Thierry Woignier
email: thierry.woignier@imbe.fr

Florence Clostre

Paula Fernandes

Alain Soler

Luc Rangon

Maria Isabel Sastre-Conde

Magalie Lesueur Jannoyer

Philippe Garrigues

The pesticide chlordecone is trapped in the tortuous mesoporosity of allophane clays

Keywords: Chlordecone, Soil contamination, Allophane, Mesoporosity, Tortuosity, Fractal microstructure

Some volcanic soils like andosols contain shortrange order nanoclays (allophane) which build aggregates with a tortuous and fractal microstructure. The aim of the work was to study the influence of the microstructure and mesoporosity of the allophane aggregates on the pesticide chlordecone retention in soils. Our study shows that the allophane microstructure favors pollutants accumulation and sequestration in soils. We put forth the importance of the mesoporous microstructure of the allophane aggregates for pollutant trapping in andosols. We show that the soil contamination increases with the allophane content but also with the mesopore volume, the tortuosity, and the size of the fractal aggregate. Moreover, the pore structure of the allophane aggregates at nanoscale favors the pesticide retention. The fractal and tortuous aggregates of nanoparticles play the role of nanolabyrinths. It is suggested that chlordecone storage in allophanic soils could be the result of the low transport properties (permeability and diffusion) in the allophane aggregates. The poor accessibility to the pesticide trapped in the mesopore of allophane aggregates could explain the lower pollutant release in the environment.

Introduction

Andosols contain clay (allophane) that presents unique structures and physical properties compared to crystalline clays. Allophane is a short-range order aluminosilicate, the unit cell of which appears as spheroids with a diameter between 3 and 5 nm [START_REF] Wada | The distinctive properties of andosol[END_REF] forming aggregates with a fractal structure [START_REF] Adachi | Application of a scaling law to the analysis of allophane aggregates[END_REF][START_REF] Chevallier | Fractal structure in natural gels: effect on carbon sequestration in volcanic soils[END_REF][START_REF] Wada | The distinctive properties of andosol[END_REF][START_REF] Woignier | Preservation of the allophanic soils structure by supercritical drying[END_REF]. The original properties of allophane have been used in various fields for trapping purpose. Recently allophane has been found to be effective in the stabilization of C in soil and composts, offering perspectives for carbon sequestration technology [START_REF] Bolan | Stabilization of carbon in composts and biochars in relation to carbon sequestration and soil fertility[END_REF][START_REF] Calabi-Floody | Nanoclays from an andisol: extraction, properties and carbon stabilization[END_REF]. Synthetic allophane has also been studied as adsorbents for organic pollutants [START_REF] Espinoza | Sorption behavior of bensulfuron-methyl on andisols and ultisols volcanic ash-derived soils: contribution of humic fractions and mineral-organic complexes[END_REF].

Chlordecone (CLD) is an organochlorine pesticide. It is a highly hydrophobic organochlorine pesticide (K ow between 4.9 and 5.4) and poorly soluble (0.35-3 mg L -1 ) [START_REF] Soler | Solid-phase microextraction and gas chromatography-mass spectrometry for quantitative determination of chlordecone in water, plant and soil samples[END_REF]; U.S. Environmental Protection Agency 2012). It is generally admitted that chlordecone is poorly biodegradable especially in environmental conditions explaining its persistence in soils [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF][START_REF] Epstein | Kepone-hazard evaluation[END_REF]UNEP 2007). The CLD poor biodegradability is attributed to its peculiar chemical structure (bishomocubane Bcage^) and the high steric hindrance caused by the ten chlorine atoms. However, [START_REF] Dolfing | Gibbs free energy of formation of chlordecone and potential degradation products: implications for remediation strategies and environmental fate[END_REF] showed that there were no thermodynamic reasons why chlordecone-respiring or chlordeconefermenting organisms should not exist. Previous work studied variations in the ratio of CLD-5b-hydro to CLD in soils (Clostre et al. 2015a). The mean ratio was lower in andosols than in nitisols. The differences in ratios between andosols and nitisols could be due to a lower biodegradation of CLD to CLD-5b-hydro, because of a lower availability in andosols. Chlordecone biodegradation is not impossible but at least very restricted in these clays.

This neurotoxic and carcinogenic molecule [START_REF] Dallaire | Cognitive, visual, and motor development of 7-month-old Guadeloupean infants exposed to chlordecone[END_REF][START_REF] Epstein | Kepone-hazard evaluation[END_REF] still persists in the soil for centuries and contaminates foodstuffs and water resources [START_REF] Coat | Organochlorine pollution in tropical rivers (Guadeloupe): role of ecological factors in food web bioaccumulation[END_REF][START_REF] Fung | Risks posed by trace organic contaminants in coastal sediments in the Pearl River Delta, China[END_REF]Clostre et al. 2015b;[START_REF] Jondreville | Contamination of free-range ducks by chlordecone in Martinique (French West Indies): a field study[END_REF][START_REF] Rossa | Linking current river pollution to historical pesticide use: insights for territorial management?[END_REF]) thus leading to human exposure through food [START_REF] Dubuisson | Impact of subsistence production on the management options to reduce the food exposure of the Martinican population to chlordecone[END_REF][START_REF] Gimou | Dietary exposure to pesticide residues in Yaoundé: the Cameroonian total diet study[END_REF]. The bioaccumulation process leads to the contamination of the entire food web. Recently, a book provides a presentation of various aspects of chlordecone pollution (Lesueur et al. 2016).

Water is the main vehicle of pesticides transport; so, water flow is an important point to consider. However, few studies have focused on pesticide transfers in andosols. When comparing cadusafos leaching in andosols and nitisols, it appeared that cumulated water pollution by cadusafos stemming from nitisol percolation was much greater than that from andosols [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF]). In the case of CLD, [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF] collected samples from lysimeters installed on an andosol and a nitisol. They observed that the concentrations of CLD in similar volumes of drainage water were 3 to 4-fold higher in the nitisol than in the andosol. This finding suggested that the desorption capacity of nitisol was higher than that of andosol.

These results are consistent with results on soil to crops contamination found in the literature. Contamination of crops by soil CLD depended on the type of soil: andosols were less contaminating than nitisols [START_REF] Cabidoche | Contamination of harvested organs in root crops grown on chlordecone-polluted soils[END_REF][START_REF] Woignier | Pollution of soils and ecosystems by a permanent toxic organochlorine pesticide: chlordecone-numerical simulation of allophane nanoclay microstructure and calculation of its transport properties[END_REF]. Studies on chlordecone transfer to water and crops led to the general assessment that andosol tends to retain the pesticide more than other soils.

So, the literature shows that andosols are generally more polluted by CLD than the other kinds of tropical soils (nitisols, ferralsols) but they release less pesticide to the environment [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF][START_REF] Cabidoche | Contamination of harvested organs in root crops grown on chlordecone-polluted soils[END_REF][START_REF] Woignier | Chlordecone retention in the fractal structure of volcanic clay[END_REF]. Organic matter and mineral surface [START_REF] Pignatello | Soil organic matter as a nanoporous sorbent of organic pollutants[END_REF][START_REF] Vlčková | A comparison of POPs bioaccumulation in Eisenia fetida in natural and artificial soils and the effects of aging[END_REF] are well known to influence pesticide retention in soils but physical properties of soil porous structure are seldom studied.

In its natural state, the clay consists of aggregations of spherical allophane particles [START_REF] Wada | The distinctive properties of andosol[END_REF][START_REF] Wells | Factors affecting the flow behavior of soil allophane suspensions under low shear rates[END_REF][START_REF] Lindner | Hollow nanospheres, allophanes 'all-organic' synthesis and characterization[END_REF]. Transmission electron microscopy shows allophane nano-spheres with a diameter of about 5 nm [START_REF] Wada | The distinctive properties of andosol[END_REF][START_REF] Calabi-Floody | Nanoclays from an andisol: extraction, properties and carbon stabilization[END_REF][START_REF] Chevallier | Fractal structure in natural gels: effect on carbon sequestration in volcanic soils[END_REF]. The allophane spherule aggregates (average diameter 100 nm) and organo-mineral complexes form small aggregates with pores in the mesopore scale (2-50 nm) and with high organic matter loadings. Seventy-five to 90% of the soil organic matter found in the pores is in the 10-1000 nm size range [START_REF] Mccarthy | Protection of organic carbon in soil microaggregates via restructuring of aggregate porosity and filling of pores with accumulating organic matter[END_REF].

The present study hypothesizes that the peculiar properties of allophane and specially the porous and tortuous microstructure of allophane aggregates favor pollutant accumulation in soils and hinder the transfer to the environment. We thus focus on the physical properties of the allophane clay to explain the pollutant trapping.

