F Yang

C J Van Der Beek

T Shibauchi

Y Matsuda

B Dabrowski

Screening of transverse ac fields and influence of supercurrent on NMR Knight shift in high-T c crystals

Keywords: Superconductor, Knight-shift, d-wave, Volovik effect PACS: 74.25.Qt, 74.25.Sv, 74.25.Nf

In the Nuclear Magnetic Resonance (NMR) configuration with perpendicular constant (dc) field and radiofrequency (rf) magnetic fields, a strong screening supercurrent is generated by the rf field. In order to estimate this current which may influence Knight shift data, we performed a model experiment in the NMR configuration on a YBa2Cu4O8 single crystal. The differential magneto-optical imaging technique is used to determine the ac screening current distribution in the sample. We find that at T > 20 K, NMR experiments can be straightforwardly interpreted. At T < 20 K, the rf field is completely excluded. In this situation, NMR only probes the crystal edges; moreover, the Knight shift may be affected by the supercurrent flow.

Introduction

The NMR Knight shift K s probes the density of states (DOS) at the Fermi surface. In a superconductor, measurements of the K s at low temperature can thus probe the symmetry of the superconducting order parameter. However, in the NMR configuration: (i) The supercurrent flow is not uniform and it changes with temperature. (ii) For superconductors with gap nodes, in the presence of a magnetic field, the DOS at the Fermi surface for quasi-particles is Doppler-shifted by the supercurrent. An example is the shift of N s (E) ∼ √ H due to the vortex currents, known as the Volovik effect [1].

Here, we determine the distribution of the screening supercurrent and its possible effect on the Knight shift by Magneto-Optical (MO) imaging of a YBa 2 Cu 4 O 8 crystal in the orthogonal field configuration proper to NMR. The sample is a stripshaped YBa 2 Cu 4 O 8 single crystal (T c = 82 K) of dimensions 1220 × 328 × 88 µm 3 . We reproduce the field configuration employed in NMR: a dc field of 250 Oe is applied along the c axis and a periodic transverse field is applied along the b axis. The penetration of the periodic field was imaged using the Differential Magneto-Optical (DMO) technique. A transverse field of 10 Oe was applied, then 10 MO images were acquired and averaged. The transverse field was then removed, another 10 images were collected, averaged and subtracted from the first average. This procedure was repeated 10 times. We thus obtained images of the flux penetration into the crystal due to a 10 Oe square wave transverse field H plane of frequency f = 50 mHz. We suppose that the dynamics of flux lines do not change significantly with the frequency.

Experiment

Figure 1 shows a DMO image of the perpendicular component of the induction, induced by H plane , obtained at T = 45 K. Measurements were done for temperatures between 15 K and 80 K.

The screening current distribution J y (x) is calculated by inversion of the Biot-Savart law [2]. Three regimes are clearly manifest: At T > 65 K, current flow is uniform. At T < 20 K, one has the critical state due to bulk flux pinning. The current flows in the regions penetrated by the ac field only. At intermediate temperature 20 K < T < 65 K, current flows only at the sample edges due to the geometrical barrier effect (Fig. 1).

From the current distribution, we reconstruct the magnitude of the transverse field H x modulation normalized to H plane (Fig. 2). This corresponds to the local probing field in NMR. For T > 20 K, the transverse field is almost homogenous, the whole sample contributes to the NMR signal. For T < 20 K, only a thin surface layer contributes to the NMR signal.

Discussion of the Volovik effect on NMR Knight shift measurements

For a d-wave superconductor, in the absence of a magnetic field,

N s (E)/N 0 = 2 π E ∆0 K( E ∆0
), where N s (E) is the superconducting density of states; N 0 is the density of states at the Fermi surface in the normal phase, ∆ 0 is the maximum gap and K(x) is the complete elliptic integral. For small E ∆0 , or when T < 0.4 T c , numerical evaluation shows that N s (E) increases linearly with E [3]. The application of a magnetic field results in a shift of the quasi-particle energy levels due to the superflow:

E k = E 0 k +p k •v s .
The modification of the DOS at the Fermi level due to the screening current is estimated as N (0,j)

N0 = p F •vs ∆0 ≈ 1 2 √ 2
TF Tc vs vF , where means the average over the Fermi surface, p F is the Fermi momentum, v F is the Fermi velocity, v s is the superflow velocity, ∆ 0 ≈ 2k B T c (weak-coupling limit).

For example, in our crystal at T = 45 K, the sheet current j d (integrated over the sample thickness d = 88µm) at the edges is 4.5×10 3 Am -1 . The maximum current density j = 5 × 10 7 Am -2 . At T < 20 K, the current density reaches 10 8 Am -2 , which results in v s ≈ 5 ms -1 , to be compared to v F ≈ 1.2 × 10 5 ms -1 [4], T F ≈ 4000K, i.e. a Doppler shift of 0.15%. This is to be compared to the Volovik effect [1]:

N (0, H)/N 0 = h p F • v s /∆ 0 ≈ K H/H c2 , with K ≈ 1, H c2 =
100 T, and H = 1 T (NMR experimental condition) yields N (0, H)/N 0 = 10 %. The effect due to the screening current due to H plane can thus be ignored for our YBa 2 Cu 4 O 8 crystal. But for other high-T c superconductors, e.g., Bi 2 Sr 2 CaCu 2 O 8 , in which screening currents of the order of 10 10 Am -2 are attained, the Doppler shift due to screening current may strongly influence the K s below 15 K.

For a d-wave superconductor like YBa 2 Cu 4 O 8 , a linear dependence of the Knight shift on temperature should be observed for 0.1T c < T < 0.4T c . While the Volovik contribution may lead to a nonvanishing Knight shift for T → 0, the Doppler shift due to the screening current may lead to a nonlinear dependence of the K s on temperature.

Fig. 1 .

 1 Fig. 1. Left: DMO image of the perpendicular component of the induction, induced by H plane , obtained at T = 45 K with a 10 Oe square wave modulation along the b axis. Right: Corresponding sheet current Jy(x) (Amm -1 ) at T = 45 K.

Fig. 2 .

 2 Fig. 2. Magnitude of the transverse field Hx modulation normalized to H plane : circle 40K, star 60K.

Preprint submitted to Elsevier Science


Samuel Coussy
email: s.coussy@brgm.fr

Sylvain Grangeon
email: s.grangeon@brgm.fr

Philippe Bataillard
email: p.bataillard@brgm.fr

Hicham Khodja
email: hicham.khodja@cea.fr

Nicolas Maubec
email: n.maubec@brgm.fr

Pierre Faure
email: pierre.faure@univ-lorraine.fr

Christophe Schwartz
email: christophe.schwartz@univ-lorraine.fr

Robin Dagois
email: robin.dagois@univ-lorraine.fr

Evolution of iron minerals in a 100 years-old Technosol. Consequences on Zn mobility

Introduction

The industrial revolution in Europe that mainly took place in the 19 th century and the concomitant growth of urban activities led to major changes in land use. Many new factories created at that time produced high quantities of wastes (smelter slags, brick residues, coke or coal) which were extensively used as backfill materials for levelling purposes [START_REF] Lehmann | Nature and significance of anthropogenic urban soils[END_REF][START_REF] Testiati | Trace metal and metalloid contamination levels in soils and in two native plant species of a former industrial site: Evaluation of the phytostabilization potential[END_REF][START_REF] Nehls | Contribution of bricks to urban soil properties[END_REF]. Since that time, these wastesoften contaminated with trace metalsbecame the parental material of soils which developed thanks to alteration mechanisms due to mechanical, geochemical and biological processes. The newly formed soils are generally called Technosols or Anthropic Soils [START_REF] Rossiter | Classification of urban and industrial soils in the world reference base for soil resources[END_REF][START_REF] Wrb | World Reference Base for Soil Resource 2006[END_REF] because they contain at least 20% of man-made materials within the first 100 cm of the profile. Although contaminated, these materials present a pedologic interest since they fulfill several functions such as biomass production, storage-filtration and transformation of nutriments, trace elements and water, or formation of ecological niches [START_REF] Morel | Ecosystem services provided by soils of urban, industrial, traffic, mining, and military areas (SUITMAs)[END_REF]. Furthermore, they are now a central compartment for urban re-developers who try to build the city in the city to preserve agricultural soils [START_REF] Reeve | A basis for a pragmatic inquiry into the policy considerations related to the increased application of Biophilic Urbanism[END_REF].

Understanding their fate is then of primary importance.

