M El Ouahabi
email: meriam.elouahabi@doct.ulg.ac.be

L Daoudi

F De Vleeschouwer

R Bindler

N Fagel

Potentiality of Clay Raw Materials from Northern Morocco in Ceramic Industry: Tetouan and Meknes Areas

Keywords: Clay Materials, Ceramic Properties, Ceramic Suitability, Morocco

This study aims at evaluating the potential suitability of Tetouan and Meknes (central Morocco) clay material as raw materials in various ceramic applications by investigating their textural, chemical, thermal and firing characteristics. Textural properties were identified by specific surface area, cation exchange capacity (CEC) and bulk density (ρs). Chemical and thermal properties were assessed using XRF and TG/DTA techniques, respectively. Firing characteristics at temperatures from 800˚C to 1100˚C were determined by linear firing shrinkage, loss on weight and water absorption capacity. The Meknes clays are characterised by medium cation exchange capacity (CEC) and specific surface area (SSA) values due to their moderate smectite content. The Tetouan clays have medium to low CEC and medium SSA values. The main oxides in the clayey samples are SiO2 (35 -54.3 wt%), Al 2 O 3 (20.6 -43.9 wt%), and Fe 2 O 3 (9.7 -22.4 wt%). The amount of CaO in Meknes clays ranges from 8 to 12 wt%, whereas CaO is only present in some Tetouan clay (TE4, TE7, TN4 and TN5). A significant densification of ceramic behaviour could be noticed for most of Tetouan clays at firing temperatures above 1000˚C. Meknes clays show earlier densification from 800˚C. The chemical, textural and ceramic properties of Tetouan and Meknes clays indicate their suitability as raw materials for the production of structural ceramics. The high amount of Fe2O3 in all clays makes them inappropriate in fine ceramics.

Introduction

Throughout the world, clays are the main raw materials exploited in the fabrication of various ceramic products for building construction. Due to inherently complex physical, chemical and mineralogical characteristics, clays have unique properties related to their own natural genesis [START_REF] Bauluz | Assessment of Technological Properties of Calcareous and Non-Calcareous Clays Used for the Brick-Making Industry of Zaragoza (Spain)[END_REF]- [START_REF] Verduch | Características de las Arcillas Empleadas en la Fabricación de Ladrillos[END_REF]. In most cases, for economic reasons, the ceramics industry relies on clays from nearby deposits. As a consequence, the clay characterization is important for the technical performance of local products [START_REF] Fabbri | Quality Assurance of Ceramic Clays-A Deeper Understanding[END_REF] [START_REF] Sánchez | Control de calidad de las arcillas empleadas en la fabricación de los soportes de baldosas cerámicas[END_REF].

Pure clays do not occur in nature, they contain mixtures of different clay and associated minerals [START_REF] Grim | Clay Mineralogy[END_REF]- [START_REF] Bergaya | Chapter 1. General Introduction: Clays, Clay Minerals, and Clay Science[END_REF]. At present, many ceramic tiles are manufactured from mixtures of mineral raw materials, composed essentially of clays and materials such as quartz, feldspar and carbonates. In the fabrication process, the raw materials are mixed in variable proportions taking into account the influence of each component on the properties of the final products [START_REF] Sousa | Development of Red Wall Tiles by the Dry Process Using Brazilian Raw Materials[END_REF] [START_REF] Sousa | Thermal Transformations of Red Wall Tile Pastes[END_REF]. The components that play fundamental roles for optimum processing, and hence performance of the final products, are clay fraction (<2 µm) for plasticity, feldspar for fluxing and silica as filler material [START_REF] Kamseu | Characterisation of Porcelain Compositions Using Two China Clays from Cameroon[END_REF]- [START_REF] Celik | Technological Characterization and Industrial Application of Two Turkish Clays for the Ceramic Industry[END_REF]. The selection of the appropriate raw materials is based on specific criteria, which are related either to the behaviour during the different stages of manufacturing or to the overall chemical composition. The microstructure and properties of any ceramic depends on the characteristics of the raw materials and processing parameters [START_REF] Kingery | Introduction to Ceramics[END_REF] to assure the quality of ceramic products.

Morocco is among the world top 20 producers and consumers of clayey building materials (http://www.lematin.ma/reader-2007/files/lematin/2011/01/30). The industry of ceramic tiles and bricks is most frequent in northern Morocco. Natural clayey materials are abundant in this region. They play an important economic role with a national production of about 45% for building materials (bricks, ceramic tiles and refractories). However, national ceramic production is still insufficient, and to fill this deficit the Moroccan government has to import ceramic products mainly from Spain, Italy and Egypt.

The Tetouan and Meknes area have large construction and development projects to meet the population growth in particular for social and luxury housing construction, which is experiencing a significant economic and tourism development. Meknes and Tetouan clays are currently used for the traditional production of smallscale ceramic factories. Meknes clays are used in small traditional pottery factories using local clays. Artisanal and semi-industrial exploitation of these clays, without any prior study, causes various problems during the manufacturing process, such as deformations and breakage of products.

The aim of this study is to evaluate the viability of the Tetouan and Meknes clay deposits as an industrial mineral resource by comparing their textural, chemical and thermal properties as well as drying and firing behaviours.

Material

The studied clayey raw materials were collected in the Tetouan and Meknes areas. The Tetouan clay deposit is located in north-western Morocco a few kilometres from the town of Tetouan, in the external Rif domain [START_REF] Durand Delga | Livre à la mémoire du Professeur Paul Fallot consacré à l'évolution paléogéographique et structurale des domaines méditerranéens et alpins d'Europe[END_REF] [17] (Figure 1). A total of nineteen clay samples were collected in this area. There are two categories of samples: 10 clay samples collected from the exploited quarries labelled (TE) and 9 clays sampled from clay deposits, which are not yet exploited, labelled (TN). The Meknes clay deposit is located in north-central Morocco, about 15 km east of the town of Meknes, in the Saїss Basin zone situated in the Pre-Rif domain (Figure 1). Six Miocene yellow sandy clay samples [START_REF] Brahim | Tectonique cassante et états de contraintes récentes au nord du Maroc[END_REF] were collected from the quarries of pottery and artisanal ceramic.

A preliminary characterization of those clay materials was done in a previous study [START_REF] El Ouahabi | Preliminary Mineralogical and Geotechnical Characterization of Clays from Morocco: Application to Ceramic Industry[END_REF]. Tetouan clays are characterized by diversified mineralogical assemblages (in particular a variable proportion of clay, quartz and calcite) in contrast with to Meknes clays (high clay content, quartz and calcite). The clay fraction of Tetouan is dominated by illite and kaolinite with variable contributions of chlorite. Meknes clays are illite and kaolinite, associated with smectite. The studied clay materials consist generally of fine particles with medium to high plasticity and low organic matter content [START_REF] El Ouahabi | Preliminary Mineralogical and Geotechnical Characterization of Clays from Morocco: Application to Ceramic Industry[END_REF].

Analytical Methods

Textural Properties

The specific surface area of the samples was characterized by the analysis of nitrogen adsorption-desorption isotherms, performed at 77˚K. The measurements were performed using a sorptomatic Carlo Erba 1900, controlled by a computer (Industrial Chemistry, Department of Chemistry, ULg). The analysis of the isotherms was performed according to the methodology of Vallée, Keller [START_REF] Lecloux | Texture of Catalysts[END_REF], which provided specific surface area (S BET ), micropore volume calculated by the Dubinin-Radushkevich equation (VDUB), and total pore volume calculated from the adsorbed volume at saturation (Vp). The bulk density (ρs) was obtained by helium pycnometry on the powdered sample, using Micromeritics Accupyc 1330. Textural analyses were done at the laboratory of Industrial Chemistry (Department of Chemistry, ULg).

Cation Exchange Capacity (CEC) was measured by the Schollenberger method [START_REF] Schollenberger | Determination of Exchange Capacity and Exchangeable Bases in Soil-Ammonium Acetate Method[END_REF]. The samples were first saturated by ammonium acetate (1 N), and then the ammonium ions in the supernatant were deprotonated into ammonia with sodium hydroxide solution (0.1 N). The ammonia content was then determined by distillation into a known amount of acid and back-titrated by the Kjeldahl method [START_REF] Mackenzie | A Micromethod for Determination of Cation Exchange Capacity of Clay[END_REF].

Chemical and Thermal Properties

The chemical composition of selected elements (Si, Al, Fe, Ca, Mn, Mg, Na, K, Ti, P and S) was measured as oxides on 2 g of dried and homogenized powder of clayey samples using a Bruker S8 Tiger wavelength-dispersive X-Ray Fluorescence (WD-XRF) spectrometer equipped with a Rh anticathode (Department of Ecology and Environmental Sciences, Umeå University). Calibration was made using 35 commercially available certified reference materials of similar matrix (sedimentary rocks, river, lake and marine sediments, sands and soils). The accuracy ranges from 3% to 7% except for S (25%) and P (20%). Reproducibilites are above 99% except for S (89%) and P (97%). More details about the method and the calibration can be found in [START_REF] Vleeschouwer | Quantitative WD-XRF Calibration for Small Ceramic Samples and Their Source Material[END_REF]. The same powdered samples were heated to 1000˚C for 2 h to determine the Loss On Ignition (L.O.I).

