compared to singly doped materials.

We point out that our investigation has left out a series of factors, such as long-range cation ordering, grain boundaries, impurities, etc, that might also affect the total conductivity of these materials. This was done on purpose, because the focus of this investigation was on bulk properties of perfect fluorite-structured materials. TABLE II. Number of vacancies surrounding another vacancy along the <100>, <110> and <111> directions of the simple cubic anion sublattice of 10% cation doped GdDC, LaDC, ScDC and Sc:LaDC, as well as a random distribution of the same number of vacancies. These values were obtained from the integration of the peaks in Figure 9 System <100> <110> <111> Random 0.150 0.300 0.200 GdDC 0.001 0.010 0.056 LaDC 0.06 0.007 0.107 ScDC 0.001 0.062 0.170 Sc:LaDC 0.006 0.023 0.213

We note here that these factors have usually a detrimental effect on the conductivity of these materials, so that our conclusions are not invalidated by leaving them out. For instance, cation ordering might be expected in Sc:LaDC [84], because the cations have significantly different ionic radii. This was not taken into account in these calculations, because the cations were randomly distributed and the simulation timescale does not allow them to diffuse. Cation ordering is known to lead to a decrease of the ionic conductivity, as observed in several oxides [66,104], so that the effects of co-doping might be even more detrimental to the material's conductivity that what we predict in this investigation.

Finally, we wonder why many studies have found an enhancement of the ionic conductivity in some co-doped materials, while others have not. We note that, as discussed above, there are many factors (grain boundaries, cation ordering, phase separation and nano-domain formation, impurity levels, etc) that affect the ionic conductivity of these materials and it is very hard to separate their effects. As an example, the conductivity of 10% GdDC, as shown in figure 3, varies by as much as a factor of 2.5 in different experiments. Such a huge variation is probably caused by a combination of these factors and shows that it is not trivial to compare the conductivity of these materials.

CONCLUSIONS

This work was motivated by the conflicting evidence that surrounds the merits of ceria co-doping as a means to improve this electrolyte's ionic conductivity. Here we used Molecular Dynamics simulations that employ accurate interionic potentials, parameterized with respect to first-principles calculations. No experimental data was used to parameterise these potentials. This methodology allowed the study of large systems at realistic operating temperatures (∼ 873-1273 K) and defect concentrations. The conclusions pertain only to bulk properties, because the models that were simulated do not include grain boundaries, impurity segregation, dislocations, etc.

The results show that co-doping can be successfully used to reproduce the lattice constant of ceria doped with a single cation which has an ionic radius equal to the effective radius from two co-dopants. However, close examination of the bulk ionic conductivity of co-doped ceria revealed that this property is not enhanced by co-doping and can be described as an average of the conductivities of the "parent" singly-doped compounds. This result was explained by the fact that the vacancy ordering tendencies of individual dopant cations remain largely unchanged in co-doped systems. For this reason, co-doping with cations that are bigger/smaller than a given ideal dopant radius (r C ) leads to the combination of unwanted defect trapping tendencies, as exemplified by the case of the significantly mismatched Sc:LaDC system, where Sc 3+ is an oxygen vacancy scavenger while La 3+ repels vacancies. In conclusion it is the local structure of these materials, rather than their average structure, that dictates their conducting properties. These results effectively reject co-doping as a possible avenue for improving ceria conductivity. More fruitful outcomes are likely to be achieved in other intensely investigated areas such as the application of strain [32,105,106].

to study doped ceria and other oxides [22,64,[66][67][68], as well as fluoride systems [69,70]. In DIPPIM the various ionic species that form part of the simulation are assigned their formal valence charges (Ce 4+ , RE 3+ , and O 2-). The model also includes the polarization effects that result from the induction of dipoles on the ions. The potential is constructed from four components, the first three of which are purely pairwise additive. The DIP-PIM components are: charge-charge, dispersion, overlap repulsion and polarization. The Coulombic interactions (charge-charge) are described by:

V qq = i≤j q i q j r ij (3)
where q i is the formal charge on ion i. Dispersion interactions (Equation 4) include dipole-dipole and dipolequadrupole terms. Those terms marked in red in Equation 4 and subsequent formulae were part of the fitting process described in Appendix .

V disp = - i≤j [ f ij 6 (r ij )C ij 6 r 6 ij + f ij 8 (r ij )C ij 8 r 8 ij ] (4)
Here C ij 6 and C ij 8 are the dipole-dipole and dipolequadrupole dispersion coefficients, respectively. The f ij n are Tang-Tonnies damping functions [110,111] which are added in order to describe the short-range penetration correction to the asymptotic dispersion term and are expressed as follows:

f ij n (r ij ) = 1 -e b ij n rij n k=0 (b ij n r ij ) k k! (5)
The short range repulsive term (Equation 6) is approximately exponential in the region of physical interionic separations. The full expression used also includes a Gaussian function which acts as a steep repulsive wall and accounts for the anion hard core. This extra term is used in cases where there are highly polarizable ions (e.g. oxygen) to avoid instability problems at very small anion-cation separations [109].

V rep = i≤j A ij e -a ij rij r ij + i≤j B ij e -b ij r 2 ij (6)
As its name indicates it, the polarization part of the DIPPIM potential incorporates dipolar effects only

V pol = i,j q i µ j,α f ij 4 (r ij ) -q j µ i,α f ji 4 (r ij ) T (1) α (r ij ) - i,j µ i,α µ j,β T (2)
αβ (r ij ) + i 1 2α i | µ i | 2 (7)
Here α i is the polarizability of ion i, µ i are the dipoles and T (1) , T (2) are the charge-dipole and dipole-dipole interaction tensors:

T (1) α (r) = -r α /r 3 T (2)
αβ (r) = (3r α r β -r 2 δ αβ )/r 5 (8) The instantaneous values of the dipole moments are obtained by minimization of this expression with respect to the dipoles of all ions at each MD timestep. This ensures that we regain the condition that the dipole induced by an electrical field E is αE and that the dipole values are mutually consistent. The short-range induction effects on the dipoles are taken into account by the Tang-Toennies damping functions (f ij 4 ) similar to those used to damp the dispersion interactions (Equation 5) where b ij determines the range at which the overlap of the charge densities affects the induced dipoles. The damping of these induction effects requires an additional pre-exponential parameter, c ij , which determines the strength of the ion response to this effect.

