Jannes Kinscher

Isabelle Contrucci

Pascal Bernard

Anne Mangeney

Jack-Pierre Piguet

Pascal Bigarre

Semi-automatic detection and localization of microseismicity induced by a shallow "salt dissolution provoked" cavity collapse

. The final optimized localization algorithm sufficienty constrained the tendency of actual hypocenter source location in the cavity region. Taken all together, our detection and localization strategy provides an appropriate first order approximation to study spatio-temporal attributes of seismicity from huge data sets associated with seismic signals of unknown or complex signature as observed for Cerville-Buissoncourt.

Introduction

Natural underground cavities, active or abandoned mine workings, particularly when they are shallow, can provoke large scale land subsidence and collapses attended by catastrophic social-economic impacts. The potential of passive microseismic monitoring to prevent such disasters is widely known. Nonetheless, to further improve monitoring reliability, a better understanding of associated seismicity is necessary. In this context, within a large multi-parameter research project at Cerville-Buissoncourt in Lorraine, France, launching in 2004, the growth of a single, shallow, about 150 m diameter salt cavity was surveyed, created by salt dissolution mining. In February 2009, the cavity reached a critical size and a collapse was initiated by controlled brine pumping. During the experiment, a large microseismic data set of > 50.000 event files was recorded. The microseismic network was constituted by a triggered, high resolution 40 Hz geophone monitoring system including five 1component and four 3-component stations [START_REF] Klein | Experimental monitoring of a solution-mining Cavern in Salt: identifying and analyzing early-warning signals prior to collapse[END_REF] (Figure 1a and1b).

Initial data inspection reveal very unusual seismic signals where about 80 % represent complex swarming sequences [START_REF] Mercerat | Induced seismicity monitoring of an underground salt cavern prone to collapse[END_REF][START_REF] Contrucci | Multi-parameter monitoring of a solution mining cavern collapse: First insight of precursors[END_REF] (Figure 1c). Conventional automatic detection algorithms as e.g. the STA/LTA yielded no reliable results as seismic events appear close to each other in time, and vary strongly in duration and size. In addition, localization techniques based on seismic phase arrival times find its limits, as body wave phase are partially poorly developed, noisy, or covered by the CODA of the preceding event.

Here, we present two methods concerning the detection and localization problem, and the first results of their evaluation. In the proposed detection design, simple spectral signal characteristics are used to detect and characterize seismic events in the complex swarming sequences. For event localization, an approach based on simple peak-to-peak amplitude ratios was tested which relied on the derivation of a site specific attenuation law. Our results demonstrate the great potential of both methods to provide a reliable, semi-automatic approach to approximate spatio-temporal characteristics of seismicity in a first order.

Detection Design

The proposed detector design is based on a spectral envelope function (SEF) calculated for each station seismogram. The SEF is defined by

( ) i t i w F SEF ) ( max =
(1), where max(|F(w)|) is the maximal spectral amplitude of the FFT spectrum gradually calculated for very small time intervals t i (0.025 s) over the entire seismogram. Each calculated spectrum is characterized by a very low resolution (sample rate = 5 kHz), whose shape and maximal spectral amplitude is very sensitive to spectral signal characteristics. If incoherent signals as noise and CODA waves are considered, the spectrum presents a flat shape and low maximal spectral amplitude. If energy is distributed over distinct frequency spectra, as expected for seismic waves, the calculated spectrum is steeper and the maximal spectral amplitude becomes much higher. In this way, contributions of noise and seismic events are clearly distinguished by the final calculated SEF. Then the SEF of all stations are combined to a network detection function (NDF) (Figure 2a) to guarantee event detection in agreement with the network detection capability and to further eliminate false detection alarms. Prior to NDF generation, all SEFs are elongated by convolution with a 0.25 s rectangular step function to avoid event extinction due to travel time differences. The convolved SEF conv for each station is then transformed to a station detection function (SDF) containing values of 0 for noise and 1 for a potential event. The trigger value of the station detection function was defined by the median of the convolved SEF:

SEF conv i -median(SEF) > 0 => SDF i = 1 SEF conv i -median(SEF) ≤ 0 => SDF i = 0
In the following, the product of the SDF is used to obtain a preliminary NDF where each value bigger than 0 comprise the centre of a detected event. The general event start and end time is determined by the point where the sum of all the SDFs becomes zero when going backwards and forwards in time from the event centre.

The great advantage of the proposed detection design is the usage of only one evaluation window of very small size which makes it applicable for all ranges of event duration. Moreover, event detection is quite sensitive with respect to very small sized events directly appearing next to a much bigger event (Figure 2a and2d). Correspondingly, seismicity represented by the first detection results of a data set, recorded in 2008 (~ 11.000 detected events), seems to follow a Gutenberg-Richter distribution (Figure 2b). However, small deviations of the apparent Frequency -Magnitude relation are observed for stronger events that are mainly associated with an abnormal long duration (> 2 s) (Figure 2a). We found evidence that these loger lasting events are constituted by a superposition of shorter isolated events associated with a duration of 0.2 -0.5 s. Accordingly, the statistical distribution of the dominant frequency is similar for isolated events and longer lasting events. Moreover, the number of apparent sub-events contained in the long duration events was estimated by calculating the SDF again for the time window of the detected event using the SEF without previous convolution (Figure 2c). From the results we found that the event duration increase linearly with the number of contained sub-events, supporting the idea of isolated event superposition. Consequently, an additional sub-event detection step might be inevitable when detection results are used to estimate seismic rates and apply probabilistic seismic analysis.

Localization design

Hypocenter inversion based on the decay of seismic wave amplitudes was introduced by [START_REF] Battaglia | Location of seismic events and eruptive fissures on the Piton de la Fournaise volcano using seismic amplitudes[END_REF]. Here, we used inter-station amplitude ratios, following the formulation of the seismic attenuation law as proposed by [START_REF] Taisne | Imaging the dynamics of magma propagation using radiated seismic intensity[END_REF] ( ) ( )

j i s i j j i j i r r e QV f r r n S S A A - ⋅ ⋅ -       ⋅ +       =       log log log log π (2),
where A is the peak-to-peak signal amplitude, S the receiver amplification site effects and r the hypocentral distance at station i and j. The coefficient n represents the degree of amplitude loss due to geometric spreading on the ray path while the third term on the right hand side of Equation 2represents intrinsic attenuation where f is the signal frequency, V S the medium specific S-wave velocity, and Q the Quality factor.

Receiver characteristics were determined by carefully inspecting the amplitude spectra related to seismic events being at the same hypocentral distance to both stations of a station couple such that attenuation effects can be neglected (see Eq. 2). Finally, differences in peak-to-peak amplitudes were used to define the ratios of receiver site effects for each station pair.

To calibrate the attenuation law we used 58 events of known source location that covered approximately the whole cavity zone. Hypocenter inversion for these events was carried out by means of manually determined polarization angles, and P and S-waves arrival times. Associated errors in epicenter location error were small ~ ± 20 m. In contrast, source depth was poorly resolved where best results comprised an error of ~ ± 50 m. For the following calibration, a fixed source depth at -180 m was assumed. At the same depth, gamma ray measurements documented significant detachment and collapse of a Marl layer (Figure 1a) between March and May in 2008 which correspond to the event registration time.

In a first step of calibration, a grid search for the quantities n and Q was performed to retrieve the contribution in amplitude decay produced by geometric spreading and intrinsic attenuation (Figure 3). The best approximation was represented by the minimal misfit between observed amplitude ratios and the right hand side of Equation 2 using the L2 norm. Used peak-to-peak amplitudes were measured for three different frequency bands 30 -70 Hz, 70 -210 Hz, 210 -630 Hz. The minimal misfit finally was found close to the asymptotic kink of the misfit function, at n = 2.1 ± 0.1 and Q = 45 ± 20 (for V S = 2000 m/s), where geometric spreading and intrinsic attenuation equally contribute to the amplitude decay with distance. Estimated errors represent the maximal deviation in parameter values considering a small degradation of the misfit function of 5 % from its minimum. Similar values for Q were found by [START_REF] Mercerat | Sismicite induite et modelisation numerique de l'endommagement dans un contexte salin[END_REF] from spectral analysis. The somewhat high value found for n is suggested to represent the increased complexity of ray paths introduced by the cavity structure.

In addition, we looked for a possible dependence of Q on frequency by using Q = Q o • f η and varying η. Frequency values were approximated by the mean value of each considered frequency band (Figure 3). The results demonstrate a slight dependence of Q with frequency which significantly improved the fit of amplitude ratios for higher frequencies.

For the entire calibration procedure and following inversion stations M6 (1-3) (Figure 1a and 1b) were completely excluded. Attenuation effects recorded by these stations (especially M63) are significantly more dependent on source depth as they are much closer to seismic sources located at the cavity roof as compared to the other stations situated around the cavity structure. Hence, the assumption of a fix source depth would produce a significant bias when modeling the attenuation parameters.

In the following, amplitude ratio based localization for the 58 events was tested. Inversion was performed by a simple grid search over the cavity region. A grid point interval of 10 m was used for horizontal coordinates. The misfit of observed and theoretical amplitude ratios calculated by our attenuation law was estimated by using the L2 norm.

From the results, we observed that localization strongly depends on the used frequency band. Localization based on amplitude ratios for 30 -70 Hz showed a poor resolution of epicenter location around 100 m (Figure 4a). As a result, attenuation effects for this frequency band are generally too weak with respect to contributions of noise in the rough peak-to-peak amplitude estimation.

Better localization results are observed when using amplitudes for higher frequency that are associated with a mean location error of ~50 m (Figure 4a). Generally, localization based on amplitudes for 70 -210 Hz give the most robust results (Figure 4a and4b). In contrast, localization results at frequencies of 210 -630 Hz clearly depend on the source-receiver distance, and the amount of high frequency energy emitted by the seismic source. This effect can be seen by the apparent low location resolution at station M7 and somewhat at station M8 (Figure 4a). Dependent on the source size, both stations receive generally less and sometimes no high frequency energy as they are located at the surface, and thus, tend to be more distant than others. As a result, amplitude ratios do not anymore reflect correctly attenuation and the localization result is biased. On the other hand, localization becomes very precise when high frequency energy is present for all stations (Figure 4b).

