Ali Fadel
email: ali.fadel@leesu.enpc.fr

Bruno J Lemaire
email: bruno.lemaire@leesu.enpc.fr

Brigitte Vinçon-Leite

A coupled hydrodynamic biological model for cyanobacteria dynamics in reservoirs

Keywords: Cyanobacteria, management, modelling

The potentially harmful phytoplankton bloom is a world wide problem that became frequent in the last decade. Microcystis aeruginosa is a toxic cyanobacterium that proliferates frequently in reservoirs deteriorating their water quality. Hence, a monitoring and warning system to forecast the risk of cyanobacteria proliferation became essential for lake managing. For that purpose, a one dimensional hydrodynamic (DYRESM) model coupled to an ecological model (CAEDYM) has been applied to simulate the growth of cyanobacteria colonies found in a French reservoir. As primary results the model was able to follow the trend of the cyanobacteria dynamics in the reservoir.

INTRODUCTION

Lakes and reservoirs are used for many purposes to complete human needs such as: drinking, agricultural irrigation, industrial and cooling water supply, sports or commercial fisheries, recreation and navigation. Reservoirs are often constructed for the specific purposes of flood control, power generation, agricultural irrigation, industrial and cooling water supply, sports or commercial fisheries, recreation, navigation and mostly for drinking purpose when the underground water are unsuitable or insufficient. Phytoplankton is microscopic, single-celled photosynthetic plants that use chlorophylls and carotenoids to absorb light for photosynthesis. The name "phytoplankton" consists of two Greek words meaning "plant" (phyto) and "wanderer" (plankton) meaning a collective of organisms that are adapted to spend part or all of their lives in apparent suspension in the open water of the sea, lakes, ponds and rivers . Cyanobacteria are an important component of the picophytoplankton in both marine and freshwater systems. They could be unicellular or colonial, lacking membrane bound plastids . Pigmentation of cyanobacteria includes chlorophyll a, blue and red phycobilins (phycoerythrin, phycocyanin, allophycocyanin, and phycoerythrocyanin), and carotenoids (Barasanti and Gualtieri 2006;Reynolds 2006). Protection of freshwater resources is of great importance. Reservoir water problems are either quantitative or qualitative. Karaoun reservoir (Lebanon) and Grangent reservoir (France) are important reservoirs that suffer from qualitative problem caused by harmful cyanobacteria in spring and summer period. Microcystis aeruginosa is the toxic cyanobacteria species that are frequent in these reservoirs, this cyanobacteria perturbates the drinking water supply, agriculture, swimming and energy production. They are colonial species that are common in nutrient enriched fresh water with low salinity.

Microcystis aeruginosa is characterized by spherical cells grouped in colonies with uniform mucilage. The colonies have many different forms: they can be spherical,cylindrical, entire or lobed and sometimes perforated (Bourrelly 1985). Colony size can reach several centimetres, while the size of a cell is between 0.8 and 10microns (Leitào and Couté avril 2005). In Lake Grangent, the local authority needs simple tools for early warning of phytoplankton proliferations. Hence, the Proliphyc research project was created, consisting of high frequency measurements by an autonomous wireless buoy, used as inputs for a model performing automatic forecasts of phytoplankton dynamics. Water quality models became a beneficial and essentially needed after the spread of eutrophication problem in many world reservoirs (Forsberg 1987;Canfield and Hoyer 1988) . These models range from zero dimensional models to three dimensional models. DYRESM-CAEDYM (DYCD) is a one dimensional model that is suitable for numerical modelling of small and medium sized reservoirs, as in the case of Grangent and Karaoun reservoirs where the horizontal variation are less important than the vertical variation in the water body. The objective of the thesis is to understand the physical determinants of phytoplankton blooms in the Karaoun reservoir,that represents a typical example of water storage in Lebanon and Mediterranean countries. For achieving this objective the following tasks will be performed: (1) to verify the ability of DYCD to simulate cyanobacteria dynamics; (2) to build a reliable database, gathering the existing data and performing field surveys for completing the data set; (3) to model Karaoun's thermal and biogeochemical functioning and (4) to provide recommendations for water management of the dam.

The predictive capabilities of an ecological model depend greatly on the accuracy and availability of data for input to the model. In order to validate the model, experimental measurements had to be performed regularly to provide all the input data needed for DYRESM-CAEDYM functioning.

The first objective of the thesis, presented in this paper, is to verify if the one-dimensional deterministic DYCD model has the ability to perform short-term simulations of cyanobacteria dynamics. For that purpose, we applied DYCD to the Grangent reservoir were the PROLIPHYC monitoring and warning system was implemented. The Proliphyc system is an autonomous buoy that performs meteorological measurements and includes profilers with underwater sensors. Measured data are transmitted daily by GPRS to the onshore laboratory to be treated and used by DYRESM-CAEDYM to perform the simulations. Based only on buoy measurements, the cyanobacterial biomass has been simulated for the year 2010 after it has been validated for the year 2009. Achieving this work will provide a deep understanding of the modelling process.

MATERIAL & METHODS

Study sites

Grangent reservoir

Grangent reservoir (45.27°N; 4.15°E) located in Massif Central (France), in the upper part of Loire River, is a eutrophic reservoir that was created in 1957. Grangent is kept full in summer (Briand, Escoffier et al. 2009). It is used mainly for energy production, drinking and irrigation, in additional to bathing and nautical activities in summer (Latour, Salencon et al. 2007). The reservoir has a surface area of 365 ha with a length of 21 km, a maximum depth of 50m and a capacity of 57.4 MCM. The average residence time for water varies from 16 to 32 days (Salençon 2004).

Measurement methods for Grangent

The predictive capabilities of an ecological model depend greatly on the accuracy and availability of data for input to the model. In order to validate the model, experimental measurements had to be performed regularly to provide all the input data needed for DYRESM-CAEDYM functioning. For that purpose the PROLIPHYC monitoring and warning system was created. It is an autonomous system that performs meteorological measurements and includes profilers with underwater sensors. Measured data are transmitted daily by GPRS to the onshore laboratory to be treated and used by DYRESM-CAEDYM to perform the simulation (Le Vu, Vinçon-Leite et al., 2010)

RESULTS AND DISCUSSION

Field observations

Analyzing meteorological conditions is essential to notice the variables that affect the phytoplankton group we are intending to model. Water temperature and wind speed have been found to be the main environmental factors controlling the initiation of cyanobacteria growth in Grangent (see Figure 4. The cyanobacterial biomass started in 26/06 while phytoplankton bloom started earlier in 22/06. Microcystis aeruginosa is the cyanobacteria species that are frequent in Grangent, its growth is known to be weak below 15 °C and optimal above 25°C (Yamamoto; Okino 1973;Chu, Jin et al. 2007) this explains their absence before 26/06 where the temperature ranged between 15 and 20 °C.

Phytoplankton biomass was more affected by the wind speed that was high (above 5 m/s) before 22/06. Their biomass was enhanced later by the increase of water temperature (after 22/06).

Mixing and stratification affect the vertical distribution of cyanobacteria and phytoplankton in Grangent. Mixing was produced by the wind speed and the decrease in temperature at night causing a night mixing; this process can be observed by comparing the phytoplankton and cyanobacteria concentration at both 1m and 5m depths, where phytoplankton concentration at 1m decreases with a simultaneous increase of phytoplankton at 5m depth The surface layer was mixed to 5m depth between 20/6 and 22/6 as a result of strong wind speed, this can be observed from the water temperature that was the same at both depths, suppressing the phytoplankton growth in that period, then stratification process became more established after 27/06, although it was interrupted by high wind speed in 01/07.

