EVALUATION OF THE CHIMERE MODEL AT HIGH RESOLUTION OVER EUROPE: FOCUS ON URBAN AREA

A comprehensive evaluation of a CHIMERE model run at high resolution was conducted for the year 2009 over Europe. The performance of the model is systematically assessed according to the type/location of the stations and time of year. Along the year, CHIMERE reproduces nicely the daily NO 2 and O 3 daily variability. Over the year, the model shows negative bias for NO 2 and a positive one for O 3 which is higher during winter and summer respectively. CHIMERE gives an overall satisfactory performance concerning the simulation of PM 10 concentrations (FB=-14%) and a good performance for prediction of PM 2.5 concentrations (FB=4.9%). For sulfate, the model performs rather well during the summer (FB=7.1%) but overestimates the concentrations at spring time (FB=41.0%). Over the year, the total nitrate concentrations are underestimated (FB=-57.8%) but are better reproduced than particulate nitrate. Finally, the total ammonia is better simulated by the model during the spring (FB=4.0%) and the autumn (FB=-1.5%) seasons than during the summer (FB=-21.2). Along with work on the aerosol module in order to improve the urban modelling of SIA, the next steps will include calculation of the modelled PM 2.5 and NO 2 urban increments for European cities and the integration of a standard methodology for the urban increment calculation to be implement in integrated assessment models.

INTRODUCTION

Over the last decades, numerous papers intended to evaluate the skills of CTMs to correctly estimate tropospheric air pollutants concentrations. Both constant evolution of model parameterisations and the increased quality of input data including meteorology and emissions should foster frequent CTMs assessments. A list of European model evaluation studies that took place during the last decade can be found in [START_REF] Pay | Spatio-temporal variability of concentrations and speciation of particulate matter across spain in the caliope modeling system[END_REF]. The CHIMERE model itself had undergone several time extensive evaluations (Vautard et al. (2007a); [START_REF] Vautard | Evaluation and intercomparison of ozone and PM10 simulations by several chemistry-transport models over 4 european cities within the city-delta project[END_REF]). However, we note that previous studies are generally coarser in terms of horizontal resolution and use fewer stations to achieve the validation part. The aim of the study is to comprehensively evaluate a fine resolution (0.0625x0.125°) CHIMERE runs throughout the Europe using the largest set of monitoring stations available in 2009. The analysis is performed for ozone (O 3 ), nitrogen dioxide (NO 2 ), PM 10 , PM 2.5 and PM compounds such as sulfate (SO 4 2-), nitrate (NO 3 -), total nitrate ( HNO 3 + NO 3 -), ammonium (NH 4 + ), total ammonia (NH 3 +NH 4 + ).

METHODOLOGY

CTM description

The purpose of CHIMERE is to calculate the concentrations of usual chemical species that are involved in the physico-chemistry of the low troposphere. CHIMERE has been described in detail in several papers: [START_REF] Schmidt | A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in western Europe[END_REF] for the dynamics and the gas phase module; [START_REF] Bessagnet | Modelling dust emissions and transport within Europe: the Ukraine March[END_REF][START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe -Focus on Secondary Organic Aerosols[END_REF] for the aerosol module; [START_REF] Vautard | On the contribution of natural Aeolian sources to particulate matter concentrations in Europe: testing hypotheses with a modelling approach[END_REF]Vautard et al. ( , 2007b) ) for the latest substantial model improvements. The aerosol model species are the primary particle material, sulfates, nitrates, ammonium, organic aerosols, sea-salts and dust.

For more detail on the latest developments one can refer to the online documentation (http://www.lmd.polytechnique.fr/chimere). For the study, we defined a nested fine resolution domain (324x410 grid boxes) that covers the whole of Europe from 10.4375°W to 29.9375°E in longitude and 35.9062°N to 61.4687°N in latitude with a resolution of 0.0625x0.125° (Figure 1). Boundary conditions are monthly mean climatology taken from the LMDz-INCA model for gaseous species [START_REF] Hauglustaine | Interactive chemistry in the Laboratoire de Meteorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation[END_REF]) and from the GOCART model for aerosols [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF]). Data for comparison with observations are extracted from the lowest vertical level (20m on average). A complete and high resolution set of both biogenic and anthropogenic emissions are needed in order to perform CHIMERE computations. Six biogenic species (isoprene, α-pinene, β-pinene, limonene, ocimene, and NO) are calculated using the MEGAN model [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF]). We also account for wildfire emissions issue from the GFED3 [START_REF] Kaiser | Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power[END_REF].

Meteorology

As CHIMERE is an off-line model, we had to select a set of meteorological data for the entire 2009 year. For this study, we shifted from the usual WRF limited area models data to the ECMWF Integrated Forecast System (IFS) data (http://www.ecmwf.int/research/ifsdocs). Motivations include the systematic over wind speed estimation by WRF (Jimenez and Dudhia) and the save in time allows when using IFS data as no meteorological pre-runs are needed. The IFS model has a 0.25x0.25° horizontal grid spacing (T799) from surface to 0.1 hPa (91 levels in total). It delivers typical meteorological variables (temperature, wind components, specific humidity, pressure, sensible and latent heat fluxes) that are vertically and horizontally interpolated onto the CHIMERE grid (8 levels).

Anthropogenic emission pre-processing

The emission pre-processor transforms raw anthropogenic emissions in ton/year/cells to CHIMERE compliant spatialised emissions dataset. VOC, NOx, CO, SO 2 , NH 3 , PPM (Primary Particle Material) annual emissions come from the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air pollutant EMEP [START_REF] Vestreng | Review and revision of emission data reported to CLRTAP[END_REF]).Two main steps can be identified in the anthropogenic emission pre-processing: the spatialisation and regridding of raw emission on the CHIMERE grid and the speciation/aggregation step. The first step consists in regridding national anthropogenic emission inventories onto the CHIMERE computational grid using the USGS database (http://www.usgs.gov). Annual NOx emissions were speciated into NO, NO 2 and HNO 2 using the coefficients recommended by IIASA (personal communication). For NMVOC, the speciation was performed over 32 NMVOC NAPAP classes [START_REF] Middleton | Agregation and analysis of volatile organic compound emissions for regional modelling[END_REF]) and for the aggregation step the lumping of NMVOCs into model species is performed following [START_REF] Middleton | Agregation and analysis of volatile organic compound emissions for regional modelling[END_REF]. Time disaggregation was done on the basis of GENEMIS data using monthly, weekly and hourly coefficients depending on the activity sector [START_REF] Society | Generation of European Emission Data for Episodes (GENEMIS) project[END_REF]). For SNAP2, we also propose a new temporal profile according to the daily ambient temperature (degree day concept).

Observation data

Observed data come from two different databases. The first one is Airbase (http://acm.eionet.europa.eu/databases) gathering regulatory data reported by Member States according to the air quality directives. For this study, we used two different Airbase types of station: the Rural Background stations (RB) and Urban Background stations (UB). We also used data from the EMEP network (http://www.emep.int/) that provides observations of SIA at remote Rural Background sites (RB) over a more extended area of the European Union. Stations with an altitude under 750 m were selected if 75% or more data are available over the year. Figure 1 displays the spatial distribution of the AIRBASE (green for RB and blue for UB) and the EMEP (red) stations used for the evaluation. Three key statistical indicators are selected for their ability to diagnose the model performance: the correlation index (R), the root mean square error (RMSE) and the fractional bias (FB). Details about the calculation of the statistics using Atmospheric Model Evaluation Tool software (AMET) can be found in [START_REF] Appel | Overview of the Atmospheric Model Evaluation Tool (AMET) v1.1 for evaluating meteorological and air quality models[END_REF]. The following section is based on yearly and seasonal mean of the statistical indicators.