In total, it can be concluded that the observed attenuation effects at Cerville can be used to resolve the tendency of epicenter source location in the cavity region by using simple signal amplitudes estimates for higher frequencies > 70 Hz. However, results should be always interpreted under consideration of the spectro-temporal characteristics of the respective analyzed event. Future work will focus on the evaluation of an additional approach that might allow for source depth determination. Source depth determination seems generally possible by using the here obtained attenuation parameters with respect to signal amplitudes observed at stations M6 (1-3).

Conclusions

An event detection and localization design was tested to study semi-automatically unusual seismic signals recorded during the growth, and the induced collapse of a salt cavity. The results show the great potential of both methods to provide a reliable first order approximation of spatio-temporal attributes of the recorded seismicity. The detector design is based on a spectral envelope function and designed to study the characteristics of complex swarming sequences. By using just one evaluation window, the detector is suitable for all ranges of event duration. Nonetheless, the detection design needs to be further tested and optimized to allow for robust and reliable estimates of absolute seismic rates from the detection results. The tested localization method uses simple estimates of signal amplitude, and thus, avoids troublesome a priori seismic phase segmentation. Observed attenuation effects at Cerville were found to be sufficiently strong to resolve the tendency of epicenter location in the cavity region especially when considering higher frequencies of >70 Hz. As location results depend on the considered frequency band, additional information about the spectro-temporal event characteristics are inevitable when interpreting the results.

Figure 1

 1 Figure 1 (a) Side view of the microseismic network at Cerville-Buissoncourt, most important geological layers, and the salt cavity structure. (b) Top view of the microseismic network, and the border of the salt cavity. (c) Typical microseismic record of a complex swarming sequence.

Figure 2

 2 Figure 2 (a) Detection result for the swarming sequence presented in Figure 1c. Colored horizontal and vertically dashed lines represent the start and end time of an event detected by the NDF as a function of all SDF (blue shaded areas). (b) Apparent Frequency-Magnitude relation for ~11.000 detected events approximated by the detected event number and Peak-to-Peak amplitude (counts) relation. (c) Apparent sub-event number for a long duration event determined by the SEF. (d) Detection example of a very short and weak seismic event.

Figure 3

 3 Figure 3 Calibration results of the attenuation law. (Upper panel) Grid search results for quantities n and Q. (Lower panel) Q frequency dependence illustrated by the Misfit function for each considered frequency band calculated for observed and theoretical amplitude ratios (Eq. 2) using the L2 norm. The final value frequency dependence η was found by the minimal mean value of all Misfit functions.

Figure 4

 4 Figure 4 (a) The error of location illustrated for its statistical distribution (lower panel), and spatial distribution in the cavity region (upper panel). The error of location represents the distance to the actual known epicenter position. Note that the illustration of the spatial location error distribution is done by linear interpolation and only representative for areas where known epicenter locations are close. (b) Examples of two individual location results.


Dominique Fleury
email: dominique.fleury@ineris.fr

Guillaume Fayet

Alexis Vignes

François Henry Ab

Emeric Frejafon

Nanomaterials Risk Assessment in the Process Industries: Evaluation and Application of Current Control Banding Methods

Nanotechnology is a rapidly growing field and industrial developments are more and more challenged by potential health and safety risks pertaining to manufactured nanomaterials. This matter is far from being solved due to the current lack of reliable data addressing occupational safety as well as environmental field. In this context, the Control Banding (CB) approach appears particularly interesting to assess ESH risks associated to nanomaterials. Our study focuses more specifically on four CB methods which have been analysed in order to highlight their a priori limits and evaluate their effectiveness for perform risk assessment in the industry. Our study concludes that too conservative frameworks, multiplicity of factors and complex algorithm are critical elements that can limit the effectiveness of the tools for risk assessment in the industry.

Introduction

Despite the numerous technological innovations brought by nanomaterials (NMs), the size reduction that is so expected to enhance product performances may also be associated to new hazards that have to be managed to ensure a safe and sustainable development. The usual risk assessment tools fail when applied to NM because toxicological data are lacking, as some relevant physicochemical information (e.g. size, shape, or state of agglomeration) which are rarely available in material safety datasheets (MSDS) [START_REF] Lee | Evaluation of information in nanomaterial safety data sheets and development of international standard for guidance on preparation of nanomaterial safety data sheets[END_REF].

The Control Banding (CB) framework has been widely adopted by decades by the pharmaceutical industry to assess risks associated to new components. Indeed, it has proven to be an effective strategy for controlling worker exposure in the absence of complete relevant information about toxicity and exposure, , i.e. without Occupational Exposure Limit (OEL). CB has been widely adopted as an alternative option for controlling exposure to NMs. It can be considered as a first step in a complex risk assessment process that may involve quantitative exposure measurements and accidental risk assessment.

A conceptual model was notably presented by Maynard using "impact" and "exposure" indexes [START_REF] Maynard | Nanotechnology: the next big thing, or much ado about nothing?[END_REF]. He highlighted that a big challenge remains for a relevant and efficient definition and weighting of the risk factors. Recently D.H. Brouwer published a very interesting review which aims at identifying the intrinsic differences and similarities of some CB tools [START_REF] Brouwer | Control banding approaches for nanomaterials[END_REF]. Our paper is complementary to the Brouwer's study as it will focused on advantages and drawbacks which may be practically encountered in using CB tools for the process industries.

Overview of the current CB methods

Our study will focused on four CB methods: Groso's method [START_REF] Groso | Management of nanomaterials safety in research environment[END_REF], CB NanoTool [START_REF] Paik | Application of a pilot control banding tool for risk level assessment and control of nanoparticle exposures[END_REF], ANSES method (ANSES, 2010) and Stoffenmanager Nano (Van Duuren-Stuurman, 2012) here designated by "STM Nano". This part consists in brief descriptions on these four methods.

Groso's method

Groso"s method does not follow exactly the classical CB framework and is rather a decision trees approach that helps managing the risks in laboratories involving NM thanks to a classification into three hazard classes (from nano 1 to nano 3). This tool, which target researchers (users) with no specific knowledge in nanosafety, provides an easy way to assess and compare the level of risk. The method focuses on the level of containment which can lower the hazard level to its minimal value "nano 1", in case of total enclosure. By using such tree-based approach, notions of hazard and exposure are merged in a unique index, pushing the simplicity of the method to the highest reachable level (no calculation algorithm) but loosing the ability to identify both aspects distinctively. Safety guidelines (technical, organizational and PPE) are provided to put in front of the different classes. It consists essentially of a mix between general good laboratory practices, safety measures and personal protective equipments (PPE).

One particularity of the Groso's method is to differentiate the impact of the quantity factor for handling and production operations. The threshold quantities has been computed from an hypothetical accidental scenario (spillage) which may occur in the surrounding space (arbitrary defined as a 10 cubic-metres volume) and compare to the BSI recommendations (BSI, 2007).

For nano-objects in suspension, a hazard class is attributed as a function of the volume of the solution and the ability to the process to generate droplets and aerosols. For liquids containing NMs, the generation of droplets by the process [START_REF] Johnson | Potential for occupational exposure to engineered carbon-based nanomaterials in environmental laboratory studies[END_REF]) is considered as a potential exposure scenario that leads to the hazard class "nano 3". Nanocomposite preparation is a potential way of exposure considered as a process step that can release aerosols in the environment [START_REF] Fleury | Identification of the main exposure scenarios in the production of CNT-polymer nanocomposites by melt-moulding process[END_REF]. Here the lower hazard class "Nano 1" is attributed if the process does not involve any mechanical or thermal treatment. [START_REF] Paik | Application of a pilot control banding tool for risk level assessment and control of nanoparticle exposures[END_REF] introduces a pilot CB tool named "CB Nanotool" based on existing knowledge of NM toxicology based on the CB framework proposed in earlier publications. CB Nanotool consists of a Risk-Level (RL) matrix, similar to that used in the implementation of CB through the HSE's COSHH Essential program, leading to four categories of "bands", namely control bands, which indicates a level of technical control that must be set up to manage the risk. The risk level (RL) is determined by severity score and the probability score which are both classified into four bands and are analogous to the impact and exposure indexes described in [START_REF] Maynard | Nanotechnology: the next big thing, or much ado about nothing?[END_REF]. The three lowest risks level (RL1 to 3) correspond to engineering control measures, whereas the higher risks level (RL4) consists in asking for a specialist advice.

CB Nanotool

Many efforts have been put in CB Nanotool hazard assessment. It is based on toxicological information available in the current literature in 2008 that seems hardly changed from nowadays. Hazard assessment is performed through a quite long list of factors that are expected to be determinant in the overall NM severity. These factors can be classified into two groups: physicochemicals properties (surface chemistry, shape, size and solubility) and toxicological properties (carcinogenicity, mutagenicity and dermal toxicity). Practically, most of these factors cannot be determined and properties of the parent material (e.g. carcinogenicity, mutagenicity, reproductive toxicity, and dermal hazard) are used to provide a starting point in the NM hazard assessment. The parent factors counts for 30% of the total score in the hazard index calculation (greater consideration is given to the NM characteristics). The second axe of the CB matrix "probability" focuses on the potential exposure by inhalation. The index gathers process information (amount of NMs, frequency, duration), physicochemical properties (dustiness/mistiness) and organisational information such as number of employees with similar exposure.

In both probability (exposure) and hazard banding cases, the user keeps the possibility to answer "unknown" when the factors remain undetermined. To avoid CB Nanotool to be overconservative, it was decided that the factor "unknown", which can play a major role for assessing NM hazard, will be assigned to 75% of the maximum value. Even if this has not been demonstrated practically yet, CB Nanotool has been design as a dynamic tool in which risk factors can evolve as a function of available scientific data, especially concerning NM toxicology [START_REF] Paik | Application of a pilot control banding tool for risk level assessment and control of nanoparticle exposures[END_REF]. These new data might change the relative importance of one factor compare to the other. The score and range of value can also be modified according to the level of risks one is willing to accept, or for large-scale manufacturing of NMs (particularly for the probability factors ranges).

