B Dewandel
email: b.dewandel@brgm.fr

J Perrin
email: perrin@ngri.res.in-

S Ahmed

S Aulong
email: s.aulong@brgm.fr

Z Hrkal

P Lachassagne
email: p.lachassagne@brgm.fr

M Samad
email: m.samad@cgiar.org

S Massuel
email: s.massuel@cgiar.org

Development of a Tool for managing groundwater resources in semi-arid hard rock regions. Application to a rural watershed in south India

Keywords: groundwater management, hard rock aquifer, overexploitation, groundwater budget, DST-GW

Until recently, aquifers located in hard rock formations (granite, gneiss, schist) were considered as a highly heterogeneous media, and no adequate methodology for groundwater management was available. Recent research studies showed that when hard-rocks are exposed to regional and deep weathering processes and when the geology is relatively homogenous, a typical hard rock aquifer is made of two main superimposed hydrogeological layers each characterized by quite homogeneous specific hydrodynamic properties: namely the saprolite and the fissured layers. Therefore, for these cases, hard rock aquifers can be considered as a multi-layered system.

Based on these works, an operational Decision Support Tool (DST-GW) designed for the management of groundwater resources in hard rock area under variable agro-climatic conditions has been developed. The tool focuses on the impact of changing cropping pattern, 1 artificial recharge and rainfall conditions on groundwater levels at the scale of small watersheds (10 to about 100 km 2 in case well-developed weathering profile). DST-GW is based on the groundwater balance and the 'Water Table Fluctuation Method', well-adapted methods in hard rock and semi-arid contexts.

Based on field data from an over-exploited south Indian watershed (58 km²), the model allows calibrating, at watershed scale, the variation in specific yield of the aquifer with depth, as well as the rainfall/aquifer recharge relationship. Seasonal basin-scale piezometric levels are computed with an average deviation of ±0.56 m compared to measurements from 2001 to 2005. The model shows that if no measure is taken the water table depletion will induce the drying up of most of the exploited borewells by the year 2012. Scenarios of mitigation measures elaborated with the tool show that change in cropping patterns could rapidly reverse the tendency and lead to a sustainable management of the resource. This work presents the developed tool and particularly the hydraulic model involved in, and its application to a case study. However, the purpose tool is applicable at watershed scale but not design for the groundwater management of a very small area or for a single bore well.

INTRODUCTION

Supplying 27 million hectares of farmland in India, groundwater now irrigates a larger area than surface water (21 million hectares). This change has been extremely rapid since the start of the 'Green Revolution' in the 1970s, when the number of borewells have shoot up from less than one million in 1960 to more than 20 million presently. In consequence, and especially in hard rock and (semi)arid areas that covers about 2/3 of the country, the groundwater resource is highly stressed due to large abstraction of water by pumping for irrigation. As a result, this overexploitation of the resource threatens the sustainability of water availability and agricultural development. Therefore in these areas, it is necessary to adapt the groundwater resource exploitation to its availability.

Until recently, aquifers located in hard rock formations (granite, gneiss, schist) were considered as highly heterogeneous media, and adequate methodologies for groundwater management were not existent. Recent studies conducted in African, European and Indian basements (e.g., [START_REF] Omorinbola | Verification of some geomorphological implcations of deep weathering in the basement complex of Nigeria[END_REF][START_REF] Omorinbola | Shallow seismic investigation for location and evaluation of groundwater reserves in the weathered mantles of the basement complex in south-western Nigeria[END_REF][START_REF] Wright | The hydrogeology of crystalline basement aquifers in Africa[END_REF][START_REF] Chilton | Hydrogeological characterization and water-supply potential of basement aquifers in tropical Africa[END_REF][START_REF] Owoade | The potential for minimizing drawdowns in groundwater wells in tropical aquifers[END_REF][START_REF] Wyns | Caractérisation multiparamètres des horizons de subsurface (0-100 m) en contexte de socle altéré[END_REF][START_REF] Taylor | A tectono-geomorphic model of the hydrogeology of deeply weathered crystalline rock: evidence from Uganda[END_REF][START_REF] Maréchal | Use of hydraulic tests at different scales to characterize fracture network properties in the weathered-fractured layer of a hard rock aquifer[END_REF][START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF][START_REF] Krásný | Hydrogeology of fractured rocks from particular fractures to regional approaches : state-of-the-art and future challenge[END_REF][START_REF] Maréchal | Hard-rock aquifers characterization priror to modelling at cahment scale: an application to India[END_REF], Courtois et al., 2008, Courtois et al., in press, etc.) showed that when hard rocks are exposed to regional and deep weathering processes and when the geology can be considered as homogenous, the aquifer is constituted of two main sub-parallel hydrogeological layers, namely the saprolite, a clayey-sandy material, and the fissured layers, generally characterized by a dense horizontal fissuring in the first few meters and a depth-decreasing density of fissures. Usually, the total thickness of the weathering profile is comprised between 50 to 100 m. Each layer can be considered as homogeneous and is therefore characterized by quite homogeneous hydrodynamic properties [START_REF] Maréchal | Use of hydraulic tests at different scales to characterize fracture network properties in the weathered-fractured layer of a hard rock aquifer[END_REF][START_REF] Maréchal | Hard-rock aquifers characterization priror to modelling at cahment scale: an application to India[END_REF][START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF]. For example in hard rock areas of India and Africa, this homogeneity in term of layers thicknesses and hydrodynamic properties is shown from 100 m to kilometric scale (e.g., [START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF]Courtois et al., 2008, Courtois et al., in press). Consequently, hard rock aquifers exposed to regional deepweathering processes can be considered as a composite aquifer constituted of two main layers characterized by their own hydrogeological properties.

Based on this conceptual model of hard rock aquifers, the Indo-French Centre for Groundwater Research (IFCGR-Hyderabad) has developed a Decision Support Tool for sustainable groundwater management in semi-arid hard rock regions at watershed scale (DST-GW). The main objective of the tool is to help stakeholders to visualise what would happen to groundwater levels under different conditions of groundwater exploitation (irrigation), implementation of artificial recharge structures, and rainfall scenarios. The hydraulic model involved in DST-GW is based on the water table fluctuation method and groundwater budgets performed at catchment scale [START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF]. DST-GW predicts mean groundwater levels at seasonal and watershed scale and is thus a lumped model. However, the purpose tool is applicable at watershed scale, so it cannot be used for the groundwater management of a very small area or a single exploited borewell.

For its scientific development, DST-GW has been implemented in a representative south Indian watershed (Maheshwaram watershed; 58 km² in area, Fig. 1) characterised by a granitic basement, a relatively flat area (from 590 to 670 masl), semi-arid climatic conditions (mean annual precipitation: 750 mm; potential annual evapotranspiration: 1800 mm; aridity index= 0.42), rural context, and groundwater overexploitation due to large amount of water pumped for the irrigation of rice (210 ha during the dry season and 500 ha during the rainy season), vegetables (15 ha), flowers (20 ha), fruit trees (47 ha) and grape (55 ha). The mean annual groundwater abstraction at catchment scale is about 10 Mm 3 (174 mm). This work presents the developed tool and particularly the hydraulic model involved in, and its application to the Maheshwaram watershed. -Scenario: it comprises a modulus for creating the "most probable scenario" (i.e., the one that predicts what should happen in the studied area according to agricultural, industrial, tourism policies) and other scenarios for testing other strategies. For all the scenarios, groundwater uses can be modified as well as rainfall conditions. This window also comprises input information on exploited borewells (i.e., elevation of the base of the fissured layer), structures favouring artificial recharge and rainfall historical data.

BRIEF DESCRIPTION OF THE TOOL

DST-GW is devoted to groundwater management in semi-arid hard rock areas. Scenarios including impacts of rainfall change and/or changing cropping patterns can be tested and groundwater levels forecasted. DST-GW is particularly well designed for watershed size ranging from 10 to 100 km 2 where geology, layers thicknesses of the weathering profile and hydrodynamic properties can be considered as homogeneous. In addition and as a basic prerequisite, the aquifer has to be unconfined to allow proper uses of groundwater budget and water table fluctuation methods (see next section).

As a main result, the tool estimates mean groundwater levels at basin scale on a seasonal time-step (for dry and rainy seasons only; two values per year) and an estimate of the drying up of exploited borewells. Therefore, the tool is particularly well-suited for encouraging discussions between stakeholders to favour the implementation of groundwater exploitations strategies and regulations.

DESCRIPTION OF THE HYDRAULIC MODEL INVOLVED IN DST-

GW

The hydraulic component of DST-GW is based on the combination of the well-known 'Water Table Fluctuation (WTF)' method and groundwater budget computation for estimating aquifer specific yield and annual recharge [START_REF] Maréchal | Importance of irrigation return flow on the groundwater budget of a rural basin in India[END_REF]2006;[START_REF] Saha | Determination of specific yield using a water balance balance approach -A case study of Torla Odha watershed in Deccan Traps[END_REF]. This combination offers two main advantages while dealing with hard rock aquifers and semi-arid context: i) no use of hydrodynamic parameters in the model except for estimating some groundwater budget components, and ii) evapotranspiration from crops, which can be the source of large uncertainties particularly in semi-arid climate, does not need to be estimated as DST-GW focuses on groundwater fluxes only (recharge is computed with WTF method). The only basic requirements to use this method are that the aquifer should be unconfined and that dry and rainy seasons should be well differentiated (significant water table rises and declines).