Simulation

Simulations were performed over a period of 2 months ranging from 28th of June till 28th of August of the year 2010. The inflow and outflow were neglected because we lacked these data. But since only the 1mdepth results are considered, it will not has a great effect since the river discharges at a average depth of 22 m thereby affecting the temperatures between 15 and 30m depth (Sellamie, 2010) For temperature modelling, the layer thickness and the incident light attenuation were calibrated, where a value of 45% of light attenuation coefficient had fit the best for the 2010 simulation.

For biological simulation, the most important parameters were calibrated: maximum potential growth rate of cyanobacteria (1.5/day), light extinction coefficient (0.25), average ratio of carbon to chlorophyll a ( 180), optimal temperature (20 °C), and rate of light dependant migration velocity (0.3 m/hr). It is assumed that there is no variation in nutrient levels.

Result interpretation

The surface temperature varied between 20 and 24 °C, comparison between field and simulated temperature data are shown in figure 5. It is clear that the model has reproduced the variation in water temperature at 1 m depth showing a good model performance with an average mean squared error less than 1 °C.

In terms of cyanobacteria dynamics, although the field and simulated data show some discrepancy, the model was able to detect and follow both the increase and decrease of the cyanobacterial biomass.

Between the 8th and 18th of July, a breakdown of the buoy data transmission occurred and we lacked data at that duration. Therefore, a comparison between field data and model results cannot be performed for this period.

CONCLUSIONS AND PERSPECTIVES

The DYCD model has shown its capability to simulate cyanobacteria dynamics after it was applied successfully to Lake Grangent for 2 years (2009 and 2010). Until starting with field campaign and modelling of Karaoun reservoir we have a period of 2 months in which we are expecting to improve the simulation Grangent reservoir for the year 2010 by simulating two or more phytoplankton classes, modelling outside the summer period, testing the effect of management scenarios and modelling with the phosphorus cycle, to understand algal succession in the reservoir.

Grangent reservoir was a good practicing tool for DYCD, all the input data that were used for DYCD were of high precision and with high frequency measurements. This is not the case for Karaoun reservoir, where we have low frequency measurements and there are many input variables to be measured. The hydrological data will be supplied by the Litani River Authority (LRA). But, there is no data base about the cyanobacteria biomass and temperature profiles for Karaoun Lake and this should be created carefully and with high precision. Our next target will be planning for an organized field campaign that would be from the mid of August till the mid of October.

We have to propose and analyze management scenarios to limit the proliferation of cyanobacteria starting from the model. The treatment of meteorological, hydrological and physiochemical data with the analysis of management procedures of Karaoun reservoir will allow us to reach a deterministic model of phytoplankton growth. This will be achieved by studying:

• The influence of water withdrawal (for power production, irrigation…) on the dynamics of the phytoplankton in the reservoir. • The role of winter rains on recolonization of sediment cyanobacteria on the water column.

• Influence of drought on the risk of algal blooms in Karaoun reservoir.

• Barasanti, L. and P. Gualtieri (2006). General Overview. Algae: Anatomy, Biochemistry, and Biotechnology, Taylor & Francis Group: 15-27. • Bourrelly, P. (1985). Les algues d'eau douce: algues bleues et algues rouges, N.Boubée.

• Briand, E., N. Escoffier, et al. (2009). "Spatiotemporal changes in the genetic diversity of a bloom-forming Microcystis aeruginosa (cyanobacteria) population." Isme Journal 3(4): 419-429. • Canfield, J. D. E. and M. V. Hoyer (1988). "The eutrophication of Lake Okeechobee." In: G.

Redfield (Editor), Lake Reservoir Manage (4: 91-100.).

Figure 1

 1 Figure 1 Grangent reservoir location in France

Figure 2

 2 Figure 2 Karaoun reservoir location in lebanon (Shaban and Nassif 2007)

Figure 3 :

 3 Figure 3: Representative schema of DYCD model run

Figure 4 :

 4 Figure 4 : Four variables: cyanobacteria concentration, phytoplankton concentration, water temperature, wind speed, air temperature.

Figure 5

 5 Figure 5 Results of the hydrodynamic model for 2010, a) temperature b) cyanobacteria

Table 1 PROLIPHYC monitoring and warning system

 1

	Variables	Time interval	Depth	Sensor name
	Temperature
	Conductivity		1 and 15 m in 2009;	Nke MPx multiprobe
	Pressure	30 min	1 and 5 m in 2010;
	Dissolved oxygen		1 to 4 metric profiles a	Aanderaa 3835 optode
	Chlorophyll-a fluorescence		Day	BBE-Moldaenke FluoroProbe100
	Air temperature
	Pressure
	Humidity
	Wind speed
	Rain

•

  Catafago, s. Restructing water in Lebanon: Litani River Authority facing the challenges of good water governance. Lebanon, Faculty of engineering.• Chu, Z. S., X. C.Jin, et al. (2007). "The effect of temperature on growth characteristics and competitions of Microcystis aeruginosa and Oscillatoria mougeotii in a shallow, eutrophic lake simulator system." Hydrobiologia 581: 217-223. • Forsberg, C. (1987). "Evaluation of lake restoration in Sweden." Scheiz. Z. Hydrol. (49: 260-274). • Hamilton, D. P. and S. G. Schladow (1997). "Prediction of water quality in lakes and reservoirs. Imberger, J. P., J. C. (1981). «A dynamic reservoir simulation model DYRESM: 5. In Transport Models for Inland and Coastal Waters (ed. H. Fischer)." Academic Press, New York: 310-361. • Jurdi, M., S. Ibrahim Korfali, et al. (2002). "Evaluation of Water Quality of the Qaraaoun Reservoir, Lebanon: Suitability for Multipurpose Usage." Environmental Monitoring and Assessment 77(1): 11-30. • Latour, D., M. J. Salencon, et al. (2007). "Sedimentary imprint of Microcystis aeruginosa (Cyanobacteria) blooms in Grangent reservoir (Loire, France)." Journal of Phycology 43(3): 417-425. • Le Vu, B., B. Vinçon-Leite, et al. (2010) High-frequency monitoring of phytoplankton dynamics within the European Water Framework Directive: Application to metalimnetic cyanobacteria Biogeochemistry 28. • Leitào, M. and A. Couté (avril 2005). Guide pratique des cyanobactéries planctoniques du Grand Ouest de la France. Honfleur, Agence de l'eau Seine-Normandie. • McDonald, C. P. and N. R. Urban "Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models." Ecological Modelling 221(3): 428-432. • Okino, T. (1973). "Studies on the blooming of Microcystis aeruginosa. II. Rapid accumulation of phosphate by Microcystis aeruginosa." FAC. SCI. 8: 135-145. • Reynolds, C. S. (2006). Phytoplankton. Ecology of Phytoplankton. M. Usher, D. Saunders, R. Peet and A. Dobson, Cambridge University Press. • Saad, Z., V. Kazpard, et al. (2006). "Use of hydrochemistry and environemantal isotopes to evaluate water quality, Litani River, Lebanon." Journal of Environmental Hydrology 14. • Salençon, M. J. (2004). Ecosystème de la retenue de Grangent, Présentation générale, EDF R&D, Laboratoire Nationale d'hydaulique et environnement, Projet E3-98-03 I, rapport interne. Chatou: 18. • Shaban, A. and N. Nassif (2007). "Pollution in Qaraaoun Lake, Central Lebanon." Journal of Environmental Hydrology 15. • Yamamoto, Y. "Contribution of bioturbation by the red swamp crayfish Procambarus clarkii to the recruitment of bloom-forming cyanobacteria from sediment." Journal of Limnology 69(1): 102-111. • Yeates, P. S. and J. Imberger (2003). «Pseudo two-dimensional simulations of internal and boundary fluxes in stratified lakes and reservoirs." Intl. J.River Bassin Mangement: 297-319.