Stoffenmanager Nano

Initially, the generic Stoffenmanager is a web-based control banding tool which was design to help smalland medium-sized companies to handle hazardous substances with more care (Marquart, 2008). Recently, TNO has developed a nano-specific module within the generic Stoffenmanager for helping in managing the risks associated to NMs. This module was first presented to the 2010 Nanosafe conference and then published in [START_REF] Van Duuren-Stuurman | Stoffenmanager nano version 1.0: a webbased tool for risk prioritization of airborne manufactured nano objects[END_REF]. Similarly to the COSHH approach. STM Nano riskbanding tool has been developed for employers and employees handling NMs and having limited experience in occupational health. Input parameters for the hazard assessment were selected based on their "theoretical" availability in the MSDS and the technical data sheets. Stoffenmanager combines the available hazard information with a qualitative assessment of the potential exposure. The authors of STM Nano highlight the importance of basing the hazard banding system on inputs parameters which are available to the user and for which information is accessible (for instance, water solubility is used as a surrogate for solubility in biological media).

ANSES method

The Anses method respects a standard CB framework by crossing hazard bands with emission potential bands. The determination of the hazard index starts from the properties of the parent material or an analogous material (CLP R-phrases) which is then passed through an incremental tree composed by 4 factors that enable to determine the final hazard classification. The emission potential is determined as a function of the physical form of the NM (solid, liquid, powder or aerosols) incremented if transformations are likely to occur during the process (e.g. spraying) Emission potential and hazard assessment lead to determine a control class (CL) for which technical recommendations are furnished (mainly containment and ventilation). Both highest CL recommend a full containment in addition to a review by a specialist (a toxicologist) for CL 5. This specialist request may be particularly annoying because it seems quite discouraging trying to find a toxicologist to bring answer that probably goes beyond the state-of-the-art research or may require long and expensive research study.

Discussions

This part aims to discuss on seven properties that have been pointed out as critical elements to ensure the relevance of the NM risk assessment in the industry. These statements are issued from the observation of INERIS in workplaces of various industries involving different type of NMs.

Quantity

Amount of NM is probably one of the most relevant and available factor to assess exposure to NM as it is usually tightly linked to the potential amount of airborne material released at workplace. Two tendencies have been observed among the studied methods: the one like STM Nano and Anses which discard the amount of NM in the risk band calculation, and the others (CB Nanotool and Groso's method) which include this factor but fail to comply with industrial activities due to an upper limit not greater than 100 mg. This 100mg threshold is reached in almost all large scale processes, then the range factor being inefficient to distinguish the likely of exposure between two different industrial operations which may involve very different quantities of nanoscale products (e.g. both 10 g and 1 kg will lead to the same score). Surprisingly, although STM Nano does not consider the amount of product, the weight fraction is a parameter which is taken under consideration to compute the exposure index.

Size and agglomeration state:

Size is also a relevant factor to assess the risks associated to NMs since it plays a relevant role in hazard assessment as size predicts how far the NMs will penetrate into the respiratory tract and the possible translocation into the body. But considering exposure, size and density parameter will also be relevant to predict the behaviour of airborne release (air capture, filtration efficiency, sedimentation). Then it is quite surprising this parameter is not part neither from exposure nor hazard index calculation in both Anses and Groso's methods (STM Nano only considers it in the hazard band calculation). Furthermore, agglomeration and aggregation state is a critical parameter that is relevant for both exposure and hazard assessment although this factor is hardly considered in the risk banding calculation. In Groso's method, the agglomeration factor is incremental and used to differentiate class "nano 2" from class "nano 3", when the amount of powder is greater than 100 mg. In Stoffenmanager Nano, aggregate and agglomerates of size exceeding 100 nm are considered as cluster of NMs and considered as such, as they could possibly retain nano-specific properties and are identified through the specific surface area and the primary particle size criterion. Practically such data are scarce in MSDS or technical data sheet and the knowledge of the state of agglomeration would require additional tests that have to be performed by an expert.

Aspect ratio and fiber paradigm

Fiber-shaped nano-objects are considered to have a high potential hazard due to the fiber paradigm (asbestos-like carcinogen effect). Anses, Groso's and STM Nano methods are quite conservative on this point since a length-to-diameter aspect ratio greater than 3 will lift up the hazard band to its highest level (unless the fibers are embedded in a solid matrix). Insoluble nanofibers exceeding a length of 5 urn are considered as persistent, in accordance with the fiber paradigm [START_REF] Donaldson | The inhalation toxicology of p-aramid fibrils[END_REF]. Unlikely, CB Nanotool considers the fiber shape of NM as factor which increase hazard but does not require automatically the highest hazard band. Moreover, the fiber shape often comes together with the notion of bio-persistent particle which require in-depth toxicological study and is generally not known neither through the generic documentation of the product (i.e. MSDS and technical datasheet) nor through the up-to-date scientific literature. Therefore, the conservative approach on the fiber-shaped NMs causes some difficulties for practical applications.

Factor availability

CB method has to deal with the strong uncertainties which affect NMs and then an efficient hazard banding will have to rely on relevant factors that will be available for the person intended to perform the risk assessment (in general a non-expert). To ensure accessibility to the numerous factors, STM Nano relies on MSDS which are supposed to contain enough information about the involve NM to give ability to the user to answer. A recent study has shown that MSDS are not so as perfect as they should be and it is often hard to access to basic parameters that are addressed in the risk banding (e.g. size, solubility, moisture content, dustiness) [START_REF] Lee | Evaluation of information in nanomaterial safety data sheets and development of international standard for guidance on preparation of nanomaterial safety data sheets[END_REF]. [START_REF] Maidment | Occupational hygiene considerations in the development of a structured approach to select chemical control strategies[END_REF] stressed the importance of limiting the number of factors in the CB model to reduce its complexity and increase its applicability for non-experts. Paik hazard assessment goes deeper in details than the other methods and allows "unknown" answers. This is particularly useful for handling toxicological parameters (e.g. carcinogenicity or mutagenicity) which are generally highly uncertain. If the unknown-answer trick offers more convenience for practical application, the lack of information causes the final hazard banding to be uncertain and mostly guided by the properties of the parent materials. Even if the number of factors is reduced in Anses method, the availability is not improved since the incremental factors like "solubility" or "evidence of higher activity" can hardly be found in MSDS too.

Role of parent materials in hazard banding

In many cases poor level of information is available to assess NM risks. Therefore the "trick" is to call for parent material properties so as to provide a first estimation of the potential hazard of the NM. This is done in CB Nanotool, which gives a 30% score to the parent in any case but also in the Anses and the STM Nano methods which propose a classification of NM based on the parent material. [START_REF] Zalk | Risk level based management system: a control banding model for occupational health and safety risk management in a highly regulated environment[END_REF] mentioned ones would have required the weight of the parent material to be higher than 30% (cumulative scores related to the parent material in the severity score calculation on the maximum severity score) but one has to be careful by the fact that nanoscale product does not necessarily exhibit the same toxicological properties than their larger counterparts (the parent material). Some NMs, like carbon nanotubes, may seriously affect human health whereas the closest parent (graphite) which does not exhibit similar hazardous properties at all. For this kind of material, increasing the weight of the parent material in the hazard scoring will tend to lower the severity level, in contradiction with the precautionary principle.

Monitor safety barriers improvements

One great advantage of STM Nano is to include the measures of exposure reduction (control measures and PPE) into the exposure score calculation, then it is possible to score any progress that will be done in term of prevention and protection (new or improved barrier). This is not the case in the other methods which do not take into account the level of prevention and protection when determining the exposure band.

However, risk assessment should be performed without considering PPEs that should not be include as a factor of reduction in the exposure score of the workplace itself and should only be considered in the last case , e.g. in accidental case, when control measures might be deactivated.

Coactivity

It is quite frequent to observe that several processes take place in the same area and then it is important to take into account the exposure caused by coactivity. Stoffenmanager takes into consideration the different sources of exposure at workplace: background, near field and far field. This is particularly useful regarding the risks of exposure due to co-activities in the same area which may be discarded with other methods focusing only on the near field sources. This point out some indirect exposure scenario in case of handling of very dusty NM or mistiness provoked by high energetic processes (e.g. mixing, spraying), [START_REF] Johnson | Potential for occupational exposure to engineered carbon-based nanomaterials in environmental laboratory studies[END_REF].

Conclusion

This study has described and compared 4 different methods which aim to manage the risk associated to NM trough a CB framework.

Anses method may be one of the most conservative because some incremental factors can lead straight to the highest hazard band (HB5), for instance if there is no parent material of if it is not possible to statue about the biopersistence in case of fiber. This over-conservative approach tends to put a large amount of workplace to the same level of hazard and may pose some difficulties in the prioritizing task that should follow the risk assessment.

If STM Nano seems to be one of most complete tool to manage the nano risks, this coverage is paid by an increased complexity (large number of parameters and complex calculation algorithm) that may limit the applicability of the method by non hygienist persons. Particularly, the on-line web application may appear like a black-box in which the computation leading to the exposure and hazard banding may not be fully understood.

The tree approach of Groso's method offers a very convenient form which may easily be exploitable by non-specialists. Unfortunately the mix between hazard and exposure factors caused by the tree-approach and the low level of recommendation which come in face of the risk levels limit the scope of the method. Finally CB Nanotool benefits from the "unknown" answer that enables the user to discard some uncertain properties (often the case in industrial production) that are partially replaced by properties of the parent material (own for 30% of the final score). However, this low-conservative approach leads to lower the hazard of NM exhibiting more hazardous properties than their larger counterparts.