RESULTS AND DISCUSSION

Figure 2 shows daily box-whisker plots time series of the NO 2 , O 3 , PM 10 and PM 2.5 observed and calculated concentration averaged over all UB Airbase stations. Table 1 presents a few yearly mean statistical scores for gas and particulate pollutants. Along the year, CHIMERE catches nicely the temporal variability of NO 2 both at RB (R=0.65) and UB (R=0.61) sites but underestimates significantly the concentration especially during the winter season (FB=-65.9). This behaviour could be due to the general underestimation of NOx urban emissions whose the impact is magnified by the winter time stagnant conditions that increase the NO 2 observed concentration at low level. Overall, daily temporal variability of O 3 concentrations is very well simulated both at rural (R=0.78) and urban background sites (R=0.77). The model reaches its lowest FB in the summer (15.8%) at UB sites. Over the year, the modelled values present a systematic positive bias which is higher at urban (FB=28.5%) than at rural sites (19.8%). This tendency could be link to the underestimation of the NO 2 previously mention especially during the winter at urban sites. For the PM 10 , at UB stations, R gets highest values during the summer and the autumn (0.46 and 0.48 respectively) and its lowest at spring (0.41). Conversely, the FB is lower in warm season (-1.4% at spring time) than in the winter (-30.5%). At RB site, the model performs better in terms of correlation (0.60 in the winter). Over the year, in opposition to the UB sites (FB=-17%), CHIMERE overestimates the concentration (FB=8.1%) at RB sites. For PM 2.5 , the behaviour is similar to PM 10 . However, we note a higher R value over the year both at UB sites (0.54) and RB sites (0.61) than for PM 10 . At UB sites, CHIMERE performs better in autumn (0.56) than in summer (0.40). The highest R is observed at RB sites during the winter (0.69). The FB is very low during the summer at UB stations (1.8%) and negative during the winter (-11.9%). At RB sites the FB is always positive and the maximum is observed at spring time (28.9%) and the minimum at winter time (6.5%). We underline that CHIMERE gives, over the year, a good performance concerning the reproduction PM 2.5 concentrations (FB=4.9%). PM 10 , PM 2.5 and PM speciation data were available on several EMEP sites. As previously observed with the Airbase RB stations, CHIMERE overestimates the PM 10 (FB=19.9%) and PM 2.5 (FB=20.1%) concentrations at the EMEP stations. A strong inter-seasonal variability is observed with a minimum FB during the winter (7.1% for PM 10 and 0.6% for PM 2.5 ). The highest correlation coefficient is found during the winter (0.55 for PM10) and is good for PM 2.5 (0.69). Concerning the secondary inorganic aerosol (SIA), an inter-seasonal variation is also noted. For the sulfate, the model performs rather well during the summer (FB=7.1%) but strongly overestimates the concentration at spring time (FB=41.0).The nitrates are strongly underestimated along the year but rather high R value is noted during the winter (0.68). This underestimate is mainly due to the slight overestimate of sulfate and the missing coarse nitrate chemistry in CHIMERE. The total nitrate concentration is much better reproduced with R>0.6 over the year except during the summer (0.17). The order of magnitude of the FB indicates an underestimation of a factor 2. Along with sulfate, ammonium appears to be the best SIA compound reproduced by CHIMERE. The FB is rather low and indicates a slight overestimation during the winter (7.7%) and an underestimation during the summer (-5.9%). The total ammonia is nicely reproduced by CHIMERE with some low bias observed during the spring (FB=4.0%) and autumn (FB=-1.5%) seasons. The performance is worse during the summer where the model is underestimating the most (FB=-21.2%).

Table 1: Yearly mean selected statistical indexes calculated using Airbase UB and RB (values in brackets) stations and using EMEP RB stations (SIA only). For observation mean, modelled mean and RMSE the units are: in ppb for NO 2 and O 3; in µg/m 3 for PM 10 and PM 2.5 ; in µgS/m 3 for sulfate, in µgN/m 3 for total nitrate and total ammonia. FB in %.

Pollutant

Nb

CONCLUSIONS

A comprehensive evaluation of the CHIMERE model was conducted for the year 2009. The performance of the model is systematically assessed according to the type/location of the stations and time of year. It reproduces correctly daily NO 2 variability along the year but underestimates significantly the concentration especially during the cold season. It simulates nicely the day to day O 3 variation similarly at urban and rural sites with an overestimation which is higher during the winter at urban sites. CHIMERE gives an overall satisfactory performance concerning the prediction of PM 10 concentrations (FB=-14%) and a good performance for PM2.5 concentrations (FB=4.9%). For the sulfate, the model performs rather well during the summer (FB=7.1%) but strongly overestimates the concentration at spring time (FB=41.0%). The total nitrate concentration is much better reproduced than nitrate stand alone with high R over the year (R>0.6). For total nitrate, the model underestimates the observation. Finally, the total ammonia is better reproduced by the model during spring (FB=4.0%) and autumn (FB=-1.5%) whereas the model performance is lower during the summer (FB=-21.2%). Along with work on the aerosol module in order to improve the urban modelling of SIA, the next steps will include calculation of the modelled PM 2.5 and NO 2 urban increments for European cities and the integration of a standard methodology for the urban increment calculation to be implement in integrated assessment models.

Figure 1 :

 1 Figure 1: Airbase rural background (green), Airbase urban background (blue) and EMEP rural stations (red) projected on the domain used for the evaluation.

Figure 2 :

 2 Figure 2: Daily box-whisker plots time series of the NO 2 , O 3 , PM 10 and PM 2.5 observed and calculated concentration averaged over all UB Airbase stations. The continuous lines represent the medians and the bars show the 25 th -75 th quantile interval. The yearly 25 th , 50th, 75 th , and 95 th quantiles are reported on the top right corner of the plots (in the legend AqIFSCHM09Fin refers to the CHIMERE simulation).

Acknowledgments

This project is funded by the ECMACS project (EU LIFE) and the French ministry of Ecology. We particularly thank Markus Amann, Wolfgang Schoepp and Chris Heyes (IIASA) for fruitful discussions.


Shazia Tanvir
email: htanvirsh@utc.fr

Gyorgy Thuroczy
email: ggyorgy.thuroczy@ineris.fr

Brahim Selmaoui
email: brahim.selmaoui@ineris.fr

Viviane Silva Pires-Antonietti

Pascal Sonnet
email: pascal.sonnet@sa.u-picardie.fr

Philippe Lévêque
email: francefphilippe.leveque@unilim.fr

Rene De Seze
email: rene.de-seze@ineris.fr

Sylviane Pulvin
email: sylviane.pulvin@utc.fr

Effets de l'exposition aux radiofréquences de téléphone portable sur le cytochrome P450 réductase humain

Keywords: Radiofréquence (RF), téléphone portable, UMTS, cytochrome P450 réductase Cell phone radiation, UMTS, radiofrequency (RF), cytochrome P450 reductase

Accelerated and widespread of cell phones has increased the human exposure to radiofrequency electromagnetic fields (EMFs) and raises serious concerns about the biological and health-related effects of RF radiation.