Groundwater budget equations

The basic equation governing the fluxes at the basin-scale into an unconfined aquifer is [START_REF] Schicht | Hydrologic budgets for three small watersheds in Illinois[END_REF]:

S Q Q PG E Q RF R bf off in Δ + + + + = + + (1)
where

h S S y Δ = Δ . ( 2
)
where S y is the specific yield (dimensionless), or the effective porosity of the unconfined aquifer.

Due to seasonal monsoon rainfall, piezometric levels display sharp rises and declines (see Table 1). Therefore, the hydrological year can be divided into two distinct seasons: dry (Rabi, November to May) and rainy (Khariff, June to October). Thus, combining the use of both groundwater budget and water table fluctuation methods twice a year allows on the one hand, the computation of the unknown parameter S y during the dry season by assuming a nil recharge in Eq.1, and on the other hand the computation of recharge during the rainy season (i.e., with known S y in Eq.1).

Estimation of the flow components

Application of the WTF method requires an accurate knowledge of all the components of the budget, except recharge and specific yield, which are considered in this approach as unknown parameters and are calculated using the combined method described above.

WTF method has been successfully applied to Maheshwaram watershed. Tables 1 and2 summarize the seasonal and annual groundwater budget for the five studied hydrological years (2001 to 2005). The next section briefly describes how the different components have been evaluated. However, the reader may find additional information in [START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF] for all components of Eq.1 and the impact of their uncertainties on the groundwater balance, Sy and R estimates, in [START_REF] Zaidi | Optimizing a piezometric network in the estimation of the groundwater budget: A case study from a crystalline-rock watershed in southern India[END_REF] particularly for h and Δh estimates according to various observation networks and impact on balance, Sy and R, and finally in [START_REF] Dewandel | An Efficient Methodology for Estimating Irrigation Return Flow Coefficients of Irrigated Crops at Watershed and Seasonal Scales[END_REF] for RF and PG estimates. Using such methods [START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF] showed that the maximum expected uncertainty introduced by the fluxes components of Eq.1 on the total groundwater recharge and the annual groundwater balance is about 20%.

Precise seasonal water level fluctuations (Δh) were obtained by establishing detailed piezometric maps at the end of the dry seasons (in June) and at the end of the rainy seasons (October-November) based on up to 165 piezometers (IFCGR and non-exploited borewells;

Fig. 1). No measurements were perfomed in pumped wells and the rare cases of observed drawdown in the monitored wells (monitoring of IFCGR observation wells; time recording time interval: 15') are never more than 10 to 20 cm, which is little compared to water table fluctuations at seasonal scale (several meters). The relative error on the water table fluctuation has been calculated by geostatistics and logically decreases with the number of measurements (Tab.1). Mean seasonal piezometric levels were obtained from mapping and kriging of piezometric data; Figure 3 presents one of the piezometric map and Table 1 the mean seasonal piezometric levels at the catchment scale and the corresponding Δh with the relative error (±).

Groundwater abstraction (PG in Eq. 1) was evaluated by combining a well inventory database (i.e., discharge rate of each exploited borewell) and daily duration of pumping, which have been deduced from piezometric data influenced by exploited wells [START_REF] Dewandel | An Efficient Methodology for Estimating Irrigation Return Flow Coefficients of Irrigated Crops at Watershed and Seasonal Scales[END_REF].

The mean annual groundwater abstraction (Mean annual in Table 2) is 174 mm, and has been evaluated for all groundwater uses, i.e.: rice (86.8% of PG), vegetable (0.7%), flowers (0.8%), fruit trees (2.5%), grapes cultivation (5.2%), domestic (1.7%) and poultries (2.3%). The groundwater abstraction was found in accordance with the one evaluated from a land use map using remote sensing technique (difference of about 5%, [START_REF] Taylor | Land use / land cover study of the Maheshwaram watershed, Ranga Reddy District, Andhra Pradesh using remote sensing and GIS techniques[END_REF]. Table 2 presents the groundwater abstraction per season and per uses.

Irrigation return flows (RF in Eq. 1) were estimated for each irrigated crops, except for grapes and fruit trees, which use drip irrigation technique; for these crops RF was assumed nil.

Irrigation return flows for rice, flowers and vegetables cultivation were estimated from a hydraulic model that combines both unsaturated and saturated flow theory and few information about the soil type, the meteorological conditions [rainfall and potential evaporation], the frequency of irrigation and the cropping calendar [START_REF] Dewandel | An Efficient Methodology for Estimating Irrigation Return Flow Coefficients of Irrigated Crops at Watershed and Seasonal Scales[END_REF].

The obtained irrigation return flow coefficients, i.e. the ratio of the irrigation return flow (RF)

to the abstracted flow (PG), are on average 47% for rice (PG rice /RF rice in Tab.2), 23% for vegetables and 10% for flowers, and are similar to coefficients found in the literature [START_REF] Apgwd | Studies on hydrologic parameters of ground water recharge in water balance computations[END_REF][START_REF] Chen | Analysis of water movement in paddy fields (II) simulation studies[END_REF][START_REF] Jalota | Model-based assessment of water balance components under different cropping systems in North-West India[END_REF]. As no information about return flow from domestic and poultries uses was available, and since return flow may exist, a value of 20% was assumed. On average, mean annual return flow from pumping corresponds to about 72 mm meaning that about 42% of the groundwater abstraction returns back to the aquifer.

Groundwater evaporation, E, was estimated using an empirical relationship established for semi-arid environment that depends on groundwater level depth, z, (E=71.9z -1.49 ; Coudrain, et al., 1998). In Maheshwaram watershed, it is a very small groundwater budget component, 2.3 mm/year, due to quite deep water levels (more than 17 m). Even if the proposed estimate is probably not the best because of the use of an empirical relationship its order of magnitude is outlined. In addition, because of its small amount compared to other fluxes this component will not affect significantly the groundwater budget.

Horizontal in and out flows, Q in and Q off , across the aquifer boundaries, were evaluated using a finite-differences model run in steady state mode (Modflow; [START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF]. The top of the fresh basement [START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF] and the piezometric map are used in computation. The aquifer permeability has been assumed constant at the watershed scale and equal to 4x10 -6 m/s [START_REF] Maréchal | Use of hydraulic tests at different scales to characterize fracture network properties in the weathered-fractured layer of a hard rock aquifer[END_REF]. Results show that Q in and Q off are also small components of the groundwater budget (annual balance: about -1 mm/year).

Computation of the specific yield and the recharge at the watershed scale

Specific yield, S y :

According to the different groundwater fluxes (see previous section), the specific yield, S y , is computed for each dry seasons at the watershed scale using Eqs. 1 and 2. As a consequence, a set of couple of S y versus range of piezometric fluctuations (Δh) is obtained, Δh being defined by the average post-and pre-monsoon piezometric levels (see Table 1).

The bars in Fig. 4 illustrate the (S y , Δh) couples for the dry seasons October 01-June 02 (S y : 0.013), November 02-June 03 (S y : 0.014), November 03-June 04 (S y : 0.015) and November 04-June 05 (S y : 0.014).

In the fissured zone of the weathering profile, aquifer permeability and storativity primarily depends on the degree of weathering and thus on the density of fissures, which itself decreases rapidly with depth [START_REF] Acworth | The development of crystalline basement aquifers in a tropical environment[END_REF][START_REF] Wyns | Caractérisation multiparamètres des horizons de subsurface (0-100 m) en contexte de socle altéré[END_REF][START_REF] Maréchal | Use of hydraulic tests at different scales to characterize fracture network properties in the weathered-fractured layer of a hard rock aquifer[END_REF], Dewandel et al., 2006). Moreover, it has been shown that fissure properties are characterized by similar hydraulic properties whatever their location in the fissured layer [START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF]. Consequently, it is proposed to model the variation in S y vs. elevation by the variation in the percentage of fissures with the elevation. The percentage of fissures is deduced from a statistical analysis based on flowmeter tests in borewells carried out in the study area (Fig. 5; [START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF]. To compute the vertical variation in S y at the watershed scale, the fissured zone is discretized in five layers of equal thickness (layers L3 to L7; Figs. 4 &5), and the saprolite layer in two layers of equal thickness (layers L1 and L2).

First, the average specific yield of the investigated fissured zone, S ypond-FZ , is computed using Eq. 3:

∑ ∑ = = - Δ Δ = n i i n i i i y FZ pond y h h S S 1 1 / (3)
where S yi are S y estimates in the investigated intervals Δh i (see Table 1).

Then, assuming a linear relationship between S ypond-FZ and the percentage of fissures encountered within the same investigated zone leads to estimate the average S y per fissure, Ratio %fiss. :

∑ ∑ = = Γ Γ = n i i i n i i FZ pond y fiss X S Ratio 1 1 _ . % (4)
where X i is the percentage of fissure in the discretized layer i, Γ i is the investigated fraction of the discretized layer i. Therefore, Eq. 4 is used for modelling the variation in S y vs. elevation for the entire fissured zone by multiplying the percentage of fissure of each layers by Ratio %fiss . For the saprolite layer, weighted values of the investigated intervals within the saprolite layer are used. Figure 4 presents the modelled S y at the watershed scale vs. elevation deduced from the four years of records.

When all the data set (June 01 to June 05, 4 years) is used for calibrating the S y model, it gives for the fissured zone of the Maheshwaram granite aquifer an average S y value of 8.0x10 -3 , which is consistent with data obtain from pumping tests, in average 6.3x10 -3 [START_REF] Maréchal | Use of hydraulic tests at different scales to characterize fracture network properties in the weathered-fractured layer of a hard rock aquifer[END_REF], this validates the high elevation-decrease in S y and shows the robustness of the methods used for modelling S y . However, if required the S y model can be changed and different S y values can be prescribed to each of the seven discretized layers. This option lets the users free to test any other hypothesis concerning the variation in specific yield with elevation.