For that, we propose to do the following:

1) Measure the water chlordecone contamination from andosols having different allophane content. 2) Show the influence of allophane concentration on porous features of soils. We measure the porous properties (specific surface area, pore volume, fractal features) of several andosols by small angles X-ray scattering and nitrogen adsorption techniques. 3) Finally, we put forth the importance of the clay microstructure (fractal range, mesoporous volume, and tortuosity) for pollutant trapping.

Experimental Soil samples

We sampled 31 soils, andosols (allophane clay) and nitisols (halloysite clay), in Martinique (14°40 N, 61°00 W) in the vicinity of the BMontagne Pelée^volcano. These two kinds of soils are representative of the main polluted soils found in French West Indies [START_REF] Brunet | Determination of soil content in chlordecone (organochlorine pesticide) using near infrared reflectance spectroscopy (NIRS)[END_REF][START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF]. The soils we selected were known to be historically polluted by CLD. The soils samples were retained in closed containers to avoid moisture evaporation and associated structural changes of allophanes. Samples were stored during 7 days at 4 °C.

The allophanic content was measured by the method of [START_REF] Mizota | Clay mineralogy and chemistry of soils formed in volcanic material in diverse climatic regions[END_REF] using Al and Si content extracted by oxalate and pyrophosphate and measured by inductively coupled plasma atomic emission spectroscopy (ICP-AES ICAP 6500, Thermo Scientific, France). The soils samples covered the allophane range 0 to 26% (w/w basis).

The crystalline and amorphous structure was studied by Xray diffraction (Cu Kα) with a Philips PW 1830. Before X-ray diffraction, the samples are dried at 90 °C and the powder is ground at 40 μm.

Environ Sci Pollut Res

The presence of allophane was characterized by infrared spectroscopy. The apparatus used was an IR-FT Nicolet 510P spectrometer and the samples were diluted in KBr pellets with a 5 10 -3 mass ratio.

Physical fractionation of soil organic matter by sieving in water [START_REF] Bruckert | Fractionnement des agrégats applique à l'analyse des complexes organominéraux des sols[END_REF][START_REF] Feller | Une méthode de fractionnement granulométrique de la matière organique des sols : application aux sols tropicaux, à textures grossières, très pauvres en humus (a method for granulometric fractionation of soil organic matter. Application to tropical soils with coarse textures and very low humus content[END_REF] as previously described (Clostre et al. 2014b). It was carried out after leaving 20 g of soil to stand overnight at 4 °C in 300 cm 3 of water added with 0.5 g of hexametaphosphate, followed by shaking for 6 h at 50 rpm with five glass balls 1 cm in diameter. This method separates the different organo-mineral soil fractions according to their particle size. In our case, we separated three fractions: (1) 0-50 μm, corresponding to the soil organic matter associated with clay and silt (fine fraction), this fraction contained stable allophane microaggregates; (2) 50-200 μm, corresponding to pre-humified or partially mineralized organic debris of plant or animal origin and fecal pellets; and (3) 200-2000 μm, corresponding to coarse sand and plant residues.

Three replicates of each fraction were prepared and CLD and organic carbon contents were analyzed in each replicate.

The carbon contents are measured with a CHN (Thermofinnigan) chromatograph analyzer.

Transfer experiments

The leaching experiment was performed through water extraction such as evaluating soil solution contamination by CLD. Micro-columns containing 5 g of soil were prepared with at least four replicates for each soil. Leaching with 12.5 mL of nanopure water was slightly forced by gentle centrifugation (500×g) directly in the micro-columns. Nanopure water was used to avoid CLD contamination by tap water as described in Clostre et al. (2014b). Pure water was produced using a water purification system (Barnstead Nanopure Diamond) and an activated carbon filter (Bioblock Scientific ORC-83005). One important parameter in the leaching experiments is the S/V ratio, where S is the contact area and V the liquid volume. In this study, the S/V ratio was around 200 cm -1 , which is comparable to the leaching test used for porous materials and nuclear waste glasses [START_REF] Rebiscoul | Etude de la pérennité des gels d'alteration de verres nucléaires[END_REF][START_REF] Woignier | Sintered silica aerogel: a host matrix for long life nuclear wastes[END_REF]. The system was designed to preserve the soil structure as much as possible. After water passed through the soil columns, water CLD concentrations were determined by solidphase microextraction and gas chromatography mass spectrometry (GC450/MS240, Varian, Palo Alto, CA, USA) according to the procedure detailed in [START_REF] Soler | Solid-phase microextraction and gas chromatography-mass spectrometry for quantitative determination of chlordecone in water, plant and soil samples[END_REF]. The soil to water transfer coefficient (WTC) is the ratio of the pesticide concentration in lixiviates to the pesticide concentration in soil expressed in microgram per liter/milligram per kilogram of dry soil.

CLD concentration in soils samples was analyzed by the LDA26 (Valence, France) as described in [START_REF] Cabidoche | Contamination of harvested organs in root crops grown on chlordecone-polluted soils[END_REF].

Soils porous properties

Porous and structural measurements generally require dried solid samples. A preliminary study [START_REF] Woignier | Supercritical drying applied to natural Bgels^: allophanic soils[END_REF] has shown that the CO 2 supercritical drying is necessary to preserve the porosity and microstructure of andosols. A critical point dryer (CPD 010, Balzers, Liechtenstein) was used in a procedure such as previously described [START_REF] Woignier | Supercritical drying applied to natural Bgels^: allophanic soils[END_REF].

The specific surface area (SSA) of the sample was calculated from the adsorption curve at P/P0 = 0.3 and the BET equation [START_REF] Brunauer | Adsorption of gases in multimolecular layers[END_REF]) by N 2 adsorption-desorption curves (Micromeritics ASAP 20120, France). The soil powder was placed in a test tube to degas for 24 h at 50 °C in a vacuum of 2-4 μm Hg.

The pore volume was calculated from the bulk density and the solid density measured by He pycnometry for the different kinds of soils. The solid density is closed to 2.5 g.cm -3 for the allophanic soils and 2.66 g.cm -3 for the halloysite soils [START_REF] Dorel | Porosity and soil water properties of Caribbean volcanic ash soils[END_REF].

Physical features of clay aggregates

The morphology of allophane and halloysite clays was characterized by electron microscopy. The transmission electron micrographs were obtained with a Transmission Electron Microscope JEOL Type 1200 EX (100 kV). The scanning electron micrographs were obtained with a Cambridge stereo scan 360 scanning electron microscope.

The fractal features of the allophane aggregates were characterized by small angle X-ray scattering experiments (SAXS) in transmission mode. A copper rotating anode X-ray source (operating at 4 kW), with a multilayer focusing BOsmicm onochromator giving high flux (10 8 photons.s -1 ) and punctual collimation, was employed. The data from the SAXS experiments provided three types of information on the fractal geometry: the maximum size of the fractal clusters (L), the size of the primary particles (a) which built the clusters, and the fractal dimension (Df) which characterized the spatial arrangement in the cluster [START_REF] Marlière | Very large-scale structures in sintered silica aerogels as evidenced by atomic force microscopy and ultra-small angle X-ray scattering experiments[END_REF][START_REF] Teixeira | Small angle scattering by fractal systems[END_REF]. These experiments which characterize the clay microstructure between 1 to 100 nm cover the range of the allophane mesoporosity.

Mesopore volumes (the pore volume of clay aggregates) were determined as cumulative values in the range between 2 and 50 nm [START_REF] Rouquerol | Guidelines for the characterization of porous solids[END_REF]) with a Barrett-Joyner-Halenda method (BJH) [START_REF] Barrett | The determination of pore volume and area distributions in porous substances. I. Computations from nitrogen isotherms[END_REF]) using desorption branches of the N 2 adsorption-desorption isotherms.

Environ Sci Pollut Res Tortuosity (t) inside the allophane aggregates is calculated from the fractal features; t is a power law function of the length scale: t ∝ l λ-1 [START_REF] Jullien | Aggregation and fractal aggregates[END_REF][START_REF] Stanley | Kinetics of aggregation and gelation[END_REF]. λ is the tortuosity exponent and can be derived from the fractal dimension (Df) and the spreading dimension (Dl) λ = Df/Dl. [START_REF] Jullien | Aggregation and fractal aggregates[END_REF].

Thus, t = L (Df/Dl)-1 .a (Df/Dl)-1 .

Statistical analysis

All statistical analyses were performed using XL STAT 2012.6.08 (Addinsoft 1995(Addinsoft -2013)). ANOVA and Tukey Honestly Significant Difference tests (P < 0.05) were used to compare means of transfer (WTC) for the different soil types and to assess the difference of CLD content in the different soil fractions for each soil type. The linear regressions and ANOVA were used to assess the equation of the linear models, their goodness of fit, and the significance of the explanatory variable.