	Part I --Model description." Ecological Modelling 96(1-3): 91-110.
	• Hipsey, M. R. (2007). Water quality modelling of west Seti hydropower reservoir using
	DYRESM-CAEDYM. Australia, the University of Western Australia.
	• Imberger, J., Patterson, J. C., Hebbert, B. & Loh, I. (1978). " Dynamics of reservoir
	of medium size." Journal of Hydraulics Division ASCE 104: 725-743.
	•

10-WWW-YES-2011-Fadel-Paper-2011_04_04.doc


Marie-Laure De Boutray
email: ml.deboutray@polymtl.ca

Bruno J Lemaire
email: bruno.lemaire@cereve.enpc.fr

Brigitte Vinçon-Leite

Bruno Tassin
email: bruno.tassin@cereve.enpc.fr

Yves Comeau
email: yves.comeau@polymtl.ca

Sarah Dorner
email: sarah.dorner@polymtl.ca

Water Quality Monitoring and Lake Ecosystem Modelling: an Integrated Approach to Assess Cyanobacterial Blooms

Keywords: Cyanobacteria, lake, water, treatment, modeling

published or not. The documents may come

INTRODUCTION

Anthropogenic eutrophication of water bodies and its consequences are of concern especially in waters used for recreation or as sources of drinking water (VASCONCELLO, 2006). High nutrient inputs, mainly phosphorus and nitrogen in their diverse forms, cause excessive algal growth and make freshwater lakes more vulnerable to the occurrence of cyanobacteria, also known as blue-green algae (BGA). Because highly toxic cyanobacteria can produce toxins, these microorganisms pose a significant threat to the quality of water [START_REF] Bartam | Toxic Cyanobacteria in Water: A guide to their public health consequences, monitoring and management[END_REF]. Specific strategies to address algal bloom problem in lakes depends on numerous considerations such as properties of the lake (size, depth, currents, physico-chemical and biological characteristics, etc), users, available budgets and activities in the watersheds. Lake management approaches can be classified into two categories: short-term management and long-term management.

In-lake treatments such as artificial mixing, ultrasound and algicides are short-term treatments. Mechanical and physico-chemical methods have been devised in attempts to manage cyanobacterial blooms, with limited success (GUMBO et al., 2008). Algicides such as copper based products (HAVENS, 1994;COOKE et al., 2005;TUBBING et al., 1994) and potassium permanganate (LAM et al., 1995) induced cyanobacterial cell lysis, followed by the release of toxins into surrounding waters. These chemicals are toxic to other aquatic microorganisms, may accumulate in the sediment at harmful concentrations and may cause long-term damage to the lake ecology (MASON, 1996). Moreover, public acceptance of algicides is continuously decreasing because of their toxic by-products and their adverse ecological impact.

Augmentative biological control or bioaugmentation is the practice of enhancing the populations of predators to help in regulating the populations of the pest in its natural habitat (GUMBO et al., 2006). With this method, biodegradation of cyanobacteria cells have been clearly demonstrated (NAKAMURA et al., 2003 a, b). Effective biodegradation of microcystin has also been reported (JONES and ORR, 1994;BOGOSIAN and BOURNEUF, 2001;HO et al., 2006;HO et al., 2007). However, very few predators have been tested and the mechanisms of cyanobacterial degradation are not well understood.

On the other hand, sophisticated monitoring programs have been developed to follow phytoplankton blooms on a longer period and to assist with effective management of cyanobacteria. In Europe, aquatic ecosystems are monitored according to the Water Framework Directive. This program requires that by 2015, lakes whose surface area is greater than 50 ha need to achieve a good ecological status (ECE, 2000). Thus, rapid and precise methods are needed in order to monitor water quality in lakes.

Submersible in vivo fluorescence (IVF) profilers are commonly used for high frequency monitoring plans (LEE et al., 2005;BRIENT et al., 2008;MCQUAID, 2009). They are particularly suitable for monitoring cyanobacteria where the spatial heterogeneity of these microorganisms becomes a major problem (CAGNARD et al., 2006). Furthermore, they are simple detection methods, faster than conventional microscopy and do not require highly trained personnel to perform chemical analyses and biological characterization of samples (CAGNARD et al., 2006;GREGOR and MARSALEK 2005). This paper presents the second part of a joint Canadian-French research project that aims at a deeper understanding of the physical, chemical and biological determinants of BGA blooms. It proposes a long-term water quality monitoring system, adapted to a Canadian lake and coupled with one dimensional modeling,. The first part of this research project -the development of a biological treatment against Microcystis aeruginosa -is beyond the scope of this paper.

Preliminary results from the data collected during summer 2010, in Missisquoi Bay (Quebec, Canada), will be presented. The one dimension modeling is undergoing and will only be briefly presented.

MATERIAL & METHODS

Study site

Missisquoi Bay (figure 1) is situated in the northeastern part of Lake Champlain which flows from Whitehall (New York, U.S.), across the U.S./Canadian border to its outlet, the Richelieu River in Canada [START_REF] Ballinger | Lake and facts[END_REF]. The bay has a surface area of 77 km 2 and a maximum depth of 4 m. The mouth of the bay, at the southern end, is delimited by two causeways, extending from each shore and a bridge section of 170 m (MENDELSOHN et al., 1997). Missisquoi Bay has three major tributaries: Missisquoi River, Pike River (also called Rivièreaux-Brochets) and Rock River which drain a watershed of 3 105 km 2 . Due to high turbidity and excessive concentrations of nitrogen (N) and phosphorus (P), the environmental department of Quebec (MDDEP) has classified water quality from these tributary rivers as "poor" or "very poor" (SIMONEAU, 2007). Missisquoi Bay is also a source of drinking water for 4100 residents. Because of the high nutrient loading, this northern shallow freshwater bay experiences recurrent toxic cyanobacteria blooms during the summer months.

Continuous water monitoring and vertical depth profiles

In 2007, a real-time monitoring program was developed and implemented in Missisquoi Bay, using an online multi-probe system. The system was an on-line 66000V2-4 multi-probe from YSI (YSI, yellow Springs, Ohio, USA). YSI probes were chosen because of their relatively low cost making them more likely to be used by small municipalities than other more expensive probes using similar technology (MCQUAID, 2009). The multi-probe system consists of eight sensors: temperature (T), depth, conductivity, turbidity, pH, dissolved oxygen, chlorophyll fluorescence and phycocyanin fluorescence.

Extensive field data have been acquired during the summer period, from 2007 to 2010. A probe was installed inside the drinking water treatment plant (DWTP) to monitor raw water and recorded measurements every hour. The probe was in service from August to early November in 2007, from May to October in 2008, from June to October in 2009 and from July to late November in 2010. Data were downloaded once week. Another probe was used to measure vertical depth profiles above the DWTP's intake point and in the pelagic zone, once or twice a week.

Calibration and field samplings

The probes were calibrated once a month. For pH, turbidity and conductivity, the sensors were calibrated with purchased standard products from the manufacturer. For chlorophyll, dissolved oxygen and phycocyanin sensors a one point calibration with de-ionized water was done according to the user manual of the manufacturer. Depth and temperature readings were compared with respectively a light depth probe and a thermometer.