Recharge, R:

Once the S y model is established, then the recharge model is computed using Eqs. 1 and 2, and the groundwater budget components during the rainy seasons. This procedure is more accurate than the classical water table fluctuation method which usually assumed a constant S y value for the entire aquifer thickness. It is why recharge values estimated in [START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF] Based on the analysis of the complete data set (four years of records) and on the previously defined S y model, a linear relationship between recharge and annual rainfall is found (Fig. 6).

This model is thus applied to rainfall values for modelling the recharge at the watershed scale.

However even if the linear character of this relation is consistent with other studies carried out in granitic areas of south India that use tritium as recharge tracer (Fig. 6; [START_REF] Rangarajan | Annual replenishable ground water potential of India -an estimate based on injected tritium studies[END_REF][START_REF] Sukhija | Ground water recharge in semiarid regions of India: an overview of results obtained using tracers[END_REF], the model computes higher values. Because of the use of the WTF method, DST-GW computes an estimation of the total recharge at the basin-scale while the tritium technique, a local approach, gives only the minimum recharge ('direct' recharge) and does not take account of both 'indirect' recharge, the percolation through rivers and tanks beds, and 'localized' recharge through local geological or topographic variations [START_REF] Lerner | A guide to understanding and estiamtiing natural recharge[END_REF][START_REF] Maréchal | Combining the groundwater budget and water table fluctuation methods to estimate specific yield and natural recharge[END_REF]. This is why values from DST-GW are higher. In turn, this difference gives the maximum expected percolation at the watershed scale through the tanks located in the study area that cover about 80 ha. The difference between the two trends (positive value only) is used for estimating the maximum expected artificial recharge from the tanks under variable rainfall conditions.

Once S y and recharge models are calculated, the annual groundwater balance can be computed for each hydrological year (Tab. 2).

For the Maheshwaram aquifer, the groundwater balance from 2001 to 2005 is very often negative which illustrates the overexploited status of this aquifer. Only the year 2003 was a 'positive' year due to high and quite exceptional rainfall (1041 mm). On average (see Mean annual in Tab. 2), annual depletion of groundwater levels is 1.5 m with a negative balance of about -20 mm. This shows that even if rainy year may provide a significant increase of groundwater levels, their frequency is too low to counterbalance the negative balance induced by the pumping during 'normal' and 'weak' rainy years.

Computation of piezometric level and model sensitivity

Computation of piezometric levels

Piezometric levels computation either for validating the model or forecasting groundwater levels under different abstraction and/or rainfall conditions, makes use of the S y model, the recharge model, and the variation in aquifer storage, ΔS which depends on the different groundwater fluxes (see Eq. 1).

Computed piezometric level, h t+1 , at time t+1 that corresponds to the next season can be written as follows, for ΔS ≥0:

⎥ ⎦ ⎤ ⎢ ⎣ ⎡ - + - - Δ + = ∑ = + + + + + n i i y i i i y t i t i y t t S Y Y S h Y S S h h 1 1 1 1 1 1 ) ( ) ( 1 (5a)
and for ΔS <0:

⎥ ⎦ ⎤ ⎢ ⎣ ⎡ - + - - Δ + = ∑ = - + - + n i i y i i i y i t t i y t t S Y Y S Y h S S h h 1 1 1 1 1 ) ( ) ( 1 (5b)
where h t is the piezometric level at time t, h t+1 , piezometric level at time t+1, S yi , specific yield of the discretized layer i, ΔS t+1 , storage variation at t+1,Y i = bottom of the discretized layer i.

Sensitivity of the hydraulic model to the duration of the calibration period

Sensitivity of the hydraulic model has been evaluated through calibrations of S y and recharge models from different periods, i.e., from one year to four years of calibration (Fig. 7 a andb).

During these tests, Sy and recharge models are generated from the different prescribed calibration periods (1, 2, 3 and 4 years), and sets of piezometric levels are computed with these models for the entire available data set (June 2001 to June 2005; Fig. 7c). Finally, the computed levels are compared to the ones observed and the piezometric observations beyond the calibration period are used for validating the model. The deviation between observed and computed water levels is expressed as follow:

∑ - = n i i sim i obs h h m Dev , , ] .[ (6)
where h obs,i and h sim,i are observed and computed piezometric levels respectively.

Figures 7 a andb show that with the analysed data set 3 years of calibration period are necessary to obtain similar recharge and Sy models. Consequently, the difference between observed and computed water levels is low for the models calibrated on 3 and 4 years of records (Fig. 7c). One may thus assumed that continuing calibration beyond for 4 years, and thus continuing to compute all groundwater fluxes components of Eq.1, should not drastically change the these models. However, the calibration on 4 years is sensibly better (dev.=±0.56 m against ±0.95 m for the 3 years calibration period; Fig. 7c) because during the fourth year of record water levels were the deepest which has for consequence to improve the S y estimate of the deepest aquifer compartment, i.e. the fissured zone.

In the following sections, the hydraulic model established from the calibration on the entire data set (4 years) is kept for further calculation. For this watershed, DST-GW models the basin-scale piezometric levels with an average deviation of ±0.56 m from 2001 to 2005, which shows the robustness of the model.

Once the hydraulic model is achieved, the user may proceed to the scenario creator modulus.

SCENARIOS

The scenario module, in addition to feed the model with additional data (borewell database, historical rainfall data), is especially devoted to create a 'reference scenario' (i.e., business as usual scenario) as well as additional scenarios. The 'reference scenario' or 'most probable scenario' describes the future evolution of a site according to changes that are expected through strategies planned at national or regional levels. The scenario module allows creating theoretical scenarios in order to test the impact of different management measures upon the groundwater resource. For every scenarios, the DST-GW's user may i) change the abstraction of the existing groundwater uses at seasonal time-step, or add new ones, ii) change future annual rainfall, or /and iii) change the number or efficiency of artificial recharge structures.

As an example, two alternative scenarios are presented to help visualise and discuss their impacts on the groundwater resource. However, these scenarios are theoretical and issued from an optimisation approach that is not socio-economically sound since the development of the socio-economic module (e.g., computation of farmer's net returns) is not yet available.

Reference scenario

In this scenario, no climatic change is considered, and the rainfall scenario uses the past 20 years annual rainfall data for the next 20 years (Fig. 8a &b). Results show that if the groundwater exploitation by pumping continues at the present rate of development (about 1.3% per year, FAO, 1997) the groundwater resource limit at the watershed scale (i.e., the bottom of the aquifer materialized by the horizontal dotted line in Fig. 8a), below which the aquifer cannot be exploited, will be reached by the year 2012-2013. As a consequence, yields of numerous farmer's borewells will hazardously decrease or will be dry at the same date with serious socio-economic consequences; according to the model about 80% of farmer's borewells should dried up in 2012-2013 (Fig. 8b). To estimate the drying up of borewells, a database containing all exploited borewells and the borewell-specific aquifer bottom elevation is compared with simulated average piezometric levels. A borewell gets dry when the piezometric level is equal or below the aquifer bottom elevation.

Impact of rainfall or climate variability on the Reference scenario

Figure 9 presents water level simulations with the occurrence of two consecutive "low" (450 mm/year) and "good" (1100 mm/year) monsoons in 2009 and 2010. Two consecutive "low" monsoons result in a strong depletion of the aquifer with a total depletion excepted in 2009-2010. With two consecutive "good" monsoons, the consequences of overexploitation are delayed by a few years and total depletion should occur by year 2014-2015. As a result and whatever the rainfall amount provided by the monsoon, the strong depletion of the aquifer will occur sooner or later, and may be faster than expected. Realistic solutions have to be found quickly and DST-GW can be useful for testing and selecting the most appropriate solutions (demand/supply measures).

Impacts of changing cropping patterns and artificial recharge on piezometric levels

DST-GW has been especially built to test the impact of changing cropping pattern measures on piezometric levels and on exploited borewells. The impact of increasing or decreasing cultivated areas onto groundwater levels is predicted.

In order to avoid unrealistic scenarios, which may not be accepted by the farmers for profitability or socio-cultural reasons, DST-GW has been designed to enable an action plan over several years where each groundwater uses can be changed at a seasonal scale. then an annual decrease of 5% up to 2020, at the same time a 20% increase of vegetable and flower cultivated areas every year. As a result, rice cultivated area, which is about 700 ha today, will be about 220 ha in 2020 and, vegetables and flowers that cover about 70 ha today, will be about 750 ha in 2020. Thus, at the end of the plan 200 additional hectares will be cultivated.

ii) Scenario 2: the same cropping pattern changes as scenario 1 and the build-up of additional percolation tanks between 2009 and 2013 (+15 ha), knowing that today they cover about 80 ha in area.

In both scenarios, the piezometric level would be more or less maintained before getting back to its original level with potential benefits for the farmer's population (+200 ha to cultivate); therefore this would bring a sustainable solution.

In addition, the scenario 2 demonstrates that artificial recharge cannot be considered as the unique measure for tackling the groundwater depletion in this area since today the existing tanks capture most of surface run off (tanks: 14% of total area). Its contribution to improve the situation is minimal compared to changing cropping patterns. But a combination of both could seriously improve the groundwater situation. Therefore, policies aiming at sustainable groundwater management may consider a package of supply/demand measures rather than only one-directional measures such as recharge augmentation.