Results and discussion

Allophanic soils

To confirm the presence of allophanes in the studied andosols, we used X-ray diffraction and infrared techniques. X-ray patterns of studied andosols showed bulges of the baseline between 2 and 7 Å, according to the literature [START_REF] Barois | Andosol-forming process linked with soil fauna under the perennial grass Mulhembergia macroura[END_REF][START_REF] Woignier | Supercritical drying applied to natural Bgels^: allophanic soils[END_REF], these bulges can be attributed to non-crystalline solids, allophane, and/or imogolite. While the X-ray diffraction patterns for nitisols showed peaks at 4.45 and 7.4 Å that can be attributed to the clay halloysite [START_REF] Dubroeucq | Weathering and soil forming processes under semi-arid conditions in two Mexican volcanic ash soils[END_REF][START_REF] Onodera | Bactericidal allophanic materials prepared from allophane soil: I. Preparation and characterization of silver/phosphorus-silver loaded allophanic specimens[END_REF]. The infrared spectroscopy confirmed the presence of allophane (characteristic bands like 506, 577 and a shoulder at 970 cm -1 ). Moreover, the IR spectra of the different studied andosols do not present doublets of the band at 577 and 967 cm -1 characteristic of the imogolite structure [START_REF] Denaix | Structure and affinity towards Cd2+, Cu2+, Pb2+ of synthetic colloidal amorphous aluminosilicates and their precursors[END_REF][START_REF] Gustafsson | Podzolisation mechanisms and the synthesis of imogolite in northern Scandinavia[END_REF][START_REF] Levard | Structure and distribution of allophanes, imogolite and proto-imogolite in volcanic soils[END_REF].

Andosols are derived from volcanic materials [START_REF] Colmet-Daage | Caractéristiques de quelques groupes de sols dérivés de roches volcaniques aux Antilles françaises (Characteristics of a few soil groups resulting from volcanic rocks in the French West Indies)[END_REF][START_REF] Dubroeucq | Weathering and soil forming processes under semi-arid conditions in two Mexican volcanic ash soils[END_REF]). These volcanic soils comprise weathering products such as allophane originating from the lixiviation of volcanic ash and glasses [START_REF] Barois | Andosol-forming process linked with soil fauna under the perennial grass Mulhembergia macroura[END_REF][START_REF] Basile-Doelsch | Mineralogical control of organic carbon dynamics in a volcanic ash soil on La Reunion[END_REF][START_REF] Quantin | Premiers stades d'altération de ponces volcaniques en climat tropical humide (montagne pelée, martinique)[END_REF]. The literature shows that the clay consists of aggregations of spherical allophane particles [START_REF] Wada | The distinctive properties of andosol[END_REF][START_REF] Wells | Factors affecting the flow behavior of soil allophane suspensions under low shear rates[END_REF][START_REF] Lindner | Hollow nanospheres, allophanes 'all-organic' synthesis and characterization[END_REF]. Allophane spherules tend to form porous nanosized aggregates [START_REF] Calabi-Floody | Nanoclays from an andisol: extraction, properties and carbon stabilization[END_REF][START_REF] Garrido-Ramirez | Catalytic wet peroxide oxidation of phenol over iron or copper oxide-supported allophane clay materials: influence of catalyst SiO2/Al2O3 ratio[END_REF], whose average diameter is around 100 nm and there is a clear analogy between allophane aggregates and synthetic silica gels [START_REF] Adachi | Application of a scaling law to the analysis of allophane aggregates[END_REF][START_REF] Clark | Cation and anion retention by natural and synthetic allophane and imogolite[END_REF][START_REF] Chevallier | Fractal structure in natural gels: effect on carbon sequestration in volcanic soils[END_REF].

We compare the structure of halloysite clay with that of allophane clay isolated from the studied nitisols and andosols (Fig. 1). The plate-like particles of phyllosilicate clay like halloysite are 300-1000 nm in size (Fig. 1a). In comparison, the scanning electron micrograph confirms the spongy structure of allophane clay with aggregates around 100 nm (Fig. 1b). The allophane aggregates are highly porous in the range of the mesoporosity [START_REF] Calabi-Floody | Nanoclays from an andisol: extraction, properties and carbon stabilization[END_REF][START_REF] Maeda | Solidification mechanism of allophane by hydrothermal reaction[END_REF].

From the transmission electron microscopy (Fig. 1c), we see that the morphology of the allophane aggregates is peculiar. Allophane has a very open structure made up of aggregated small particles that form clusters of around 10-20 nm. The clusters can stick together and form larger and larger aggregates up to L ∼ 100-300 nm in size (Fig. 1c). This hierarchical aggregation is characteristic of a fractal microstructure. The fractal features of the studied samples will be measured in BInfluence of allophane content on soil pore properties^section.

Transfer experiments

The literature suggested that the desorption capacity of nitisol was higher than that of andosol [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: a simple leaching model accounts for current residue[END_REF]). Through the leaching experiment, we evaluated the contamination of the soil solution by chlordecone. The transfer of chlordecone to water (WTC, Table 1) was twice lower in allophane clay (andosol) than halloysite clay (nitisol) despite similar organic carbon content. The WTC were significantly different for the two soils (P < 0.0001).

It is generally admitted that the pesticide retention is soils is related to the organic matter content [START_REF] Pignatello | Soil organic matter as a nanoporous sorbent of organic pollutants[END_REF]) and to the sorption capacity of the mineral surface [START_REF] Vlčková | A comparison of POPs bioaccumulation in Eisenia fetida in natural and artificial soils and the effects of aging[END_REF][START_REF] Fushiwaki | Adsorption of pesticides and their biodegraded products on clay minerals and soils[END_REF]. It has already been shown that organic carbon content could not account for CLD intra-field variability (Clostre et al. 2014a) and could only partially explain the lower uptake observed for plants growing in andosol compared to ferralsol and nitisol (Clostre et al. 2015b).

In this study, we showed that the higher the allophane content, the lower the WTC (Fig. 2, WTC = -0.2548 allophane (%) + 4.5096, P < 0.0001 and r 2 = 0.7378) despite similar organic carbon content. In Fig. 2, the carbon content is 2.1 wt% for the nitisols (0% allophane weight), and 2.9, 2.3, and 3.1 wt% for the andosols with 5.9, 10.2, and 11.8 allophane weight %, respectively (organic carbon was not measured for the 2.5 allophane weight %).

Likewise, andosols show higher sorption capacity for a range of pesticides, such as atrazine, parathion, and cadusafos compared to other soils [START_REF] Prado | Transport, sorption and degradation of atrazine in two clay soils from Mexico: andosol and vertisol[END_REF][START_REF] Olvera-Velona | Sorption and desorption of organophosphate pesticides, parathion and cadusafos, on tropical agricultural soils[END_REF][START_REF] Müller | Spatial variability of atrazine dissipation in an allophanic soil[END_REF]. And, due to its adsorption capacity, allophane clay has been studied for use in water purification for benzene derivatives [START_REF] Nishikiori | Adsorption of benzene derivatives on allophane[END_REF], fluoride [START_REF] Kaufhold | Allophane compared with other sorbent minerals for the removal of fluoride from water with particular focus on a mineable Ecuadorian allophane[END_REF], and boron [START_REF] Reinert | Characterization and boron adsorption of hydrothermally synthesised allophanes[END_REF].

Environ Sci Pollut Res

The structural properties of the mineral matrix with which pollutants are associated may influence pollutant bioavailability and retention in soils [START_REF] Peters | Differences in p,p′-DDE bioaccumulation from compost and soil by the plants Cucurbita pepo and Cucurbita maxima and the earthworms Eisenia fetida and Lumbricus terrestris[END_REF][START_REF] Chung | Effect of soil properties on bioavailability and extractability of phenanthrene and atrazine sequestered in soil[END_REF][START_REF] Liu | Mechanisms associated with the high adsorption of dibenzo-p-dioxin from water by smectite clays[END_REF][START_REF] Rana | Probing the microscopic hydrophobicity of smectite surfaces. A vibrational spectroscopic study of dibenzo-p-dioxin sorption to smectite[END_REF]). Allophane is short-range order clay, whose porous structure and physical properties strongly differ from those of the crystalline clays as halloysite found in nitisols.

