Keywords:

The role of microcrystalline structure on the temperature-dependent thermo-optical properties of semi-crystalline thermoplastics and non-invasive temperature measurements Sinan BOZTEPE 1 , Remi GILBLAS 1 , Olivier de ALMEIDA 1 , Fabrice SCHMIDT 1 , Yannick Le MAOULT 1* 1 Université de Toulouse ; Mines Albi, ICA (Institut Clément Ader) ; Campus Jarlard, F-81013 Albi cedex 09, France * (auteur correspondant : sboztepe@mines-albi.fr)

Résumé -

The relation between the microcrystalline structure of unfilled semi-crystalline polyolefin (PO)-based polymer and its thermo-optical characteristics was studied under room and varying temperatures. The temperature-dependence of its optical properties was observed in NIR range while this effect vanishes after certain wavelengths in MIR range. This is critical in order to determine a methodology for an accurate non-invasive IR thermography on the polymer surface. The preliminary IR thermographic analyses showed that accurate surface temperature measurements may not be simply performed on the PO-based polymers due their optical characteristics.

Nomenclature

Introduction

Infrared (IR) radiation heat transfer is widely used for thermoforming process of thermoplastic polymers and their composites. Thermoforming process involves heating and forming stages where preforms are heated prior to the forming. For thermoforming of unfilled semi-crystalline thermoplastics, the effect of their microcrystalline structure on the optical properties is the key to develop a successful predictive model for the IR-heating stage of thermoforming process. The key reason behind this is that the microcrystalline structure of semi-crystalline polymers introduces an optically heterogeneous medium where the radiation penetrated into such polymer changes its direction due to optical scattering. Previous studies in literature showed that there is a strong coupling between microstructure of semi-crystalline thermoplastics and their thermo-optical properties in near-infrared (NIR) spectral range [START_REF] Hakoume | Effect of Processing Temperature on Radiative Properties of Polypropylene and Heat Transfer in the Pure and Glassfibre Reinforced Polymer[END_REF]- [START_REF] Denis | Dependence on the spectral scattering coefficient on crystallinity into semicrystalline polyester[END_REF] where also this relation may be negligible in middle-infrared (MIR) range [START_REF] Hakoume | Effect of Processing Temperature on Radiative Properties of Polypropylene and Heat Transfer in the Pure and Glassfibre Reinforced Polymer[END_REF], [START_REF] Boztepe | Experimental analysis on the coupled effect between thermo-optical properties and microstructure of semi-crystalline thermoplastics[END_REF]. As semi-crystalline thermoplastics are thermoformed in semi-molten state, their microcrystalline structure may evolve under varying temperature and change the scatterer characteristics. The optical properties of unfilled semi-crystalline polymers was analyzed in [START_REF] Geiger | Optical properties of plastics and their role for the modelling of the laser transmission welding process[END_REF] and its temperature-dependence was demonstrated. Therefore, this temperature-dependent relation may not be neglected both for building an accurate radiation heat transfer model and, for noninvasive IR thermographic temperature measurements for experimental validation of such a model. In order to obtain a reliable surface temperature measurement via IR thermography it is required to know the optical characteristics of a heated material and the operating spectral band of an IR camera. It is desired that the surface of a heated material to be measured has a spectral band where the material is opaque -with a very low (or theoretically zero) optical penetration depth-and, an employed IR camera operates in the corresponding spectral band. As the optical characteristics of unfilled semi-crystalline thermoplastics are related to their microcrystalline structure extra attention is required on their optical characteristics when an IR thermography is applied under varying temperature.

In this study, the optical scattering phenomenon due to microcrystalline structure in semicrystalline PO-based polymer was experimentally analyzed performing bi-directional and directional-hemispherical transmittance measurements at room temperature. The analyses showed that the optical scattering greatly affects the transmittance in NIR range whereas this effect vanishes in MIR range for the wavelengths greater than 6 µm. The temperaturedependence of its transmittance was studied in NIR range using an in-house developed device that is built combining a Fourier Transform Infrared (FT-IR) spectrometer and a heating stage. Theses analyses provided a guide to develop an experimental approach for quantitative IR thermography. The suitable spectrums for IR thermography were chosen based on the notransmittance spectral bands of tested samples. In addition, the optical penetration depth of the polymer was analyzed in the corresponding spectral bands. It was found that the material has relatively high optical penetration depth (D p ) that introduces a challenging surface temperature measurement via IR thermography. For the preliminary IR thermographic analyses, an IR-camera with an optical filter was used and a calibration curve was established performing measurements on a blackbody source. A plate-like PO-based sample was heated using a thermofoil heater and the surface temperature of the sample was registered using a resistance thermometer -also called resistance temperature detector (RTD)-. The registered RTD measurements were compared to the ones obtained via the IR camera. The preliminary analyses showed that IR thermographic measurements may not be simply performed on the PO-based polymers and require a correction method due to their semi-transparency.

Microcrystalline structure and optical scattering relations in unfilled semi-crystalline PO-based polymer

The optical scattering phenomenon in the PO-based polymer was experimentally analyzed performing transmittance measurements over the spectrums of NIR (0.8-2μm) and MIR (2-25μm). Polymer samples with different thicknesses were prepared using identical processing conditions and two different transmittance (T λ ) measurements were done on each sample employing a FT-IR spectrometer (Bruker Vertex 70) and an integrating sphere. The physical meaning of T λ is the ratio of the total amount of transmitted (Φ λ t ) radiative flux over the incident flux (Φ λ i ) [START_REF] Zalewski | Radiometry and photometry[END_REF] at which the Φ λ i is the initial photon flux emitted by the spectrometer.

In the case of analyzing an optically heterogeneous medium, the transmittance may only provide the directly-transmitted light named as directional-directional transmission or "in-line transmission" in literature [START_REF] Apetz | Transparent Alumina: A Light-Scattering Model[END_REF]. Although some amount of small angle forward-scattered radiative flux may also be detected by the detector of the spectrometer it is assumed to be negligibly small. In addition, it may be taken into account that the scattered light inside of an optically heterogeneous medium may come back into the direction of the incident light in case of multiple internal scattering. In other words, the light propagated inside of the medium may be scattered multiple times due to the optical heterogeneity and some part of the scattered light may be back to the direction of the incident light and detected by the spectrometer which is called in radiation theory as "radiative energy gain due to in-scattering" [START_REF] Howell | Thermal Radiation Heat Transfer[END_REF]. However, determination of the multiple scattering phenomena may require an extensive experimental analysis and therefore the effect of multiple internal scattering was neglected that in the current study. Based on the adopted assumptions, the measurements performed using the spectrometer lead to have the bi-directional transmittance of the tested samples. In order to analyze the effect of optical scattering qualitatively, the identical samples were positioned at the entrance port (Port-1) of the integrating sphere so that directional-hemispherical transmittance of the samples were also measured [START_REF] Manara | Integrating sphere reflectance and transmittance intercomparison measurements for evaluating the accuracies of the achieved results[END_REF]. The experimental setups for the bidirectional and directional-hemispherical transmittance measurements are displayed in Figure 1(a) and (b) respectively. For the sake of simplicity, the bi-directional and the directionalhemispherical transmittance are named as T λ-directional and T λ-hemispherical respectively. It should also be noted that some amount of the radiative flux may also be back-scattered inside of the polymer and may not enter the integrating sphere [START_REF] Manara | Integrating sphere reflectance and transmittance intercomparison measurements for evaluating the accuracies of the achieved results[END_REF]. It was therefore assumed that radiative flux detected by the detector in the integrating sphere is the combination of both the forwardscattered (Φ λ s ) and the directly-transmitted light (Φ λ t ). The T λ-directional and T λ-hemispherical measurements were performed using the identical samples that were prepared using an identical processing conditions, but with different thicknesses (0.42, and 1.82 mm). Thanks to the analyses, two different transmittance values were obtained on each sample and the information about the scattering characteristics of the PO-polymer medium was qualitatively analyzed. In Figure 2, the T λ-directional and T λ-hemispherical of the prepared samples are displayed in NIR (a) and MIR (b) ranges. As illustrated in Figure 2 (a), considering the spectral band around 1-1.2 µm the difference between the two measurements are around 60% and 40% for the 0.42 and 1.82 mm thick samples respectively. The difference in transmittance in the spectral range of 1-1.2 µm is crucial for IR-heating radiation models as the maximum emission wavelength of a typical IR lamp is in the corresponding range. In MIR range, the differences between the measurements on the identical samples are around 20% for the wavelengths lower than 3.5 µm where after the difference gradually decreases and becomes negligibly small for the wavelengths higher than 6 µm. A similar trend was reported in [START_REF] Hakoume | Effect of Processing Temperature on Radiative Properties of Polypropylene and Heat Transfer in the Pure and Glassfibre Reinforced Polymer[END_REF] for polypropylene where it was concluded that the effect of microcrystalline morphology becomes negligible considering an increase for the wavelengths in MIR range.