From that study, it should be underlined as well that current control banding approaches are intrinsically limited and cannot cover all types of hazards: if they are mainly focused on health risks and do not address safety (fire/explosion) or environmental risks, these aspects should not be neglected for an efficient-inpractice risk assessment [START_REF] Vignes | Risk assessment of the ignitability and explosivity of aluminum nanopowders[END_REF]. Similarly, degraded working conditions (maintenance, cleaning) are not covered although these scenarios often increase the risk due to safety barriers deactivation (hood stopped, glovebox opened...).

As mentioned in the 3rd RIP-oN final report (European Commission 2011 ), it is challenging to see how CB tools could be used without a critical review of the input parameters and collection if much more information about them in relation to each case of its use. This comment, which was initially dedicated to CB Nanotool and Anses tool, indeed can be extended to all CB tools for which this kind of review is still scarce. As [START_REF] Brouwer | Control banding approaches for nanomaterials[END_REF] did in his paper, this work provides first elements to fill the lack raised by the RIP-oN report and aims to be complemented by the results of on-going studies at INERIS.

Table 1 :

 1 Comparison of seven properties that have been pointed out as critical elements to ensure the relevance of the NM risk assessment in the industry over the four studied CB tools.

		Groso	CB Nanotool	STM Nano	Anses
		Not adapted to	Not adapted to
	Quantity	industrial activities	industrial activities	Not considered	Not considered
		(100 mg)	(100 mg)
	Size	Not considered	Included in the hazard factors	50 nm limit hazard factor	Not considered
	Aspect ratio / shape	Fibre (L/D > 3)	Included in the hazard factors	Highest class if bio-persistent fibre	Highest class if bio-persistent fibre
	Factor availability	Mostly available	Mostly unavailable forNM	Based on MSDS but often unavailable	Often unavailable
	Parent material	Not considered	Count for 30% of the hazard score	Use in case of NM uncertainties	Starting point for the hazard banding
	Monitor safety barrier improvements	Not possible	Not possible	Yes, through factors exposure reduction	Not possible
	Coacticity	Not considered	Not considered	Near filed and far filed concepts	Not considered


Guillaume Fayet

Patricia Rotureau

Vinca Prana

Carlo Adamo

Prediction of Physico-Chemical Properties for REACH Based on QSPR Models

Quantitative Structure Property Relationship models have been developed for the prediction of flash points of two families of organic compounds selected in the PREDIMOL French Project: amines and organic peroxides. If the model dedicated to amines respected all OECD validation principles with excellent performances in predictivity, the one dedicated to organic peroxides was not validated on an ex ternal validation set, due to the low number of available data, but already presented high performances in fitting and robustness. This work highlighted the need of gathering experimental data, as in progress in the PREDIMOL project, to achieve validated reliable models that could be used in a regulatory framework, like REACH. Such models are expected to be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox) to be available for use by industrials and regulatory instances.

Introduction

The new EU regulation REACH requires the evaluation of the physico-chemical properties of a large number of existing substances (143 000 pre-registered substances in 2008) before 2018 in order to allow their use. Taking into account the number of substances and properties, the timing, the economic costs, the feasibility at the R&D level and the risks for the operator, in particular for the characterization of the hazardous physico-chemical properties (explosibility, flammability), the experimental measurement of all the data reveals not realistic. Thus, the development of alternative predictive methods for the evaluation of the properties of substances was recommended in the framework of REACH.

In this context, the French PREDIMOL (molecular modeling prediction of physico-chemical properties of products) project (2012) funded by ANR (National Research Agency) has started in November 2010 for 3 years. This project is conducted by INERIS in partnership with several public and private partners. Its objective is to demonstrate that molecular modeling, notably through use of QSPR (Quantitative Structure-Property Relationships) models, is a credible alternative approach to experimental characterization to access, in a reliable and fast manner, to the whole range of physico-chemical properties of substances required by EU-REACH's regulation (annexes VII and IX) as well as for the industry in terms of propertyscreening method. QSPR methods allow predicting properties from the molecular structures of chemicals. The project is focused on the prediction of physico-chemical properties related to particular families of compounds, like amines and organic peroxides.

In this paper, we describe in a first part existing QSPR models applicable to predict properties relevant to these substances, debating onto their performances and limits. An inventory of existing experimental data from literature was also established in this part. Furthermore, as predictivity of QSPR models highly depends on the database suitability in terms of number of data and uncertainties of measurement, experimental databases were also consolidated in a c onsistent way in this project for these families of compounds. In the second part of this paper, new QSPR models developed for the prediction of the flash point property of amines and organic peroxides are presented.

Inventory of existing QSPR models and experimental data

Inventory of existing QSPR models

Even if a l arge number of QSPR models have been developed for the prediction of physico-chemical properties required by REACH, only few references have been dedicated to models specifically developed for amines or organic peroxides. Considering amines, [START_REF] Koziol | Neural network modelling of physical properties of chemical compounds[END_REF] developed neural networks for the prediction of the boiling point of 190 compounds. This model based on 46 constitutional descriptors demonstrated a good predictive power with a standard deviation of 6°C (and R²= 0.98) on an external validation set. Two references have been found about QSPR models focused on organic peroxides. Firstly, [START_REF] Romanelli | Ameliorate QSPR study of alkyl Hydroperoxides[END_REF] have proposed several models for the prediction of the density based on 14 molecules with a R² reaching 0.999 but without any external validation. A more recent QSPR study presented (MLR-multilinear regressions and PLS-partial least square) models developed by [START_REF] Lu | Prediction of the Reactivity Hazards for Organic Peroxides Using the QSPR Approach[END_REF] based on 16 compounds for the onset temperature and the heat of decomposition. Even if PLS models presented interesting performances in terms of fitting and robustness (e.g. R 2 =0.957 and Q 2 = 0.859 for heat of decomposition), once again, they were not validated on an external validation set. Other more global models (dedicated to organic compounds) are applicable for amines and or ganic peroxides, but their reliability for these particular families of compounds have not been robustly evidenced since they only used few representatives (typically less than 10 in large datasets of thousands of molecules).

Inventorying experimental data

In order to develop QSPR models, an inventory of available experimental data was made. Concerning amines, different sources of data were investigated like DIPPR, NIST, Handbook of Chemistry and Physics [START_REF] Haynes | Principles of QSAR models validation: internal and external[END_REF]. In particular, the CarAtex database (2012), available online, gathers properties related to process safety (auto-ignition temperature, lower and upper flammability limits, flash points) for about 80 amines. Considering organic peroxides, a particular attention focused on the Datatop (2005), a database developed by TNO collecting until 40 explosive properties for 116 compounds at different concentrations and diluents. Indeed, for stability reason, organic peroxides are rarely pure compounds. These data allow classifying organic peroxides (represented by a large variety of structures including hydroperoxydes, peroxyesters and peroxydicarbonates for example) according to the Transport of Dangerous Goods regulation [START_REF] Un | Recommendations on the transport of dangerous goods: Manual of tests and criteria, ST/SG/AC.10[END_REF]. In this database, reliability of data is not guaranteed and all data have to be regarded as indicative. Moreover, the source is unknown for many data. Nevertheless, it allowed observing that some property values vary for the same substance with concentration in organic peroxides. In this context, a robust database is under consolidation in the framework of the PREDIMOL project with new data obtained in homogenous experimental conditions for 30 or ganic compounds. The targeted properties are heats and temperatures of decomposition, impact sensitivities, densities and flash points that will allow developing new QSPR models.

Development of QSPR models for the prediction of flash points

In this study, QSPR models to predict the flash point (FP) of amines and organic peroxides were developed as data were available for these both families. This property is a key measure of the flammability hazard of liquids. It is defined as the lowest temperature, corrected to a barometric pressure of 101.3 kPa, at which the vapour/air mixture above the liquid can be ignited. Substances with low flash points present higher flammability than those with higher flash points. All experimental data were obtained in closed cup apparatus.

Principle of the QSPR method

The QSPR method is based on the principle that molecules with similar structures have similar properties. The chemical structure is represented at molecular level by a series of descriptors that can be mathematically connected to experimental properties by a Q SPR model. So, such model will have the following form: Property =f(descriptors)

(1)

A large number of descriptors (constitutional, topological, geometrical and quantum chemical) can be calculated to describe the structure of molecules [START_REF] Karelson | Molecular Descriptors in QSAR/QSPR[END_REF]. Many statistical tools can be also used to develop QSPR models (multi-linear regression, neural network, etc...).

In this paper, multilinear regressions were developed using the Best Multi Linear Regression (BMLR) approach [START_REF] Karelson | Molecular Descriptors in QSAR/QSPR[END_REF] as implemented in Codessa software (2002). This stepwise approach started with constructing two-parameter MLR models based on non-intercorrelated descriptors (with R² between descriptors lower than 0.1) and then it built higher rank models by adding new non-intercorrelated descriptors (i.e. with R² lower than 0.6 with each of the previous ones). By this way, the method guaranteed that two intercorrelated descriptors were not selected in the same model. The algorithm gave, at each rank (i.e. for each number of descriptors), the model presenting the highest correlation with the studied property. The final model was chosen as the best compromise between correlation refinement and number of descriptors.

Within the context of REACH, the development of QSPR models is encouraged providing that they respect the 5 driving principles for the validation of QSPR models drawn up by OECD (2007):

1. A defined endpoint (including experimental protocol);

2. An unambiguous algorithm; 3. A defined domain of applicability; 4. Appropriate measures of goodness-of-fit, robustness and predictive power; 5. A mechanistic interpretation, when it's possible.

The fourth OECD principle requires suitable measures of performances. To measure the goodness-of-fit of a model, the determination coefficient R² is calculated between predicted and experimental values. For robustness, leave-one-out (LOO) and leave-many-out (LMO) cross-validation were performed [START_REF] Haynes | Principles of QSAR models validation: internal and external[END_REF]. Y-scrambling [START_REF] Lindgren | Model validation by permutation tests: applications to variable selection[END_REF][START_REF] Rücker | y-Randomization and Its Variants in QSPR/QSAR[END_REF] was processed to prevent from chance correlation. Moreover the predictive power was evaluated on an ex ternal validation set on a s eries of coefficients: R²ext (characterizing the correlation between predicted and experimental values in the validation set) and coefficients Q²F1 [START_REF] Tropsha | The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models[END_REF]), Q²F2 (Schüürman et al., 2008), Q²F3 [START_REF] Consonni | Comments on the Definition of the Q2 Parameter for QSAR Validation[END_REF] and CCC [START_REF] Lin | A Concordance Correlation Coefficient to Evaluate Reproducibility[END_REF][START_REF] Lin | Assay Validation Using the Concordance Correlation Coefficient[END_REF].