Pedogenesis processes generally take place in a timescale of thousands of years [START_REF] Minasny | Quantitative models for pedogenesis -A review[END_REF][START_REF] Salvador-Blanes | Modelling long-term in situ soil profile evolution: application to the genesis of soil profiles containing stone layers[END_REF] and involve structural changes in the soil profile, including incorporation and transformation of organic matter as well as mineral dissolution and/or precipitation [START_REF] Costantini | Using the analysis of iron and iron oxides in paleosols (TEM, geochemistry and iron forms) for the assessment of present and past pedogenesis[END_REF][START_REF] Duchaufour | Dynamics of organic matter in soils of temperate regions: Its action on pedogenesis[END_REF][START_REF] Néel | Factors affecting natural development of soil on 35-year-old sulphide-rich mine tailings[END_REF]. Technosols are often contaminated (e.g. [START_REF] Tsolova | Pb, Cu and Zn geochemistry in reclaimed soils (Technosols) of Bulgaria[END_REF][START_REF] Wanat | Potentials of Miscanthus x giganteus grown on highly contaminated Technosols[END_REF] and, by analogy with natural and cultivated soils [START_REF] Huot | Characteristics and potential pedogenetic processes of a Technosol developing on iron industry deposits[END_REF], pedogenetic evolution will certainly influence the speciation, mobility and bioavailability of pollutants they contain. However, the kinetics and mechanisms of this evolution, as well as the consequences on pollutant speciation and mobility, are poorly constrained. It is currently admitted that the pedogenic processes occurring in Technosols are comparable to those occurring in natural soils, but can be accelerated due to the large thermodynamic disequilibria that can exist between the media of formation of the initial wastes and their new context of deposition [START_REF] Séré | Early pedogenic evolution of constructed Technosols[END_REF][START_REF] Leguédois | Modelling pedogenesis of Technosols[END_REF]. Coherently, several studies showed that early pedological transformations could be observed in Technosols, including oxidation, decarbonatation, secondary minerals precipitation, roots colonization and even humification in the first centimeters of the solum [START_REF] Bataillard | Mobilité des éléments traces dans un Anthroposol développé sur des sédiments de curage fortement contaminés -1 -Migration à l`échelle du profil[END_REF][START_REF] Norra | Urban Soil Science on the 18th WCSS[END_REF][START_REF] Séré | Early pedogenic evolution of constructed Technosols[END_REF], although the first industrial activities started in Europe at maximum 200 years ago.

Investigation of the kinetics and mechanisms of Technosols formation can be performed by studying soil profiles where the deeper horizons are considered less evolved than the upper horizons submitted to climatic and biologic alteration [START_REF] Brahy | Estimation of soil weathering stage and acid neutralizing capacity in a toposequence Luvisol-Cambisol on loess under deciduous forest in Belgium[END_REF][START_REF] Demattê | Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations[END_REF][START_REF] Egli | Weathering, mineralogical evolution and soil organic matter along a Holocene soil toposequence developed on carbonate-rich materials[END_REF]. Hence, the deeper horizons in a selected Technosol profile could be considered as the parent material of the upper parts of the same profile. In this hypothesis, the characterization of the surface and deep horizons gives valuable information about the evolution of the parent material and the rates of alteration. However, because these materials have been deposited by human activities, it may be pointed out that these horizons may also differ due to their origin. This complicates the understanding of alteration impact on the chemistry of Technosols: each observation and analytical result must be interpreted with care.

In this study, the iron (Fe) status evolution is characterized during the early alteration processes occurring in a 100-year old Technosol, and its consequences in terms of Zn mobility are assessed. Fe, an ubiquitous element in natural and anthropic materials, is of special interest because Fe minerals (i) are reported to be reliable and well-accepted markers of soil evolution in soil sciences and (ii) control to a large extent the distribution and mobility of trace metals in soils, especially in the absence of Mn species [START_REF] Bakker | A micromorphological study of pedogenic processes in an evolutionary soil sequence formed on Late Quaternary rhyolitic tephra deposits, North Island, New Zealand[END_REF][START_REF] Buol | Soil genesis and classification[END_REF][START_REF] Uzarowicz | Technogenic soils developed on mine spoils containing iron sulphides: Mineral transformations as an indicator of pedogenesis[END_REF]. The slow transformation of primary Fe-bearing silicates in oxic and well-drained conditions lead to Fe depletion and precipitation of Fe (hydr)oxides such as goethite, lepidocrocite, ferrihydrite or magnetite [START_REF] Bakker | A micromorphological study of pedogenic processes in an evolutionary soil sequence formed on Late Quaternary rhyolitic tephra deposits, North Island, New Zealand[END_REF][START_REF] Tawornpruek | Properties of red Oxisols on calcareous sedimentary rocks in Thailand[END_REF]. Formation of free Fe (hydr)oxides can lead to the coating of soil particles with a thin film of these Fe (hydr)oxides [START_REF] Ben-Dor | Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor[END_REF]. These mechanisms can compete with Fe complexation within the soil organic matter [START_REF] Tipping | Al(III) and Fe(III) binding by humic substances in freshwaters, and implications for trace metal speciation[END_REF].

For this study, a ~2m depth profile was sampled in a 100 years-old Technosol developed on backfills that contain significant concentrations of metals (e.g. Zn).

Samples were characterized at different depths in order to quantify the changes in the chemical status of Fe, to identify the main mechanisms responsible for the mobility of trace elements such as Zn and to understand its consequences in terms of long-term potential emission by the Technosol.

Material and Methods

Field site description

The present study was conducted at the "Union" former industrial site (Tourcoing, France), located 50° 42' 35" N, 3° 9' 52" E. The site was occupied from the 19 th century by several types of industries, such as textile industries, breweries, smelters, chemical industries, transport companies, and a gas plant (Figure 1). A rail yard was built at the end of the 19 th century at the north-west part of the site. Its reconversion for new activities is currently in progress.

A parcel of about 1,500 m² was chosen at the northern part of the former rail yard (Figure 1).

The parcel shows a gentle slope from west to east, so that the railway (SW) is higher than the road (NE). The parcel is free of any buildings.

A total of 5 pits were dug in the parcel and briefly investigated. Only one pit was held up and kept for further investigation, whereas the remaining pits were filled up. Pit selection was done according to the following criteria:

-Absence of material recently backfilled at the surface (e.g. recent natural earthy material was found in some pits), and presence of aged backfills with evidences of vegetation development; -Presence of a similar backfill material in depth which was not submitted to surface alteration.

Technosol profile dating

Investigations were carried out at the archives of the SNCF (French railways) to assess the probable date of setting of the backfills in the Technosol profile. A mail dating back October 6 th , 1884 has indicated the establishment of a rail yard in the sector. Other archive documents pointed out the construction of a railway in 1889, other earthworks in 1897 and the construction of a special rail in 1898. Since then, no major works were done in the sector. The study of aerial photographs taken between 1950 and 2015 proved that the frame of the rail yard always remained similar to the one represented in a map dating back 1898 (see supplementary material). Those photographs indicate also that the area where the Technosol is located has never been built since then. Thus, the area of study was not reworked for a century.

2.3.Technosol sampling and analyses

Soil profile sampling

The whole soil profile was sampled as a function of depth. Two types of samples were collected: composite samples (~1 kg per sample) for bulk analyses and samples representative of a given depth in the profile for punctual investigations (hereafter denominated "slices").

Composite samples were taken within the following range of depth: 0-10 cm (samples A), 30-40 cm (samples C), 110-130 cm (samples E) and 175-185 cm (samples F). At each depth range, three replicates were taken on a same horizon (Figure 2). The samples were kept as it is in plastic bags and stored at 4 °C before analyses.

Soil slices were taken at 5 cm (LM1), 90 cm (LM2) and 180 cm (LM3) deep. The soil structure was preserved (non-destructive sampling using cylinders) and 3 thin sections embedded with epoxy resin were elaborated (final thickness: 30 µm).

Bulk analyses

In laboratory, after measuring the water content of the composite samples, the soil pH was measured in a 1:5 (volume fraction) suspension of soil in deionized water according to NF ISO 10390. The total organic carbon content (TOC) was determined by dry combustion analysis according to NF ISO 10694. Organic carbon was analyzed after removal of carbonates by acidification using hydrochloric acid. Grain size distribution and total elemental concentrations were determined according to standardized methods (AFNOR, 1996, NF X 31-147). Cation exchange capacity (CEC) and exchangeable cations were measured with the cobaltihexamine chloride method [START_REF] Orsini | The use of the chloride of cobaltihexamine for the simultaneous determination of the exchange capacity and exchangeable bases in soils[END_REF], which can safely be used here, as samples contain little or no Fe(II)-bearing minerals [START_REF] Hadi | Pitfalls in using the hexaamminecobalt method for cation exchange capacity measurements on clay minerals and clay-rocks: Redox interferences between the cationic dye and the sample[END_REF].