At the same time depth profiles were conducted, water samples were taken in duplicates every meter in clean 500 mL plastic bottles for chemical analysis and biological extractions, and in sterile 20 mL vials for taxonomic counts and toxins analysis. Raw water samples were also collected in duplicate inside the DWTP. Bottles and vials were rinsed three times with sampled water before they were filled completely and tightly closed. Lugol's iodine was added in vials for taxonomic counts according to LUND et al. (1958). All samples were preserved on ice in coolers for transport and processed later the same day. Samples for microcystin analysis were frozen at -15ºC.

Biological and chemical analyses

Vials with Lugol's iodine were sent to Quebec Environmental Analysis Expertise Centre and to Université du Québec à Montréal's (UQAM) Biological Sciences department for species identification, taxonomic counts, biomass and biovolume calculations using inverted microscopy according to LUND et al. (1958).

Microcystin LR was monitored using ELISA kits (Abraxis, Warminster, Pennsylvania, USA). Water samples were filtered through a Whatman GF/C glass filter before freezing. Total intra and extra cellular toxins were measured after three thaw cycles to lyse cells.

Chlorophyll-a and phycocyanin extractions were performed according to Standard Methods [START_REF] Apha | Standard Methods For the Examination of Water and Wastewater: Centennial Edition, 21st edn[END_REF].

Field samples were also analyzed for total phosphorus (TP), total Kjeldhal nitrogen (TKN), ortophosphate (o-PO 4 ) and nitrate/nitrite (NOx). All chemical analyses were carried out according to Standard Methods. For TP and TKN determination, 5 mL H 2 SO 4 were added to 20 mL water samples. o-PO 4 and NO x determinations were carried out on filtered samples.

One dimension modeling

The study uses the one-dimensional coupled hydrodynamic-ecological model DYRESM-CAEDYM for the simulation of phytoplankton dynamics. The model simulates the temporal course of vertical temperature and salinity distribution, and quantify the feedback from the algal population to the thermal structure of the lake. Expected results will yield information to assess cyanobacterial blooms occurrence.

RESULTS AND DISCUSSION

Depth profiles using the multiprobe were used to understand the spatial distribution of cyanobacteria throughout the water column. Depth profile made on July 20 th , 2010 in a pelagic zone indicated homogenous distribution for T and pH (Figure 1). Concentration of chl-a and cyanobacteria were also relatively uniform. A previous study by MCQUAID (2009) has highlighted that in this shallow bay, turbulent mixing of the water matrix, due to wind and waves, caused relatively homogenous distribution of phycocyanin readings. Temperature, pH and chl-a concentrations also follow similar trends. However, on July 20 th wind speed was low and never exceeded 9 km/h (Figure 2). This unusual profile can be explained by considering wind and precipitation of the three previous days (Table 1). Strong winds on July 17 th and 18 th have mixed the water. Mixing of the water was also facilitated by the near uniform temperature from top to bottom (Figure 1) and the shallowness of the bay. In addition, abundant rain on July 19 th swelled the tributary rivers. The important water inflows may have created strong currents that also contributed to mixing in the bay. High concentrations of cyanobacteria (between 1*10 4 and 1*10 5 cell/mL; figure 1) and homogenous density profiles increased the risk that toxic cyanobacteria entered the DWTP through the water intake situated at the bottom of the bay, thereby creating a more difficult situation for operators of the DWTP to ensure that less than 1µg/L of microcystin is present in water, according to the World Health Organization guidelines and standards for cyanotoxins in treated drinking water.

CONCLUSION

Preliminary results from this study demonstrated that real-time monitoring system was useful to better understand the spatial distribution of cyanobacteria and to detect high risk situations. Thus, real-time, autonomous monitoring program can provide reliable information for DWTP operators and help them anticipating cyanobacteria bloom and adjusting their treatment. Such a system can also assist lake managers in decision making.

Globally, most countries have experienced or routinely experience cyanobacterial presence in surface waters (CODD et al., 2005). The availability of information is, however, highly variable. In North America, some parts of Europe, Australia, New Zealand and highly developed parts of Asia the access to scientific and management information and reports is relatively easy. It is more complicated in much of Africa and for parts of Latin America and Asia. Awareness of the threats posed by eutrophication, and its consequences for health and water supply, is limited or non-existent in many developing countries (CODD et al., 2005). The presence of cyanotoxins is not commonly reported in all countries. The non-reportingof cyanotoxins, may be due to the non-availability of the necessary facilities and skills (CODD et al., 2005).

Long-term monitoring programs with sophisticated tools are relatively costly. Therefore they are mostly used in developed countries and are generally not accessible to developing countries. However, both in developed and developing countries, the availability of monitoring and analytical facilities, appropriately trained personnel and facilities capable of undertaking reliable cyanobacterial identification and enumeration, are severely under-resourced (CODD et al., 2005).

• BOGOSIAN G. and Bourneuf E. V. (2001). A matter of bacterial life and death. European Molecular Biology Organization Reports 2(9): 770-774. • BRIENT L., Lengronne M., Bertrand E., Rolland D., Spiel A., Steinmann D., Baudin I., Legeas M., Le Rouzic B. and Bormans M. (2008). A phycocyanin probe as a tool for monitoring cyanobacteria in freshwater bodies. Journal of Environmental Monitoring 10(2): 248-55.

• CAGNARD O., Baudin I., Lemoigne I., and Cartnick K. (2006). Assessment of emerging optic sensors (fluoroprobes) for algae on-line monitoring. American Water Works Association-Water Quality Technology Conference, Denver, Colorado, USA. • MCQUAID N. ( 2009). Establishment of an early system for cyanobacteria using an online multiprobe system measuring physicochemical parameters, chlorophyll and phycocyanin. Doctorate thesis. Ecole Polytechnique de Montreal, Montreal, Canada, pp.108.

• MENDELSOHN D., Swanson C. and Isajit T. (1997). Hydrodynamic Modeling of the Missisquoi Bay in Lake Champlain. Final report, Applied Science Associates, Vermont Agency of Natural Resources, Waterbury, Vermont.Project 95-136, 106 p. and 5 appendices.

• NAKAMURA N., Nakano K., Sugiura N. and Matsumura M. (2003a): A novel control process of cyanobacterial bloom using cyanobacteriolytic bacteria immobilized in floating biodegradable plastic carriers. Environmental Technology, 24(12): 1569-1576.

• NAKAMURA N., Nakano K., Sugiura N. and Matsumura M. (1994). The contribution of complexed copper to the metabolic inhibition of algae and bacteria in synthetic media and river water. Water Research, 28(1): 37-44.

• VASCONCELLO V. ( 2006). Eutrophication, toxic cyanobacteria and cyanotoxins: when ecosystems cry for help. Limnetica, 25(1-2): 425-432.

11-WWW- YES-2011-De-Boutray-Paper-2011-06-30.doc

Figure 1 :

 1 Figure 1: Missisquoi Bay study and sampling locations (adapted from www.icsd.k12.ny.us and www.maps.google.ca)

Figure 2 :

 2 Figure 2: Depth profile of cyanobacteria density, pH, temperature and chl-a concentration in the middle of the bay, on July 20 th 2010.

Figure 3 :

 3 Figure 3: Mean hourly wind speed on July 20 th 2010 at the weather station of Frelighsburg. Data from the Climate Weather Office of Environment Canada.