(

( ) ∑ ∑ = = - - - = ext ext n i TR i n i i i F ² y y ² y y Q 1 1 2 1 1 )
With i y ˆ the predicted value of the property, i y the experimental value of the property, TR y the mean experimental value in the training set and next the number of molecules in the validation set.

(

( ) ∑ ∑ = = - - - = ext ext n i EXT i n i i i F y y y y Q 1 1 2 2 ² ² 1 (3) )
With EXT y the mean experimental value in the validation set.

(

( ) TR n i TR i EXT n i i i F n y y n y y Q TR EXT       -       - - = ∑ ∑ = = 1 1 2 3 ² ² 1 (4) )
With TR n the number of molecules in the training set.

)² y x ( )² y - (y )² x - (x ) y - )(y x - (x 2 n 1 i i n 1 i i n 1 i i i - + + = ∑ ∑ ∑ = = = n CCC (5)
In this last formula, x and y represent the experimental and predicted values, respectively.

The applicability domain [START_REF] Netzeva | Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52[END_REF][START_REF] Jaworska | QSAR applicability domain estimation by projection of the training set descriptor space: a review[END_REF] required by the third OECD principle was determined based on the descriptors included in the model. The Euclidean distance method available in Ambit discovery software [START_REF] Jeliazkova | Ambit Discovery[END_REF] was used with a 95% threshold, i.e. the domain was calculated to contain 95% of the molecules of the training set. Then, the performances inside the applicability domain were also calculated based on the sole molecules of the validation set that belong to this domain using coefficients previously presented.

Amines

A first QSPR model was derived to predict the flash point of a series of 68 amines, including both aromatic and aliphatic compounds and also ethanolamines which were extracted from the CarAtex database (2012) of INRS. For each of these compounds, 165 des criptors were calculated in Codessa software (2002) based on geometric structures optimized at the AM1 level using Gaussian09 (2009).

To allow an external validation of models, the data set was then divided into a training set, containing two thirds of the molecules of the data set and a validation set constituted by the remaining molecules. To ensure that the validation set well represented the domain of property of the model (defined by the property values in the training set), molecules were classified by increasing order of flash points and one molecule out of four was selected (2nd, 6th, etc.) to build up the validation set. Moreover, the chemical structures of the molecules in both sets were analyzed and no bias was evidenced. Then, the BMLR approach was applied to the 51 m olecules of the training set and the three-parameter equation 6 was found as the best compromise between the correlation performance and the number of descriptors.

FP(°C)= 337. where nH is the relative number of H atoms, HDCA2 is the area-weighted surface charge of hydrogendonor atoms and PPSA1 is the partial positive surface area. It is worth noting that HDCA2 is related to the hydrogen bonding ability of the molecule which can be related also to its volatility as already highlighted by [START_REF] Katritzky | QSPR analysis of flash points[END_REF]. This model is well correlated with a coefficient of determination R²=0.956. It is also robust as evaluated by LOO and LM O cross validations (Q²LOO= 0.947, Q²10CV= 0.946 and Q²5CV= 0.949). The Y-scrambling approach was used to check that it did not issue from chance correlation. Indeed, low R² were exhibited for the models obtained from randomized data. In a future step of the project, other descriptors, methods and level of theory will be investigated to achieve potentially more accurate models.

Organic peroxides

Another QSPR model was developed for 23 organic peroxides including diverse structures like hydroperoxides and per oxyesters, based on experimental results obtained in the framework of the PREDIMOL project. In this section, the DFT (density functional theory) at PBE0/6-31+G(d,p) level was used to optimize the structures of organic peroxides with the Gaussian09 (2009) software from which quantum chemical descriptors were calculated. From more than 350 descriptors and using the BMLR method, a 5-parameter model was found:

FP(°C)= -6127.1 + 3.46 nH + 2.51 WNSA2 + 5300.2 FPSA3 -3.55 PNSA3 + 1534.7 Vavg,C

Where nH is the number of H atoms, WNSA2 is the surface-weighted negative charged surface area, FPSA3 is the fractional atomic charge-weighted positive surface area, PNSA3 is atomic charge-weighted negatively charged surface area and Vavg,C is the average valency of a C atom.

The model was characterized by a good c orrelation (R²= 0.921) as shown in Figure 3 and robustness (Q²LOO= 0.866, Q²10CV= 0.868 and Q²5CV=0.888). The Y-scrambling method also validated the model because of low values of R² for the models obtained after randomisation. However, due t o the small number of data available for organic peroxides, no external validation was performed in this study. A data acquisition phase is in progress to overcome this point. It must be also pointed out that the concept of flash point as to reflect the flammability hazard of organic peroxides is limited due to potential decomposition issues, in comparison with conventional flammable liquids.

Conclusion and perspectives

Browsing existing models and experimental data performed in the PREDIMOL project highlighted the need to consolidate robust databases, notably for organic peroxides, in order to develop accurate QSPR models for the prediction of the hazardous physico-chemical properties required by REACH. Two QSPR models were developed to predict the flash point of amines and organic peroxides respectively. The first one respects all the OECD principles and presents excellent performances in fitting, robustness and predictivity. The second one dedicated to organic peroxides was not validated on an ex ternal validation set, notably due to the low number of available data but it already presents good performance in fitting and robustness. Further investigation will be done in this project to obtain additional data for hazardous properties of physico-chemical nature that will allow the development of new QSPR models. These models are expected to be available to industrials and regulatory instances in order that predictive data could be used for registration. For this reason, accurate QSPR models will be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox).

  The model was applied to the 17 molecules of the validation set as shown in Figure2. It demonstrated high predictive power based on the various coefficients dedicated to the external validation of QSPR models (R²EXT = 0.905, Q²F1 = 0.897, Q²F2 = 0.897, Q²F3 = 0.966, CCC = 0.949).

Figure 2 :

 2 Figure 2: Calculated vs. experimental values of flash point (in °C) for amines Finally, the applicability domain (AD) was computed and two molecules of the validation set were evidenced as out of the determined AD. So, the external validation of the model was computed again taking into account the AD. The external validation coefficients reached higher values (R²EXT = 0.905, Q²F1 = 0.907, Q²F2 = 0.902, Q²F3 = 0.971, CCC = 0.946).In a future step of the project, other descriptors, methods and level of theory will be investigated to achieve potentially more accurate models.

Figure 3 :

 3 Figure 3: Experimental vs. predicted flash point for organic peroxides

Acknowledgments

The PREDIMOL project (ANR-10-CDII-007) is financed by ANR and t he French ministry in charge of environment.


I Zdanevitch

O Bour

S Lejal

M Briand°

MEASUREMENT OF PERSISTENT ORGANIC POLLUTANTS IN LANDFILL LEACHATES

leachates from two landfills which receive the stabilized waste (site A) and sorting refuses (site B) from mechanical biological treatment plants (MBT) have been sampled. Classical parameters, as well as organic persistent pollutants (POPs) have been measured and compared between the two sites, and to data from litterature (classical parameters and POPs) and to limit values for drinking waters, when applicable, for POPs. Though the study was limited to 3 samples, the concentrations in organic micropollutants varied, depending on the compounds and on the treatment process.

INTRODUCTION

Leachates from two French landfills which receive biologically stabilized municipal solid waste -MSWhave been previously studied (Zdanevitch et al, 2009a). Classical parameters were measured on all samples, and values were compared between leachates of the same site, and between sites. Leachates showed different behaviours depending on the measured compounds: some values were typical of young waste, while others were more representative of old, stabilized waste.

To date, there is some knowledge on the fate of organics contaminants in landfills, but more research is needed, especially on emerging compounds in leachates. Samples of leachates were taken on two landfills previously studied, for biogas emissions (Zdanevitch et al, 2009b) on both sites, and for leachates on site (A): Zdanevitch et al, 2009a. These sites receive the outputs of aerobic treatments. Materials which are landfilled are different between the two sites:  site A has two landfill zones: the old one -K1 -received untreated MSW until 2006; the new zone -K2, organized in cells -receives waste which has been aerobically treated to stabilize the organic matter, since 2006. Leachates are collected on these two zones separately, allowing the sampling on each zone,  site B has a high grade separation of the biodegradable fraction of MSW; this fraction is treated by a composting process. The compost is used by local farmers. The landfill receives only the refuses of sorting, which contains less biodegradable material than non-treated waste.

Leachate was sampled in the existing well at the bottom of cell 2 (C2).

EXPERIMENTAL

One sample of leachate was taken from each zone: site A, zone K1 and K2, and site B, cell C2. Each sample accounted for approximately 11 liters. Smaples were kept cold (4 °C) until return to the laboratory, between 24 and 48 hours. Depending on the compounds to be analyzed, sampling bottles were either made of plastic or brown glass. The analytical Laboratory in INERIS is used to measuring POPs at trace levels in surface waters and ambient air. Yet, measuring these compounds at low concentrations in biogas or leachates (heavy polluted matrixes) is not very easy. Samples were carefully taken and quickly returned to the Laboratory. In addition, "classical" parameters (suspended matter, metals, salts...) were measured outside INERIS, in the Laboratory which already measured the leachates for the previous study. Methods used by INERIS for measuring the POPs are summarized below.

Volatile fatty acids, aldehydes, ketones were analysed by dilution of an aliquot of leachate in a DNPH (2,4-dinitrophenylhydrazine) solution, followed by direct analysis by UV/HPLC. Limit of quantification varied from 0.2 mg/L (formaldehyde) to 5.0 mg/L (volatile fatty acids).