Potential parameterization

A total of 19 2 x 2 x 2 fluorite-structured supercells were used to fit this IP set. They included YSZ (1), ScSZ (1), ceria-zirconia (3), pure ceria (1), reduced ceria (1) and two configurations of composition Ce 0.5 RE 0.5 O 1.75 for each of the systems. Each model supercell was obtained from high temperature (2000 K) ab initio MD simulations that were run for a few pico seconds in order to reach structural equilibrium. The forces on each species were determined directly from each DFT calculation, and the dipoles were obtained from a Wannier analysis of the Kohn-Sham (KS) wave functions [112]. In each case, hybrid density functional theory (h-DFT) calculations using the Heyd, Scuseria, Ernzerhof (HSE06) functional [113,114], as implemented in the VASP code [115] were performed. The inclusion of a fraction of nonlocal Hartree-Fock exchange to standard DFT in functionals such as HSE is known to be necessary to correctly describe the electronic strucuture of lanthanide oxides, such as, reduced ceria [54,55] due to the highly correlated f -electrons present in these elements. This DFT functional also represents an improvement over other more commonly used functionals like LDA (Local Density Approximation) or GGA (Generalized Gradient Approximation) in terms of a closer agreement to experimental lattice constants [54] in cases where there are no f -electrons present, e.g. YDC, LaDC, etc.

Although dispersion energies constitute only a small fraction of the total energy, they have a considerable influence on transition pressures and, in particular, on the material density, thus the lattice constant, and the stress tensor. However, the dispersion terms were not included in the initial fit due to the well known uncontrolled representation of dispersion within the framework of DFT [START_REF] Koch | A Chemist's Guide to Density Functional Theory[END_REF]. Instead, values for the C ij 6 and C ij 8 terms were determined from the dipole polarizabilities for each element that resulted from the initial potential fit. The α values thus obtained have been previously found to be in very good agreement with other theoretically derived values [68,[START_REF] Heaton | [END_REF]. The relationship between the dispersion coefficients and the dipole polarizability is given by the Slater-Kirkwood equation [118,119]. The final values for the DIPPIM parameters were then obtained by fixing the values of the polarizabilities and dispersion terms in a last round of optimization and re-fitting. The final χ 2 values for fit were 0.216 for dipoles and 0.335 for forces. Table III presents the parameters for the DIPPIM interionic potential set that was obtained.

Vacancy analysis

The vacancy analysis of fluorite structured materials probes the occupancy changes of the tetrahedral sites formed by the face centred cubic cation sublattice. These are the sites where the oxide ions and, thus, their vacancies are normally present. The oxygen vacancy analysis is performed making use of the fact that the cations in the systems under study do not diffuse, even at high temperatures. Thus, it is possible to specify the cation sublattice in terms of tetrahedra, each of which may be empty (vacancy) or occupied by an oxide anion. For a set of time-correlated instantaneous ionic configurations, i.e., frames in an MD simulation, one can determine which tetrahedral sites are empty. However, distinction must be made between the vibrations undergone by the oxide anions, which are large in amplitude at the temperatures of interest, and instances when a given oxide anion has truly vacated a given tetrahedron. This distinction is made by imposing the condition that a site be considered a vacancy only if it has been vacant for a period of at least two frames. The position of each vacancy is then defined as the centre of the tetrahedron formed by the average positions of the four surrounding cations. These positions can then be used to calculate partial radial distribution functions (RDFs), from which vacancy ordering in real space can be studied. Integration of the vacancyvacancy RDF (g v-v ) peaks from zero out to the position r c gives the vacancy-vacancy coordination number, n v-v :

n v-v = 4πρ rc 0 r 2 g v-v dr ( 9
)
where ρ is the vacancy density in the simulation cell. This method has been previously used to study similar materials, such as, Zr 2 Y 2 O 7 -Y 3 NbO 7 [66], YDC [22] and PbO 2 [109,120].

Figure 5

 5 Figure 5 presents the DIPPIM calculated (filled symbols) and experimental lattice constants (open symbols) from Buyukkilic et al.[81] at 300 K for Ce 1-x RE x O 2-x/2 , where x = 0.05, 0.10, 0.15, 0.20, 0.25 and RE = Sm, Nd, Nd:SmDC and Sc:LaDC. The codoped systems shown correspond to an effective dopant cation radius of 1.093 Å for the stoichiometries specified in Section . It is evident from the figure that the calculations predict the correct lattice constant for the singly doped systems over the entire composition range, and that this carries over to the co-doped cerias under study. The agreement is particularly good for the systems that are the focus of this study, namely Ce 0.90 RE 0.10 O 1.[START_REF] Lang | Solid Oxide Fuel Cells (SOFC VI): Proceedings of the Sixth International Symposium[END_REF] . The results show that co-doping can be successfully used to reproduce the lattice constant of a cation with a critical dopant radius, r C .

30 x 2 NdFIG. 6 .

 3026 FIG.6. DIPPIM ionic conductivities shown as a ratio of the values for co-doped (CD) ceria divided the weighted average (WA) of their singly-doped parent oxides. Panel a) corresponds to the Sc:LaDC system and panel b) corresponds to Nd:SmDC.

FIG. 8 .FIG. 9 .

 89 FIG.8. Cation-vacancy partial radial distribution functions (gCat-Vac(r)) at 873 K. Top panel: Random (solid black line), La 3+ -Vacancy in Sc:LaDC (dashed orange line) and La 3+ -Vac in LaDC (dotted turquoise line). Bottom panel: Random (solid black line), Sc 3+ -Vacancy in Sc:LaDC (dashed green line) and Sc 3+ -Vacancy in ScDC (dotted magenta line)

  is that the DIPPIM potentials used in this work deliver conductivity values which Lattice constants for Ce0.90RE0.10O1.[START_REF] Lang | Solid Oxide Fuel Cells (SOFC VI): Proceedings of the Sixth International Symposium[END_REF]. DIPPIM values at 300 K (this work) are shown as a solid black line. The errors correspond to the standard deviation obtained from three simulations. Hong and Virkar[74] values are represented by a

			Sc				Gd Sm Nd	La
		5.44	DIPPIM Hong and Virkar						l m n
			ScDC
			GdDC						j
	Lattice Constant (Å)	5.4 5.42	a b c SmDC NdDC LaDC			e d	f	i h g	k
		5.38
		0.8	0.85	0.9	0.95	1	1.05	1.1		1.15	1.2	1.25
						Ionic Radius (Å)
	FIG. 1.

dashed red line. Literature data from a) Grover et al.

[75]

, b) Lee et al.

[76]

, c) Gerhardt-Anderson and Nowick

[77]

, d) Huang et al.

[78]

and Zhang et al

[79]

e) Huang et al.

[80]

f) Omar et al.

[37]

, g) Huang et al.

[80]

, h) Buyukkilic et al.

[81]

, i) Omar et al.

[37]

, j) Buyukkilic et al.

[81]

, k) Omar et al.

[37]

, l) Huang et al.