Differential scanning calorimetry (DSC) and thermogravimetry (TG) were conducted simultaneously using a NETZSCH STA 409 PC instrument (Industrial Chemistry, Department of Chemistry, ULg). Samples were heated from room temperature to 800˚C at 10˚C min -1 under atmospheric air [START_REF] Abajo | Manual sobre Fabricación de Baldosas. Tejas y Ladrillos[END_REF].

Ceramic Properties

Industrial tests were carried out as part of the ceramic evaluation process. Clay samples were dried at 105˚C for 24 hr and ground to a fine powder and then sieved using a mesh-size of 100 µm. Each clay sample was wetted in order to achieve the proper plasticity for modeling. The samples obtained with these shaping techniques were 4 cm long, 2 cm wide and 2 cm thick. The drying was done in a shaded and ventilated room. For different drying times, the mass and the value of the length were measured to calculate the linear shrinkage. The dried samples (24 h at shaded room plus 12 h at 105˚C in oven) were kiln-fired at different temperatures (800˚C, 850˚C, 900˚C, 950˚C, 1000˚C, 1050˚C and 1100˚C) over 1 h. The fired samples were tested for loss on ignition, shrinkage and water absorption capacity after firing. The linear shrinkage (LS) was determined following the conventional techniques. The water absorption capacity (WAC) was determined according to standard procedure UNE 67-027 [START_REF][END_REF], in fired clay pieces after each cooking cycle [START_REF]International Standard for Ceramic Tiles-Part 3. Determination of Water Absorption, Apparent Porosity, Apparent Relative Density and Bulk Density[END_REF]. After preliminary measurements at the end of firing and cooling, the specimens of each batch were kept dry in an oven until their submission to water absorption. Each dry and cool specimen was weighed (P1) and the three were then immersed into clean water at 25˚C for 24 h. The specimens were removed from the water, their surfaces were wiped off and the weight (P2) of each was measured immediately. The water absorption capacity (WAC) was calculated as WAC = (P2 -P1)/P1 × 100%.

Results and Discussion

Textural Properties

Specific surface area (S BET ), pore volume (Vp), micropore volume (VDUB) and cation exchange capacity (CEC) of the samples are listed in Table 1. The surface area of the Meknes samples ranges from 33.3 to 37.9 m 2 •g -1 . Sample M1 has the highest specific surface area and M3 the lowest. According to these specific surface area values, M1 has less illite and smectite content, knowing that the specific surface area of kaolinite is usually smaller (10 -20 m 2 •g -1 ) with respect to that of illite (80 -100 m 2 •g -1 ) [START_REF] Ferrari | The Use of Illitic Clays in the Production of Stoneware Tile Ceramics[END_REF], but similar values for smectite (19 m 2 •g -1 ) [START_REF] Ravichandran | Properties and Catalytic Activity of Acid-Modified Montmorillonite and Vermiculite[END_REF]. The Tetouan clays have specific surface area (S BET ) values ranging between 17 and 36 m 2 •g -1 . Among them TE1, TE3 and TN9 display the highest SSA values due to their illite, kaolinite and interstratified clay contents [START_REF] Omotoso | Surface Area of Interstratified Phyllosilicates in Athabasca oil Sands from Synchrotron XRD[END_REF].

The CEC values of the Meknes samples (9.0 -20.6 meq•100 -1 g) are higher than the Teouan clays (7.1 -18.5 meq•100 -1 g). The high CEC values of the Meknes samples may be explained by their smetite content [START_REF] El Ouahabi | Preliminary Mineralogical and Geotechnical Characterization of Clays from Morocco: Application to Ceramic Industry[END_REF], a mineral characterized by higher CEC value ranging between 80 and 150 meq•100 -1 g [START_REF] Grim | Clay Mineralogy[END_REF].

The highest S BET and CEC of Meknes and some Tetouan clays (TN and TE) reflect their fine grain size, which is represented by the high amount of clay fraction. The highest S BET and CEC of those clays could cause a difficulty to dry and lead to the development of cracks in the drying process. The bulk density (ρs) of all clay samples are almost similar, ranging from 2.7 to 3.2 g•cm -3 .

Chemical and Thermal Properties

The most abundant oxides in the studied samples are SiO 2 , Al 2 O 3 , CaO and Fe 2 O 3 (Table 2). According to the major element abundances, the samples are divided into two groups (Figure 2). The first group is characterized by very low CaO (<1.9%), slightly high Fe 2 O 3 (from 12% to 22%) and high Al 2 O 3 (from 29% to 44%) in comparison to the second group. On the basis of their low CaO content, the first group is qualified as a non-calcareous clay group, which is mainly represented by the Tetouan clays (TE and TN). The second group displays higher CaO (12% -22%), slightly lower Al 2 O 3 (20% -25%) and lower Fe 2 O 3 (8% -12%) concentrations and is labelled as a moderate calcareous group. Group 2 consists of all the Meknes samples and TE4, TE7 from the exploited Tetouan samples and TN4 and TN5 from unexploited Tetouan clays. The higher contents of Fe 2 O 3 (>5%) in the samples give the reddish color of the clay-based products after firing. The L.O.I at 1000˚C (Table 2) ranges from 6% to 24%, which is associated with the presence of clay minerals, hydroxides and organic matter [7] [START_REF] Milheiro | Densification Behaviour of a Red Firing Brazilian Kaolinitic Clay[END_REF]. These material losses are confirmed by the thermal analysis and organic matter content as indicated in Figure 3 and Table 2.

The DSC/TG (Figure 3(a) and Figure 3(b)) show at least 4 peaks. The first endothermic peak is observed at 90˚C -150˚C for Tetouan samples and between 96˚C and 113˚C for Meknes samples. Such peaks can be attributed to the removal of adsorbed and interlayered water. The associated mass lost ranges from 0.4% to 0.8% for Meknes clays and between 0.4% and 1.3% for Tetouan clays. A large exothermic peak is observed between 200˚C -450˚C. Due to organic matter decomposition [START_REF] Baccour | Influence of Firing Temperature on the Ceramic Properties of Triassic Clays from Tunisia[END_REF], it was observed in all studied clays, but was especially well marked at 450˚C for M6. A broad endothermic band, sintered at 520˚C to 550˚C for Meknes samples and at 523˚C to 568˚C for Tetouan area samples, is due to clay mineral dehydroxylation and α quartz → β quartz transformation [START_REF][END_REF]. A substantial loss of weight (5% -7%) is associated with this endothermic peak for Tetouan samples. As an exception, some Tetouan samples (TN4, TN5, TE7 and TE4) lost less than 3% weight. Meknes samples lost about 4%, demonstrated by elevated clay and quartz content in this area. Small additional endothermic peaks occurred in all samples at 700˚C, which are due to carbonate decomposition [START_REF] Yariv | The Role of Charcoal on DTA Curves of Organo-Clay Complexes: An Overview[END_REF].

Ceramic Properties

Drying Behaviour

Bigot's curves (Figure 4) were used as preliminary indicators in the choice of raw materials [START_REF] Štubna | Acoustic Emission Study of Quartz Porcelain during Heating up to 1500˚C[END_REF] for the ceramic industry. The drying is accompanied by shrinkage of the clay materials, due to porewater loss. The Bigot's curves exhibit the two characteristic phases of the drying process: an initial weight loss with shrinkage and successive weight loss with no further shrinkage. The behaviour of all Meknes clay samples was somewhat similar (Figure 4), although there were some small differences in shrinkage, ranging from 12% to 17%, accompanied by a loss of weight from 19% to 22%. Tetouan clay samples showed a low drying shrinkage (2% -6%), in contrast to the Meknes clay samples. Tetouan clays showed a greater increase of the loss on weight (20% -23%). The highest linear shrinkage of Meknes clays can be explained by its smectite content and its high PI value [START_REF] Maitra | Effect of Compaction on the Kinetics of Thermal Decomposition of Dolomite under Non-Isothermal Condition[END_REF]. Consequently Meknes clays have more problematic drying behaviour. By contrast, Tetouan (TN and TE clays) showed a low drying shrinkage, and they have more suitable behaviour.