As also seen in Figure 2 (b), there are three narrow spectral bands around 3.4, 7.6 and 13.6 µm where the 0.42 and 1.82 mm thick PO-based samples have no transmittance. Regarding whole the spectrum analyzed in NIR and MIR ranges, it can be stated that the polymer is highly semi-transparent. The spectral band around 3.4 µm is known to be as carbon-hydrogen (C-H) absorption band causing to have zero transmission as reported for PO-based polymers [START_REF] Dewitt | Theory and Practice of Radiation Thermometry[END_REF]. In order to do an IR thermographic measurement on the PO-based polymer under varying temperature this spectral range may not be the best to operate an IR thermal camera. Because the microcrystalline structure of a semi-crystalline thermoplastics may evolve under varying temperature and therefore change the optical characteristics which may eventually affect the accuracy of IR thermographic measurements. Hence, the most accurate IR thermography of the PO-based polymer may not be obtained for the spectrum lower than 6 µm. In addition, directional-hemispherical reflectance (R λ-hemispherical ) measurements were done at room temperature and R λ-hemispherical was found out around 7% considering the whole MIR range where the difference between R λ-hemispherical of the samples was negligibly small.

Temperature-dependent optical properties of unfilled semi crystalline thermoplastics

Transmittance of the PO-based polymer was experimentally analyzed under heating conditions using an in-house developed test set-up. For the tests, a heating stage (Bruker A599) was mounted in the FT-IR spectrometer and the sample was positioned inside of the heating stage (Figure 3). T λ-directional of the polymer was measured at discrete temperature points where the polymer was heated up to a set temperature (T set ) and waited for 2 minutes at each measurement point in order to sustain a constant temperature during the measurements. It was observed that the temperature in the measurement zone (T middle ) of the polymer was different than the T set due to convection cooling on its surface. The variation is critical in order to determine the size of the measurement zone since the temperature variation should be negligible so that a reliable correlation between the actual measured temperature and the transmittance can be obtained. Therefore, before doing the temperature-dependent transmittance measurements, the variation between T set and T middle was analyzed using a PET polymer sample prepared with the identical thickness at which its surface temperature was monitored using an IR camera. PET was chosen as a reference material as it is opaque in the operating range of a typical IR camera thus, a reliable surface temperature can be obtained [START_REF] Monteix | Quantitative infrared thermography applied to blow moulding process: measurement of a heat transfer coefficient[END_REF]. It was considered that the T middle of the PO-based polymer is identical to the PET based on the (simplifying) assumption that conduction heat transfer and convection cooling is the same for both the polymers. Based on the analyses, the beam width of the Φ λ i that is sourced from the spectrometer was set to 1.5 mm as the temperature variation in the measurement zone is 0.3 °C. The details of this correction method can be found in [START_REF] Boztepe | Identification of the temperature dependent relation between thermo-optical properties and morphology of semi-crystalline thermoplastics for thermoforming process[END_REF]. The T λ-directional of the sample were measured for the temperature range between room temperature up to 125 °C. The temperature presented here reflects T middle which is the temperature where the T λ-directional measurements were obtained. The changes in the transmittance between 25 °C and 83 °C was less than 1% and therefore they are not presented in Figure 4 for the sake of simplicity. As displayed in Figure 4, T λ-directional of the polymer changes dramatically after 114 °C and surpasses its T λ-hemispherical value after 120 °C. The changes in the color of the polymer can also be seen clearly where the polymer has a milky color at lower temperatures and becomes totally transparent after 120 °C. Such a change in its color is indeed due to optical scattering -which is also called haze in literature-. A typical IR lamp for thermoforming processes has a temperature around 2400 K so that 96% of its emitted energy is between 0.6 and 6 µm which becomes crucial for IR heating of such type of semi-crystalline polymers. It should also be mentioned that T λ-directional measurements under heating in MIR range showed there is no significant change with temperature where the effect of microcrystalline structure may be negligible, as similarly observed at room temperature.

IR thermography of PO-based polymer 4.1. The semi-transparency and the optical penetration depth of the PO-based polymer

As aforementioned, the microcrystalline structure does not affect the transmittance properties of the PO-based polymer for the wavelengths greater than 6 µm. Hence, the most accurate IR thermographic measurements may be obtained in the spectral bands around 7.6 and 13.6 µm where the transmittance is zero. In addition, a typical IR camera has an operating spectral range between 7-14 µm [START_REF] Maoult | Infrared Radiation Applied to Polymer Processes[END_REF] that covers these two spectral bands and therefore makes them the most suitable bands. However, these no-transmittance bands are relatively narrow in comparison to the operating range of a typical IR camera. Therefore a tailored optical filter is necessary for quantitative IR thermographic measurements. The term -tailored-is used here to define the specific bandwidth where an IR-camera captures photons only in preferably selected wavelengths thanks to an optical filter mounted on the camera.

At very first step of the study, both the spectral ranges around 7.6 and 13.6 µm were analyzed where two different optical filters were used for the IR thermography. The optical filters have spectral ranges around 7.6 µm and 13.6 µm, with bandwidths of ±0.10 µm and ±0.21 µm respectively. The initial analyses showed that the sensitivity of the employed IRcamera around 13.6 µm was too low. Therefore, the optical filter with a spectral range around 7.6 µm and with a bandwidth of ±0.10 µm was chosen. The D p of the polymer was calculated considering the chosen spectral band. Since its microcrystalline structure has no effect on its optical characteristics in the chosen band homogeneous medium can be assumed: β λ = κ λ + D λ where; D λ = 0;

(

The integrated absorption coefficient -which is reciprocal of D p -was calculated based on the transmittance of the PO-based polymer in the corresponding spectral band and at the temperature (T = 373 K) close to its thermoforming process temperature. The D p of the polymer was found around 350 µm indicating that the absorbed and therefore emitted radiation may not be considered on the surface of the polymer but, in a range close to surface which may require a correction method on the IR thermographic analyses. (2)

               d T I d T I K T K o K o ) ( ) ( .