The present study aimed at the effect of cell phone RF (1.9 GHz) emitted by Universal Mobile Telecommunication System (UMTS), the third generation (3G) of cell phones on structure and function of human cytochrome P450 reductase. The ability of the cytochrome P450 reducatese to transfer the electron was measured by an exogenous electron acceptor cytochrome C with UV-vis spectrophotometer. Structural changes were studied by fluorescence spectroscopy, dynamic light scattering, Circular dichroism and capillary electrophoresis (Zeta potential). The results indicated that UMTS-like one hour RF exposure at 1 W/kg SAR slightly altered electron transfer ability and structure of cytochrome P450 reductases.

Introduction

The accelerated cell phone usage has increased human exposure to radiofrequency (RF) electromagnetic fields (EMFs). The question whether these EMFs exert any specific effect on biological systems is still debatable. In the present study the effect of 1.9 GHz RF emitted by Universal Mobile Telecommunication System (UMTS, 3G) on structure and function of human cytochrome P450 reductase (CPR) in-vitro was investigated. CPR is an electron transport enzyme of the microsomal redox chain of the endoplasmic reticulum. Our research strategy was to study the effects of RF exposure at the molecular level using a solution of highly purified human reductase. It is reasonable to assume that any possible biological damage could start at molecular level involving biological macromolecules. Proteins are the macromolecules found in a living cell and play a crucial role in almost every biological process. One simple approach to this problem is studying the effect of irradiation on protein function and conformation. We have chosen human CPR as a protein model because it is a key electron donor to P450-mediated microsomal electron transport system. This enzyme appears to be universally present [START_REF] Strobel | Cytochrome P450: Structure, Mechanism, and Biochemistry[END_REF][START_REF] Shen | Role of Acidic Residues in the Interaction of NADPH-Cytochrome P450 Oxidoreductase with Cytochrome P450 and Cytochrome c[END_REF]. CPR is a complex multidomain protein consists of diflavin and tryptophan residues making this enzyme suitable to examine structural changes, possibly taking place after exposure, by fluorescent spectroscopy.

The purpose of the present investigations was to determine if the short term in vitro exposure to UMTS signals as used by 3G cell phones could alter structure and activities of CPR.

Materials and Methods

The Recombinant human CPR (Sigma, St. Louis, MO) was exposed to RF in two TEM-cell systems (control and exposed) set in a temperature controlled incubator. The CPR were diluted in 100 mM potassium phosphate buffer and filtered to clear off large sized aggregates and subjected to protein content determination by Modified Lowry assay. Horse heart cytochrome C was also purchased from Sigma. RF signal with WCDMA modulation was generated by Generic UMTS signal generator (GUS 6960S, University of Wuppertal connected to a high power amplifier (Ophir, 5303069, USA) to guarantee the power requirements for the study [START_REF] Mbonjo Mbonjo | Generic UMTS test signal for RF bioelectromagnetic studies[END_REF]. The 3 mL CPR solution contained in a polymethylmetacrylate cuvette was inserted into the TEM-cell for one hour. The specific absorption rates (SAR) was 1 W/kg [START_REF] Desai | Pathophysiology of cell phone radiation: oxidative stress and carcinogenesis with focus on male reproductive system[END_REF]. The SAR in the solution was determined by elevation of temperature measurement by non-perturbing optical temperature probes (Luxtron 790F 590F, USA) following short time RF exposure.The change of temperature inside the cuvette during RF radiation of 1 W/kg SAR was below 1°C. The treated sample was matched with unexposed control which was kept in the same experimental conditions. The ability of the CPR to transfer the electron was measured by a non-physiological electron acceptor cytochrome C with UV-vis spectrophotometer. Structural changes were studied by fluorescence spectroscopy, circular dichroism, capillary electrophoresis and dynamic light scattering.

Results and discussion

The results indicated that electron transfer ability and structure of cytochrome P450 reductase was altered under WCDMA modulated RF exposure (Table 1). After 1 hour of irradiation, CPR structural changes are irreversible and consistent with a new thermodynamically stable structure of the enzyme, providing reproducible spectroscopic and kinetic data. In this context, comparative CPR activities measured via the reduction of cytochrome c, the surrogate electron acceptor most commonly used for measuring diflavin reductase activity.

The RF exposure produced 22% decrease in both Km and V max as compared to control values at 1W/kg after one hour of exposure. These results suggested that EMFs has reduced enzyme activity means lowered the rate of electron transport under the given set of experimental conditions. Among such enzymatic complexes, embedded in the biological membranes and potentially affected by EMF, are cytochrome P450 dependent monooxygenases as well as enzymatic proteins (NADH cytochrome b5, b5 reductase). The above complexes are the key enzymes of phase I reactions, which initiate the metabolism of lipid soluble xenobiotics [START_REF] Reed | Formation of P450; P450 complexes and their effect on P450 function[END_REF]. Since many of the physiological effects attributable to RF radiation may be eventually traced to alterations in cell membrane function. The membrane protein like human CPR is suitable as a model for intensive investigation as being involved in electron transportation to cytochrome P450.

Conclusions

Learning how UMTS exposure affects the structure and function of the CPR is essential for understanding the RF mediated bio-effects on the electron transport chain and eventually on metabolism. The relevance of these findings to real RF exposure scenario however demands further biochemical and in-vivo confirmation. If CPR of a healthy human exposed to these frequencies transfer less electron to their redox partners, the metabolic abilities especially xenobiotic transformation could be disturbed. It is quite possible that the effects of RF radiation on biological systems may be either masked by repair and regulation mechanisms or transient.

Experiments Results

Tryptophan emission fluorescence

Exposure of CPR to RF radiation resulted in the loss of intrinsic tryptophan fluorescence. The absence of sift clearly argues that the environment of most of the molecules is not altered significantly by the exposure to RF.

Flavin emission fluorescence

The exposed protein showed enhancement of flavin fluorescence intensities. These changes in the fluorescence properties are likely to reflect loosing of protein-flavin contacts.

Circular dichroism

The spectral difference and data indicate that structure of exposed protein has slightly more secondary structure. The data showed that RF exposure induced small but visible changes in the secondary structure of protein.

Dynamic Light Scattering

The average hydrodynamic diameters have shown minor changes in the unexposed (15±2) to exposed (10±3) samples.

Zeta potential

A shift in the Zeta potential is observed at pH 7, from -21 ± 4 mV to -10 ± 3 mV, indicating that the total surface charge at the sliding plane becomes, as a mean value, more positive in the exposed sample. Perhaps, the topological distribution of charges and their interaction with the electromagnetic fields can cause conformational and functional modification of biological activity.

Cytochrome C reductase activity

The RF exposure produced 22% decrease in both Km and V max as compared to control values. The inhibition pattern looks like uncompetitive inhibition, enzyme's apparent affinity for the substrate is increased (K m is lowered) and decreases the maximum enzyme activity (V max ), as it takes longer for the product to leave the active site.


