Alexandre Soupramanien
email: alexandre.soupramanien@mines-ales.fr

Luc Malhautier

Eric Dumont

Yves Andr

Jannick Rocher

Jean-Louis Fanlo

Gepea

Biological treatment of a mixture of gaseous sulphur reduced compounds: identification of the total bacterial community's structure

Keywords: malodorous gases, sulphur reduced compounds, biological processes, removal efficiency, pH, microbial communities

BACKGROUND:

This study deals with the potential of biological processes combining biofiltration and a biotrickling filter to treat a mixture of sulphur reduced compounds (SRC) including dimethylsulfide (DMS), dimethyldisulfide (DMDS) and hydrogen sulphide (H 2 S). The first step in this work is to evaluate the influence of pH on SRC biodegradation in microcosms seeded with planktonic biomass from activated sludge of a rendering facility. In a second step, for each tested pH, evaluation of the influence of pH on total bacterial community diversity and structure has been investigated using denaturing gradient gel electrophoresis (DGGE). RESULTS: At pH 7 and 5, H 2 S, DMS and DMDS were completely removed. In return, the abatement of DMS and DMDS is low, around 20%, for pH 3 and pH 1 microcosms. The selective pressure imposed in the microcosms (concentration and pH) is sufficient to influence strongly the community diversity and composition. The inoculum community seems to make a significant contribution to the 67-day community in the considered pH microcosms.

CONCLUSION: These results suggest that distinct communities from different inocula can achieve high and stable functionality. The results obtained provide relevant information for improving the treatment of different SRC using biotrickling filter/biofilter combination.

INTRODUCTION

Rendering facilities generate odorous gaseous emissions that are a serious concern as they may induce odour annoyance to the surrounding populations, generate complaints and lead to a lack of acceptance of the facility. While odorous emissions are not considered to cause health problems directly, they may well be associated with negative health effects, which may cause defensive reactions of people due to psychological effects. [START_REF] Schlegelmilch | Odour control at biowaste composting facilities[END_REF] Depending on weather and topographical conditions, odours may be carried away several kilometers but are generally only judged as significant within 500 m of the site. [START_REF] Héroux | Evaluating odour impacts from a landfilling and composting site: involving citizens in the monitoring[END_REF] Hence, legislation in developed countries and states is becoming more and more restrictive and sets minimum distances from the facility to the nearest housings (Germany: 300 m, France: 200 m) [START_REF] Schlegelmilch | Odour control at biowaste composting facilities[END_REF] .

Field data analysis and literature survey reveal the emission of a large variety of volatile compounds such as oxygenated (volatile fatty acids, ketones, aldehydes and alcohols), nitrogen and sulphur compounds. Among these molecules, the sulphur reduced compounds (SRC) are an important class of odorous compounds. Because of their very low odor thresholds (around 1 µg-N m -3 ), these compounds deserve special attention. [START_REF] Langenhove | GC-MS identification of odorous volatiles in wastewater[END_REF] Moreover, they a potentially corrosive effect on some materials. Generally the most frequently studied SRCs are hydrogen sulphide (H 2 S), dimethylsulphide (DMS), dimethyldisulfide (DMDS) and methanethiol (MT). [START_REF] Kim | Sulfuric odorous compounds emitted from pig-feedings operations[END_REF][START_REF] Panetta | Management strategy impacts on ammonia volatilization from swine manure[END_REF] For these reasons an adequate waste gas management strategy has to be implemented by industrial facilities to control odor emissions. [START_REF] Both | Directive on Odour in Ambient Air: an established system of odour measurement and odour regulation in Germany[END_REF]7 Among the gas treatment processes, biotechniques, and more particularly biofiltration [START_REF] Chung | structure of the bacterial community in a biofilter during dimethyl sulfide (DMS) removal process[END_REF][START_REF] Kennes | Bioprocess for air pollution control[END_REF][START_REF] Rehman | Perfomance of biofilter for the removal of hydrogen sulphide odour[END_REF] and biotrickling, [START_REF] Arellano-Garcia | Dimethyl sulphide degradation using immobilized Thiobacillus thioparus in a biotrickling filter[END_REF][START_REF] Chung | Removal of high concentration of NH 3 and coexistent H 2 S by biological activated carbon (BAC) biotrickling filter[END_REF] are proving to be efficient, economic and environment-friendly technologies for the treatment of industrial airstreams characterized by high flow rates and low pollutant concentrations. These biotechniques are based on the ability of microorganisms to use volatile pollutants for growth and have been successfully applied for the treatment of gaseous emissions emitted from different industrial processes. Nevertheless, as such gaseous emissions contain a mixture of chemical molecules (oxygenated, sulphur and nitrogenous compounds), the performance of these techniques has to be improved to reach removal efficiency levels sufficient to prevent these odour annoyances. Consequently, the complete elimination of sulphur reduced compounds has to be assessed as the perception threshold of these compounds is particularly low.

Sulphide oxidizing bacteria (SOB) allowing oxidation of sulfide encompass several genera such as Thiobacillus, Acidithiobacillus, Achromatium, Beggiatoa, Thriothrix, Thioplaca, and Thermotrix. Thiobacillus is one of the most widely studied. [START_REF] Tang | Bacteria of the sulphur cycle: an overview of microbiology, biokinetics and their role in petroleum and mining industries[END_REF] According to literature, [START_REF] Sercu | Inoculation and start-up of a biotrickling filter removing dimethyl sulfide[END_REF] it has been shown that these bacterial communities involved in the biodegradation of SRC are sensitive towards environmental stress conditions as pH.

This study deals with the potential of a combination of biofiltration and biotrickling filter for the treatment of a mixture of SRCs including DMS, DMDS and H 2 S. Hence, the first step of this work was to evaluate the influence of pH on the SRC biodegradation activity under controlled operating conditions. The optimal pH range within which the SRC mixture is completely removed from the gas phase has been determined in microcosms seeded with a planktonic biomass.

In a second step the aim of this work was then to characterize the total bacterial community in terms of diversity and structure, using denaturing gradient gel electrophoresis (DGGE). An increasing number of studies propose statistical investigations of DGGE banding patterns, which undoubtedly leads to refined results. These advanced analyses are based on computer-assisted characterization of the banding patterns and subsequent treatment of the data using a statistic approach. Guidelines for the computer assisted analysis of fingerprinting profiles was proposed by Rademaker [START_REF] Rademaker | Computer-assisted analysis of molecular fingerprints and database construction[END_REF] using the GelCompar software package (Applied Maths, Kortijk, Belgium). Clustering techniques, such as the unweighted pair group method using arithmetic averages (UGPMA), are applied to DGGE profiling with the aim of identifying those samples which generate similar patterns. [START_REF] Boon | Microbial community dynamics during production of the mexican fermented maize dough pozol[END_REF][START_REF] Ibewke | Impact of fumigants on soil microbial communities[END_REF][START_REF] Yang | 16S rDNA fingerprinting of rhizosphere bacterial communities associated with healthy and Phytopthora infected avocado roots[END_REF] One advantage of this presentation is that coherence of the fingerprinting patterns can be assessed rapidly.