IR-Camera Calibration and the Test-up for IR Thermographic Analyses

For the IR thermographic measurements a cooled long-wave (LW) IR-camera (AGEMA 880) was employed and the chosen narrow-band optical filter (Northumbria Optical Coatings) was mounted on the IR-camera. Before doing IR-thermographic analyses with the employed LW IR-camera, the optical filter was also mounted on an uncooled microbolometric IRcamera (FLIR SC325) and tested however enough sensitivity could not be obtained. The typical operating spectral range of the employed LW IR-camera is between 8 and 12 µm indicating that spectral sensitivity of the IR camera detector is nominal in this range, whereas the detector is still sensitive at the wavelengths beyond, but close to, this range. In the current study, it was found out that the LW IR camera is still sensitive enough for the measurements in the spectral band around 7.6µm, however an extra step was required to be applied to adjust the sensitivity of the camera. A blackbody calibration source (Landcal P550P) was used for establishing a correlation between the IR radiation captured by the camera and a surface temperature to be measured. The calibration was done choosing reference temperatures on the blackbody source between 60 and 120 o C and the radiometric value (UI) detected by the camera was registered for each corresponding reference temperature. Thus, a calibration curve was established that enables to convert the detected energy emitted from the surface of the polymer to a temperature value. In Figure 5 and 6, the test set-up for the calibration and the registered radiometric values in the chosen reference temperatures are displayed respectively. Thanks to the calibration curve, a preliminary IR thermographic analysis was done on the polymer. For the analysis, a plate-like polymer sample was prepared and heated by conduction using an electric thermofoil heater (Minco). A copper foil was embedded between the heater and the sample for performing a uniform heat transfer throughout the sample. The contact between all the components was secured using a clamp on each side. In addition, black coating was applied on the two small zones of the sample surface with the aim of creating a reference measurement zone. The emissivity of the black coating is 0.9 which was previously determined in our laboratory. The surface temperature measurements on the PO-based polymer were performed between 60 and 80 o C where the measurements were registered at each 5 o C increment via both the RTD and the IR camera. Thus, five measurements were obtained in the chosen temperature range. The energy detected by the IR camera was converted into temperature using the calibration curve and the registered IR thermographs of the polymer were compared to the RTD measurements that were obtained at each corresponding measurement point. In Figure 7, the test set-up for heating the PO-based sample (a), a thermographic image of the sample registered under heating (b) and the recorded temperature values that were obtained via the RTD and the IR-camera at each measurement point (c) are presented. As displayed in Figure 7 (b), a comparison zone was defined on the bare polymer surface and, the same zone was analyzed on each obtained IR-thermographic image to compare them with the RTD measurements. This comparison zone was chosen close to the area where the RTD is mounted on the sample surface. The comparisons between both the measurements showed that error in the recorded values is less than 1% for the measurements obtained at 60 and 65 o C whereafter the temperature read by the IR-camera shows higher value than the RTD measurements at higher temperatures and the error reaches up to 17.5% at which the difference in the measured temperatures is 15°C. The reason behind this may be twofold: The temperature measurement obtained via RTD may be erroneous at higher temperatures due to the polymer softening, as it may change the contact quality between the RTD and the sample and, eventually RTD may read lower values than the IR-camera. Also, thermal resistance alongside the metallic wires of the RTD may affect the measured temperature at which the heat losses increase proportionally with temperature. These preliminary analyses revealed that RTD may not be fully reliable to make comparison and establish a correction methodology. Therefore the accurate comparisons may be only obtained between the IR thermographs and the reference measurement zone created using black coating, which is the future work of this study.

CONCLUSIONS

The IR thermography of PO-based polymer was analyzed and its optical properties were evaluated in terms of bi-directional and directional-hemispherical transmittance measurements under room and varying temperatures. The effect of its microcrystalline structure on the optical properties was experimentally analyzed and it was found that that the optical scattering greatly affects the transmittance in NIR range whereas this effect becomes negligible in MIR range, for the wavelengths greater than 6 µm. Bi-directional transmittance measurements obtained in NIR range and under varying temperature demonstrated the temperature dependence of its optical properties due to evolution in its microcrystalline structure under heating.

These analyses provided a guide to develop an experimental approach for the IR thermography of the PO-based polymer. After the initial analyses with the employed IRcamera, the optical filter with a spectral range around 7.6 µm and with a bandwidth of ±0.10 µm was chosen for the IR-thermographic analyses. A preliminary IR thermographic analysis was performed on the PO-based polymer and its surface temperature was also registered using a RTD at which those measurements were compared to the ones obtained through the IR camera. The preliminary analyses demonstrated that quantitative IR thermography on the POpolymer may only be obtained after a correction methodology is applied adopting a comparative analysis between IR thermographs of the bare polymer surface and the reference measurement zone created using a black coating, as D p of the polymer introduces a challenging step in terms of emitted energy by its surface.

Figure 1 :

 1 Figure 1: The experimental test set-ups for the bi-directional and directional-hemispherical transmittance measurements of the polymer using FT-IR spectrometer (a) and integrating sphere (b)

Figure 2 :

 2 Figure 2: T λ-directional and T λ-hemispherical of the identical samples in NIR (a) and MIR ranges (b).

Figure 3 :

 3 Figure 3: The test set-up for temperature-dependent bi-directional transmittance measurements

Figure 4 :

 4 Figure 4: Temperature dependence of the bi-directional transmittance (solid lines) and its comparison to the directional-hemispherical (dashed line) obtained at room temperature

Figure 5 :Figure 6 :

 56 Figure 5: Test set-up for calibration of IR camera with the optical filter at 7.6 µm

Figure 7 :

 7 Figure 7: Test set-up for heating the PO-based polymer (a), its IR thermography at 75 o C (b) and the comparison between the surface temperatures measured via the RTD and the IR-camera (c)


Sylvain Ravier

Marine Désert

Grégory Gille

Alexandre Armengaud

Henri Wortham

Etienne Quivet
email: etiennequivet@univ-amu.fr

Monitoring of Glyphosate, Glufosinate-ammonium, and (Aminomethyl)phosphonic acid in ambient air of Provence-Alpes-Côte-d'Azur Region, France

Keywords: Pesticides, Herbicides, Atmosphere, Transport Highlights

Glyphosate, AMPA, its main metabolite, and Glufosinate-ammonium were monitored in ambient air samples collected for two years (2015)(2016), at four sampling sites in Provence-Alpes-Côte-d'Azur Region (PACA, France) in different areas typologies (non-agricultural areas: city center, 'zero pesticide' policy, and industrial area but also agricultural sectors: mainly orchards and vineyards). Neither Glufosinate-ammonium nor AMPA were detected.

Glyphosate was detected at a global frequency of 7% with frequencies ranging from 0% (Nice) to 23% (Cavaillon), according to the sampling site. Glyphosate concentration reached a maximum level of 1.04 ng m -3 in the rural site of Cavaillon. This is despite the physicochemical characteristics of Glyphosate which are not favorable to its passage into the atmosphere. The absence of simultaneous detection of Glyphosate and AMPA suggests that drift during spraying operation is the main atmospheric source of Glyphosate and that resuspension from soil particles is minor. The present study offers one of the few report of Glyphosate, Glufosinateammonium, and AMPA in the air.

Introduction

Herbicides are chemical substances formulated to control or manipulate undesirable vegetation.

They can be applied directly to the plant, applied to the soil, or sprayed onto the foliage.

Herbicides may be applied before or after crop planting or may be used to control weeds in an already established crop. They are extensively used in farming but also in gardening, landscaping, turf management, roadways, and railways. In 2016, according to the most recent statistics on agriculture, forestry, and fisheries for the European Union (Eurostat, 2018), the annual quantity of herbicide sold in Europe amounted to close to 112,000 tons, i.e., around 32% of the total quantity of pesticide sold.

Among herbicides, Glyphosate (2-(phosphonomethylamino)acetic acid) and Glufosinateammonium

(2-amino-4-[hydroxy(methyl)phosphoryl]butanoic acid) are both organophosphorus compounds with broad-spectrum systemic actions. Glyphosate is the nonselective herbicide the most sold worldwide [START_REF] Benbrook | Trends in glyphosate herbicide use in the United States and globally[END_REF]. After spreading, Glyphosate is rapidly biotransformed in soils into (Aminomethyl)phosphonic acid (AMPA). Despite its high biodegradability and its strong adsorption on soil particles (i.e., weak leaching potential) [START_REF] Nguyen | Large variation in glyphosate mineralization in 21 different agricultural soils explained by soil properties[END_REF], Glyphosate can contaminate water resources. Glufosinate-ammonium was presented by the European Commission as "one of the very few alternatives to Glyphosate" (European Commission, 2017). However, sales of Glufosinate-ammonium are much lower than that of Glyphosate (BNVD, 2017). As a result, because of both their physico-chemical properties and intensive use, these compounds were extensively monitored in soils and waters [START_REF] Székács | Monitoring Pesticide Residues in Surface and Ground Water in Hungary: Surveys in 1990-2015[END_REF][START_REF] Karanasios | Monitoring of glyphosate and AMPA in soil samples from two olive cultivation areas in Greece: aspects related to spray operators activities[END_REF].