CONCLUSION

The present study reinforces that simple techniques like groundwater budget and water table fluctuation methods can be very useful for evaluating the groundwater fluxes balance in hardrock areas in addition to some watershed scale parameters like hydrodynamic parameters (efficient porosity) or recharge from rainfall. The presented methodology is adapted to unconfined aquifer, hard-rock formations relatively homogenous in term geology and exposed to regional deep-weathering processes, i.e., where the weathering profile is characterized by thick sub-horizontal and stratiform layers, and well-differentiated dry and rainy seasons. As a consequence these methods are particularly well designed for watershed size ranging from 10 to 100 km 2 , where geology, layers thicknesses of the weathering profile and hydrodynamic properties can be considered as homogeneous. The presented methods are suitable to other similar geological and climatic context around the world such as in Africa where evidence of deep-weathering and stratiform hard rock aquifers have been reported, e.g. in Malawi [START_REF] Chilton | Hydrogeological characterization and water-supply potential of basement aquifers in tropical Africa[END_REF], Uganda [START_REF] Taylor | A tectono-geomorphic model of the hydrogeology of deeply weathered crystalline rock: evidence from Uganda[END_REF], Burkina Faso (Courtois et al., in press). However, where high spatial variations in the weathering profile thickness, or a highly heteregeneous geology, characterizes the area (e.g., in mountainous areas, areas densely fractured/faulted), the methodology will be not applicable.

The developed DST-GW is based on groundwater budget and water table fluctuation methods at basin-wide scale, and is especially designed for groundwater management in hard rock area experiencing semi-arid conditions. The present tool is able to estimate groundwater levels and drying-up of borewells at the catchment scale at seasonal time-step. It is a tool where the stakeholders can build-up scenarios (i.e., changing the groundwater uses or climatic conditions, testing artificial recharge solutions, etc.) and visualize the outcome at the watershed scale. DST-GW is thus an interactive tool useful to provoke discussion between community and policy makers, which should help the implementation of sustainable solutions to reduce or control the stress imposed by human activities on aquifers.

The tool has been tested and validated in Maheshwaram watershed (Andhra Pradesh, India)

and it shows that if no adequate measures are taken, alarming depleted groundwater levels will be reached soon due to overexploitation. Solutions exist (e.g., scenarios with changing cropping patterns and additional artificial recharge) but they need to be validated by a socioeconomic study. In addition to this water shortage and because of the closed character of the studied aquifer, a deterioration of the groundwater quality due to a enrichment of elements by evapotranspiration is also expected, e.g. increase of water salinity, of contents in pesticide or in other contaminants.

This tool is a first version and for its future development it is planned to include socioeconomic parameters (e.g., farmers categories, net return of crops, etc.), and hence to forecast farmer's incomes according to different groundwater management scenarios. This will be tested within a new case study in Andhra Pradesh. Socio-economic parameters and census data at basin scale will be integrated to DST-GW. This should give more information on the impact of land use changes over the farmers' average income per farmers' categories. This Figure 5: Variation in percentage of fissures with elevation (or vs. mean ground level) in the fissured zone for Maheshwaram aquifer, deduced from flowmeter measurements [START_REF] Dewandel | A generalized hydrogeological conceptual model of granite aquifers controlled by single or multiphase weathering[END_REF].

Figure caption:

Figure 6: Computed annual recharge at the watershed scale vs. annual rainfall model according to groundwater budget data and S y model (Fig. 4). Is also plotted the rainfall-recharge relationship estimated with tritium techniques in hard rock aquifers of India [START_REF] Rangarajan | Annual replenishable ground water potential of India -an estimate based on injected tritium studies[END_REF][START_REF] Sukhija | Ground water recharge in semiarid regions of India: an overview of results obtained using tracers[END_REF].

Figure 2

 2 Figure2presents the break down structure of the tool.

  are slightly different (Jun 02 -Nov 02: 70.5 against 70.8* mm; Jun 03 -Nov 03: 156.5 against 160.4* mm; * this study).

  For example, Figures 10a & b consider two 15-years action plan scenarios: i) Scenario 1: 10 % decrease of rice cultivated area every year from 2009 to 2017

  new development will help to propose more economically-sounded solutions, and should facilitate the acceptability and the implementation of groundwater resource management strategies.Today and particularly in India where groundwater resource suffer the consequence of an uncontrolled exploitation, it is very important to assess the availability of the water resources in a quantitative way and balance it with the demand. The difference could and should be managed by demand measures such as changes in agricultural practices (cropping patterns, irrigation techniques, etc.) and supply measures such as artificial recharge. Decision Support Tools are a well adapted approach because users can play on the different components of the water demand/supply in an interactive way.

Figure 1 :

 1 Figure 1: Maheshwaram watershed, 700 borewells in use for irrigation and up to 165 observation wells used for establishing piezometric maps.

Figure 2 :

 2 Figure 2: Breakdown structure of the DST-GW.

Figure 3 :

 3 Figure 3: Example of piezometric map (June 05, 165 observations); average piezometric level: 608.5 m. The insert presents the variogram used for data interpolation.

Figure 4 :

 4 Figure 4: Calibrated vertical distribution of the specific yield with elevation at basin-wide scale (or mean ground level) for the Maheshwaram watershed. S y is deduced from dry seasons October 01-June 02 (S y : 0.009), November 02-June 03 (S y : 0.014), November 03-June 04 (S y : 0.015), November 04-June 05 (S y : 0.013) and modelled S y according to variation in % of fissures vs. elevation (Fig. 5).

Figure 7 :

 7 Figure 7: (a) Variations in S y model according to the duration of the calibration period, (b) variations in recharge model according to the duration of the calibration period, and (c) Right axis: observed and computed piezometric levels according to the duration of the calibration period. Left axis: deviation between observed and computed piezometric levels according to the duration of the calibration period.

Figure 8 :

 8 Figure 8: (a) Reference scenario. Simulation of water levels in Maheshwaram watershed, no climatic change and ~1.3%/year increase of irrigated area (FAO, 1997). Triangles: mean seasonal piezometric levels during the calibration period (2001 to 2005) and (b) dryingup of borewells according to the Reference scenario. The vertical bars depict the degree of uncertainty, which depends on the spatial variability of the aquifer thickness (about 25% of uncertainty). The interrogated borewell database contains 706 borewells.

Figure 9 :

 9 Figure 9: Reference scenario. Case of two consecutive "low" (450 mm/y) and "good" (1100 mm/y) monsoons in 2009 and 2010.

Figure 10 .

 10 Figure 10. (a) DST-GW, testing scenarios on groundwater levels. Scenario 1: 10 % decrease of rice cultivated area every year from 2009 to 2017 then an annual decrease of 5% up to 2020, at the same time 20% increase of vegetables and flowers cultivated area every year. Scenario 2: considers scenario 1 + build-up of 15 ha of additional percolation tanks between 2008 and 2013, and (b) impact on borewells. Results of Scenario 1 and 2 (fig. 10a).

  Fig. 1

  Fig. 6

  Fig.9

Table caption :

 caption Table 1: Mean piezometric levels post and pre-monsoon from June 2001 to June 2005 (h, in masl), corresponding water table fluctuation (Δh in m), and number of observation wells

	(IFGCR borewells and abandoned wells) used for the establishment of piezometric maps
	(e.g., Fig.3). * ± : relative error (deduced from geostatistics), ** from 2002 to 2005, 90
	borewells have a common location. masl: meters after sea level. Catchement area: 58
	km 2 .

Table 2 :

 2 Groundwater fluxes (see Eq.1) and groundwater budget from June 01 to June 05, Maheshwaram watershed 58 km 2 . * not used in the groundwater budget computation, and annual recharge (Rech.) is computed using the Sy vs. elevation model (see text for explanation). Veg.: vegetables, Flow.: flowers, Grap.: grapes, Dom.Use: domestic use, Poul.: poultries, Sum GW abstr.: sum of all groundwater abstraction and Sum GW RF: sum of all return flows.

Acknowledgements

The authors are grateful to the research-sponsorship from many sources such as: IFCPAR (New Delhi), BRGM (France), Embassy of France in India, NGRI (India) and the European Commission (SUSTWATER AsiaProEco Project). Colleagues from NGRI (Hyderabad), BRGM (France), Department of Rural Development, AP Govt. and AP Groundwater Department, CGWB are thanked for their fruitful comments and discussions. The two Journal's referees, J. Krásný and an unknown reviewer, are thankful for their fruitful remarks and comments that improved the quality of the paper.

Table 2


G Renaudin

A Bertrand

M Dubois

S Gomes

P Chevalier

A Labrosse

A study of water releases in ground (GCC) and precipitated (PCC) calcium carbonates

Keywords: Precipitated calcium carbonate (PCC), ground calcium carbonate (GCC), water release, calcite, portlandite, structural water

Precipitate Calcium Carbonates (PCCs) are important industrial products mainly used as fillers. Because of their regular, synthesized characteristics (e.g. grain shape or grain size distribution) PCCs are distinct from natural Ground Calcium Carbonates (GCCs). A thermal study on GCC samples showed only the presence of surface physisorbed water with a monotonic weight loss up to the carbonate decomposition. In the case of PCC samples, two supplementary water releases were observed. The first one appeared at around 525 K and the second one at around 725 K. The nature of the water present in two different PCC samples was investigated by thermal analyses (thermogravimetric and Karl Fischer analyses), Rietveld analyses on X-rays powder diffraction, infrared and Raman spectroscopies and solid state 1 H MAS NMR. The second water release at about 725 K was clearly identified as being portlandite dehydration. Ca(OH) 2 was present in the PCC in an amorphous state. Its crystallization occurred simultaneously to the first water release at about 525 K. Structural effects observed on calcite during the first water release led to the assignment to structural water molecules inserted in the structure of calcite.