Leaching tests and selective dissolution

The release of trace elements was assessed by leaching of the composite samples. The 3 replicate samples taken at each depth range were tested. The samples were air dried and sieved (cut-off diameter of 2 mm). Then, the leaching tests were conducted at room temperature (20°C±2°C), in closed PTFE reactors with continuous stirring during 24h. A liquid/solid ratio of 10 was used with deionized water, corresponding to 10 g of dry sample in 100 mL water. The leachates were then filtrated (cut-off diameter of 0.45 µm) and analyzed by ICP-AES (major and trace elements such as Zn).

The Fe status was assessed by selective dissolution, using both Dithionite-Citrate-Bicarbonate (DCB) and pyrophosphate methods, done in parallel on aliquots. The methods used are detailed in Table 1. DCB selectively solubilizes pedogenic oxides and hydroxides [START_REF] Mehra | Iron oxide removal from soils and clays by a dithionitecitrate system buffered with sodium bicarbonate[END_REF]. This method can solubilize crystalline Fe (hydr)oxides (hematite, goethite), non-crystalline Fe (hydr)oxides and Fe and Al organic complexes, whereas magnetite is slightly attacked and clays are unaffected. The pyrophosphate method was used to dissolve amorphous Fe(III), Fe(II) complexes and/or very poorly crystalline compounds [START_REF] Pansu | Mineralogical Separation by Selective Dissolution[END_REF]. Well-crystallized Fe (hydr)oxides like goethite and hematite are not attacked and slightly ordered Fe (hydr)oxides are only slightly solubilized. [START_REF] Claff | A sequential extraction procedure for acid sulphate soils: partitioning of iron[END_REF] tested the effect of these sequential chemical extractions on synthetic Fe minerals representative of some of those most abundant in the environment (e.g., akaganeite, ferrihydrite, goethite, hematite, jarosite, magnetite, pyrite and schwertmannite). They demonstrated that sodium pyrophosphate does not dissolve any of the tested minerals. The DCB procedure dissolves totally or significantly akaganeite, magnetite, goethite, hematite and ferrihydrite, and to a minor extent, jarosite.

Powder X-Ray diffraction analysis (XRD) on composite samples

The mineralogical assemblage of each composite sample was assessed by XRD, using a Siemens D5000 diffractometer with cobalt X-ray tube (CoKα radiation). The powdered composite samples were scanned with steps of 0.03°2θ and a counting time of 10s per step, in the 4-84°2θ angular range. Mineral identification and semi-quantitative determination of relative abundances of crystalline phases was performed using the software DIFFRAC plus EVA.

XRD patterns of composite samples were subjected to quantitative mineralogical analysis using the SIROQUANT TM software. The quantification is based on the principles of Rietveld refinement [START_REF] Rietveld | A profile refinement method for nuclear and magnetic structures[END_REF]. In addition to crystalline phases, SIROQUANT TM was used to indirectly determine the amorphous phase content by spiking the samples with corundum internal standard. A precisely evaluated proportion of corundum by weight (target value of 10%) was added to the samples. The amorphous content was calculated by a routine within the SIROQUANT TM program using a correction based on the weight-in proportion in the spike added prior to the XRD analysis.

Fe and Zn X ray absorption spectroscopy (XAS) analysis on composite samples

To complement selective dissolution for the study of Fe status, XAS analyses were carried out on composite samples. Moreover, the status of Zn, a recurrent trace element occurring in this Technosol, was also studied by XAS. The experiments were carried out at the Pacific Northwest Consortium Synchrotron Radiation Facility (PNCSRF) bending magnet beamline of the Advanced Photon Source (Argonne, IL, USA). The XAS spectra of composite samples were recorded, as well as those of reference minerals: natural hematite, natural 6L ferrihydrite, synthetic goethite and synthetic 2L and 6L ferrihydrite. Experiences of Zn coprecipitation and sorption onto ferrihydrite and goethite were also carried out. All ferrihydrite and goethite syntheses were performed using previously developed protocols [START_REF] Schulze | The influence of aluminum on iron oxides. VIII. Unit-cell dimensions of AI-substituted goethites and estimation of A1 from them[END_REF][START_REF] Waychunas | Surface chemistry of ferrihydrite: Part 1. EXAFS studies of the geometry of coprecipitated and adsorbed arsenate[END_REF]. X-ray absorption near-edge spectroscopy (XANES) spectra were systematically recorded. In addition, if the concentration of the element targeted (Fe or Zn) in the sample was sufficient, X-ray absorption fine structure spectroscopy (EXAFS) spectra were also collected. Fe XANES and EXAFS spectra were recorded in the range 6962-7912 ev, in transmission mode. The monochromator used was a double Si(111)-Si(220) crystal. A Fe foil was placed between the second and third ionization chamber for energy calibration.

Each sample was scanned between 6 and 10 times, to increase the signal-to-noise ratio in the EXAFS region. All absorption spectra were calibrated according to the Fe K-edge position at 7128 eV corresponding to the maximum of the first derivative. EXAFS spectra were extracted using a spline function adjusted by minimizing the Fourier transform magnitude at radial distances <1 Å. EXAFS spectra were deglitched if necessary. Radial structure functions were obtained by Fourier transform of the EXAFS spectra, using a Kaiser-Bessel apodization function with a smoothing parameter of 3.0. Data reduction was done using the Iffefit software package [START_REF] Ravel | ATHENA, ARTEMIS, HEPHAESTUS: data analysis for Xray absorption spectroscopy using IFEFFIT[END_REF].

The Fe-bearing minerals building up the composite samples were determined by linear combination fit of the EXAFS spectra. The minimum number of components needed to reproduce the EXAFS spectra was determined by principal components analysis [START_REF] Manceau | Quantitative Speciation of Heavy Metals in Soils and Sediments by Synchrotron X-ray Techniques[END_REF] using the entire set of composite samples spectra. The minimum of the indicator function (0.017) shows that two components are needed to reproduce the whole set of spectra (Table 2).

Linear combinations were done between 3 and 11 Å -1 in the EXAFS region. During the simulation, the following commonly used parameter was minimized:

Where n is the number of data point, data i is the data point number i, and fit i is the fit value at point i.

The sum of the wt.% of each compound obtained after least square fitting calculations was also used as a goodness-of-fit parameter, since it should reach values close to 100 wt.% in a reliable reconstitution.

For Zn, XANES spectra were recorded in fluorescence mode (multi-element Canberra Ge detector), in the range 9511-10361 eV. Each sample was scanned 12 times and resulting spectra were averaged.

Nuclear microprobe investigations

Textural information was provided by nuclear microprobe analyses on thin sections made from samples collected at a precise depth. These experiments were performed using the Laboratoire d'Etude des Eléments Légers nuclear microprobe in Saclay, France [START_REF] Khodja | The Pierre Süe Laboratory nuclear microprobe as a multi-disciplinary analysis tool[END_REF]. µ-PIXE and µ-RBS analyses were carried out on Fe-bearing phases pre-selected by SEM-EDS mapping. PIXE and RBS were used simultaneously to check the consistency of the results. Analyses were performed with 3.07 MeV 4He+ [START_REF] Isaure | Characterisation of zinc in slags originated from a contaminated sediment by coupling μ-PIXE, μ-RBS, μ-EXAFS and powder EXAFS spectroscopy[END_REF] and oxygen quantified through resonant scattering. The size of the incident 4 He + beam was approximately 4 x 4 µm 2 . Prior analyses, both PIXE and RBS detection setups were calibrated with suitable standards.

All measurements were performed by electrostatically scanning the beam on pre-selected areas, and elemental contents from regions of interest were obtained by combining SIMNRA software [START_REF] Mayer | SIMNRA User's Guide[END_REF] for RBS and GUPIXWIN software [START_REF] Campbell | The Guelph PIXE software package IV[END_REF] for PIXE.

Major and trace elements were quantified by µ-PIXE and oxygen by µ-RBS. H and C cannot be quantified (only semi-quantification).

Results

Soil characteristics

The Technosol profile is made of several horizons of anthropogenic backfills from the surface to ~190 cm (Figure 2). These backfills consist in successions of homogeneous yellow-white pebbles horizons with heterogeneous backfills composed of a sandy brownish/darkish material mixed with bricks and concrete fragments. Coal and slag fragments are also found in these materials. Roots are present from the surface to ~170 cm depth. Below the backfill materials, quaternary yellow silts are observed. Brick fragments were found at the transition of the backfills and the quaternary silts (~200 cm). Below, the silts seem un-impacted by human activities.