From a health point of view, Glyphosate and Glufosinate-ammonium are poorly absorbed both orally and via the dermal route and they are rapidly eliminated with no biotransformation and no accumulation in tissues [START_REF] Gupta | Toxicity of Herbicides[END_REF]. More, several epidemiologic studies have concluded that there is no apparent association between Glyphosate and any solid tumors or lymphoid malignancies overall [START_REF] Acquavella | Glyphosate epidemiology expert panel review: a weight of evidence systematic review of the relationship between glyphosate exposure and non-Hodgkin's lymphoma or multiple myeloma[END_REF][START_REF] Andreotti | Glyphosate Use and Cancer Incidence in the Agricultural Health Study[END_REF]. AMPA, the main metabolite of Glyphosate, is of no greater toxicological concern than its parent compound (JMPR, 2004).

On the other hand, the main environmental concern lies in the fact that some pesticides are persistent particularly in the atmosphere which make them possible to be transported over long distances [START_REF] Socorro | Heterogeneous Reactions of Ozone with Commonly Used Pesticides Adsorbed on Silica Particles[END_REF][START_REF] Socorro | The persistence of pesticides in atmospheric particulate phase: An emerging air quality issue[END_REF]Mattei et al., 2018 and references therein). Hence, the atmosphere is an important spread vector at local, regional, and global scales. Atmospheric pesticide contamination was observed in urban and rural areas with concentration levels from some picograms per cubic meter (pg m -3 ) to several nanograms per cubic meter (ng m -3 ) (Désert et al., 2018 and references therein). Unfortunately, atmospheric concentrations of Glyphosate and AMPA are poorly documented because very few studies have monitored them in the atmosphere, and none regarding Glufosinate-ammonium. Due to their low Henry's Law Constant and their low vapor pressure, their presence in the atmosphere seems to be more dependent on the drift of the finest droplets after spraying [START_REF] Hewitt | Spray droplet size, drift potential, and risks to nontarget organisms from aerially applied glyphosate for coca control in Colombia[END_REF] or on the suspension of the soil particles on which they are adsorbed [START_REF] Bento | Glyphosate and AMPA distribution in wind-eroded sediment derived from loess soil[END_REF]. In 2004, Glyphosate was searched in 59 atmospheric samples in Hauts-de-France Region (France), with a detection frequency of 14% and a maximum concentration of 0.19 ng m -3 [START_REF] Prouvost | Exposition de la population aux pesticides dans la région Nord-Pas-de-Calais : apports du programme[END_REF]. In 2007-2008, Glyphosate and AMPA were sampled in the atmosphere of two agricultural areas (soybeans, rice, corn) of Mississippi and Iowa, USA [START_REF] Chang | Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere[END_REF].

Atmospheric concentrations of Glyphosate reached 9.1 ng m -3 and 5.4 ng m -3 in Mississippi and Iowa, respectively, whereas atmospheric concentrations of AMPA reached 0.49 ng m -3 and 0.97 ng m -3 in Mississippi and Iowa, respectively. These data were supplemented by measurements in rainwater. Authors estimated that 97% of Glyphosate in the air would be removed by weekly rainfall greater than 30 mm. More, a study was carried out in Malaysia to determine Glyphosate atmospheric concentrations in a treated field [START_REF] Morshed | Determination of glyphosate through passive and active sampling methods in a treated field atmosphere[END_REF]. They reached 42.96 µg m - 3 during spraying using a calibrated mist blower. It should be noted that a first modeling attempt of an estimated emission of Glyphosate to the atmosphere was done at a regional scale (Atmo Auvergne-Rhône-Alpes, 2017) but without any measurement to confirm the model output.

In the present work, the results of a two year (2015)(2016) obtained over the same period and at the same sampling sites for 50 current-use pesticides [START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF]. The present study offers one of the few reports of Glyphosate, Glufosinateammonium, and AMPA in the air.

Material and methods

Chemicals and reagents

Glyphosate (99%), Glufosinate-ammonium (95%), and (Aminomethyl)phosphonic acid (AMPA, 99%) reference standards were purchased from Sigma-Aldrich. The main physicochemical properties, the agricultural uses and the legal situation of pesticides studies are summarized in Table 1. MΩ cm -1 at 25°C) was obtained by tap water passed through a Milli-Q water purification system (Direct 8 MilliQ, Merck Millipore). Underivatized standards were dissolved in UHQ water and the stock solutions of each compound at 0.5 g L -1 for Glyphosate and Glufosinate-ammonium, and 0.9 g L -1 for AMPA were stored in a polypropylene bottle (PP) at 4°C.

Sampling and site characterization

Sampling was undertaken at four sampling sites distributed throughout the Provence-Alpes-Côte-d'Azur (PACA) region, France (Figure 1), from January 2015 to December 2016. The description of sampling sites and sampling periods are summarized in Table 2. The three urban sampling sites (i.e., Avignon, Nice, and Port-de-Bouc) were located in the city centers, whereas the rural site of Cavaillon (hamlet of Les Vignères) was located in an intensive arboriculture area. According to a gas/particle distribution model of semi-volatile organic compounds in the atmosphere, Glyphosate and Glufosinate-ammonium are expected to exist solely in the particulate-phase (AEROWIN program; [START_REF] Bidleman | Sorption to aerosols. Chapter 10[END_REF]Harner, 2000, Boethling et al., 2004).

As a result, Glyphosate, Glufosinate-ammonium, and AMPA concentrations in the atmosphere are assumed to be equal to their particulate-phase concentrations.

Sampling was carried out using a high-volume sampler (Digitel Aerosol Sampler DHA-80)

equipped with a PM-10 size selective inlet. Particulate samples (n = 142) were collected on 150 mm diameter ashless quartz microfiber filter (ALBET LabScience). The sampling flow was 30 m 3 h -1 for 24 h. A total of 71 analyses were performed. Each analysis groups two filters, giving a total volume of filtered air around 1,400 m 3 .

Once collected, samples were stored and protected from light at -18°C until analysis. Moreover, in order to quantify the background contamination from sample handling and storage, field air blanks were done at each site. Typically, they consisted in a brief installation of a filter in the high-volume sampler without air pumping to simulate the sample handling. No contamination was detected, i.e., below the limit of detection.

Sample extraction and derivatization

Extraction: Extractions of samples and blanks were carried out using PolyTetraFluoroEthylene (PTFE) or PolyPropylene (PP) vessels to avoid any loss of studied compounds by wall adsorption. In a 70 mL PTFE centrifugation tube, two filters (i.e., one sample) were spiked with 40 µL of IS solution (15.4 mg L -1 ). The sample was then extracted with 20 mL of UHQ water added by 2 mL of Borax (0.05 M) and 0.8 mL of EDTA (0.1 M) solutions using first a mechanical shaker (30 s), then an ultrasonic bath (10 min). Sample was finally centrifuged at 12,000 rpm (12 min). A second extraction was performed with half volume of solutions according to the same procedure. The supernatants of the two successive extractions were collected and filtered together through a polyethersulfone (PES) membrane of 0.45 µm pore size under vacuum.

FMOC (FluorenylMethylOxyCarbonyl) derivatization:

The filtrate was derivatized in 10 mL of acetonitrile with 2 mL of FMOC-Cl (50 g L -1 in acetonitrile). The mixture was stirred, cap closed, for 90 min in the dark at room temperature. After derivatization, acetonitrile was evaporated under nitrogen flow using a concentration workstation (TurboVap II, Biotage) with pressure 1.1 bar and a water bath at 40°C. To remove unwanted by-products and FMOC excess, 6 mL of dichloromethane were added at the residual aqueous solution then removed by settling.

Purification and concentration: Prior to purification and concentration on Solid Phase Extraction (SPE), the pH of the aqueous fraction was adjusted to pH 3 with formic acid 5% which corresponds to the optimum analyte retention. The extraction cartridge (OASIS HLB cartridge, 6 mL, 150 mg, Waters) was successively conditioned by 2 mL of methanol then 2 mL of formic acid 0.1%. Impurities were eliminated by a selective washing step constituted by 2 mL of formic acid 0.1% then 2 mL of UHQ water. Elution was achieved by 4 mL of [methanol/H2O (70/30) (v/v) + NH4OH 2%] solution. The extract was reduced to 1.5 mL by evaporating methanol using a concentration workstation and filtered through a PTFE membrane of 0.2 µm pore size before analysis.