Christophe Didier

Nielen Van Der Merwe

Frederic Poulard

Back analysis on the Lorraine iron ore pillars behaviour

Keywords: risk, subsidence, pillars, mines, iron, flooding, long term failure

After one hundred years of activity, mining in the Lorraine iron ore basin has ceased. Following several surface instabilities, investigations into methods of evaluating underground stability were initiated few years ago. This paper concerns the latest developments in the field of stability evaluation as input into the risk assessment procedures. The methodology is based on the empirical back analysis philosophy developed by Salamon in South African coalfields. A strength formula resulting in the best possible separation of data bases of safety factors of failed and intact pillar cases was identified. Work is ongoing into the kinematics of failure, distinction between different zones of the basin and the development of a Failure Potential Index.

Introduction and context

The Lorraine iron ore basin in north-east France extends over 120 km from north to south, and over 30 km from east to west. The basin was mined throughout the 20 th century, but activity declined progressively as of the 1980s. The end of extraction led to cessation of the pumping works and therefore to the progressive flooding of mine workings. In the months that followed the onset of this flooding, several subsidence events occurred with a major impact on the population. On 4 October 1996, approximately 70 buildings had to be demolished in the city of Auboué and 150 families relocated. In the following months, other instabilities developed in vicinity (Moutiers, Moyeuvre-Grande, Roncourt). The Administration sought to implement a procedure to identify rapidly the sectors most susceptible to the risk of subsidence [START_REF] Didier | Postmining management in France: situation and perspectives[END_REF]. For each sector identified, a risk management procedure was developed (monitoring, treatment, evacuation). Development and implementation of the procedure, entrusted to Geoderis, were centred on a committee of national experts.

The risk management policy was applied to the entire basin. The later flooding of the northern sub-basin of Briey-Longwy area was not started until all the ground stability studies had been completed and measures to mitigate risk were implemented above the sectors considered likely to instabilities. Given the chronology of events in the other subbasins, it was reasonable to fear new ground movements, or at least triggering of seismic events in the monitoring networks implemented in the critical areas [START_REF] Couffin | Permanent real time microseismic monitoring[END_REF], in the months following the onset of flooding. However, disturbances turned out to be few,.

In order to take advantage of the available information on workings flooding over a vast geographical sector where precise geological and mining data were available, analytic work was undertaken between INERIS and the University of the Witwatersrand, in collaboration with Geoderis. The objective was to seek to better understand the behaviour of the pillars and their a priori different response to flooding according to mining sectors. To do this, the approach focussed on the long-term stability of the pillars. The present paper reports on the current work and outlines the prospects for continuation of research.

Objectives of the analysis, choice of approach and constitution of the database

The approach developed focuses exclusively on the concept of "potential for pillar failure". It thus does not incorporate the concept of the predictable kinetics of the various disruptions and gives priority to a "back analysis" approach, seeking to better understand the onset (or absence) of instabilities in various areas. To do this, the empirical approach proposed by [START_REF] Salamon | A study of the strength of coal pillars[END_REF] was used. This model, a standard in the Englishspeaking mining world, relies on an empirical determination of pillar strength, contrary to the more "deterministic" approach of the procedure usually used in France (laboratory tests, then extrapolation to the in situ scale).

Salamon's approach is as follows: having available a database of pillars, some of which have failed and the others remaining stable, how do we determine a relation for pillar strength that, based on the available characteristics, allows the category of failed pillars to be best differentiated from that of intact pillars? The main hypothesis inherent to the Salamon approach is the following: on the scale of a consistent "geographical risk basin", the strength of a pillar depends less on the variations in properties of the rock masses than on its dimensions, and especially its shape. This shape is mainly defined by two magnitudes: the width w and the height h. Thus we have:

   h w k s  [1]
With  s : Strength of the pillars in MPa w and h, respectively the width and height of the pillars k,  and , parameters to be specified according to the environment considered.

To adapt the formula to the environment considered, it is of primary importance to use a reliable and accurate database describing a sufficient number of pillars, some failed and others stable. In this case, with mining extraction having ceased and access to most of the former areas now impossible, it was not possible to sample various pillars considered representative in several mining areas.

An experience-based approach was thus developed. A database specifically intended to establish the "Lorraine iron basin" empirical relation was built up. The failed pillars are cases known to have affected the mines in the basin in the past [START_REF] Didier | Importance of failure mechanisms for management of surface instability risk[END_REF]. Sixteen cases were found, the oldest dating from 1902 (Audun-le Tiche) and the most recent from 2009 (Angevillers). For each of these sectors, a "representative mesh" was defined to characterise as well as possible the mining layout corresponding to the failed area.

The initial stable data base consisted of pillars not known to have failed (i.e. stable to the best of our knowledge) in the vicinity of the failed cases, in order to have a contrasting stable data base of approximately the same age, depth, etc as the failed cases. This was reported in van der [START_REF] Van Der Merwe | Investigation Into the Strength of iron ore pillars in the Lorraine Basin[END_REF]. Subsequent to that investigation, the stable data base was extended substantially by the inclusion of more randomly selected cases over the entire mined area that were as certain to be stable as could possibly be determined. By doing this, the initial stable data base of 33 cases was extended to 107 cases. Finally, in an innovation with regard to the approach developed by Salamon, a third category was instituted: pillars considered "suspect", that is, those for which no information is available on their present state of failure, but that are subject to a level of vertical stress greater than the long-term strength value specified for the ore (7.5 MPa). This category thus corresponds to an "intermediate" class, a priori stronger than the "failed" category but less so than the "stable" one. Eighty-two "suspect" samples were thus identified, taking care here too to ensure a geographic distribution of sampling as satisfactory as possible.

Methodology of development of the formula used and application to the case studied

To seek to distinguish stable pillars from unstable pillars, we reasoned on the basis of safety factors that require calculation of the stresses acting on the pillars. These are established on the basis of the simple but proven model of the tributary area, corrected by a factor taking account of the context of mining in the area likely to generate stresses in excess of the Tributary Area stress on the pillars considered (Table 1).

Table 1: values of the corrective factors for various mining configurations We then find:

     1 H gf L L [2] then       w h H gf k f L L S s ) 1 (    [3]
With:

H: depth of the works (in m) τ: extraction ratio ρ: density of the cover (assumed equal to 2500 kg/m 3 )

For each sample of the database, a series of safety factors is established by a parametric approach, varying the values of  and  in a range from 0 to 2, with increments of 0.2.

This allows 100 simulations of safety factors (k initially assumed equal to 7.5 MPa) to be produced for each area studied. For each pair of values (, ), two distributions of safety factors are thus established: one for intact pillars and one for stable.

The objective in parametrising the model is to look for the pair of values for which the "overlap area" between the distributions of values of safety factors calculated for the failed pillars and for the stable pillars is found to be least. The perfect formula would allow complete separation of the two categories of samples but because of the partial understanding of the mechanisms and the incomplete nature of the data available, a minimisation approach is used.

This "Minimum Overlap" approach is different from the one used by Salamon, who preferred the maximum likelihood approach to find that combination of parameters that would result in a safety factor closest to 1.0 for the failed pillar data base. The reason for preferring the Minimum Overlap approach, first used by [START_REF] Van Der Merwe | Linear coal pillar strength formula for South African Coal[END_REF], is that the original pillars were not designed with the intention of having the same factor of safety and also that there was more merit in finding the combination of parameters that would result in the greatest separation of failed and stable cases, bearing in mind that the main purpose of the formula is to distinguish between failed and intact cases and not to design pillars.