Firing behaviour

The results for linear firing shrinkage, loss on mass and water absorption of the fired clays are displayed in Table 3 and Figure 5, respectively. Linear firing shrinkage and water absorption have been frequently used as quality and process control parameters in the development and manufacturing stages of the production of structural ceramics such as floor and wall tiles [35] [36]. The linear firing shrinkage could be used as a direct measure of the extent of densification [START_REF] Correia | Using Statistical Techniques to Model the Flexural Strength of Dried Triaxial Ceramic Bodies[END_REF]. The linear firing shrinkage increased gradually from 1000˚C to 1110˚C for most of the Tetouan clays (TN and TE). This evolution reflects densification of the fired clays. As an exception, some fired clays (TE5, TE7 and TE8) do not show any shrinkage. At 800˚C, Meknes clays shrank from 17% to 29% and their shrinkage increased again gradually from 850˚C to 1110˚C. Meknes clays are characterized by an early densification from 800˚C. This is due to the formation of a glassy phase, because they contain large amounts of fluxing agents (Na 2 O, K 2 O and MgO) (Table 2). Loss on weight stability is indicated from 1000˚C for most of the sintered clay bodies (Table 3). It can be seen that an increase in the shrinkage with a decrease in water absorption are associated with an increase in firing temperature. This trend is found especially above 1000˚C. Shrinkage is related to the formation of high temperature crystalline phases, and the rates of shrinkage are directly related to the development of glassy material. Formation of gases during vitrification can also have a significant effect on the net dimensions of fired clay minerals. The greatest shrinkage above 1000˚C is due to a more significant liquid phase formation. During liquid phase formation, the liquid surface tension and capillarity help to bring particles close together and reduce porosity [START_REF] Correia | Simultaneous Optimization of Linear Firing Shrinkage and Water Absorption of Triaxial Ceramic Bodies Using Experiments Design[END_REF]. A significant decrease of the water absorption is observed for Tetouan clays (TE and TN) from 1000˚C (Figure 5). As an exception, TE4 and TN4 present the highest water absorption capacity (about 18%). The Meknes clays have high water absorption capacity, ranging from 9% to 21%. At 1100˚C, the water absorption of Tetouan clays decreased to 0.4% and 2.6% for TE6 and TN8, respectively, indicating the total vitrification of those clays. For Meknes clays, the water absorption decreased from 6% to 13%, with the exception of M1, which had 20% (Figure 5).

Suitability for Ceramics Applications

The suitability of raw clay for their use in the manufacture of ceramic products is determined by mineralogy, chemistry and physical properties of the material. These factors will determine the behaviour of the clay during forming, drying and firing with direct influence on the final product [START_REF] Shepard | Nomenclature Based on Sand-Silt-Clay Ratios[END_REF]. In order to evaluate the suitability of the studied clays for different ceramic products, a ternary diagram from [START_REF] Monteiro | Influence of Firing Temperature on the Ceramic Properties of Clays from Campos dos Goytacazes, Brazil[END_REF] based on XRD results [START_REF] El Ouahabi | Preliminary Mineralogical and Geotechnical Characterization of Clays from Morocco: Application to Ceramic Industry[END_REF] is shown in Figure 6(a). Two groups can be identified: those with high quartz contents and those that are rich in oxides, carbonates and feldspars. The mineralogical data for the samples (Figure 6(b)) also suggest that most of the Meknes clays can be used for vitrified red floor tile making. However Tetouan clays can be used for different ceramic applications due to their high quartz amount and their low amount of iron oxide. TN7 and TE9 may be used for making clinker products and TE7 for vitrified red floor tiles (Figure 6 was estimated by Fiori et al., [START_REF] Monteiro | Influence of Firing Temperature on the Ceramic Properties of Clays from Campos dos Goytacazes, Brazil[END_REF], Wilson [START_REF] Mardare | Deposition of Bioactive Glass-Ceramic Thin-Films by RF Magnetron Sputtering[END_REF] and Baccour et al., [START_REF] Fiori | Mineralogical Composition of the Clay Bodies Used in the Italian Tile Industry[END_REF], who stated that clays containing 8% or more of Fe 2 O 3 are red-firing clays. In this context, none of the Meknes and Tetouan clays can be used for the production of fine ceramics, due to their high Fe 2 O 3 amount (>8%). Such application would require processing to reduce this iron oxide content. However, Tetouan clays could be considered as raw materials for structural ceramic products. The high firing shrinkage for M1 and M3 from Meknes area (>10%) make them inappropriate to make ceramics, due to the risk of dimensional defects. Water absorption can restrict the use of ceramics as building material [START_REF] Correia | Using Statistical Techniques to Model the Flexural Strength of Dried Triaxial Ceramic Bodies[END_REF] [START_REF] Machado | Structural Ceramics Made with Clay and Steel Dust Pollutants[END_REF]. Ceramics used as flooring should have a water absorption <1%, roofing tiles <20%. Some Tetouan (TE1, TE2, TE3, TE6, TE9 and TN8) clays can also be used as flooring. Meknes clays and most of unexploited Tetouan clays show adequate water absorption for a use as roofing tiles.

Conclusions

Tetouan and Meknes clay materials are composed mainly of SiO 2 , Al 2 O 3 , and Fe 2 O 3 . Meknes clays are characterized by a higher CaO content.

The ceramic behaviours of Tetouan and Meknes clays were interpreted by drying shrinkage, linear firing shrinkage, loss on weight and water absorption. Meknes clays are characterized by a high drying shrinkage, and therefore there will be more difficulty to dry the samples. To solve this problem, sand could be added to reduce their plasticity. Tetouan clays show optimal drying shrinkage. All the fired ceramic properties show moderate changes from 800˚C to 1000˚C. However, significant changes are observed above 1000˚C. An increase in the firing shrinkage and associated with a decrease in the water absorption are especially observed from 1100˚C.

Meknes clays and most of Tetouan clays (TE1, TE2, TE3, TE6, TE9 and TN8) can be used on flooring production. In addition some exploited Tetouan clays and currently unexploited Tetouan clays are also suitable for making roofing tiles. Meknes clays and most of the Tetouan clays can be used for different ceramic applications due to their high quartz amount and their small iron oxide amount. Only TN7 and TE9 are being used for making clinker products.

This study demonstrates that exploited Tetouan and Meknes raw clay are suitable for the manufacture of structural ceramic products, with or without additives depending on the ceramic type. New clay outcrops such as unexploited Tetouan clays are also suitable. They constitute a potential ceramic raw material for growing Moroccan ceramic tile industry, and are thus not limited to artisanal production.

Figure 1 .

 1 Figure 1. Structural sketch map of the northern occidental part of the Rif Chain and the Northern central of Morocco (modified after [45]). 1-Foreland basement, 2-Meseta and Atlas cover series; 3-Foredeep basins; 4-Detached Atlasic cover at Prerif front; 5-Prerif; 6-Alpujarrides-Sebtide nappes; 7-Intrarif, Mesorif, Rif nappes; 8-Sud-Betic Zone; 9-Maghrebian Flyschs; 10-Dorsale calcaire units, 11-Malaguide-Ghomaride nappes.

Figure 2 .

 2 Figure 2. Ternary plot of: (a) Al 2 O 3 -CaO-Na 2 O + K 2 O; and (b) Al 2 O 3 -CaO-Fe 2 O 3 (all in wt%) for studied clay samples.

Figure 3 .

 3 Figure 3. (a) Schematic representation of the thermal analysis (ATD/TG) of the clay samples; (b) Some examples of TG/ DSC curves.

Figure 4 .

 4 Figure 4. Bigot's diagram of the examined clay samples.

  (c)). Some Tetouan samples (TN5, TN9 and TE7) are located close to the porous light-coloured field; they can therefore be used for porous light-coloured wall tile product. The ternary diagram (Fe 2 O 3 + CaO + MgO)/Al 2 O 3 /(Na 2 O + K 2 O) (Figure 6(c)) is used to classify raw clay

Figure 5 .Figure 6 .

 56 Figure 5. Influence of firing temperature on the variation of linear shrinkage and loss on weight.materials and industrial ceramic bodies[START_REF] Monteiro | Influence of Firing Temperature on the Ceramic Properties of Clays from Campos dos Goytacazes, Brazil[END_REF]. All Meknes clays plot in the field of red ceramics, close to the field for porous light-coloured ceramics. Most of the Tetouan clays were located outside of the red ceramics field, with the exception of TE4, TE7, TN4 and TN5, which are close to the field for porous light-coloured ceramics. The ideal composition for an optimum white body product (SiO 2 = 72 wt%, Al 2 O 3 and total oxides = 8 wt%)

Table 1 .

 1 Textural properties of the clay samples. S BET : specific surface area; Vp: pore volume; VDUB: micropore volume; CEC: specific surface area; ρs: bulk density.