BTX (benzene-toluene-xylenes) and HVOC (halogenated compounds): analytical methods for these compounds are standardized (NF ISO 11423-1, NF EN ISO 10301). 13 ml aliquots of leachate were taken and analyzed by:  Headspace gas chromatography / flame ionization detector for BTX,  Headspace gas chromatography / electron capture detector for halogenated compounds.

Quantification limits varied from 0.01 to 0.1 g/L for most halogenated compounds, to 5 g/L for BTX and 8 g/L for dichloromethane.

Polycyclic aromatic compounds (PAH) were extracted with an INERIS method based on ISO 17993. Analysis was performed by HPLC with fulorimetric detection. Quantification limits were uniform: 10 ng/L.

Polychlorobiphenyl compounds (PCBs) were extracted by liquid/liquid extraction with hexane, dried on sodium sulfate, concentrated, purified on Cu and florisil cartridges, and analyzed by double GC / electron capture detector. Qunatification limits were uniform at 2 ng/L. Polybrominated diphenyl ethers (PBDEs) and phtalates were extracted by liquid/liquid extraction, either with dichloromethane or a mixture of nC6/acetone (for solid phase of PBDEs), dried on sodium sulfate, concentrated and analyzed by GC/MS. Quantification limits were: 1 ng/kg for all PBDEs except BDE209 (10 ng/kg), 0.1 ng/g (liquid phase) and 30 ng/g (solid phase) for phtalates.

RESULTS AND DISCUSSION

Inorganic and global parameters

50 parameters were measured on the 3 leachates. Detailed results are not reported here but highlights are given in Table 1.

First, on site B, pH was rather acidic (value of 6.5), although leachates of site A (from both old untreated waste and stabilized waste) had a rather basic pH (7.6 to 8.6), which were more consistent with literature (Kjeldsen et al, Robinson et al).

The leachate from cell 2 of site B (receiving little organic material), though having a rather high concentration of suspended matter (970 mg/L), had much lower concentrations than leachates of site A for organic compounds, which is shown by lower values of COD, BOD5, adsorbable organic halides (AOX)... Waste in cell 2 of site B could be considered as stabilized, as BOD5 < 100 mg/L, and COD < 1000 mg/L, beside the ratio BOD5/COD falled below 0.1 (Ravelojaona L., 2005). It was not the case for site A, though the ratio BOD5/COD was below 0.1 for both zones.

Leachate of site B had a higher content in sulphates than both samples of site A, but lower concentrations for the other salts. Therefore, the total ion contents (ion balance) of leachate B were lower than on site A. Ion balances and conductivities were similar on the two zones of site A, while conductivity was lower on leachate from site B, and the ion balance was reversed (more cations than anions).

Content of leachates in heavy metals was usually rather low for all samples, except for aluminium and iron. Manganese concentration of leachate from site B had fallen between concentrations in leachates from K1 and K2, whereas chromium, tin, nickel and zinc

-NO 3 -HCO 3 -SiO 2 ) (2) (NH 4 -Na-K-Ca-Mg)
concentrations were lower in the sample from site B.

Organic pollutants

Organic compounds and persistent organic pollutants which were measured are: aromatics (benzene-toluene-xylenes) and chlorinated compounds, volatile fatty acids, aldehydes, ketones, 15 polycyclic aromatic compounds, 7 PCB, 8 PBDE, phtalates. Some of the results can be compared to limit values for drinking waters, when they exist, or to values published in other studies on landfill leachates, in particular [START_REF] Baun | Xenobiotic organic compounds in leachatesfrom ten Danish MSW landfillschemical analysis and toxicity tests[END_REF]: 10 Danish landfills receiving other waste as well as MSW, Oman & Junestedt (2008): 12 Swedish landfills receiving principally MSW.

BTX and HVOC

Table 2 presents the BTX and HVOC concentrations measured on the 3 leachates.

Benzene was not detected on either sample, which means that benzene concentration, if present, should be smaller than the detection limit (1.5 g/L). This value is smaller than the concentrations reported by Baun et al: from 2.3 to 38.9 g/L. Toluene was measured only on leachate from the old zone of site A (K1), while xylenes were measured which similar concentrations on both leachates of site A.

Most halogenated compounds were not found, or at low concentration (around the quantification limits) on the 3 samples. Only dichloromethane was measured in the leachate of the old zone K1 of site A. It should be noticed than, although trichlorethene and tetrachlorethene undergo biodegradation, they were found in lower concentrations in young leachates (K2 and C2) than in the old one: this demonstrates what we have noticed during other studies (on trace compounds in biogases for instance), that MSW which is presently collected contains less chlorinated compounds, and is therefore less toxic that it used to be. Another hypothesis is that the aerobic stabilization treatment lowers the organic content of the treated waste, and the level of contaminants which are associated to the organic matter.

Aldehydes, ketones and volatile fatty acids

Errore. L'origine riferimento non è stata trovata. presents the measurements for aldehydes, ketones and volatile fatty acids for the 3 leachates. All the target compounds were detected on the 3 samples, but no one was quantified on either of the samples. The quantification limit of the method should be improved in order to quantify these compounds, but this may be difficult to achieve, due to the high level of all pollutants in leachates. These compounds were not studied by Baun et al, nor by Oman & Junestedt.

Polycyclic aromatic compounds (PAH)

Table 4 presents the measurements for PAHs on the 3 leachates. On site A, all the PAH which were measured were in lower concentrations on the young leachate (K2) than on the old one (K1): see in Figure 1. As for the BTX and HVOC, this could indicate that: 1) the toxic content of MSW is lower nowadays than it was some years ago, and 2) the aerobic stabilization lowers the organic content of the treated waste, and consequently, the level of contaminants which are bound to the organic matter. Also, the more volatile pollutants (such as naphthalene) may be lost by evaporation during the stabilization process.

Concentrations measured here are similar to those published by Oman & Junestedt, but much lower than those published by Baun et al (naphtalene ranges from 600 to 91,200 ng/L).

Figure 1. PAH contents for leachates of site A On leachate of site B, except naphthalene and phenantrene, no PAH were detected.

There is a limit value for drinking waters in France (Arrêté du 11 janvier 2007): the sum of 4 PAHs (Benzo(b)fluoranthene; Benzo(k)fluoranthene; Benzo(g,h,i)perylene; Indeno(1,2,3cd)pyrene) should be lower than 0.1g/L (100 ng/L). All leachates fulfill this limit value (Benzo(g,h,i)perylene and Indeno(1,2,3-cd)pyrene are not detected on either of the samples).

Polychlorobiphenyl compounds (PCBs)

Table 5 presents the measurements for 7 PCBs on the 3 leachates. These compounds, among the 209 congeners, are assumed to represent PCB pollution as they are the most abundant, persistant and because of their toxicological properties.

All these compounds were detected and quantified in leachates of site A. Except PCB28, which showed a similar concentration between the two leachates, all the heavier PCBs are more abundant in the old leachate from K1 tant in the young one from K2: see Figure 2. PCBs are persistant. A lower concentration in the leachate from the treated waste could be due to a lower content of the waste, or to a lower leaching of these compounds, the waste being partially stabilized.

On leachate from cell 2 of site B, only 2 of the 7 PCBs were measured, and their concentrations were of the same order of magnitude as the quantification limit.

Polybrominated diphenyl ethers (PBDEs)

PBDEs are flame retardant. They are not chemically bound to plastics, foam, fabrics, or other products in which they are used, making them more likely to leach out of these products.

Analysis were done on both the liquid and solid phases, and measured on raw sample (before filtration). Table 6 presents the measurements for 8 PBDEs on the 3 leachates. Concentrations are given by weight. For these compounds, only 5 of the 8 target compounds were detected and quantified on leachates from site A : concentrations are higher in leachates from the young zone (K2) than on the old waste (K1). One hypothesis could be that newer waste (K2 receives waste since 2006) contain more EEEW than older waste, and as PBDEs are subject to leaching, they are found in the leachates. PBDEs are reported by Oman & Jundestedt betwwen 0.002 to 7.1 g/L (maximum for tetrabromodiphenyl ethers), depending on the compounds. None of these compounds was detected on the leachate of site B.

Phtalates

Phtalates are widely present in the environment. They are used in plastics and other materials to make them flexible. Table 7 presents the measurements for phtalates on the 3 leachates. As they are mostly present on the solid phase, concentrations are also reported by weight.

3 phtalates are highly abundant in the solid phase of leachate from K2 (site A), compared to the other leachates. Origin of these compounds should be explored. One phtalate (the diethylphtalate) is used as solvent and vehicle for fragrance and cosmetic ingredients.Another one (the diethylhexylphtalate ) is is the most common of the class of phtalate plasticizers.

All the other compounds, and the other leachates, show no phtalates or small concentrations, similar to those reported by Oman & Jundestedt. Baun et al report higher concentrations, up to 340 g/L for monoethylphtalate in one landfill leachate. Only 4 out of 8 phtalates were measured in leachate from site B, and concentrations were of the same order as the quantification limits: 0.1 ng/g (liquid phase) and 30 ng/g (solid phase).

CONCLUSIONS

This preliminary study was limited to 3 samples of leachates, one issued from untreated MSW, the two others coming from landfills which receive outputs of MBT. In spite of this small number of samples, different behaviours are noticeable, on "classical" parameters, and also on volatile and persistent organic pollutants.

Site A is interesting as comparison can be made between leachates from untreated and treated MSW. Leachate from the untreated waste has globally a smaller content in salts (except ammonia) and metals than the young leachate from treated waste. However, the ion balances are similar for the two leachates. Leachate from untreated waste has higher concentrations of many organic compounds, such as toluene, dichloromethane, PAHs (especially the lighter ones), and PCBs, except PCB28, which is similar on the two leachates. On the other hand, the leachate from treated waste shows higher concentrations for 4 out 5 of the measured PBDEs, and for 3 phtalates. These high concentrations prove that these compounds are probably more abundant in the actual waste than they were some years ago, and that they are not reduced by the aerobic treatment. In addition, some of those compounds are not bound to the materials in which they are added, and they are subject to leaching.