To calculate the overlap area between the two distributions, the principles described by [START_REF] Harr | Reliability-Based Design In Civil Engineering[END_REF] were applied. To do this, we introduce the parameter  defined as: According to the value of , the overlap area (A) can be estimated as [START_REF] Harr | Reliability-Based Design In Civil Engineering[END_REF]:

If  > 2.2:            2 exp ) 2 ( 1 2 5 . 0 f f A  [5] if  ≤ 2.2: ) ( 5 . 0 f A    [6]
where (f) can be taken from the statistical tables of [START_REF] Harr | Reliability-Based Design In Civil Engineering[END_REF]

       f f f f f f f  [7]
The approach then consists of calculating the value of the area of overlap between the normal distributions of the safety factors for the intact and failed pillars for each of the 100 simulations obtained for the various values of the pair  and . The pair of values to be selected is that which minimises the overlap area. Figure 2 summarises the results of the simulations conducted to optimise the values of  and  in the case of the Lorraine iron ore basin It shows a map of the overlap area value between the distributions of stable and failed safety coefficients. The centre of gravity of the minimum class (less than 8%) corresponds to the combination  = 0.4 and  = 0.5. At this exact point, the overlap area is 7.9%. The value of k then remains to be determined.. We note in this connection that the overlap area is independent of the value of k, as k increases or decreases the safety coefficients of the two populations in the same proportion. The value of k was then that value which resulted in the mean safety factor of the failed pillars being 1.0. In the context that interests us, for the combination = 0.4 and  = 0.5, the value of k is 8.3 MPa. This value inspirers confidence in the strength formula as it is derived by a different route, yet is in good agreement with the strength value of the ore of 7.5 MPa that was derived analytically.

The resulting optimal formula is thus:

5 . 0 4 . 0 3 . 8 h w s   in MPa [8]

Initial results and prospects for continuation of the work

Initial evaluations of the database and the strength formula for the pillars

Figure 3 shows various distributions for categories of pillars, based on equation [8]. At this stage, safety factors for the sub set of data in the "Suspect" category were also calculated with Equation 8and compared to the data sets for failed and intact cases. The three main categories used in constituting the (failed, suspect, stable) database are shown. We note that the area of overlap between the "failed" and "stable" categories is in fact relatively low. Logically enough, the distribution for the pillars considered "suspect" shows a profile intermediate between the other two, although very appreciably closer to the "failed" category than the "stable" category. This confirms that the suspect pillars show characteristics that differentiate them very clearly from the long-term stable pillars. Moreover, although of concern, their stability characteristics turn out to be on the average slightly more favourable than those of the already failed pillars (mean safety factor of 1 for the failed and 1.4 for the suspect). This assessment is however only valid at a given time.

Everything in fact leads one to expect that several "suspect" sectors will fail in the years to come, thus modifying the present distributions. It will then be particularly interesting to analyse the increase, according to time, in the mean safety factor of the failed pillars.

Figure 4 shows the distribution of the failed and suspect pillars for the south and north basins. An initial analysis reveals that the category of failed pillars in the southern subbasin shows a noticeably less favourable stability configuration than the others. This may provide an initial factor in explaining the large number of failures that occurred during flooding of this part of the basin (presence of very fragile sectors). Moreover, the "stability potential" of the suspect pillars seems very low (both for north and south), as they show a safety factor profile very similar to that of the failed sectors of the north basin. Everything thus leads one to believe that, given the ageing of the rock in addition, several sectors now considered suspect will fail in the years or decades to come.

A more thorough analysis of various parameters likely to play a key role in creating instabilities (before or after mining, before or after flooding, nature of the iron layer, nature of the mining operations in the vicinity of the collapsed area) should be conducted in the months to come to extract the maximum information from the database. But monitoring and interpretation of the development of the profile of the failed areas over time presents in particular the most interesting prospects for scientific advances.

Possible considerations on the kinetics of progressive subsidence

Among the instabilities that have affected the Lorraine iron ore basin, some have appeared as "progressive subsidence" developing over periods on the order of a day to several months, damaging to structures but with little danger for people when sectors are subject to monitoring. Conversely, others have given rise to "sudden collapses" that occur with the failure of the overburden as a whole in several seconds, often accompanied by a strong seismic tremor. Due to their suddenness, these potentially disastrous events are especially hazardous and should be identified with the greatest care before taking the safety measures best adapted to the context (reinforcement of the workings, moving surface features).

A specific methodology has been established by the group of experts led by Géoderis to identify the sectors at risk of sudden collapse, based on cross-analysis of the weakness of the underground works and the "rigidity" of the soil cover [START_REF] Josien | Lessons learned from experience to ensure security in Lorraine iron mines[END_REF]. Figure 5 gives rise to interesting prospects in terms of use of the approach described in the present paper to identify areas at risk of subsidence and those susceptible to collapse. It shows the location on the normal distribution of the failed pillars, the cases of sudden collapses (in red) and those of progressive subsidence (in green). From the analysis, it emerges that with one exception (Moutiers 97) the formula allows a very clear distinction: the lowest safety factors correspond to sudden events, the others to progressive subsidence. It appears that, in cases of progressive subsidence, the approach even allows the slowest events, hardly perceptible by the monitoring networks (Auboué 72, Roncourt 99, Angevillers), that correspond to the highest safety factors to be distinguished

Development of Failure Potential Index concept

The concept of a safety factor only has merit in describing relative stability of pillars, but it is not possible to quantify by how much pillars with high safety factors will be more stable than pillars with lower safety factors. The concept of a probability of failure is considered to be more useful in this regard.

The historical probability of failure can be determined by comparing the number of failed pillar cases to the stable ones for each category of safety factor, using the total populations of failed and stable cases. That was not practical in this case, due to the magnitude of the total population of stable pillar cases. A pragmatic approach was then followed, by statistically extending the existing sample of the population of failed pillars according to the ratio of the sum of the sample areas to the total area mined. Furthermore, as only the as mined dimensions were known and not the dimensions of the pillars at the time of failure and to avoid confusion, the term "Failure Potential" was preferred over the term "Probability of Failure", but it was derived in the same way. This work is ongoing and publishing results at this stage is premature, yet we considered it appropriate to mention that this extension and hopefully refinement of the procedures is already being attended to. Preliminary results are very encouraging..

Conclusions

The principle implemented by Salamon to determine in situ strengths of coal pillars in South African mines has been adapted to the context of the abandoned mines of the Lorraine iron ore basin. A formula relying mainly on the dimensions of the pillars has been established, with the parameters determined using a database of nearly 200 pillars considered representative of the basin. Among these pillars, some have already failed and others are assumed long-term stable, with the remainder considered "suspect", that is, susceptible to eventual failure. The distributions of the various categories provide a very satisfactory distinction between the failed pillars and the intact pillars, a fundamental parameter in the possibility of using the approach to assess the "potential for failure" of suspect pillars. A probabilistic approach is presently being developed. The initial results show that the failed pillars in the south sub-basin, particularly after inundation, had appreciably less favourable configurations in terms of stability than those encountered in the north sub-basin overall. This can contribute to explaining the difference in terms of occurrence of failures after flooding of both sub-basins. They also indicate the relevance of using the approach developed to assist in distinguishing the kinetics of failure (rapid or progressive) of the various mining configurations encountered.