	Samples	S BET (m 2 •g -1 )	Vp (cm 3 •g -1 )	VDUB (cm 3 •g -1 )	CEC (meq 100 -1 •g)	ρs (g•cm -3 )
		±5	±0.05	±0.01
			Meknes
	M1	37.9	0.055	0.015	12.9	2.7
	M2	36.3	0.055	0.015	9.1	2.7
	M3	33.3	0.053	0.015	9.7	2.7
	M4	36.3	0.053	0.015	10.1	2.7
	M5	34.7	0.049	0.015	20.6	2.7
	M6	35.2	0.094	0.015	12.5	2.7
			Unexploitable Tetouan clays
	TN1	32.4	0.069	0.015	18.5	2.7
	TN2	32.9	0.072	0.015	17.1	2.8
	TN3	31.0	0.072	0.015	17.2	2.8
	TN4	34.6	0.08	0.02	8.5	2.8
	TN5	26.2	0.061	0.014	10.0	2.8
	TN6	27.9	0.064	0.015	18.3	2.7
	TN7	26.2	0.086	0.012	13.1	2.8
	TN8	30.2	0.091	0.012	9.3	2.8
	TN9	36.4	0.101	0.015	17.9	2.8
			Exploitable Tetouan clays
	TE1	35.5	0.077	0.015	13.8	2.8
	TE2	34.5	0.075	0.015	14.6	2.8
	TE3	36.4	0.078	0.015	10.3	2.8
	TE4	31.0	0.068	0.014	10.7	2.9
	TE5	26.0	0.069	0.011	7.6	2.9
	TE6	17.7	0.047	0.008	12.0	2.8
	TE7	18.1	0.048	0.01	7.1	2.8
	TE8	21.3	0.056	0.01	13.7	3.0
	TE9	18.2	0.048	0.008	18.1	3.2
	TE10	27.0	0.062	0.015	17.6	2.8

Table 2 .

 2 Chemical composition of the clay samples.

		SiO 2	Al 2 O 3	Fe 2 O 3	CaO	MnO	MgO	Na 2 O	K 2 O	TiO 2	P 2 O 5	SO 2	Loss On Ignition
							Meknes
	M1	44.9	24.83	14.20	11.38	0.06	2.69	0.81	4.12	0.75	0.28	0.22	17.85
	M2	42.7	24.83	12.16	12.67	0.05	2.34	0.78	4.07	0.75	0.25	0.08	17.37
	M3	41.3	21.05	15.63	8.15	0.04	2.67	0.84	3.37	0.65	0.24	0.42	18.77
	M4	43.1	22.67	13.18	9.95	0.05	2.42	0.84	3.71	0.70	0.24	0.08	17.61
	M5	41.0	23.84	14.83	11.70	0.07	1.82	0.67	3.61	0.73	0.22	0.02	18.29
	M6	43.1	24.15	12.40	10.47	0.05	2.55	0.92	3.83	0.72	0.26	0.52	16.77
						Unexploitable Tetouan clays
	TN1	45.5	30.53	15.84	0.69	0.08	2.06	0.94	3.57	0.90	0.13	0.10	9.90
	TN2	47.7	34.58	18.96	0.39	0.03	2.44	0.92	3.49	1.05	0.13	0.00	9.24
	TN3	47.1	35.48	22.39	0.25	0.05	2.59	1.16	3.30	1.03	0.14	0.02	8.93
	TN4	35.9	20.63	9.75	20.94	0.14	2.14	1.05	3.54	0.53	0.17	0.84	23.90
	TN5	35.9	20.90	9.86	22.16	0.15	2.24	1.05	3.52	0.52	0.15	0.68	24.41
	TN6	47.6	32.69	14.53	0.48	0.06	1.94	0.86	3.08	0.97	0.12	0.00	8.87
	TN7	54.3	30.95	13.38	0.73	0.04	1.94	1.16	2.84	0.90	0.11	0.02	7.48
	TN8	51.1	30.46	12.24	1.90	0.03	1.72	1.29	2.89	0.93	0.13	0.10	7.94
	TN9	44.2	29.66	15.07	4.55	0.10	1.63	0.89	2.89	1.05	0.27	0.02	11.65
						Exploitable Tetouan clays
	TE1	48.6	32.39	17.01	0.45	0.03	1.68	1.78	4.36	0.97	0.18	0.00	7.20
	TE2	52.7	31.44	18.01	0.45	0.05	2.36	1.35	2.91	0.83	0.22	0.08	6.82
	TE3	54.3	31.48	11.49	0.42	0.03	1.76	1.73	3.95	0.85	0.16	0.02	7.05
	TE4	36.7	25.39	9.92	11.84	0.10	6.00	0.94	5.13	0.67	0.10	0.02	19.52
	TE5	51.9	33.97	18.10	0.45	0.03	1.89	1.91	4.43	1.00	0.19	0.00	8.72
	TE6	51.5	33.78	17.47	0.49	0.03	1.76	1.91	4.41	1.00	0.18	0.06	6.57
	TE7	35.0	20.63	8.61	17.67	0.18	5.36	0.81	3.88	0.55	0.11	0.02	21.35
	TE8	44.3	43.95	19.87	0.38	0.03	1.19	0.84	6.91	0.78	0.08	0.02	11.13
	TE9	48.3	42.40	16.64	0.31	0.02	0.85	1.13	6.07	0.72	0.05	0.02	8.95
	TE10	47.6	34.46	20.87	0.41	0.05	2.40	1.11	3.32	1.02	0.14	0.02	8.96

Table 3 .

 3 Linear shrinkage (%) of the studied clays during firing process.

		800˚C	850˚C	900˚C	950˚C	1000˚C	1050˚C	1100˚C
				Meknes clays
	M1	29.8	23.9	21.7	21.5	21.6	21.4	20.3
	M2	19.3	15.3	10.2	9.7	10.2	9.6	8.8
	M3	21.8	16.0	14.4	13.9	14.2	14.1	13.8
	M4	18.6	13.7	9.3	9.0	9.1	9.0	7.4
	M5	18.2	14.2	9.2	8.5	8.8	8.0	7.2
	M6	17.4	12.9	9.1	8.8	9.2	8.6	6.7
				Unexploitable Tetouan clays
	TN1	0	0	0	0	3.2	3.2	3.2
	TN2	0	0	3.1	3.1	3.1	3.1	6.3
	TN3	0	0	3.1	3.1	3.1	3.1	3.1
	TN4	0	0	2.9	2.9	2.9	2.9	2.9

Acknowledgements

The WD-XRF data acquisition was made possible at Umeå University through a research grant from the Kempe Foundation to Richard Bindler. The authors would like to thank René Pirard (Laboratoire de Génie Chimique, Université de Liège, Belgium) for the Calorimetry (ATD-TG) and specific surface area (SSA) analyses. We would like to thank Anne Iserentant (Université catholique de Louvain) for the CEC analysis. Finally, our thanks go to Dekayir (Université de Marrakech, Morocco) for his help on the field.


Maud Leriche

Laurent Deguillaume

Nadine Chaumerliac

Modeling study of strong acids formation and partitioning in a polluted cloud during wintertime

Keywords: Multiphase chemistry, strong acid formation, gas partitioning in clouds

A multiphase chemistry model coupled with a quasi-spectral microphysical model has been applied to measurements from the European CIME campaign to quantify the formation of the strong acids nitrate and sulfate, and to evaluate the role of microphysical processes in redistributing reactive species among the different phases (gas versus cloud and/or rain). Significant formation of nitrate and sulfate are found to be due to the reaction of pernitric acid with the sulfite ion. Moreover, pernitric acid, because of its equilibrium in the gas phase and its high solubility, is always available both in cloud water and in rainwater via mass transfer from the gas phase. The sulfite ion comes from the mass transfer from the gas phase of sulfur dioxide in cloud water. When rain formation begins, it is efficiently transferred to the rainwater by collision/coalescence processes. This leads to an enhancement in strong acid production when microphysics is activated in the model. Modeled results have been compared with experimental data, in an effort to retrieve a behavior law related to the partitioning between the gas and aqueous phases of the cloud. In particular, when collision/coalescence processes are considered, an improvement in retrieving the partitioning of soluble species and especially nitrate is observed. A higher production in sulfate could help interpret the discrepancy of global model calculations with observed sulfate concentrations in Europe in wintertime.

Introduction

For several years, significant efforts have been made by the scientific community to understand atmospheric chemistry. However, these efforts have concentrated mainly on atmospheric chemistry in the homogeneous gas phase, which currently begins to be well documented [START_REF] Atkinson | Evaluated kinetic and photochemical data for atmospheric chemistry: Supplement V. IUPAC subcommittee on gas kinetic data evaluation for atmospheric chemistry[END_REF]IPCC, 2001]. The role of clouds in the chemical composition of the atmosphere is significant in two aspects of planetary evolution: the atmospheric oxidizing capacity and the greenhouse effect linked to the role of aerosols (direct and indirect) in radiative forcing. Even if some modeling studies have tried to understand the overall role of cloud chemistry in global climate studies [START_REF] Jacob | Chemistry of OH in remote clouds and its role in the production of formic acid and peroxymonosulfate[END_REF]Lelieveld and Crutzen, 1990;[START_REF] Dentener | Aqueous phase reaction of HNO4: The impact on tropospheric chemistry[END_REF], our knowledge is still at a rudimentary stage when looking at the details of cloud processes [START_REF] Facchini | Clouds, atmospheric chemistry and climate[END_REF].