[80]

m) Hisashige et al.

[82]

and n) Dikmen et al.

[83]

  Ionic conductivities for singly doped ceria (Ce1-xRExO 2-x/2 ) at 1273 K (blue diamonds), 1073 K (black dots) and 873 K (red triangles). The vertical dashed red line represents the critical ionic radius (rC) introduced by Andersson et al.[29].

		0.1			Sc						Gd Sm Nd	La
			1273K
	(S/cm) σ	0.01	1073K									0.05 0.10 0.15 0.20
														0.25
			873K
		0.001 0.8		0.85	0.9		0.95	1		1.05	1.1	1.15	1.2	1.25
									Ionic Radius (Å)
	0.0001 0.001 0.1 FIG. 2. 1.04 σ (S/cm) 0.01	e d a b c	Gd	1.06	i h g f	1.08 Sm	Pm	1.1	k j	Nd	1.12	1.14 873K	1.16 La m
									Ionic Radius (Å)

l FIG.

3

. Ionic conductivities for Ce0.90RE0.10O1.95 at 873 K (blue triangles). Open symbols correspond to experimental values, while filled symbols were obtained from computational data in the literature: a) Steele

[85]

, b) Huang et al.

[78]

and Omar et al.

[38]

c) Xia and Liu

[87]

, d) Zhou et al.

[86]

, e) Dholabhai et al.

[88]

, f) Kasse and Nino

[46]

, g) Grope et al. (at 893 K)

[89]

and Omar et al.

[37]

, h) Shemilt and Williams

[90]

, i) Jung et al.

[91]

, j) Kasse and Nino

[46]

, k) Omar et al.

[37]

l) Aneflous et al.

[92]

and m) Dikmen et al.

[83]

. The vertical dashed red line represents the critical ionic radius (rC) introduced by Andersson et al.

[29]

  clearly show that the local environment, and thus the local strain, of the dopant cations undergoes few changes in going from singly doped sys-

		0.75	Sc				Gd Sm Nd Pm	La
				Sc:LaDC DIPPIM
				Nd:SmDC DIPPIM
		0.7
	Activation Energy (eV)	0.6 0.65						a b
		0.55 0.8	0.85	0.9	0.95	1	1.05	1.1	1.15	1.2	1.25
						Ionic Radius (Å)
	FIG. 7. DIPPIM activation energies (eV) for Ce0.90RE0.10O1.95 from Figure

TABLE I .

 I Number of vacancies in the Nearest Neighbour (NN) and Next Nearest Neighbour (NNN) positions with respect to the cations in a random distribution, GdDC, Sc:LaDC, LaDC and ScDC. These values were obtained from the integration of the peaks in Figure8

	Peak	Random GdDC LaDC Sc:LaDC ScDC Sc:LaDC
		Ce-O	Gd-Vac La-Vac La-Vac Sc-Vac Sc-Vac
	1 st (NN) 0.200	0.168 0.062 0.059	0.734 0.878
	2 nd (NNN) 0.600	1.356 1.502 1.578	0.412 0.467

TABLE III .

 III Parameters for the DIPPIM potential. All values are in atomic units, except those corresponding to the ionic radii, which are given in angstroms ( Å) and are shown in parentheses alongside the dipole polarizabilities α. The parameters b O 2- -D and b -O 2-Dwere given the same value. Here, represents a placeholder for the identity of the ionic species specified in a given column

	Interaction A ij	a ij	B ij	b ij	C ij 6	C ij 8	b ij 6	b ij 8
	O 2--O 2-7.15	18.52	50000	1.00	83	1240 1.30 1.70
	Ce 4+ -O 2-82.20	1.19	50000	1.55	47	595 1.50 1.96
	Zr 4+ -O 2-89.79	1.29	50000	1.75	21	271 1.62 2.10
	La 3+ -O 2-102.63	1.25	50000	1.30	57	731 1.46 1.88
	Ce 3+ -O 2-100.02	1.25	50000	1.20	71	902 1.47 1.90
	Nd 3+ -O 2-94.24	1.25	50000	1.36	56	709 1.49 1.94
	Sm 3+ -O 2-87.79	1.25	50000	1.38	49	630 1.51 1.97
	Gd 3+ -O 2-79.98	1.25	50000	1.38	23	293 1.54 2.00
	Y 3+ -O 2-118.0	1.38	50000	1.50	21	264 1.60 2.08
	Sc 3+ -O 2-61.66	1.28	50000	1.75	15	197 1.77 2.30
	Ion	α	radius ( Å) b O 2-D	-	c O 2-D	-	c -O 2-D
	O 2-	13.97	1.38	2.18	3.03	-
	Ce 4+	5.86	0.97	1.75	1.85	0.17
	Zr 4+	2.38	0.84	1.74	1.56	-0.60
	La 3+	7.51	1.16	1.50	1.43	-0.20
	Ce 3+	9.72	1.143	1.59	1.71	0.05
	Nd 3+	7.24	1.109	1.67	1.94	0.00
	Sm 3+	6.28	1.079	1.67	1.97	-0.14
	Gd 3+	2.56	1.053	1.69	1.75	-0.89
	Y 3+	2.31	1.019	1.47	1.08	-0.60
	Sc 3+	1.70	0.87	1.67	1.36	-0.39

ACKNOWLEDGEMENTS

MB acknowledges the HPC-EUROPA2 project (project number: 228398) with the support of the European Commission Capacities Area -Research Infrastructures Initiative for access to the computing resources at CINES in France.

Calculations were performed on the Stokes supercomputer (allocations tcche026b and tcche031b) maintained by ICHEC and Kelvin maintained by TCHPC. GWW acknowledges Science Foundation Ireland (PI Grant numbers 12/IA/1414 and 08/RFP/MTR1044). DM wishes to thank the Government of Ireland for an EMPOWER Postdoctoral Fellowship and is thankful to Profs. Harry Tuller and Bilge Yildiz for his recent appointment as visiting scientist at MIT. The authors also thank Prof. Paul A. Madden (Oxford) and Dr Jonathan Yates for helpful discussions.

The DIPPIM model

In this section we provide a brief description of DIPole Polarizable Ionic Model (DIPPIM) potential employed in this work. The reader is referred to [107][108][109] and references therein for further information concering the form of the potential. This model has been previously used


INERIS, Verneuil S Denys

En Halatte

INERIS K Tack

INERIS J Caboche
email: julien.caboche@ineris.fr

Assessing metals bioaccessibility to Man in human health risk assessment of contaminated sites

INTRODUCTION

Soil ingestion is a significant exposure pathway to Man for sites contaminated with heavy metals. In France, risk assessments on polluted soils currently consider the total soil metal content for calculation of the dose exposure to Man. However several studies done on animals showed that this assumption was not accurate as the total soil metal content was not available for absorption though the gastro-intestinal system. The fraction that is actually entering the systemic circulation is the bioavailable fraction and was shown to be dependent on the solid metal distribution [START_REF] Casteel | Refining the risk assessment of metalcontaminated soils. Short Communication[END_REF][START_REF] Henningsen | Differential bioavailability of Pb mixtures from twenty different soil matrices at Superfund mining sites[END_REF].

