Ikram Naifar
email: ikramnaifar@hotmail.com

Fernando Pereira

Raja Zmemla

Moncef Bouaziz

Boubaker Elleuch

Daniel Garcia

Spatial distribution and contamination assessment of heavy metals in marine sediments of the southern coast of Sfax, Gabes Gulf , Tunisia

Keywords: Heavy metals, Marine sediments, Spatial distribution, Pollution assessment, Gabes Gulf

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Spatial distribution and contamination assessment of heavy metals in marine sediments of the southern coast of Sfax, Gabes Gulf, Tunisia

Ikram Naifar, Fernando Pereira, Raja Zmemla, Moncef Bouaziz, Boubaker

Elleuch, Daniel Garcia

To cite this version:

Ikram Naifar, Fernando Pereira, Raja Zmemla, Moncef Bouaziz, Boubaker Elleuch, et al.. Spatial distribution and contamination assessment of heavy metals in marine sediments of the southern coast of Sfax, Gabes Gulf, Tunisia. Marine Pollution Bulletin, 2018, 131 (Part A), pp.53 à 62. 10.1016/j.marpolbul.2018.03.048. hal-01765167

Introduction

Integrated management of coastal areas takes into account all environmental parameters that may affect its development. In this context, Tunisian coastal areas (southern Mediterranean Sea) are the focus of many studies including Sfax area (Gulf of Gabes GG) which is considered as the second principal pole of economic activities. Due to the numerous industrial activities, the southern coast of Sfax, became one of the most damaged Mediterranean coasts inducing significant degradations of the environment (ecosystem, quality of life, local population ...).

Marine environment can be considered as one of the most fragile areas, and as a result, the extension of urban activities on the south-eastern Tunisian coasts increased marine pollution, especially in Sfax coasts. This city is one of the most industrialized cities. The main operating industries, such as the phosphate treatment factory (Tunisian Chemical Group (GCT)/SIAPE) and the lead secondary melting industries are collectively generating many liquid and solid wastes. Those are usually loaded with both mineral and organic pollutants that are released within the littoral zone without treatment [START_REF] Bouzid | La pollution des nappes phréatiques côtiers et son impact sur le domaine marin (cas de Sfax, Tunisie). Actes du Workshop sur la circulation des eaux et pollution des côtes méditerranéennes du Maghreb, à Rabat[END_REF][START_REF] Azri | Performance evaluation of the wastewater treatment plant of Sfax City (Tunisia): Influence of intrinsic and extrinsic factors[END_REF][START_REF] Azri | Seasonal evolutions of the ozone (O3) and its Nitrogen Precursors (NO, NO2) in Urban Sfax, Tunisia[END_REF].

Due to their toxicity, non biodegradability and accumulative behaviors, heavy metals are considered as dangerous environmental pollutants that need to be monitored with care [START_REF] Billah | Mangrove macroalgae as biomonitors of heavy metal contamination in a tropical estuary, Malaysia[END_REF][START_REF] Pekey | Heavy metal pollution assessment in sediments of the Izmit Bay, Turkey[END_REF]Yu et al., 2008). These elements play a role in many biological and geochemical cycles due to their presence in both solid and dissolved form.

Marine coastal sediments are usually dominated by terrigenous particles and shell debris; they are considered as a reservoir where an important range of pollutants such as heavy metals can be stored [START_REF] Pekey | Heavy metal pollution assessment in sediments of the Izmit Bay, Turkey[END_REF][START_REF] Santos | Heavy metal contamination in coastal sediments and soils near the Brazilian Antarctic Station, King George Island[END_REF][START_REF] Sin | Assessment of heavy metal cations in sediments of Shing Mun River Hong Kong[END_REF].

Heavy metals have a rapid and efficient tendency to be associated with solid sediments when dropped in solution to the natural environment through different mechanism: adsorption or incorporation into a biogenic material or precipitation [START_REF] Szefer | Heavy metal pollution in surficial sediments from the southern Baltic Sea off Poland[END_REF]Cho et al., 1999). Heavy metals in sediments casue various deletrious effects to both benthic and aquatic biota as they can be released into the overlying water via biological and chemical processes [START_REF] Ip | Trace metal distribution in sediments of the Pearl River Estuary and the surrounding coastal area, South China[END_REF]. Their distribution and accumulation are influenced by many factors, including sediment texture, mineralogical composition and hydrodynamic conditions.

Most of recent studies aiming at the assessment of marine environment quality in GG revealed an enrichment of surface sediments with a wide range of pollutants especially in heavy metals, showing a potential degradation of sediments quality [START_REF] Serbaji | Utilisation d"un SIG multi-sources pour la compréhension et la gestion intégrée de l"écosystème côtier de la région de Sfax (Tunisie)[END_REF][START_REF] Ayadi | Assessment of contaminated sediment by phosphate fertilizer industrial waste using pollution indices and statistical techniques in the Gulf of Gabes (Tunisia)[END_REF][START_REF] Gargouri | Heavy metal concentrations in the surface marine sediments of Sfax Coast, Tunisia[END_REF][START_REF] Ghannem | Spatial distribution of heavy metals in the coastal zone of "Sfax-Kerkennah" plateau, Tunisia[END_REF][START_REF] Houda | Impact of a Mixed "Industrial and Domestic" Wastewater Effluent on the Southern Coastal Sediments of Sfax (Tunisia) in the Mediterranean Sea[END_REF][START_REF] El Zrelli | Heavy metal contamination and ecological risk assessment in the surface sediments of the coastal area surrounding the industrial complex of Gabes city, Gulf of Gabes, SE Tunisia[END_REF]Rabaaoui et al., 2015). It also resulted in the appearance of red tides [START_REF] Hamza | Les eaux colorées dans le golfe de Gabès: bilan de sept ans de surveillance (1989-1994)[END_REF], the disappearance of some marine species [START_REF] El Kateb | Correlation between pollution and decline of Scleractinian Cladocora caespitosa (Linnaeus, 1758) in the Gulf of Gabes[END_REF], unbalance in different local benthic communities [START_REF] Rabaoui | On the relationship between the diversity and structure of benthic macro invertebrate communities and sediment enrichment with heavy metals in Gabes Gulf, Tunisia[END_REF] and malformations in different benthic foraminifera species [START_REF] Ayadi | Impacts of several pollutants on the distribution of recent benthic foraminifera: the southern coast of Gulf of Gabes, Tunisia[END_REF].

However, none of the studies in the southern coast of Sfax have reported the distributional patterns of heavy metals according to the granulometric and mineralogical characteristics of the surface marine sediments.

In this context, metals like As, Cd, Cr, Ni, Mn, Sr, V, Zn... are often used for marine contamination monitoring and assessment due to their dependence on anthropogenic activities [START_REF] Caccia | The distribution of trace metals in Florida Bay sediments[END_REF][START_REF] Burton | Trace metals and nutrients in bottom sediments of the Southport Broad water, Australia[END_REF]. The evaluation of their distribution in surface marine sediment is useful to assess the pollution status of the studied area. For this purpose, we investigated the current metals distribution and concentrations in coastal sediments of Sfax to evaluate its environmental status after more than 65 years of industrialization and to update the relevant data for an effective environmental management in the region.

We analyzed many geochemical and some physical parameters, such as pH, heavy metals, fluoride and granulometry in both waters and coastal sediments. Then, the distribution maps of concentrations were estabished using a geographic information system (GIS) approach coupled with geostatistical and multivariate statistical techniques to identify covariations among them and to discuss their significance and potential source.

Materials and methods

Study area

The studied area covers the southern coastal line of Sfax to the northern edge of the Gabes Gulf (south-east of Tunisia). It is characterized by a semi-arid Mediterranean climate influenced mainly by a mild and gentle topography.

This coast experiences a semidiurnal and low tide with an amplitude approximately 1.4 m during spring tides and 0.3 m during neap tides. The wave currents precede towards the N-NW and those of ebb towards the S-SE [START_REF] Amari | Contribution à la connaissance hydrologique et sédimentologique de la plateforme des Kerkennah[END_REF].

Sfax region contains large flat plains characterized by soils rich in limestone, siliciclastic sediments, and gypsum [START_REF] Watson | Structure chemistry and origins of gypsum crusts in southern Tunisia and the central Namib Desert[END_REF][START_REF] Mtimet | Atlas des Sols Tunisiens[END_REF]. From a sedimentological viewpoint, surface sediments are dominated by slightly gravelly, sandy mud and poorly sorted, muddy sand. This coastline is subjected to an increasing pressure due to the development of several industrial activities that generate industrial and domestic wastewaters. These effluents are drained to the sea causing deleterious effects to waters, marine sediments and the whole natural ecosystem [START_REF] Illou | Impact des rejets telluriques d"origine domestique et industrielle sur les environnements côtiers : cas du littoral nord de la ville de Sfax (Tunisie)[END_REF][START_REF] Ghannem | Spatial distribution of heavy metals in the coastal zone of "Sfax-Kerkennah" plateau, Tunisia[END_REF][START_REF] Gargouri | Heavy metal concentrations in the surface marine sediments of Sfax Coast, Tunisia[END_REF].

Our study area, is located downstream a mixed industrial and domestic wastewater effluent on the southern coast of Sfax (latitude 34°43"N and longitude 10°46"E) transported through a three kilometers long channel. Its outlet is connected directly to the sea (Fig. 1). This channel drains together an acidic industrial effluent originated from the phosphate treatment factory since 1952 and the wastewater generated by the Wastewater Treatment Plant (WWTP) of Sfax City since 1983. This effluent also contains phosphogypsum particles; It causes a severe contamination of marine waters and sediments by heavy metals [START_REF] Illou | Impact des rejets telluriques d"origine domestique et industrielle sur les environnements côtiers : cas du littoral nord de la ville de Sfax (Tunisie)[END_REF][START_REF] Gargouri | Heavy metal concentrations in the surface marine sediments of Sfax Coast, Tunisia[END_REF], fluoride and phosphorus (Ayadi et al., 2015) and hydrocarbons [START_REF] Fourati | Gabès (Tunisia, Southern Mediterranean Sea)[END_REF][START_REF] Aloulou | Contamination des sédiments marins par les hydrocarbures et les métaux lourds et utilisation des foraminifères comme bio-indicateurs de pollution[END_REF][START_REF] Serbaji | Utilisation d"un SIG multi-sources pour la compréhension et la gestion intégrée de l"écosystème côtier de la région de Sfax (Tunisie)[END_REF]. These pollutants pose a potential ecological risk to marine organisms and human health [START_REF] Wong | Ecotoxicological assessment of persistent organic and heavy metal contamination in Hong Kong coastal sediment[END_REF].

Samples and analysis

Thirty nine surface marine sediments were sampled, in March 2016, from the southern coast of Sfax receiving the mixed industrial and domestic effluents. Sampling was performed in spaced sites (from 150 to 250 m) through different radials covering an area approximately of 4.3 km 2 . Location was recorded using a Global Positioning System (GPS) technology (Fig. 1). Eleven samples were collected from the upper shoreface where water depth does not exceed 0.3 m.