Calcium carbonate is used as fillers for numerous applications, including thermoplastics.

Ground Calcium Carbonates (GCC: ground from natural chalk, limestone or marble) are generally used as fillers with an interesting performance/price ratio. However, more expensive Precipitated Calcium Carbonates (PCC, synthetic CaCO 3 ) can be used to fulfil additional needs like processing aid, impact modification and better weatherability [1][START_REF] Mathur | Polymer Modifiers and Additives, Chapt. 5[END_REF][START_REF] Han | [END_REF].

PCCs are much more than a filler, namely because of their regular and controlled crystalline shape, ultrafine particle size and narrowness of particle size distribution. PCCs are obtained by reaction of gaseous carbon dioxide with a colloidal suspension of calcium hydroxide and can be decomposed by the two simple following reactions:

-thermal decomposition of GCC:

+ ⎯→ ⎯ + (2)
The synthesis process is sufficiently versatile to allow a number of different morphological characteristics to be developed [START_REF] Mathur | Polymer Modifiers and Additives, Chapt. 5[END_REF][START_REF] Han | [END_REF][4]. However, calcium carbonates are containing various levels of moisture, which impose careful storage or the use of desiccant additives such as CaO to preserve the final properties of e.g. thermoplastic films [5].

Indeed, compared to GCC, two additional water releases were observed when heating PCC at around 525 and 725 K. These two water losses were superimposed on the monotonic weight loss observed from room temperature (RT) up to the temperature of decomposition of the carbonate (i.e. about 1025 K). The monotonic weight loss was present in both PCC and GCC cases and was assigned to both physisorbed surface water molecules and to the beginning of the decarbonation process. The physisorbed water was correlated to the specific surface area of the samples. The present study, realised in partnership with Specialty Minerals Inc., was aimed at identifying the origin and nature of the two additional kinds of water present in PCCs, that can be considered as the main impurity. Water losses were identified and quantified by several techniques, namely thermogravimetric analysis (TGA) and Karl Fischer analysis (KF), correlated with a previous study by thermal analysis at controlled kinetics coupled together with mass spectroscopy [START_REF] Labrosse | Synthèse, flottation, épuration et morphologie des carbonates de calcium précipités[END_REF]. Powder X-rays diffraction, vibrational spectrometry and solid state 1 H MAS NMR were further used to elucidate the origin of these two water losses from PCCs.

Samples and thermal treatments

Three different samples were investigated in this study. Two commercial PCCs, named PCC-1 and PCC-2, were selected because of their different thermal behaviour. Indeed, the PCC-1 weight losses were more pronounced than the ones of sample PCC-2. This study was realised in partnership with Specialty Minerals Inc., the minerals producer of PCC-1 and PCC-2 under the respective trade names Albafil and Calofort U. A third GCC sample (Millicarb from Omya) was used as a reference material exhibiting only a monotonic weight loss before the thermal decomposition of carbonate. Table 1 gives the physical properties of the samples. The characterization made on the PCC-1 and GCC samples agreed with the indication given by the manufacturers. In the case of the PCC-2 the measured specific surface area was accurate but the median grain size and grain size distribution were larger than the given indications.

This has to be explained by the grains agglomeration in this sample. Nevertheless the three samples presented quite equivalent physical characteristics. Figure 1 shows the thermal behaviour of these three samples from RT up to 825 K.

Thermal treatments were applied to these three calcium carbonates: 15 hours in a furnace from 325 K up to 775 K by steps of 50 K. Eleven samples (as-synthesized, 325 K, 375 K, 425 K, 475 K, 525 K, 575 K, 625 K, 675 K, 725 K and 775 K) for each carbonate (PCC-1, PCC-2 and GCC) were prepared for this work. The named as-synthesized samples in the text correspond to commercial as-received samples. Infrared and Raman spectroscopy experiments were also carried out on PCC-1 thermally treated at 500 K and 550 K. After each thermal treatment, samples were analysed by TGA to check the water content and were then stored in a dry box before being studied by the following different analytical techniques.

Thermal analyses

Thermogravimetric analyses (TGA) have been performed on a Mettler Toledo TGA_SDTA 851e apparatus. About 30 mg of powder were introduced in an alumina crucible. Measurements were realized from 300 K up to 1275 K with a heating rate of 5 K.min -1 under a nitrogen flow of 25 ml.min -1 . A blank curve, obtained in the same condition with the same empty alumina crucible, was systematically subtracted.

Identification of the different weight losses was made by derivative DTA curves.

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

4 Karl Fischer analyses (KF) were performed on a Metrohm coulometric model 756 titrator using a model 768 oven. Experimental KF parameters were optimized to be able to compare TGA and KF data, therefore the experiments were carried out in two steps: one at 393 K and the second one at 573 K (see Table 2). Other parameters like sample mass of around 2 g; start and relative drift of around 15 mg.min -1 and nitrogen flow of around 60 ml.min -1 were applied.

Powder X-rays diffraction (PXRD)

Powder X-rays diffraction measurements were carried out in order to check the presence of portlandite in PCC. The weight loss measured at 725 K was close to 0.5 wt % (weight percent) which corresponds to approximately 2 wt % of portlandite (Figures 1 and2).

Long time measurements in a quite short two theta range were performed in order to obtain a good statistic counting that allowed to extract low intensity, and possibly broad, peaks from the background. X-rays powder patterns were recorded on a Siemens D5000 diffractometer at room temperature, Bragg-Brentano geometry, Cu K α radiation (λ = 1.54184 Ǻ), two theta range 15-60°, two theta step 0.02°, 22 seconds per step leading to a measuring time of 14 hours for each sample. The chosen two theta range allowed identification of portlandite by its non-overlapped diffraction peaks at 2θ = 18.06°, 34.14° and 50.88° [START_REF]JCPDS -International Centre for Diffraction Data, PDF2 Data Base[END_REF] and Rietveld refinement of CaCO 3 structure (calcite type) by the presence of twelve Bragg peaks [START_REF]JCPDS -International Centre for Diffraction Data, PDF2 Data Base[END_REF]. 20 wt % of pure silicon were added to the sample as internal standard in order to increase the accuracy in two theta position and to determine the instrumental resolution function of the diffractometer. Powder pattern Rietveld refinements were performed with FullProf.2k [START_REF] Rodriguez-Carvajal | PROGRAM FullProf.2kversion 3.30[END_REF]. The diffraction peak profiles (both instrumental and intrinsic sample) were modelled by using a Thomson-Cox-Hastings pseudo-Voigt function [START_REF] Rodriguez-Carvajal | PROGRAM FullProf.2kversion 3.30[END_REF] (convolution of Gaussian and Lorentzian components having different full widths at half maximum, FWHM). Lattice parameter of Si (cubic m Fd 3 space group) was fixed at a = 5.43088 Å. Lattice parameters, oxygen position and microstructure parameters of calcite (rhombohedral c R3 with a = 4.988 Å and c = 17.061 Å [START_REF] Effenberger | [END_REF]) were refined. Lattice and microstructure parameters of portlandite (trigonal 1 3m P with a = 3.589 Å and c = 4.911 Å [10]) were refined when observed. Background pattern was modelled with a five coefficients polynomial function. The refined parameters from the X-rays diffraction patterns are gathered in Table 3. The Rp and χ 2 values indicate the quality of the refinement. The standard deviations (indicated in bracket in Table 3)

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

5 correspond to 3σ weighted by χ 2 in order to avoid an overestimation on the accuracy of the refined parameters (σ is the standard deviation determined by FullProf.2k).

DRIFT spectroscopy

Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectra were performed on a

PerkinElmer System 2000 FTIR spectrometer equipped with a Diffuse Reflectance sphere accessory from SpectraTech. 5% of sample were intimately mixed and ground with pure KCl and background spectra were recorded with pure KCl only. Spectra were obtained at 4 cm -1 spectral resolution from 450 to 5600 cm -1 , 15 scans (165 s), apodization "strong", KBr beamsplitter, DTGS detector. For peak area measurements, spectra were previously normalized on the calcite peak at 2512 cm -1 at the arbitrary absorbance unit of 0.3.

Raman spectroscopy

Raman spectra were performed on a Renishaw InVia Reflex Raman Microscope equipped with a CCD detector and a 514 nm laser source. Powder samples were analysed under a microscope using a x100 magnifying objective and laser power of around 10 mW at the sample focus. Spectra were typically recorded from 100 to 4000 cm -1 (Notch filter) with a 1800 grooves.mm -1 grating leading to a spectral resolution of around 4 cm -1 . 3 scans of 15 s were usually recorded. Like for DRIFT, spectra were normalized on a calcite band (1087 cm -1 ) before peak area measurements.

Solid state 1 H MAS NMR

The solid state 1 H MAS NMR spectra were recorded at room temperature with a Bruker MSL300 spectrometer operating at 300.1 MHz. MAS spectra were performed at 14 kHz with a Bruker 4 mm MAS probes using a simple sequence (τ -acquisition). The processing and acquisition parameters were recycle time 5 s, 5 μs single π/2 pulse duration and 64 scans. The 1 H chemical shifts are given in ppm and refer to tetramethylsilane (TMS) by using adamantane as an external standard (1.74 ppm from TMS). The reproducibility of the measured chemical shift values was of around 0.1 ppm.