UPLC-MS/MS analysis

Sample extracts were analyzed using an Ultra Performance Liquid Chromatographic (UPLC) system (Acquity, Waters) interfaced with a Quadrupole-Time-of-Flight Mass Spectrometer (Synapt G2 HDMS, Waters) equipped with an electrospray ion source (ESI). The mass spectrometer was used in its resolution mode, up to 18,000 FWHM (Full Width at Half Maximum) at 400 Th and allowed extracted chromatograms with 0.01 Th mass accuracy. The chromatographic separations were carried out on an Acquity UPLC column BEH C18, 1.7 µm particle size, 100 mm × 2.1 mm i.d. (Waters, Milford, MA, USA), at 40°C. The mobile phases consisted in (A) UHQ Water + 5 mM ammonium formate and (B) acetonitrile (Optima®, LC/MS grade, Fisher Scientific). The gradient elution was performed at a flow rate of 0.6 mL min -1 using 5% to 95% of (B) within 7.5 min and held at 95% of (B) for 1.5 min. The injection volume was 10 µL. Analyses were carried out in negative ionization mode and optimum ESI conditions were found using a -0.85 kV capillary voltage, -15 V sampling cone voltage, 450°C desolvation temperature, 120°C source temperature, 20 L h -1 , and 1,200 L h -1 cone gas and desolvation gas flow rate respectively. Dwell times of 0.25 s scan -1 were chosen. Data acquisition and mass spectra treatments were provided by the MassLynx software (v.4.1, Waters). The negative ion electrospray of Total Ion Chromatogram (TIC), selected ion chromatograms, and ion spectra of Glyphosate-FMOC, AMPA-FMOC, and Glufosinateammonium-FMOC are available in Supplementary Information.

Analytical performance of the method

Method validation was carried out using spiked quartz filter as solid sorbent. The accuracy (including the recoveries) of the analytical method was integrated during calibration (i.e., each concentration levels were spiked on quartz filter and followed by the extraction, derivatization, and analytical protocol). Each concentration level (from 0.04 to 0.63 ng m -3 for Glyphosate, from 0.17 to 2.67 ng m -3 for Glufosinate-ammonium, and from 0.25 to 4.06 ng m -3 for AMPA, n = 6) are triplicate. Calibration plots showed good linearity with correlation coefficients R 2 ≥ 0.98 for Glyphosate, R 2 ≥ 0.95 for Glufosinate-ammonium, and R 2 ≥ 0.99 for AMPA.

The detection limit (LOD) and quantification limit (LOQ) were determined using the calibration graph residuals for each compound (ICH, 2005). The LOD and LOQ obtained using spiked quartz filter, when air volumes of 1,400 m 3 were collected, are equal to 0.05 and 0.14 ng m -3

for Glyphosate, 0.30 and 0.90 ng m -3 for Glufosinate-ammonium, and 0.28 and 0.84 ng m -3 for AMPA, respectively.

Results and discussion

Detection frequency and atmospheric concentrations

Sales and application

In 2015 and 2016, French sales of Glyphosate were 8,790 and 9,110 tons respectively. At the local scale, over the same period, the Glyphosate sales were stable in Alpes-Maritimes (Nice),

i.e., 11.7 and 10.6 tons, and in Bouches-du-Rhône (Port-de-Bouc), i.e., 43.6 and 44.4 tons, respectively, whereas they were significantly reduced in Vaucluse (Avignon and Cavaillon), from 165.1 to 48.5 tons. The same pattern was observed for Glufosinate-ammonium for much smaller sold amounts, i.e., 255 and 185 kg in Alpes-Maritimes, 3.0 and 2.7 tons in Bouches-du-Rhône, and 3.5 and 1.0 tons in Vaucluse, in 2015 and 2016 respectively (BNVD, 2017).

In the areas under study, Glufosinate-ammonium is mainly used in agriculture (commercial formulation Basta® F1) while Glyphosate can be used for agricultural practice (e.g., Missile® 360 and Clinic® Ace for the main formulations used) or in non-agricultural areas, i.e., public areas, roadways, amateur gardens… (e.g., Barclay® Gallup Super 360 Jardin and Glyfos® Jardin for the main formulations used) (BNVD, 2017). In France, in 2015 and 2016, nonagricultural uses of Glyphosate were estimated respectively at 18.6% and 16.1% of the total amount of sales.

The nature of soil and meteorological conditions (relative humidity (RH), temperature…) are important parameters for the weeding process. Indeed, as Glyphosate and Glufosinateammonium are systemic foliar and contact herbicides, their efficiencies are enhanced during humid (RH > 70%) and temperate (15-25°C) periods, without wind and rain causing pesticides dispersion by drift and leaching. They were mainly spread in late winter (February), during the spring and early summer periods (April to July), and to a lesser extent, during the fall period (October to December). However, since these active substances are not only used in agriculture (but also by individuals), it is difficult to know the exact treatment schedule and to delimit the area of use.

Detection frequency and atmospheric concentrations

Glyphosate was detected at a global frequency of 7% with frequencies ranging from 0% (Nice)

to 23% (Cavaillon), according to the sampling site. These detection frequencies were of the same order of magnitude as those observed in the Hauts-de-France region (France), i.e., 14% (2004, Prouvost and[START_REF] Prouvost | Exposition de la population aux pesticides dans la région Nord-Pas-de-Calais : apports du programme[END_REF]. However, Glyphosate was observed with a higher frequency in the air of Mississippi and Iowa (USA), i.e., 94% and 67%, respectively (200794% and 67%, respectively ( -200894% and 67%, respectively ( , Chang et al., 2011)). In this work, AMPA, the main Glyphosate degradation product, was never detected at any sampling sites. In the atmosphere, it was previously analyzed only in

Mississippi and Iowa (USA), with detection frequencies of 78% and 58%, respectively (2007( -2008[START_REF] Chang | Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere[END_REF]. However, it should be noted that the LOD determined in the present study is too high to detect traces in some samples of Mississippi and Iowa. As AMPA is a biodegradation product formed only in soils, its atmospheric concentrations could be only due to soils aeolian erosion [START_REF] Bento | Glyphosate and AMPA distribution in wind-eroded sediment derived from loess soil[END_REF]. Since no simultaneous detection of Glyphosate and AMPA was observed in the present work, it can be assumed that the aeolian erosion was a pesticide atmospheric source of minor importance and thus, the atmospheric Glyphosate concentrations were mainly due to the drift during spraying [START_REF] Hewitt | Spray droplet size, drift potential, and risks to nontarget organisms from aerially applied glyphosate for coca control in Colombia[END_REF].

As well as AMPA, Glufosinate-ammonium was never detected. Besides its low Henry's Law Constant and its low vapor pressure, two other hypotheses may be put forward to explain this result: its sales amount (i.e., its application) was far below of Glyphosate and its LOD was maybe too high to detect atmospheric concentrations. Anyway, to the best of our knowledge, no atmospheric monitoring is reported in the literature for this active substance and this work is the first effort to quantify it.

During the same period (2015)(2016), 50 other active substances, including 21 herbicides, were monitored in the atmosphere on the sampling sites under study [START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF]. Detection frequencies of herbicides, fungicides, and insecticides reached a maximum of 57% for Pendimethalin, 58% for Tebuconazole, and 98% for Lindane, respectively. Considering only herbicides, 12 actives substances were never detected, and Glyphosate would be in 6/22 position ranking behind Pendimethalin (57%), S-Metolachlor (23%), Diflufenican (16%), Chlorpropham (14%), and Prosulfocarb (9%), which makes its atmospheric detection frequency relatively important considering its physicochemical properties.