Figure 1 :

 1 Figure 1: in red, example of failed sector that has caused a surface disruption;in green, nearby sectors in which the pillar dimensions predict stability.

  s = Mean of the population of safety factors for the intact pillars M f = Mean of the population of safety factors for the failed pillars S s = Standard deviation of the population of safety factors for the intact pillars S f = Standard deviation of the population of safety factors for the failed pillars

Figure 2 :

 2 Figure 2: Values of the "overlap areas" of the distributions of safety coefficients for intact and failed pillars according to the values of  and .

Figure 3 :

 3 Figure 3: Probability density distributions of safety factors for the stable, suspect and failed pillars.

Figure 4 :

 4 Figure 4: Distributions of the safety factors for the pillars of the northern and southern basins.

Figure 5 :

 5 Figure 5: Location of past instabilities (in red collapses, in green subsidences).


B Truchot

G Leroy

F Fouillen

Y Perin

MODELLING FIRE IN TUNNELS: A LARGE SCALE VALIDATED TWO STEPS MODELLING METHOD

Keywords:

Fire is a quite common phenomenon in tunnel and being able to model its conséquences with a good précision is crucial to design adapted safety measures. Modelling the fire behaviour in tunnel is quite challenging. Managing large scale experiment for ail the possible configurations is however economically unrealistic. This paper présents an expérimental real scale fire test that was used not only for demonstrating the fire behaviour but also for evaluating the capabilities of the FDS fire code to model fire conséquences too. It enables highlighting the importance of wall and inlet boundary condition treatment. Keeping in mind that predicting fire development using a CFD code is quite impossible, a two levels approach is discussed with an analytical model to predict the fire curve and a CFD model for predicting smoke propagation, température and toxic gases distribution inside the tunnel. The comparisons show a good agreement between expérimental fire test and CFD modelling but also let appear requirements when using CFD.

INTRODUCTION

Fire in tunnels can generate dramatic conséquences as shown in the past. Following the "Mont Blanc" fire in 1999, a new régulation was created in France with différent prescriptions for safety measures. Those measures are of course hardly applicable to existing tunnel and must be adapted to the real risk.

The "tunnel du Roux", in Ardeche, a région in the middle of France, is a typical case where a compromise has to be found. This tunnel is located in a mountainous région invaded by snow during long periods in winter. This tunnel is then crucial for the local inhabitants. This tunnel is however not provided with safety equipment and the question of conséquences in case of fire was asked. To demonstrate the smoke propagation and fire conséquences inside the tunnel in case of a car fire, a real test was managed with différent measures.

Then, to explore the différent possibilities in terms of fire, and considering that it was unrealistic to manage several fire tests, for obvious économie reasons a coupled approach was validated on the experiments and then used for predicting conséquences in other cases. This coupled approach includes a CFD model for predicting fire conséquences associated with an algebraic model to predict the fire curve. This paper présents the resuit of the expérimental test and the comparison with numerical prédictions.

TWO STEP MODELLING STRATEGY

Fire conséquences modelling in tunnels requires predicting température raise inside the tunnel, smoke behaviour and toxic gases distribution inside the infrastructure. One of the key points in such a modelling consists in the définition of the évolution of the fire heat release rate with time used as an input in the 3D model. Regarding car fire, several tests were realised [START_REF] Okamoto | Burning behavior of sedan passenger cars[END_REF]) [START_REF] Okamoto | Burning behavior of sedan passenger cars[END_REF] or (Lônnermark et al. 2006) [START_REF] Lonnemark | Emissions form an automobile fire[END_REF] and lead to normalized fire curve for such vehicles (CETU 2003) [START_REF] Cetu | Guide to road tunnels safety documentation, booklet 4, Specific hazard investigations[END_REF]. Such a curve does not however enable to predict real car fire considering thèse curve give just an estimation of the maximum power and duration linked with linear curve. Furthermore those curves are only provided for one "small vehicle" and one "large vehicle" while there is différences between vehicles. To make a better prédiction of the heat release rate from a vehicle fire, a spécifie model was developed. The fire curve predicted using analytical model was then introduced in the FDS CFD fire code to evaluate the ability of this code to predict the fire conséquences. Having validated the numerical approach, it was then used to model some other scénarios.

Mathematical modelling of a car fire

Considering total heat release rate for a vehicle is governed by the heat release of the différent components, this tool enables to compute the fire curve by summing the individual heat release, considering also a propagation time. The car is split into 5 parts, namely: wheels, engine block, interior, trunk and fuel tank. The average combustion velocity and heat of combustion is then computed for each part considering the distribution between following materials. The non-combustible materials are considered as energy well. Each component fire curve follows three phases: the fire growth, a steady state and a linear decrease phase. The contribution of each élément is then summed.

CFD fire modelling

As mentioned previously, the température and smoke distribution along the tunnel was modelled with a CFD code. The FDS fire code [START_REF] Mcgrattan | Fire Dynamics Simulator (Version 5) User's Guide[END_REF] [START_REF] Mcgrattan | Fire Dynamics Simulator (Version 5) User's Guide[END_REF] was used. It is clear that predicting the fire growth and évolution with a CFD code is quite difficult [START_REF] Sanchez | Fundamentals for the development and application of a design train fire scenario[END_REF] [START_REF] Sanchez | Fundamentals for the development and application of a design train fire scenario[END_REF] but this code was largely validated for smoke propagation prédiction in tunnel [START_REF] Truchot | Numerical analysis of smoke layer stability[END_REF] [START_REF] Truchot | Numerical analysis of smoke layer stability[END_REF] or [START_REF] Hwang | The critical ventilation velocity in tunnel firesa computer simulation[END_REF] [START_REF] Hwang | The critical ventilation velocity in tunnel firesa computer simulation[END_REF], this paper provides a validation for a real car fire in naturally ventilated tunnel. It also highlights the importance of the boundary conditions. It was then chosen for the présent modelling. This code enables solving the fluid mechanics équations, continuity, momentum and energy in the computational domain. It also permits to manage largely parallel computations with an acceptable speed-up.

One important point to be mentioned is that turbulence modelling is based on an LES approach. This of course influences the mesh génération because of the constraints imposed by such a modelling approach. For such an LES modelling approach, the cutting scale, defined by the cell size, must be placed in the inertial zone of the turbulence spectrum. This will be highlighted in the numerical description of the paper. It is also important to hâve in mind when using an LES approach for smoke propagation in tunnel with natural ventilation that the turbulent fluctuations due to external wind perturbations must be considered. The turbulence intensity has to be modelled through velocity fluctuations on tunnel portai. This was done using a sinusoidal variation along time with measurement based frequencies.

It was also demonstrated that predicting the correct smoke behaviour, including the backlayering phenomena requires to model tunnel walls characteristics with a great précision [START_REF] Truchot | Numerical analysis of smoke layer stability[END_REF] [START_REF] Truchot | Numerical analysis of smoke layer stability[END_REF]. This is to hâve a good prédiction of the energy transfer from the smoke to the walls. This energy transfer is also crucial when considering the stratification of the smoke. This stratification results from the equilibrium between density gradient due to thermal effect that générâtes stratification and turbulent mixing due to the flow that destroys the stratification [START_REF] Boehm | Smoke stratification stability: Results of experiments, Tunnel safety and ventilation[END_REF] [START_REF] Hwang | The critical ventilation velocity in tunnel firesa computer simulation[END_REF].