For other stations, the depth of water varied from 0.5 to 2m. At each sampling station, the upper 0-10 cm layer of surface sediment was collected using a Van Veen grab sampler. Immediately after their collection, samples were transported to the laboratory and stored in polyethylene bags at 4°C for subsequent analysis.

Samples were oven dried at 60°C for five days and lightly ground to reduce aggregates, then sieved to <63 µm to be used later in the chemical analysis. Grain size plays a significant role in the determination of heavy metals concentrations in sediments. Fine-grained sediments tend to have relatively high metal contents [START_REF] Horowitz | A primer on sediment trace element chemistry[END_REF][START_REF] Tam | Spatial variation of heavy metals in surface sediments of Hong mangrove swamps[END_REF][START_REF] Morillo | Heavy metal distribution in marine sediments from the southwest coast of Spain[END_REF] because of their high specific surface [START_REF] Horowitz | The relation of stream sediment surface area, grain size and composition to trace element chemistry[END_REF]. pH was measured for both of marine waters (pH L ) and sediments (pH S ) using a pH meter (INOLAB WTW 720). The granulometric analysis was performed on the bulk sample sieved below 1 mm using the laser analyzer (Masteriser 2000).

Chemical analysis was performed on the particle size fraction of <63 μm. About 1g of calcinated sediments was dissolved with Aqua Regia (3:1 HNO 3 : HCl). The concentrations of Al, Ca, Fe, Mn, P, Zn and Y were measured using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES, Jobin Yvon 138 Ultrace). As, Cr, Cu, Cd, Ni and Pb concentrations were measured by Inductively Coupled Plasma Mass Spectrometry (ICP-MS, Agilent 7800).

Data reported in this study are recalculated on a dry weight basis.

Fluoride concentrations were determined by dissolving 1 g of marine sediment samples in 3 ml of 6 M HCl and diluted to 100 ml with deionized water. Then, an aliquot of the extract was used for the determination of fluoride by selective electrode method (perfectION™) [START_REF] Hilal | Phosphate pollution in the Jordan Gulf of Aqaba[END_REF].

A subset of the collected fine-grained fractions ( <63 μm) was analyzed by Xrays Diffraction (XRD) to assess the mineralogy of the marine material, and to compare their content with the solids collected in the bedload of the wastewater channel.

Enrichment Factor EF

In order to assess the impact of the anthropogenic activities through the abundance of trace metals, enrichment factor (EF) was calculated using the following equation [START_REF] Ergin | Heavy metal concentrations in surface sediments from the two coastal inlets (Golden Horn Estuary and Izmir Bay) of the northeastern sea of Marmara[END_REF][START_REF] Choi | Distribution and accumulations of heavy metals in the sediments of harbors and coastal areas in Korea[END_REF][START_REF] Garcia | Anthropogenic influences on heavy metals across marine habitats in the western coast of Venezuela[END_REF]:

( ⁄ ) ( ⁄ ) (1)
Where (C⁄Al) S is the metal to reference ratio in the samples of interest and (C⁄Al) B is the same ratio for the used background reference (earth crust or unpolluted reference baseline). To minimize the influence of grain size and mineral composition on elemental concentrations and to identify possibly anomalous metal concentrations, heavy metal concentrations were generally normalized by conservative elements, such as Al, Fe and Sc [START_REF] Aloupi | Geochemistry of natural and anthropogenic metals in the coastal sediments of the island of Lesvos, Aegean Sea[END_REF][START_REF] Amin | Anthropogenic impacts on heavy metal concentrations in the coastal sediments of Dumai, Indonesia[END_REF]. In this study, we selected Al as the commonly used normalizer element [START_REF] Zhang | Riverine composition and estuarine geochemistry of particulate metals in China-Weathering features, anthropogenic impact and chemical fluxes[END_REF]. The choice of background values plays an important role in the assessment of pollution status. The average shale value [START_REF] Turekian | Distribution of the elements in some major units of the earth's crust[END_REF] or the average crustal abundance data (Taylor and McLennan, 1985) can be used as background metal values. However, the use of these data for a specific area may give misleading results. The regional background values might be more appropriate [START_REF] Gao | Chemical composition of the continental crust as revealed by studies in east China[END_REF][START_REF] Xu | Spatial distribution and pollution assessment of heavy metals in the surface sediments of the Bohai and Yellow Seas[END_REF]. Moreover, in the case of partially digested samples, it seems more appropriate to use a sample processed with the same analytical protocol as a reference.

In this study, a sample of marine sediment (S T ), 8 kilometers apart from the study area, was employed as the reference for the background values (Table 2).

According to [START_REF] Zhang | Riverine composition and estuarine geochemistry of particulate metals in China-Weathering features, anthropogenic impact and chemical fluxes[END_REF], an EF value of less than 1.5 suggests a geogenic origin of the studied metal (may be entirely from crustal materials or natural weathering processes). A high EF (> 1.5) means a significant input of trace metal derived from non-crustal materials. Instead, they are provided by an anthropogenic source. [START_REF] Sutherland | Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii[END_REF] used a five categories ranking system to assess the degree of contamination : EF <2: deficient to minimal contamination; 2<EF<5: moderate contamination; 5<EF<20: significant contamination; 20<EF<40: very high contamination and EF >40: extremely high contamination.

Geoaccumulation Index Igeo

According to Muller (1979), the geoaccumulation index (Igeo) may contribute to the estimation of the degree of pollution by heavy metals. The equation ( 2) that describes Igeo is as follows:

(2)

Where C x and B x are concentrations of metal (x) in the sediment and in the background, respectively. To eliminate the effect of possible lithological variations in sediments that can affect background data, a correction factor ( K =1.5) was used accordingly. Based on Igeo values, sediments can be classified into seven levels as follows: <0: unpolluted; 0-1: unpolluted to moderately polluted; 1-2: moderately polluted; 2-3: moderately to strongly polluted; 3-4: strongly polluted; 4-5: strongly to extremely polluted; >5 extremely polluted (Muller, 1979).

Geostatistical and statistical analysis

In environmental investigations and pollution mapping, geographic information system (GIS) has been widely applied [START_REF] Xu | Spatial distribution and pollution assessment of heavy metals in the surface sediments of the Bohai and Yellow Seas[END_REF]. The Inverse Distance Weighted (IDW) technique is the selected interpolation method as the best normalizer to picture the spatial distribution of metals. Continuous surfaces are predicted through a linear combination of the available data and greater weighting values; they are assigned to those closer to the interpolated point [START_REF] Zhou | Spatial distribution of heavy metals in Hong Kong"s marine sediments and their human impacts: A GIS-based chemometric approach[END_REF][START_REF] Li | A Review of Spatial Interpolation Methods for Environmental Scientists[END_REF][START_REF] Xie | Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis[END_REF]. Statistic results of IDW interpolation showed a good agreement between measured and predicted lines with an acceptable mean error and Root Mean Square (<0.05). Distribution maps were performed using ArcGIS 10.4.

In this paper, correlation coefficients were calculated, and principal component analysis PCA was performed to examine the relationship between metals, fluoride and < 63µm fraction. These calculations were carried using the version 13.0 of SPSS software.

Most multivariate statistical methods require variables to conform to the normal distribution. Thus, the normality of the distribution of each variable was checked by analyzing kurtosis and skewness statistical tests before multivariate statistical analysis [START_REF] Papatheodorou | A long-term study of temporal hydrochemical data in a shallow lake using multivariate statistical techniques[END_REF][START_REF] Zhou | Application of multivariate statistical methods to the water quality assessment of the watercourses in the Northwestern New Territories, Hong Kong[END_REF]. The original data demonstrated that most of the variables were heavily skewed, with kurtosis values much higher than zero (p < 0.05), ranging from -1.089 to 32.464 and skewness ranging from -2.397 to 5.677; This may indicate that distributions were far from normal with 95% confidence. To remove the effect of magnitude differences between variables, an additive logarithmic transformation log(1+X) of the raw values is required [START_REF] Frontalini | Benthic foraminifera for heavy metal pollution monitoring: a case study from the central Adriatic Sea coast of Italy[END_REF]. This may further contribute to data normalization and increase of the importance of smaller values. After log transformation, kurtosis and skewness values ranged from -1.615 to 5.796 and from -0.575 to 1.93, respectively.

PCA (Varimax rotation mode) is also an effective method to understand the relationships between elements [START_REF] Reid | Use of principal components analysis (PCA) on estuarine sediment datasets: the effect of data pre-treatment[END_REF][START_REF] Gu | Multivariate statistical and GIS-based approach to identify source of anthropogenic impacts on metallic elements in sediments from the mid Guangdong coasts, China[END_REF] via a multivariate technique used to analyze data tables and extract important information for later representation as a set of new orthogonal variables: Principal components PCs. In this case, PCA is used to clarify the relationship between heavy metals. Before applying this method, it is compulsory to examine the adequacy of sampling data examination through the Kaiser-Meyer-Olkin (KMO) test.

Further classification was performed via the hierarchical cluster analysis (HCA), using centroid clustering methods and squared Euclidean distance for similarity measurement, was applied to better understand the spatial distribution pattern in each cluster.

Results and discussion

Physical and geochemical results

Particle size Median value D 50 is defined as the value where half of the population resides above this point. It was calculated as a handy tool to understand the overall sediment size. Our results indicated medium-sized sand (D 50 between 500 and 250 μm) for samples near the discharge channel (Distance ≈ 250 m). However, when distance reached 500 m or more from the pollution source sands become fine to very fine for the majority with D 50 between 250 and 100 μm.

pH L and pH S of the study area ranged between 3.37-8.06 and 7.4 -8.84, respectively. The pH of the acidic effluent drained from the phosphate treatment plant is 2.73, while the pH of domestic wastewater is 8.3. In addition, its chemical properties are characterized by high values exceeding 10 to 37 times the standard loads of wastewater discharge into the marine environment (Table1). Acidic pH in the effluent favored the dissolution of heavy metals in the surrounding areas, leading to higher mobility of these metal cations in seawater and also in interstitial water. In addition, spatial distribution showed a great variation of pH values according to the distance from the discharge channel. Satellite pictures proved the presence of mouth bars at the outlet of the wastewater channel, and it is thus likely that the channel delivers solids to the sea as bedload and suspended load, in addition to dissolved species. XRD data suggest that Fluorite is transported to the sea, while solid phosphogypsum is dissolved in seawater at the channel mouth.

Moreover, the lack of marine calcite at the channel mouth suggests that it is readily dissolved, or converted to Fluorite, by the acidic and fluoride rich wastewater. Many other pollutants are not abundant enough for their host to be identified by XRD. Hence, we rely on their spatial distributions to assess their importance and to discuss their origins.