The Technosol investigated here belongs either to the sandy loam class or to the silt loam class according to the FAO guideline for soil description (2006) because it contains 41-61% sand, 30-50% silt and only 8% clay at maximum (Table 3). Total organic carbon is abundant (> 20 g 100g -1 ) over the whole profile, probably due to the coal fragments found in the backfills. No particular biological investigation was performed, but it is noteworthy that no earthworm activity was observed in the profile. pH and CEC are similar for the three replicate samples located on a same horizon, indicating that soil properties are homogeneous for a given depth. pH steadily increases with depth from 7.5 in surface sample to 8.5 in the deepest horizon. Oppositely, CEC decreases from ~23 meq 100 g -1 to 8-9 meq 100 g -1 . Fe concentrations vary from 6 to 2 wt. % in the profile, with no specific trend with depth. Such generally high Fe concentration cannot be quantitatively accounted for by the presence of crystalline Fe phases because hematite, the main Fe-bearing mineral detected by XRD, accounts for 0-3 wt. % of the mineral assemblage (Table 4). Thus, amorphous phases certainly contain Fe. Indeed, the mineralogical assemblage (Table 4) of the Technosol is mainly composed of an amorphous phase which can be formed by organic matter (Table 3) and contributes to 45-68 wt.% of the sample. Major minerals such as quartz and calcite are also present. In addition to hematite, accessory phases are constituted of phyllosilicates (illite, kaolinite) and sulphates (natrojarosite, syngenite). Such assemblage is typical of oxidized slags [START_REF] Navarro | The use of soil-flushing to remediate metal contamination in a smelting slag dumping area: Column and pilot-scale experiments[END_REF]. Note that this Technosol contains significant amounts of trace metals such as Zn (Table 3), but also As and Pb (see total concentrations in supplementary material).

Evolution of the Fe status along the profile

Fe status was first assessed by selective dissolution, using DCB and pyrophosphate methods (Figure 3). The fraction of total Fe complexed to surfaces (including organic matter) is negligible, as only 0.5-2 % of total Fe content is extracted by pyrophosphate, with no dependence on depth in the profile. Contrastingly, 30-60 wt % of total Fe content is extracted by DCB, indicating the presence of "free" Fe ("free" Fe covers different status: Fe oxides and hydroxides as well as exchangeable Fe).

DCB results show that 20-30 % more of the total Fe is present as "free" Fe at the surface as compared to the bottom of the profile. Such higher Fe extraction near surface could be due to a depth-dependent mechanism of oxidation of primary Fe-bearing species. However, the studied materials already contain oxides inherited from the past industrial activities, either under crystallized (hematite) or glassy (amorphous phase) states, the latter having a typical structure of melting-quenching, coming from slags (Figure 4). To distinguish between Fe (hydr)oxides inherited from the past and (hydr)oxides newly precipitated since the deposit of the backfills, XAS was employed.

A first analysis of all XANES spectra, collected on samples originating from various depths, shows a high degree of similarity between them, witnessing generally similar Fe-bearing phases and Fe oxidation state over the whole profile (Figure 5a). In particular, the pre-edge features located at ∼10-20 eV before the edge jump and which are particularly sensitive to the Fe oxidation state are similar for all samples, with a maximum located at ~7114.3 eV, indicative of the predominance of Fe(III) species throughout the profile [START_REF] Sundman | XAS study of iron speciation in soils and waters from a boreal catchment[END_REF]. Upon closer examination, however, the first derivatives shows a change with depth in the respective contributions of two inflexion points at 7124 eV and 7128 eV. As the 7124 eV and 7128 eV contributions are respectively attributable to Fe(II) and Fe(III) [START_REF] Sundman | XAS study of iron speciation in soils and waters from a boreal catchment[END_REF], such variations are attributed to a slightly higher contribution of Fe(II) in the lower part of the profile and not to statistical fluctuations because the first derivative of XANES spectra of two samples from a same horizon showed similar contributions of each inflexion point (Figure 5b andc). SEM-EDS observation of a sample taken at 175-185 cm depth (sample F1) evidenced the regular presence of particles of ~10 µm in width and containing solely Fe and S (Figure 6). These Fe sulphides could be at the origin of the slightly higher Fe(II) signal observed in depth by XANES. They were not detected by XAS and XRD (see Table 4, Figure 5 and Figure 7) because of their minor abundance as compared to the other Fe-bearing phases.

The mineralogy of the Fe-bearing phases was further assessed by least-square fitting of EXAFS spectra. Assuming that the evolution as a function of depth is due to weathering, and assuming that the sample collected in the bottom is representative of the lowest degree of weathering, samples from the bottom to the top can be described as a mixture of the sample from the bottom, plus additional component(s) that were formed during weathering. From PCA analysis (see above), only two components are required to describe the whole system. Following this approach, simulation of the spectra from samples collected in the surface horizon (A1) and at intermediate depth (E1) was performed, assuming that they could be described as a weighted sum of sample from the bottom (F1), plus an additional mineral that results from weathering. For the surface sample, the best fit (Figure 7) was obtained with 77% of sample from the bottom plus 25% of ferrihydrite. This is consistent with the DCB extraction results, as the increase in the DCB-extractable fraction between depth (sample F) and surface (sample A) was equal to 20-30%. Note that the sum of the weighted fraction of the two components is close to 1, thus reinforcing the confidence in simulation results. For the intermediate sample, the best fit (Figure 7) was obtained with 85% of sample from the bottom plus 15% of ferrihydrite, confirming that ferrihydrite proportion decreases with depth.

These results indicate a change in the status of Fe along depth. An assumption is that this change could influence the speciation of trace elements. The specific case of Zn is addressed in this paper since it is present in significant concentrations (up to 4,000 µg g -1 ) in the profile.

The status of Zn was investigated in relation with Fe but also with Mn status since Mnbearing phases can also be major scavengers of trace metals [START_REF] Grangeon | Zn sorption modifies dynamically the layer and interlayer structure of vernadite[END_REF][START_REF] Koschinsky | Uptake of elements from seawater by ferromanganese crusts: solid-phase associations and seawater speciation[END_REF][START_REF] Manceau | Quantitative Zn speciation in smeltercontaminated soils by EXAFS spectroscopy[END_REF][START_REF] Peacock | Crystal-chemistry of Ni in marine ferromanganese crusts and nodules[END_REF].

Evolution of the Zn status along the Technosol profile and assessment of Zn mobility

The evolution of Zn speciation was first assessed using Zn-K edge XANES analysis of composite samples, as it provides an integrating view. Data are presented as a function of depth for two sets of replicate samples (Figure 8).

The XANES spectra are similar for each replicate samples, indicating that Zn speciation is similar at any location at the same depth. Contrastingly, changes with depth are observed. The contribution at 9668.5 eV is higher in the deepest samples (F1 and F3) whereas the peak at 9665.5 eV is more pronounced in the surface samples (A, C and E). In Zn-ferrihydrite reference sample, the main contribution is located at 9665.5 eV. According to [START_REF] Cismasu | Properties of impuritybearing ferrihydrite II: Insights into the surface structure and composition of pure, Al-and Sibearing ferrihydrite from Zn(II) sorption experiments and Zn K-edge X-ray absorption spectroscopy[END_REF] and [START_REF] Waychunas | Surface complexation and precipitate geometry for aqueous Zn(II) sorption on ferrihydrite: II. XANES analysis and simulation[END_REF], the position of the edge is indicative of Zn coordination.

When coordination is octahedral, the XANES spectrum only has one well defined edge, whereas when coordination is tetrahedral, the XANES presents several maxima, as it is the case here. Indeed, all XANES spectra have two maxima at ~9665.5 eV and ~9668.5 eV. Thus, in presently studied samples, Zn coordination is tetrahedral. With increasing depth, the intensity of the maximum at ~9665.5 eV relative to that at ~9668.5 eV evolves, and is lowest at the maximum depth. As discussed by [START_REF] Waychunas | Surface complexation and precipitate geometry for aqueous Zn(II) sorption on ferrihydrite: II. XANES analysis and simulation[END_REF] this is indicative of an increasing number of heavy (e.g. Fe) scattering atoms around Zn, and thus of sorption to better-crystallized phases. Consequently, in the deeper horizons, Zn is associated to phases having a higher degree of crystallinity than in the surface horizons. This demonstrates that the weathering processes during soil evolution influence Zn speciation, but do not necessarily relate to Zn mobility as, for example, it gives no information on the solubility of the sorbing phases. To study Zn mobility, and to compare this element to other major elements, leaching of the composite samples was performed. The results indicate that Fe, Mn and Zn are all very little labile throughout the whole profile (Figure 9). Thus, all these elements probably are under the form of mineral phases (Fe, Mn) or strongly bound to mineral phases (Zn), which are determined in the following.