MATERIALS AND METHODS

Inoculum and mineral medium

The inoculum was obtained from a rendering facility (ATEMAX and SOLEVAL Sud Est, Viriat, France) and comprises an aqueous suspension sampled from the retention tank where waste water is collected after primary treatment and deposit of solid sulfur. The composition of the mineral medium used for microbial growth is as follows: KH 2 PO 4 : 1.36 g L -1 ; Na

Experimental setup and operation

Polluted gas generation and microcosms

The dimethylsulfide (DMS) and dimethyldisulfide (DMDS) liquid mixture is continuously injected and vaporized into an air stream regulated at 5 L min -1 (humidity content 5%), via a syringepump (Precidor, Infors AG, Basel, Switzerland). Gaseous hydrogen sulphide is directly injected in the compressed airstream using a flow rate regulator (5850TR, Brooks Instrument, Hatfield, PA, USA) controlled at 0.35 mL.min -1 (Fig. 1). The concentration of each compound in the synthetic polluted gas is 100 mg m -3 . This synthetic gaseous effluent passes continuously through homogeneous flasks. The flow rate of the airstream is 100 mL min -1 and the concentration of each compound of the mixture is 100 mg m -3 (Fig. 2). Tests were performed in 1 L sterile flasks containing 1 L of mineral salt solution and sealed with a Tefloncoated cap (Interchrom, Montluc ¸on, France). The gaseous effluent inlet and outlet are equipped with a 0.22 µm filter in order to avoid microbial contamination. Each flask except the negative control is seeded with 25 mL of aqueous suspension and 11 mL of solid deposit sulphur suspended in the mineral medium. Incubation was performed at 30 • C at an agitation speed of 150 rpm for 2 months.

Compressed Air Microcosms

Two experiments (C1 and C2) were realized with duplicates (C1.1 and C1.2; C2.1 and C2.2). At the beginning, pH of the mineral medium of each flask was adjusted to neutrality. Then, the pH value of the microcosms was maintained constant at specified levels (7, 5, 3, 1) using 5N NaOH solution. Two controls are also realized, one containing mineral medium and inoculum and fed by compressed air and the other containing just mineral medium and supplied with the polluted gas. The pH of these two controls was maintained at neutrality.

Analytical methods

The biodegradation was monitored by gas phase measurement at inlet and outlet sampling ports. Hydrogen sulphide concentration was measured using a gas chromatograph equipped with a photoionization detector (PID) (HNU Systems Model 311, Newton, MA, USA). The minimum detectable level for hydrogen sulphide is estimated at 1 mg m -3 . DMS and DMDS concentrations were measured using a gas chromatograph equipped with a flame ionization detector (FID) (Trace, Thermo Fisher Scientific Inc., Waltham, MA). A fused silica capillary column was used (Equity-1, Sigma Aldrich, St Louis, MO, USA): length 30 m, internal diameter 0.32 mm, film thickness 0.25 µm and quantification limit 1 mg m -3 . The detector and injection temperature are 220 • C and 100 • C, respectively, gas vector flows are 350 mL min -1 for air and 35 mL min -1 for H 2 .

Removal efficiency

The removal efficiency (RE) defined the functional performance in the different cultures. It is based on the SRC concentration (C) inlet and outlet at the different microcosms and was calculated according to:

RE = C inlet -C outlet C inlet (1)
DNA isolation and 16S RNA gene amplification DNA was extracted from the aqueous suspension present in each microcosm using a FastDNA  SPIN Kit for Soil (MP Biomedicals, Irvine, CA, USA). Replicates were realized for each DNA extraction. For that, 200 mL of aqueous suspension were taken and centrifuged at 10 000 rpm for 15 min. The amount of cell pellet used for extraction and the elution volume for final DNA recovery were standardized for each microcosm (200-350 mg and 50 µL, respectively). Extracted DNA (5-7 µL) was visualized by gel electrophoresis on 1% agarose gel. The V3 region of the eubacterial 16S rRNA gene was PCR amplified using primers V3F (5 -ACTCCTACGGGAGGCAGCAG-3 ) and V3R

(5 -ATTACCGCGGCTGCTGGCAC-3 ), both slightly modified from Muyzer. [START_REF] Muyzer G De | Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA[END_REF] A 40-base GC clamp was attached to the 5 end of the forward primer. Amplification was performed on ∼100 ng DNA template (approx.

Denaturing gradient gel electrophoresis

Denaturing gradient gel electrophoresis (DGGE) was performed according to the protocol of Muyzer, [START_REF] Muyzer G De | Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA[END_REF] using 8% polyacrylamide gels prepared with denaturing gradient ranging from 30% to 60% (where 100% denaturant contained 7 mol L -1 urea and 40% (v/v) formamide). 500 ng of PCR samples product were loaded on the gel, along with reference standards for further pattern alignment.

Electrophoresis was run at 60 • C and 100 V in 1× TAE buffer, for 16 h. Gels were stained with Sybr green I (Sigma-Aldrich), washed, and photographed with a camera (Vilber, Quantum ST4-1100/26M. X-Press, Eberhardzell, Deutschland). Gel images were analyzed with Gel Compar II (Applied Maths, Saint-Martens-Latem, Belgium).

Sequencing and cloning of DGGE fragments DNA fragments to be sequenced were excised from the gel, placed into sterile Eppendorf tubes containing 25 µL of sterilized water, and incubated overnight at 4 • C. A 4 µL volume of DNA diffused in water served as template for PCR amplification. The amplified products were subjected to a new DGGE step to confirm their electrophoretic mobility. The PCR product was cloned by using a TOPO TA Cloning kit with plasmid vector pCR4-TOPO (Introvigen, Carlsbad, CA). The sequences obtained were investigated on the RDP-II database.

Statistical analysis

The diversity of the total bacterial community is evaluated by calculating the Shannon index (I), which took into account both the number of bands (obtained by DGGE) and their relative intensity according to Equation ( 2) where p i is the relative abundance of the ith band of the profile:

I = - n i p i log(p) i ( 2
)
The uhted pair group method using arithmetic averages (UGPMA), are applied to the DGGE profiling to identify those samples which generate similar patterns. In effect, the initial data matrix (relative intensities according to position) was reduced and transformed in order to downweight the influence of more abundant species. The pair-wise similarity index S i,j between a profile i and j was calculated by Bray-Curtis coefficient, on the basis of both band positions and their relative abundance:

S i,j = 1 - s k=1 |p i,k -p j,k | p i,k + p j,k (3)
where p i,k is the relative abundance of the kth band in the profile i, and p j,k is the relative abundance of the kth band in the profile j.

RESULTS AND DISCUSSION

SRC removal efficiency

The synthesis of all results of this study is presented on Fig. 3(a), (b) and (c).

The removal efficiency of H 2 S (Fig. 3(a)) is very high (approximately 90-95%) from the fourth day of functioning. After the 10 days of functioning, H 2 S is completely removed, whatever the pH of the microbial suspension.

The efficiency of removal of DMS and DMDS (Fig. 3(b),(c)) follows a similar temporal evolution. Nevertheless, two groups can be distinguished: pH 7 and 5 microcosms on the one hand and pH 3 and 1 microcosms on the other. After 10 days of functioning, the efficiency of removal of these compounds increases, whatever the microcosms considered (pH 7, pH 5, pH 3 and pH 1), reaching removal efficiency values close to 60% for DMS and 40% for DMDS. For pH 1 and 3 microcosms, a decrease in the efficiency of removal of these compounds is then exhibited. The abatement values remain stable around 10 and 15% for DMS and DMDS, respectively. In return, for pH 7 and 5 microcosms, complete elimination of both compounds is obtained after 20 days of functioning.

These results can be explained by several phenomena. First, for pH 1 and pH 3 microcosms, it is probable that there is competition between substrates; the biomass would first metabolize the most available or biodegradable compounds. Second, competition between species can be assumed as SRC oxidizing bacteria are diverse and hence different specialized groups can grow. Furthermore, this result could be explained by the accumulation of sulfuric acid, which is a by-product of the aerobic oxidation of sulfide by SOB. It seems that sulfuric acid induces a decrease in DMS and DMDS removal efficiency by inhibiting the growth of the bacteria concerned. [START_REF] Adib | Effect of pH and surface chemistry on the mechanism of H 2 S removal by activated carbons[END_REF][START_REF] Lee | Removal of hydrogen sulfide by sulfate-resistant Acidithiobacillus thiooxidans AZ11[END_REF][START_REF] Shinabe | Characteristics of hydrogen sulfide removal by Thiobacillus thiooxidans KS1 isolated from a carrier-packed biological deodorization system[END_REF] For pure DMDS-degrading strains isolated from various environments, it has been shown that optimum growth was observed for pH values between 6 and 8. [START_REF] Ito | Degradation of dimethyl sulfide by Pseudomonas fluorescens strain 76[END_REF][START_REF] Smith | Mechanism of oxidation of DMDS by Thiobacillus thioparus strain 56[END_REF] Moreover, the outcome of competition for common substrate in the mixed cultures seems to be dictated mainly by pH value of the microbial suspension. What is the impact of this operating parameter (pH) on total bacterial community diversity and structure?