  (2003b): A novel cyanobacteriolytic bacterium, Bacillus cereus, isolated from a eutrophic lake. Journal of Bioscience and Bioegineering, 95(2): 179-184. • SIMONEAU M. (2007). État de l'écosystème aquatique du bassin versant de la baie Missisquoi : faits saillants 2001-2004. Québec, ministère du Développement durable, de l'Environnement et des Parcs, Direction du suivi de l'état de l'environnement, ISBN 978-2-550-49625-0, pp.18.• TUBBING D. M. J., Admiraal W., Cleven R. F. M. J., Iqbal M., Vandemeent D. and Verweij W.

00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 Wind speed (km/h) Time of the day (hours)

	10
	9
	8
	7
	6
	5
	4
	3
	2
	1
	0
	01:

Table 1 : Total rain and speed of maximum guts, from July 17 th to 20 th 2010 at the weather station of Frelighsburg. Data from the Climate Weather office of Environment Canada.

 1

	Date

  European Commission on Environment (ECE). (2000). The EU Water Framework Directiveintegrated river basin management for Europe, web pages, last updated: 25/03/2011, http://ec.europa.eu/environment/water/water-framework/index_en.html • GREGOR J. and Marsalek B. (2005). A simple in vivo fluorescence method for the selective detection and quantification of freshwater cyanobacteria and eukaryotic algae. Acta Hydrochimica and Hydrobiologica, 33(2):142-148. • GUMBO R. J. and Cloete T. G. (2006). Antagonism of Bacillus spp. towards Microcystis aeruginosa. Ph. D. thesis, Department of Microbiology and Plant Pathology, Faculty of Natural and Agricultural Sciences, University of Pretoria, South Africa, pp. 168.

	• CODD G.A., Azevedo S.M.F.O, Bagchi S.N., Burch M.D., Carmichael W.W., Harding W.R., Kaya K. and Utkilen H.C. (2005). CYANONET A Global Network for Cyanobacterial Bloom and Toxin Risk
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11thWWW YES, Arcueil, France, 6 -10 June 2011 de BOUTRAY et al.


Gerardo Rodríguez-Fuentes
email: gerardo@imre.oc.uh.cu

Sabine Devautour-Vinot

Sekou Diaby
email: sekoudiaby@yahoo.fr

François Henn

INSIGHTS INTO CATION EXCHANGE SELECTIVITY OF A NATURAL CLINOPTILOLITE BY MEANS OF DIELECTRIC RELAXATION SPECTROSCOPY

Keywords: natural clinoptilolite, dielectric relaxation, cation exchange

Purified natural clinoptilolite from the Tasajeras deposit, Cuba, and some of its metal exchanged forms are studied, at the dehydrated state, by means of Dielectric Relaxation Spectroscopy (DRS) using two different modus operandi: Complex Impedance Spectroscopy and Dielectric Dynamic Thermal Analysis. Data analysis yields the determination of the extra-framework cation (EFC) population into the various possible crystallographic sites of the zeolitic framework as well as of the activation energy characterizing the localized hopping mechanism of EFC. First, it is shown that the DRS responses obtained here match well with the previous reported data which were previously localized EFCs in positions close to M1 and M2 sites when the clinoptilolite is modified to almost homoionic form. From this outcome, it can be concluded that all EFCs are in the same crystallographic situation regarding solvation or, in other terms, that no steric effect can be taken into account to explain cationic selectivity. Second, based on the assumption that the activation energy for EFC hopping is directly connected to the EFC/framework interaction and on simple thermodynamics consideration, we show this interaction does not govern the EFC 2 exchange reaction. So, it is emphasized that EFC/H 2 O interaction is the key factor for cation exchange selectivity.

Introduction

Cationic exchange is one of the most relevant properties of zeolites [1,2]. However, understanding of the microscopic mechanisms responsible for this property is still under debate and many questions remain posed. For instance, one may ask whether the localization of the extra-framework cations (EFC) at the inner-surface of the considered zeolites plays a key role into the cation selectivity. If so, it can be due either to i) steric effects, i.e. water molecules cannot solvate some given EFCs because of geometric restriction or the coordination sphere corresponding to the solvated cation is too large to cross some of the framework doorways, or to ii) energetic factors, i.e. depending on their position some EFC are too deeply trapped at the zeolites surface so that their solvation is not energetically favorable. In the later case, cationic exchange is therefore driven by the competition between EFC/framework and EFC/water interactions. In order to answer these questions, one needs to get deeper insights into EFC location at the surface and EFC/framework interaction. Dielectric Relaxation Spectroscopy (DRS) was shown to be a powerful tool for investigating charge dynamics in ionic solids. In these solids, the dielectric response arises from the dipolar reorientation associated with the thermally activated ionic hops occurring between neighboring ionic positions. In zeolites, it has thus been well established that dielectric relaxation is associated with the localized displacement of EFC located at the surface in the microporosity of the crystalline structure [3]. It can then be expected that the dielectric relaxation properties of a given zeolite is strongly dependent on its framework features, i.e. crystallographic structure, Si/Al ratio and localization of the Al atoms, and on the nature of EFC. DRS thus appears as a spectroscopy well adapted to characterize zeolites features along with XRD, Neutron diffraction, Infra-Red and NMR. More specially, it can be used to estimate the EFC localization among the various possible crystallographic ionic positions, the energy barrier associated to EFC hopping, later referred as the de-trapping energy, and, hence, the energy depth of the cationic sites. Furthermore, it was shown that DRS is also very sensitive to the presence of guest molecules adsorbed in the nanoporosity where EFC sit [3][4][5][6][7]. This is particularly true when the adsorbed molecule is small and polar, e.g. water, and consequently when its interaction with EFC is significant. DRS is thus well suited to investigate water adsorption in zeolites. In that case, one of the key questions is to determine the precise role played by EFC in regards to other important parameters such as confinement in the zeolite nanoporosity and interaction of the adsorbed molecule with the non-cationic part of the inner-surface. The competitive interplay between the EFC/zeolites framework and the EFC/guest molecule interactions thus appears essential. This is particularly true when considering the zeolite properties and its Cationic Exchanged Capacity (CEC) in aqueous electrolytes. To that respect, DRS allows us to qualitatively compare the energy depth of the cationic sites that can be determined at the dry state with that of the cation solvation energy, and consequently to hierarchically distinguish the role played by the various interactions taking place upon hydration and ionic exchange. Besides, most of the dielectric studies carried out on zeolites have been conducted in synthetic types with high-symmetry. Only a few have been performed in natural ones as in heulandite-clinoptilolite [8], though this zeolite has lower symmetry and suffers from structural changes due to thermal effect and the evolution of the EFC locations. The dielectric response of a natural clinoptilolite from the Tasajeras deposit, Cuba, was studied by A. Delgado et al. [9]. They found that two dielectric relaxations take place at room temperature at about 34 Hz and 500 kHz and that they could be associated to two different types of ionic hops.

In this paper, we aim at investigating the dielectric relaxation properties of a purified natural clinoptilolite Cli Natand of its metal-exchanged forms, i.e. dielectric losses at a given fixed frequency under thermal ramping. The later is called Dielectric Dynamic Thermal Analysis (DDTA). As already reported [4,10], the isothermal procedure allows us to determine the de-trapping energy, whereas the second one is more suitable for analyzing the shape of the dielectric signal. The de-trapping energy and the crystallographic EFC position are thus confronted to previous data obtained from structural investigations [22,23].

Finally, the cation de-trapping energy is compared both to that of the water solvation and to cationic selectivity.

The paper is constructed as followed. In section 2, the description of the samples studied here is given. A brief recall of DRS principle, data analysis and experimental procedure is provided in section 3. DRS results are discussed in section 4 where they are compared to the structural information provided by previous investigations, to the energy for cation solvation and to cationic selectivity. Conclusion is given in section 5.