Spatial distributions of the studied metals in surface sediments are given in Fig. 2. The proportion of fine particles varies as a function of the distance to the shoreline, with a marked dissymmetry on both sides of the discharge channel (more fine particles to the North and coarser material to the South). On the contrary, pH L and many metal concentrations varied strongly and directly about the distance from the discharge channel. (29.90 -375). The abundance decreasing sequence of those elements was found to be as follows: Ca>Al>Fe>P>F>Y>Zn>Cr>Mn>Cu>Ni>Pb>Cd>As.

According to Fig. 2, the highest values of As, Cd, Cr, Cu, Ni, Pb, Y and Zn concentrations were observed in the channel mouth samples (S0 and S38), in sediments near (less than 300 m) the discharge channel (S9, S12, S16, S20, S26, S27, S35, S36 and S37) and for samples located on the coastline up to a distance of 1300 m from the wastewater outlet (S1, S2, S3, S4, S29, S30, S31 and S32). Their significant extension through these sites may be influenced by the coastal hydrodynamic conditions. For these sediments, concentrations of Cr, Cu, Ni and Zn are higher than the current regional background and other values revealed in previous studies for the same area (Table 3). Compared to these different studies carried between 2000 et 2010 in the southern coast of Sfax, concentrations of Cu, Cr, Ni and Zn in the surface marine sediments found in our study are much higher. The increasing values may be caused by the development of phosphate treatement activities or by the accumulation of pollutants in sediments as they represent an important reservoir for metals.

These metals also showed higher values when compared to some published studies, in Tunisia and all over the world, and Sediment Quality Guidelines SQGs. Threshold effects level (TEL) and probable effects level (PEL) are also two widely used guidelines for bio-toxic sediments risks. Below TEL, concentrations of metals in sediments have a rare biological effect. However, PEL is a level above it, concentrations of metals cause important biological risks [START_REF] Macdonald | Development and evaluation of sediment quality guidelines for Florida coastal waters[END_REF]. In the case of our study, for the majority of analyzed sediments concentrations in Cd, Cu, Cr, Zn and Ni far exceed PEL level especially for Cd. Thus, high ecological and biological risks are expected in this coast.

As, Cd and Y in all the analyzed samples far exceed values in other studies carried out in coastal areas in Sfax. By far, the highest amounts are recorded in S0 and S38, at the wastewater outlet, and all sediments in the coastline.

Although it exceeds normal levels, concentrations in Cd still lower than those recorded from the coast of Gabes, due to the high amount of discharged phosphogypsum into the sea ( El Zrelli et al., 2015;[START_REF] Ayadi | Assessment of contaminated sediment by phosphate fertilizer industrial waste using pollution indices and statistical techniques in the Gulf of Gabes (Tunisia)[END_REF]. In contrast, for all surface sediments, Al, Fe and Pb (except S0 and S12) were found to be low and in agreement with natural levels.

Ca and Mn showed very high concentrations far exceeding the reference used to calculate the EF and the Igeo for all samples in the study area. This is due to the important amount of these pollutants rejected by the phosphate treatment plant. These latter findings are confirmed by the spatial distributions of F and P (Fig. 3), the leading elements discharged by the GCT [START_REF] Darmoul | Effets des rejets industriels de la région de Gabès (Tunisie) sur le milieu récepteur[END_REF]Zrelli et al., 2017), contributing to a clear degradation pattern of the marine water and sediment quality in areas including this factory.

Enrichment and pollution assessment

Based on a regional background (S T ), enrichment factor (EF) was used as an index to assess the status of environmental contamination. According to Fig. 4a, the EF values of As, Ni and Pb are between 047-5.37, 0.77-6.19 and 0.59-5.12 , respectively. Results of EF and Igeo are in concordance with the spatial distribution maps where the nearest samples to the pollution source are qualified as the most polluted and enriched in all the heavy metals, except As, Ni and Cu where their EF and Igeo are below 2 and 1, respectively.

Statistical analysis

In order to establish relationships among metals and to discuss their source in the southern coastal area of Sfax, a correlation matrix was created for heavy metals, major elements and F.fine. Generally speaking, the strong correlation between metallic contaminants is already evident in the distribution maps (Figs. 2 and3). In addition, granulometry and carbonate content are important controlling factors of adsorption and trace metals abundance. The main types of mineral particles to which metals can be adsorbed are silicates (silica, quartz and feldspar), aluminosilicate clays (kaolinite, smectite, illite...), carbonate (calcite, dolomite...), phosphate (apatite), Fe-Mn oxides/hydroxides and sulphate (Honeyman et Santschi, 1988).

According to Table 4, Pearson correlation matrix reveals a strong relationship between heavy metals. The very significant positive correlation could indicate that these elements in sediment share common anthropogenic sources (Garcia et al.2008) and may underwent analogous pathways and geochemical process [START_REF] Wang | Distribution of heavy metals in core sediments from Baihua Lake[END_REF]Zhuang and Gao, 2014). Medium correlations exist between Al, Fe and mud content (fractions <63µm) with r= 0.400 and r= 0.373, respectively. This suggests that these metals may be moderately associated with silt/clay-sized constituents of the surface sediments. Mn correlates moderately with Fe (r=0.402), indicating that those lithogenic elements are bound to the fine-grained sediments.

In the marine surface sediments, the very high correlation between P and the analyzed heavy metals like Zn (r= 0.975), Y (r=0.901), Cr (r=0.804), Cd (r=0.979), Pb (r=0.796) and Ca (r=0.864) not only indicates that these elements are derived from the same anthropogenic source but also some of them could be hosted in phosphate (dissolved or released by the dissolution of phosphogypsum in the channel).

A very high correlation is revealed between Zn, Y, Cr, Cd and Pb, indicating that these elements share a common anthropogenic origin (i.e., industrial and WWTP effluent). Ni highly correlates with As (r=0.715) and Cu (r=0.908). It is probably due to common influential factors. Frequently, heavy metals are associated with the calcite. In particular, Cd and Pb have a strong affinity to calcite [START_REF] Zachara | Sorption of divalent metals on calcite[END_REF][START_REF] Van Proosdij | Immobilization of contaminants with in situ calcite precipitation: a preliminary evaluation[END_REF]. These two elements showed a significant correlation with Ca (r= 0.746 and r= 0.834). Ca was also strongly correlated with metals like P, Cr, Y and Zn, which indicates that there is a probability that these metals are getting co-precipitated with CaCO 3 [START_REF] Nirmala | Geochemistry of surface sediments of a tropical brackish water lake in South Asia[END_REF], except sediments located around the channel mouth where calcite is dissolved under low pH. This is confirmed by the absence of calcite in XRD analysis for S0 and S38.

In the case of our study, adsorption does not seem to be the main fixing mechanism because under a pH 8.5, positive surface charge of calcite does not allow cationic adsorption [START_REF] Van Proosdij | Immobilization of contaminants with in situ calcite precipitation: a preliminary evaluation[END_REF]. A significant correlation between Al and Zn, Y, Cr, Cd and Pb may indicate that these elements can be also associated with aluminosilicate minerals [START_REF] Pereira | Nickel, vanadium, and lead as indicators of sediment contamination of marina, refinery, and shipyard areas[END_REF]. High concentrations in all metals for S26 (300 m distant from the pollution source) may be due in part to the presence of microcline ( KalSi 3 O 8 ) and kaolinite (Al 2 Si 2 O 5 ) as shown in XRD diffractograms.

Although their distance from the discharge channel outlet , S3, S4, S5, S6, S8, S9 and S10 showed moderate concentrations in Zn, Ni, Pb and especially in Cu. These sediments are located in the northern part of the study area where the depositional energy is low. The existence of very small amounts of microcline in these samples could be responsible in part for fixing some elements.

In addition to heavy metals, the wastewater effluent transport very high concentrations of fluoride (≈3500 mg/l). For the majority of samples, surface marine sediments also showed high concentrations of fluoride. XRD analysis revealed the abundance of Fluorite (CaF 2 ) in the channel and the nearing sediments (S18, S35, S36, S37 and S38). This indicates that Fluorite could be transported as a solid phase from the phosphate treatment plant. In addition, neither calcite nor gypsum exist in S38 which means that the majority of extracted CaO may be derived from Fluorite as a result of the reaction between waters rich in fluoride and carbonate precipitated by their major source: shells.

In order to elucidate the potential sources of heavy metals in the surface marine sediments of the southern coast of Sfax, we performed factor analysis on heavy metals based on principal component method with a varimax rotation. For the present study, KMO was equal to 0.795 (close to 1) which means that PCA is useful.

PCA applied to all data (elementary concentrations and F.fine) resulted essentially in three principal components (PC). A significant correlation between the studied variables and three extracted components is obtained with approximately a total variance of 85.13%. According to Table 5, the first group (G1) of metals represented by Ca, Cd, Cr, F, P, Pb, Y and Zn showed a correlation (exceeding 0.700) with PC1 with 59.24% of the total variance. The second group (G2) is positively displayed over PC2 with 13.94% of the total variance and articulated around Fe, Al and F.fine.

The third group(G3) is positively correlated with PC3 (11.94% of the total variance) and presented by Ni, Cu and As.

PC3 also showed a moderate correlation with PC2 and PC1. According to Fig. 5, the loading plot of the three components, confirmed the three obtained groups: G1: Ca, Cd, Cr, F, P, Pb, Y and Zn originated from anthropogenic resources, confirming the results of calculated EF and Igeo. The main source of these elements seemed to be the phosphate treatment factory by generating important amounts of impurities due to the presence of phosphogypsum and phosphate particles transported in the fluoride-rich wastewater to the sea ; G2: in this group, Al and Fe are considered as natural components. Their concentrations are in concordance with natural values. Spatial distribution patterns of these metals are almost identical in all the studied area; G3: As, Cu and Ni may be influenced by both natural and anthropogenic sources. The high concentrations of As and Cu in local sites, especially in the northern part of the study area, may be related to the muddy sediments with low organic matter contents.

Hierarchical cluster analysis (HCA) has identified three distinct clusters (I, II, III) based on metal concentration distributions (Fig. 6). Cluster I is associated with samples S0, S35, S36, S37 and S38, the nearest sediments to the pollution source. They are characterized by the highest concentrations of pollutants . Cluster II, with a lower hierarchical order, associate with samples with medium distance from pollution source (S12 and S20) and coastline samples (S3, S5, 29 and S30). This group is characterized by lower concentrations than Cluster I. Finally, Cluster III groups sediment with the lowest contamination level. This is consistent with EF and Igeo measurements.

Conclusions

This study has been undertaken to assess the contamination level of the costal area to the southern edge of sfax city.

Our results indicated high metals concentrations in the surface marine sediments of the southern coast of Sfax, far exceeding natural values. Spatial distribution of those metals shows different patterns with higher concentrations near the coastline. It was also found that this part of the coast is a favorable environment for the accumulation of some pollutants because of its proximity to pollution sources, a mixed «industrial and domestic» wastewater effluent and variable depositional energy. Tidal currents played key role in sediment particles and heavy metals distribution.