In an atmospheric concentrations point of view, Glyphosate concentration reached a maximum level of 1.04 ng m -3 in Cavaillon. Until this study, the maximum peak concentration measured in France was only 0.19 ng m -3 (2004, [START_REF] Prouvost | Exposition de la population aux pesticides dans la région Nord-Pas-de-Calais : apports du programme[END_REF]. These maximum values were much smaller than those measured in US agricultural areas where they can reach 9.1 ng m -3 in Mississippi (2007[START_REF] Chang | Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere[END_REF] during the application period. However, these concentrations should be compared with the highest concentration (i.e., 42.96 µg m -3 ) measured in the atmosphere near a spray application [START_REF] Morshed | Determination of glyphosate through passive and active sampling methods in a treated field atmosphere[END_REF]. In 2015 and 2016, among herbicides searched on the sampling sites under study, only Pendimethalin was quantified at a higher concentration, i.e., 1.924 ng m -3 in Cavaillon.

Spatial and temporal detections of Glyphosate

According to sampling sites and years, spatial and temporal detection frequencies varied from 0% (e.g., Nice) to 66% (i.e., Cavaillon in 2015). If some of these results may be explained by the context of sources (e.g., rural vs. urban), it is not always easy to correlate the detections and the environment of the sampling sites.

Spatial distribution

In Nice, sampling was performed in a wooded square in city center, near a cemetery (~550 m South-West), urban parks (~400 m East), and port (~500 m South). Nice was the only site where Glyphosate has never been detected (0/22 analysis). The explanation probably lies in the fact that, since 2009, Nice has adopted a 'zero pesticide' policy for the maintenance of green spaces, cemeteries, and roads.

On the other hand, with a detection frequency of 23% (3/13 analysis), Cavaillon is the sampling site where the atmosphere is the most contaminated by Glyphosate. In addition, the highest concentrations, until 1.04 ng m -3 in April 2015 were measured on this site. Back-trajectories calculated using the NOAA HYSPLYT model (Figure 4) indicate two regional sources: from North (May 2015) and South-West (April 2015 and June 2016). Samples of Cavaillon were collected in a hamlet named "Les Vignères", a rural site located in an intensive arboriculture area (the nearest orchard is less than 200 m from the sampler). According to the French National Institute for Agricultural Research [START_REF] Reboud | Usages et alternatives au glyphosate dans l'agriculture française[END_REF], mechanical weeding is not always possible in established orchards if it has not been thought upstream, which leads to use of herbicides and especially Glyphosate. The amounts of active substance applied per hectare and per year range from 62 to 3,600 g ha -1 yr -1 . Moreover, these results were consistent with the monitoring of 59 other active substances during the 2012-2017 period [START_REF] Désert | Spatial and temporal distribution of current-use pesticides in ambient air of Provence-Alpes-Côte-d'Azur Region and Corsica, France[END_REF], The sampling site of Port-de-Bouc is located at the harbor near the train station (~600 m North) and less than 2 km from an industrial complex (refinery, petrochemical facilities…). As in (2008), according to meteorological conditions [START_REF] Chang | Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere[END_REF]. In contrast to these results, in 2003-2004, measurements in Caudry, a suburban area in Hauts-de-France Region (France), show only detections in the summer period between July and September [START_REF] Prouvost | Exposition de la population aux pesticides dans la région Nord-Pas-de-Calais : apports du programme[END_REF].

which
It should be noted that of the three sampling sites where Glyphosate has been detected (i.e., Avignon, Cavaillon, and Port-de-Bouc), there is no reproducible detection pattern from 2015 to 2016.

Influence of meteorological conditions

The meteorological data collected at the four sampling sites allow observing the influence of precipitation, temperature, and wind speed on the Glyphosate concentrations. However, it is necessary to be cautious because only 5 out of 71 samples contained Glyphosate. No literature data are available on the influence of temperature and wind speed. However, the weeding efficiency of Glyphosate is enhanced during humid (RH > 70%) and temperate (15-25°C) periods, without wind and rain to avoid drift and leaching.

The 5 detections of Glyphosate were registered when mean daily temperatures ranged between 9.7°C (Port-de-Bouc, March 2016) and 21.0°C (Cavaillon, June 2016), which is consistent with the temperatures commonly measured at the spreading period.

In France, it is forbidden to treat as soon as the wind speed reaches an intensity greater than about 19 km h -1 (Index Acta Phytosanitaire, 2018). During the days when Glyphosate was detected, the wind speed exceeded this value 33% of the time (hourly measurement), reaching up to a maximum of more than 40 km h -1 in Port-de-Bouc. These wind speeds can lead to greater resuspension and then long-range transport by aerial drift which will cause injury to nontarget plants. The probability of drift injury occurring increased when winds are gusty or when wind speed will allow spray drift to occur [START_REF] Franz | Glyphosate: A Unique Global Herbicide[END_REF].

Due to its high solubility in water, Glyphosate is expected to be removed by rainfall. More, its detection in precipitation is probably due to its association with particulate matter [START_REF] Anderson | Glyphosate residues in Alberta's atmospheric deposition, soils and surface waters[END_REF]. In previous work [START_REF] Chang | Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere[END_REF], it was estimated that 87 to 92% of Glyphosate in the air would be removed by weekly rainfall lower than 30 mm and 97% by weekly rainfall greater than 30 mm. Only the sampling collected in Port-de-Bouc in March 2016 showed Glyphosate detection during a rainy period (precipitation 18.6 mm), suggesting that the measured concentration (0.38 ng m -3 ) was potentially higher before the rain even.

Conclusion

This work is one of the few monitoring studies in the atmosphere of Glyphosate, AMPA, its main metabolite, and Glufosinate-ammonium. Neither Glufosinate-ammonium nor AMPA were detected. However, at the same sampling sites, during the same period, detection frequency and maximum concentration of Glyphosate were sometimes higher than those found for other pesticides, especially herbicides. This is despite the physicochemical characteristics of Glyphosate which are not favorable to its passage into the atmosphere.

The absence of simultaneous detection of Glyphosate and AMPA suggests that drift during spraying operation is the main atmospheric source of Glyphosate and that resuspension from soil particles is minor.

However, in the worst-case scenario (1.04 ng m -3 ), the expected dose of Glyphosate for an average consumer (70 kg body weight) respiring at a rate of 1.5 m 3 h -1 during light exercise is 0.54 ng kg -1 day -1 . In these conditions, this value remains well below of the chronic reference dose for Glyphosate of 1.75 mg kg -1 day -1 (US EPA, 2009).

Finally, the implementation of an extensive air monitoring network for Glyphosate control is needed to collect more data in order to be able to model the concentrations in the atmosphere.

Acknowledgment

Field sampling and laboratory analysis have been made for the Regional Observatory of Pesticides (ORP PACA) that receives financial support from the PACA Region, the Regional Health Agency of PACA, the Regional Directorate for Environment, Planning, and Housing of PACA, and the Regional Network for Air Quality Monitoring AtmoSud.

Fig 1 .

 1 Fig 1. Sampling sites (red: urban sites; green: rural site) distributed throughout the Provence-

Fig. 4 .

 4 Fig. 4. Geographical environment of Avignon and Cavaillon: a-Calculated back-trajectories

Table 1 .

 1 Physicochemical properties, agricultural uses, and legal situation.

	Chemical

name CAS number Molecular weight (g mol -1 ) Vapor pressure (Pa, 25°C) a Henry's law constant (Pa m 3 mol -1 , 25°C) a Solubility in water (g L -1 , 20°C) a Acceptable Daily Intake (mg kg bw -1 day -1 ) b Principal agricultural uses c

								General treatment,
								cereals, vegetable
	Glyphosate	1071-83-6	169.1	1.3•10 -5	2.1•10 -7	10.5	0.3	crops, orchards,
								vineyards, non-
								cropped areas
								General treatment,
	Glufosinate-ammonium	77182-82-2	198.2	3.1•10 -5	4.5•10 -9	500	0.02	cereals, potatoes, vineyards, non-
								cropped areas
	(Aminomethyl) phosphonic acid (AMPA)	1066-51-9	111.0	-	0.16	1,467	0.3	Transformation product
	a PPDB: Pesticide Properties DataBase (Lewis et al., 2016). b APVMA, 2017.
	9-Fluorenmethylcholoroformate (FMOC-Cl, ≥ 99%) and isotope-labeled Glyphosate (2-13 C, 99
	atom % 13 C) from Sigma-Aldrich were used as derivatization reagent and internal standard (IS),
	respectively. HPLC-grade dichloromethane (Sigma-Aldrich), ethylenediaminetetraacetic acid
	(EDTA), sodium tetraborate decahydrate (Borax), ammonium formate, formic acid, ammonia
	solution (35%), LC/MS-grade acetonitrile, and LC/MS-grade methanol (Fisher Scientific) were
	used for extraction and chromatographic elution. Ultra-High Quality water (UHQ water, 18.2

Table 2 .