X-rays fluorescence

A c c e p t e d m a n u s c r i p t 6 X-rays fluorescence measurements have been realised on the three as-synthesized samples. Measurements have been performed in a semi-quantitative multi-elements analysis on a Bruker AXS S4 Pioneer equipment. The three samples present quite the same purity level with the same impurities (see Table 1). Magnesium is the main impurity presents at around 0.5 wt % expressed in elementary oxide: 0.41 wt % of MgO in GCC, 0.58 wt % in PCC-1 and 0.50 wt % in PCC-2. The others impurities are minors, with levels below 0.4 wt % for P 2 O 5 , below 0.2 wt % for SiO 2 , and even below 0.1 wt % for Al 2 O 3 , SrO, SO 3 and Fe 2 O 3 , when applicable.

Results and discussion

Thermal analyses

The two weight losses observed at about 525 K and 725 K in case of PCCs are shown in Figure 1 by TGA and derivative curves. These two thermal events were characteristic of PCCs, as indicated by a comparison with the GCC TGA curve presenting only the monotonic weight loss from RT up to carbonate decomposition. The calculated weight loss values relative to each event were not the same in the two PCCs: PCC-1 weight losses were significantly larger than the PCC-2 weight losses: respectively 0.79 wt % and 0.24 wt % at around 525 K for respectively PCC-1 and PCC-2, and 0.59 wt % and 0.06 wt % at around 725 K for respectively PCC-1 and PCC-2. In the case of GCC, a very small weight loss of about 0.02 wt % can be observed for the first thermal event at around 525 K. A good agreement was found between TGA and KF analyses for the three samples (Table 2), bringing evidence for the assignment of the two weight losses to water releases. The higher values found for the PCCs by KF compared to TGA were due to a static thermal process versus a dynamical one by TGA. On the other hand, variations of the physisorbed water level due to storage conditions were not excluded. The smaller value obtained by KF for the GCC sample between 393 and 573 K (0.08% versus 0.16%) proves the contribution of the decarbonation process to the monotonic loss above 393 K. Thermal analyses coupled with mass spectroscopy on PCCs confirmed these attributions to water release by evidencing two peaks coming from the OH + signal at 525 K and 725 K [START_REF] Labrosse | Synthèse, flottation, épuration et morphologie des carbonates de calcium précipités[END_REF]. another, but also the ratio between the first and the second releases did. From 1.34 for PCC-1, it increased to 4.00 for PCC-2. Figure 2 illustrates also the irreversibility of both water releases: samples thermally treated above the thermal event did not show the corresponding weight loss anymore, contrarily to the reversible monotonic weight loss that was always observable for all the thermally treated samples, as usual with physisorbed water. As a matter of fact, PCCs usually contain a higher amount of surface adsorbed water molecules because of their higher specific surface areas compared to GCC: in our case, respectively 7 m 2 .g -1 , 26 m 2 .g -1 and 3 m 2 .g -1 for samples PCC-1, PCC-2 and GCC, as measured by nitrogen at 77 K after an outgasing step at 120 °C (BET method, see Table 1). Moreover, the level of physisorbed water (Table 2) correlated well with the BET specific surface area extend. The water release observed at around 725 K should correspond to hydroxyl condensation of portlandite Ca(OH) 2 . The calculated weight losses led to portlandite amounts of 2.43 wt % and 0.25 wt % for respectively PCC-1 and PCC-2. These amounts indicated that portlandite could be detected by powder X-rays diffraction for PCC-1, but not in the case of PCC-2, whose Ca(OH) 2 amount was clearly too small.

Rietveld analyses of the powder X-rays diffraction patterns

Rietveld analyses were performed for each as-synthesized samples and each thermally treated PCC-1, PCC-2 and GCC samples. Four phases were simulated as follow: i/ silicon Si (internal standard), ii/ calcite CaCO 3 (largely majority phase), iii/ portlandite Ca(OH) 2 (when observed) and iv/ lime CaO (when observed for thermal treatment above 725 K).

Portlandite was identified in PCC-1 for thermal treatments above 475 K. It disappeared at 775 K and lime was observed. Figure 3 shows the evolution of the refined diffraction powder patterns when heating PCC-1. Careful analysis of low intensity peaks (see inserts in Figure 3) revealed that portlandite was not visible in the as-synthesized PCC-1 sample.

No change was observed for treatments up to 425 K (powder patterns still correspond to top of Figure 3). Next, portlandite was identified for treatments between 475 K and 725 K, 3). The starting temperature of Ca(OH) 2 crystallisation (i.e. 475 K according to the 15 hours thermal treatment) was close to the first thermal event (i.e. 525 K according to dynamical TGA experiments). These two simultaneous events (water release and portlandite crystallisation) could either be dependent or independent. In the hypothesis of dependent events it should indicate that amorphous portlandite contained excess water molecules and could be expressed as Ca(OH) 2 ·xH 2 O amorph . In this hypothesis, and according to TGA results, the x value would be of about 1.34 and 4.00 for PCC-1 and PCC-2, respectively. Refinement on diffraction patterns from PCC-2 and GCC did not allow the identification of portlandite. In case of PCC-2, the weight loss of 0.06 wt % observed at 725 K led to an amount of 0.25 wt % of Ca(OH) 2 , which is to small to be detectable by X-rays diffraction, even after crystallisation. In relation to GCC, no portlandite could be detected since no weight loss appeared in TGA at around 725 K.

Figure 4 shows the evolution of calcite refined parameters as a function of the thermal treatments. PCCs did not show the same behaviour as GCC. X-rays fluorescence results on GCC and PCCs indicated that differences between samples could not been explained by the presence of impurities like Mg, Si, Al or Sr. Contrary to PCCs, the GCC lattice parameters were not sensitive to thermal treatments. In case of PCC lattice parameters evolution, we observed a break at 475 K. Before 475 K, the unit cell volume of PCC was superior to the one of GCC (and unit cell volume of PCC-1 is superior to the one of PCC-2), and after the break at 475 K the three samples converged on the same unit cell volume.

Positions of oxygen site in PCCs and GCC showed also some interesting evolutions (Table 3). Refined x coordinate of oxygen site in as-synthesized PCC and GCC samples were different (0.258 for as-synthesized PCC-1 and PCC-2 compared to 0.252 for assynthesized GCC), but became equivalent after 525 K (0.257, 0.257 and 0.258 for respectively PCC-1, PCC-2 and GCC thermally treated at 575 K). This led to interesting

Vibrations

Vibrational spectroscopies, DRIFT and Raman, were performed on the three assynthesized samples and on some thermally treated samples (mainly PCC-1 treated at around 525 K). Figure 5 shows the wavenumber range 4000 cm -1 -2000 cm -1 (water and hydroxyl vibrations, along with calcite overtone and combination modes coming from lattice or multiphonon absorptions [START_REF] White | The Infrared Spectra of Minerals[END_REF]) and a zoom around 3644 cm -1 (A 2u asymmetric stretching mode of hydroxyls in portlandite). Figure 6 shows two zooms, around 1087 cm - 1 (A 1g vibration mode of carbonate in calcite) and 3620 cm -1 (A 1g symmetric stretching mode of hydroxyl in portlandite) from the Raman spectra. Observations on hydroxyl vibrations modes were in perfect agreement with previously identified portlandite crystallisation. Asymmetric stretching at 3644 cm -1 on DRIFT spectra and symmetric stretching at 3620 cm -1 on Raman spectra are characteristic of portlandite [START_REF] Lutz | [END_REF]13] and in agreement with measurements performed on pure Ca(OH) 2 samples, not shown here. As observed by X-rays diffraction, the signal of portlandite increased upon thermal treatment, indicating its crystallization process which occurred between 475 K and 500 K. Before

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

10 this temperature treatment, the hydroxyl vibrations bands were already observable, but with a weaker intensity. This confirmed that Ca(OH) 2 was already present in the assynthesized samples but with a low crystallinity; i.e. amorphous state according to long range order necessary for X-rays diffraction. In its amorphous state, and due to hydrogen bonding, Ca(OH) 2 hydroxyl vibrations can not be easily detected because they should rather give rise to much broader signals occuring at a lower wavenumber than 3644 cm -1 in the DRIFT spectra (means within the broad water band centered at 3450 cm -1 ) or than 3620 cm -1 in the Raman spectra. The evolution of the relative amount of detected portlandite, respectively in DRIFT and Raman studies, was determined by the integrated peak area corresponding respectively to the A 2u (OH -) and A 1g (OH -) modes from portlandite. In both cases, the maximum calculated peak area was assigned to a relative amount of 1.00. Table 4 indicates the calculated relative amounts (see also Figure 7) of portlandite together with the absolute values deduced from TGA and PDRX experiments.

The differences between TGA relative values and the three other series come from the amorphous features of portlandite before 475 K.

The extremely broad band in DRIFT spectra, with maximum at around 3450 cm -1 , corresponds to stretching vibrations of hydroxyl bonds which could be assigned to four different species: i/ hydrogen bonded physisorbed water; ii/ water inserted in the calcite structure (hydrogen bonded 'structural' water, CaCO 3 ·yH 2 O); iii/ over-stoechiometric water from amorphous portlandite (Ca(OH) 2 ·xH 2 O amorph ) hydrogen bonded; and iv/ hydrogen bonded hydroxyls from amorphous portlandite. The number of species involved in this band makes its use inappropriate. Besides, the use of the bending mode of water near 1620 cm -1 was not be possible due to the strong underlying carbonate modes in this region. Moreover, the use of the first (ν + δ) water combination band at around 5200 cm -1 [14], which eliminates the contribution of hydroxyls from the amorphous portlandite, was not possible either because of the different kind of water involved and as well because this spectral range is strongly dominated by the physisorbed water contribution, whose level is higher. This explained as well the differences in the intensity of the band at 3450 cm -1 , as seen in Figure 5 left, where it was more important in PCC-1 and quasi-absent in GCC, in good agreement with the physisorbed water level from KF (Table 2) and the specific surface areas (Table 1). Finally, neither the mid infrared range (3450 or 1620 cm -1 ) nor the near infrared one (near 5200 cm -1 for sole water contribution -no other changes relative to pure Ca(OH) 2 hydroxyl contributions were seen in this range either, like between 4800

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

11 and 4000 cm -1 [14]) were usable to confirm the presence of excess water in the amorphous portlandite.