Measurement of metal bioavailability can be done using animals (pigs and monkeys) having similar digestive conditions than Humans. However these experiments are costly and can be ethically problematic. These last years, both North American and European scientists developed chemical tests to measure the bioaccessible fraction of the soil metals. These tests are based on two or three steps extraction method that simulates the extraction of the metal by the digestive fluxes (saliva, gastric and intestinal conditions). These fractions were shown to be correlated to the bioavailable fraction for some metalloids (arsenic) or metals (lead). The bioaccessible fraction of metal is then used to correct the soil metal total concentration. This data can be used to improve the estimation of the external fraction of the contaminant that is available for absorption through the digestive tract.

The objectives of this paper are to give a short overview of the methods available in the literature to assess the soil metal bioaccessibility, focusing on the tests developed by the European BARGE working group. One of these tests is then used on four soils sampled on a residential area on which soils are strongly contaminated with lead. Results of bioaccessibility are then discussed regarding the soil solid phase repartition of this metal using Tessier sequential extractions. Finally, a theoretical human health risk assessment is then carried out considering both the total and the bioaccessible lead fraction. Impact of bioaccessibility in terms of management of contaminated sites is then discussed.

2

Definition and Measurement of the bioaccessibility

Definition

Bioaccessibility is defined as the fraction of a pollutant that can be dissolved by the digestive fluids. This is the theoretical fraction available for adsorption through the gastrointestinal tract.

Overview of existing protocols to assess bioaccessible fraction of soil contaminant

Numerous tests are available in the literature to assess soil metal bioaccessibility. Most of these tests consist in a two steps extraction protocol: the intestinal and the stomachal step.

A common characteristic among these tests is the difference in pH, ranging from low value in the intestinal step to close-toneutral value in the stomachal step. Beyond this similarity, huge differences exist among these tests regarding experimental conditions or composition of the extractants used to simulate the digestive fluids. These differences might strongly influence measurement of bioaccessibility.

2.3

The BARGE protocol

To homogenized protocols, a European working group (Bioaccessibility Research Group for Europe, BARGE) was created. This group aims to define and normalize one protocol for the measurement of the bioaccessibility for inorganic or organic3 compounds. Even if few adjustments of the protocol are still in progress, a general description of the method can be done. The protocol consists in a three steps extraction procedure conducted on a 0.6 g of a soil sample. A 9.0 mL simulated saliva fluid is added and thoroughly mixed to the soil sample. 5 to 15 min after addition of this saliva fluid, 13.5 mL of simulated gastric fluid is added to the suspension that is end-over rotated for 1 hour. After homogenization, pH of the suspension is adjusted to 1.2 to 1.7 using HCl. The stomach phase is then collected by centrifuging the soil suspension at 3000 g for 5 minutes. The supernatant is removed and metal content measured as the bioaccessible contaminant in the stomach phase. The gastric bioaccessible contaminant is measured after addition to the soil suspension of a simulated gastric fluid. pH of the suspension for the gastric step is adjusted to 6.3 using NaHCO 3 and the suspension is mixed for 4 hours. The soil suspension is then centrifuged at 3000 g for 5 minutes, the supernatant removed and metal content in this latter measured as the bioaccessible contaminant in the gastric phase.

Simulated fluids are composed with both organic and inorganic reagents having specific composition. To these fluids, a simulated bile phase reagent is added to be as close as possible to the actual digestion conditions.

Assessment of lead bioaccessibility on a former mining extraction area

Material and methods

Lead bioaccessibility was determined on 4 soils sampled on 4 locations at a site contaminated with mining waste and located in a high carbonate soil area in the South of France. Each location corresponded to different soil use: garden (2 samples), waste material sample on a pedestrian pathway (1 sample) and a soil naturally developed on the former mined bedrock (1 sample). For each location, a representative sample was obtained by combining three point samples of the surface horizon (0-5 cm).

After sampling, the soils were air-dried and sieved to < 250 urn particle size. This particle size is believed to be ingested or directly available for inhalation by children (Grön et Andersen, 2003;Oomen et al., 2003). Soil characteristics and lead content are given below (Table 2). 1). Temperature was maintained at 37°C throughout the extraction procedure. 0.6 g of soil was mixed to 9 mL of saliva (pH 6.5). This suspension was shaken for 5 minutes. Then 13.5 mL of gastric solution (pH 1.7) was added to the soil suspension. The pH of the solution was reduced to 1.2 using HCl. The suspension was end-over mixed for 2 hours and an aliquot of 0.25 mL was sampled for measurement of Pb in the solution (saliva-gastric phase). During the third step, bile and intestinal solution were added And the pH was increased to between 5.5 to 6. Solutions were mixed end over end for a further 2 hours and centrifuged at 3 000 g. The supernatant was then sampled to determine the Pb concentration in the solution (intestinal phase).Bioaccessibility tests were carried out on the four samples and the two reference materials. Results of bioaccessibility are expressed as the percentage of the total soil Pb content and for each sample, tests were carried out on three replicates.

Pb distribution within the four soil samples was evaluated using a slightly modified sequential extraction according to the [START_REF] Tessier | Sequential extraction procedure for the speciation of particulate trace metals[END_REF] protocol. For the waste material only, which was the most contaminated material, the solid residue was kept for further physical analysis.

The high concentration of Pb in the waste material allowed analysis of this matrix by physical methods. X Ray Diffractometry (XRD), Scanning electron microscopy couple to EDX analysis and Infra-red spectrometry analysis were done both on the waste material and on the residues obtained after each step of the sequential extraction.

Results

Pb bioaccessibility

For all soil samples, Pb bioaccessibility was significantly higher in the saliva-gastric phase compared to the intestinal phase (a = 5%) (Table 5). Recovery of the total Pb content using the in vitro bioaccessibility test varied between 20% on the G2 soil and 65% on the waste material. For the saliva-gastric phase, it ranged between 15% and 56% of the total Pb content; for the intestinal phase, it ranged from 5% (G2) to 25% (G1) of the total Pb content. For each phase, bioaccessibility values were significantly different between samples (a = 5%).