Zn content was investigated first using pyrophosphate extraction, which showed that the fraction of total complexed Zn, partly borne by organic matter is, within uncertainties, constant and equal on average to 18% (Figure 10a). Thus, although organic matter is a significant Zn reservoir, it does not represent the only Zn bearing-phase. The Zn content of the DCB-soluble fraction was investigated next. Fe extraction yields are presented here above (Figure 4b), and those from Mn and Zn are shown in Figure 10b. Both Zn and Mn are significantly affected by DCB extraction. Zn extraction yield (35% on average) is, within uncertainties, constant over the whole profile, while Mn has a slightly different behavior, 69 ± 4 % being extracted in the first 40 cm depth, 51 ± 6 % at 120 cm depth and 62 ± 4 % at 180 cm depth (Figure 10b). As DCB solubilizes Mn (hydr)oxides [START_REF] Mehra | Iron oxide removal from soils and clays by a dithionitecitrate system buffered with sodium bicarbonate[END_REF], it can be concluded that Mn is mainly present in the profile as discrete Mn (hydr)oxide(s). Here above, it was shown that Fe is also mainly present as (hydr)oxides. These two (hydr)oxides can potentially bear Zn, which is confirmed by the tetrahedral coordination of Zn assessed by EXAFS. Note that, although within uncertainties, the fraction of Zn extracted by DCB might increase with depth. However, as Zn concentration in the deepest sampling point (175-185 cm depth) is 10 times lower that in the surface horizon (0-10 cm depth), and even 26 times lower than in the sampling point just above (110-130 cm depth), a small quantity of Fe and Mn (hydr)oxides that would have migrated from the upper horizons (or a limited contamination, for example during sampling) may have led to such apparent slight change extraction yield.

According to this assumption, Zn is probably associated in depth with the Fe (hydr)oxides originally present in the backfills, but also with the newly formed Fe (hydr)oxides at the surface of the profile that would have migrated.

To summarize, in this profile and on average, less than 0.1 % of total Zn is H 2 O-extractable, up to 18 % of total Zn would be bound to organic matter, 35 % of total Zn would be adsorbed onto Fe/Mn (hydr)oxides, and about 47 % of total Zn is present under a residual form (that cannot be accessed with any of the extractant used here). To better constrain this distribution, µ-PIXE and µ-RBS are now used with a twofold aim: first, cross-check results from chemical extraction and in particular the association between Zn and Fe/Mn pedogenic (hydr)oxides, second, determine the nature of the residual form. These analyses were performed on sample LM2 sample (90 cm depth).

The association between Zn and Fe (hydr)oxides could be straightforwardly and regularly observed, as exemplified in Figure 11. The grain showed in this image is made of coal, with a Fe coating having a size of about 50 µm on the edge. The morphology suggests that the Fe (hydr)oxide is a secondary phase and was formed during soil evolution. Using quantitative analyses performed two regions of interest (ROI -Table 5), this Fe (hydr)oxide is probably the ferrihydrite identified using EXAFS spectroscopy, since Fe/O atomic ratios in the two ROIs (respectively 0.42 and 0.51) are lower than the theoretical Fe/O ratio of hematite (0.66), but are consistent with Fe/O ratio of ferrihydrite (theoretical value from 0.42 to 0.57 depending on the hydration state). Table 5 indicates also that Zn/Fe atomic ratio could reach up to 17%, which further confirms that ferryhydrite is a very efficient sink for metallic trace elements.

The nature of the Zn-bearing phase forming the residual phase was found to be complex, as demonstrated in Figure 12 and Table 6, where complex associations between Zn, Mn, Fe, S and O could be quantified. The quantitative analysis carried out in some regions of interest (ROI) show various elemental proportions (Table 6). The grain is composed of different Fecompounds: Fe (hydr)oxides (ROI 3,4,6) and Mn, Zn, Fe-bearing sulphates (ROI 1 and 5).

Interestingly, these latter are likely a mix of sulphides undergoing oxidation and forming sulphates (see the white arrow in Figure 12 b andc), since the S/O ratio of 0.42 (ROI 1 and 5), are higher than the expected ratio in a sulphate (0.25). It is interesting to note that the external part of the grain (ROI 3 and 4) do not contain sulphur. It is only composed of Fe (hydr)oxides, and contain less than 0.2 atomic percent of Zn. Fe/O atomic ratio calculated for all the Fe (hydr)oxides (see Table 6) range from 0.45 to 0.48. These values are lower than Fe/O ratio of hematite (0.66) and goethite (0.5), but are consistent with Fe/O ratio of ferrihydrite (0.42-0.57 depending on the hydration state).

Discussion

The study of the Fe status by several methods of investigation throughout the Technosol profile indicates that (i) Fe is mainly under Fe(III) form, with a contribution of Fe(II) in the deeper samples, (ii) some Fe species inherited from the past have been oxidized and secondary minerals are constituted of Fe-bearing sulphates (jarosite) and Fe (hydr)oxides, (iii) Fe(III) is mainly under the form of ferrihydrite, as evidenced by XAS, µ-PIXE and µ-RBS (iv) 20 to 30% more Fe (hydr)oxides are present in the surface than in depth. Ferrihydrite forms coatings surrounding different grains of the Technosol, in consistence with a neoformation induced by weathering processes. Previous studies have shown that in slags, secondary minerals formed by oxidation can be present as coatings on surfaces and as precipitates of various phases such as amorphous Fe, Al (hydr)oxides, chalcanthite, siderotil, jarosite, brochantite or gypsum [START_REF] Navarro | The use of soil-flushing to remediate metal contamination in a smelting slag dumping area: Column and pilot-scale experiments[END_REF][START_REF] Piatak | Mineralogical and geochemical controls on the release of trace elements from slag produced by base-and precious-metal smelting at abandoned mine sites[END_REF]. Fe sulphate and Fe (hydr)oxides are both secondary minerals formed by oxidation of Fe-bearing sulphides [START_REF] Blowes | The geochemistry of acid mine drainage[END_REF]. Such reaction typical of acid mine drainage can be encountered in all kind of wastes containing sulphides. In the present study, this assumption is consistent with the sulphates found by XRD and µ-PIXE, as well as the Fe sulphides encountered in depth (see Figure 6 above). However, the pH remains near-neutral to slightly basic since the netacid-generating potential was probably exceeded by the net-neutralizing potential due to a low sulphide/neutralizing minerals ratio (e.g. Plante et al., 2011). Thus, the main evolution driver of this 100 years-old Technosol is oxidation, which affects the Fe status, the CEC and the related trace elements mobility. The presence of free Fe at the surface of this Technosol is of interest since it provides the soil with an efficient trace metals sink and since such Fe is easily accessible to plants [START_REF] Pansu | Mineralogical Separation by Selective Dissolution[END_REF]. According to [START_REF] Navarro | The use of soil-flushing to remediate metal contamination in a smelting slag dumping area: Column and pilot-scale experiments[END_REF], the formation of secondary phases (e.g. jarosite, gypsum, goethite, ferrihydrite) may control the distribution of trace elements in the pore water and the degree to which they are mobilized into the environment. Dissolution of primary minerals leads to trace elements release in pore water solution. These trace elements can precipitate to form secondary minerals, or can be sorbed onto these secondary minerals.

In the studied Technosol, XANES revealed that Zn has tetrahedral coordination and is sorbed to phases of increasing crystallinity when depth increases. This could indicate that the amorphous Fe (hydr)oxides inherited from the past are slightly more crystalline than the newly formed Fe (hydr)oxides at the surface. Soil formation on such material does not induce a dramatic increase of Zn solubility since efficient scavengers are concomitantly formed in the system. Zn-bearing phases identified in the present study are: complex Fe, Mn, Zn sulphides, which may be the primary Zn-bearing phase, sulphates which may be the oxidation product of the sulphides, organic matter, and ferrihydrite. On the contrary, no Zn-bearing Mn (hydr)oxides were found, even if there are known to be important sinks of trace elements such as Zn.

The assumption that the deeper horizons are the parent material in this Technosol profile is difficult to prove since the characteristics of the original material at the time of backfill deposit are unknown. However, the observed evolution of Fe mineralogy with depth, as well as Zn speciation change and CEC decrease with depth, is a clue for the validity of this assumption.

This study also highlights that DCB selective extraction method is relevant to investigate Fe status in a Technosol, since the relative proportion of "free" Fe -mainly Fe (hydr)oxidesquantified by DCB (20-30% in surface) is similar to the ones estimated by EXAFS linear combination fitting (25% in surface). DCB is also interesting to assess the relations between all the "free" Fe species and trace elements such as Zn, even if the concentrations of Zn bound to Fe (hydr)oxides are probably underestimated by this extraction. Indeed, dithionites are labile compounds which undergo rapid disproportionation in aqueous solution due to their sensitivity to oxygen. This decomposition is rapid at pH < 5.5 but also occur at neutral and basic pH [START_REF] De Carvalho | Polarographic determination of dithionite and its decomposition products: kinetic aspects, stabilizers, and analytical application[END_REF]. It produces mainly thiosulphate and bisulphite which will be the final product of the disproportionation. But intermediate sulphide and elemental sulfur, playing a catalytic role, are also encountered in solution as minor decomposition products [START_REF] De Carvalho | Polarographic determination of dithionite and its decomposition products: kinetic aspects, stabilizers, and analytical application[END_REF][START_REF] Wayman | Decomposition of aqueous dithionite. Part II. A reaction mechanism for the decomposition of aqueous sodium dithionite[END_REF].