It is likely that the propensity of andosols for pollutant retention is related to the allophane microstructure and nanopore features. Data show that physical protection and adsorption of persistent pesticide are both higher in nanosized structures [START_REF] Pignatello | Soil organic matter as a nanoporous sorbent of organic pollutants[END_REF][START_REF] Calabi-Floody | Improving bioavailability of phosphorous from cattle dung by using phosphatase immobilized on natural clay and nanoclay[END_REF][START_REF] Baldock | Role of the soil matrix and minerals in protecting natural organic materials against biological attack[END_REF]. The combination of pore size and network tortuosity may play a key role in controlling pesticide inaccessibility to microorganisms and water. The decrease in bioavailability has often been explained by slow diffusion in small pores and can be physically explained as the result of entrapment of molecules [START_REF] Arias-Estévez | The mobility and degradation of pesticides in soils and the pollution of groundwater resources[END_REF][START_REF] Puglisi | Bioavailability and degradation of phenanthrene in compost amended soils[END_REF][START_REF] Reid | Bioavailability of persistent organic pollutants in soils and sediments-a perspective on mechanisms, consequences and assessment[END_REF]. It is necessary to explore the physical processes that explain these differences.

Influence of allophane content on soil pore properties

The data show that the pore volume Vp and the specific surface area S were well correlated to the allophane contents (Fig. 3a,b). We found that S (m 2 .g -1 ) = 43.39 + 4.99 allophane (%) (P < 0.0001 and r 2 = 0.87) and Vp (cm 3 .g -1 ) = 0.58 + 0.059 allophane (%) (P < 0.0001 and r 2 = 0.79). The specific surface area could be as high as 200 m 2 .g -1 and Vp close to 2 cm 3 .g -1 .

Large specific surface area is generally the signature of an important mesoporosity contribution [START_REF] Gregg | Adsorption, surface area, and porosity[END_REF]. The literature [START_REF] Adachi | Application of a scaling law to the analysis of allophane aggregates[END_REF][START_REF] Wada | The distinctive properties of andosol[END_REF][START_REF] Woignier | Preservation of the allophanic soils structure by supercritical drying[END_REF] describes the allophane aggregates structure as fractal in the range of a few nanometer up to almost 100 nm. This new study confirms that the fractal dimension is close to 2.6 ± 0.1 and the particles size (a) extracted from the scattering spectra (SAXS) is constant (in the range 4-5 nm). The Df constant means that the aggregation mechanism is quite the same, whatever the allophane concentration. The measured Df is rather close to the fractal dimension (2.2) corresponding to the BReaction limited cluster aggregation^ [START_REF] Kolb | Scaling of kinetically growing clusters[END_REF]) and the higher measured value (2.6 compared to 2.2) is likely the result of a restructuring with time [START_REF] Jullien | Aggregation and fractal aggregates[END_REF].

We show that the size of the fractal aggregate (L) increases with the allophane concentration (Fig. 3c, L (nm) = 6.65 + 1.95 allophane (%), P < 0.0001 and r 2 = 0.89). When particles stick together to form clusters, they also create voids, i.e., pores, inside the clusters. These clusters stick together to form larger porous clusters with larger pores and so on, up to L the maximum size of the fractal aggregates. Another important feature of fractal aggregates is their internal porosity (mesoporous volume Vmeso). Figure 3d shows a strong increase in the mesoporous volume Vmeso with the allophane Vmeso = 0.0008 + 0.0197 allophane (%) (P < 0.0001 and r 2 = 0.8018).

This kind of aggregation process leads to a porous structure with a high tortuosity (t) which is another important property of fractal aggregates. t can be derived from the fractal dimension (Df) and the spreading dimension (Dl), the fractal dimension of the minimum path [START_REF] Jullien | Aggregation and fractal aggregates[END_REF]

: t = L (Df/ Dl)-1
.a (Df/Dl)-1 . For nanoporous microstructure close to allophane (silica gels), the spreading dimension Dl has been found experimentally to be close to 1.7 [START_REF] Vacher | Crossovers in the density of states of fractal silica aerogels[END_REF]) and thus the exponent λ should be in the range 1.47 to 1.55 with Df in the range 2.6. These λ values are in a good agreement with the data found experimentally to be between 1.4 and 1.8 [START_REF] Vacher | Crossovers in the density of states of fractal silica aerogels[END_REF][START_REF] Courtens | Brillouin-scattering measurements of phonon-fracton crossover in silica aerogels[END_REF]) and with the predictions of aggregation models [START_REF] Herrmann | The fractal dimension of the minimum path in two-and three-dimensional percolation[END_REF][START_REF] Meakin | Topological properties of diffusion limited aggregation and cluster-cluster aggregation[END_REF]. We have no Dl value for the allophane aggregates, but we can assume that the Dl value is not very different from the Dl of nanoporous silica Dl = 1.7.

Figure 3e shows the increase of the aggregates tortuosity with the allophane content (calculated with Dl = 1.7 and the data found from the SAXS experiments: Df = 2.6, a = 5 nm and the L values from Fig. 3c). Figure 3e shows the strong positive correlation of the allophane content with the aggregates tortuosity (t): t = 1.3628 + 0.082 allophane (%) (P < 0.0001 and r 2 = 0.8593). The allophane aggregate microstructure resembles a labyrinth at the nanometer scale. This description suggests that the accessibility in the allophane microstructure decreases when the size of the labyrinth, the internal porosity, and tortuosity increase. This hypothesis is discussed thereafter.

Influence of allophane features on the CLD retention

A previous work [START_REF] Woignier | Chlordecone retention in the fractal structure of volcanic clay[END_REF]) has demonstrated the strong influence of the allophane content on the soil contamination. The new data confirm that the soil chlordecone content increases with the allophane content: CLD = 0.6223 + 0.8951 allophane (%) (P < 0.0001 and r 2 = 0.8257). It is reasonable to hypothesize that the ability for a soil to retain chemical species could be related to the soil structure and pore features (size of the cluster, mesopore volume, and tortuosity).

For pesticides, there is a need to establish the relationship between the retention mechanism and such physical parameters in allophanic soils. Figure 3f, g shows that the CLD concentration increases with the soil specific surface area S: CLD = 0.13 S -4.36 (P < 0.0001 and r 2 = 0.80) and with the pore volume CLD = 9.56 Vp -4.11 (P < 0.0001 and r 2 = 0.78).

Figure 3h, i, j shows that the CLD soil concentration increases with the features of the clay fractal structure, i.e., the size of the fractal allophane aggregates L (CLD = 0.47 L -6.40, P < 0.0001 and r 2 = 0.71), the internal porosity of the aggregates Vmeso (CLD = 37.3 Vmeso -0.40, P < 0.001 and r 2 = 0.72), and the tortuosity t (CLD = 4.6 t -2.36, with P < 0.0001 and r 2 = 0.61). This confirms the effect of the allophane microstructure on the pesticide trapping.

Trapping mechanism in allophane microstructure

The observed characteristics of allophane, high specific surface area and pore volume, and fractal features correspond to a highly tortuous microstructure and small mesopores. The fractal structure of allophane aggregates is typically in the range 5-100 nm and we may hypothesize that the accessibility inside the aggregates is reduced. Indeed, for example, the trapping inside allophane aggregates is sought to contribute to the protective effect of allophane on biodegradation of soil organic matter, the later becoming inaccessible to microorganisms and enzymes [START_REF] Boudot | Relative efficiency of complexed aluminum noncrystalline Al hydroxide, allophane and imogolite in retarding the biodegradation of citric acid[END_REF][START_REF] Mayer | Surface area control of organic carbon accumulation in continental shelf sediments[END_REF][START_REF] Mayer | Organic matter in small mesopores in sediments and soils[END_REF][START_REF] Mccarthy | Protection of organic carbon in soil microaggregates via restructuring of aggregate porosity and filling of pores with accumulating organic matter[END_REF][START_REF] Zimmerman | Mineral mesopore effects on nitrogenous organic matter adsorption[END_REF][START_REF] Dahlgren | The nature, properties and management of volcanic soils[END_REF].

Data show that the soil to water transfer coefficient (WTC) decreases with L, Vmeso, and t, respectively: WTC = -0.0966 L + 4.5798 (with P < 0.0001 and r 2 = 0.75), WTC = -1.3752 t + 4.90 (with P < 0.0001 and r 2 = 0.82), and WTC = -15.12 Vmeso + 4.65 (with P < 0.0001 and r 2 = 0.65) (Fig. 4a-c).

These results emphasize the role of the clay fractal features (L, Vmeso, and t) in entrapment of pesticides. The retention mechanism is developed in the following.

To put forth the importance of the allophanic microstructure, we fractionated an andosol (allophane) and a nitisol (halloysite) and analyzed the CLD content in the three size classes obtained: >200 μm (class a), 50 to 200 μm (class b), and <50 μm (class c).