Hydrodynamic condition and fine particle (silt and clay) distribution show that the study area can be subdivided into two dissymmetry zones: a north characterized by a low depositional energy that facilitated fine particles and pollutant accumulation and a south characterized by longshore currents where sediments are dominated by coarse-grained particles (sand).

EF, Igeo and PCA analysis, are different methods used for pollution assessment. It appears that Al and Fe derived from natural sources. However, the strong enrichment recorded for Cu, P, Y and Zn and the extreme enrichment in Cd confirms that these metals predominately have an anthropogenic source. The comparison of EF and Igeo with previous studies elaborated in 2005 and 2010 for the nearby areas showed that the enrichment in pollutants and the pollution degree are increasing year by year. Also, metals pollution affected all the samples but values of elements concentrations, EF and Igeo decreased with increasing distance from the source. These results were confirmed by HCA.

Multivariate analysis PCA helped to conclude that majority of metals are related to the anthropogenic sources.

Results show no correlation between these elements and Silt/clay minerals (except few samples in the northern part of the study area) and Fe-Mn oxides which suggest that pollution is mainly derived from similar sources (GCT effluent) in solid phases transported by the mixed channel to the sea, or in secondary solid (but unidentified) phases formed where the acidic wastewater is neutralized at the channel outlet.

When compared to previous data reported in the literature, concentrations of the majority of analyzed metals were higher than their background values, metal concentrations in earth crust and those of Interim Sediment quality guidelines (SQGs).The concordance between the WWTP discharge analyzed parameters and Tunisian standards for industrial discharge in the maritime field led to deduce that industrial discharges, mainly from GCT, are predominantly responsible for the marine waters and sediment degradation in the northern coast of Gabes Gulf.

As severe pollution impacts on the southern coast of Sfax, several hectares of azoic areas are created. In addition, the north-south direction of water currents seems to extend the influence and to increase the transfer of hazardous pollutants toward the southern areas, to be accumulated by marine species and integrated into food chains and to end in humans. This situation is leading to serious health risks if significant effective strategies of management are not adopted. It is important to know that the coastal ecosystems of the northern part of gulf of Gabes are able to establish their auto-remediation once the discharge of this effluent into the sea is stopped or properly treated. Ca -0,049 0,402 -0,060 1 Mn 0,261 0,308 0,475 -0,201 1 F -0,152 0,215 0,018 0,364 0,007 1 P -0,072 0,499 0,046 0,975 -0,183 0,451 1 Zn 0,004 0,583 0,159 0,858 -0,009 0,548 0,901 1 Y -0,087 0,455 0,081 0,729 -0,020 0,872 0,804 0,869 1 Cr -0,055 0,541 0,101 0,964 -0,156 0,287 0,979 0,865 0,697 1 Ni -0,100 0,457 0,330 0,371 0,245 0,879 0,469 0,626 0,825 0,366 1 Cu -0,177 0,177 0,046 0,277 0,162 0,940 0,349 0,481 0,789 0,204 0,908 1 As -0,074 0,611 0,459 0,516 -0,048 0,561 0,583 0,629 0,649 0,558 0,715 0,553 1 Cd 0,030 0,517 0,087 0,746 -0,093 0,418 0,796 0,916 0,763 0,773 0,485 0,334 0,496 1 Pb -0,018 0,545 0,183 0,834 0,092 0,593 0,864 0,940 0,854 0,815 0,696 0,546 0,663 0,828 1 *Correlation is Bold number: correlation is significant at the 0.01 level (two-tailed)

X

  -ray diffactograms indicate that quartz, apatite, calcium sulfate (i.e., phosphogypsum), fluorite and barite are present in the bedload of the wastewater channel downstream the phosphate treatment plant. All these minerals are also found in the phosphogypsum stockpiles of the plant: quartz and apatite are unreacted minerals while phosphogypsum, fluorite, barite and a chukrovite-like phase (Ca 4 [FSO 4 SiF 6 AlF 6 ],12H 2 O) are likely secondary minerals of the production process(Papageorgiou et al., 2015). The sediment located in the mouth of the wastewater channel contains quartz, fluorite and apatite, but neither gypsum nor calcite were detected by XRD. Quartz and calcite are ubiquitous in all the other marine samples. They are often associated with feldspars (microcline and sodic plagioclase) which are very common detrital minerals in the sand mode, and less frequently with some clay minerals (illite and/or kaolinite). In marine sediments, XRD patterns also indicate the presence of halite, which may represent evaporated seawater.

Fig. 2

 2 Fig. 2 Distribution maps of heavy metals concentrations in surface marine sediments of the Southern coastal area of Sfax.

Fig. 3

 3 Fig. 3 Distribution maps of Fluorine and Phosphorus concentrations in surface marine sediments of the Southern coastal area of Sfax.

Fig. 5

 5 Fig.5 Loading plots of the three components obtained with PCA for the data set.

Fig. 6

 6 Fig.6 Hierarchical cluster analysis for sampling stations based on metals concentrations

  Physical and chemical parameters of both metals and background values are summarized in Table2. It was found that major and trace metals concentrations in sediments ( in mg/kg dry weight) were found as follows: Al (2487 -21393), As (1.90 -10.30 ), Ni (4 -60),Ca (12657.2 -137772.6), Cd (0.55 -24.52), Cu (11.10 -359), F (65 -29000), Mn (44 -106), P (367 -15110), Pb (3.68 -39.70), Cr (21.90 -381), Y (4.40 -946) and Zn

  Ni and Pb, Igeo values show that the marine sediments in our study area can be classified as unpolluted to moderately polluted in these metals. Cr and Cu display a moderate pollution. For P, Y and Zn, the calculated Igeo indicates that sediments range from moderately to strongly polluted. In terms of Cd, the majority of marine sediments (64.11%) can be classified from strongly to extremely polluted, confirming the EF results. An exception is always made for the sediments located in the surrounding area to the effluent outlet (S0, S35, S36, S37, and

	and Pb for some samples (S0); a moderate enrichment in Y for 82.05% of samples (significant enrichment for S0, S35,
	S36, S37, and S38) and Cr for 74.36% of samples (significant enrichment for S0, S35, S36, S37, S38); a significant
	enrichment in Zn for more than 80% of analyzed sediments; from moderate to very strong enrichment in P ( S38 is
	extremely contaminated in P); For Cu, 80% of samples are moderately to significantly contaminated while S5, S20,
	S27, S31, S35, S37 and S38 are weakly enriched. For Cd , 92.31% of samples are extremly contaminated. Their EF in
	Cd far exceed the value of 40 especially for S4, S12 and S38 where EF attended 100. This confirms the very high
	amount of Cd rejected by the phosphate treatment plant (GCT) in the discharge channel. In conclusion, S0, S35, S36,
	S37 and S38 are the most enriched samples for the majority of pollutant due to their proximity to the pollution source
	and to the low hydrodynamic activity in this area.
	In the same way, Igeo ranged from -1.03 to 1.42 (average -0.01) for As, 1.40 to 6.90 (average 4.77) for Cd, -0.37 to
	3.77 (average 0.88) for Cr, 0.08 to 5.12 (average 1.34) for Cu, -1.14 to 2.78 (average 0.09) for Ni, 0.14 to 5.53 (average
	1.81) for P, -0.94 to 2.51(average 0.30) for Pb, 0.83 to 4.28 (average 2.31) for Y and 0.35 to 4.01 (average 1.75) for Zn
	(Fig. 4b). For As,

Those of Cd, Cr, Cu, P, Y and Zn range from 2.

10-115.38, 0.99-13.10, 1.10-29.61, 0.69- 45.03, 0.52-15.30 and 1.12-14.06

, respectively. The average EF for As (1.81) in the study area is below 2, while those for Cd (56.44), Cr (3.81), Cu (6.13), Ni (2.17), P (9.06), Pb (2.45), Y (4.04) and Zn (6.51) exceed 2. Consequently, metals can be classified as follows : weak to moderate enrichment in As, Ni and Pb with a significant enrichment in Ni S38). The calculated Igeo indicates that these samples are strongly polluted in Cu and Zn and extremely polluted in P and Y. Based on the mean values of Igeo, sediments were enriched with metals in the following sequence : Cd > Y > P > Zn> Cu > Cr > Pb > Ni > As.

Table 1

 1 Physical and chemical characteristics for the industrial and domestic wastewater effluent (ppm)

Table 2

 2 Ranges (Min, Max), Average and Standard Deviations of Geochemical parameters in surface marine sediments from the southern coast of Sfax and regional background values

		pH	F	Fe	Zn	Cd	Cu	Pb	Reference
	Phosphate treatement plant effluent	1,9	1245	16	18	3,1	0,3	0,2
	WWTP effluent	7,6	5,3	2,2	1,9	<0,05	0,5	0,3	Serbaji et al. (2000)
	Mixed wastewater effluent 2,8	252	3,8	7,2	1,1	0,3	0,1
	Tunisian standards	6,5-8,5	5	1	10	0,01	1,5	0,5	NT106.02
								Background
			Min	Max	Average	SD	values ST
								(This study)
	D50	80.44	495.40	232.11		118.30	-
	F.fine	0	6.31	1.46		1.46	-
	PHL	2.23	8,06	6.97		1.53	-
	PHS		7.40	8.65	8.06		0.30	-
	Al (mg kg -1 )	2 487	21 393	7 289		4 513	7 285
	As (mg kg -1 )	1.,90	10.30	4.17		1.87	2.58
	Ca (mg kg -1 )	12 657.2	137 772.6 42 011.62 29 584.29 18 461
	Cd (mg kg -1 )	0.55	24.52	8.14		6.95	0.14
	Cr (mg kg -1 )	21.90	381	77,22		90.88	18.90
	Cu (mg kg -1 )	11.10	359	37		56	7.00
	F (mg kg -1 )	65	29 000	1 446		4 965	-
	Fe (mg kg -1 )	1 837	15 740	4 339		2 602	4 911
	Mn (mg kg -1 )	44	106	73		11.84	50
	Ni (mg kg -1 )	4	60	11.08		9.27	5.87
	P (mg kg -1 )	367	15 110	2 456		3 819	222
	Pb (mg kg -1 )	3.68	39.70	10.71		8.70	4.68
	Zn (mg kg -1 )	29.90	375	104.90		89.51	15.70
	Y (mg kg -1 )	4.40	946	147.68		156.31	36.80

Table 3

 3 Comparison of heavy metal concentrations in sediments of the southern coast of Sfax with other published values from the Gulf of Gabes and other selected areas and SQGs