Dissolved sulphide can react with Zn to precipitate ZnS during DCB extraction [START_REF] Rao | A review of the different methods applied in environmental geochemistry for single and sequential extraction of trace elements in soils and related materials[END_REF]. This would explain the gap between DCB results which show that only 35% of total Zn is dissolved whereas XANES and nuclear microprobe indicate a major contribution of Zn associated with ferrihydrite.

In the future, this Technosol will potentially evolve again because some phases are not thermodynamically stable in conditions of pH observed here. For instance, jarosite is known to be stable at pH<3 [START_REF] Blowes | The geochemistry of acid mine drainage[END_REF], whereas the pH in the Technosol is near-neutral to slightly basic. Moreover, the input of organic matter at the surface based on organic C determination, is slightly visible after 100 years of evolution. Pyrophosphate extractions have shown that Fe and Zn are variably complexed to surfaces (including organic matter).

However, the pyrophosphate extract does not only determine organic forms but also dissolves poorly crystalline mineral phases (e.g. [START_REF] Kaiser | Defects in estimation of aluminium in humus complexes of podzolic soils by pyrophosphate extraction[END_REF]. Mineralogical investigations confirmed that Fe and Zn are not complexed on coal which is the most abundant carbon phase of the Technosol. Indeed, Fe and Zn were not observed at the surface of the coal grains (see Figure 11). Though, other clues of evolution are visible in the studied Technosol. CEC is higher near surface and could indicate that the soil surface reached a higher evolution stage than the deeper horizons, either due to weathering or to bioturbation. Organic matter will probably play an important role in the future on major and trace element speciation.

Conclusions and environmental implications

Technosols are highly heterogenous and widely differ from one place to another because of the variety of their constitutive man-made materials. In this study, a Technosol made of smelter slags, i.e. containing high amounts of coal and Fe (hydr)oxides was characterized. The results indicate that 100 years of soil evolution has led to the precipitation of secondary minerals (including Fe (hyrdr)oxides and Fe sulphates) at the surface of the profile. This behavior is similar to pedogenic processes occurring in natural soils, but other specific evolutions due to large thermodynamic disequilibrium can be highlighted, in particular sulphide oxidation and sulphate precipitation. This specific behavior of Technosols over time can be generalized to all kinds of Technosols, even if the final reaction products are not similar. Such evolution influences pollutants mobility including trace elements release. In this specific study, Technosol evolution does not induce a dramatic increase of Zn solubility since efficient scavengers are concomitantly formed in the system. However, this behavior is not generic and will depend on the type of Technosol and on the secondary minerals formed as well as on the nature and amount of organic matter. If the trace metals sinks are not abundant and/or efficient, this could potentially represent a risk of mobilization of trace metals. This must be carefully examined through case studies. b Error on these elements is comprised between 1 and 5% when the atomic fraction is lower than 1%, elsewhere the error is lower than 1%. b Error on these elements is comprised between 1 and 13% when the atomic fraction is lower than 1%, elsewhere the error is lower than 1%.

Figure 1 .

 1 Figure 1. Aerial photograph (1951) of the Union site (red) and location of the main industries (white). The parcel of study is located in the orange square.

Figure 2 .

 2 Figure 2. Soil profile. A General overview. B. Details of the central zone of the profile. C. Schematic representation of the soil profile.

Figure 3 .

 3 Figure 3. Fe extracted by pyrophosphate (a) and DCB (b) throughout the soil profile (normalized by the total amounts in Fe of each sample).

Figure 4 .

 4 Figure 4. Iron (hydr)oxide showing a texture of melting-quenching (in sample A1).

Figure 5 .

 5 Figure 5. Fe-K edge XANES spectra (a) and corresponding first derivatives (b) and (c) of the composite samples from the Technosol profile located at the following range of depth: 0-10 cm (replicates A1 and A3), 30-40 cm (replicates C1 and C3), 110-130 cm (replicates E1 and E3) and 175-185 cm (replicates F1 and F3). For the replicate samples A3, C3, E3 and F3, only the first derivatives are given.

Figure 6 .

 6 Figure 6. Fe-S bearing phases dispersed in a silicate mineral in the composite sample F1 (175-185 cm).

Figure 7 .

 7 Figure 7. Least squares fit of EXAFS spectrum for A1 (0-10 cm, replicate N°1) and E1 (110-130 cm, replicate N°1) composite sample with a combination of ferrihydrite reference (Nat fhyd) and F1 composite sample (175-185 cm, replicate N°1).

Figure 8 .

 8 Figure8. Zn-K edge XANES spectra of the composite samples from the Technosol profile for two sets of replicate samples located at the following range of depth: 0-10 cm (replicates A1and A3), 30-40 cm (replicates C1 and C3), 110-130 cm (replicates E1 and E3) and 175-185 cm (replicates F1 and F3)., and Zn-K edge XANES spectra of Zn-sorbed ferrihydrite reference sample.

Figure 9 .

 9 Figure 9. Fe, Mn and Zn released by leaching (% extracted from the total Fe, Mn and Zn content).

Figure 10

 10 Figure 10. a. Zn extracted by pyrophosphate throughout the soil profile (normalized by the total amounts in Zn of each sample).b. Mn and Zn extracted by DCB throughout the soil profile (normalized by the total amounts in Mn and Zn of each sample).

Figure 11 .

 11 Figure 11. Quantitative elemental mapping showing distribution of Si, S and O (map b) and Fe, Zn and Mn (map c) of a selected grain in LM2.

Figure 12

 12 Figure 12 Quantitative elemental mapping showing distribution of Si, S and O (map b) and Fe, Zn and Mn (map c) of a selected grain in LM2. The white arrow in maps b and c indicate a specific Zn, Fe, Mn and S bearing phase.

Figure captions

 captions Figure captions

Table 1

 1 Summary of selective dissolution procedures (from[START_REF] Bataillard | Iron and arsenic speciation in marine sediments undergoing a resuspension event: the impact of biotic activity[END_REF]

	Fraction	Reagent

Tables

Table 2 :

 2 Results of the principal component analysis on the 8 composite sample spectra. The minimum of the indicator (IND) function shows that two components are needed to reproduce the whole set of spectra.

	Component Eigenvalue IND a
	1	13.7	0.018
	2	1.7	0.017
	3	0.9	0.022
	4	0.8	0.028
	5	0.6	0.040
	6	0.4	0.085
	7	0.4	0.32

a Indicator function

Table 3 .

 3 Technosol characteristics and properties (ND: not determined)

	Soil	Depth		Texture (%)	pH Total organic carbon (g	CEC	(meq	Total Fe (wt.	Total Mn (wt.	Total Zn (mg
	sample	(cm)	Clay Silt Sand	100g -1 )	100g -1 )		%)	%)	kg -1 )
	A1	0-10	8	50 42	7.5 25	22.8		4.5	0.06
	A2			ND	7.6 26.3	21.3		4.8	0.06
	A3			ND	7.7 24.9	20.1		4.2	0.06
	C1	30-40	8	31 61	7.8 35.3	15.6		5.3	0.06
	C2			ND	8.2 38.0	14.8		5.3	0.06
	C3			ND	8.2 28.8	14.3		3.9	0.06
	E1	110-130	8	39 53	8.3 38.9	10.1		4.2	0.06
	E2			ND	8.3 27.4	10.7		4.5	0.09
	E3			ND	8.4 25.3	10.9		5.3	0.12
	F1	175-185	5	30 65	8.4 31.8	9.5		2.2	0.03
	F2			ND	8.5 48.7	8.1		2.0	0.03
	F3			ND	8.5 46.8	9.2		2.0	0.03

Table 4 .

 4 Relative proportion of crystalline minerals in the bulk composite samples of the Technosol profil estimated by Rietveld analysis (ND: not detected)

	Mineral proportion (wt.%) a A1 C1 E1 F1
	Quartz	31 14 15 23
	Calcite	4	2	1	5
	Albite	2	1	<1 1
	Microcline	1	<1 <1 1
	Dolomite	4	1	1	ND
	Natrojarosite	<1 <1 ND 1
	Hematite	2	3	<1 1
	Mullite	3	3	2	1
	Syngenite	1	<1 <1 <1
	Illite/mica	4	4	10 6
	Kaolinite	3	4	4	1
	Amorphous phase	45 68 67 60

a Error on these results: +/-5 wt%

Table 5 .