Bacterial community analysis Influence of pH on total bacterial community

The reproducibility of the whole experiment from sampling until DGGE is represented on Fig. 4. The similarity index within the eightreplicate pool was 88%. This reproducibility was the same range as previous studies where duplicate profiles were characterized by high similarity (≥85% according to the Jaccard coefficient [START_REF] Cabrol | Assessing the bias linked to DNA recovery from biofiltration woodchips for microbial community investigation by fingerprinting[END_REF] ).

The diversity of the total bacterial community is evaluated by calculating the Shannon index, which took into account both the number of bands (obtained by DGGE) and their relative intensity (Fig. 5).

As indicated on Fig. 5, a diminution of the diversity of the total bacterial community is observed when the pH of the aqueous solution becomes acid (1 ± 0.09 to 0.6 ± 0.02). This diminution of diversity has been linked with the removal efficiency of SRC. At steady state, for pH 3 and pH 1 microcosms, the abatement value of DMS and DMDS is low (around 20%). It can be assumed that the acidification of the aqueous phase inhibited the growth of specific populations having the ability to degrade DMS and DMDS. In return, around neutrality (5-7), higher bacterial community diversity seems to induce an enhanced activity biodegradation level as SRC are completely removed. These first results indicate that the bacterial community involved in the biodegradation of these compounds is sensitive towards environmental conditions. Figure 6 presents the DGGE profiles of the total bacterial community for each tested pH at steady-state (day 67) for C2.2. After 67 days of functioning, the total bacterial community structure differs from the inoculum community whatever the tested pH. This result could be due to the adaptation of bacterial communities to the environmental conditions. According to pH, the bacterial community structure differs. As indicated in DGGE profiles, the bacterial community of pH 1 microcosm reveals two main bands, while the DGGE profiles of pH 7 microcosm exhibits various prevailing populations. A similar evolution is observed for C1 and C2.1 (data not shown). Hence, the selective pressure imposed in the microcosms (contaminant concentration and pH) is sufficient to influence strongly the community diversity and composition. These results have to be confirmed by statistical analysis. It has been reported [START_REF] Egli | Community analysis of ammonia and nitrite oxidizers during a start-up of nitrification reactors[END_REF] that despite similar reactor performance with respect to chemical parameters (pH and temperature), the underlying community structures are different.

The analysis of DGGE profiles of pH 5 and pH 7 microcosms suggested that a stable function could be maintained, notwithstanding different bacterial community structure. It can be suggested that some populations seem to be favored with specific environmental conditions as some main bands are only visualized in pH 5 microcosms DGGE profile for example. For acidic environments, the low DMS and DMDS removal efficiency levels could be explained by the inhibition of specific bacterial populations. As indicated on Fig. 6, some dominant bands for pH 7 and pH 5 microcosms are not detected by the analytical technique.

The major bands from the DNA-based DGGE pattern of pHmicrocosms were excised (Fig. 6) and sequenced (Table 1).

The identified bacteria belonged to the same bacterial class: Gammaproteobacteria. Similarities less than 80% to classified It has been reported that alkaliphilic sulfur oxidizing bacteria (ASB) can oxidize reduced sulfur compounds such as sulfides and Thioalkalibacter are used in H 2 S removal studies. [START_REF] Olguín-Lora | Evaluation of haloalkaliphilic sulfur-oxidizing microorganisms with potential application in the effluent treatment of the petroleum industry[END_REF] These chemolithoautotrophic microorganisms can also grow in saline environments.

Microorganisms like Dyella, Burkholderia, Alcaligenes, are able to grow chemolithotrophically with a medium containing dimethylsulfide and dimethyldisulfide and exhibit growth coupled with their oxidation. It has been reported that optimal pH range is between 5 and 7.

In an acid environment, the chemolitrophic biooxidation of sulphide (H 2 S) has been widely investigated using a number of organisms including thiobacillus and acidithiobacillus. [START_REF] Sercu | Inoculation and start-up of a biotrickling filter removing dimethyl sulfide[END_REF][START_REF] Rappert | Odor compounds in waste gas emissions from agricultural operations and food industries[END_REF] Influence of the inoculum Figure 7 shows UPGMA clustering of DGGE patterns based on Bray-Curtis similarities for pH 7 and pH 1 microcosms and for both experiments.

As illustrated by clustering (Fig. 7), three distinct groups are separated on the basis of species composition and relative abundances: sludge inocula, pH 7 or pH 1 microcosms seeded with inoculum1 (T01), pH 7 or pH 1 microcosms seeded with inoculum2 (T02). The sludge inocula constitute the most distinct group, with only 16 and 13% similarity with pH 7 or pH 1 microcosms, respectively. Moreover, even in the presence of contaminants, the inoculum community (T0) seems to make a significant contribution to the 67-day community in pH 7 or pH 1 microcosms. After 67 days, the pH 7 microcosm community does not result in the same structure as the pH 7 C1 microcosm community, and is only 22% similar to the pH 7 C2 microcosm community. The same trend is observed for the communities of the other microcosms (pH 3 and 5 microcosms). Nevertheless, for a given pH microcosm, the SRC removal efficiency is equivalent, whatever the chosen inoculum. These results indicate that distinct communities from different inocula can achieve high and stable functionality.

The long-term impact of inoculation has been discussed. In bioreactors under constant operating conditions, achieving the same functionality but differently seeded, distinct communities can develop at steady-state (in terms of density, diversity and structure), indicating the emergence of different fitted species due to the inoculum origin. [START_REF] Sercu | Inoculation and start-up of a biotrickling filter removing dimethyl sulfide[END_REF][START_REF] Khammar | Link between spatial structure of bacterial communities and degradation of a complex mixture of volatile organic compounds in peat biofilters[END_REF] These interesting data must be cautiously interpreted as the extent of microbial dynamics has not been surveyed during the steady-state period. To conclude, it seems that inoculation brings enough diversity to allow the emergence of adapted species.

CONCLUSIONS

The present study assesses the influence of pH of the environment (aqueous phase) on SRC biodegradation and community structure in bioreactors seeded with planktonic biomass and under constant operating conditions.

The results obtained show that the pH value of the aqueous phase has a great impact on the abatement of SRC. Complete elimination of SRC is observed when pH level decreases from 7 to 5. In return, for acidic pH (3 and 1), the efficiency of removal of DMS and DMDS is low at around 20%. The abatement of hydrogen sulfide is complete, whatever the pH level of the considered microcosm.

Moreover, the selective pressure imposed in the microcosms (contaminant concentration and pH) is sufficient to influence strongly the community diversity and composition. The inoculum community (T0) makes a significant contribution to the 67-day community in the pH microcosms investigated. These results indicate that distinct communities from different inocula can achieve high and stable functionality. Sulphur oxidizing bacteria (SOB) represent a wide-ranging and highly diversified group of microorganisms such as Thiobacillus, [START_REF] Malhautier | Biological treatment process of air loaded with ammonia and hydrogen sulphide mixture[END_REF] Hyphomicrobium, [START_REF] Sercu | Inoculation and start-up of a biotrickling filter removing dimethyl sulfide[END_REF] Pseudomonas, [START_REF] Chung | Biological elimination of H 2 S and NH 3 from waste gases by biofilter packed with immobilized heterotrophic bacteria[END_REF] and Arthrobacter. [START_REF] Lee | Simultaneous removal of H 2 S and NH 3 in biofilter inoculated with Acidithiobacillus thiooxidans TAS[END_REF] Competition between species can be assumed as SRC oxidizing bacteria are diverse and hence different specialized groups can emerge. At the different pH levels it will be of interest to identify, from DGGE profiles, those emergent populations that contribute to SRC biodegradation.