	Locations			Metals concentrations (mg/kg dw)			References
		Cd	Cu	Pb	Cr	Zn	Ni	Fe
				Analysis method: Aqua regia
	This study	0.55 -24.52	11.1 -359 3.68 -39.7	21.87 -381	29.9 -375	4.00 -59.9	1837 -15740	Present study
	Aegean Sea	0.14 -0.74 2.96 -8.92	2.23 -13.68	7.33 -29.10	29.42 -72.39	-	-	Tunca et al. (2017)
	Persian Golf	-	1.90 -304.83	0.97 -14.48	5.70 -52.40	5.10 -123.60	5.40 -80.0	-	Delshab et al. (2016)
	Coast of Gabes	0.11 -950 0.59 -5.8 3.8 -13.9 9 -69.3	5.2 -7165	-	-	El Zrelli et al. (2015)
	Coast of Cameroon 0.01-0.21 1.0-64	1.14-21	5.13-328	7.9-212	1.59-457	3473.9-136762	Ngeve et al. (2015)
	Gulf of Gabes	0 -1225	1.5 -58.7 0 -10.2	3 -120	6.75 -10.1	-	-	Ayadi et al. (2014)
	Southern coast of Sfax	6.0 -56	15 -44	19 -59	44 -137	42 -391		52344 -54543	Houda B et al. (2011)
	Coast of Sfax	5.5 -7	13 -29	18 -88	41 -82	39 -117	7 -5.5	41011-50163	Gargouri et al. (2011)
	Southern coast of Sfax	3.21-5.87 2.25-2.28 1.75-3.04 11.4-15.6 19.7-23.8	1.62-2.87	-	Aloulou et al. (2010)
	Sfax-Kerkenah shelf 35.9	38.4	125	196	2077	348	8835	Ghannem et al. (2010)
	Coast of Sfax	5.5	13	18	41	39	7	52000	Serbaji (2000)
				Analysis method: Total digestion
	Sancti Petri estuary, Spain	0.08 -0.4	12.9 -90.82	9.8 -70.6 -	31.70 -162	4.6 -58.5	-	Bhuiyan et al. (2018)
	TEL* (threshhold effect level) PEL* (probable effect level)	0.68 4.21	18.7 108	30.2 112	52.3 160	124 271	15.9 42.8	--	Macdonald et al. (1996)
	*American Sediment Quality Guidelines

Table 4

 4 Pearson correlation matrix of studied elements in the southern coastal area of Sfax

		F.fine Al	Fe	Ca	Mn	F	P	Zn	Y	Cr	Ni	Cu	As	Cd	Pb
	F.fine 1
	Al	0,400 1
	Fe	0,373 0,843 1

Table 5

 5 Factor loading and accumulated variance of metals

		PC1	PC2	PC3
	F.fine	-0,044	0,669	-0,547
	F	0,738	0,067	0,215
	Al	0,507	0,83	0,127
	Fe	0,089	0,937	0,206
	Ca	0,921	-0,065	0,031
	As	0,444	0,255	0,569
	Cd	0,916	0,021	0,067
	Cr	0,891	0,301	0,175
	Cu	0,062	-0,108	0,883
	Ni	0,355	0,411	0,785
	P	0,961	0,224	0,094
	Pb	0,831	0,268	0,381
	Zn	0,899	0,262	0,249
	Y	0,923	0,178	0,253
	% of Variance	49,25	18,45	17,43
	% of Cumulative variance	49,25	67.706	85,13

Acknowledgment

The authors express their sincere thanks to the editor and the anonymous reviewers for their constructive comments that improved the quality of the original manuscript. We warmly thank Didier Graillot and all members of "Département GéoSciences et Environnement (SPIN): École Nationale Supérieure des Mines de Saint-Étienne" for their technical help during chemical analysis.

(This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors).


Anita Gaspard D'assignies

Edouard Paisant

Anne Bardou-Jacquet

Elise Boulic

Fabrice Bannier

Martine Lainé

Jeff Ropert

Hervé Saint-Jalmes Yves Morcet

Gandon

MD, PhD G D'assignies

MD A Paisant

MD, PhD E Bardou-Jacquet

MD A Boulic

PhD E Bannier

MD F Lainé

PhD M Ropert

PhD J Morcet

PhD) ² H Saint- Jalmes

MD Yves Gandon
email: yves.gandon@chu-rennes.fr

Non-invasive measurement of liver iron concentration using 3-Tesla magnetic

Keywords: Iron, Liver, Magnetic resonance imaging, Hemosiderosis, Non-alcoholic fatty liver disease, Hemochromatosis Signal intensity ratio LIC Non-alcoholic steatohepatitis DIOS: Dysmetabolic iron overload syndrome MRI: Magnetic resonance imaging BMI: Body mass index AUC: Area

MAIN TEXT

Introduction

Liver iron content (LIC) is a surrogate marker of whole-body iron load. In overload diseases such as primary or secondary hemochromatosis, LIC measurement is mandatory for guiding therapeutic decisions. Liver iron overload may also be present in non-alcoholic steatohepatitis (NASH) and dysmetabolic iron overload syndrome (DIOS), which are both highly prevalent in the Western population [START_REF] Blachier | The burden of liver disease in Europe: a review of available epidemiological data[END_REF]. The main complications are cirrhosis and hepatocellular carcinoma. Many studies [START_REF] Moukhadder | Hepatocellular carcinoma as an emerging morbidity in the thalassemia syndromes: A comprehensive review[END_REF][START_REF] Deugnier | Pathology of hepatic iron overload[END_REF] have suggested a close correlation between iron deposition and carcinogenesis.

The gold-standard method for detecting and quantifying liver iron overload is histopathological analysis of a liver sample collected by biopsy with biochemical analysis of the core fragment. The biopsy procedure is both invasive and painful and carries some risk of complications [START_REF] Hatfield | Percutaneous imaging-guided solid organ core needle biopsy: coaxial versus noncoaxial method[END_REF]. In addition, the very small liver sample may not be representative of the whole liver in cases of heterogeneous iron distribution [START_REF] Wood | Liver MRI is more precise than liver biopsy for assessing total body iron balance: a comparison of MRI relaxometry with simulated liver biopsy results[END_REF].

Non-invasive, quantitative assessment of LIC by 1.5T magnetic resonance imaging (MRI) has been extensively validated against histology by calculating the relaxation rates R2 and R2* [START_REF] Anderson | Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload[END_REF][START_REF] St Pierre | Noninvasive measurement and imaging of liver iron concentrations using proton magnetic resonance[END_REF][START_REF] Wood | MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients[END_REF][START_REF] Hankins | R2* magnetic resonance imaging of the liver in patients with iron overload[END_REF][START_REF] Garbowski | Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan[END_REF][START_REF] Henninger | R2* relaxometry for the quantification of hepatic iron overload: biopsy-based calibration and comparison with the literature[END_REF] and/or the signal intensity ratio (SIR) between the liver and paraspinal muscles [START_REF] Gandon | Non-invasive assessment of hepatic iron stores by MRI[END_REF][START_REF] Alústiza | MR quantification of hepatic iron concentration[END_REF][START_REF] Ernst | Hepatic iron overload: quantification with MR imaging at 1.5 T[END_REF]. MRI is thus now used in routine clinical practice to diagnose, quantify and monitor iron overload [START_REF] Brissot | Current approach to hemochromatosis[END_REF].

In recent years, 3T MRI has become more widespread. In view of the shift in magnetic field strength, acquisition parameters need to be adapted and new reference values proposed.

Better sensitivity and accuracy can be expected at 3T, improving diagnosis of DIOS with low iron burden. Conversely, quantification of high overload cases may prove more difficult [START_REF] Meloni | Feasibility, reproducibility, and reliability for the T*2 iron evaluation at 3 T in comparison with 1.5 T[END_REF].

Recently, the SIR method, based on several single-echo GRE sequences, has been validated against histology at 3T (17).

A c c e p t e d m a n u s c r i p t

The purpose of our study was to evaluate the ability of the R2* method to detect and quantify liver iron at 3T using biochemical quantification as the reference method. Our secondary goal was to compare, at the 3T field strength, two major LIC quantification methods: R2* and SIR.

Materials and Methods

Patient population

Between January 2007 and January 2013, all patients referred for liver biopsy and in whom liver iron overload was suspected according to their disease were prospectively recruited. All patients provided written informed consent to participate in this prospective single-center clinical trial. In addition to usual care, an MRI scan was scheduled to assess hepatic iron stores. Age, sex and body mass index were recorded.

Biochemical liver iron concentration

Liver biopsy was indicated as per the guidelines of the American Association for the Study of Liver Diseases [START_REF] Rockey | Liver biopsy[END_REF][START_REF] Bacon | Diagnosis and management of hemochromatosis: 2011 practice guideline by the American Association for the Study of Liver Diseases[END_REF]. A biopsy sample was taken from the right lobe of the liver using a 16 gauge needle (Hepafix 16G, Braun, Melsingen, Germany) under ultrasound guidance. Biochemical liver iron concentration (LICb) was measured using Barry and Sherlock's method for biopsy samples taken from paraffin-embedded blocks [START_REF] Barry | Measurement of liver-iron concentration in needle-biopsy specimens[END_REF]. Liver iron overload was defined as an LICb greater than 35 µmol/g (dry liver). Biochemical analysis was blinded to MRI results.

Magnetic resonance imaging protocol

The study was performed with two 3T MRI scanners: first with Achieva (Philips, Best, Netherlands) and then with Magnetom Verio (Siemens Healthcare, Erlangen, Germany). The body coil was used as the receive coil to achieve homogeneous signal intensity in the imaged section and avoid signal depth fall-off. Only the Siemens scanner had a compensation method for better B1 homogeneity. There was a slight difference in resonance frequency (127.79 vs. 123.24 MHz) between the two scanners. Using the body coil, one multi-echo gradient echo (mGRE) sequence was performed, with 11 echoes. The selected TEs were slightly different depending on the scanner: a multiple of 1.15 ms for the Philips group and 1.23 ms for the Siemens group. Pixel bandwidth was 1161 Hz for the Philips group and

MRI data analysis

Measurements were conducted using an in-house Java program integrating ImageJ functions (NIH, Bethesda, USA). All data were analyzed by a radiologist (with 10 years' experience in abdominal radiology) who was blind to clinical information and to the biopsy result.

On the selected slice, 3 ROIs with a diameter of 2.5 cm (4.9 cm²) were placed in the right liver area, taking care to avoid large vessels, biliary tracts, parenchymatous lesions and artifacts, 2 ROIs with a diameter of 2 cm (3.1 cm²) in the right and left paraspinal muscles and 1 ROI with a diameter of 3 cm (7.1 cm²) in the air outside of the body for noise measurement. All ROIs were automatically copied to the same place on each echo of this selected location. The placement of the ROIs is illustrated in Figure 1.

Before performing fitting, we applied a noise subtraction algorithm to subtract the mean background noise from the liver signal. Then T2* values were automatically calculated using a simplex non-linear algorithm to fit the magnitude of the complex signal from all echoes or only from in-phase echoes when the signal of the first out-of-phase echo was lower than the signal of the first in-phase echo. The liver-to-muscle signal intensity ratio (SIR) method was used to calculate LICSIR with the algorithm derived from the same patient series using 5 single-echo GRE sequences [START_REF] Paisant | Assessment of liver iron overload by 3 T MRI[END_REF]. Only the first four echoes of this formula were used to calculate LICSIR since the longest fifth echo (14 ms) was not obtained in the mGRE acquisition.