Table 2 shows the CLD concentration in milligram CLD per kilogram DS (dry soil) for the different soils samples. For halloysite soil samples, the CLD concentrations are close, between 0.66 and 0.99 mg kg -1 dry soil, for a, b, and c fractions. However, in the case of the allophanic soils samples, the CLD concentration strongly increases for the class c, CLD concentration being 8.8-fold higher than the a fraction, these differences being highly significant (P = 0.007). This higher CLD content in the c class is due to its richness in allophane aggregates [START_REF] Filimonova | The role of allophane nano-structure and Fe oxide speciation for hosting soil organic matter in an allophanic andosol[END_REF]. We observe that the CLD relative to C % is systematically higher in andosol than in nitisols which comforts the assumption that the CLD retention cannot be explained only by the OC content.

Pesticide availability and allophane microstructure

The fate and behavior of organic pollutants in soils is governed by many different factors including soils characteristics and chemicals properties. The behavior of organic pesticides in soils has been reviewed by several excellent papers [START_REF] Pignatello | Soil organic matter as a nanoporous sorbent of organic pollutants[END_REF][START_REF] Pan | Part IV-sorption of hydrophobic organic contaminants[END_REF][START_REF] Cornelissen | Extensive sorption of organic compounds to black carbon, coal, and kerogen in sediments and soils: mechanisms and consequences for distribution, bioaccumulation, and biodegradation[END_REF][START_REF] Weber Wj | Geosorbent organic matter and its relationship to the binding and sequestration of organic contaminants[END_REF]. It has been well documented that soil organic matter controls the behavior of pesticides, such as sorption, mobility, extractability, solubility, and bioavailability [START_REF] Pan | Part IV-sorption of hydrophobic organic contaminants[END_REF][START_REF] Chiou | A physical concept of soil-water equilibria for nonionic organic compounds[END_REF]). Adsorption-desorption behavior is one of the key processes affecting the bioavailability and the fate of agrochemicals. It is commonly accepted that sorbed chemicals are less accessible to microorganisms and that adsorption limits their transport [START_REF] Arias-Estévez | The mobility and degradation of pesticides in soils and the pollution of groundwater resources[END_REF]. Adsorption-desorption hysteresis has been explained as the result of non-attainment of equilibrium [START_REF] Huang | A distributed reactivity model for sorption by soils and sediments. 10. Relationships between desorption, hysteresis, and the chemical characteristics of organic domains[END_REF][START_REF] Barriuso | Solvent extraction characterization of bioavailability of atrazine residues in soils[END_REF]) and irreversible Fig. 3 Increase of the specific surface area S (a), the total pore volume Vp (b), the size of the fractal aggregate L (c), the mesopore volume Vmeso (d), and the tortuosity t (e), with allophane content. And, increase of soil contamination with specific surface area S (f), total pore volume Vp (g), the size of the fractal allophane aggregates L (h), the mesopore volume Vmeso (i), and the tortuosity t (j) Environ Sci Pollut Res binding to soil constituents such as organic matrices or inorganic structures [START_REF] Huang | Quantifying effects of primary parameters on adsorption-desorption of atrazine in soils[END_REF]. [START_REF] Kleber | Chapter One-Mineral-organic associations: formation, properties, and relevance in soil environments[END_REF] identify several factors that would facilitate binding of organic molecules in soils like the presence of reactive mineral phases with large specific surface areas.

Very reactive with respect to organic molecules sorption are weathering products such as the variably charged and usually nanometer-sized Fe oxides and short-range ordered Alsilicates like allophane. [START_REF] Kleber | Poorly crystalline mineral phases protect organic matter in acid subsoil horizons[END_REF] explain that hydroxyl-bearing charges such as Fe hydroxides and poorly crystalline aluminosilicate protect the organic compounds by ligand exchange between mineral surface hydroxyls and negatively charged organic groups. Particle size controls the specific surface area and thus the amount of reactive surface groups per unit mass of mineral. Due to their small size and high specific surface area, nanoparticles are highly reactive with contaminants [START_REF] Kleber | Chapter One-Mineral-organic associations: formation, properties, and relevance in soil environments[END_REF]. Last but not least, the soil porous features can limit the transport of chemicals compounds in the soil pore system and control exchanges with microorganisms. For example, the sequestration of organic matter within mineral mesopores has been hypothesized to protect organic matter from enzymatic degradation [START_REF] Mayer | Surface area control of organic carbon accumulation in continental shelf sediments[END_REF][START_REF] Mayer | Organic matter in small mesopores in sediments and soils[END_REF][START_REF] Mccarthy | Protection of organic carbon in soil microaggregates via restructuring of aggregate porosity and filling of pores with accumulating organic matter[END_REF][START_REF] Zimmerman | Mineral mesopore effects on nitrogenous organic matter adsorption[END_REF][START_REF] Boudot | Relative efficiency of complexed aluminum noncrystalline Al hydroxide, allophane and imogolite in retarding the biodegradation of citric acid[END_REF].

To explain the high pollutant content in allophanic soils, we can invoke the mesopore protection mechanism already proposed for organic matter. In addition to the mesopore protection mechanism, we point out the role of the fractal and tortuous porous microstructure. Organo-mineral complexes like allophane form small aggregates with pores in the mesopore scale (2-50 nm) and with high organic matter loadings: 75 to 90% of the soil organic matter found in the pores is in the 10-1000-nm size range [START_REF] Mccarthy | Protection of organic carbon in soil microaggregates via restructuring of aggregate porosity and filling of pores with accumulating organic matter[END_REF]. Such aggregate pore widths are far below the cutoff for accessibility by bacteria, which is considered to be 2 μm. Furthermore, enzymes can penetrate micropores and small mesopores but with difficulty, [START_REF] Zimmerman | Mineral mesopore effects on nitrogenous organic matter adsorption[END_REF] showed that the enzyme activity was 3-40 times lower in mesoporous silica or alumina than that observed for nonporous minerals. The fractal aggregates in allophane clay likely sign tortuous diffusion pathways. In a previous work [START_REF] Woignier | Application of the DLCA model to natural gels: the allophanic soils[END_REF], numerical simulations on porous structures have shown that permeability in allophane soils is strongly affected by the allophane content. The permeability at the scale of allophane aggregate has been calculated to be low (40 nm 2 ) in these tortuous pore networks. These results are close to those measured in silica gels (∼10-20 nm 2 ) [START_REF] Reynes | Permeability measurement in composite aerogels: application to nuclear waste storage[END_REF][START_REF] Scherer | Bending of gel beams: method for characterizing elastic properties and permeability[END_REF]; Anez et al. 2005), 6 orders of magnitude smaller than the diffusion coefficient in aqueous solution. Thus, allophane aggregate structure might be highly porous but has a low permeability and diffusion coefficient; fluids and chemical species migrate with difficulty inside the fractal structure [START_REF] Woignier | Pollution of soils and ecosystems by a permanent toxic organochlorine pesticide: chlordecone-numerical simulation of allophane nanoclay microstructure and calculation of its transport properties[END_REF]. In addition, the fractal pore structure imposes a range of pore sizes including small as well as large ones. As an allophane aggregate increases in size, the tortuous diffusion pathway will become blocked by pore throats that prevent chemical or biological species passage. The fractal structure of the pore networks may, therefore, retard the enzyme diffusion to the adsorbed CLD and affect the degradation by microorganisms. The high CLD retention capacity is the result of different kinds of mechanism: (1) the high chemical affinity of chlordecone for organic matter would stabilize the pesticide in the mesoporosity.

(2) Microorganisms supposed to be able to degrade the pesticide have a size higher than the allophane aggregates.

(3) The low transport properties in allophane aggregate could explain the lesser chlordecone release in the environment, crops, and water resource. The synergy between organic matter content in allophane and tortuous microstructure prevents chlordecone from degradation when trapped inside the mesopore network. This trapping may reduce considerably the bioavailability of the pesticide and reinforce the poor pesticide degradation observed in this type of soil.

In Situ Chemical Reduction (ISCR) was tested to reduce the chlordecone soil content [START_REF] Mouvet | Remediation by chemical reduction in laboratory mesocosms of three chlordecone-contaminated tropical soils[END_REF]. ISCR treatment showed a poorer efficiency in andosol compared to nitisol and ferralsol. Due to the poor accessibility inside the allophane clusters, the ISCR efficiency may be limited by lower contaminant availability.

Conclusion

Allophanic clays are poorer secondary contaminant sources than phyllosilicates thus reducing the pesticide diffusion in the environment and food products. This study shows that there are clear correlations between clay microstructure and the soil pesticide content and retention:

1. The porous properties of clay aggregates strongly depends on the allophane content, 2. The soil contamination increases with the allophane content but also with the mesopore volume, the tortuosity, and the size of the fractal aggregate. 3. The pore structure of the allophane aggregates at nanoscale also favors the pesticide retention. It is suggested that chlordecone storage in allophanic soils could be the result of the low transport properties (permeability and diffusion) in the allophane aggregates.