3.2.2

Pb distribution within the soil samples as determined by sequential extractions For each French soil sample, results of sequential extractions are described. Whatever the sample, Pb was mostly extracted during the steps III (acid-soluble fraction),Vb (oxidable fraction). For step III the concentration of the extracted Pb ranged from 20% (waste) to 72% (G2) of the total soil Pb content. For step Vb, the concentration of the Pb in the extract varied between 31% (G1) and 62% (geochemical background) of the total soil Pb content.

Pb content was always higher in extracts following step Vb except for the G2 sample in which Pb concentration was higher in the extract following step III. Pb distribution within the initial waste material or its residues from each step of the sequential extraction XRD showed the presence of cerrussite (PbCO 3 ) as the predominant Pb bearing mineral in the waste material. The same technique carried out on the residues showed that cerrussite was detected after only the first two steps of the extraction procedure.

Scanning electron microscopy couple to EDX analysis showed that Pb was not detected after the step V of the sequential extraction. Morevoer, Pb in the residues from the first and second step was associated to carbon and oxyge wheras it was mostly associated to sulfur, iron and zinc in the residues obtained in the following steps of the extraction procedure.

Infra red spectrometry conducted on the initial waste material was used to detect the two bands (678 cm -1 and 839 cm -1 ) characterizing cerrussite. Quantification of this mineral using calibration curves showed that between 30% and 40% of Pb was occurring in the cerrussite form in the initial waste material. This technique allowed to detect occurrence of cerrussite in the residual fractions obtained from the steps I and II of the sequential extraction whereas PbCO 3 was not occurring anymore in the fraction obtained from the following steps of the extraction procedure. This latter technique was also conducted on the residual waste material after the in-vitro bioaccessibility measurement test and showed that cerrussite was not detected in this matrix. The aims of this study were to verify this hypothesis on soil samples from a high carbonate dolomitic area in which Pb was supposed to occur mostly as carbonates by studying the solid phase distribution of Pb in these soils and the Pb bioaccessibility obtained by an in-vitro test.

Discussion

Bioavailability of

Pb bioaccessibility results were significantly different between soil samples. Bioaccessible Pb measurements on the NIST reference material were similar to the results published in the literature using the same protocol, confirming the accuracy of our measurements [START_REF] Oomen | Comparison of five in vitro digestion models to study the bioaccessibility of soil contaminants[END_REF][START_REF] Oomen | Effect of bile type on the bioaccessibility of soil contaminants in an in vitro digestion model[END_REF].

Physical analysis of the contaminated waste material confirmed that 40% of Pb was present as cerrussite (PbCO 3 ). These analyses show that about 20% of this mineral is dissolved between the first and the third step of the sequential extraction.

So, even if PbCO 3 was not occurring anymore in the fractions after step due a too high detection limit for IRS technique, it is clear that PbCO 3 is also dissolved during the following steps or the extraction procedure. This confirms that bioaccessibility of Pb is partly controlled by the presence of cerrussite. Moreover, this also confirms that cerrussite is a highly bioaccessible Pb-bearing mineral because of its entire dissolution during the bioaccessibility test.

Assuming that cerrussite strongly controls Pb bioaccessibility in high carbonate soils, then there should be a link between the bioaccessible Pb and the Pb concentration as measured in the third step of the sequential extraction.. However, the bioaccessibility tests shows that 65% of Pb was bioaccessible in the waste material. This means that a significant fraction (25%) of the Pb which is not occurring as cerrussite is also bioaccessible.

In addition, for the three other soils, Pb bioaccessibility is significantly different among them whereas the Pb concentration in the extract following the step III is quite similar (c.1000 mg kg -1 ).

Moreover, bioaccessibility of Pb is higher for soil G1 compared to the waste material whereas the Pb concentration in the sequential extract of the third step of soil G1 is a factor of ten lower than the Pb concentration for the same extract from the waste material.

Assuming that most of the carbonated forms were totally

Integration of bioaccessibility in human health risk assessment and in the calculation of rehabilitation level

Bioaccessibility can simply be integrated to human health risk assessment to adjust the dose exposure to Man by soil ingestion.

Equation 1 shows how the bioaccessibility fraction has to be integrated to the calculation of the dose exposure.

Equation 1 :

Dose Exposure adujust ed = Soil Total Content (mg kg -1 ) * Bioaccessibility (%) * Amount of ingested soil (mg day -1 ) Impact concerning the integration of this value in human health risk assessment is shown here, based on a theoretical residential scenario. A hazard quotient can be calculated for the soil ingestion pathway, considering a child chronic exposure to the garden soil 2 (total lead content = 2142 mg kg -1 ; lead bioaccessibility = 20%; yearly exposure time = 350 days; toxicological reference value = 3.5 ug kg -1 day -1 ). The hazard quotient varies from 5.8 when bioaccessibility is not considered to 1.1 when bioaccessibility is considered.

A rehabilitation level might also be calculated for this soil considering or not bioaccessibility. To reach a hazard quotient of 1, a lead concentration of 380 mg kg -1 is needed without considering bioaccessibility. When bioaccessibility of lead in the soil garden 2, the rehabilitation level is around 1 800 mg kg -1 .

Conclusion

The bioavailability of a soil contaminant to Man can be approached by the measurement of the fraction of this metal that is dissolved in the gastro-intestinal tract (bioaccessible fraction). Implication of the bioaccessibility concept in human health risk assessment allows for an improved estimate of the contaminant which is actually available for adsorption through the gastrointestinal tract. However, some studies are still needed even if bioaccessibility is ever currently used in European countries.

Particularly further researchs are needed on the relationships between bioaccessibility values and bioavailability values as measured using animal models on the same soil samples.

*

  Table 4 : Bioaccessibility (% of total lead content) of lead in the four soil samples Garden the same letter are not significantly different (Neuwmann-Keuls test, a = 5 Bioaccessibility values for both phases varied among samples.

  Beyond steps III and Vb, Pb concentration measured after the other steps of the extraction were below 1 % of the total soil Pb content, except for the step IVc (iron-oxide fraction) in which Pb concentration could reach 4% of the total soil Pb content. Non negligible Pb concentration were also obtained in the residual fraction (step VI).