 5 Quantitative elementary results obtained by µ-PIXE and µ-RBS on two regions of interests (ROI) of the grain in the isolated sample LM2 as shown in Figure11.

	Atomic fraction (%) ROI 1 ROI 2
	O a	36.6	30.4
	Si b	2.4	6.3
	P b	0.3	0.7
	S b	0.8	0.9
	Cl b	0	0
	K b	0	0.1
	Ca b	0.1	0.6
	Ti b	0	0
	Mn b	0	0
	Fe b	18.8	12.6
	Zn b	0.7	2.1
	As b	0	0
	Pb b	0.2	0.2
	Fe/O atomic ratio	0.51	0.42

a Error on O is ± 5%

Table 6 .

 6 Quantitative elementary results obtained by µ-PIXE and µ-RBS on five regions of interests (ROI) of the grain in the isolated sample LM2 as shown in Figure12.

	Atomic fraction (%) ROI 1 ROI 2 ROI 3 ROI 4 ROI 5
	O a	36.2	40.2	40	34.2	37.8
	Si b	13.8	0.5	0.2	13.4	0.6
	P b	0	0.5	0.1	0.2	0.1
	S b	15.3	0.2	0.1	14.0	0.9
	Cl b	0.2	0	0	0	0.1
	K b	0.3	0	0	0.2	0
	Ca b	2.2	0.3	0.2	1.7	0.1
	Ti b	0.2	0	0	0.1	0
	Mn b	3.0	0	0	2.0	0
	Fe b	3.8	18.1	19.0	4.6	17.2
	Zn b	7.5	0.2	0.1	6.5	0.1
	As b	0	0.1	0	0	0
	Pb b	0.8	0	0	0	0
	Fe/O atomic ratio	-	0.45	0.48	-	0.46

a Error on O is ± 5%

Acknowledgments

The authors would like to acknowledge the French Environmental Agency (Ademe; Hélène Roussel) for their financial support (REITERRE research program) and advices through this work. S.G. acknowledges partial funding by the ANR (NACRE-ANR-14-CE01-0006). The XAFS experiments were performed at the Advanced Photon Source (sector 20). Sector 20 facilities at the Advanced Photon Source, and research at these facilities, are supported by the US Department of Energy -Basic Energy Sciences, the Canadian Light Source and its funding partners, the University of Washington, and the Advanced Photon Source. Use of the Advanced Photon Source, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory, was supported by the U.S. DOE under Contract No. DE-AC02-06CH11357. Special thanks to Robert Gordon and Michael Pape, who helped with the experiments at the beamline.


Arnaud Prouzeau
email: prouzeau@lri.fr

Collaboration around Wall-Displays in Command and Control contexts

Keywords: Wall-display, Collaboration, Graph, Critical System ACM Classification H.5.2 [Information Interfaces and Presentation]: User Interfaces -Graphical user interfaces

High resolution wall-dispays are more and more used in research contexts in order to visualize, explore and study big dataset. They provide a huge visual space to display data, physical navigation rather virtual, which leads to a more natural pan and zoom, and finally an easy way to collaborate due to their large shared space. Their use in a more operational context like control rooms is more limited, they are most of the time considered as just a big display with which you interact with mice and keyboards. During this PhD, we would like to study how we could embrace the benefits of a wall-display in command and control contexts. To answer this question, we first analyzed the activities in a control room in general, and then focus in a specific use case: road traffic control. In this specific context, we are now working on an experiment to compare the use of a wall-display and several workstations. Then we focussed on collaboration, and study the use of a wall display to perform multi user exploration of graphs, a data structure commonly used in command and control centre. Finally, we studied the use of the capacity of a high resolution wall to help operators assess the impact of their action.

INTRODUCTION

High density information spaces are more and more common, some of them containing several millions of data points, such as social networks, scientific database (DNA, Molecules) but also transportation networks. To analyse, understand but also monitor these data, complex interactive visualizations are needed. Wall-sized displays represent a convenient way to immerse and allow users to navigate and manipulate data in these kind of spaces due to several inherent benefits: (i) physical rather than virtual navigation affords a natural panand-zoom in the information space to see overview from afar and details up-close; (ii) an enlarged physical space in front of the display enables collaborative viewing; (iii) and millions of pixels support viewing large amounts of data.

Wall-displays are for now mostly used to their full interactive and visualization potential in research contexts only [START_REF] Bezerianos | Perception of visual variables on tiled wall-sized displays for information visualization applications[END_REF], in more operational and industrial contexts they are used as big computer displays, in which most of the interactions are distant and done with a mouse and a keyboard. This is especially the case in command and control contexts. Indeed, in several control rooms, they are used to give an overview of the system to operators in order to enhance situation awareness, but all the manipulations are done on individual workstations. This situation provokes a lack of group awareness, as it is harder for an operator to be aware and understand what another colleague is doing when focusing on their desktop, and so harder to coordinate their actions.

The lack of adoption of wall-displays as more than monitors leads to our research question: Could interactive wall-displays be useful in collaborative command and control environment?

In the remaining of this paper, we will first perform an analysis of control room activities, then we study how the use of an interactive wall-displays can impact coordination between two operators, compared to the use of several workstations. We will then discuss our work on multi-user exploration of graphs on a wall display, as graphs are a data structure which is use a lot to visualize transportation networks. Finally, we introduce a specific use-case: road traffic control, and how to use the full potential of an high-resolution wall in order to help operators monitor, but also forecast the effect of possible interventions on the traffic in big cities like Paris.

ANALYSIS OF CONTROL ROOM ACTIVITIES

In control room contexts, it is crucial to be aware of what the other operators are doing in the room, in order to collaborate and coordinate their actions. This is particularly necessary in cases of emergency, where coordinate cost should be as low as possible to act as fast as possible. This is illustrated in the study of the London subway control room by Smith et al. [START_REF] Smith | Questioning, exploring, narrating and playing in the control room to maintain system safety[END_REF], in which to enhance group awareness, subway controllers put on speaker their conversation with train drivers, giving others information about the current situation with this train, and allow them to act quickly in case of incident.

Interviews of operators from different fields (road traffic controllers from before, one air traffic controller and one nuclear power plant operator), visits of control rooms (two Paris road traffic control room, the French police operation centre) and my past experiences (Visits of French and Scottish air traffic control centres and airport control towers) showed us that we can find mainly two types of situations in this context: (i) The normal situations, in which the operators mainly monitor the system,don't need to act a lot and don't need to coordinate their actions with others. It represents most of their time during a shift in the control room. And (ii) the critical situations, that can correspond to an accident, a failure in the system, or just a situation in which the system is saturated (traffic jam for road traffic control) and for which the automation can't provide an optimized solution. In that case, the operators need to act, but also to coordinate their actions with others. As usually these situations are time critical the coordination has to be done at the same time as the action.

These also showed us the main general layout of a control room: the operators work on individual workstations, and they have a shared large visualization of the system projected on a shared surface. The large visualization is used to provide context information, while the workstation is used to provide detailed information and to act on the system.

A high resolution wall-displays provides a large space that can be used to display general information about the state of the system, but also detailed information about a specific part of it when viewed up-close due to its high resolution. It can also provide more direct way to interact with the system by directly touching it, which can attenuate divided attention issues. This way of interacting with the wall provides more information about an operator's action to others which improves group awareness, and can facilitate group coordination, as operators can just point with their fingers at a part of the system they are talking about.

COMPARISON OF A WALL DISPLAY WITH WORKSTA-

TIONS

Empirical studies support the idea that large displays foster collaboration. Nevertheless, to our knowledge, no study quantitatively compares collaboration using a large vertical display, with a setup that doesn't possess its characteristics, i.e., the large and shared surface. In this paper, we measure performance and coordination differences when pairs use a large display, compared to two workstations that share a common view. Workstations are far enough to avoid face-to-face and deictic communication, while allowing verbal communication.

The configuration of the two workstations are similar to the configuration seen in control rooms.

To quantitatively study collaboration, we chose a low level task, to better control task difficulty across setups, and allow for multiple repetitions. Inspired by previous work on collaboration [START_REF] Tang | Collaborative coupling over tabletop displays[END_REF] and by real tasks performed in control rooms, we used a simplified path-finding task with constraints. We expected our pairs to develop efficient collaboration strategies over multiple trials, eventually reducing the need for coordination and decision making that are essential in collaboration [START_REF] Mandviwalla | What do groups need? a proposed set of generic groupware requirements[END_REF]. As such, we did not provide any training to our participants, but rather compared the learning phase across settings, as this is where pairs need to communicate and coordinate to improve their strategy.