The Raman spectra on CO 3 2-A 1g symmetric stretching vibration mode at 1087 cm -1 , characteristic of calcite [START_REF] White | The Infrared Spectra of Minerals[END_REF], showed a sharpening of HWHM (half width at half maximum, as presented in Table 4) for samples thermally treated above 500 K. This indicates an increase of the local environment homogeneity of carbonate anions in the calcite structure. This observation is in agreement with the departure of some inserted water molecules from the calcite structure, and cannot be explained by the crystallisation process of portlandite, in agreement with the PXRD analyses.

Solid state 1 H MAS NMR

Solid state 1 H MAS NMR spectroscopy was performed on the three as-synthesised samples and on thermally treated PCC-1 samples at 425 K, 475 K, 575 K and 725 K (Figure 8). Spectra from as-synthesised samples (top of Figure 8) underline the differences between PCC and GCC samples. Whereas free water was visible on the GCC spectrum by the line centred at chemical shift 4.8 ppm/TMS (indicated by a star in Figure 8), two other contributions centred at 8.6 ppm, and as well relative to water (one broad and one sharp part), are also present in the GCC spectrum. The two PCC spectra both exhibit one sharp signal at 10.3 ppm and one broad line with a higher area centred close to

Conclusion

Three distinct water releases were observed in PCCs: one monotonic up to the carbonate dissociation, and two thermal events at 525 K and 725 K. Solid measured by laser granulometry with a Malvern Mastersizer Instrument using the dry-sieving technique. measured by nitrogen at 77 K after an outgasing step at 393 K and using the BET method. Raman spectra on PCC-1 thermally-treated at 375 K, 475 K, 500 K and 550 K; carbonate vibration from calcite (a, range 1070 cm -1 to 1100 cm -1 ) and hydroxyl stretching mode from portlandite (b, range 3500 cm -1 to 3750 cm -1 ).

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 3

 3

Figure 2 A c c e p t e d m a n u s c r i p t 7 steps

 27 indicates the weight losses calculated from TGA at 525 K (i.e. exclusively due to the first water release) and at 725 K (i.e. exclusively due to the second water release) according to the previously applied thermal treatment. The indicated water loss values in Figure2were obtained after removal, from TGA curves, of their own continuously monotone weight loss. This monotonic weight loss was extrapolated in two : i/ from a linear fit of the weight decrease from 300 K up to 455 K (the subtraction of this linear fit from the TGA curve, over the whole thermal range, allowed the determination of the weight loss corresponding only to the first thermal event); ii/ followed by a new linear fit performed on the as-obtained curve from 600 K up to 675 K (the new subtraction of the second linear fit allowed the determination of the weight loss corresponding only to the second thermal event). Not only the amounts of water losses did vary from one PCC to

A c c e p t e d m a n u s c r i p t 8 namely

 8 by broad diffraction peaks at 2θ = 18.0° and 34.2° (powder patterns corresponding to middle of Figure3). Finally, at 775 K, diffraction peaks of portlandite disappeared and those of lime appeared (powder pattern corresponding to bottom of Figure3with broad peaks of CaO at 2θ = 32.1° and 37.3°). This confirms that the second thermal event observed at about 725 K corresponds to the dehydroxylation of portlandite. It means that Ca(OH) 2 is present in small amounts in PCCs, but not in GCC. In course of the PCC synthesis, few weight percent of amorphous Ca(OH) 2 were precipitated together with CaCO 3 formation. A heating treatment of almost 475 K led to its crystallization. The broad diffraction peaks of Ca(OH) 2 indicated its very small coherent domain size (Table

A c c e p t e d m a n u s c r i p t 9 evolution

 9 of the inter-atomic d Ca-O distances: smaller in PCCs below 525 K, and identical in PCCs and GCC above 525 K. These several observations on calcite structure parameters showed that CaCO 3 was affected by the first water release at around 525 K. It indicated that the first thermal event was due to a release of water molecules coming from the calcite structure: water inserted in the calcite structure as a limited solid solution CaCO 3 -H 2 O. The previous hypothesis of excess water in amorphous Ca(OH) 2 ·xH 2 O amorph could not explain the observed variations in calcite structure parameters but, at this stage, we can not exclude the presence of some Ca(OH) 2 ·xH 2 O amorph though. 'Structural' water molecules inserted in the CaCO 3 network induced a repulsive interaction with the oxygen coordinated to calcium atoms, leading to the refined small contraction of the d Ca-O distance in PCCs (compared to GCC), which disappeared after 525 K. The calcite composition can be expressed as CaCO 3 ·yH 2 O with y = 0.04 and 0.01, for respectively PCC-1 and PCC-2 samples. A very small weight loss was also observed for GCC at 525 K, corresponding to the composition CaCO 3 ·0.001H 2 O. As-synthesized PCCs contain small amounts of amorphous portlandite and small amounts of water molecules inserted in their own crystallographic structure. Microstructure parameters did not show important changes according to the thermal treatment.

9. 0 A c c e p t e d m a n u s c r i p t 12 agreed

 012 ppm (9.1 ppm for PCC-1 and 8.7 ppm for PCC-2). A first interpretation of these 1 H NMR spectra seems to assign the fixed sharp contribution at 10.3 ppm to water inserted in the calcite structure and the broad contribution close to 9.0 ppm to amorphous Ca(OH) 2 . It must be noted that the well resolved spinning side bands were relative to the line at 10.3 ppm. Two facts can explain the sharpness of the line at 10.3 ppm: i/ 1 H nuclei were diluted in the CaCO 3 ·yH 2 O solid solution resulting in a lowering of the 1 H-1 H homonuclear dipolar coupling and then in a narrower line, ii/ the well-ordered crystalline structure reduced the possible number of slightly different environments, leading also to line width reduction. The spectrum obtained with a commercial portlandite sample is shown at the bottom of Figure8(dotted line). The crystallized Ca(OH) 2 spectrum (extremely broad band at 5.1 ppm) was different from the contributions observed in the PCC samples. Nevertheless, this difference can be explained by the difference in crystallinity: in well crystallized Ca(OH) 2 , the proton coupling is optimized involving a line broadening of the band.1 H NMR observations on thermally treated PCC-1 samples with these interpretations. Changes in the 1 H NMR spectrum after a treatment at 425 K were low. On the contrary, drastic changes appeared for a treatment at 475 K: large decrease in intensity of the contribution at 10.3 ppm, in addition to sharpening and shift of the most important peak from 9.1 ppm to 8.9 ppm. Treatment at 575 K led to the same observations. The sharp contribution at 10.3 ppm significantly decreased and the main band centred at 8.8 ppm became sharper. Another broad contribution was well observable at 5.0 ppm chemical shift: it can be assigned to well crystallized portlandite. The spectrum from sample PCC-1 treated at 725 K showed the disappearance of the main band at 8.8 ppm and the broad band at 5.0 ppm. Taking into account the presence of their spinning side bands and their disappearance at 475 K (beginning of the first water release) the sharp band at 10.3 ppm was indeed assigned to water molecules inserted in the calcite structure. The broad line close to 9.0 ppm which became sharper and sharper, before disappearing at 725 K (temperature of the second water release), was indeed assigned to amorphous portlandite. The 1 H NMR signal of Ca(OH) 2 contained in PCCs was different from the one of commercial portlandite by both a shift of the maximum peak and its sharpness. Two facts had to be considered: i) the hydrogen bonds network could be completely different in Ca(OH) 2 ·xH 2 O amorph before crystallization of portlandite, ii) the nanometric feature of crystalline Ca(OH) 2 in PCCs after 525 K (as indicated by PDRX patterns and Rietveld refinements) can decrease the 1 H-1 H coupling. Nevertheless, an amount of well crystallized portlandite was observable by the broad signal at 5.0 ppm after a treatment at 575 K. 1 H NMR experiments showed again the simultaneity of the two phenomena upon heating: disappearance of the contribution of structural water inserted in the calcite CaCO 3 ·yH 2 O, simultaneously with the crystallisation process of portlandite. But no evidences were brought about the possible excess water present in amorphous portlandite Ca(OH) 2 ·xH 2 O amorph .