Site B, which receives only the reject of organic sorting (organic matter being valorized under a compost form), emits a rather small volume of leachate. This leachate, considering the BOD and COD values, seems to be issued from "stabilized" waste. This leachate has a very low content of most organic compounds, and intermediate values for "classical" parameters.

Aldehydes, ketones and volatile fatty acids were never detected on neither of the 3 samples. Due to the low content in certain hazardous pollutants, the treatment of these leachates could be rather light. Nevertheless, the content in "classical" pollutants (especially salts) remains high, which implies a high grade treatment of these leachates.

Table 1 .

 1 classical parameters measured on the 3 leachates

	Parameter	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	pH		7.85	8.1	6.55
	Conductivity @ 20°C	mS/cm	15.5	15.95	5.42
	Suspended matter	mg/L	380	1,800	970
	COD	mg O/L	3,150	14,400	736
	BOD5	mg O/L	105	943	23
	BOD5/COD		0.033	0.065	0.031
	Sulfates	mg SO4/L	11	700	1,430
	Chloride	mg/L	1,660	2,490	555
	Ammonia	mg NH4/L	2,190	1,930	420
	Sum anions (1)	meq/L	157	153.25	47.55
	Sum cations (2)	meq/L	89	119.5	64.28
	Phenol index AOX Al As Cd Cr Cu Sn Fe Hg Mn Ni Pb Zn	g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L	97 1,690 1,200 170 <1 540 <20 510 43,000 <0.5 630 160 <10 210	170 1,490 140,000 430 9.7 2,800 350 130 280,000 <0.5 4,000 370 260 5,100	<25 120 7,600 5 <1 91 <0.02 <100 21,000 <0.5 2,300 84 <10 160
	(1) (Cl-SO 4

Table 2 .

 2 BTX and HVOC concentrations measured on the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	Benzene Toluene m,p-Xylenes o-Xylene Dichloromethane Chloroforme Carbon tetrachloride Trichlorethene Dichlorobromomethane Tetrachlorethene Chlorodibromomethane Bromoforme Hexachlorobenzene	g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L g/L	ND 5 6 4 8.2 ND ND 0.06 ND 0.04 0.09 <0.17 ND	ND ND 5 5 ND ND ND <0.04 ND 0.03 ND ND ND	ND ND ND ND ND ND ND <0.043 ND 0.02 <0.05 ND ND
	ND: not detected

Table 3 .

 3 Aldehydes, ketones and volatile fatty acids contents of the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	Formaldehyde	mg/L	<0.2	<0.2	<0.2
	Acetaldehyde	mg/L	<0.4	<0.4	<0.4
	Acroleine	mg/L	<0.7	<0.7	<0.7
	Propionaldehyde	mg/L	<0.4	<0.4	<0.4
	Butanal	mg/L	<1.0	<1.0	<1.0
	Pentanal	mg/L	<1.4	<1.4	<1.4
	Hexanal	mg/L	<1.6	<1.6	<1.6
	Heptanal	mg/L	<0.5	<0.5	<0.5
	Octanal	mg/L	<0.9	<0.9	<0.9
	Benzaldehyde	mg/L	<0.7	<0.7	<0.7
	Butanone	mg/L	<0.8	<0.8	<0.8
	Pentanone	mg/L	<2.5	<2.5	<2.5
	Hexanone	mg/L	<1.6	<1.6	<1.6
	Heptanone	mg/L	<1.8	<1.8	<1.8
	Octanone	mg/L	<2.0	<2.0	<2.0
	Acetic acid	mg/L	<5.0	<5.0	<5.0
	Formic acid	mg/L	<5.0	<5.0	<5.0

Table 4 .

 4 Polycyclic aromatic compounds measured on the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	Naphtalene	ng/L	1,986	215	270
	Acenaphthene	ng/L	546	318	ND
	Fluorene	ng/L	223	58	ND
	Phenanthrene	ng/L	816	314	19
	Antracene	ng/L	53	23	ND
	Fluoranthene	ng/L	323	186	ND
	Pyrene	ng/L	197	121	ND
	Benzo(a)antracene	ng/L	50	30	ND
	Chrysene	ng/L	83	43	ND
	Benzo(b)fluoranthene (a)	ng/L	32	20	ND
	Benzo(k)fluoranthene (a)	ng/L	13	<10	ND
	Benzo(a)pyrene	ng/L	28	15	ND
	Dibenzo(a,h)antracene	ng/L	ND	ND	ND
	Benzo(g,h,i)perylene (a)	ng/L	ND	ND	ND
	Indeno(1,2,3-cd)pyrene (a)	ng/L	ND	ND	ND

(a): sum of these 4 PAH must be < 100 ng/L for drinking waters

(French Arrêté du 11 janvier 2007)

Table 5 .

 5 Polychlorobiphenyl compounds measured on the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	PCB28	ng/L	45.9	47.7	<2.0
	PCB52	ng/L	55.8	35.3	2.0
	PCB101	ng/L	65	23.5	3.3
	PCB118	ng/L	68.1	22.5	<2.0
	PCB153	ng/L	39.9	13.6	<2.0
	PCB138	ng/L	48.4	17.9	<2.0
	PCB180	ng/L	16.5	6.8	<2.0

Figure 2. PCB contents for leachates of site A

Table 6 .

 6 Polybrominated diphenyl ethers contents of the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new) Site B, C2
	BDE28	ng/Kg	ND	ND	ND
	BDE47	ng/Kg	25	38	ND
	BDE99	ng/Kg	5.3	10	ND
	BDE100	ng/Kg	16	40	ND
	BDE153	ng/Kg	3	8	ND
	BDE154	ng/Kg	9	4	ND
	BDE183	ng/Kg	ND	ND	ND
	BDE204	ng/Kg	ND	ND	ND

Table 7 .

 7 Phtalates contents of the 3 leachates

	Coumpound	Units	Site A, K1 (old) Site A, K2 (new)** Site B, C2
	Dimethyl-phtalate	ng/g	ND	400 (a)	3
	Diethylphtalate	ng/g	16	7,012 (a)	46
	Dipropylphtalate	ng/g	ND	3	ND
	Di-isobutyl-phtalate	ng/g	5	ND	ND
	Dibutylphtalate	ng/g	4,5	5	3
	Butylbenzylphtalate	ng/g	ND	ND	ND
	Dicyclohexylphtalate	ng/g	ND	ND	ND
	Diethylhexylphtalate	ng/g	72	2,125 (a)	4
	Di-n-octylphtalate	ng/g	ND	ND	ND
	** : analysis on both liqid and solid phases (high content in suspended matter)
	(a): high phtalate content in the solid phase

Ravelojaona L. (2005): "Définition de l'état éco-compatible des installations de stockage de déchets ménagers et assimilés". Synthèse finale, conv. ADEME-SUEZ n° 0206003, 33 pp.


O Bour

I Zdanevitch

Z Pokryszka

A Lalet

A Akerman

TEMPORAL VARIATION OF LFG EMISSION FROM DIFFERENT TYPES OF LANDFILL COVER

The main goal was to identify the amplitude and importance of the methane and carbon dioxide emissions variations observed on a landfill site and to give hints for a more robust assessment of the global greenhouse gas emission of a landfill site in operation. Nine monitoring areas were selected on 3 types of cover (daily, intermediate and final cover) of a pilot site and different time scales were considered in order to allow the LFG emission monitoring on an hour, day, week and seasonal basis. The whole methane flux assessment conducted with the LFG surface fluxes variations monitoring for each area allows to precise the diffusive and convective fraction of the methane fluxes for each type of cover. These results explain the difference between the high oxidation rates observed for the diffusive fluxes and the medium oxidation rate observed for the whole methane emissions.

INTRODUCTION

Control and reduction of fugitive emissions of methane is one of the challenges of the landfilling of Municipal Solid Waste (MSW). This reduction of the landfill gas (LFG) emissions requires the ability to measure low methane emissions and to use the results in order to evaluate these diffuse emissions on an annual basis. Moreover, the experience of low emission measurements made at INERIS demonstrates that the variations of methane fugitive emissions are even more pronounced than these emissions are low. The uncertainty of the measurements thus tends to increase with the reduction of emissions, and this difficulty is observed for all measurement methods. Factors such as the duration of the lag between the maintenance of the LFG network and the emissions measurement periods, as well as changes in climatic parameters also strongly influence methane emissions. A research project "OSSIMED" funded by the French Environment and Energy Management Agency (ADEME) began in the year 2011. The main goal was to identify the importance and the amplitude of the LFG emissions variations from a landfill site in operation and to give hints for a more robust assessment of the global greenhouse gas emission from MSW landfill sites. The goals were also to observe the LFG fluxes variations for the principal types of cover used during landfill operations.

The study was conducted on a pilot site which uses a temporary cover for the exploitation area. Nine monitoring areas were selected on 3 types of cover (daily, intermediate and final cover) and the measurements were made with different time frequencies in order to allow the monitoring of the emissions on an hour, day, week and seasonal basis.

METHODOLOGY

2.1 Methodes used for localisation and determination of the gas fluxes.

In this study, we use a combination of two methods for localisation and determination of the gas fluxes, in order to have a better coverage of the surface. The methane concentration measurements were made with FID (Flame Ionisation Detector) and Inspectra® methane analysor (laser spectroscopy technology using a laser diode). The scanning of the methane emissions has two goals. The first goal was to give a first view of the distribution of the methane emissions and also to give the hints to sort the convective or diffusive emissions. The presence of the cracks was generally associated with convective fluxes or rather thin clay covers. In the case of the pilot site, the convective methane emissions were observed only near the landfill gas (LFG) drainage wells. These locations could be explained by a sufficient humidity of the cover.