A c c e p t e d m a n u s c r i p t

Statistical analysis

Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

Qualitative variables were expressed as numbers and percentages. Quantitative data were expressed as means ± standard deviations (SD) if normally distributed and medians (Q1-Q3) if not normally distributed.

Given that LIC quantification variables were not normally distributed, we calculated non-linear correlation coefficients (Spearman) to estimate the strength of the linear relationship between LICR2* or LICSIR and LICb.

Similarly, in order to compare measurements using the Philips or Siemens scanner, Generalized Poisson Mixed Models (GLIMMIX procedure) were used with or without adjustment for sex, BMI and age.

Agreement between LIC quantifications was assessed using the Bland and Altman method, calculating the mean difference (estimated bias, d), the standard deviation of the differences (precision, SD), and the limits of agreement (d±1.96SD). Student's t-test was used to determine whether the bias between measurement methods was significant.

Optimal cut-off values for the threshold of LICb at 36 µmol/g were obtained by optimization of the Youden index from AUROC curve analysis.

The area under the curve, sensitivity, specificity, positive and negative predictive values were calculated for both LICR2* and LICSIR.

In order to compare the two methods at different levels, the cohort was divided into equal successive classes according to the values of LICb. Then, LICb -LICR2* and LICb -LICSIR were calculated and compared at the different LICb levels. A similar comparison, corresponding more to the practical intent to diagnose, was also done by using LICR2* classes. P values less than 0.05 were considered statistically significant.

RESULTS

Patient population

One hundred and five patients were prospectively included between January 2007 and January 2013 (Figure 2). Fifty-eight (55%) had hyperferritinemia, 52 (50%) had metabolic syndrome, 15 (14%) had A c c e p t e d m a n u s c r i p t chronic alcohol intoxication, 15 (14%) had either hepatitis B or C, and 6 (6%) had other liver diseases (autoimmune hepatitis, primary biliary cirrhosis).

The first 36 patients (34%) included were scanned with the Philips scanner and the following 69 (66%) with the Siemens scanner. Mean (±SD) age was 55.8±12.7 and 50±12.8 years for women and men, respectively (p=0.03). One hundred and one patients underwent the MRI examination and the biopsy on the same day while 4 patients experienced an interval between the biopsy and MRI of less than 15 days. The LICb concentration ranged from 0 to 630 µmol/g, and 49 patients (47%) had normal LICb values < 36 µmol/g. Fifty six patients had a liver iron overload. It was due to genetic hemochromatosis in 31 patients (LICb mean=286±148 µmol/g, range=43-630 µmol/g), to dysmetabolic syndrom in 22 patients (LICb mean=62±27 µmol/g, range=36-123 µmol/g) and to other causes (alcoholic or viral hepatitis) in 3 patients (LICb mean= 42±5 µmol/g, range=36-46 µmol/g).

Patient characteristics are provided in Table 1.

Our analysis without / with the adjustment for BMI, sex and age yielded no difference in the distribution of LICb (p=0.65 / p=0. [START_REF] Bacon | Diagnosis and management of hemochromatosis: 2011 practice guideline by the American Association for the Study of Liver Diseases[END_REF]), LICR2* (p=0.49 / p=0.14) and LICSIR (p=0.50 / 0.27) results between the two groups using MRI machines from different manufacturers.

R2* and LICR2* measurements

Linear regression between LICb and R2* is shown in Figure 3a showed an area under the curve (AUC) of 0.987. The best threshold was given for LICR2* at 32 µmol/g, corresponding to an R2* of 77 s -1 , and a T2* of 13 ms, with 47 true positives, 4 false positives, 52 true negatives and 2 false negatives. The sensitivity was 0.96 (95% CI: 0.9; 1.01) and the specificity 0.93 (95% CI: 0.86; 1.0).

Taking in consideration only the 76 patients with LICb below 130µmol/g, linear regression yielded the following equation: LICR2* (µmol/g) = 0.314 R2* -0.96

The best threshold was then given for LICR2* at 27 µmol/g, corresponding to an R2* of 89 s -1 , and a T2* of 11 ms, with 44 true positives, 2 false positives, 25 true negatives and 5 false negatives. The sensitivity was 0.90 (95% CI: 0.81; 0.98) and the specificity 0.93 (95% CI: 0.83; 1.0).

LICSIR measurement

Linear regression between LICb and LICSIR is shown in Figure 3c. The Spearman correlation coefficient (r = 0.92 p < 0.001) indicates a strong positive correlation between LICSIR and LICb. Figure 3d shows the Bland-Altman plot of the difference vs. mean values of LICb and LICSIR measurements.

The bias (SD) or average difference between the results of the two methods was 14.8 (37.6) and the 95% limits of agreement were -59.0 and 88.5 µmol/g. The bias was statistically significantly different to zero (p<0.0001).

With the reference threshold established at LICb = 36 µmol/g, the ROC curves obtained with LICSIR results showed an AUC of 0.965. The best threshold was given for LICSIR = 20 µmol/g with 45 true positives, 3 false positives, 53 true negatives and 4 false negatives. The sensitivity was 0.92 (95% CI: 0.84; 0.99) and the specificity 0.95 (95% CI: 089; 1.0).

Comparison between LICR2* and LICSIR measurements

The Spearman correlation coefficient (r = 0.95 p < 0.001) indicates a strong positive correlation between LICR2* and LICSIR. Figure 4 presented the lowest differences for patients with LICb values under 130 µmol/g and the highest differences for patients above 190 µmol/g (Figure 4a). Using LICR2* classes, the differences increased above 130 µmol/g (Figure 4b).

DISCUSSION

With a shortest TE of 1.2 ms, liver iron overload can be reliably quantified by MRI at 3T with the R2* for patients with biopsy-proven LIC under 130 µmol/g but the SIR method appears more robust for higher iron overload.

The R2* calculation is well known and its clinical use is well established at 1.5T. In the literature, there are 5 main publications, validating R2* against LIC determined by biopsy [START_REF] Anderson | Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload[END_REF][START_REF] Wood | MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients[END_REF][START_REF] Hankins | R2* magnetic resonance imaging of the liver in patients with iron overload[END_REF][START_REF] Garbowski | Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan[END_REF][START_REF] Henninger | R2* relaxometry for the quantification of hepatic iron overload: biopsy-based calibration and comparison with the literature[END_REF]. Conversion formulas have been proposed to estimate LIC from R2* (s -1 ) with a slope of 0.025 to 0.032 to obtain the LIC value in mg/g. Pooling the data from the main publications, Henninger found a mean slope of 0.029 [START_REF] Henninger | R2* relaxometry for the quantification of hepatic iron overload: biopsy-based calibration and comparison with the literature[END_REF]. Then, to obtain the LIC in µmol instead of mg/g, we multiplied this mean slope by 18 to obtain 0.52. So, at 1.5T, simply by dividing by 2 the value of R2* expressed in s -1 we have a correct approximation of LIC expressed in µmol/g.

No such validation with biopsies has been done at 3T. Theoretical calculations suggest a doubling of R2* from 1.5 to 3T [START_REF] Storey | R2* imaging of transfusional iron burden at 3T and comparison with 1.5T[END_REF]. Then the mean slope to obtain the LIC value in µmol/g should be divided by 2 and should be approximately 0.26. Anwar's [START_REF] Anwar | Hepatic Iron Quantification on 3 Tesla (3 T) Magnetic Resonance (MR): Technical Challenges and Solutions[END_REF] results in 5 patients seem to confirm this hypothesis but with significant delay between MRI and biopsy. However, in our series we obtained a slope of 0.316, slightly higher than the slope expected by extrapolation of 1.5T polled data but close to half the higher slope proposed at 1.5T by Garbowski, who used the same laboratory reference [START_REF] Garbowski | Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan[END_REF]. In our series, the background noise subtraction leads to a higher value of R2* and partly explains the residual difference with Garbowski's results. This emphasizes the need for a standardized protocol to obtain more comparable results.

A c c e p t e d m a n u s c r i p t

The higher magnetic susceptibility observed at 3T introduced limitations in the R2* calculation. For high overloads, there is a strong decrease in liver signal intensity. It is then difficult to obtain a correct exponential curve fit.

The SIR method is also widely recognized and used for hepatic iron quantification at 1.5T. Our study evaluated this method at 3T using the algorithm defined from single-echo sequences [START_REF] Paisant | Assessment of liver iron overload by 3 T MRI[END_REF]. The results we obtained with an mGRE sequence showed good correlations but with a slight overall overestimation and a slight underestimation for low values because the longest TE, around 14 ms, was not included in the mGRE sequence. For slight to moderate overloads, below 130 µmol/g, almost exclusively patients with DIOS, our study showed a better correlation of the R2* method than the SIR method to LICb. However, in patients with high LICb above 130 µmol/g, corresponding exclusively, in our study, to patient with genetic hemochromatosis, the SIR method provides a better correlation to LICb. At 1.5T, quantification was possible by SIR up to 350 µmol/g by using the shortest in-phase TE of 4 ms. Rose overcomes this limit by using a shorter first TE of 1.8 ms [START_REF] Rose | Liver iron content assessment by routine and simple magnetic resonance imaging procedure in highly transfused patients[END_REF]. At 3T, a first TE of 1.2 ms is short enough to give a liver signal over the signal noise and to allow a SIR estimation in high overload.

Our study is the largest series calibrating R2* versus LICb, for any magnetic field strength. It validates the use of 3T MRI for hepatic iron quantification. In comparison to the biopsy with biochemical determination of iron, we propose a formula to convert R2* at 3T to LICb. Despite variation in technical characteristics, there was no significant difference between the two machines used. Although the use of 3T MRI is becoming more widespread, some centers only have a 3T magnetic field for abdominal imaging. There is a strong need for reference values at 3T. Moreover, the use of a 3T magnetic field allows for more accurate quantification of slight to moderate overloads. Improving sensitivity is clinically relevant regarding the increasing incidence of DIOS with low iron overload.