These results provide interesting data in order to improve the abatement of SRC by using a biotrickling filter/biofilter combination. It is worth noticing that the adjustment of pH level of the aqueous phase is easier to control in a biotrickling filter than in biofilters. Moreover, in these bioprocesses, microorganisms are grown on an organic or inorganic support and form a biofilm. Hence, as the growth conditions change, the impact of pH level on fixed microbial communities can be slightly different, as has been observed in microcosms seeded with planktonic biomass.

Figure 1 .

 1 Figure 1. Gaseous effluent generation system: (1) syringe pump; (2) homogeneous flask; (3) flow rate regulator.

Figure 2 .

 2 Figure 2. Microcosm culture: (1) mineral medium; (2) magnetic stirring bar; (3) magnetic agitator.

Figure 3 .

 3 Figure 3. SRC removal efficiency versus time for different tested pH: (a) H 2 S; (b) DMS; (c) DMDS.

Figure 4 .Figure 5 .

 45 Figure 4. Test of reproducibility based on Bray-Curtis index.

Figure 6 .

 6 Figure 6. Total bacterial community structure for each microcosms: (1, 9) marker; (2) pH 7; (3) pH 5; (4) pH 3; (5) pH 1; (6) control 1; (7) control 2; (8) Inoculum.

Figure 7 .

 7 Figure 7. UPGMA clustering of DGGE patterns based on Bray-Curtis similarities for pH 7 and pH 1 microcosms and for both tests (C1 and C2).

  bacterial species were found except for pH 7 and pH 5 microcosms. Moreover, different studies have shown that these microorganisms are involved in the biodegradation of SRC and H 2 S.

  C for 4 min, 30 cycles consisting of 30 s denaturation at 98 • C, 30 s annealing at 68 • C and 30 s extension at 72 • C, and final extension at 72 • C for 10 min. Negative controls were included to verify the absence of contamination. PCR products were visualized by electrophoresis on 2% agarose gel.

1-2 µL) in a final volume of 50 µL containing 0.6 µmol L -1 of each primer (Sigma Aldrich, St Louis, MO, USA), 10 mmol L -1 of dNTP mix (200 µmol L -1 each dNTP, Abcys, Paris, France), 5 µL of 10× ThermoTaq Reaction Buffer (Thermo Fisher Scientific Inc., Waltham, MA, USA), and 0.75 U of Thermo Taq High fidelity DNA Polymerase (Thermo Fisher Scientific Inc., Waltham, MA, USA). PCR was achieved by Tpersonal thermocycler (Biometra, Goettingen, Germany) using the following program: initial denaturation at 98 •

Table 1 .

 1 Similarity percentages of excised DGGE bands as compared with the RDP database

	Microcosms Microorganisms Similarity(%)	Class
	pH 7	Thioalkalibacter	43	Gammaproteobacteria
	pH 5	Dyella	73	Gammaproteobacteria
	pH 3	Acidithiobacillus	99	Gammaproteobacteria
	pH 1	Acidithiobacillus	98	Gammaproteobacteria

ACKNOWLEDEGMENTS

The present research was funded and supported financially by the Ecole des Mines d'Al ès and the Ecole des Mines de Nantes.


Anne Rondeau
email: rondeau.anne@gmail.com

Agn ` Es Mandon

Luc Malhautier

Franck Poly

Agn ` Es Richaume

Biopurification of air containing a low concentration of TEX: comparison of removal efficiency using planted and non-planted biofilters

Keywords: biofiltration, TEX, removal efficiency, planted biofilter, microbial activity

BACKGROUND: Underground car parks are confined spaces where traffic generates many pollutants such as toluene, ethylbenzene and xylene (TEX) and nitrogen oxides (NOx). The objective of this work was to evaluate the removal efficiency of low concentrations of TEX using planted and non-planted biofilters. Two pilot-scale units of biofiltration were set up. The gaseous mixture was generated to obtain a total TEX concentration of 600 µgm -3 . Elimination performance of both biofilters was monitored for 96 days. Microbial behavior and the potential capacity to degrade the NOx, were investigated.

RESULTS:

The removal efficiency (RE) was 30% in the non-planted and 15% in the planted biofilter at day 3. The RE decreased more rapidly in planted biofilter than in non-planted until nutrient addition at day 77. Consequently, the RE increased to 70% in both reactors. The potential for carbon mineralization was higher than those found in soils. The denitrification activity indicated good conditions for synthesis and maintenance of the denitrifying enzyme pool in the mound of soil. This suggested that NOx could also be treated in such a system. CONCLUSION: The efficiency of the planted biofilters in removing TEX was evaluated and found to have good potential for improving air quality.

INTRODUCTION

Urban air pollution is increasingly recognized as a major public health problem and environmental issue. [START_REF] Krzyzanowski | Introduction in Health Effects of Transport-related Air Pollution[END_REF]2 Nitrogen oxides (NOx) and volatile organic compounds (VOCs), originating mainly from transport and heating modes, are among the pollutants of most concern because they are behind the formation of tropospheric ozone and contribute to global warming. [START_REF] Ineris | Institut National de l'Environnement et des Risques. Fiche de donn ées écotoxicologique et environnementales des substances chimiques : les oxydes d'azote[END_REF] Often in cities, car parks are a confined environment in which complex pollution containing about 275 identified substances accumulates. The majority of these compounds are carbon oxides (COx), nitrogen oxides (NOx) and VOCs such as benzene, toluene, ethylbenzene and xylenes (BTEX). [START_REF] Franc | Recommandations pour la qualité de l'air dans les parcs de stationnement couverts[END_REF] The average concentrations of TEX (toluene, ethylbenzene and xylene) and NOx found in underground car parks, of the order of 10 1 -10 2 µg m -3 , often surpass the values recommended by public authorities. [START_REF]COPARLY Comit é pour le contr ôle de la Pollution Atmosph érique dans le Rh ône et la r égion Lyonnaise, Qualité de l'air dans les parcs de stationnement couverts[END_REF] In France, mechanical systems to extract the contaminated air from underground car parks were set up to manage fire risk regardless of indoor air quality. The polluted air released into the atmosphere through ventilation systems is then a source of pollution for underground car park surroundings. Constraints imposed on air quality by European policies were strengthened recently. [START_REF]Directive 2004/35/CE du Parlement europ éen et du Conseil, du 21 avril 2004, sur la responsabilité environnementale en ce qui concerne la pr évention et la r éparation des dommages environnementaux[END_REF]7 Therefore, the development of innovative solutions to treat various gaseous pollutants has received special attention in order to improve air quality.

The effectiveness of biological processes for the treatment of VOCs has been highlighted. [START_REF] Leson | Biofiltration: an innovative air pollution control technology for VOC emissions[END_REF]9,[START_REF] Mudliar | Bioreactors for treatment of VOCs and odours -a review[END_REF] Among the solutions developed, biofiltration has been demonstrated to be an effective air pollution control technology for a wide range of applications such as wastewater treatment plants, rendering plants, chemical industries and composting facilities. [START_REF] Pagans | Biofiltration for ammonia removal from composting exhaust gases[END_REF] Furthermore, biofiltration processes are appropriate for treating high flow rates and low concentration of pollutants. [START_REF] Malhautier | Biofiltration of volatile organic compounds[END_REF] Their relatively low operational costs and their limited environmental impact contribute to the increasing success of biofiltration compared with other gas treatment strategies. [START_REF] Van Groenestijn | Recent developments in biological waste gas purification in Europe[END_REF] Industrial biofiltration systems are designed so that pollutant concentrations ranging from mg m -3 to g m -3 can be treated. Such concentrations are far above those observed in underground car parks.