The fractal and tortuous aggregates of nanoparticles play the role of nanolabyrinths.

The high pesticide content in allophanic soils is likely the result of a synergy between the high pesticide affinity with the organic matter and the poor accessibility to the pesticide trapped in the mesopore. Thus, these nanostructures both reduce the risk of a potential release of chlordecone and promote a protection of degradation by soil microorganisms of the organic matter.

Future research for chlordecone degrading microorganisms and other bioremediation tools to clean up polluted soils should take into account the reduced accessibility of chlordecone in the confining structure of the allophane nanolabyrinths.

Fig. 1

 1 Fig. 1 Scanning electronic micrographs showing the structure of phyllosilicate clay (a) and allophane clay (b). Panel c shows the transmission electron micrographs of allophane clay

Fig. 2

 2 Fig. 2 Decrease of the soil to water transfer coefficient WTC with the allophane content

Fig. 4

 4 Fig. 4 Decrease of the soil to water transfer coefficient WTC with the size of the fractal allophane aggregates L (a), the mesopore volume Vmeso (b), and the tortuosity t (c)

Table 1

 1 Transfer of chlordecone to water: the soil to water transfer coefficient (WTC) is the ratio of the pesticide concentration in lixiviates to the pesticide concentration in soil expressed in microgram per liter/ milligram per kilogram of dry soil. Differences between clay types were tested by ANOVA (P = 0.05) with seven replicates for andosol and eight for nitisol. The mean carbon content is 2.3 and 2.1 wt% for the andosol and nitisol, respectively

	Soil and clay type	Soil contamination	WTC
		range (in mg CLD
		kg -1 dry soil)	Mean	Standard
				deviation
	Andosol (allophane)	3.4-6.7	2.33	0.66
	Nitisol (halloysite)	0.5-0.91	4.68	1.07

Table 2

 2 CLD concentration in the different size class a, b, and c for andosol and nitisols. Values are means of three replicates ± standard error of the mean. Differences between fractions were tested by ANOVA.

				test, P = 0.05). The carbon contents (weight %) are 0.99, 1.77, and 7.01
				for the fractions a, b, and c, respectively (andosol), and 1.12, 1.76, and
				2.34 for the fractions a, b, and c, respectively (nitisol)
	Different letters in the same row indicate significant differences (Tukey
		CLD content (in mg CLD kg -1 dry soil)				ANOVA
								Pr > F
	Soil type	Fraction a		Fraction b		Fraction c
	Andosol (allophanic clay)	1.16 ± 0.39	a	2.14 ± 0.26	a	10.25 ± 4.2	b	0.007
	Nitisol (halloysite clay)	0.66 ± 0.11	a	0.99 ± 0.17	b	0.95 ± 0.07	a,b	0.031

Acknowledgements Funding was provided by the French Chlordecone National Plan (BJAFA^project), the French National Research Agency (BChlordexco^project), and the French Ministry for Overseas development (MOM).


P Besson

E Dousset

C Bourdin

L Bringoux

T Marqueste

D R Mestre

J L Vercher

Bayesian Network classifiers inferring workload from physiological features: compared performance

This paper presents an approach based on Bayesian Networks to estimate the workload of operators. The models take as inputs the entropy of different number of physiological features, as well as a cognitive feature (reaction time to a secondary task). They output the workload variation of subjects involved in successive tasks demanding different levels of cognitive resources. The performances of the classifiers are discussed in term of two criteria to be jointly optimized: the diversity, i.e. the ability of the model to perform on different subjects, and the accuracy, i.e., how close from the (subjectively estimated) workload level the model prediction is.

I. INTRODUCTION

The ability to manage cognitive workload (denoted simply by workload from now on) during multitask activity is crucial for operators involved in driving complex engine such as car or aircraft. Intelligent systems can assist the operator in such situations, but for this assistance to be really efficient, it should be adapted to the current operator's workload. Thus, task demand should be decreased in case of overload, whereas more functions should be delegated to the operator in case of low workload [START_REF] Hancock | Human factors and safety in the design of intelligent vehicle-highway systems (IVHS)[END_REF], [START_REF] Liao | Toward a decisiontheoretic framework for affect recognition and user assistance[END_REF]. For example, in the context of car driving, where low workload is likely to result in a lack of vigilance, Advanced Driving Assistance Systems should be able to deactivate some functions (such as lateral and longitudinal control, speed regulation, etc.) to force the driver to focus on the task. Being able to characterize the operators workload is therefore the prerequisite to adaptive intelligent systems.

Computational models have been proposed to infer some cognitive states, such as workload or distraction, from task performance analyses or sensorimotor features (gaze, head movements, etc.) [START_REF] Di Nocera | Using the distribution of eye fixations to assess pilots' mental workload[END_REF], [START_REF] Zhang | Learning-based driver workload estimation[END_REF], [START_REF] Tango | Evaluation of distraction in a Driver-Vehicle-Environment framework: An application of different Data-Mining techniques[END_REF]. However, for these features to make sense, they have to be compared to nominal values that are dependent on the task context.

More direct and task independent features can be extracted from physiological measurements. Indeed, changes in the subject's cognitive state may drive to changes in physiological data [START_REF] Cacioppo | Inferring psychological significance from physiological signals[END_REF], specifically (but not exclusively) when they are under the control of the autonomic nervous system. The latter is responsible for maintaining the body's homeostasis, noticeably through the orthosympathetic branch which mobilizes energy resources in response to the changing demands of the external and internal milieu [START_REF] Wallin | The sympathetic nervous system in man aspects derived from microelectrode recordings[END_REF].

Thus, electrocardiogram (ECG), electromyogram (EMG), skin conductance (SC), and respiration were used in [START_REF] Healey | Detecting stress during Real-World driving tasks using physiological sensors[END_REF] to infer the stress level by drivers using linear discriminant analysis. In [START_REF] Wilson | Real-time assessment of mental workload using psychophysiological measures and artificial neural networks[END_REF], the authors developed an Artificial Neural Network (ANN) taking electroencephalogram (EEG), electrooculographic (EOG) and respiration as inputs to assess workload levels. ECG, EEG and EOG were also used in [START_REF] Cannon | An algorithm for online detection of temporal changes in operator cognitive state using real-time psychophysiological data[END_REF] to derive an information-theoretic indicator of cognitive state. Support Vector Machine and ANN were applied to workload estimation in [START_REF] Putze | Multimodal recognition of cognitive workload for multitasking in the car[END_REF], using EEG, SC, respiration, and hear rate (HR) data. Notice that these models rely on physiological measurements to infer cognitive states but not necessary workload specifically (for example, stress, inferred in [START_REF] Healey | Detecting stress during Real-World driving tasks using physiological sensors[END_REF], should not be confused with workload thought it partly results from overload [START_REF] Sanders | Towards a model of stress and human performance[END_REF]).

In this work, our objective was to develop a task independent model, able to infer workload from objective measurements. We wanted to use non-intrusive and minimally invasive sensors, therefore, we did not measure EEG (incompatible with helmets wore by helicopter or fighter pilots for example) but restricted the measurements to EMG, ECG, SC and respiration. The reaction time (RT) to a secondary task was also used as a cognitive measure of workload [START_REF] Donnel | Workload assesment methodology[END_REF], [START_REF] Hancock | Human factors and safety in the design of intelligent vehicle-highway systems (IVHS)[END_REF].

We propose Bayesian Network (BN) models that take as inputs the entropy of these physiological measurements and output the change of the subject's workload while they are involved in task demanding different levels of cognitive resources. Entropy of ECG signal has been shown to be a good indicator of distraction [START_REF] Yu | Driving distraction analysis by ECG signals: An entropy analysis[END_REF] but, to the best of our knowledge, it has never been applied to other physiological signals in a computational model of workload.

Different BN structures, built from expert knowledge, are tested, using in turn several combinations of physiological features. Their performances are evaluated in term of two criteria to be jointly optimized: the diversity, i.e. the ability of the model to be functional for different subjects, and the accuracy, i.e. how close from the workload level the model prediction is. The ground truth is provided by subjective evaluations of workload collected during the experiment.

Rather than assessing the ability of the BN models to infer the workload level, we focused on how good they are in predicting the workload change between successive tasks. Indeed, in the context of defining adaptive intelligent systems, an erroneous prediction of the workload level that results in a false prediction in term of workload change (i.e. in a predicted variation opposite to the reality) should be absolutely avoided. It might drive the system to undertake actions opposite to those required by the operator's state, and have dramatic consequences.

The experimental protocol used to collect representative data and an analysis of these data are presented in sec. II. Sec. III describes the proposed models, whose performances are assessed in sec. IV in term of two criteria to be jointly optimized: the diversity and the accuracy.