Our study has certain limitations. First, the shortest TE was about 1.2 ms, a value which is also the first TE usually proposed by MR vendors in most built-in protocols dedicated to hepatic iron and fat quantification. Obviously, this TE is not short enough at 3T to correctly calculate R2* in the case of A c c e p t e d m a n u s c r i p t high overload. It is technically difficult to use a first TE of 0.4 ms, which is half the shortest TE of 0.8 ms proposed by Wood at 1.5T [START_REF] Wood | MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients[END_REF]. Very short TEs will be available using ultrashort echo time (UTE) imaging [START_REF] Krafft | Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla[END_REF]. In the meantime, the main risk is not being unable to quantify correctly a high overload, which has only a small impact on patient management, but miscalculating R2* and hence underestimating liver iron overload. For example a patient with an R2* of 512 s -1 , corresponding to an LICR* of 130 µmol/g, actually had an LICb of 480 µmol/g. This type of error explains how the difference between LICR2* and LICb increases faster with LICR2* classes than with LICb classes. So, to overcome this limitation, we propose either greatly reducing the shortest TE or combining both SIR and T2* methods. Second, we used two different machines with a slight magnetic field difference (3%). Acquisition parameters were as close as possible. However, there were also slight TE differences (8%). This could have produced errors particularly for the SIR method which does not take into account TE differences between the two units. The absence of B1 heterogeneity correction with the first machine may also lead, in some cases, to an overestimation of LICSIR through reduction of the paraspinal muscle signal, as described with single-echo sequences [START_REF] Paisant | Assessment of liver iron overload by 3 T MRI[END_REF]. Third, we used the body coil for both methods. This coil is necessary for the SIR method. A surface coil allows a higher signal for R2* calculation but this is offset by larger voxels (17mm 3 ) and T2* fitting to the entire ROI instead of producing a pixel-wise map. Fourth, we only use 4 of the 5 echoes used by the 3T SIR algorithm based on single-echo sequences. This explains the bias observed for the low values of LICb with a LICSIR cut-off of 20 µmol/g for determining overloaded patients. A new version of the algorithm taking into account the reduction in the number of echoes obtained has now been incorporated into our dedicated software. Nevertheless, this has no practical impact since at that level of overload R2* is the most precise method.

This study validates hepatic iron quantification by MRI at 3T, with a conversion formula to LICb obtained from biopsy material. With the selected TEs, the R2* method is more accurate for slight to moderate hepatic iron overload whereas the SIR method is more accurate for high overloads. Shorter TEs are needed to improve performance for quantifying massive iron overload by R2* [START_REF] Krafft | Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla[END_REF]. In the meantime, both methods should be used simultaneously with a breath-hold mGRE sequence acquired A c c e p t e d m a n u s c r i p t

  s c r i p t 1048 Hz for the Siemens group. The remaining parameters were identical for both machines: 400x400mm 2 field of view; 128*121 acquisition matrix; 256x256 reconstruction matrix with a pixel size of 1.56x1.56 mm²; 120 ms repetition time; 20° flip angle; 7 mm slice thickness; 1 excitation. The breath-hold acquisition lasted 15s.

  Thus T2* was calculated according to the formula: 𝐿𝑖𝑣𝑒𝑟 𝑠𝑖𝑔𝑛𝑎𝑙 -𝑛𝑜𝑖𝑠𝑒 = 𝑀0. calculated as follows: R2* = 1/T2*, and we used the linear correlation with LICb to determine LICR2*.

  and yielded the following equation: LICR2* (µmol/g) = 0.316 R2* +7.6 The Spearman correlation coefficient (r = 0.95 p < 0.001) indicates a strong positive correlation between LICb and R2*.

Figure

  Figure3bshows the Bland-Altman plot of the difference vs. mean values of LICb and LICR2*

  shows the mean differences (absolute values) between LICSIR and LICb or between LICR2* and LICb according to LICb or LICR2* class. With LICb classes, LICR2* A c c e p t e d m a n u s c r i p t

A

  c c e p t e d m a n u s c r i p t using the body coil. The sequence protocol we propose can be applied to the majority of MRI scanners without the need to purchase a specific option. Detailed sequence parameters and a dedicated DICOM software program, incorporating both calculations with cross-checks, are available at www.mrquantif.org A c c e p t e d m a n u s c r i p t A c c e p t e d m a n u s c r i p t

Figure 1 :

 1 Figure 1: Screen-copy of the in-house software viewer with example of ROIs placement.

Figure 2 :

 2 Figure 2: Flow chart.

Figure 3 :Figure 4 :

 34 Figure 3: Comparison between LICb and R2* or LICSIR: a) Linear regression between R2* and LICb, b) Bland-Altman plot of the difference vs. average of LICR2*, c) Linear regression between LICSIR and LICb, and d) Bland-Altman plot of the difference vs. average of LICSIR in comparison to LICbFigure 4: Mean of absolute differences between LICb and LICR2* or LICSIR according to a) (Figure 5a) LICb classes or b) (Figure 5b) LICR2* classes

Table 1 : Patient characteristics (n=105)

 1

	TABLES
	Male/Female


E Monsalve
email: eduardo.monsalve@espci.fr

A Maurel

V Pagneux

P Petitjeans

Experimental measurements of perfect absorption on surface water waves

We present experimental measurements of perfect wave absorption on surface gravity-capillary waves. The equilibrium between friction losses and coupled resonance yields a zero reflection coefficient. As a simple resonator, among other possibilities, the trapped modes produced by a non-symmetrical cylinder are used to generate absorption.

I. INTRODUCTION

The absorption of water waves is a problem that catches the interest of scientists and engineers due to the direct applications. Theoretical studies, as well as experimental investigations, have been performed in useful and promising problems like power generation or coastal protection. Regarding the vast theory developed in the last decades, one can mention [START_REF] Evans | A theory for wave-power absorption by oscillating bodies[END_REF], which gives a theory for predicting the wave absorption by oscillating damped floating bodies. Later works [START_REF] Evans | Wave-power absorption by systems of oscillating surface pressure" distributions[END_REF][START_REF] Evans | Wave energy extraction by coupled resonant absorbers[END_REF][START_REF] Martins-Rivas | Wave power extraction from an oscillating water column at the tip of a breakwater[END_REF] present new devices, as the oscillating water column, that improves the energy absorption. Now, we turn our attention to a simpler problem. The absorption of water waves by means of friction losses, which in this case plays the role of the mechanical absorber. In this case, when the friction losses are equal to the radiation of the coupled resonator the reflection coefficient is zero. This type of perfect absorption has been proposed recently in acoustics by [START_REF] Romero-García | Use of complex frequency plane to design broadband and sub-wavelength absorbers[END_REF].

II. EXPERIMENTAL SET-UP

Experiments were carried out in a narrow channel designed to measure the wave deformation by means of Fourier Transform Profilometry (FTP) [START_REF] Cobelli | Experimental study on water-wave trapped modes[END_REF], as described in figure 1. The water depth was fixed at 50 mm, which permits us to generate easily linear waves in intermediate depth conditions by a flap-type wavemaker. The position of the cylinder is defined by the parameters δ and γ corresponding to the distance from the longitudinal axis and to the end wall respectively.

III. OBTAINING THE REFLECTION COEFFICIENT

Considering a linear regime, with negligible contribution of higher harmonics, we average the FTP data in time by means of a Fourier Transform at the forcing frequency ω. Thus, the fundamental mode η 1 (x, y) is obtained as:

η 1 (x, y) = 2 T T 0 η(x, y, t) • e iωt dt (1)
where T represents an integer number of wave periods.

Once we have the complex field of the fundamental mode, we average in the transverse direction y, considering plane waves. Thus, we have a one-dimensional wave, which can be approximated by the addition of two linear waves in opposite directions:

η1 (x) = a e ikx + Re -ikx ( 2
)
where a is the amplitude of the incident wave, R represents the reflection coefficient and k the wavenumber.

IV. VARIATION OF RESONATOR PARAMETERS

The influence of the cylinder position has been inspected by varying the parameters δ and γ indicated in figure 1. The variation of these parameters influences the absorption due to the amplification of the resonance.

A. Variation of δ

The first analysis consists of varying the transverse asymmetry of the cylinder located near the end-wall. As indicated in figure 1, the parameter δ = 0 corresponds to the cylinder located at the center line of the channel. The increase of δ produces excitation of non-symmetrical trapped modes resonance, and consequently a growth in the absorption. In figure 2 we compare four different values of δ with the reflection measured without cylinder (called wall in the figure). The absorption increase almost twice when δ changes from 2 mm to 5 mm. That is an experimental confirmation of the larger contribution of trapped modes to the resonance. In the vertical line k c L represents the resonant frequency of trapped modes [START_REF] Cobelli | Experimental study on water-wave trapped modes[END_REF].

B. Variation of δ for a larger γ and smaller cylinder

In order to allow more freedom of movement to the cylinder, we use in this case a smaller cylinder, with a diameter D/L = 0.64. Likewise, we consider that the best absorption in the γ varying experiment was obtained at γ/L = 1.45, which we fix as well in this experiment. The reduction of the cylinder size gives us, as expected, an improvement in the absorption with values of |R| 2 < 0.05, as shown in figure 3. This improvement can be explained by the greater asymmetry given by the smaller cylinder that amplifies the trapped modes.

V. CONCLUSION

We have presented an ongoing work that has given so far promising results, showing experimental evidence of the perfect absorption with a simple resonator.

We have verified that the excitation of non-symmetrical trapped modes grows with the asymmetry of the cylinder. When the cylinder is located sufficiently close to the end-wall, the asymmetry is the main parameter that controls the absorption. The maximum absorption was found for the maximum transverse eccentricity.

When we vary the distance between the cylinder and the end-wall, the absorption grows significantly. We observed that when the cylinder is sufficiently distant from the end-wall, the transverse position of the cylinder does not affect the absorption. Therefore, we consider in this case the existence of other resonances that are excited at the same frequency, for instance, a harbor-type resonance can be generated in the partially-closed region behind the cylinder. This project will be continued with the trial of other types of resonators, with the objective of finding the best configuration that produces perfect absorption. One possibility is a rectangular Helmholtz-type resonator, which permits us to better control the resonance in terms of the quality factor.

Fig. 1 :

 1 Fig. 1: Experimental set-up

Fig. 2 :

 2 Fig. 2: Reflection coefficient measured at the wall position. The distance to the end γ = 55 mm or γ/L = 0.88. The size of the cylinder is D/L = 0.8.

Fig. 3 :

 3 Fig. 3: Reflection coefficient measured at the wall position with D/L = 0.64; γ/L=1.45; δ max = 11 mm.

ACKNOWLEDGEMENT

The authors wish to acknowledge Agence Nationale de la Recherche projet DYNAMONDE ANR-12-BS09-0027-01.


Marc Bocquet

Damien Deleruyelle

Christophe Muller

Jean-Michel Portal

Self-consistent physical modeling of set/reset operations in unipolar resistive-switching memories

HAL is

This letter deals with a self-consistent physical model for set/reset operations involved in unipolar resistive switching memories integrating a transition metal oxide. In this model, set operation is described in terms of a local electrochemical reduction of the oxide leading to the formation of metallic conductive filaments. Beside, reset operation relies on the thermally-assisted destruction of the formed metallic filaments by Joule heating effect. An excellent agreement is demonstrated with numerous published experimental data suggesting that this model can be confidently implemented into circuit simulators for design purpose.