  Pb in human health risk assessment is supposed to be dependent on geochemical factors controlling Pb speciation in soil. From US EPA reports, it was underlined that cerrussite contains highly bioavailable Pb (US EPA, 1999). Bioaccessibility, which significantly controls bioavailability, should also be dependent on the soil Pb speciation. Consequently, risk management of soils contaminated by Pb might be based on the soil mineralogical composition. Particularly, special attention should be given to high carbonate areas in which Pb could occur in carbonates and therefore being strongly bioaccessible to humans.

  dissolved during step III of the sequential extraction procedure, this means that non-carbonate forms of Pb may be significantly bioaccessible. From physical analysis, Pb in the residues obtained after the last three steps of the extraction seems to be associated to sulphur and oxygen. The results of sequential extraction show that Pb is mostly extracted during the last step of the protocol (excepted for the G2 soil). These observations confirm a significant association of Pb to more stable mineralogical forms even if that have not been fully quantified in this present study. The bioaccessibility of Pb contained in these stable minerals can be explained by the chemical conditions of the digestion test. Indeed, the intestinal phase consists of an acid extraction (pH = 1.2) in which quite stable minerals are likely to be soluble. Another parameter to consider for bioaccessibility is the residence time of the matrix in the gastric compartment. The higher the residence time of the matrix in the gastric compartment, the higher the Pb extraction for even quite stable minerals. Therefore, even in carbonated areas, Pb bioaccessibility cannot be simply predicted from the carbonated minerals content of soil. The other Pb bearing mineral also play a significant role in overall Pb bioaccessibility to humans.

  This fraction can be measured by chemical extraction tests based on two or three steps simulating the different steps of the digestion process. Numerous tests can be found in the literature. The European Barge Group aims to currently develop and normalize one homogeneous test involving a three steps extraction procedure (saliva, gastric and intestinal extraction steps). Here one test selected among the available tests in the litterature and defined by the Dutch RIVM was chosen to characterized lead bioaccessibility in four soils sampled around a former mining extraction area. Lead bioaccessibility in these samples ranged from 20 to 80% of the soil total lead content.Discussion of these values regarding lead distribution within the soil solid phase was done using both chemical and physical methods. Even if carbonate lead (cerrussite) was identified as a highly bioaccessible form of lead, dissolution of lead associate to quite stable mineral (lead associated to sulfur) also occured during the chemical tests showing that these forms might contribute sensitively to overall lead bioaccessibility.

Table 1

 1

	gives an overview of

Table 1

 1 Overview of the different methods available in the litterature to estimate a soil pollutant bioaccessibility to Man

	Method	Type	Simulated digestive compartment	pH	T (°C)	Ratio L/S *	Residence Time	Tested metals
			Stomach	2.5	37	100/1	1 H
	PBET	Batch						As, Pb
			Small intestine	7	37	100/1	4H
	SBET	Batch	Stomach	1.5	37	100/1	1H	As, Cd, Pb
			Stomach	1.8	37	150/1	1H
	IVG	Batch						As
			Small intestine	5.5	37	150/1	1H
	USP	Batch	Stomach	1	37	1000/1	2H	Pb, Cr, As, Cd, Ni
			Mouth	6.4	37	160/1	5s
	MB&SR	Batch	Stomach	2	37	2160/1	2H	Pb, Cr, As, Cd
			Small intestine	7.5	37	4770/1	4H
	DIN	Batch	Mouth Stomach Small intestine	6.4 2 7.5	37 37 37	15/1 50/1 100/1	0.5 H 2 H 6H	As, Cd, Pb, Cr, Hg
	SHIME	Batch	Stomach Small intestine	5.2 6.5	37 37	2.5/1 4/1	3H 5H	As, Cd, Pb
	RIVM	Batch	Mouth Stomach	6.5 1.5	37 37	15/1 37.5/1	5 min 2 H	As, Cd, Pb
			Small intestine	5.5	37	97.5/1	2H
	TIM	Dyamic	Mouth Stomach	5 2	37 37	5/1 30/1	5 min 1.5 H	As, Cd, Pb
			Small intestine	7	37	51/1	6H
	AOAC	Batch	Stomach	1.12	37	150/1	16H	Cu, Zn, Mn, Fe, Al
	* Liquid/Solid Ratio used in the extractions

Table 2 :

 2 Soil characteristics and total lead content

		Organic C	Total Carbonate	Pb content
	pH	3 kg-1	g kg-1	mg kg -1
	Garden 1 7.1	53.4	170.0	4 767
	Garden 2 7.8	60.0	298.0	2 141
	Waste material 7.9	9.4	346.0	77 007

Table

Table 3 :

 3 Step of the sequential extraction procedure according to[START_REF] Tessier | Sequential extraction procedure for the speciation of particulate trace metals[END_REF] and slightly modified

	Step	Fraction	pH	Reagents
	I	Soluble	5.7	Ultrapure Water
	II	Exchangeable	5.0	Magnesium nitrate 1M
	III	Acid-soluble	4.5	Sodium acetate/
				Acetic acid
	IV a	Manganese oxydes	3.5	Hydroxylammonium chloride
	IV b	Amorphous Iron Oxydes	3.0	Ammonium oxalate 0,2M/ oxalic acid 0,2M
	IV c	Crystalline Iron Oxydes	2.25	Ammonium oxalate 0,2 M-oxalic acid 0,2 M-
				ascorbicacid 0,1 M
	Va	Organic Matter		H 2 O 2 35%
	Vb	Sulfite		Nitric Acid 7N
	VI	Residual		HF+HNO3


Mercerat Diego

Driad-Lebeau Lynda

Bernard Pascal

Souley Mountaka

INDUCED SEISMICITY MONITORING OF AN UNDERGROUND SALT CAVITY UNDER A TRANSIENT PRESSURE EXPERIMENT

Keywords: Cavern Opération, Instrumentation and Monitoring, Seismic

Within thé framework of a research project launched to assess thé feasibility of seismic monitoring of underground growing cavities, this spécifie work focus on thé analysis of thé induced microseismicity generated by a controlled pressure experiment recently carried out in a sait mine environment. A local seismic array has been installed over a stable underground cavity within a sait layer located in thé Lorraine basin (north-east France). The array includes four 3D components and three 1D component geophones deployed at depths between 30 m to 125 m in cemented boreholes drilled in thé vicinity of thé study area. The underground cavity under monitoring is located within a sait layer at 180 m depth and it présents a rather irregular shape that can be approximated by a cylindrical volume of 50 m height and 180 m diameter.

Presently, thé cavity fully saturated with brine inducing a significant pressure on its walls (-2.5 MPa) to keep thé overburden mechanically stable. Nevertheless some small microsesimic events were recorded by thé array and analysed (-500 events in two years of recording). In October 2005, a controlled pressure transient experiment has been carried out in thé cavity in order to analyse thé mechanical response of thé overburden by tracking thé induced microseismicity. The recorded events are mainly grouped in clusters of 3 to 30 seconds of signal duration with non-emergent first arrivais and rather low frequency content (between 20 to 120 Hz). Some of thèse events hâve been spatially located by travel-time picking close to thé actual cavity and its immédiate roof. Preliminary spectral analysis suggests sources possibly related to résonant fluid-filled cracks, and/or cavitations due to thé dynamic excitation of thé brine-filled cavity. Rock-debris falling into thé cavity from delamination of clayey maris in thé immédiate roof is possibly another source of seismic excitation. No clear évidence of classical brittle ruptures in thé compétent layers of thé overburden has been observed up to now. Current work is focused on thé discrimination of ail thèse possible mechanisms to better understand thé damage processes within thé rock mass.