Moreover, to study possible trade-offs between the setups, we also measured other metrics that could shed light to differences in collaboration, such as the amount of communication between pairs and their coordination strategies.

We were unable to measure a difference in learning between setups, but pairs were generally faster using workstations. Nevertheless, quality was more consistent with the large display, and pairs communicated and planed more in this setup. With workstations, pairs divided the task as much as possible, requiring less communication, and affecting their quality of work. Now that we show that the wall facilitates communication and coordination compared to two workstations, we want to see what can have an influence on collaboration. We then study the influence of interaction technique on collaboration strategy.

MULTI-USER GRAPH EXPLORATION

Graph structures, consisting of vertices and edges, exist in various application areas, such as traffic networks, they are frequently represented as node-link diagrams. But like many visual representations of large datasets today, they can be too wide to view comfortably on regular screen monitors. We thus propose viewing and exploring them on a wall-displays.

We perform a first systematic study of how pairs use a walldisplay to solve topology based tasks, that are common com-un-propagate (undo) chained undo ponents of more complex graph analysis tasks [START_REF] Lee | Task taxonomy for graph visualization[END_REF]. We study how the choice of interaction technique supports or hinders pairs collaborating on these tasks, as it is shown for a data manipulation task by Liu et Al. [START_REF] Liu | Shared interaction on a wall-sized display in a data manipulation task[END_REF]. We focus on techniques for selection, a fundamental visualization task, as it is a prerequisite to many interactions such as filtering, comparisons, details on demand, etc.

We adapt two general purpose graph selection techniques for use by multiple users on a touch-enabled wall-display. Our baseline is a basic node/edge selection. It is easy to master, and has a limited, and thus fairly localized, visual footprint on the wall display, that does not interfere with colleagues' work. We called this technique Basic. The propagated selection extends, for multiple users, the idea of transmitting a selection to neighboring nodes/edges [START_REF] Mcguffin | Interaction techniques for selecting and manipulating subgraphs in network visualization[END_REF] It highlights the connectivity structure of the graph, but may have a large visual footprint that disturbs colleagues. We called it Propagation (Figure 2).

We first assess the impact of selection technique on pairs conducting a specific topology analysis task, namely identifying a shortest path. As this is the first study of pairs working on such tasks on wall-displays, we tease out effects due to technique vs. collaboration, by also studying single user selections. We then examine how propagation, the most promising technique, is used by pairs on other graph analysis tasks [START_REF] Lee | Task taxonomy for graph visualization[END_REF]. Our studies are conducted on a touch enabled wall-display, instead of interacting using mice and keyboards, as mobility allows viewers to perform implicit zooming [START_REF] Ball | Move to improve: Promoting physical navigation to increase user performance with large displays[END_REF] and correct for visual distortions [START_REF] Lobo | An evaluation of interactive map comparison techniques[END_REF].

The first study showed Propagation to be faster in both individual and multi-user contexts, to be more accurate for multiple users, and to require less movement than Basic in a shortest path identification task. It is also versatile enough to be used in a series of topology tasks, observed in our second study.

Nevertheless, as Propagation has a large visual footprint, it requires higher coordination when used by multiple users, we often found pairs alternate their interactions to coordinate actions. When working in pairs, propagation selection increases accuracy overall, but due to a coordination cost it improves time only for complex graphs. When using basic selection, that has a small visual footprint, accuracy dropped for pairs, most noticeably in complex graphs. Indeed, we observed that using basic selections, participants tended to work inde-pendently and lose awareness of each other's work, which proved detrimental for the task we consider, that is not clearly divisible.

ROAD TRAFFIC MANAGEMENT ON WALL-DISPLAYS

Finally we want to study the use of a wall display in a specific use-case: road traffic control management. Traffic congestion in major cities and highways is a growing problem in most countries. Perturbations and incidents such as accidents and breakdowns, or exceptional events such as demonstrations, can overload a road network that may already be operating at its limit, e.g. during rush hour. To prevent and to react efficiently to incidents and perturbations, road traffic in cities and highways is monitored in dedicated control centers.

Even for experienced operators, it is often challenging to evaluate the impact of an intervention on the network. While they are equipped with predefined traffic plans (sets of compatible interventions on a sector or area), it is still sometimes unclear which plan will work best for the current state of the network, in particular during exceptional events. This is where simulation models of road-traffic can help operators better understand and chose among possible intervention alternatives. Road-traffic is a complex system that is particularly challenging to model, as it involves multiple agents (cars) that can behave in a non-deterministic manner. Nevertheless, researchers are able to approximate road-traffic using methods from physics or statistics and machine learning Their simulations can perform short-term traffic forecasting, identify problematic sectors with high-risk of traffic-congestion, and test new concepts to improve road-traffic such as dynamic adjustment of speed limits.

Following interviews and observations of road-traffic control centers, we extracted user needs related to both possible interventions they make, and the lack of support tools to help them understand the impact of these interventions. We then designed a prototype system for road-traffic monitoring and management that runs on a touch enabled ultra high-resolution wall display (Figure 1). Taking advantage of their high resolution and large real-estate, our prototype extends the visualizations currently used in road-traffic control centers, to allow operators to concurrently explore and visualize results from multiple simulations that can help them test alternative interventions on the network, both in a local and global scale.

We propose two visualization techniques for viewing multiple simulations in combination with real traffic: (i) multiple views [START_REF] Javed | Exploring the design space of composite visualization[END_REF][START_REF] Tufte | The Visual Display of Quantitative Information[END_REF] to show global views of the network; and (ii) Drag-Magic, a combination of DragMags [START_REF] Ware | The dragmag image magnifier[END_REF] and magic lenses [START_REF] Bier | Toolglass and magic lenses: The see-through interface[END_REF] to visualize localized sectors. For the first time, we adapt and combine these techniques to visualize and compare several forecast visualizations using wall displays in control centers. Our choice of techniques is influences by both controllers' comments on the type of feedback they require for monitoring and forecasting (on the whole network, but also more specifically on one intersection), but also by our previous findings that global versus local focus can influence performance. To better understand when to use each of these techniques, we compared them in a lab experiment. Non expert participants were asked to follow several simulations of road traffic and to find the more fluid one.

Participants performed well in comparison tasks of up to 6 different simulations, contrary to previous findings on multiple views [START_REF] Plumlee | Zooming versus multiple window interfaces: Cognitive costs of visual comparisons[END_REF] that predicted decreased performance with the increase of comparisons. Our results also suggest that DragMagic is easier to master and may be beneficial when the number of simulations to compare is high, but that both techniques are viable alternatives. Early feedback on our prototype from experts, were very promising. They particularly appreciate the DragMagic, and they say it can also be used to compare the present traffic with past traffic data to detect unusual events. Our study was conducted on a single user setup to first assess the impact of such novel visualization on user's abilities to follow multiple simulations on the wall. It remains future work to also test these designs with multi-users.

We took advantage of the high resolution of the wall-displays to show more information on the shared display, in the form of multiple simulation results. But we feel it was important, then, to study how people collaborate and coordinate in front of it. In order to isolate the coordination effect, we did our study in a more abstract context: graph exploration.

CONCLUSION

Due to their large size and high resolution, wall-displays can be an interesting solution to answer to current control rooms issues like group awareness for example. In that case, visualization and interaction techniques have to be designed with the the type of collaboration wanted in mind (close or loose). We want now to understand how operator will perform the transition between their workstations and the wall.

Figure 1 .

 1 Figure 1. Visualization of traffic in a city with two "DragMagics" (white rectangles) showing one (left) and two (right) simulations associated with different possible interventions on the traffic. The simulation visualizations use difference color maps to highlight differences with the real traffic.

Figure 2 .

 2 Figure 2. On the left multiple propagations: (a) a first tap on node 0 selects it; (b) a second tap propagates the selection to immediate neighbors; (c) and a third tap to 2nd degree neighbors (notice the difference in link width according to distance); (d) a tap on node 7 selects it with a new color; (e) a second tap selects its neighbors, one of which (node 8) is shared with the first propagation and has both colors; (f) a fourth tap on node 0 propagates the first selection a third time, resulting in nodes 6,7,8, and link 8-7 being shared between propagations, with the color and width on shared link 8-7 alternating. On the top right gesture to undo one propagation step on a node (left) and chained undo for backtracking multiple steps (right). On the bottom right design variations for displaying propagation distance using color intensity (top) and node-link size (bottom).

AKNOWLEDGEMENT

I thank my two supervisors, Anastasia Bezerianos and Olivier Chapuis, for their help and support and all the members of the ILDA team. This work has been partially supported by the ANR grant ANR-10-EQPX-26-01 "Digiscope". Details about part of this work can be found in these publications [11,12].