Memory devices based on resistive switching materials are currently pointed out as promising candidates to replace conventional non-volatile memory devices based on charge-storage beyond 2x nm-technological nodes. [START_REF] Hong | Proc. IEEE Int. Electron Devices Meeting (IEDM)[END_REF] In particular, devices integrating a transition metal oxide (so-called TMO) such as NiO, TiO 2 , ZnO or Cu x O, are of growing interest due to their simple Metal/Insulator/Metal (MIM) structure, oxides compatible with complementary metal-oxide-semiconductor technology and low process temperature. [START_REF] Chen | Proc. IEEE Int. Electron Devices Meeting (IEDM)[END_REF] So far, in TMO-based memory devices, the unipolar switching between low resistance state (LRS) and high resistance state (HRS) is explained in terms of creation/destruction of conductive filaments within the oxide. [START_REF] Park | [END_REF]4 Waser et al. 5 explained that set, i.e. the transition from HRS to LRS, originates from a local reduction reaction leading to the creation of metallic conductive filaments (CF). During reset, local dissipation of Joule power enhances the thermally activated diffusion of defects and/or of different atomic species constituting the CF combined with a local oxidation process. 6,7 Based on this phenomenological description, several models for reset were reported in Refs. 8-10 but very few offer a model for set. [START_REF] Cagli | Proc. IEEE Int. Electron Devices Meeting IEDM 2008[END_REF] Furthermore, it has to be stressed that there is currently no complete model taking into account both set and reset operations that could be easily implemented in circuit simulators for design purpose.

In this context, this paper proposes a self-consistent physical model accounting for both set/reset operations in NiO-based unipolar resistive switching devices. After uncovering the theoretical background and the set of relevant physical parameters, the model is confronted to quasi-static and dynamic experimental data from literature.

Fig. 1 schematically describes the set/reset mechanisms controlling the resistive switching phenomenon. Set (resp. reset) is assumed to be due to the creation (resp. destruction) of CF between two electrochemically inert electrodes. Starting from LRS, the existing CF may a) Electronic mail: marc.bocquet@im2np.fr be heated by Joule effect thanks to the application of an electric field ξ, as reported in Ref. 12. CF top and bottom boundaries are kept at thermal equilibrium with connected electrodes at room temperature (T amb ) whereas the sidewalls are expected to exchange heat flow with the surrounding oxide. Thus, in one dimension geometry the heat equation can be simply written as:

σ CF (x) • ξ(x) 2 = -K th CF ∂ 2 T CF (x) ∂x 2 + h T CF (x) -T Ox t Ox (1)
where σ CF is the CF conductivity, T CF (x) the CF temperature, T Ox the oxide temperature, K th CF the CF thermal conductivity, t Ox the oxide thickness and h the heat transfer coefficient between CF and surrounding oxide.

In LRS CF is assumed to be metallic with a high conductivity σ CF . In contrast, when CF is dissolved, the resistance of MIM structure is controlled by the resulting oxide gap separating the two CF fragments (cf. Fig. 1). As a consequence, a lower conductivity noted σ Ox accounts for the electronic transport in HRS. Assuming the current conservation through the whole structure, an analytical expression of the electric field ξ can be derived (∇J = 0): where the resistance per unit length R(x) is

ξ(x) = I 0 • R(x) (2
R(x) = 1 r 2 CF (x)π(σ CF (x) -σ Ox ) + r 2 CFmax πσ Ox (3)
I 0 = V Cell t Ox 0 R(x)dx (4)
I 0 is the ohmic current in the MIM structure, V Cell the applied voltage, r CF (x) and r CFmax denote respectively the radius and the maximal radius of the CF in cylindrical geometry. Two distinct mechanisms were considered to describe the creation and the destruction of CF: a local redox process 5,[START_REF] Pan | Proc. Int Simulation of Semiconductor Processes and Devices (SISPAD) Conf[END_REF] and a thermally-assisted diffusion 8 . One can consider a classical redox equations such as:

Ni 2+ + 2 • e -ox red Ni (5)
in which the reaction velocities for both reduction and oxidation processes may be expressed classically by the Butler-Volmer equation: 14 6)

ν red = k 0 e - ∆rG 0 + 2(1 -α)F (E -E eq ) R • T ox (1-C N i ) (
ν ox = k 0 e - ∆rG 0 -2αF (E -E eq ) R • T CF (x) C N i (7)
α is the asymmetry factor, k 0 kinetics constant of electrochemical reaction, R is the ideal gas constant, F is the Faraday constant, ∆rG 0 is the free energy of the reaction at equilibrium and finally E eq is the equilibrium potential. Moreover C N i denotes the dimensionless concentration of metallic species. To determine the potential of the reduced and oxidized species such as Ni and Ni 2+ respectively, the mechanism is supposed to be isotropic and the redox potential E is assumed to be equal to -|V Cell |. Finally, in order to properly account for the metallic nature of the CF, its conductivity relies on a temperature coefficient α T as described by Russo et al. [START_REF] Russo | Proc. IEEE Int. Electron Devices Meeting IEDM[END_REF] where σ CF0 is the CF conductivity at room temperature (T amb ):

σ CF (x) = σ CF0 1 + α T • (T CF (x) -T amb ) (8)
As already proposed by Russo et al. 8 , the thermal rupture of CF in reset operation can be described by a local diffusion velocity (ν dif f ) of the metallic species:

ν dif f = k dif f • e - E a k b • T CF (x) • C N i (9)
where E a is the activation energy governing the thermally-assisted exodiffusion of metallic species. Regarding Eq.5, we assumed that electron density available during set could not limit the reduction process. Indeed, during set the growth of the CF is initiated

k dif f = 5 × 10 10 s -1 Ea=1.9 eV k0 = 10 12 s -1 rCF max =60 nm αT = 1.1 × 10 -3 K -1 h = 100 W/(K • m 2 ) TOx = T amb K th = 91 W/(K • m) ref.7 α=0.5 σOx = 12.5 kΩ -1 cm -1 ref.8 ∆rG0 -2(1 -α)F • Eeq = 222 kJ • mol -1
σCF 0 = 6.67 mΩ -1 cm -1 ref. at the cathode side. As the CF grows toward the anode, it is assumed to act as "virtual cathode" 5 allowing electrons to flow freely from the real cathode toward the reduction site.

Finally, the description of set and reset mechanisms enables proposing a self-consistent master equation relying on a chemical kinetics:

dC N i dt = ν red -ν ox -ν dif f (10)
CF radius (r CF = r CFmax ×C N i ) is calculated from the electric field (Eq. 2), the CF temperature (Eq. 1), the diffusion and reaction velocities (Eqs. 6, 7&9,) and finally from the concentration of metallic species (Eq. 10). It is has to be mentioned that considering energies involved in Eq. 7&9, the CF destruction is mainly governed by the diffusion process rather than oxidation.

To validate the proposed theoritical approach, the model was confronted to quasi-static and dynamic experimental data from the literature. Fig. 2 I. The CF profile obtained after the set operation is used as the initial state to simulate the subsequent reset operation.

sured on NiO-based memory elements by Cagli et al. [START_REF] Cagli | Proc. IEEE Int. Electron Devices Meeting IEDM 2008[END_REF] and Lee et al.. [START_REF] Lee | Proc. IEEE Int. Electron Devices Meeting IEDM 2007[END_REF] In both studies, memory elements consisted in Pt/NiO/Pt stack with respective nickel oxide thicknesses of 160 nm 11 and 30 nm. [START_REF] Lee | Proc. IEEE Int. Electron Devices Meeting IEDM 2007[END_REF] Using the set of physical parameters given in Table I together with the actual nickel oxide thickness, the present model shows an excellent agreement with experimental data for both set and reset operations. Moreover, it must be underlined that the model is used continuously since it relies on a self-consistent kinetics equation accounting for both CF creation and destruction mechanisms. This numerical feature is a key point for a model dedicated to an implementation in computer-aided design tools. From a practical point of view, the CF profile obtained after the set operation is used as the initial state to simulate the subsequent reset operation.

Fig. 3(a&b) shows that the proposed model satisfactorily matches the experimental increase of set and reset voltages along with the programming ramp speed reported by Cagli et al.. [START_REF] Cagli | Proc. IEEE Int. Electron Devices Meeting IEDM 2008[END_REF] The increase of reset voltage may be interpreted in terms of competition between creation and destruction mechanisms. Indeed, as ramp speed increases, a larger current (i.e. a larger applied voltage) is necessary to achieve reset in a shorter time. Through Eq. 6, a larger applied voltage implies a strengthening of the reduction process that limits the filament destruction. Finally, the turn back of the structure toward HRS is achieved thanks to an increase of V reset . Beside, the closing of the gap between V set and V reset at highest sweep rates may lead to some reset/set instabilities as proposed in Ref. 17.

A reported in numerous works, the resistance in LRS (noted R LRS ) and reset current strongly depend on the maximum current reached during the preceding set operation [18][START_REF] Kinoshita | Proc. 22nd IEEE Non-Volatile Semiconductor Memory Workshop[END_REF][START_REF] Lee | [END_REF][21] (referred as I SetM ax ). This feature can be understood in terms of reduction of CF radius that concomitantly increases the resistance of the The reset current may be scaled down by limiting the maximum set current through an integrated select device (e.g. transistor or load resistor) in series with memory element. Finally, it has to be mentioned that the present model may also be used to simulate retention properties. Nevertheless, a complete set of experimental retention data is required to calibrate the model. This work is under way. In conclusion, this letter deals with a self-consistent physical model well suited for describing simultaneously set and reset operations in unipolar resistive switching memories based on transition metal oxide. By gathering local electrochemical reactions and a thermal diffusion mechanism in a single master equation, the model enables accounting for both creation and destruction of conductive filaments. The simulation results satisfactorily match numerous quasi-static and dynamic experimental data published in literature on resistive switching devices. Beside, the model can be easily implemented in circuit simulators and enables predicting relevant trends required for designing innovative architectures such as memory matrix, distributed memories or Field-Programmable Gate Array.

Authors would like to thank Prof. Daniele Ielmini (Politecnico di Milano), Dr. Robert Müller (IMEC) and Dr. Renaud Bouchet (LCP) for fruitful discussions.

FIG. 1 .

 1 FIG.1. Schematic representation of a formed and dissolved conductive filament resulting from set and reset operations respectively in MIM structure. During reset, the inner temperature is raised by Joule effect and leads to the destruction of the CF. In contrast, during set a electrochemical local reduction process enables restoring CF.

FIG. 2 .

 2 FIG. 2. Experimental I(V ) characteristics measured on a NiO-based memory structure presented in (a) ref.11 and (b) ref.15 and the corresponding simulation by using the present model and the physical parameters given in TableI. The CF profile obtained after the set operation is used as the initial state to simulate the subsequent reset operation.

  FIG. 3. (a) Set and (b) reset voltages as a function of the programming ramp speed based on the experimental results reported in Ref. 11 The calculated curves were obtained for the physical parameters given in TableI.

FIG. 4 .

 4 FIG. 4. (a) Simulation of set and reset I(V ) characteristics for ISetMax = 5 mA and 10 mA. (b) Maximum current during the reset operation (IReset) and (c) the resistance in LRS as a function of the maximum current during the preceding set operation (ISetMax). Experimental data were extracted from Ref. 21-25. The parameters of theTable I were used for simulations.

TABLE I .

 I Physical parameters used for simulations

  8, 9, and 16 tOx = 160 nm for ref.11 or tOx = 30 nm for ref.15