DESCRIPTION OF THE EXPERIMENTAL CAMPAIGN

Tunnel description

The "Tunnel du Roux", is a 3 325 m long French tunnel located in Ardeche, in the middle of France, at 950 m of altitude. The tunnel section is about 41 m 2 , Figure 1, and it has the particularity to be strictly linear. Safety measures in this tunnel are limited and it is not equipped with ventilation System. Consequently, smoke propagation in the tunnel is governed by pressure différence between tunnel portais, except in winter when one of the two portais is closed to prevent ice formation; there is then no flow inside. However, this closing door is equipped with an automatic System to let vehicle go through the tunnel.

Introduction of the fire source

The fire in the tunnel was produced using a real car, a Ford Fiesta, from which the main polluting materials as battery and oil were removed, Figure 2. The fuel tank was filled with 101 of gas-oil and the fire was ignited on the passenger seat after having made a eut in its envelope to uncover the foam, several centilitres of heptanes were used for ignition. Because of the natural ventilation of the tunnel and of the complex wind profile in the surrounding, it was impossible to predict the flow direction for the morning of the test. It was decided to locate the car at 275 m of the South portai of the tunnel.

The tunnel was protected from thermal effects along about 20 m with minerai wool to avoid damages during the test. Such a protection is of course to be considered when modeling the fire because it has an influence on thermal exchanges between smoke and tunnel walls.

Metrology

Several sensors were distributed along the tunnel to measure température, velocity and carbon dioxide concentration.

First of ail, velocity (Mac Caffrey probes) and température were measured along the crosssection on each side of the fire. This enables to measure the convective power produced by the fire. This quantity is of course crucial for comparison between numerical modelling and expérimental measurements. Then thermocouples were positioned in the fire area to follow the température évolution ail along the fire near the tunnel roof, Figure 3. On top of those measurements, gas analysers were distributed along the tunnel with, mainly, one located 100 m downstream the fire.

MAIN EXPERIMENTAL RESULTS

It first must be notice that the wind was from North to South when fire test was made. The velocity of the air flow in the tunnel before the fire ignition was around 2.5 m/s, this means a natural air flow rate of 102.5 m 3 /s.

Evaluation of the power release

The fire curve developed by the ignited car was computed from the expérimental data based on the energy balance between fresh air and smoke. This consists in applying the first principle of thermodynamics on an open System.

Considering 0 is an energy flux, the conservation of energy is then written:

0S1 + Cf) s 2 + <\> X = 0
This means that outflow energy flux, § s2 , equals the sum of entrance energy, cj) s i, and the energy produced by the fire, § x . The power release by the fire was obtained considering both chemical, kinetic and sensible energy, Figure 4. This yields the convective heat release rate. The total heat release rate was then obtained assuming a radiative fraction of 0. This curve shows a slow inflammation phase during the first minutes after ignition, then a quick increase of heat release rate due to the inflammation of ail the combustible materials inside the car and finally a quite steady state before extinction. The maximum heat release rate reached by this fire is around 3 MW, 10 minutes after ignition.

This curve also shows the ability of the analytical model to predict the car fire curve including both the maximum heat release and the combustion duration. Both those values are over estimated using the standard fire curves. One improvement in this model should be the considération of the incubation time to improve the fire growth prédiction.

Température distribution

Because of the low power developed by the fire in relation with the air flow, no backlayering was observed, the maximum température 10 m upstream the fire on tunnel ceiling was not modified from ambient, and fire influenced températures are located in the fire area and downstream the fire. The maximum température reached under ceiling in the fire zone is about 100X. This is also the case for températures measured 50 m downstream the fire, Figure 5.

\

À.

Visibility

Visibility measures inside the tunnel were done using a video caméra and a calibrated source. One of the main results is the quick diminution of this visibility and the important smoke cloud generated by the fire.

COMPARISON BETWEEN NUMERICAL MODELLING AND EXPERIMENTS

Requirements for modelling

Following constraints mentioned in the FDS spécifie description, the mesh was built putting a minimum of 20 cells to capture the intégral scale. For the validation part, a 1 300 m long part of the tunnel was modelled including the entire tunnel downstream the fire and around 1 000 m upstream to ensure an inlet independent flow. The total number of cells used for that case is 1 900 800 cells with a characteristic size of 20 cm in the fire zone.

The fire was modelled using the fire curve measured during the test considering a ramp based on the measurement. The fire was started 1200 s, 2.5 convective times, after the beginning of the simulation to let time for the flow to be established. To take into account the wind velocity fluctuations that occur outside and of course generate turbulence inside the tunnel, a sinusoidal signal was used [Mouilleau 2009] [8]. This enables to introduce some turbulence inside the domain as in the real configuration. Thèse fluctuations were constructed on the basis of velocity measurement inside the tunnel.

Finally, the tunnel walls were considered as rock with a thickness of 2 m with a conductivity of 3.5 W/m/K, a density of 2 600 kg/m3 and a spécifie heat of 1 000 kJ/kg/K. In the fire région, the product used for the tunnel thermal protection was modelled with its real characteristics including the évolution of the conductivity with température.

Comparisons

Numerically computed températures were first compared with experiment and show a good agreement, Figure 6. The fluctuations on the numerical curves are the conséquences of the LES turbulence model used.

Temperature under ceiling beside the fire.

Temperature under ceiling, 10 m downstream the fire. Carbon monoxide concentration 100 m downstream the fire 1.5 m above ground were also compared between experiment and numerical simulation and confirm the ability of the CFD code to give a quite good overview of the fire consequences, Figure 7.

^^Computed CO concentrations

• Experimental CO concentratio These comparisons show the good agreement that can be obtained between experiments and simulations on some important parameters: the energy exchanges between smoke and walls and correct turbulence introduction in the numerical domain. This first simulation case was used to validate the numerical approach and the use of CFD to reproduce other configurations such as more powerful fires or other wind directions considering that all cases cannot be explored experimentally.

6.

CONCLUSIONS The aim of this paper was to present the experimental results and comparisons between experiments and numerical simulation. This paper shows that the analytical model developed for predicting the heat release by a fire of vehicle is able to predict the fire curve with quite a good precision including mainly the maximum value for heat release rate and the combustion duration. This is useful considering CFD codes encountered difficulties in such a prediction. This paper also showed that, when using correct hypothesis, a CFD code is able to give good predictions of temperature, toxic gas concentration and visibility, i.e. all fire consequences, in the tunnel. The different simulations achieved in this study let appear 2 major elements that have to be accounted for to obtain quite a good prediction. The first is the turbulence in the entrance of the domain, if this is quite simple with a RANS approach, using LES introduces a difficulty and the velocity fluctuation must be considered. The second crucial point is the thermal effect of walls. The exchange between cold walls and hot smokes is fundamental for smoke behaviour modelling and mainly stratification that is based on the thermal gradient. In the present case, the various wall properties were considered.

7.

  used for averaging:<P = ~k m,nii is the mass of f h combustible and <p the interested quantity.

Figure 1 :

 1 Figure 1: Section of the "tunnel du Roux".

Figure 2 :

 2 Figure 2: Images of car used for the experiment.

Figure 3 :

 3 Figure 3: Position of sensors: thermocouples (circles) and Mac Caffrey probes (stars).

Figure 4 :

 4 Figure 4: Fire curve determined using energy balance.

  Evolution of température in the fire zone.Températures 50 m downstream the fire.