Recently, the reaction pathway of pernitric acid with the sulfite ion in aqueous phase has been proposed to play an important role in tropospheric chemistry [START_REF] Warneck | Chemistry of the Natural Atmosphere[END_REF][START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF]. Furthermore, recent studies by [START_REF] Warneck | Chemistry of the Natural Atmosphere[END_REF], [START_REF] Dentener | Aqueous phase reaction of HNO4: The impact on tropospheric chemistry[END_REF] and [START_REF] Williams | The influence of cloud chemistry on HOx and NOx in the moderately polluted Marine Boundary Layer: a 1-D modeling study[END_REF] have also suggested the potential role of aqueous phase pernitric acid reactivity in tropospheric chemistry. The kinetic constant of this reaction has been measured by [START_REF] Amels | Kinetic investigation of the stability of peroxonitric acid and of its reaction with sulfur(IV) in aqueous solution[END_REF]. Due to its thermal decomposition in the gas phase, its impact on tropospheric chemistry via cloud chemistry could be important in winter and/or at high latitudes. The reaction of pernitric acid with the sulfite ion produces both sulfate and nitrate and is the only reaction pathway, which links N-chemistry and S-chemistry. This reaction could have an impact on the amounts of nitrate and sulfate in aerosols for non-precipitating clouds and on the wet acid deposition when clouds are precipitating. Also, this reaction could have an effect on NO x levels because pernitric acid is a NO x reservoir species [see [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF]. Finally, pernitric acid can lead to a release of nitrous acid by the cloud droplets at moderate pH in moderately polluted conditions [START_REF] Williams | The influence of cloud chemistry on HOx and NOx in the moderately polluted Marine Boundary Layer: a 1-D modeling study[END_REF]. The possible role of this reaction in the production of sulfate aerosol could be a first hypothesis to explain the systematic under-estimation of sulfate in winter in Europe by global models as described for example in [START_REF] Feichter | Simulation of the tropospheric sulfur cycle in a global climate model[END_REF], in [START_REF] Chin | Atmospheric sulfur cycle simulated in the global model GOCART: Comparison with field observations and regional budgets[END_REF] and in [START_REF] Barrie | A comparison of large-scale atmospheric sulphate aerosol models (COSAM): Overview and highlights[END_REF]. Global models, however, often do not contain a very good description of cloud processes. Many models exclude the explicit implementation of intermediate chemical reactions, and have resolution too coarse to provide a detailed comparison with measurements in polluted regions.

The present modeling study aims at:

• quantifying the formation of strong acids like nitrate and sulfate when considering multiphase chemistry during an observed cloud event,

• verifying the importance of intermediate species such as HNO 4 in this strong acid production and

• evaluating the role of microphysical processes in redistributing reactive species among the different phases (gas versus cloud and/or rain).

We use the model from [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF], which presents a coupling between a fully explicit multiphase chemistry model [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] and a quasi-spectral microphysics model based upon Berry and Reinhardt's parameterization [1974a, b, c, d]. The microphysical scheme considers two categories of particles (cloud drops and raindrops) and aerosol particles are not taken into account.

This model has been used for the modeling of a cloud event [START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF] during the European CIME experiment [Cloud Ice Mountain Experiment, Atm. Res., Special Issue, 2001], which is a typical polluted wintertime event. The simulation is described in detail in [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF]. This paper is the continuation of [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF] paper and focuses on the formation pathway of strong acids, sulfate and nitrate for a precipitating and non-precipitating cloud. We also compare measurements of the partitioning between the gas and aqueous phase with model calculations. Of particular focus on is the very soluble and reactive species nitric acid which is found to be more efficiently produced when microphysical processes are considered.

Description of the model

The model is the result of a coupling between a multiphase chemical box model as described in [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] and a quasi-spectral microphysical model based upon a parameterization of a cloud evolution following Berry and Reinhardt [1974a, b, c, d] and [START_REF] Huret | Influence of different microphysical schemes on the prediction of dissolution of nonreactive gases by cloud droplets and raindrops[END_REF]. The detailed description of the coupling between the chemical and the microphysical modules can be found in [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF].

The chemistry included in the chemical module is explicit and describes a rural environment. The gas-phase mechanism includes the oxidation of methane, and the chemistry of NO y and ammonia, which is derived and updated after [START_REF] Madronich | The NCAR Master Mechanism of the gas phase chemistry[END_REF]. The exchange of chemical species between the gas and aqueous phase is parameterized by the mass transfer kinetic formulation developed by [START_REF] Schwartz | Mass-transport considerations pertinent to aqueous phase reactions of gases in liquid water clouds[END_REF]. The thermodynamical data involved in mass transfer are listed in Table 1 with accommodation coefficients and Henry's law constants. In Table 2, for aqueous phase equilibrium, only new references for updated values after [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are given. The aqueous phase chemistry includes the detailed chemistry of HO x (Table 3), chlorine (Table 4), carbonates (Table 5), NO y (Table 6) and sulfur (Table 8) and the oxidation of organic volatile compounds (VOCs) with one carbon atom (Table 7). These aqueous phase chemical mechanisms have been updated and only new references for updated values are indicated in Table 3-8. The pH of the droplets is calculated at each time step by solving a simplified ionic balance equation.

The microphysical module takes into account two categories of liquid water, cloud water and the rainwater, which is distributed according to a lognormal spectrum defined by the median size diameter D 0 and the standard geometric deviation σ. The microphysical processes taken into account are collision/coalescence represented by autoconversion, accretion and self-collection, and evaporation and sedimentation. The time step of this module is ten seconds.

The chemical and microphysical processes act together to redistribute the chemical species concentrations as described in Figure 1. This model can be used with two different options. In one case, the microphysical processes are not taken into account, which means that only cloud water is considered varying with time but with a constant radius for the droplets.

In the other case, the microphysics is considered, and due to the quasi-spectral nature of the Berry and Reinhardt parameterization, allows for partitioning of the liquid phase among the two water categories (cloud and rain) and for varying mean raindrop diameter. The first case simulates a non-precipitating cloud while the second case corresponds to a precipitating cloud with maritime parameters (defined through the median size diameter and the standard geometric deviation for the lognormal distribution of cloud droplets).

Results and discussion

Description of the simulation

The conditions of the simulation are described in details in [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF]. We only briefly summarized these conditions below.

To initialize the model, we used data from the Cloud Ice Mountain Experiment (CIME campaign). CIME was a European experiment, which took place during winters 97 and 98 at the top of The Puy de Dôme mountain in the center of France. During December 1997, chemical measurements were performed in both gas and aqueous phases by Voisin et al. [2000] in the presence of clouds. We chose to simulate the 13 th of December because there was a stable meteorological situation which produced drizzle without either ice or rain. The air mass was polluted coming from the North-North-East. In the simulation, the chemical measurements used to initialize the chemistry are typical of a wintertime-polluted air mass with high NO x (7.1 ppbv) and high formaldehyde (6 ppbv) concentrations.

Meteorological parameters are prescribed by the time change of the temperature, which simulates the temperature variation in an orographic cloud at the top of the Puy de Dôme mountain, the site of the CIME experiment. The simulation begins at noon and its duration is half an hour. The liquid water content of the cloud initialized with 0.3 g m - 3 , further evolves as a function of time and temperature through microphysical conversions, providing the partitioning between cloud and rainwater, and the change in raindrop diameter. At the end of the simulation, the cloud evaporates as the temperature increases. The maximum in the cloud water and rainwater content are respectively found at 12.7 p.m. and at 12.12 p.m. If the microphysical processes are not considered (when autoconversion is turned off), the liquid water content of the cloud only evolves through the effect of varying the temperature across the simulation (which describes a non-precipitating case). In this case, the maximum in the liquid water content arises at 12.15 p.m.

As discussed before, two options are available for considering cloud chemistry: either the liquid water content is in the only one form of cloud with no conversion allowed for rain production (non-precipitating case) or liquid water is shared out between cloud phase and rain phase with varying raindrop diameter (precipitating case).

Formation pathway of strong acids: sulfuric and nitric acids

Simulation results: Precipitating versus non precipitating cloud.

We now examine the formation pathway of strong acids (sulfate and nitrate) in the gas phase, in cloud and in rain.

We present results for two simulations, with and without microphysical conversions. Results for the formation of nitric acid and sulfate through chemical reactions are presented in Figure 2 for both cases. Only the main production pathways are indicated in both gas and aqueous phases. First, total production of both nitric acid and sulfate (gas plus liquid phases) are higher in the case where microphysics is activated, a factor of 1.2 more important for nitric acid and a factor 1.45 more important for sulfate than for the case without microphysics. The production pathway of pernitric acid (A69) is an important contribution to the formation of both nitric acid and sulfate. This pathway accounts for 58% of the total sulfate production for the case with microphysics considering cloud plus rain contributions, and 53% for the case without microphysics. For nitric acid formation, it accounts for 37% of the total production for the case with microphysics considering cloud plus rain contributions, and 28% for the case without microphysics. The budget of HNO 4 indicates that the reaction with the sulfite ion contributes 82% to the total pernitric acid destruction (gas and liquid phases) in the case with microphysics considering cloud plus rain contributions. The other predominant destruction pathways of HNO 4 are A66 and A68 (respectively around 7% and 10% in the case with microphysics considering cloud plus rain contributions). For nitric acid, the main production pathway arises via the gas phase production from reaction of NO 2 with OH (58% for the case with microphysics and 68% for the case without microphysics). This could be expected in our run conditions describing a high NO x regime. The contribution of hydrolysis of N 2 O 5 (A74) to the production of nitric acid is less significant with 5% contribution to the total production for the case with microphysics considering cloud plus rain contributions and 4% for the case without microphysics. The sulfate formation in the gas phase is negligible and the formation in the aqueous phase by oxidation of sulfur dioxide by hydrogen peroxide (A161) contributes 39% to the total production for the case with microphysics considering cloud plus rain contributions and 46% for the case without microphysics. If we examine the formation pathway in the aqueous phase for the case with microphysics, we observe that the contribution of one reaction is the same in cloud and in the rain phase. This fact explains the more important total production of nitric acid and sulfate when microphysics is considered.