II. MATERIAL AND METHOD

A. Subjects

Ten subjects (9 males and 1 female, aged 30 ± 10.7 years) with normal or corrected to normal hearing and seeing, have participated to the experiment.

B. Material

The subjects sit in the dark, using a non-force feedback joystick and facing a standard 24" monitor, where the graphical dynamic flying scenes generated by the homegrown ICE software [START_REF]Ice software[END_REF] were displayed. An experimenter's computer was used to acquire all the data synchronously, using the Captiv Software [START_REF]Captiv software[END_REF]. These data were made of the simulation data (e.g., aircraft position) sampled at 100Hz, and of physiological data, acquired at a sampling rate of 2048Hz using the FlexComp Infinity sensors and encoder [START_REF]Flexcomp infinity hardware manual[END_REF]. The subjects bore stereo headphones, so that they could hear the pre-recorded instructions (the instructions' tone and content were then strictly identical for each subject) and the task related noises such as the engine noise (leading to a greater immersion) or the possible alarms.

C. Procedure

The subjects were asked to pilot a flying aircraft and to do their best to follow a trajectory made of 60 rings, alternatively red and yellow. The trajectories varied only along the vertical dimension. The aircraft's speed was maintained constant at the same predefined value for all the trajectories. The ratio of hit rings over the total number of rings in the trajectory appeared on the cockpit dashboard. There was also a green or red light indicating whether the last ring had been hit or missed.

The experiment was organized in 5 sessions of 6 trials. Each trial lasted approximately 90sec1 . In the three first sessions (labeled D1A0, D2A0 and D3A0), the subjects were presented with three different trajectories of increasing difficulty (D1, D2, and D3). The trajectories remained the same for the 6 trials of each session. The trajectory difficulty was an independent variable meant to manipulate the task workload requirement. It was varied by changing the vertical distance between two successive rings, while keeping their depth distance constant. In the two last sessions (labeled D1A1 and D3A1) the subjects were asked to fly again on the simplest and the hardest D1 and D3 trajectories, and to try to beat their own mean scores over these trajectories.

Moreover, a strident alarm was emitted in case of a missed ring. This challenge and the alarm were introduced in order to maintain the subjects' motivation and implication in the task.

For each of the five sessions, a secondary task was also required from the subjects. Two geometrical shapes (a square or a triangle) appeared on the screen during 1sec, at pseudorandom positions (the ring apparition zone was avoided, and the same number of targets appeared in each of the four screen quarters) and at pseudo-random times (no apparition while the ring was crossed, and minimum time interval of 1.5sec between two successive targets). The subjects had to press a button on the joystick with the forefinger as quickly as possible in response to the square target apparition. They should not react to a triangle target.

Fig. 1 shows a typical screen shot of the simulated scene.

D. Dependent variables

Performance on the primary (percentage of hit rings) and on the secondary (false and good detection rates; reaction times (RT)) tasks was recorded. The physiological variables comprised the following measurements:

• Hear Rate (HR) estimated from the ECG by the Captiv software, using the R-R intervals;

• Root mean squares of the flexor digitorum EMG (RMS1) and of the right trapezius descendens EMG (RMS2);

• Respiration (R), measured through chest expansion;

• Skin Conductance (SC), measured using electrodes placed on the first and little fingers of the left hand (temperature in the room equal to 19.33 ± 0.98 • C). Finally, psychological data were also collected at the end of each session. The subjects evaluated their own workload during the performed task, using the NASA Task Load Index (TLX) scale [START_REF] Hart | NASA task load index (TLX)[END_REF]. The NASA TLX asks the subjects to rate their perceived workload on six different subscales (Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration). At the end of the experiment, these six components are matched two by two and the subjects have to choose for each couple which component best described the workload in the performed task. Each component score can thus be weighted accordingly to the number of times it has been chosen in the matching phase. In the present experiment, the NASA TLX rates on the six subscales are weighted and summed for each sessions to result in a single TLX score per session.

E. Analysis of the experimental data

Prior to build a model that will take the collected data as input, it has to be checked that these data are representative of the problem at hand. That is, we have to ensure that the workload has been effectively manipulated using our experimental paradigm, so that variations observed in the physiological signals can effectively correspond to variations of workload.

The impact of the primary task difficulty on the performance in both the primary and secondary tasks is assessed through Analysis of Variance (ANOVA) statistical tests. There is no significant difference between trials for a same difficulty (p=0.13) whereas the difference is significant between sessions (F(4,36)=26.708, p=0.000). A post-hoc analysis (Student Neuwman-Keuls) indicates that scores on all sessions are statistically different (p < 0.001) but for the sessions of the same difficulty levels (D1A0 and D1A1, D3A0 and D3A1). The difficulty also impacts the true positive (TP) detection rate of the secondary task (F(4,36)=8.84, p < 0.01), though the statistical difference only holds for D3 in the Student Neuwman-Keuls post-hoc analysis. Finally, the reaction times associated to these TP detections also differ significantly between the different difficulty levels (F(4,24)=14.084; p=0.0000) (but not between each session's trials (p=0.03)).

This statistical analysis establishes firstly that subjects behave with a coherent resource allocation strategy inside a given session. Secondly, as it is well-known that secondary task competes for the limited brain resources with the primary task (see e.g. [START_REF] Donnel | Workload assesment methodology[END_REF], [START_REF] Hancock | Human factors and safety in the design of intelligent vehicle-highway systems (IVHS)[END_REF]), it indicates that the cognitive resources allocated by each subject on the primary task have increased at the expense of the secondary task. Nevertheless, these results do not insure that the overall level of involved resources has been increased: subjects might have been only partially committed in the task and have simply changed the resource allocation strategy as the primary task difficulty increased. Investigation of the subjective data (TLX scores) can give us some clues about this point. Generally speaking, the TLX scores increase with the session difficulty. An ANOVA on the TLX scores shows a significant difference between sessions (F(4,26)=12.284, p=0.000).

Table I summarizes the correlation values found for each subject between the TLX scores and the RT, per session. In most cases, the correlation is greater than 0.5. which indicates that the subjective evaluation of workload is consistent with the objective measure (RT) and that subjects committed in the task. Values below 0.5 are not necessarily due to a mismatch between subjective and objective metrics: some subjects experiencing high workload totally gave up on the secondary task, so that RT were not available2 (thus, the relation with TLX scores is not linear anymore).

Finally, this analysis establishes that subjects experienced different levels of workload during the task. This should be reflected in the physiological data and be captured by the model.

III. MODEL

A. Selection of output and input features

A data driven approach to modeling problem requires to prealably assign some data with the correct class labels so that the relationship between the input features (derived from the physiological data in the present case) and the classes (the model's output) can be automatically discovered and extracted in the learning phase. As stated in sec. II-E, we can only rely on a subjective rating scale to label our ground truth. This adds some noise in the pattern recognition process. To reduce the noise in the process as much as possible, the input features have also to be optimized: the more representative the features, the simpler the task for the classifier, thus the better its expected performance [START_REF] Besson | Extraction of audio features specific to speech production for multimodal speaker detection[END_REF].

We have decided to use the Shannon's entropies of the physiological data as inputs for our model. The entropy of a random variable (rv) X is a measure of the average uncertainty in X [START_REF] Cover | Elements of Information Theory[END_REF]. Stated in a different and simpler way, it is a measure of disorder. As such, it is likely to capture differences in physiological data related to workload variations.

Before the entropies to be estimated, the noise in the raw signals is firstly smoothed using a low-pass median filter. The first and last seconds of each trial's signal are also removed to avoid possible starting and ending effects. Then, the data are normalized between 0 and 1, taking the minimal and maximal values observed on the three first sessions (used as training sessions). Entropies are estimated on 15sec long windows slided by 5sec along the signals, using an histogram of 41 bins that ranges on [0, 1]. Therefore, there is about 90 values per sessions and per subject. Entropy values are also normalized between 0 and 1 by taking the maximal and minimal values over the three first sessions for each subject.

It be observed that the variation of the mean entropy features is consistent with the variation of the performance on the primary task, the RT and the TLX scores (see Fig. 2 showing the subject 4's features as an illustrative example). However, we observed the variation of the physiological data to be idiosyncratic: for some subjects, the mean entropy values of some physiological data might decrease with difficulty levels, whereas they decrease for other subjects. As a results, the models will be individual (trained and tested on each subject separately).