Planted biofilters involve the venting of contaminated gas streams through a porous packed solid media in which plants can grow. They constitute a promising alternative, which combines the capacity of bacteria and plants to reduce pollutants and provide an aesthetic value. The operation of the biofilter relies on both the bioremediation capacities of microorganisms from the soil and the purifying capacities of plants (CO 2 fixation, and absorption of VOCs). The planted mound of soil sustains a rich and diverse rhizosphere microflora. These microorganisms can degrade many pollutants (VOCs, NOx, etc.), using them as substrates or energy sources. [START_REF] Roux | Stimulation of soil nitrification and denitrification by grazing in grasslands: do changes in plant species composition matter?[END_REF][START_REF] Glick | Using soil bacteria to facilitate phytoremediation[END_REF] Although the plant-bacteria associations are well studied with the agronomic perspective of improving crop yields, their use in pollution control remains poorly studied despite their promising use for improving air quality in an urban context.

In the present study, the capacity of biofiltration to reduce gaseous pollutants from confined spaces such as underground car parks or road tunnels before releasing them into the atmosphere was investigated. A synthetic gaseous mixture was generated to be representative of emissions from underground car parks. This gaseous mixture was characterized by a very low concentration of TEX corresponding to 200 µg m -3 air for each compound and a superficial gas velocity of 100 m h -1 . The elimination performance of planted and non-planted biofilters supplied with this polluted gas was monitored. Both the microbial behavior and the potential capacity to degrade NOx were also investigated.

MATERIALS AND METHODS

Laboratory scale biofiltration system

The experimental set-up used for continuous experiments is schematically represented in Fig. 1. Two biofilter columns made of transparent PVC tubing with an internal diameter of 15 cm and a height of 40 cm, corresponding to a packing volume of 7.6 × 10 -3 m 3 , were filled with 2.143 kg of filtering material. The filtering material was provided by Canevaflor (Tarare, France) and mainly consisted of a mixture of coco fibres and perlite. It was characterized by a dry bulk density of 90.4 kg m -3 and a void fraction of 95%.

Each column was equipped with two gas sampling ports located at the inlet and at the outlet of the biofilter.

The polluted gas was obtained by volatilizing a liquid mixture containing toluene, ethylbenzene and xylenes (TEX) (99+% Extra Pure, Alpha Aesar, Karlsruhe, Germany) in compressed air via a syringe pump (Apparatus Pump 11 Pico Plus, Harvard, Massachusetts, USA) in order to provide a total pollutant concentration of 600 µg m -3 (200 µg m -3 for each compound). The gaseous stream was homogenized and humidified in a scrubber (>98% relative humidity). Each column was supplied in an upward flow mode with polluted air containing TEX with a volumetric flow of 1.8 m 3 h -1 corresponding to a superficial gas velocity of 100 m h -1 and a constant empty bed retention time (EBRT) of 14 s. The pressure drops were followed during the experiment and never exceeded 80 Pa (data not shown).

One biofilter was planted with Hedera helix grown in 9 cm diameter pots containing soil. The planted mound of soil was introduced in the centre of the top column in the filtering material near the gas outlet. In the non-planted biofilter, the plant was removed before placing the mound of soil in the filtering material. In both biofilters, the mound of soil was used as inoculant. The biofilter columns were maintained at room temperature (between 19 and 21 • C) under standard horticultural fluorescent tubes alternating 12 h of light and 12 h in the dark.

The composition of nutrient solution added to each column from day 78 is given in Table 1. This solution provided nitrogen, phosphorus and potassium sources (24/20/02 at a concentration of 0.7 g L -1 ). Every 2 days, 80 mL of the nutrient solution were homogeneously distributed drop by drop on the top of the packing material. The fertilization provided 4 mg nitrogen day -1 and enabled a C/N/P ratio of 100/5/1 to be obtained.

Analytical methods

Inlet and outlet synthetic polluted air streams were sampled daily. An AirmoBTX gas chromatograph (GC) equipped with a column (MXT30CE; 30 m; 0.28 mm internal diameter; 1 µm film) and a flame ionization detector (FID) (Chromatotec group, Bordeaux, France) was used to measure the TEX gas-phase concentrations. Gas-phase samples (460 mL) were withdrawn from the biofilter sampling ports and automatically injected into the GC/FID. TEX concentrations were determined from an internal calibration curve. The biodegradation was monitored by calculating the TEX removal efficiency from the average of five successive quantifications as follows:

RE = 1 - C o C i × 100
where RE is removal efficiency, C o is TEX concentration at the column outlet, and C i is TEX concentration at the column inlet.

Measurements of bacterial activities

At the end of the experiment, the packing material was removed from the PVC columns and separated according to depth from the inlet (0-10 cm, 10-20 cm, 20-30 cm and 30-40 cm). The soil was separated from the packing material in the section near the gas outlet (30-40 cm). The water content was determined after drying a portion of each sample at 105 • C overnight. Investigation of the bacterial activity was performed by measuring the carbon mineralization potential using substrate induced respiration (SIR) [START_REF] Anderson | A physiological method for the quantitative measurement of microbial biomass in soils[END_REF] and denitrifying enzyme activity (DEA) on samples over a short period. [START_REF] Smith | Phases of denitrification following oxygen depletion in soil[END_REF][START_REF] Tiedje | Perspectives on measurement of denitrification in the field including recommended protocols for acetylene based methods[END_REF] For the SIR measurements, samples of filtering material (equivalent to 5 g oven dried) extracted at 20-30 cm and 30-40 cm were placed in 150 mL sterile plasma flasks with a rubber stopper. 1 mL of glucose was added to obtain a final concentration of 1.2 mg glucose g -1 dry soil. Additional water was added to achieve 70% of the water holding capacity. The plasma flasks were closed and incubated at 28 • C for 6 h. Gas samples were analyzed every 30 min to measure the CO 2 concentration using a gas chromatograph equipped with a catharometer (µGC R-3000; SRA Instruments, Marcy l'Etoile, France). SIR was expressed as µg C-CO 2 h -1 g -1 dry filtering material. For the DEA measurements, samples (5 g equivalent ovendried) taken from the mound of soil and in the filtering material (30-40 cm) were placed into 150 mL plasma flasks. The atmosphere of each flask was evacuated and replaced by a 90 : 10 He : C 2 H 2 mixture to provide anaerobic conditions and to inhibit N 2 O-reductase activity. Ten ml of distilled water containing KNO 3 (200 µg NO 3 -N g -1 dry soil), glucose (0.5 mg C g -1 dry soil) and glutamic acid (0.5 mg C g -1 dry soil) were added. Additional water was provided to achieve soil saturation and flasks were sealed with rubber stoppers. During incubation at 28 • C, gas samples (200 µL) were taken at 2, 4 and 6 h and immediately analyzed for N 2 O production using the µGC R-3000 (SRA Instruments, Marcy l'Etoile, France). DEA was expressed as µg N-N 2 O h -1 g -1 dry filtering material.

RESULTS AND DISCUSSION

Efficiency of TEX removal in planted and non-planted biofilter

An overview of the performance for both biofilters during the whole experimental period of 96 days is presented in Fig. 2. Operation and performance of biofilters is reported in terms of TEX removal efficiency (RE) as a function of time (days).