These results underline the efficient production of nitrate and sulfate by the pernitric acid pathway in the aqueous phase, which enhances total production of sulfate and nitrate in aerosols for a non-precipitating cloud and leads to an acidification of rain for a precipitating cloud. This aqueous production pathway of both nitrate and sulfate is not taken into account in global models such as the GOCART model for example [START_REF] Chin | Atmospheric sulfur cycle simulated in the global model GOCART: Comparison with field observations and regional budgets[END_REF]. As suggested by Dentener et al. [2002], the production of sulfate aerosols by pernitric acid in cloud could improve the comparison between sulfate concentrations simulated by global models with observations. In fact, global models tend to under-estimate sulfate concentrations over Europe in winter.

Sensitivity tests.

We have made some sensitivity tests on this simulation on microphysical processes or on initial chemical conditions to understand their relative contribution to the observed behavior of the strong acid formation. A first test on sedimentation shows that sedimentation acts as a sink for soluble species but has no significant role on chemical pathways. Secondly, to test how the contribution of pernitric acid in nitric acid and sulfate formation is linked to NO x levels, we have performed a sensitivity test in which we have reduced the initial NO x concentration by a factor of 50.

Results of this new run are presented in Figure 3, which is directly comparable to Figure 2, except that total nitric acid production is 6 to 9 times smaller when reducing NO x by 50 and total sulfate production is 1.2 to 1.16 times smaller. As expected, when diminishing the initial NO x concentration, the contribution to total nitric acid production of gas phase pathway becomes less important and represents 10% and 16% of the total nitric acid production respectively, when microphysics is considered, or not.

But surprisingly, when diminishing the initial NO x concentration, the production of strong acids by the pathway involving pernitric acid is still important.

For nitric acid, pernitric acid pathway contributes 90% to the total production for the case with microphysics considering cloud plus rain contributions and 84% for the case without microphysics. For sulfate, it represents 28% of the total production for the case with microphysics considering cloud plus rain contributions and 21% in case without microphysics. The most important production pathway for sulfate production is now the oxidation of the sulfite ion by hydrogen peroxide because the diminution of NO x involves a new chemical regime of peroxides production. This can explain the smaller diminution of total sulfate production in comparison to the diminution of total nitric acid production.

The differences observed in the low NO x regime (Figure 3) between the two cases with and without microphysics are still remaining the same as in the high NO x regime (Figure 2). The productions via aqueous phase are also equivalent in the cloud and rain phases and contribute equally to the total production for both nitric acid and sulfate. This has to be directly related to the indirect effect of microphysical conversion. To understand this equal production in cloud and rain phases, we have examined the sources of the precursors of nitric acid and sulfate in the aqueous phase. We only focus on the reaction with pernitric acid since the source of hydrogen peroxide in the aqueous phase is the same as pernitric acid.

On the one hand, nitric acid and sulfate are produced in the aqueous phase through the oxidation of the sulfite ion by pernitric acid continuously formed in the gas phase and very soluble. On the other hand, the sulfur dioxide is transferred from the gas phase to the cloud phase through mass transfer, before rain production and produces the sulfite ion that leads to nitric acid and sulfate production. A schematic diagram is drawn in Figure 4 to investigate the origin of those chemical precursors of nitric acid and sulfate in aqueous phase. The pernitric acid, available in the gas phase, is efficiently transferred to the aqueous phase through mass transfer with a relative contribution of pernitric acid production in the cloud phase of 90% and a relative contribution of pernitric acid production in the rain phase of 87%.

The other production pathways of pernitric acid in the aqueous phase are reactions A64 and A65, which contribute respectively 8% and 2% to total pernitric acid production in the cloud phase and 8.5% and 2% in total pernitric acid production in the rain phase. The remaining production in the rain phase comes from conversion of cloud water into rainwater by collision/coalescence processes. Although the kinetic constant of reaction A65 is greater than that of reaction A64, reaction A65 has a more important contribution in HNO 4 production because HO 2 prevails on O 2 -for simulated low pH values (pKa of HO 2 4.8 and simulated pH 3.7). For sulfite ion production in the aqueous phase, the pattern is slightly different. The sulfite ion already exists in cloud phase from the mass transfer of sulfur dioxide, and as the rain appears, it is transferred through collision/coalescence processes to the rain phase. This source of the sulfite ion in the rain phase represents 99% of the total production in the rain phase (chemical and microphysical processes).

Moreover, the destruction of the sulfite ion in the cloud phase by collision/coalescence processes is only 1% of the total destruction (chemical and microphysical processes). The resulting effect of this precursor production is an increase in the production of nitric acid and sulfate equally shared among the cloud and the rain phases.

Potential uncertainties in the chemical mechanism.

The above discussion on the formation of strong acids in cloud and rain strongly depends on the value of the reaction constant of the oxidation of sulfite ion by pernitric acid (A69). This constant has only been measured once in [START_REF] Amels | Kinetic investigation of the stability of peroxonitric acid and of its reaction with sulfur(IV) in aqueous solution[END_REF] study, which is part of the PhD work of [START_REF] Götz | Kinetische Untersuchung von Reaktionen der Peroxosalpetersäure in wäßriger Lösung[END_REF] and this value can be a source of potential error. Despite the lack of data on the reaction constant A69, the reactivity of peroxo compounds R-OOH towards the sulfur(IV) has been investigated in details by [START_REF] Drexler | Kinetic investigation of sulfur(IV) oxidation by peroxo compounds R-OOH in aqueous solution[END_REF]. In this study, looking at the reactivity of hydrogen peroxide (R = H), peroxonitrous acid (R = NO) and peroxoacetic acid (R = Ac), a general relationship for the rates of oxidation of sulfur(IV) by peroxo compounds following a three-term rates law was established including available data for reactivity of peroxomonosulfuric acid (R = SO 3 -) and methyl hydroperoxide (R = CH 3 ). These three-term rates are proton catalysis, general acid catalysis and water catalysis. For hydrogen peroxide, only the proton catalysis is significant for atmospheric chemistry. An interesting result of this study is the relationship correlating the rate constant for water catalysis (independent of pH value) with the pKa of the peroxo compounds. Using this relationship to estimate the reaction constant of A69 gives a value of 2.2 10 5 M -1 s -1 , which is in the same order of magnitude than the value measured by [START_REF] Amels | Kinetic investigation of the stability of peroxonitric acid and of its reaction with sulfur(IV) in aqueous solution[END_REF].

However, as the main conclusion about strong acids formation depends on the value of reaction constant of A69, additional sensitivity tests have been performed on this reaction constant taking into account the maximal potential error: the constant is divided or multiplied by a factor 10. Figure 5 shows the relative contribution of each production pathway to the total production of nitric acid (a) and sulfate (b) for three cases: the reaction constant of A69 is divided by 10, the reference simulation (using A69 constant) is corresponding to Figure 2 and the reaction constant of A69 is multiplied by 10. For each case, the total production in ppbv h -1 is indicated. The most important result from this test is that the contribution of the reaction A69 is still significant even if the reaction constant is divided by 10 for both production of nitric acid and sulfate. The relative contribution of this reaction is respectively 16.9% and 33% of the total production of nitric acid and sulfate when the reaction constant is divided by 10, is respectively 36.6% and 59% of the total production of nitric acid and sulfate for the reference simulation and is respectively 40% and 64% of the total production of nitric acid and sulfate when the reaction constant is multiplied by 10. For both nitric acid and sulfate, we can notice that the increase of the contribution of A69 as well as the increase of the value of the total production is more important between the simulation where the reaction constant is divided by 10 and the reference simulation than between the reference simulation and the simulation where the reaction constant is multiplied by 10. This is due to the non-linearity of the chemistry.

In conclusion, this test shows that, despite the potential error on the reaction constant of A69, it is clearly demonstrated that this reaction significantly contributes to the production of both nitric acid and sulfate in winter.

Partitioning of chemical species between phases.

In this paper, we are also interested in investigating the effect of microphysical conversions on the partitioning of chemical species as described by [START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF]. This partitioning was represented by the q factor, defined as:

i g i w RTC LH C q * =
(1) where i g C and i w C are respectively the gaseous and aqueous concentrations of the species i in molec.cm -3 , L is the liquid water content in vol/vol, H * is the Henry law effective constant of the species i in M.atm -1 and R = 0,08206 atm.M -1 .K -1 ). This factor indicates whether the species i is at Henry's law equilibrium (q=1), under-saturated in the aqueous phase (q<1) or over-saturated in the aqueous phase (q>1).