INERIS static chamber method

The INERIS static closed chamber was used in order to measure precisely gas flux and to give reference values for the modified ISM method. The INERIS static chamber method consists in setting a box of 0,25 m 2 ground surface over a typical representative surface of the site with a recirculation of the gas. This method gives local flux values that need to be extrapoled to estimate the whole emissions. In the closed chamber, the increase of methane and carbon dioxide was monitored by two analysors (Inspectra® and Ecoprobe®). The slopes of the curves give the flow rate of methane and carbon dioxide coming in the box. This method gives on the laboratory a precision and a repeativity better than 5%. In the field, the quality of the sealing of the chamber on the surface and the relative low velocity of the winds are the major parameters for a good reproductibility of the measurements. The procedures involved in this method are protected by a French patent (No. 96-05996,filed on May 14th 1996) and an European invention patent (No. EP0807822B1) under the heading "Measurement of gas flows through surfaces". In practice, this method has been used for over 15 years for landfills, polluted soils and closed mines to measure the flux of gaseous hydrocarbons [START_REF] Pokryszka | Evaluation of gas emission fromclosed mines surface to atmosphere[END_REF] and CO2 emissions [START_REF] Pokryszka | Development of methods for gaseous phase geochemical monitoring on the surface and in the intermediate overburden strata of geological CO2 storage sites[END_REF]. A specific design of the INERIS accumulation chamber and of the ventilation was tested in order to allow high frequency flux measurements (more than 2/hour). This design was used to monitor fluxes from an hour to a week time scale, in order to observe the influence of different physicals parameters. Due to the sealing of the chamber on the ground surface, the measurements were not as fast as the use of a simple chamber. Also, this type of measurement was only used for the locations with methane fluxes.

Modified ISM method (Instantaneous Flux Measurement)

The methane emissions measurements used a modified instantaneous surface measurements (ISM) methodology in order to evaluate the whole gas fluxes of each cover type. A gas probe was used with a simple closed chamber in order to give a fast measurement of the instantaneous flux (20 s) in order to give an Instantaneous Flux Measurement (IFM). The correlation between the IFM fluxes and sealed static chamber with recirculation fluxes was significantly better than the correlation between ISM concentrations and the sealed static chamber with recirculation fluxes. Contrary to the reference method (static chamber sealed with recirculation), the ISM and IFM results need a specific calibration and are more sensible to the variations of the wind velocities. The correlation between the IFM and the INERIS chamber reached a value of 0,95 (r 2 ) for a mixed diffuse/hot spot emissions with good surface flatness (for final cover). In the case of the pilot site and monitoring of the 3 types of cover (temporary, intermediate, final), the coefficient of correlation was in the range 0.8 -0.87. The density of the measurements was adapted to the type of emission: the base density was 100 IFM points/ha with a refinement of the measurement mesh near the convective fluxes. In the specific case of the LFG drainage well, the distance between the measurement points could be as low as 50 cm, with a number of IFM points for each LFG drainage well which could reachs 30 IFM points. As we can see later, the convective fluxes associated with the LFG drainage well could be more important than the diffusive fluxes: the duration of the measurements was finally a function of the type of cover and the number of LFG drainage wells. The same two analysors (Ecoprobe® and inspectra®) were used with the IFM method and the static chamber sealed with recirculation for a selection of 10 to 20 points for each cover and survey.

Distinction between convective and diffusive fluxes

During the last years, different authors have presented the relation between the methane load of the cover and the rate of methane oxidation. Methanotrophic bacteria are ubiquitous: we can use the assumption that all the diffusive fluxes exhibit a significant methane oxidation if the methane load generates a methane surface flux lower than 150 g/m 2 /day. For the diffusive fluxes measurements, the CH4/CO2 ratio was significantly lower than 0.6 Finally two criterias help to distinguish the methane diffusive fluxes:  ratio CH4/CO2 << 0.6;  methane fluxes < 150 g/m 2 /day.

RESULTS

Assessment of the convective/diffusive emissions

Temporary cover

This area received the "fresh" waste and have been covered each end of week by a layer of sand (30 cm). Due to the landfill exploitation, the duration of this type of cover was limited (half a week). The spatial and statistical distribution of the LFG emissions were without large heterogeneities: the maximum of the methane flux (61 g/m 2 /day) do not reachs a value of one order of magnitude more than the methane flux arithmetic mean (8 g/m 2 /day). The whole methane flux reach a value of 1 m 3 /h for the 2 200 m 2 of this cover surface.

Intermediate cover

Two cells were investigated. The cover texture of these two cells was similar to a sandy clay soil. The thickness of the cover was approximately 50 cm. The duration of this cover is several months to one year. Due to the presence of clay, the gas fluxes were observed only on 10% of the measurement points. We observed "hot spots" points with a methane maximum flux of 180 g/m 2 /day and an average methane flux of 4 g/m 2 /day. The diffusive fluxes were highly reduced, but the convective fluxes represent an important part of the fluxes. The mixing of the diffuse and convective fluxes was observed on each cell. The whole methane flux reach 1 m 3 /hour (for a surface of 5 300 m 2 ).

Final cover

Three parts of the pilot site have a final cover. In one part, the thickness of the clay layer of the cover was very high (7 m). In this part no diffuse emissions occurred. We could only observe low methane emissions on one side of this area and near a LFG well. Only one part of this particular cell was investigated. If we extrapolated the convective flux observed for the whole area (12 ha), we estimate a convective flux of approximately 6.3 m 3 methane/hour for this area. The others two parts have a cover with a clay layer which thickness vary between 50 cm and one meter. The average diffusive fluxes were very low (approximately 1 to 1.4 g CH4/m 2 /day). The average convective fluxes were in the range 1.7 to 4.7 g CH4/m 2 /day. The whole methane fluxes of these two parts reach a value of 15 m 3 /hour.

Emissions of methane of the pilot site

The pilote site has also an area with a geomembrane. In this particular area the fluxes were convective near the gas collection system but low (< 0.5 m 3 /hour). The whole pilot site methane emissions reach 24 m 3 /hour, for a surface of 28 ha. The fraction of the convective fluxes of the pilot site was high and reached approximately 13 m 3 /hour. We could observe that one half of the whole methane emissions was emitted by convective fluxes, with very low methane oxidation percentage. On the contrary, with assumptions concerning the quality of the LFG, the oxidation rate of the methane diffusive emissions was higher than the value of 10% to 25% generally used for the assessment of the methane emission on the annual basis. The oxidation rate could not be assessed precisely for each cover without assumptions concerning the LFG composition for each area of the pilot site.

Variation of the LFG emissions

The LFG emissions mean values and variations observed on individual monitoring points were compared with the assessment of the methane emission for each type of cover (IFM methane measurements). The LFG flux variations could be observed at different time scales. The temporal scale of a few days or a one-day basis represents the time scale of a flux survey. The flux variations observed during this time scale could modify the primary data for the fluxes assessment.

Temporary cover

The variations of the fluxes were observed for three points (H1, H2 and H3) located on new emissions points selected during the methane emission scanning of each new temporary cover. For a time scale of a few hours, we observe in the Figure 1 more variations for the higher fluxes. The CH4/CO2 ratio was low and the fluxes could be considered as diffusive fluxes for this criteria. Due to the fresh waste deposit, the stage of the stabilized methanogenesis could not be reached, also the criteria of CH4/CO2 ratio was more difficult to use for this particular case. Nevertheless, methane emission fluxes were low for the entire period of monitoring. The average methane flux coming from the IFM scan (8 g/m 2 /day) was in the range of the monitoring points methane fluxes (2.3 -22 g/m 2 /day).

If we observe the emission variations for a period of severals days, the flux variations are generally in the range of one order of magnitude from the average value.

Intermediate cover

The selected three points (I1, I2, I3) could be monitored for a longer period for this type of cover. If we observe the variation of the methane and carbon dioxide fluxes (Figure 3), we can see that the punctual methane fluxes variations reach more than one order of magnitude for a time scale of a few days. The variations of the carbon dioxide flux were lower. Due to the mixing of diffuse and convective fluxes, the average fluxes of the selected points were much higher than the IFM average fluxes. The CH4/CO2 ratio was also generally too high for diffusive fluxes.

If we consider the variations of the particular point I3, we could observe a correlation between the trend of the variations and the barometric pressure.

Final cover

In this particular case, the three selected points (F1, F2, F3) were located on two area with a clay layers of approximately 50 to 100 cm thickness. These monitoring points exhibit very high flux amplitude, which could reachs more than 4 orders of magnitude for one point (F1). We can observe a mixing of diffuse flux for the F2 and F3 points with high convective fluxes on point F1. Methane was only release on the point F1: The spatial concentration of the methane fluxes ("hot spots", point F1) explains the difficulties to extrapolate the results. Methane diffuse emissions were very low (F2, F3 points), but a few hot spots modify significantly the whole methane emissions.

CONCLUSIONS

One goal of this study was to distinguish the real diffusive emission from the semi convective emission coming from the cover cracks and from the surroundings of the LFG drainage wells. If methane diffuse emissions could be evaluated by a 1D diffusion model [START_REF] Bogner | CALMIM (CAlifornia Landfill Methane Inventory Model) A New Field-Validated Greenhouse Gas Inventory Methodology for Landfill Methane Emissions[END_REF] and could also be evaluated with surface fluxes measurements and geostatistical tools, the fraction of local convective emission is more difficult to assess. On the pilot site the methane convective fraction is higher than observed in previous studies, and reachs more than 50% of the whole site methane emissions. The presence of low permeability engineered covers is the main reason of the predominance of this type of emissions. Also one of the challenges of this ongoing "OSSIMED" project is to study this more important convective fraction of the methane emissions by high density IFM measurements.

AKNOWLEDGEMENTS

To Fabrice Richez, Francis Guillot, Gaetan Bentivegna who have conducted the flux measurements in two teams, in order to allow the monitoring of 4 flux locations in the same time. We thank ADEME and the French Ministry of Environment for the funding of the project (OSSIMED) and SITA France for the use of their landfill sites.

Figure 1 Figure 2 .

 12 Figure 1. variations of the methane and carbon dioxide fluxes on a few hours time scale.

Figure 3 Figure 4 .

 34 Figure 3. variations of the methane and carbon dioxide fluxes on a few days time scale.

Figure 5

 5 Figure 5. variations of the methane and carbon dioxide fluxes on a few days time scale.

A research project "OSSIMED" funded by the French Environment and Energy Management Agency (ADEME) started in 2011.