The trends in abatement were similar in both biofilters. After the peak of 75 to 80% efficiency, two periods could be distinguished. This rapid and transient removal was probably due to the adsorption phenomenon of aromatics in the packing material. [START_REF] Aizpuru | Biofiltration of a mixture of volatile organic compounds on granular activated carbon[END_REF] In the first phase (day 3 to 77), the biofilters behaved differently. The removal efficiency was higher in the non-planted than in the planted biofilter with about 30% and 15% RE, respectively (Fig. 2(A),(B)). The pollutant removal efficiency decreased rapidly in the non-planted biofilter, as indicated by the regression coefficient (-0.26). In the planted biofilter, the RE remained very low and almost constant with a regression coefficient of -0.12. This observation might be due to the balance between available nitrogen and carbon sources in the biofilters as suggested by the increase in efficiency following the fertilization. The competition for nitrogen between plant roots and bacteria [START_REF] Jackson | Short-term partitioning of ammonium and nitrate between plants and microbes in an annual grassland[END_REF][START_REF] Wang | Competition for nitrogen during mineralization of plant residues in soil: microbial response to C and N availability[END_REF] as well as the limitation of microbial growth by the availability of mineral nutrients rather than energy substrates [START_REF] Helal | Effects of plant roots on carbon metabolism of soil microbial biomass[END_REF][START_REF] Merckx | Production of root-derived material and associated microbial growth in soil at different nutrient levels[END_REF] are well known in planted soil. In the non-planted biofilter, nitrogen was presumably more available than in planted ones which would explain the difference in RE.

Several authors have found that the performance of VOC degrading biofilters was improved when an extra source of ammonium and phosphate was provided to the reactor. [START_REF] Song | Nitrogen utilization in a vapor-phase biofilter[END_REF][START_REF] Aizpuru | Fungal biofiltration of toluene on ceramic rings[END_REF] The lack of nutrients in a planted biofilter due to plant nutrition as well as the consumption of available nutrients by microorganisms in a non-planted biofilter might be responsible for the low RE observed, as suggested by Delhomenie et al. [START_REF] Delhomenie | Influence of nitrogen on the degradation of toluene in a compost-based biofilter[END_REF] In order to test this hypothesis, a nutrient solution containing nitrogen and trace elements was applied during the second phase (day 78 to 96) to obtain a C : N : P ratio of 100 : 5:1.

In this second phase, shortly after the nutrient addition, both reactors showed a significant increase in RE, ranging from 10 up to 70% (Fig. 2(A),2(B)). This observation confirmed that the low phosphate and nitrogen content in the packing materials and in the water used for watering were probably responsible for the limitation of microbial activity observed during the first phase.

Microbiological analysis

In biofilters, pollutants contained in the gaseous mixture are oxidized or converted into biomass by the action of microorganisms. [START_REF] Malhautier | Biofiltration of volatile organic compounds[END_REF] In many cases, biofilters are run for long periods of time with an appropriate substrate or with selected acclimatized microorganisms from diverse sources. They are inoculated into the packing material to enhance the biofilter start-up procedure and to ensure pollutant removal efficiency. [START_REF] Mudliar | Bioreactors for treatment of VOCs and odours -a review[END_REF] In this study, microorganisms originated from the mound of soil which was placed into the filtering material both in the planted and non-planted biofilters. The potential for carbon mineralization was measured from packing material sampled at 30-40 cm and 20-30 cm bed height (Table 2). Activities were slightly higher than those found in cultivated soils indicating that the microorganisms could grow and colonize the packing material. [START_REF] Orwin | New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances[END_REF][START_REF] Patra | Effects of grazing on microbial functional groups involved in soil N dynamics[END_REF] Since the initial maximum respiratory response is related to the current size of living microbial biomass, these results suggested the presence of a high number of microorganisms in the filtering material. [START_REF] Anderson | A physiological method for the quantitative measurement of microbial biomass in soils[END_REF] Using a compost biofilter to remove toluene, Juteau et al. found densities of culturable heterotrophic bacteria up to 10 10 per gram. [START_REF] Juteau | Analysis of the relative abundance of different types of bacteria capable of toluene degradation in a compost biofilter[END_REF] The higher activity observed in the 30-40 cm samples compared with the 20-30 cm samples suggests a higher number of microorganisms in the upper zone of the biofilter. This is probably due to the presence of the soil mound in this zone.

Whatever the depth, the carbon mineralization potential was higher in the planted than in the non-planted biofilter. Plant roots are known to sustain microbial growth via root exudates by providing carbon and energy sources. [START_REF] Bais | The role of root exudates in rhizosphere interactions with plants and other organisms[END_REF] The presence of a plant in the biofilter enables high numbers of microorganisms to be maintained and thus may contribute to keeping a high potential for metabolizing organic pollution.

Monoaromatic hydrocarbons are known to be mineralized under both aerobic and anaerobic conditions. Foght and Evans et al. reported the growth of enrichment cultures on toluene and xylene under denitrifying conditions. [START_REF] Foght | Anaerobic biodegradation of aromatic hydrocarbons: pathways and prospects[END_REF][START_REF] Evans | Degradation of toluene and mxylene and transformation of o-xylene by denitrifying enrichment cultures[END_REF] Denitrifying bacteria are also capable of converting nitrogen oxides (NOx) into bioavailable nitrogen via a dissimilatory reductive pathway. [START_REF] Wang | Feasibility of denitrifying phosphorus removal technique using nitrite as electron acceptor[END_REF] NOx are emitted from all combustion processes, and they are probably present in underground car parks. Therefore, DEA was measured along the biofilter height (0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm) from samples of the packing material and of the mound of soil in the 30-40 cm zone.

DEA was detected only in the 30-40 cm zone of the filtering material and in the soil mound (Table 3). These activity levels were slightly higher than those measured in pasture soils and indicated good conditions for the synthesis and maintenance of the denitrifying enzyme pool especially in the soil mound but also in the filtering material. [START_REF] Patra | Effects of grazing on microbial functional groups involved in soil N dynamics[END_REF] These results showed that active bacteria can persist in planted biofilters and suggested that NOx could also be degraded in such a system.

CONCLUSIONS

The performance of a new type of biofilter that has the potential for improving air quality of urban environments in an aesthetically pleasing manner was investigated experimentally. The efficiency of planted and non-planted biofilters in removing low concentrations of TEX was evaluated and found to have good potential for improving air quality. Results show that planted and non-planted biofilters can remove low concentrations of TEX, and endogenous soil microorganisms can be used as an inoculum in these filters. Furthermore, no acclimation to pollutant was required. The induction of enzymes participating in metabolic pathways of aromatic hydrocarbon degradation was likely to occur in both planted and non-planted biofilters. As previously reported in the literature, the addition of a nutrient solution was essential to maintain TEX degradation in a non-planted biofilter. This study emphasizes how essential this supply is for TEX removal in a planted system. Nevertheless, the optimization of nutrient supply needs to be further investigated.

The denitrifying activity measured in both planted and nonplanted biofilters suggested that these biofilters could be efficient in removing NOx.

Further investigations will also be needed in order to gain insights into the dynamic diversity of the bacterial community in the biofilter with and without plants. Investigations of microbial communities in biofilters remain relatively scarce, although the structure of the communities would provide key information for understanding the coupling or uncoupling between microbial diversity and removal efficiency. Different techniques such as denaturing gradient gel electrophoresis (DGGE) could provide useful information about species richness, and relative abundance. The quantification and characterization of functional bacterial populations actively involved in degradation processes are also required to understand and to control the functioning of such planted biofilters. This could be achieved by targeting specific genes involved in key functions by real time PCR. The xylE1 gene encoding catechol 2,3 di-oxygenase which is involved in the early stage of TEX degradation could be used to estimate the density of TEX degraders.

Figure 1 .

 1 Figure 1. Experimental setup of the laboratory-scale biofiltration system.

Figure 2 .

 2 Figure 2. Evolution of removal efficiency of the biofilters vs. time (day): (A) non-planted biofilter; and (B) planted biofilter. Toluene, Ethylbenzene, Xylene. Arrows indicate the beginning of the nutrient supply.

Table 1 .

 1 Composition of the nutrient solution used for fertilization after day 78

	Nitrogen	24%
	NO 3	10%
	NH 4	12.2%
	CO(NH 2 ) 2	1.8%
	P 2 O 5	20%
	K 2 O	2 %
	MgO	2%
	Trace elements
	B	0.0125%
	Cu	0.0025%
	Mn	0.025%
	Mo	0.0013%
	Zn	0.01%
	Fe	0.03%

Table 2 .