An important point is that, while measuring q, the liquid water content involved in the partitioning includes both cloud water and drizzle. In order to adequately compare the measurements with model calculations, we introduce a bulk q factor defined as:

i g total ra in cloud i total w bulk RTC H L L C q * , ) ( + = (2)
with

ra in cloud ra in i ra in cloud i cloud i total w L L L C L C C + + = , ,
and * total H is the effective Henry's law constant that includes possible dissociation and hydration, using the bulk pH.

The bulk pH, calculated from resolving the ionic balance equation in the mixed solution of cloud water and rainwater, has a mean value of 3.7 whereas the bulk pH measured during the cloud event is more acidic with a value of 3.3. The difference between the experimental and simulated pH values is firstly due to the precision of the experimental pH value, around 0.1 units. Secondly, we use chemical measurements from bulk samples to initialize the model, which may not be representative of the initial chemical composition of cloud droplets determined by aerosol nucleation and not known in detail in the measurements.

Figure 6 shows the partitioning factor between the gas and condensed phase through a comparison between experimental results versus two model results (without and with microphysics) for several chemical species ordered by increasing solubility. Experimental results correspond to values for the simulated cloud event of December13 th . The values of the q factor for the radical HO 2 has been indicated as an example of the partitioning of a very reactive radical but no measurements were available for this species.

We notice a rather good agreement between experimental and numerical results, except for nitric acid and sulfur dioxide when microphysics is not considered. For sulfur dioxide, the discrepancy between model and measurements comes from an overestimation of measured sulfur dioxide concentrations in cloud because of HMSA conversion into S(IV) due to some experimental biases. However, coupling chemistry and microphysics improves considerably the comparison for almost all the soluble species, and more particularly for nitric acid. The remaining difference between numerical and experimental results for nitric acid comes from the discrepancy between pH values that are measured and simulated with an experimental value more acidic. In fact, [START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF] show that the q factor for nitric acid decreases when the pH increases.

The under-saturation of nitric acid in the aqueous phase comes from its high solubility in connection with its production in the gas phase, which is faster than its production in the aqueous phase. In fact, the rate of nitric acid production in the gas phase is greater than the rate of transfer to aqueous phase. This means that the equilibrium time of phase exchange is greater than the characteristic time of nitric acid production in the gas phase. When microphysics is considered, the gaseous nitric acid must pass two aqueous frontiers corresponding to cloud and rain phases. Nitric acid is more under-saturated in the aqueous phase when microphysics is taken into account.

Finally, coupling chemistry and microphysics allows for a more realistic nitric acid partitioning between the gas and aqueous phases, closer to the measurements.

Conclusion

A coupled model of multiphase chemistry [START_REF] Leriche | Coupling quasi-spectral microphysics with multiphase chemistry: A case study of a polluted air mass at the top of the Puy de Dôme mountain (France)[END_REF] has been applied to a cloud event taken from the European CIME experiment [START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF] during a polluted chemical situation.

First, the production pathways of the strong acids, nitric and sulfuric acid are examined. The results show a significant contribution of the reaction between pernitric acid and the sulfite ion in the production of both acids.

Moreover, the comparison between a case without microphysics corresponding to a non-precipitating cloud and a case with microphysics corresponding to a precipitating cloud shows a greater production of strong acids when microphysics is considered. This is due to an equivalent production in cloud and rain phases, which arise from interaction between microphysics and chemistry. The study of the main sources of strong acids precursors highlights the role of collision/coalescence processes in the enhancement of strong acids production.

Secondly, modeling results have been compared with data, based upon a behavior law that relates the partitioning among gas/aqueous phases and species solubility. Coupling multiphase detailed chemistry with microphysics allows for retrieving this behavior law that could be possibly used as a parameterization in global models.

The numerical results and their comparisons with observational data demonstrate the interest of such a modeling approach in linking laboratory kinetics with in-situ measurements. It also represents a potential tool for sensitivity analysis on chemical species reactivity and partitioning among various phases of the cloud, which can now be assessed using new instruments such as a counter virtual impactor [START_REF] Noone | Design and calibration of a counterflow virtual impactor for sampling of atmospheric fog and cloud droplets[END_REF]. In that sense, if completed by some aerosol chemistry and microphysics, it could help to interpret data from such an instrumentation platform. More generally, this modeling study has put evidence on the importance of intermediate compounds such as pernitric acid, which is often neglected in models and leads to sulfuric and nitric acid production in clouds [Hermann et al., 2001].

Further laboratory investigations in the conditions of a real atmosphere (temperature, pressure, concentration) for aqueous phase pernitric acid reactivity are needed. However, considering the actual knowledge, the linkage between sulfur and nitrogen chemistry through pernitric reactivity in aqueous phase can bring a possible explanation of the discrepancy between models and observations that is found in winter in sulfate aerosol concentrations, which are actually underestimated in global models [IPCC, 2001]. Comparison between the partitioning q factor measured by [START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF] and the simulated partitioning q factor, with and without microphysical conversions. [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are indicated.

Equilibrium K (M) ΔH/R (K) CO2 + H2O ⇔ H + + HCO3 - 4.2 10 -7 HCO3 -⇔ H + + CO3 2- 4.8 10 -11 SO2 + H2O ⇔ H + + HSO3 - 1.3 10 -2 a -2130 a HSO3 -⇔ H + + SO3 2- 6.4 10 -8 a -1460 a H2SO4 ⇔ H + + HSO4 - 1.0 10 3 b HSO4 -⇔ H + + SO4 2- 1.0 10 -2 c HNO3 ⇔ H + + NO3 - 2.2 10 1 HO2 ⇔ H + + O2 - 1.6 10 -5 d HCOOH ⇔ H + + HCOO - 1.8 10 -4 150 H2O2 ⇔ H + + HO2 - 2.2 10 -12 -3730 HNO2 ⇔ H + + NO2 - 5.3 10 -4 e 1760 e HNO4 ⇔ H + + NO4 - 1.26 10 -6 f CH2O + H2O ⇔ CH2(OH)2 2.5 10 3 g -4030 HCl ⇔ H + + Cl - 1.7 10 6 -6890 NH3 + H2O ⇔ NH4 + + OH - 1.7 10 -5 4320 a Maahs, 1982. b Cotton and Wilkinson, 1980. c Eigen et al., 1964. d Bielski et al., 1985. e Park and Lee, 1988. f Goldstein and Czapski, 1997. g K without unity.

Cloud water Rainwater

Figure 1 .

 1 Figure 1. Schematic diagram of reactive gases distribution among different cloud phases by microphysical processes.

Figure 2 .

 2 Figure 2. Chemical production of nitric acid (a) and sulfate (b) through the main chemical reactions in the gas phase, in the cloud phase and in the rain phase for two simulations: with and without microphysical conversions.

Figure 3 .

 3 Figure 3. Same as Figure 2 but with an initial NOx concentration divided by 50.

Figure 4 .

 4 Figure 4. Schematic representation of the sulfite ion and pernitric acid sources in cloud water and in rainwater, percentages of contribution in the total production (+) or destruction (-) are indicated.

Figure 5 .

 5 Figure5. Relative contribution pathways to the total production of nitric acid (a) and sulfate (b) for three simulations: the reaction constant of A69 is divided by 10, the reference simulation (A69) and the reaction constant of A69 is multiplied by 10. The total production is indicated for each case.

Figure 6 .

 6 Figure 6. Comparison between the partitioning q factor measured by[START_REF] Voisin | Investigations of the scavenging of acidic gases and ammonia in mixed liquid solid water clouds at the Puy de Dôme mountain (France)[END_REF] and the simulated partitioning q factor, with and without microphysical conversions.

Table 1 .

 1 Values of mass accommodation coefficients and Henry's law constants. Only new references for updated values after[START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are indicated.Hanson et al., 92. b Schwartz and White, 83. c Rudich et al., 96. d Clegg and Brimblecombe, 90.

	Species	α	H298 (M/atm)	ΔH/R (K)
	O3	0.05	1.1 10 -2	-2300
	O2	0.05 (estimated)	1.3 10 -3	-1500
	H2O2	0.11	8.33 10 4	-7400
	HO2	0.01 a	4.0 10 3	-5900
	OH	0.05 (estimated)	30	-4500
	NO	0.0001	1.9 10 -3	-1400
	NO2	0.0015	1.2 10 -2 b	-1260 b
	NO3	0.0002 c	6.0 10 -1
	N2O5	0.0037	2.1	-3400
	HNO2	0.05	50	-4900
	HNO3	0.054	2.1 10 5	-8700
	HNO4	0.05 (estimated)	1.2 10 4	-6900
	NH3	0.04	61 d	-3920 d
	HCl	0.064	1.1	-2000
	SO2	0.11	1.4	-2900
	H2SO4	0.07	2.1 10 5 = HHNO3	-8700 = ΔHHNO3
	CO2	0.0002	3.6 10 -2	-2200
	CH3O2	0.05 (estimated)	15	-3700
	OHCH2O2	0.05 (estimated)	8.05 10 4	-8200
	CH2O	0.02	3.0 10 3	-7200
	HCOOH	0.012	8.9 10 3	-6100
	CH3OOH	0.0038	3.11 10 2	-5200
	OHCH2OOH	0.05 (estimated)	1.7 10 6	-9700
	CH3OH	0.015	2.2 10 2	-5200
	CH2(OH)(OH)	0.05 (estimated)	1.21 10 7
	CH3(ONO2)	0.05 (estimated)	2	-4700
	a

Table 2 .