 2 Description of sampling sites

	Sampling site						Total
	(French	Latitude	Longitude Altitude	Typology	Land use description a	analysis
	department)						number
	Avignon (Vaucluse)	43.94976 N	4.80451 E	21 m	Urban	Complex cultivation patterns (33%), plantations (14%) Urban fabric (10%) Vineyards (30%), Fruit trees and berry	14
	Cavaillon (Vaucluse)	43.88128 N	5.00611 E	60 m	Rural	Complex cultivation patterns (52%), Urban fabric (11%) Fruit trees and berry plantations (18%),	13
	Nice (Alpes-Maritimes)	43.70207 N	7.28539 E	0 m	Urban	Urban fabric (47%), Forests (24%), associations (16%) Scrub and/or herbaceous vegetation	22
	Port-de-Bouc (Bouches-du-Rhône)	43.40195 N	4.98197 E	1 m	Urban	Scrub and/or herbaceous vegetation associations (51%), Urban fabric with industrial area (27%), Forests (11%)

Table 3 .

 3 Precipitation and atmospheric concentrations of Glyphosate, Glufosinate-ammonium, and AMPA in all sampling sites.

	219
			Avignon		Cavaillon		Nice		Port-de-Bouc
	Date	Precipitation	Concentration (ng m -3 )	Precipitation	Concentration (ng m -3 )	Precipitation	Concentration (ng m -3 )	Precipitation	Concentration (ng m -3 )
		(mm)		(mm)		(mm)		(mm)

  Avignon, Glyphosate was detected only once in March 2016 (1/22 analysis, 5%). However, the origin of the air mass coming from the East does not indicate specific areas where Glyphosate is intensively used.

	These results highlight a higher detection frequency of Glyphosate in rural areas than in urban
	areas, i.e., 87% (3/13 analysis) against 13% (2/58 analysis), respectively. If rural and urban sites
	correspond rather to agricultural and non-agricultural applications, respectively, this is
	consistent with French sales with non-agricultural applications estimated at 18.6% in 2015 and
	16.1% in 2016.

Temporal distribution

All detections were made between March and June which is consistent with the main phase of Glyphosate spreading in late winter and during spring and early summer periods. In Mississippi, Glyphosate is detected throughout 2007 and 2008 but maximum concentration occurred in May during the application period and at a lesser extent in July and August. In Iowa, maximum concentrations of Glyphosate occurred in mid-June and mid-July (2007) and from late May to early June

a Corine Land Cover nomenclature (zone of 10 km radius around the sampling site)

Competing financial interests

The authors declare no competing financial interests.


Edwin Bourget

Frédéric Cuppens

Nora Cuppens-Boulahia

Samuel Dubus

Simon Foley

Youssef Laarouchi

Probabilistic Event Graph to Model Safety and Security for Diagnosis Purposes

Diagnosing accidental and malicious events in an industrial control system requires an event model with specific capacities. Most models are dedicated to either safety or security but rarely both. And the latter are developed for objectives other than diagnosis and therefore unfit for this task. In this paper, we propose an event model considering both safety and security events, usable in real-time, with a probabilistic measure of on-going and future events. This model is able to replace alerts in the context of more global scenarios, including with reinforcements or conflicts between safety and security. The model is then used to provide an analysis of some of the the security and safety events in the Taum Sauk Hydroelectric Power Station.

Introduction

With the increasing interconnection of Industrial Control Systems (ICS) through cyberspace, new challenges arise when ensuring their safety and security. Once rather well separated, both characteristics of the system are now completely interleaved: a cyber-attack may have consequences on an industrial process and the response to a safety incident can inadvertently impact the security of the system. Therefore, when an incident occurs in an ICS, it is more and more difficult, yet essential, to understand what is happening in order to select the appropriate response. The analysis of the incident is the diagnosis.

Providing explanation of purely accidental and malicious incidents is typically done using, respectively, fault trees [START_REF] Vesely | Fault tree handbook[END_REF] and attack trees [START_REF] Bruce | Attack trees[END_REF]. Risk analysis or incident tracking can be done using more advanced methods such as Bayesian networks [START_REF] Frigault | Measuring network security using dynamic bayesian network[END_REF] or Petri networks [START_REF] Kumar | A pattern matching model for misuse intrusion detection[END_REF] However, they can in practice be viewed as a refinement of fault and attack trees.

There exist several models dedicated to one characteristic, either safety or security, but they are hardly adaptable to the other one. The reason is that safety and security have their different semantics and using one for the other is not always meaningful. Therefore, the propagation of the incident is different and most models cannot represent both. A small number of models exist that can represent both safety and security at the same time, such as BDMP [START_REF] Bouissou | A new formalism that combines advantages of fault-trees and Markov models: Boolean logic driven Markov processes[END_REF][START_REF] Piètre-Cambacédès | Beyond attack trees: Dynamic security modeling with boolean logic driven markov processes (bdmp)[END_REF] or the FIGARO language [START_REF] Bouissou | Knowledge modelling and reliability processing: presentation of the figaro language and associated tools[END_REF]. However, they were not originally developed for diagnosis purposes and therefore, lack essential features considered in this paper, such as real-time monitoring or alert instantiation, towards achieving this objective.

According to [START_REF] Pernestål | A Bayesian approach to fault isolation with application to diesel engine diagnosis[END_REF], diagnosis can be divided into three sub problems: detecting the problem, locating it and determining its scope. Detecting the problem means having the knowledge that a safety or a security incident is happening. Locating the problem is knowing what components are at fault or targets of an attack. Determining its scope is being able to identify the affected subsystems. In a nutshell, it means explaining what is happening. In order to do so, a model of what can happen is required. Risk analysis models based on fault and attack trees are suited for that. Then, mapping the ongoing situation to the model allows for finding the roots of the problem or generating hypotheses on evolutions of the problem, such as the objective of an attacker or the next components to fail. Sometimes, though, the ongoing situation might not completely fit the model. Therefore, a measure of the variation of the incident from the model should be provided. Being able to reason beyond the scope of the system is an important part of diagnosis and is what we provide with our model.

The objective of this paper is to lay the foundations of a way to model safety and security in order to perform diagnosis of incidents in ICS. We propose a model to represent safety and security events, draw logical connections between them in order to generate complete scenarios and a probabilistic component to compute the likelihood of both the complete scenarios and the elementary events.

The paper is organised as follows: Section 2 introduces the LAMBDA language for describing security attacks and upon which the proposed model, described in Section 3, is based. Section 4 illustrates the capabilities of the model on a use case. Section 5 compares the proposed model to existing research and Section 6 gives insights on future uses and evolutions of the model.

Background

LAMBDA [START_REF] Cuppens | In: LAMBDA: A Language to Model a Database for Detection of Attacks[END_REF] is a language used to describe security attacks. It is based on the general concept that an attack can succeed if a set of conditions (called preconditions) is satisfied, and will have effects (called postconditions) on the system if it succeeds. It is also described by a scenario, being the different actions needed to be combined to perform the attack. Attacks described in LAMBDA can be of any granularity going from elementary steps that an attacker needs to carry out to progress towards his objective, to the description of a complete scenario.

CRIM [START_REF] Cuppens | Alert correlation in a cooperative intrusion detection framework[END_REF] is a correlation engine that draws connection between attacks described in LAMBDA: if a postcondition of an attack A 1 matches with a precondition of an attack A 2 , then the realisation of A 1 favours the realisation of A 2 . That is A 1 enables the attacker to then perform A 2 . In this particular example, the attack graph generated would have a connection from A 1 to A 2 .