B. Model definition

The model aims at inferring the subject's TLX score on each session, from the physiological features SC, R, HR, RMS1 and RMS2 3 . To this end, different BN classifiers are tested, each taking one, two or three of the possible physiological features as inputs. As a result, 25 classifiers with different physiological nodes are trained and tested. Moreover, three BN structures are tested. Structure 1 is a naive BN were the TLX is a direct child of the physiological nodes. Structure 2 is also a naive BN but TLX is now a child of the RT, which is itself a child of the physiological nodes. Structure 3 has a more complex structure, where TLX is a direct child of both the physiological nodes and of RT. The different structures are presented on Fig. 3 for a 2-node classifier made of physiological features Φ and Φ 2 . ). There can also be 1 or 3 physiological nodes. 3 For simplification purposes, the rv denoting the entropy features are named as the acronyms of the corresponding physiological data.

The joint probability density functions (pdf) described by the BN are estimated on the training set using histograms with the following parameters (rv take on values in [0, 1], but RT taking on values in [0, +∞[): 5 bins of width 0.2 for the physiological rv, 20 bins of width 0.05 for TLX, and 16 bins of width exp (0.2) , with the first bin centered on exp (-3.7) and the last bin taking all the values greater than exp (-0.9) for RT. For each subject, the training set is made of the data collected on the three first sessions D1A0, D2A0 and D3A0. The testing set is made of the two last sessions D1A1 and D3A1. the learning and inference stages have been implemented using the Bayes Net Toolbox for Matlab [START_REF] Murphy | Dynamic Bayesian networks: Representation, inference and learning[END_REF]. Because there are some missing data (HR in particular could not be reliably recorded sometimes, and there is not necessary one RT value per measurement window), the Expectation Maximization algorithm has been used (with a stopping criterion of 10 iterations).

C. Assessing the model performance

The performance of the models is assessed by looking at the differences in the TLX scores between the D1A1 and the D3A1 sessions. The model output will be deemed as correct if the observed and inferred TLX scores are evolving the same way, that is, if the performance index ρ, defined as follow, is positive:

ρ = sign(∆) • sign(∆ * ) • | ∆ * ∆ |, if ∆ * < ∆ (1) = sign(∆) • sign(∆ * ) • | ∆ ∆ * |, else, (2)
where ∆ is the difference between the subjects' TLX scores on sessions D3A1 and D3A1, and ∆ * the difference between the predicted TLX scores on these two sessions. The quality of the model performance is given by the distance to 1 (the closer, the better). A fine analysis of the false model's detections is useless since we want this false detection rate to be null. Indeed, as stated in sec. I, a false estimation of the workload variation between successive tasks can not be accepted: it would lead the assistance system to undertake unadapted measures, which could have worse consequences than doing nothing.

For each model, we are looking at the performance over the subject set. Thus, we want the maximum number of subjects to be correctly detected, with a ρ score as close as possible to 1. This is a two-variable optimization problem, where the first parameter to be optimized is the model diversity and the second, its accuracy. The model diversity is assessed by looking at the percentage of subjects correctly detected (S). Also, to allow for comparisons between the accuracy performance, θ, the normalized area under the ρ curve, plotted as a decreasing function of S, is used rather than ρ:

θ = 10 • ρ S , θ ∈ [0, 1]. (3)
IV. RESULTS

From the Fig. 4, it can be observed that most of the models are performant in either one of the two diversity (S) or and θ = 0.66, is the three physiological node classifier HR; RM S 2 ; SC with the structure 2. Its performance is plotted as a function of its diversity in Fig. 5. To analyse the impact of the number of nodes and of the structure on models' performance, let us look at the best models, i.e., the models with a performance greater than 50% over the sets for both diversity and accuracy criteria (these are the classifiers lying in the upper right hand side quarter of Fig. 4). Indeed, the mean performance over the model sets do not tell us whether good models for one criterion are also good models for the other. The results are presented on Fig. 6. Generally speaking, the percentage of models fulfilling the required "best performance" criterion is not high (less than 15%). It is null for the models with the simplest structure (structure 1), but it increases as soon as RT is added to the model (structures 2 and 3), with the best performance being obtained with structure 3 (12%). When comparing the models on the basis of their node number (whatever the structure), the use of two nodes leads to the smallest percentage of good classifiers (thought the best results are obtained with a 2-node classifier), whereas the largest number of good classifiers are obtained when using three nodes.

Notice that each of the five physiological features appears in one of these good classifiers. This certainly indicates that none of these features is specific enough of the workload change. We can do the hypothesis that a classifier with five physiological nodes would outperform the performance of the classifier proposed in this work. However, since we are training subject-dependent models, our sample sizes were too small to deal with a classifier with 5 physiological nodes, so that we were not able to check this hypothesis in this study. Fig. 6. Percentage of good models (with performance greater than 50% for both accuracy and diversity criteria) for the different structures and the different physiological node numbers.

V. CONCLUSIONS AND FUTURE WORK

In this paper, Bayesian networks are proposed to infer the variation of the workload for operators involved in multitask activities, from physiological and cognitive (RT) measurements. The advantage of physiological measurements is that their interpretation if more independent from a specific operator task than sensorimotor features for example. Entropy based features are firstly derived from the raw measurements so that the models receive inputs more specific from the studied phenomenon. Different structures and number of inputs are tested and compared in term of two criteria to be jointly optimized: the accuracy and the diversity of the classifier.

The best model is the three physiological node classifier HR; RM S 2 ; SC with the structure 2 (workload inferred from the physiological nodes, via the RT). However, a finer analysis of the model performances points out that each of the five proposed physiological features might appear in classifiers with good performance. Also, the performances increase with the number of physiological inputs in the model. This suggests that each of the five features yields information related to the workload, and that a model including all these information would outperform the proposed classifier. Tests on larger samples should be performed for being able to draw conclusion on that specific point.

Though the structure of the best model is the structure 2, the relative number of good classifiers is more important with the third tested structure (where the workload rv is a child of both the physiological nodes and of the RT). This indicates that physiological and cognitive features carry complementary information about the subject's cognitive state, extracted and used by the models. Including the RT in the model yields better workload prediction, at the expense of a slightly more task-dependent method, since it requires a secondary task to be performed. However, there are a lot of situations where routine tasks can be used to infer RT values.

These are only preliminary results and refinements should be brought to the models, as well as tests on larger sample sizes (which should result in improved models' performance). It should be checked whether the best classifier HR; RM S 2 ; SC, which shows a good diversity performance, remains performant when tested on new subjects. It is also possible that some combinations of specific physiological features are better for some categories of subjects (labile versus stabile for example). This could be checked by a deeper (subject by subject) analysis of the models' performance.

Fig. 1 .

 1 Fig. 1. Screen shot of a typical flying scene created by ICE. The ratio of hit rings over the total number of rings in the trajectory appeared on the cockpit dashboard (e.g. 1/60) and a green or red light indicated whether the last ring had been hit or missed.

Fig. 2 .

 2 Fig. 2. Mean physiological feature values (entropy values of the physiological signals, in bit), performance on the primary task (in %), reaction times (RT, in sec) and NASA Task Load Index (TLX) scores for each session performed by subject 4.

Fig. 3 .

 3 Fig.3. BN models inferring the TLX scores from physiological features Φ 1 and Φ 2 either directly (Structure 1), via RT (Structure 2), or from both RT and the physiological inputs (Structure 3). There can also be 1 or 3 physiological nodes.

Fig. 4 .

 4 Fig. 4. Performance of the models in terms of diversity (S) and accuracy (θ), depending on the number of nodes (top) or on the structure (bottom) of the models. The best models lie in the upper right-hand side quarter of the graphes.

Fig. 5 .

 5 Fig. 5. Performance of the best classifier (HR; RM S 2 ; SC with the structure 2).

TABLE I CORRELATION

 I BETWEEN THE TLX SCORES AND THE RT OVER THE 5

				SESSIONS, FOR EACH SUBJECT
	Subject	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10
	r	0.67 0.79 0.83 0.80 0.68 0.48 0.32 0.75 0.90 0.45

TABLE II BEST

 II PERFORMANCE IN TERMS OF ACCURACY AND DIVERSITY TableIIpresents the best classifiers in term of a single performance criterion solely. However, we are interested in classifiers performant in both dimensions simultaneously. The model that gives the best performance in term of both accuracy and diversity, namely, S = 60%

		INDEPENDENTLY
	Performance criterion	Structure	Physiological nodes	S	θ
	Maximal Accuracy	1	SC	20	0.89
	Maximal Diversity	2	HR and SC	80	0.47
	accuracy (θ) criteria.

Though the speed is maintained constant, the duration of each trial is not necessary the same, since the aircraft's trajectory can be more or less sinusoidal.

In that case, an arbitrary mean RT value of 1.5sec has been used in the correlation computation.

VI. ACKNOWLEDGMENTS

The authors gratefully acknowledge C. Goulon and M. Huet for their help in building the graphical dynamic scene as well as C. Valot for fruitful discussions. They also acknowledge the students who have collaborated on the project and all the subjects that took part in the experiment.

This work was supported by Eurocopter