 2 Aqueous phase equilibrium. Only new references for updated values after

Acknowledgments. This work was supported by the "Programme National de Chimie Atmosphérique" (PNCA) of the INSU (Institut des Sciences de l'Univers). Computer resources were provided by I.D.R.I.S (Institut du développement et des Ressources en Informatiques Scientifique), project n°000187. The first author is very grateful to Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME) and to Electricité de France (EDF), who financed this work. The authors would like to thank Professor Horst Elias for his help on the reactivity of pernitric acid with the sulfite ion measured by his team.

Table 3. HOx chemistry in droplets. Only new references for updated values after [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are indicated. New reactions are indicated by (AN) instead of (A).

Reactions k298(M -n+1 .s -1 ) Ea/R (°K) O3 + hν + H2O → H2O2 + O2 (A1) calculated H2O2 + hν → 2OH (A2) calculated OH + O3 → O2 + HO2 (AN3) 1.0 10 8 a OH + HO2 → H2O + O2 (A4) 2.8 10 10 0 OH + O2 -→ HO -+ O2 (A5) 3.5 10 10 720 H2O2 + OH → H2O + HO2 (A6) 2.7 10 7 1700 HO2 + HO2 → H2O2 + O2 (A7) 8.3 10 5 b 2700 b HO2 + O2 -+ H2O → H2O2 + O2 +OH - (A8) 9.6 10 7 c 910 c O3 + HO2 → OH + 2O2 (A9) <1.0 10 4 O3 + O2 -+ H2O → OH + 2O2 + OH - (A10) 1.6 10 9 d 2200 d OH + HSO3 -→ SO3 -+ H2O (A11) 2.7 10 9 OH + SO3 2-→ SO3 -+ OH - (A12) 4.6 10 9 a Sehested et al.,1984. b Bielski et al., 1985. c Christensen et Sehested, 1988. d Hesper and Herrmann, 2002. Table 4. Chlorine chemistry in droplets. Only new references for updated values after [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are indicated. New reactions are indicated by (AN) instead of (A).

Reactions

) 2.9 10 7 470 a Buxton et al., 1988. b Sehested et al., 1983. Table 6. N-chemistry in droplets. Only new references for updated values after [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF]

(A77) 3.0 10 9 =k76 NO3 + H2O2 → NO3 -+ H + + HO2 (A78) 4.9 10 6 2000 NO3 + OH -→ NO3 -+ OH (A79) 9.4 10 7 2700 NO3 + HSO4 -→ NO3 -+ H + + SO4 - (A80) 2.6 10 5 d NO3 + SO4 2-→ NO3 -+ SO4 - (A81) 1.0 10 5 NO3 + HSO3 -→ SO3 -+ NO3 -+ H + (A82) 1.3 10 9 2200 NO3 + SO3 2-→ NO3 -+ SO3 - (A83) 3.0 10 8 a Barker et al., 1970. b Herrmann and Zellner, 1998. c Jacobi, 1996. d Raabe, 1996. (A107) 1.0 10 9 600 CH3OH + NO3 + O2 → NO3 -+ H + + OHCH2O2 (A108) 5.4 10 5 4300 CH3OH + CO3 -+ O2 → HCO3 -+ OHCH2O2 (A109) 5.7 10 3 3100 CH3OH + Cl2 -+ O2 → 2Cl -+ H + + OHCH2O2 (A110) 5.0 10 4 5500 CH3OH + SO4 -+ O2 → SO4 2-+ H + + OHCH2O2 (A111) 9.0 10 6 2200 H2C(OH)2 + OH + O2 → HCOOH + HO2 + H2O (A112) 7.8 10 8 1000 H2C(OH)2 + NO3 + O2 → NO3 -+ H + + HO2 + HCOOH (A113) 1.0 10 6 4500 H2C(OH)2 + CO3 -+ O2 → HCO3 -+ HO2 + HCOOH (A114) 1.3 10 4 H2C(OH)2 + Cl2 -+ O2 → 2Cl -+ H + + HCOOH + HO2 (A115) 3.1 10 4 4400 H2C(OH)2 + SO4 -+ O2 → SO4 2-+ HCOOH + HO2 + H + (A116) 1.4 10 7 1300 HCOOH + OH + O2 → CO2 + HO2 + H2O (A117) 1.010 8 1000 HCOO -+ OH + O2 → CO2 + HO2 + OH - (A118) 3.4 10 9 1200 HCOOH + NO3 + O2 → NO3 -+ H + + CO2 + HO2 (A119) 3.8 10 5 3400 HCOO -+ NO3 + O2 → NO3 -+ CO2 + HO2 (A120) 5.1 10 7 2200 HCOO -+ CO3 -+ H2O + O2 → CO2 + HCO3 -+ HO2 + OH -(A121) 1.4 10 5 3300 HCOOH + Cl2 -+ O2 → CO2 + 2Cl -+ HO2 + H + (A122) 5.5 10 3 4500 HCOO -+ Cl2 -+ O2 → CO2 + 2Cl -+ HO2 (A123) 1.3 10 6 HCOOH + SO4 -+ O2 → SO4 2-+ H + + CO2 + HO2 (A124) 2.5 10 6 b HCOO -+ SO4 -+ O2 → SO4 2-+ CO2 + HO2 (A125) 2.1 10 7 b a Neta et al., 1990. b Reese, 1997. c Estimated from kinetic constant of aqueous phase reaction H2O2 + OH and the branching ratio of the gas phase reaction CH3OOH +OH.

Table 8. S-chemistry in droplets. Only new references for updated values after [START_REF] Leriche | A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment[END_REF] are indicated.

Reactions k298(M -n+1 .s -1 ) Ea/R (°K) HSO3 -+ CH2O → HOCH2SO3 - (A126) 7.9 10 2 2900 SO3 2-+ CH2O → HOCH2SO3 -+ OH - (A127) 2.5 10 7 2450 HOCH2SO3 -→ HSO3 -+ CH2O (A128) 7.7 10 -3 9200 HOCH2SO3 -+OH -→ SO3 2-+ H2C(OH)2 (A129) 3.7 10 3 HOCH2SO3 -+ OH + O2 → HO2 + HCOOH + HSO3 - (A130) 3.0 10 8 HOCH2SO3 -+ NO3 → NO3 -+ H + + CH2O + SO3 - (A131) 4.2 10 6 a HOCH2SO3 -+ Cl2 -→ 2Cl -+ H + + CH2O + SO3 - (A132) 5.0 10 5 HOCH2SO3 -+ SO4 -→ SO4 2-+ H + + CH2O + SO3 - (A133) 2.8 10 6 SO3 -+ O2 → SO5 - (A134) 2.5 10 9 SO5 -+ HSO3 -→ HSO5

2.1 10 5 5.5 10 5 SO5 -+ HO2 → HSO5 -+ O2 (A139) 1.7 10 9 SO5 -+ O2 -+ H2O → HSO5

) 3.2 10 8 1200 SO4 -+ HO2 → SO4 2-+ H + + O2 (A150) 3.5 10 9 SO4 -+ O2 -→ SO4 2-+ O2 (A151) 3.5 10 9 =k150 SO4 -+OH -→ SO4 2-+ OH (A152) 1.4 10 7 SO4 -+ H2O2 → SO4 2-+ HO2 + H + (A153) 2.8 10 7 c SO4 -+ NO3 -→ SO4 2-+ NO3 (A154) 5.0 10 4 SO4 -+ NO2 -→ SO4 2-+ NO2 (A155) 7.2 10 8 c HSO4 -+ OH → H2O + SO4 - (A156) 3.5 10 5 HSO4 -→ SO4 2-+ H + (A157) 1.0 10 9 -2700 SO4 2-+ H + → HSO4 - (A158) 1.0 10 11 HSO3 -+ O3 → HSO4 -+ O2 (A159) 3.7 10 5 5500 SO3 2-+ O3 → SO4 2-+ O2 (A160) 1.5 10 9 5300 HSO3 -+ H2O2 + H + → SO4 2-+ 2H + + H2O (A161) 9.1 10 7 3600 HSO3 -+ CH3OOH + H + → SO4 2-+ 2H + + CH3OH (A162) 1.8 10 7 3800 a Herrmann et al., 1996. b Buxton et al., 1996. c