 2 Substrate-induced respiration (µg C-CO 2 g -1 filtering material h -1 ) measured at the end of the biofiltration run

	Non-planted	Planted
	biofilter A	biofilter B

Table 3 .

 3 Denitrification enzyme activity (µg N-N 2 0 g -1 filtering material h -1 ) measured at the end of the biofiltration run

	Non-planted	Planted
	biofilter A	biofilter B

ACKNOWLEDGEMENTS

The authors are greatly indebted to M. Fages, S. Bayle and P. Legrand (Ecole des Mines d'Alès) for their help in monitoring the pilot unit and for their support. Thanks also to N. Guillaumaud (Laboratoire d'Ecologie Microbienne, UMR5557, Lyon) for technical assistance in the measurement of DEA. We thank Canevaflor for technical and financial support, and John Hughes, Thomas Pommier and Cirus Mallon for improving the English of the MS.


Saber Aloui
email: saber.aloui@gmail.com

Vincent Chapurlat
email: vincent.chapurlat@ema.fr

System engineering and risk assessment in complex organization: application to health care organizations

This paper presents an approach merging on one hand System Engineering, Enterprise Modeling and Risk assessment concepts, techniques and methods; on the other hand formal verification techniques. This approach aims to model and to analyze complex socio technical organization when facing risks which may impact the performance, the stability and the integrity of the organization. It is applied here to Health Care Organizations modeling and analysis.

Introduction and problematic

In a moving socio-economic environment companies must not only answer requirements in term of cost, of quality and of reactivity but must also hold account of the risks related to their activity (financial, human, social, technical, and industrial (Guinet et Chaabane, 2003)).

Their management becomes increasingly difficult. To be effective, the manager must improve his understanding of the organization and he must in same become able to detect and to anticipate the possible risks which can induce loss of performance, of stability and integrity of the organization. To achieve simultaneously these goals, it is necessary to have concepts, methods and tools allowing first to model from an adapted and unambiguous manner the organization and second to analyse the obtained (set of) model(s) in order to predict and if possible to prevent any potential risk. This article presents a framework of modeling, from semi formal to formal models, and then analysis method to prove that models are coherent and consistent. This approach will be used for the management of organization by risks. The application is made in health care organizations.

Requirements

Classically, a model is a representation of a system intended to enhance human's ability to understand, predict, or control its behavior (AIAA, 1998). According to (Koubarakis et Plexousakis, 2002) and to Enterprise Modeling domain (Vernadat 1996), an enterprise model is instructive in itself, revealing anomalies, inconsistencies, inefficiencies and opportunities for improvement. According now to (INCOSE, 2004) and (Le Moigne, 1977), a system musty be modeled within three views which details separately a particular aspect of the system: -Functional view: The goal is to define the mission and the objectives of the system taking into account the customer viewpoint.

-Structural view: The goal is to define without ambiguity how the mission will be done and who is getting involved.

-Behavioral view: The system, due to its composition (organizational unit, human resources, material resources, etc.) and its organization can have a wide range of behaviors that have to be defined.

Being most exhaustive as possible, each view is to be represented by using dedicated formalisms i.e. languages recognized in same time by the scientific and industrial communities such as proposed for example in the enterprise modeling community (Petit et al. 2006). Then several levels of detail must be taken into account in each view and put in prospects various points of view each ones corresponding to different actor's points of view. Last, existing modeling approaches do not include the risk dimension as an inherent knowledge which cannot be represented separately (CAS 2003).

So, modeling a complex organization requires disposing of relevant framework allowing:

• To use and to merge simultaneously several languages chosen for their ability to represent any of the required views considered and including risks representation whatever may be the needed level of details. By consequence these languages must be interoperable (EICTA 2004) i.e. they must be able to represent but also to exchange and to share without ambiguity or loss of sense the knowledge corresponding to different views or actors' points of view.

• To guide the actors during modeling process in order to facilitate the description of their own advice and to obtain in same time their consensus around the organization model.

Concerning the analysis of the model(s), rigor of the approach, relevance to the actor's objectives and actor's autonomy are requested. As seen before, the model(s) may be numerous, and they focus on different aspects of the organization: behavior, function, structure. So it is necessary to provide concepts and mechanisms allowing first to gain confidence into the models. This is the verification goal consisting to check errors, mistakes and misunderstanding. Second, these mechanisms must be also used to detect risky situations and to highlight problems in the organization.

The proposed approach can be then considered as a composite approach allowing modeling and analyzing complex systems facing to risks. It is based on:

• A modeling framework and the reference guide presented in the next part.

• A formal analysis set of mechanisms presented and illustrated in the fourth part.

Modeling framework

For several years, modeling languages have proven more or less their effectiveness and accuracy. There is thus no question of developing another one, but of re-using those in a coherent and homogeneous way called the modeling framework. This one takes advantage of three disciplines; System Engineering, Enterprise Modeling and risk modeling inspired by the Cindynics (Kervern, 1994).

System Engineering is defined by (IEEE, 1994) as "a co-operative and interdisciplinary approach for the progressive development and the checking of a solution for the system, balanced on the whole of its life cycle, satisfying waitings of a customer and acceptable by all". According to (Meinadier, 1998) system engineering is a collaborative and interdisciplinary process of problems resolution based on knowledge, methods and techniques from several sciences and implemented to define a system which satisfies an identified need. We can also define system engineering like a process based on heterogeneous conceptual and technical knowledge associated to contribute to problems resolution.

So modeling framework is based on an adaptation of the system engineering framework called SAGACE (Penalva, 1997;Aloui et al., 2007). This framework allows to conceptualize the three classical views but it do not provide means and guide for the modeling steps it self. So the proposed adaptation consists:

• To integrate dedicated modeling languages coming then from Enterprise Modeling domain. Indeed, according to SAGACE each view is divided into point of view. The views offered are interconnected and each requires describing one or more points of view. Each viewpoint then call to one or more dedicated modeling languages.

• To integrate a property modeling language called LUSP (Unified Properties Specification Language) (Lamine, 2001). In order to improve the modeling phase and to detail more precisely the system characteristics and requirements, the approach introduces a fourth view called property view (Aloui et al., 2006b). A property may be defined differently in the literature (Manna et Pnuelli, 1992;Henzinger et al., 1994;Meinadier, 1998). We will consider in the following the definition given by (Lamine, 2001): a property translates an expectation, a requirement (behavioral, functional, structural or organic, dependent or not of time) or an objective (performance, safety or reliability) which have to be respected, strictly or with a reliable level being enough by a model. Then, on the one hand the property view allows users to enrich their knowledge and thus enrich by the same occasion the information already contained in each model coming from any view. This is done by specifying properties highlighting some essential characteristic which must be respected by each object composing the organization and handled in the different views and models: processes, resources, scenario and so on. On the other hand, it allows covering analysis requirements as shown in the following.

• To develop a user's guide allowing to help actors involved during the modeling process to formalize, to answer specific questions and to compare their knowledge with another users.

The result is then an integrated set of modeling paradigms and languages expressed at a high level of formalization. This allows the use of proof tools presented below. However, considering a dedicated modeling language for each view requires to assume their interoperability (Aloui et al., 2006a). To overcome this problem the Model Driven Architecture (Bézivin et Gerbé, 2001;OMG, 2003) approach is used within the formalization of meta models. Therefore we established for each modeling language a Meta Model in UML. In what follows, an example of this use is presented for the functional view and the point of view mission, for a more detailed description see (Aloui, 2007).

Functional view. This sight makes it possible to describe the mission, the objectives and the finality of the system. The modeler has to define in a rigorous and formal way the aims of the system (in terms of performance, stability and reactivity). This sight makes it possible to initially describe the objectives on the basis of a high level of abstraction corresponding to a strategic vision, that of the managers. In the second time, it makes it possible to break up and refine these objectives in order to obtain a unit arranged hierarchically and ordered more concrete by employing a formal language extracted from KAOS (Van Lamsweerde, 2000) to face objectives refinement.