Introduction

A large research project within thé GISOS1 program has been launched in order to identify and evaluate thé potential of geophysical (microseismic, hydroacoustic) and geotechnical (extensometer, inclinometer) techniques for monitoring underground growing cavities due to sait dissolution (solution mining). A part of this program focus on two complementary axes: (1) thé validation of induced seismicity monitoring techniques in sait mine environments, and (2) thé numerical modelling of déformation and failure mechanisms with their associated acoustic émissions, as well as thé induced microseismicity [START_REF] Mercerat | Evaluation of overburden damage in a sait mine environment evaluated by a coupled continuum-discrete numerical modelling approach[END_REF]. In this article, we présent thé analysis of thé microseismic activity induced by a transient pressure experiment carried out in a stable cavity of thé Lorraine basin (France).

Previous studies on induced seismicity of underground growing cavities (Mendecki et al. 1999) confirm thé présence of microseismic events associated with thé progressive damage of thé overburden until its final collapse. Continuous monitoring of such microseismicity may provide crucial information for stability anaiysis of thé area surrounding thé cavity. It is in this optics that a local microseismic network has been installée! over a site test at Cerville-Buissoncourt (Lorraine, France), where a stable brine cavity is nowadays found. When thé sait exploitation restarts, thé spatial and temporal évolution of thé seismic event locations and magnitudes will be recorded, and further analized to identify, and eventually quantify, thé damage progression in thé overburden. The expérimental results are expected to be correlated with thé geomechanical modelling to confirm backanalysis results about rock strength parameters (Cai etal, 2006).

The microseismic monitoring is operationai since January 2005. Although thé sait dissolution opération is stopped and thé whole overburden is relatively stable, microseismic activity has been recorded by thé array and it can be associated to thé initial state of thé sait cavity until thé future mining opérations. Moreover, in October 2005, a transient pressure experiment has been carried out in thé brine filled cavity, in order to check thé instrumentation layout and to calibrate some other monitoring techniques deployed on thé site. This paper présents thé first results of thé microseismic anaiysis, and discusses some possible causes for thé induced seismicity recorded up to thé présent days.

The site test

Brine cavity and overburden characteristics

The brine cavity of Cerville-Buissoncourt (Lorraine, France) is actually located within a sait layer at about 180 m depth and présents an irregular shape that, in first approximation, can be considered a cylindrical cavity of 180 m of diameter and 50 m high. A plan of thé instrumented site with a schematic geological section of thé area is shown in Figure 1. Typically, thé overburden is characterized by a séries of intercalated anhydritic maris lying over thé sait layer, and on top of them, thé présence of a compétent dolomite layer of 8.5 m thickness which présents elasto-brittle mechanical behaviour, and it is located approximately 60 m above thé sait layer. This layer corresponds to what is known as thé 'Beaumont dolomite', and it represents thé level where most of thé seismic damage is expected, when thé sait exploitation restarts and thé cavity will migrate towards thé surface. A microseismic network has been installée! over thé stable underground brine filled cavity of Cerville-Buissoncourt. The network includes four 3D components and three 1D component seismometers located at différent depths (between 35 m and 125 m) in cemented boreholes distributed around thé study area (see Figure 1). The frequency band of thé geophones is between 40 Hz and 1 kHz. There is one station per borehole, except for thé deepest one at thé centre of thé array right above thé cavity (M6), where one 1D station at thé surface and two 3D stations are located at 60 m and 125 m depth (Beaumont dolomite level). The latéral extension of thé array is about 400 m by 600 m, and it présents reasonable azimuthal coverage (major gap of -130° at thé centre of thé array).

Recorded microseismicity

More than 500 microseismic events were recorded between January 2005 and November 2006. As an event, we dénote in this work, a recorded file by thé acquisition System used in trigger mode with recording time window of 0.8 seconds. In fact, more than 75 % of thé events are grouped in clusters of time duration between 3 to 30 seconds (recorded by almost continuous triggering of thé acquisition System). This activity is clearly not related to classical shear slip mechanisms induced by hard rock fracturing. The frequency content of thèse long events is between 20 Hz and 100 Hz, and they présent an émergent P wave onset, which is in général immersed in thé coda of thé previous event, hence it results in hardly possible P wave manua! picking. In Figure 2, we can appreciate thé time frequency analysis of 6 seconds of one of thèse recordings. It must be stressed that thé amplitudes are well above thé noise level in thé study area, estimated from recordings with clear isolated events to be less than 10 A -7 m/s. In addition, some small isolated events (less than 0.5 sec of time duration) were also recorded by thé array. For thèse events, thé frequency content is sensibly higher, with P wave onsets better resolved that allowed more précise manual picking of first arrivais. Polarization analysis of thé waveforms indicates thé présence of S waves in some recordings around 40 -80 msec after thé P wave arrivai. Despite thé higher uncertainties, they were also manually picked and used to better constraint thé spatial localisation of thèse events.

Transient pressure experiment

Currently, thé cavity is full of saturated brine inducing a significant pressure on its walls (-2.5 MPa) to keep thé overburden mechanically stable. In October 2005, a transient pressure experiment has been carried out in thé cavity, in order to analyse thé mechanical response of thé overburden and to check thé sensitivity of thé whole instrumentation. The experiment consisted of a pressure drop of around 0.4 MPa (30 m of brine column height), followed by a stable phase of one week, and a latest phase of pressure raise up to thé original level of 2.5 MPa (see Figure 3). During thé pressure raise, two of thé clustered groups of events were recorded by ail thé stations (16 and 18 seconds of duration, respectively). While thé pressure drop did not produce any significant seismic activity, except for a small cluster of 3 seconds of duration. This observation suggests that thé increase in pressure within some fractured layers on top of thé cavity could produce thé observed induced seismicity.

SepGS QctOS

Spatial Localisation

For thé hand-picked events (isolated and/or immersed in continuous clusters), thé hypocentral localisation was possible using thé NonLinLoc software based on thé Oct-Tree global search approach [START_REF] Lomax | An online guide to software for Probabilistic, Non-Linear[END_REF]. The 1D velocity model of thé monitored area was defined on thé basis of vertical seismic profiling data, acoustic laboratory measurements carried out on samples from thé site, and a geological log of thé study area. The model includes three layers, thé first one with a uniform vertical gradient from thé surface (Vp=2400 m/s) up to 120 m depth (Vp=3000 m/s), then thé Beaumont dolomite of 10 m thickness (Vp=5000 m/s), and finally thé marls-salt formation (Vp=4000 m/s) up to thé bottom of thé model. A fixed Vp/Vs ratio of 1.73 is assumed for ail layers. We can assume that layer depths are relatively well constrained, but thé largest uncertainties lie in thé absolute values of thé acoustic velocities of each formation, and possible latéral variations not taken into account in thé model.