Description of the studied samples

The natural zeolite from the Tasajeras deposit, Villa Clara province, Cuba, has been well characterized elsewhere [11]. This zeolite is a clinoptilolite, heulandite -IUPAC code HEU-.

The exchanged forms of the purified natural clinoptilolite Cli Nat --

+ 2 Ca , + 2
Mg , + K and + Na -are obtained by the hydrothermal exchange process using 1.0 mol.L - 3 Brief recalls of DRS principle, data analysis and experimental procedure

Basic principle and data analysis

Hereafter, DRS consists of measuring the sample response to an ac electrical field. The basic principles can be found in text books [START_REF]Impedance Spectroscopy: Theory, Experiment, and Applications[END_REF].

When a dipole relaxes because of local atomic re-arrangements, i.e. localized ionic hop in our case, the relaxation time associated to this movement is:

      ∆ = kT E τ τ exp 0 ( 1
)
where k is the Boltzmann constant, T the temperature, E ∆ the activation barrier and 0 τ the inverse characteristic vibration which is typical of atomic frequency, i.e. 10 13 s -1 .

Thus, following a first order relaxation mechanism, the dielectric loss spectrum ) , ( " T f ε which characterizes the ac response measured at a given temperature T and frequency f is given by:

( ) 2 2 " 2 1 . 2 ) , ( τ f π ε f π T f ε + ∆ = (2)
where ε ∆ is the so-called dielectric strength of the dipole.

However, most of real systems are not characterized by a single type of dipole and consequently by a dielectric signal corresponding to eq.2. Instead, their dielectric response can be analyzed assuming there are different types of dipoles i whose the respective populations can be described by a distribution function

) ( i τ G
. Eq. 2 can thus be written:

( ) ∫ ∞ + ∆ = 0 2 2 " . 2 1 . 2 ). ( ) , ( τ τ π ε π τ ε d f f G T f i i i (3)
For a population of dipoles which corresponds to ionic hops involving the same type of ions and of framework, i , 0 τ and i ε ∆ can be assumed to be constant and equal to 0 τ and ε ∆ respectively. ε ∆ can then be factorized and

) ( i τ G
replaced, according to eq.1, by a distribution of activation energies

) ( i E G ∆ : ( ) ∫ ∞       ∆ - + ∆ ∆ = 0 2 0 2 " . ) exp( . 2 1 2 ). ( . ) , ( τ τ π π ε ε d kT E f f E G T f i i (4) ) ( i E G ∆
characterizes the system under study and, in the present case, can be related to the various crystallographic sites in which EFC are located [4,10,[START_REF] Devautour | [END_REF] and around which they can hop. The fit of ) , ( " T f ε with eq. 4 is an inverse and ill-posed problem [15,[START_REF] Schäfer | La Physique au Canada[END_REF] so that no unique solution for ) ( i E G ∆ can be extracted. However, a "guessed" ) ( i E G ∆ function can be input into the minimization procedure based on some assumptions derived from the knowledge of the crystallographic structure of the studied zeolites. The minimization allows us to check whether the initial

) ( i E G ∆
fits well with the dielectric signal and, if so, to refine it. When ) ( i E G ∆ is refined, it gives us access to the mean activation energy and the relative population characterizing each different types of ionic hop. This procedure was proved to be productive in many zeolites [4,10,[START_REF] Devautour | [END_REF].

Experimental procedure

The zeolite powder is pressed to form disk-shape pellets whose diameter and area are 1mm and 133mm 2 respectively. The zeolite pellet is sandwiched between two insulating layers [START_REF] Henn | [END_REF],

i.e. PTFE 10 µm thin film, and placed between the metallic plates of the sample holder which is maintained under a continuous dry N 2 flow. Prior to DRS measurements, the pellet is treated insitu at 220ºC for 18 hours. So, it can be assumed that the studied sample remains totally dehydrated during the course of the experiment.

Ac permittivity is measured with a broadband dielectric spectrometer (Novocontrol BDS 4000), by using two experimental procedures (nb: the combination of two experimental procedures reduces the number of variables in the fitting procedure and, hence, makes it more reliable). In the first case, named Complex Impedance Spectroscopy (CIS) hereafter, the imaginary part of the permittivity, ) ( " f ε T , is measured, under constant temperature ranging from 220°C to 70°C, as a function of the ac electric field frequency in the 10 -2 Hz-10 6 Hz domain. In the second case, called Dielectric Dynamic Thermal Analysis (DDTA), the temperature evolution of the dielectric loss, ) ( " T f ε is measured at a fixed frequency f , while the temperature is ramping at a constant heating rate, i.e. 3°C.min -1 , from -50°C to 220°C. The fixed frequency is chosen, accordingly to the sample response, so that the ) ( " T f ε peak is entirely scanned in the explored temperature domain. In the present study, f turns out to be around 0.1 to 10 Hz.

Results and discussion

Analysis of the samples CIS and DDTA response

Figure 1a reports a typical example of CIS signal, obtained in the case of Cli Na -. ) ( " f ε T exhibits a rather broad peak, accompanied at low frequency by a signal increase. The later corresponds to the so-called Maxwell Wagner Sillars effect which results from the accumulation of ionic charges at the sample/insulator layer interface and, hence, does not correspond to an intrinsic dielectric relaxation phenomenon. As expected from eq. 4, the dielectric relaxation peak shifts towards higher frequency upon increasing temperature. The same trend is observed for all the clinoptilolite samples studied in the present work.

The ) ( " f ε T peak maximum corresponds to ( ) 1 max max 2 - = f π τ
. The Arrhenius plot of max τ , i.e.

( )

max τ Ln vs T / 1
(see figure 1b), can then be used to determine E ∆ ~ (eq.1) the activation energy of the most probable EFC hop (figure 1b). Table 2 lists the values of E ∆ ~ for our different samples. The following classification can thus be made:

) ( ) ( ) ( ) ( ) ( ~Cli Na E Cli Ca E Cli Nat E Cli Mg E Cli K E - ∆ > - ∆ = - ∆ > - ∆ = - ∆ (5)
It means that + Na cations are less deeply trapped at the surface of the dehydrated clinoptilolite framework than + K ones are, or in other words that the interaction energy between the zeolite framework and the cation is lower for + Na than for + K . It can be also underlined that there is no direct correlation between the cation electric charge and the value of E ∆ ~.

It has already been pointed out that Cli Kis more stable than Cli Naunder high temperature treatment because + K cations maintain the channel open upon dehydration, while + Na cations do not avoid the channel breakdown [18,19]. This observation is in accordance with the classification obtained from our DRS measurements (eq.5), which shows that + K are deeper embedded than + Na . + K cations motion is therefore less responsive to temperature. Moreover, it has also been reported that the + Na -framework bond is weaker than that of the other EFC [20]. Mg in M4, each of these cations having different number of water molecules in their first coordination shell. Furthermore, it was shown [24] that dehydration induces a displacement of the cations from M1 and M4 to M3 sites which is closer to the framework wall, meaning that in the dehydrated Cli Natonly the M2 and M3 sites are occupied. The existence of two distinct cation locations as determined from all the DDTA signals is therefore in agreement with these former structural investigations.

) ( i E G ∆ is determined
In the case of the exchanged samples, the existence of two Gaussians function is also consistent with previous experimental works [22,23] and with computational simulations carried out by A.R. Ruiz-Salvador et al. [20,25,26]. In every case, the most occupied sites are M1 and M2 in dehydrated samples, with more accuracy in sites close to these experimentally determined position.