Meta model. Figure 1 shows the meta model language developed. An objective is broken down into sub-objectives and so on up to the level of granularity chosen. This level of granularity agrees with the experts in the field and depends on the organization goals to be achieved. In this meta model:

• The class Objective models the objective concept, the attribute InformalDescription allows natural language to describe the goal.

• The class Indicator represents any indicator (quantified or qualified) that can be associated with each of the objectives.

Model.

Figure 2 is a partial representation of a given organization objectives refinement using the meta model Figure 1. This tree is built on the basis of the Accreditation Manual of the High Authority of Health (HAS 2005). To be accredited each care organization must meet a series of references themselves divided into sub references.

Analysis mechanisms

By definition, verification must provide rigorous arguments in order to convince users of the correctness and coherence of a model. The proposed technique is based on reasoning mechanisms by using of existing model checkers or by using Conceptual Graphs (Sowa 1984) as proposed by (Kamsu Foguem, 2003).

Reasoning. The reasoning aims two goals. The first goal is to ensure the coherence between models of a given organization, thus improving communication and exchange among the different users, or ensuring coherence between the different abstraction levels, each one represented by a specific model. The second goal is to detect where and what are the main deficiencies and to evaluate their possible impact by analyzing the resulting behaviors (scenarios, configurations and functioning modes).

In this case each property P has to be proven whatever may be the resources configuration and the available scenarios in which the organization may be considered. Knowing in which condition or for which scenario the property P cannot be verified allows detecting a modeling error or a mistake, a real dysfunction or a risk opportunity.

Even partial, the checking up of the properties makes it possible to handle a knowledge each time more relevant, and especially more consensual between the actors. Organization model can be modified and possible errors or gaps are eliminated. The model is enriched by a whole of properties which cannot be objected thereafter. However, by assumption, any new modification of whole or part of the models requires to check again all the properties. That requires having tools making it possible to manage a great quantity of information and allows applying, if it is possible in an autonomous and not guided way. Theorem provers or model checkers are generally used at this stage (Yahoda, 2003). However, the proposed approach is based on several interacting models and paradigms which have to be merged with a common set of verification mechanisms. So, property proof is done by adapting and using a conceptual graphs (Sowa, 1984) analysis approach as proposed by (Kamsu-Foguem et Chapurlat, 2006).

Conceptual graph.

A conceptual graph is a formal knowledge representation. It is a finite, connected, directed and bipartite graph composed of an alternation of nodes called concepts and nodes called relations.

A concept is a double:

[<type>: <marker>]
Where:

-type represents the occurrence of the object's class. They are grouped in a hierarchical structure called concepts lattice. The concepts lattice is obtained by translating each object class and each attribute described in the meta model by using translating rules (Chapurlat et Aloui, 2006).

-marker specifies the meaning of a concept by specifying an occurrence (i.e. an instance) of the type of concept. For example, the concept [Scenario: 'to deliver medicine'] describes an object of type scenario identified by 'to deliver medicine'. These markers correspond to the instances of each object (the marker description is provided by the name of the instance) contained into the system models. A relation binds two concepts according to the following diagram:

[Concept1]←(relation)←[Concept2]
For example, the following relation means that the object of type Configuration called 'C1' authorizes the object of type Scenario called 'S1':

[Scenario: 'S1']←(Authorize)←[Configuration: 'C1']
As for the concepts, all the possible relations between concepts are gathered into a relations lattice. This relations lattice is obtained by translating each relation role between object of the meta model in a relation between concepts described in the concept lattice.

Application

The following example concerns the Delivering process of medicine in a hospital. This process organizes the medicine delivery to patients taking into account information coming from the doctor, the pathology, and the hospital rules and so on. System model. A partial view of the system model is shown in Figure 3 and is decomposed as follow:

• Fuinctional view. The mission consists to deliver medicine. The objectives are:

-A total dispensation of the medicine after control of the prescriptions. -To assume in the same time the transmission of biological sampling of analysis and their results. -To gather and to memorize medical information using the documentary and data-processing resources of pharmacy.

• Structure: There is one process composed of 8 main activities: diagnosis, prescription, prescription analysis, preparation, transcription, transport, administering, monitoring. There are two organizations units called 'Clinical activity' and 'Pharmacy'. They involve human resources (Doctors, Nurses, Pharmacist, Chemist assistant), machine (medicine trolley for each patient, PC, etc.) and software applications (information system, dedicated software, etc.).

• Behavior:

-Scenario: The study can consider two scenarios called 'Normal flow of prescriptions' and 'Important flow of ordinance'. -Configuration: There are two possible configurations called 'The system is at full strength' and 'The system isn't at full strength'. Properties modeling : Tab. 1 describes risks related to the system deficiency (Kervern, 1995) which describe traditional deficiencies of a group of actors within an organization (Chapurlat et al. 2006). This table shows how these properties are translated in the modeling framework, initially in natural language then by using the language LUSP (Lamine, 2001;Chapurlat et al., 2006).

Tab. 1 Properties: deficiency modeling

The property P Mission who specifies that the components of the system have always objectives can be broken up into P1 for the processes, P2 for the resources, P3 for activities, and P4 for the subsystems, etc. Within the selected framework, the objectives are modeled by using graphs of decomposition of objective (we start from a high level objective that will be refined thereafter). Thus by formalizing using the language LUSP (Chapurlat et al., 2006), we obtain: Properties verification. The hypothesis adopted is: if a property, in particular that representing a potential deficiency is not verified, the system runs a risk. Analysis mechanisms allowed by conceptual graphs are then used for checking each property on the resulting conceptual graph. These analysis mechanisms are:

P1 = (∀ ∀ ∀ ∀ A ∈ ∈ ∈ ∈ System.Processus), [nature(A) = Type.BusinessProcessus] ⇒ ⇒ ⇒ ⇒ [∃ ∃ ∃ ∃ M ∈ ∈ ∈ ∈ System.
• Projection: This involves comparing the obtained conceptual graph coming from the translation of the model with another one translating the property. If the projection fails, then the modeled property cannot be verified and the causes are highlighted.

• Constraint: a property describes what the links and/or constraints are between facts. In this case, the property is translated on a positive or negative conceptual graph constraint. A positive constraint between two facts A and B must be interpreted as: "If A is true, then B must also be true". Conversely, a negative constraint must be interpreted as: "If A is true then B must be false" (if B is true, A must be true or false).

• Dynamic and static rules: A property is directly modeled as a rule composed of a cause and an effect. If the graph corresponding to the causes match with a part of the conceptual graph translating the system models, then the effect must be checked in the same way.

Conclusion

The proposed methodology allows to model and to analyze from a rigorous manner a complex socio technical organization when facing occurring risky situations and in order to improve its resiliency i.e. its ability to face any situation without loss of performance, of integrity and of stability. The associated tool of modeling and analysis is now under test on a hospital. The main perspective of development consists to integrate an agent based simulator into the modeling framework. This will allow to validate when possible and to facilitate the emergence of new behaviors in the organization by modeling and interpreting the human behavior of resources involved into the organization.

Figure 1 .

 1 Figure 1. UML meta model of objectives modeling language inspired by KAOS

Figure 2 .

 2 Figure 2. HAS objectives refinement

Figure 3 :

 3 Figure 3: System model (partial view)

  Now, the studied system is represented by a model composed of several sub models coming from each view. The used languages are more or less formal. To exploit the organization model and allowing analysis, it is necessary to re write the different sub models and the properties described in LUSP (Figure4) in an unique conceptual graph which gathers then all the knowledge represented in the organization model but now represented by using only one modeling language.

Figure 4 :

 4 Figure 4: Translation from the formal language LUSP to conceptual graph

AIAA (1998). (American Institute of

Aeronautics & Astronautics) -Guide for the Verification and Validation of Computational Fluid Dynamics Simulations: American Institute of Aeronautics & Astronautics, 1998. Aloui S (2007)