From thé hypocentral location results, supposing a travel-time picking error less than 10 msec, we estimate an uncertainty of 20 m to 30 m in thé horizontal hypocentral coordinates. In Figure 4, âll microseismic events that could be satisfactorily located (maximum station residual < 0.05 s and maximum semi-axis length of thé 68% confidence ellipsoid < 50 m) are shown. The vast majority of them hâve been spatially located close to thé actual cavity and in its immédiate roof. Up to thé présent days, there is no further évidence of ruptures near, or through, thé Beaumont dolomite. The spatial corrélation with thé actual cavity limits in thé horizontal plan is quite consistent.

Discussion

The brine filled cavity is in a rather stable period where thé overburden does not deform considerably. The microseismic activity recorded, in particular during thé transient pressure experiment, can be related to delamination of thé clayey maris on top of thé sait layer, followed by bloc drops within thé cavity. As a possible example, we show in Figure 5 a group of four isolated events recorded on October 2, 2005, just at thé beginning of thé transient pressure experiment. In Figure 6, we plotted thé spatial location of two of them (picked events) with its location uncertainties. The events may be associated to cavity roof detachments. From thé time frequency analysis, although thé inelastic atténuation has not been taken into account, we can observe a relatively high frequency content at thé beginning of each event, followed by lower frequency content in thé coda.

Note that thé current cavity has a rather irregular shape and its base is partially filled by non-soluble rock débris that was found in thé sait layer during thé mining opération (sait dissolution). The signal expected from falling blocs of degraded material in a highly irregular surface is far from being simple, but some dues can be find in thé récent work of [START_REF] Wust-Bloch | Pre-collapse identification of sinkholes in unconsolidated média at Dead Sea area by 'nanoseismic monitoring' (graphical jackknife location of weak sources by few, low-SNR records)[END_REF], where field experiments of falling blocs in both brine filled and empty superficial sait caverns (less than 30 m depth) were carried out. They hâve used a time-frequency analysis to discriminate between them. Although their conditions are rather différent from ours (in particular cavity depth and pressure), thé frequency range and général signal characteristics are quite alike. Very similar seismic activity has been also recorded on a local seismic network in Northridge (England) to monitor an abandoned underground sait mine [START_REF] Styles | [END_REF]. The hypothesis conceming thé origin of this activity (progressive delamination of clayey maris and bloc drops) is quite similar to thé one described in this paper. In addition, [START_REF] Fortier | Seismic Monitoring of Underground Storage in Sait Cavity in a Seismo-tectonic Context, Froc[END_REF] studied thé microseismicity of brine filled cavities used for hydrocarbon storage in thé Geosel-Manosque exploitation, where they found évidences of small brittle ruptures followed by falling blocs within thé underground cavities that generate clear monochromatic résonant waves.

In our case, it must be stressed that thé cut-off frequency of thé array sensors (40 Hz), tuned to register microseismic activity generated once thé solution mining restarts, is just too high to neatly record résonant frequencies corresponding to cavitations. The current cavity dimensions are between 20 m to 50 m height and 180 m width. Therefore, assuming a P wave velocity in thé brine of 1600 m/s, gives fundamental frequencies between 4.4 Hz and 40 Hz, exactly thé frequency range that is highly damped by thé geophones. Even if some évidences of roof detaching, followed by falling in thé brine cavity, can be argued from thé recorded signais, some extra work should be done in order to clarify thé rupture mechanisms.

Conclusions

A local seismic network has been installée! and further calibrated in thé site test of Cerville-Buissoncourt (Lorraine, France). This site has been chosen by thé GISOS working group to test and validate some monitoring techniques that could be used in thé future in other sait mine environments, and not to pronounce about thé conséquences, in terms of environmental impact or security, of some spécifie sait exploitation methodology implemented in thé test site.

Up to thé now, thé cavity if fuli of saturated brine what causes a significant pressure on its walls. The seismic activity recorded by thé local network, after thé transient pressure experiment, is probably related to cavity roof detachments and delamination of clayey maris on top of thé sait Iayer. There is no clear évidence of classical fragile ruptures in thé overburden, in particular in thé compétent Iayer of Beaumont dolomite. Therefore thé whole System remains mechanically stable. Further research work will be carried out to analyse thé microseismic activity induced by thé cavity growing until thé final overburden collapse. The resumption of thé sait dissolution will offer a unique opportunity to record precursor phenomena, which is actually thé most interesting part of thé running program.

Figure 1

 1 Figure1(Left) Plan of thé instrumented sait cavity (Right) Schème of thé E-W vertical section with main lithological units and location of microseismic 3D stations (red triangles) and 1D stations (blue triangles)

Figure 2

 2 Figure 2 Example of long event recording (vertical component M62) of 6 seconds of time duration. Time frequency analysis by short time Fourier transform (Hanning time window of 0.1 seconds)

Figure 3

 3 Figure 3 Number of events per day recorded while thé transient pressure experiment in thé cavity. The activity is concentrated in thé pressure raise (two clusters of 16 and 18 seconds of duration).

Figure 5 Figure 6

 56 Figure 5 Example of isolated events (vertical component M63) less than 0.5 seconds of time duration. Time frequency analysis by short time Fourier Transform (Hanning time window of 0.1 seconds)

  Spatial localisation of hand picked events (maximum likelihood locations). In pink, thé events recorded during thé transient pressure experiment(October 2005), in black some others from April, September and November 2005. Actual limits of thé cavity in red. At thé bottom right corner, thé time residuals at each station are plotted.

		X[km]			Z[km]
		893.2	893.4	0.2	0,1
	116.3			D>	1 * " * I * *	116.3
				ï>
	116.1				• • 116. 1
	Surface
	M
				Ml M2 M3 M4 M5 M63M62M61 -0.04
	893	893.2	893.4
	Figure 4.

Research Group for thé Impact and Safety of Underground Works involving French institutions INERIS, BRGM, INPL and Ecole des Mines de Paris.

ACKNOWLEDGEMENTS:This work is performed within thé GISOS framework. We acknowledge with gratitude thé financial support of thé French Ministry in charge of Industry. The authors also thank thé company SOLVAY S.A. for thé site, thé technical support and thé data for site characterization.