A c c e p t e d m a n u s c r i p t 13 informationA c c e p t e d m a n u s c r i p t

 13 state 1 H MAS NMR spectroscopy confirmed the existence of three sets of proton in PCCs, leading to three different natures of water. The water content in PCCs is sample dependent, which means synthesis dependant. A complete characterization of these PCC water releases should give about their natures, and subsequently information about the improvements to implement in the PCC synthesis process. The monotonic water release has been associated to physisorbed surface water molecules and early decarbonation (also present in case of GCCs). Identification of the second thermal event at around 725 K was clearly assigned to portlandite Ca(OH) 2 dehydroxylation. Ca(OH) 2 was present and at amorphous state in the as-synthesized PCCs. Its crystallisation happened at about 525 K (Figure7), simultaneously with the first water release. These two simultaneous phenomena were not linked. PXRD (Figure4) and Raman spectroscopy (right insert, Figure7) gave indication about the nature of the first water release which results from departure of water molecules inserted in the calcite structure. The reaction occurring at 525 K was CaCO 3 ·yH 2 O → CaCO 3 + y H 2 O. An amount of 0.001 inserted water molecule per mole of CaCO 3 was estimated for GCC, and this amount reached 0.01 and 0.04 in case of PCC-2 and PCC-1 respectively. The compositions of the three commercial studied calcium carbonates were the following: GCC was a ground calcium carbonates composed at 100% by CaCO 3 ·0.001H 2 O, PCC-2 was a precipitated calcium carbonate composed by 0.25 wt % of amorphous Ca(OH) 2 and 99.75 wt % of CaCO 3 ·0.01H 2 O, and PCC-1 was a precipitated calcium carbonate made of 2 wt % of amorphous Ca(OH) 2 and 98 wt % of CaCO 3 ·0.04H 2 O. Physisorbed surface water molecules could not be estimated from the TGA curves as the beginning of the carbonate decomposition contributes also to the monotonic weight loss (simultaneously to the progressive physisorbed water departure).Besides, we can suppose that the origin of the inserted water present in PCCs was due to the speed of the synthesis process. Referring to the PCCs synthesis reactions (1) and (2) given in the introduction, a less than complete stoichiometric reaction (2) can easily explain the presence of a residue of Ca(OH) 2 and a quick precipitation rate during reaction (2) can be at the origin of some H 2 O trapped into the structure of the freshly precipitated calcite.

2 .Figure 1 .

 21 Figures captions

Figure 2 .

 2 Figure 2. Weight losses calculated from the TGA curves for the first thermal event at about 525 K (a, ■) and the second thermal event at about 725 K (b, •) for GCC (A), PCC-1 (B) and PCC-2 (C). The calculations have been performed after removal of the monotonic weight loss (i.e. without taking into account the physisorbed water). Dashed lines are only guides for eyes.

Figure 3 .

 3 Figure 3. Rietveld plots of as-synthesized PCC-1 (a), thermally-treated at 525 K (b) and thermally-treated at 775K (c). Observed (dots) and calculated (black lines) powder patterns (λ = 1.5418 Ǻ) are shown with the difference curve (lines above). The ticks indicate the Bragg peak positions of Si, CaCO 3 , Ca(OH) 2 and CaO. Small amounts of Ca(OH) 2 and CaO were detected; see arrows in the inserts in (b) and (c).

Figure 4 .

 4 Figure 4. Rietveld analysis on calcite phase from GCC (A), PCC-1 (B) and PCC-2 (C): evolution of lattice parameter a (a), lattice parameter c (b) and unit cell volume (c). Dashed lines are only guides for eyes.

Figure 5 .

 5 Figure5. DRIFT spectra realized on as-synthesized samples (a, range 4000 -2000 cm -1 ), and thermally-treated samples (b, range 3660 -3620 cm -1 , with indication of the temperature of treatment, bold lines for as-synthesized samples). Spectra were recorded on GCC (A), PCC-1 (B) and PCC-2 (C).

Figure 6 .

 6 Figure6. Raman spectra on PCC-1 thermally-treated at 375 K, 475 K, 500 K and 550 K; carbonate vibration from calcite (a, range 1070 cm -1 to 1100 cm -1 ) and hydroxyl stretching mode from portlandite (b, range 3500 cm -1 to 3750 cm -1 ).

Figure 7 .

 7 Figure 7. Relative weight fraction of portlandite determined by DRIFT, Raman, TGA and PXRD as a function of thermal treatment of PCC-1 (a). Dashed line and dotted curve are only guides for eyes for respectively the total (i.e. amorphous and crystallized) relative amount of portlandite as determined by TGA, and the relative amount of crystallized portlandite as determined by DRIFT, Raman and PXRD. Evolution of the Half Width at Half Maximum (HWHM) of the A 1g carbonate vibration for PCC-1, from the Raman spectra, is shown in insert (b). Error bars correspond to the standard deviations of the different profile fitting procedures.

Figure 8 .

 8 Figure 8. Solid state 1 H MAS NMR spectra on as-synthesized samples (a) and on thermallytreated PCC-1 samples with portlandite Ca(OH) 2 (dotted line) as a comparison (b). Spectra (A) correspond to GCC, (B) to PCC-1 and (C) to PCC-2. Stars (*) indicate the contribution of physisorbed water. 'S' indicates the position of the spinning sidebands.

A c c e p t e d m a n u s c r i p t 16 Fig. 1 A c c e p t e d m a n u s c r i p t 17 Fig. 2 A c c e p t e d m a n u s c r i p t 18 Fig. 3 A c c e p t e d m a n u s c r i p t 19 Fig. 4 A c c e p t e d m a n u s c r i p t 20 Fig. 5 A c c e p t e d m a n u s c r i p t 21 Fig. 6 A c c e p t e d m a n u s c r i p t 22 Fig. 7 A c c e p t e d m a n u s c r i p t 23 Fig. 8

 161172183194205216227238 Fig. 1

Table 1

 1 Physical characterization (grain sizes distribution, specific surface area and grain shape) and elemental analysis of the three assynthesized samples.

	GCC
	PCC-2
	PCC-1

Table 2

 2 Comparison of TGA weight losses and Karl Fischer (KF) water contents for the three as-synthesized samples.

	KF water content (wt %)	RT -393K 393 -573K	0.22 1.00	0.50 0.83	0.17 0.08
	TGA weight loss (wt %)	RT -393K 393 -573K	0.17 0.88	0.49 0.80	0.19 0.16
	Samples	PCC-1	PCC-2	GCC

Table 3

 3 Structure and microstructure parameters Rietveld refinement results on as-synthesized and thermally-treated PCC-1, PCC-2 and GCC samples.

	Portlandite refinement	Quantitative analysis (d) (b) Grain size (Å) Strain (‰) (c) Rp, χ 2 (e)	0.3(4) --0.077, 2.35	0.3(4) --0.064, 1.57	0.3(4) --0.068, 1.68	0.4(4) --0.078, 2.42	1.2(3) 50 10.75 0.083, 2.90	1.6(3) 50 10.50 0.074, 1.97	1.9(3) 50 11.50 0.069, 1.73	1.6(3) 100 9.00 0.064, 1.45	1.5(3) 100 6.00 0.071, 1.82	1.0(4) 100 9.25 0.064, 1.59	-0.075, 2.28	-0.070, 1.56	-0.076, 2.21	-0.064, 1.48	-0.073, 1.85	-0.078, 3.23	-0.061, 1.35	-0.061, 1.36	-0.083, 2.40	-0.078, 2.34	-0.062, 1.34	-0.082, 2.34	-0.084, 2.64	-0.088, 2.97	-0.081, 2.38
		Strain (‰) (c)	2.75	2.75	2.50	2.50	2.75	2.75	2.50	2.50	2.50	2.50	2.50	2.25	2.50	2.75	2.50	2.75	2.50	2.50	2.50	2.75	2.75	3.00	2.75	2.75	2.75
		(b) Grain	size (Å)	750	750	850	750	650	650	600	650	700	750	650	500	750	600	650	750	550	550	650	550	500	500	450	500	650
	Measurements Calcite refinement	Sample Thermal treatment a (Å) c (Å) V (Å 3 ) Oxygen x (a)	PCC-1 as-synthesized 4.9984(6) 17.040(2) 368.69(7) 0.2583(4)	325 K 4.9994(3) 17.046(1) 368.98(4) 0.2592(4)	375 K 5.0000(4) 17.047(2) 369.08(5) 0.2589(4)	425 K 5.0006(7) 17.055(3) 369.35(9) 0.2583(5)	475 K 4.9951(6) 17.066(5) 368.8(1) 0.2577(6)	525 K 4.9948(4) 17.069(2) 368.79(7) 0.2561(5)	575 K 4.9907(4) 17.060(2) 368.00(6) 0.2570(4)	625 K 4.9884(3) 17.060(1) 367.66(4) 0.2562(4)	675 K 4.9894(3) 17.065(2) 367.92(5) 0.2555(4)	725 K 4.9880(3) 17.064(1) 367.67(4) 0.2570(4)	775 K 4.9876(5) 17.065(2) 367.63(8) 0.2562(5)	PCC-2 as-synthesized 4.9931(3) 17.052(2) 368.16(5) 0.2576(4)	325 K 4.9936(5) 17.053(3) 368.25(7) 0.2591(5)	375 K 4.9931(4) 17.051(2) 368.15(5) 0.2585(4)	425 K 4.9944(4) 17.053(2) 368.38(7) 0.2577(4)	475 K 4.9904(9) 17.056(5) 367.9(1) 0.2580(6)	525 K 4.9909(2) 17.062(1) 368.06(4) 0.2570(4)	575 K 4.9884(3) 17.060(1) 367.64(4) 0.2573(4)	625 K 4.9868(6) 17.066(3) 367.55(9) 0.2568(6)	675 K 4.9843(6) 17.064(3) 367.14(8) 0.2573(5)	725 K 4.9847(3) 17.075(1) 367.44(4) 0.2566(4)	775 K 4.9834(4) 17.076(3) 367.47(7) 0.2569(5)	GCC as-synthesized 4.9870(7) 17.049(4) 367.2(1) 0.2520(5)	325 K 4.9897(9) 17.057(2) 367.8(1) 0.2504(6)	375 K 4.9889(4) 17.055(3) 367.61(6) 0.2530(5)

Acknowledgements

The authors would like to thank Specialty Minerals Inc. for providing the PCC samples and for giving permission to publish the present results.

1.9 (3) 0.89 ( 8) ).

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