The relative area of each Gaussian function yields the relative population of cationic site, cf equation 4 [4,[START_REF] Devautour | [END_REF]21]. Providing the incertitude due both to the experimental signal and to the fitting modus operandi, it can be concluded (see table 3) that, independently on their nature, the cations are equally distributed in these two sites. This important outcome also implies that during the exchange procedure, M1 and M2 sites being occupied in the same proportion, all EFC can be solvated in the same way without geometric restriction. So, there must be no steric effect due to the size of the cation hydration shell.

As reported for the dry Cli Na -, the coordination number of the sodium cation with the oxygen atoms of the zeolite framework is lower for M1 than for M2 [25], i.e. the de-trapping energy is lower for M1 than for M2 cations. Therefore, it can be assumed that the Gaussian function characterized by the highest activation energy corresponds to EFC embedded in M2 sites whereas the second one is related to M1 sites.

Relationship between the exchange selectivity and the de-trapping energy

Since the de-trapping energy measured from DRS is directly related to the EFC/framework interaction, a correlation between its value and the EFC selectivity of the clinoptilolite could be anticipated. Indeed, it could be thought, at first sights, that the stronger is the EFC/framework interaction, the more difficult the cation extraction and, hence, the higher its selectivity.

However, this too simple prospect does not take into account the fact that EFC are exchanged via the aqueous solution and, consequently, that a succession of solvation/desolvation steps are also involved. So, if we put apart steric effects, i.e. the accessibility of a given cation site, ionic selectivity must result from the competition between the EFC/framework and EFC/H 2 O interaction. The comparison of both can therefore be useful for a better understanding of the cationic selectivity.

As reported in the literature [12,27], the exchange selectivity of Clinoptilolite is as follows:

+ + + + > > > 2 2 Mg Ca Na K (6)
Eq. 6 means that, in the natural clinoptilolite, it is more difficult to exchange + K than

+ 2 Mg .
For + K the situation is unambiguous since only + K are present in the K-Cli sample (see table 1). For the other exchanged clinoptilolites, it is not possible to achieve such a high degree of cationic exchange. Again, according to the chemical composition reported in table 1, we can note that

+ 2
Ca and to a less extend

+ 2
Mg are easily replaced by + Na , while Cli Caand Cli Mgstill contain + Na . All these data are in full agreement with eq.6.

The question is now to correlate eqs 5 & 6, i.e. to check whether there is a correlation between cationic selectivity and cation de-trapping energy measured at the dry state. + K appears to be the most deeply trapped cation (eq.5) at the dehydrated state and the less exchangeable one in aqueous solution (eq.6). At first sights, this outcome seems consistent if we assume that it is energetically favorable to exchange an ion by another one having stronger EFC/framework interaction, i.e. higher de-trapping energy. However, considering the other cations, the comparison of eqs.5 & 6 does not provide clear evidence that this simple assumption is valid.

Indeed, cation exchange also involves solvation energy so that it is likely to rely on the competition between EFC/framework and EFC/H 2 O interactions. A comparison between these two types of interaction appears therefore necessary.

According to the Cli Natchemical formula (table 1), EFC which can be exchanged for the greater part are

+ 2
Ca and + Na . Then, the EFC exchange in aqueous solution can be featured by the two following chemical reactions:

) / ( 6 2 6 2 ) ( ) ( ) ( ) ( + ∆ + - = + - + + + + n Y Na exch n n n H O H nNa Cli Y O H Y Cli Na ( 7a
) ) / ( 6 2 2 2 6 2 2 ) ( ) ( ) ( 2 ) ( + ∆ + - = + - + + + + n Y Ca exch n n n H O H Ca Cli Y O H Y n Cli Ca (7b)
for the + Na and

+ 2
Ca exchange, respectively. In eqs.7,

exch H ∆ is the corresponding exchanged reaction enthalpy. 6 2 ) ( O H Y n+
represents the solvated cation, with

+ + + = 2 , , Mg K Na Y or + 2
Ca and the cation valence, i.e. n = 1 or 2. To make it simple, we assume that, in solution, the solvated cation is hexa-coordinated to water molecules, whatever its nature.

Noteworthy, we refer here to The reaction enthalpy exch H ∆ (eqs.7) can be writen: Ca exchange reaction:

6 2 6 2 ) ( ) ( ) ( ) ( ) / ( O H Y Cli Na O H Na Cli Y Y Na exch n n n n H H nH H H + + + + + - - + = ∆ - - (8a) 6 2 2 6 2 2 2 ) ( ) ( ) ( ) ( ) / ( 2 O H Y Cli Ca O H Ca Cli Y Y Ca exch n n n n H n H H H H + + + + + - - + = ∆ - - (8b)
1 H H H exch ∆ + ∆ = ∆ (9)
6 2 6 2 ) ( ) ( ) / ( 1 O H Y O H Na Y Na n n H nH H + + + - = ∆ and Cli Na Cli Y Y Na n n n H H H - - + + + - = ∆ ) ( ) ( ) / ( 2 (10a) 6 2 6 2 2 ) ( ) ( ) / ( 1 2 O H Y O H Ca Y Ca n n H n H H + + + - = ∆ and Cli Ca Cli Y Y Ca H H H n n - - + + + - = ∆ ) ( ) ( ) / (
) ( ~+ ∆ n Y E
).

Consequently, we can establish the following qualitative connection between E ∆ ~ and Mg is the EFC that is the less easily trapped at the clinoptilolite surface, it can be emphasized that the main factor for Ca cations, we can also observe that the exchange is mainly governed by the cation hydration enthalpy and not by the EFC/framework interaction. Indeed, though

2 H ∆ : ) / ( ) ( ) ( ) ( 2 ) ( ) ( + + + + ∆ ∆ - = ∆ - ∆ - ∝ ∆ n n Y Na Na Y Na E E E H ( 11a
) ) / ( ) ( ) ( ) ( 2 ) ( ) (
+ + + + ∆ ∆ - = ∆ - ∆ - ∝ ∆ n n Y Ca Ca Y Ca E E E H (11b)
+ 2
Ca is more deeply trapped at the surface than Ca /H 2 O interaction is so high compared to that of + Na , i.e. Ca are replaced by + Na , the opposite being not true.

It can be then concluded that, in all the cases investigated here, the cation exchange reaction enthalpy (eqs.7) is mainly governed by exch H ∆ , the difference of hydration enthalpy. The differences in de-trapping energy between the various EFC examined in this work are not sufficient to drive the EFC exchange selectivity. To sum up, it means that, in our case, the EFC/framework interaction given by eq.5 cannot be compared to the cation selectivity featured by eq.6.