Figure 5 :

 5 Figure 5: Experimentally measured températures on fire zone and 50 m downstream the fire.

Figure 6 :

 6 Figure 6: Temperature comparisons, legend is identical for all graphs.

Figure 7 :

 7 Figure 7: CO concentration comparisons.


Ruddy Branka

Twelve years of fireworks market surveillance in France

In the view of market surveillance, more than 4400 fireworks have been taken on the spot by sworn people or bought on the market in France since 1999 for inspection purposes.

This paper presents

-the market surveillance sampling evolution during 12 years, carried out by the PYRO unit of the Accidental Risks Division of INERIS as testing body, -the related measures implemented: additional audits in importer plants, interlaboratory tests for guarantying the reliability of the results, EN17025 accreditation of the testing laboratory, -the typology of detected defects, -the results in terms of defects evolution trends versus time.

Introduction

Directive 2007/23/EC of the European Parliament and of the council of 23 may 2007 on the placing on the market of pyrotechnic articles explains in its article 14 that Member States shall take all appropriate measures to ensure that pyrotechnic articles may be placed on the market only if, when properly stored and used for their intended purpose, they do not endanger the health and safety of persons. For these reasons, Member States shall carry out regular inspections of pyrotechnic articles on entry into the Community and at storage and manufacturing sites.

Even if certified fireworks are not yet completely on the way, these articles are put on the French market (French approvals remain applicable until July 4 th , 2013), a procedure has been put in place in France in order to withdraw an article from the market if it does not comply with the French safety rules (French decree n°2010-455 of May 4 th , 2010). This procedure will be continued after the putting in place and the application of the newly introduced certification (CE marking) rules.

Control rules

In the target of market surveillance, INERIS began to take samples for inspection purposes on the market as early as 1998 on explosives and from 1999 on fireworks and pyrotechnic articles.

The sampling procedure changes in 2010. From 1999 until 2009, the sampling was made by sworn peoples directly in the importer's warehouses. Due to difficulties to obtain the suitable quantities of same articles, since year 2010, articles are bought on the market (chosen by INERIS in catalogues or Internet sites, and after validation of choices by the French administration).

Additionally, from 2008 to 2010, INERIS performed audits within the importers (in the same time that the sampling) in the view of giving an opinion about the procedures applied by each importer to guarantee the full compliance of the imported articles with the original agreed products.

All controls are financed by the French administration and performed by the explosives testing body of INERIS (the PYRO unit).

Actual procedure initiated in 2012

A list of articles (currently, 100 per year from 2010 until 2014) is proposed to the French administration. After validation, a sample of 12 items per type is bought on the market in the view of their control (1 is disassembled, 10 are tested and 1 is kept as witness model).

Among these samples, 20 pyrotechnic compositions are chosen and collected in the view of their analysis.

A report, giving the disassembly results in terms of dimensions, angles and net weight, the functioning tests results and a comparison with the original test results obtained when the approval was delivered (INERIS has in charge to keep all these files) is produced per type of article selected.

The final report for each type controlled summarizes information about all detected non conformities, classified in 2 sub categories; -Level I non conformities: all criteria including a risk for the users -Level II non conformities: all differences with the original file without any risk for the users

In brief, the main level I non conformities examined are presented in table 1.

Difference of bursting height modifying the safety distance for the public, or with average difference more than 20%

Ignition phase out of the critical limits Noise level more than 161 dB lin pic at 2.38m (French regulation for bangers), or more than 10 dB from the original file Dangerous functioning or debris Pyrotechnic composition analysis with forbidden substances or false Dangerous modification of dimensions of angles (i.e. for fan shaped batteries)

Difference of net weight more than 50% (20% for flash compositions)

Bad safety distance on labelling

Composition leakage

Tab.1: Level I non conformity typology

The actions implemented

INERIS is certified according to the ISO9001 standard and, additionally, the PYRO unit is accredited according to EN17025 standard since 1995 for testing explosives and fireworks in the view of their approval or for classification to transport of dangerous goods. These certification and accreditation guarantee the competence of the operators and the quality of the reporting.

Tab.2: Interlaboratory tests on fireworks testing

The above table give the results of the PYRO unit compared to the other laboratories, in terms of difference between the average of results pertaining to INERIS only compared to the average pertaining to all laboratory test results expressed, in numbers of standard deviations.

Except 2 values, INERIS results show limited deviations compared to overall mean laboratories values, e.g. as low as less than 1 standard deviation. These results attest for the high performance and the competence of the INERIS operators.

Evolution of the sampling typology versus time

The following table gives, for each year since 1999, the number and types of fireworks sampled for market surveillance. To some extent these variation reflect the French market trends evolution as well as intensification of market surveillance by the authorities. 1 332 4419 percentages 35,2 15,7 14,5 4,5 9,0 0,6 3,3 4,2 1,8 4,5 2,4 0,6 0,9 0,9 0,6 0,6 0,3

Tab.3: Sampling table

The evolution of total items sampled is given hereafter. The scheduled sampling for the 3 next years is 100 types per year (from 1200 to 1500 items).

Results in terms of conformity

The following table 3 gives, for each year since 1999, the number and percentages of non conforming and conforming products. Figure 2

Pyrotechnic composition statistical analysis

The following table present the number of pyrotechnic composition characterisations performed per year. Only 55% of the controlled compositions are acceptable. The purpose of the analysis is first to verify the absence of prohibited chemical compounds, and then to compare the results with the compositions reported in the initial approvals of the articles.

The results show the presence of chlorate in a banger composition (1999) or even the presence of chromium, lead, or HCB in various compositions in 2010.

The other detected non-complying compositions are mass contents of elements declared noncomplying or presence of other compounds than those declared initially for approval.

Withdrawal procedure of original approval

To date, 32 Fireworks among those sampled for inspection were the subject of a final withdrawal procedure, including 8 at the request of the importer itself. The procedure lies as follows:

(1) The 6 month suspension can be renewed if additional tests are required.

Conclusion

As resulting of the poor quality of the products placed on the French market (the very high requirements in the criteria for the monitoring of the market may also have influenced their decision), the French competent authority has reinforced the inspection procedure.

Among the visible signs of this strategy, we may quote the increase of the sampling quantity, the fact that articles for sampling are anonymously purchased on the inner market, the request for systematic analysis of pyrotechnic compositions and the regular recourse to suspension or withdrawal procedures.

However, despite the large number of non-compliant products detected, and after careful examination of their effects and their importance according to the importer and the necessary consecutive related technical adjustments, at the end, final withdrawal procedures remain limited.

  Fig.1: Sampling evolution

Fig. 2 :Fig. 3 :

 23 Fig.2: Evolution of the percentage of conformity per year

  gives the evolution of the percentage of compliance per year.

				non conformities (typology) in numbers of
						items
	year of control	types sampled	items sampled	dimension, angle	net weight	ignition phase	functionning,	debris, labeling	noise level	height of explosion	number of items	acceptable and %
	1999	10	256		6	10				36		32 12,5
	2000	5	77		10							67 87,0
										15		29
												65,9
					40					10		35
												46,7
					20			2		10		18
												45,0
					45	15				10		80
												59,3
					30	30				20		30
												37,5
				40 334 130		80	52 122		209
												27,4
					134	12		22	46 158		289
												58,3
					379 208		36 140 224		366
												35,0
				31 390 225 177	90 336		508
												36,1
				71 1388 630 317 328 941	1663
				1,6	31,4	14,3		7,2	7,4	21,3		37,6