Conclusion

A

  in terms of EFC de-trapping energy and of cationic population in the various crystallographic sites of the clinoptilolite framework. For that purpose, two DRS experimental procedures can be used: i) dielectric losses versus frequency under isothermal conditions and ii)

  the cation exchange reaction is about the same for all EFC studied in this work. Therefore the chemical equilibrium symbolized by eqs.7 can be investigated using only exch H ∆ .

  where

  interaction, respectively. Finally, eqs. 8 can simply reformulated:

2

 2

+ 2 Mg

 2 exchange selectivity is the hydration enthalpy and not the EFC/Framework interaction. Finally considering the competition between + Na or + 2

  from the fit of the DDTA signal, i.e.

	studied here, the chemical composition of	Ca -	Cli	is the closest to that of the natural sample
	Nat -	Cli	(table 1)
		In all cases, two Gaussian functions are necessary to recover the experimental data. This
	means that, independently on EFC, DDTA signal analysis reveals the presence of two distinct
	dipole populations. Table 3 compiles the parameters of	( i E G ∆	)	obtained from the fitting of each
	zeolite DDTA signal.
		As described in figure 4, the possible cationic sites in a non-dehydrated natural clinoptilolite
	are noted M1, M2, M3 and M4. The M1, M3 and M4 sites are located in the 10 members ring
	cavity, while M2 is in the 8 members one. The occupation of these sites was experimentally
	determined from DRX by Koyama and Takeushi [22] and later by Alberti and Vessalini [23]. It
	was then concluded that	+ Ca in M2, + 2 K in M1 and M3 and
		2	+
									ε	( " T f	)	, which was shown to be
	more reliable than the fit of	( " f ε T	)	obtained from the isothermal CIS method [4,10,21]. The value
	of	E ∆	max	, determined from CIS (see fig.1b), allows us to calculate the pre-exponential 0 τ value
	from the maximum of the	ε	( " T f	)	peak and then to initiate the fitting procedure of the whole
	signal.
			The		ε	( " T f	)	signal obtained on the dehydrated	Ca -	Cli	is reported in Figure 2. The
	increase of	ε	( " T f	)	at high temperature also corresponds to the so-called Maxwell-Wagner-Sillar
	effect as already mentioned in the case of	( " f ε T	)	(fig. 1a). It is not an intrinsic feature of the
	sample and, hence, must not be taken into account in the experimental data analysis. First, we
	considered	) G ∆ as a simple Gaussian function whose the maximum value corresponds to the ( i E
	value of E ∆ ~ obtained from CIS. As the output was fairly poor, the addition of one more
	Gaussian function into	( i E G ∆	)	appeared unavoidable. Figure 3 illustrates	( i E G ∆	)	obtained for
	all samples. The shape and position of	G	E (∆	)	i	differ according to the considered cation
	exchanged clinoptilolite sample.	) G ∆ characterizing ( i E	Nat -	Cli	is very similar in shape and
	position to that of	Ca -	Cli	. This result is consistent with the fact that among the clinoptilolites

+

Na cations are located in M1,

Table 4

 4

			resumes the values of	H ∆	1	and of	-	∆	( E ∆	)	for all the possible cationic exchange
	reactions.
	H ∆	1	reported here are extracted from ref. 28 where they were calculated using the DFT
	(B3LYP/6-311++G(3d,3p)) method. Noteworthy, other values of	H ∆	1	can be found in the
	literature [see Tables I and II of ref 28] though the qualitative trend remains the same.
	From eqs.10, it can be concluded that the higher is	H ∆	1	, the more favorable the hydration of
	+ Y with respect to n	+ Na and	+ Ca , meaning that the substitution of the later with 2	+ Y is not n
	encouraged. Likewise, eqs.11 indicates that the higher is	-	∆	( E ∆	)	the less favorable the trapping

+

Na and

Table 1 .

 1 series of metal exchanged clinoptilolites is studied by means of Dielectric Relaxation Spectroscopy (DRS). The signal analysis is shown to be in accordance with previous structural studies which emphasized the existence of two distinct types of extra-framework cation (EFC) position, i.e. close to M1 and M2 crystallographic sites, at the clinoptilolite inner-surface. Furthermore, analysis of the DRS signals makes possible the determination of the activation energy associated to the localized re-orientational motion of EFC. This activation energy is proportional to the ionic trapping energy and, hence, features the EFC/framework interaction.Cationic exchange capacity and cation selectivity properties of zeolites can be represented by a pseudo-chemical reaction (eq.7). It can then been assumed that, apart from specific steric effects, this chemical equilibrium is governed by the competition between the EFC/framework and EFC/H 2 O interactions. The former is extracted from DRS analysis while the later can be determined from first principle computations already reported in the literature. Then, according to the experimental data reported on the cationic selectivity and to the chemical composition obtained for our sample after various cationic exchange (section 2), it turns out that EFC/H 2 O interaction is the predominant parameter for cationic exchange in the clinoptilolite zeolites investigated in this work. Chemical formula of the purified natural clinoptilolite and of its cationic exchanged forms. Na 2.47 Ca 2.11 K 0.94 Mg 0.41 Fe 0.93 (Al 6.86 Si 32.65 O 72 ) 22H 2 O Na 5.71 K 0.69 Mg 0.18 Fe 0.41 (Al 6.99 Si 33.18 O 72 ) 25H 2 O Cli Ca -: Na 0.22 Ca 5.29 K 0.78 Mg 0.14 Fe 0.51 (Al 6.94 Si 29.58 O 72 ) 22H 2 O Fe 0.36 (Al 7.09 Si 33.66 O 72 ) 23H 2 O Cli Mg -: Na 0.11 Ca 0.99 K 0.68 Mg 4.33 Fe 0.84 (Al 6.95 Si 29.19 O 72 ) 26H 2 O

	Cli : Cli Nat -Na -: Cli K -: K 6.73

Table 2 .

 2 Activation energy of the hopping process obtained from the Arrhenius plot for all samples

	samples				Na -	Cli	Ca -	Cli	Nat -	Cli	K -	Cli	Mg -	Cli
	E ∆ ~ in (eV)	±	0	02 .	0.78	0.99	0.99	1.06	1.06
	E ∆ ~ in (kJ.mol -1 ) 2 ±	75	95	95		102	102

Table 3 .

 3 Parameters of )( i E G ∆ determined the fit of the experimental DDTA signal with eq.4,

	assuming that	( i E G ∆	)	is the summation of two Gaussian functions. E ∆ is the energy value at
	the Gaussian maximum and "Occupancy" is the area relative to the global distribution function
							) G ∆ . ( i E
						1 st Gaussian	2 nd Gaussian
		Sample	E ∆ ±0.02	Occupancy	E ∆ ±0.02	Occupancy
						[eV]	[%]	[eV]	[%]
		Nat -	Cli	0.99	49	0.90	51
		Na -	Cli	0.78	53	0.70	47
		Ca -	Cli	0.99	60	0.88	40
		Mg -	Cli	1.06	53	0.93	47
		K -	Cli	1.06	46	0.93	54

Table 4 .

 4

			1 ∆ and H		H ∆	2	(eqs. 9 & 10) for the exchange reaction of	+ Na and	+ Ca by 2	+ Y ions, n
	with Y = [28]). H ∆ is calculated using eqs.11 from the values of E + Na , + K , + 2 Mg or + 2 Ca . H ∆ are determined from ref 27 (see tables I and II in 1 2 ∆ ~ reported in table 1
	Y	n	+	(	Na	+	)	n	-	Cli	+	Y	n	+	(	H	2	O	)	6		=	( Y	n	+	)	-	Cli	+	nNa	+	(	H	2	O	)	6
									H ∆	( 1 Na	)																							-	∆	( ∆ E	)	(	Na	+	/	Y	n	+	)
									(kJ/mol)																										(kJ/mol)
	K + Ca 2+								+ 104 + 141																										-27 -20
	Mg 2+								+ 446																										-27
			(	Ca	2	+	)	-	Cli	+	2	n	Y	n	+	(	H	2	O	)	6	=	(	Y	n	+	)	2	n		-	Cli	+	Ca	2	+	(	H	2	O	)	6
									H ∆	( 1 Na	)																							-	∆	( ∆ E	)	( 2 Ca	+	/	Y	n	+	)
	Na + K +						(kJ/mol) -141 -341																										(kJ/mol) + 20 -7
	Mg 2+								+ 304																										-7

Acknowledgements

The authors thank the Universidad de La Habana for the support to this study.