Unfortunately, LAMBDA does not fit to model safety events. Indeed, every attack is to be described by a scenario, being a set of actions. When modelling safety, a component failure, like the one of a hard-drive, can happen without being the result of a particular action: it is its regular wear. Moreover, LAMBDA in itself is devoid of any probabilistic evaluation. [START_REF] Kanoun | Success likelihood of ongoing attacks for intrusion detection and response systems[END_REF] proposes a modification of LAMBDA to overcome this issue but it only computes a mean time to success (MTTS) and this is not sufficient for a complete evaluation of the likelihood of an incident.

Nonetheless, we will base the model we present in Section 3 on a heavily modified version of LAMBDA in order to reuse its event-tracking ability and the pre/postconditions modelling, as well as CRIM.

The model

In this section, we present our model. First we define the events, then we construct scenarios out of events, and we finally compute the probabilities required for diagnosis.

The Event

In the model we propose, the system in which incidents happen is represented by a set of variables. This set is called the system state. We define an event as a modification in the system state. Therefore, an event is the result of the occurrence of an attack or an accidental failure.

An event is characterised by its attributes: a set of preconditions, a set of postconditions, the nature of the event, a realisation process and a detection process.

The preconditions set and the postconditions set are inherited from LAMBDA. They are composed of predicates combined with the logical connectives ∧, ∨ and ¬. The former set is used to describe the value that the variables of the system state must have for the event to be feasible. The later set is used to describe the value that the variables of the system state will have after the event has occurred.

The nature of the events is a label used to quickly know the type of the event: so far, we only used safety and security but any other type of events could potentially be added, such as countermeasures or regular events whose occurrence is part of the industrial process.

We define the realisation process as a probability distribution function (PDF). It is used to describe the evolution of the occurrence probability of the event over time, given that all of the conditions for the event to happen are met. Any PDF can be used, even custom ones. This is where the difference in modelling security and safety events lies in our model. Indeed, their realisation process is different and is represented, in this model through the PDF. With this approach, the model can therefore acknowledge for any kind of propagation: when modelling a specific event, one simply has to use its most appropriate PDF.

The detection process is used to link the modelled event to an alert A collected by the SCADA or SIEM system. It is used to inform the model that the event has been realised in order to update the system state and trigger instantiation of said alert A.

Figure 1 gives the model of the failure of a hard-drive. The PDF associated with the event is a Weibull distribution. The distributions and their parameters are chosen by the experts modelling the system, but can be derived from sizeable return on experience. For instance, when it comes to hard-drive failures, [START_REF] Schroeder | Disk failures in the real world: what does an mttf of 1,000,000 hours mean to you?[END_REF] has determined that the Weibull distribution is much more suited than the exponential distribution. Modelling this event with a PDF allows us to compute the mean time to failure of the event (5 years) or the probability of failure after ninety days (7.52 × 10 -9 ) for instance.

The Attack Graph

After the set of elementary events have been defined, it can be used by CRIM to correlate the dependencies between the events and generate event graphs corresponding to complete scenarios. CRIM says that events A and B are correlated if P ost(A), the postconditions of A, and P re(B), the preconditions of B, are correlated. And this happens when at least one of the predicates of P ost(A) and one of the predicates of P re(B) are unifiable through a most general unifier (mgu) [START_REF] Cuppens | Recognizing malicious intention in an intrusion detection process[END_REF]. If such an mgu is found, a directed connection from A to B is present in the event graph output by CRIM. CRIM tries to correlate every pair of events and then generates its event graph displaying the logical dependencies between the events, as illustrated in Figure 4.

Recombining PDF

Once the logical dependencies have been established between the different events and a scenario has been selected, its PDF can be computed. To do so, the local PDF associated with each event needs to be recombined in order to express the global PDF of the scenario. There exists three different situations: the sequence, the AND, and the OR. On a side note, we will assume that the random variables associated with two events are always independent. It means that, the time taken to realise one event has no influence on the time taken to realise another event.

The Sequence. Let us consider that events A and B happen in sequence. C4 corresponds to "A happens then B happens". A, B, and C are respectively associated with the PDF f A , f B , and f C . The probability that C happens in a given timespan I given that nothing has happened yet is actually the probability that both A and B happen in I. This can be written:

f C : R → R x → +∞ -∞ f A (t)f B (x -t)dt
In other words, f C is the convolution of f A and f B :

f C = f A * f B .
The AND. Let us consider that both events A and B need to happen for C to happen. A and B are respectively associated with the PDF f A and f B , CDF F A and F B (the cumulative distribution function evaluated at x is the probability that the event will be realised at x), and with random variables X and Y . The probability that A and B happen in a given timespan I can therefore be written P (X ∈ I, Y ∈ I). X and Y being independent, we have:

P (X ∈ I, Y ∈ I) = P (X ∈ I)P (Y ∈ I)
If we call g the PDF associated with the event "A and B has happened", we obtain:

g = f A F B + F A f B
The OR. The case of the OR is similar to the one of the AND, with the difference that the AND is the set intersection when the OR is the set union. The resulting PDF, using the same notations as the AND case is:

g = f A + f B -f A F B -F A f B
The nature of the calculus used to obtain the PDF corresponding to the three situations make it so that they can be done in any order. For instance, the PDF associated with (A or B) or C is the same as A or (B or C).

Perspectives of the Probabilistic Recombinations

In this paragraph, we illustrate how to recombine the distributions with a generic example. Let us consider three events A, B and C respectively associated with PDF f A , f B and f C and with their cdf F A , F B and F C . We use the same events calculus algebra as LAMBDA [START_REF] Cuppens | In: LAMBDA: A Language to Model a Database for Detection of Attacks[END_REF] to combine the events: "A; B" represents A in sequence with B and "A&B" means that both A and B must happen. Let us consider the situation A; (B&C). Using the formulas demonstrated in the precedent paragraph, we obtain the distribution function g associated with this situation:

g = f A * (f B F C + F B f C ).
Distribution g can then be used to evaluate the mean time that it will take for a composition of events to occur, or to compute the probability that this composition will happen in any given duration. The recombination formulas we provide in this section are valuable tools for the diagnosis. Indeed, where a system expert can realistically provide accurate distributions for elementary events, it becomes much more arbitrary for more complex events. By decomposing a complex event in elementary ones, then defining PDF for these elementary events and recombining them to have the one of the complex event, we obtain an accurate description of the realisation process of this event. The PDF can then be used as a metric and combined with other ones, such as the impact, in order to give comprehensive feedback to a decision-maker who is looking for the most appropriate response, for example.

The use case

The case study is based on an actual power station: Taum Sauk Hydroelectric Power Station [START_REF] Rogers | OVERVIEW OF THE TAUM SAUK PUMPED STORAGE POWER PLANT UPPER RESERVOIR FAILURE[END_REF][START_REF] Team | Report of findings on the overtoppingand embankment breach of the upper dam -taum sauk pumped storage project[END_REF][START_REF]Before the public service commission state of missouri -staff's initial incident report[END_REF]. For our study, we will consider the upper and the lower reservoir, the pump, the Operator Control Center (OCC) and the two automatons Common PLC (Common PLC) and Upper Reservoir PLC (UR PLC). OCC, Common PLC and UR PLC are in the same network Operator Control Network (OCNet). When the water level in the upper reservoir overcomes a certain threshold, sensors send this information to the PLCs that forward the information to the OCC that can turn off the pump. If for whatever reason the pump does not stop, other sensors sends the information to the two PLCs who directly trigger a hard stop on the pump. If the water level keep rising, it will eventually overtop the reservoir, erode the relief upon which it is built and will cause a massive breach as it happened in december 2005 [START_REF] Rogers | OVERVIEW OF THE TAUM SAUK PUMPED STORAGE POWER PLANT UPPER RESERVOIR FAILURE[END_REF][START_REF] Team | Report of findings on the overtoppingand embankment breach of the upper dam -taum sauk pumped storage project[END_REF][START_REF]Before the public service commission state of missouri -staff's initial incident report[END_REF].

Following the methodology of our model, in order to get the scenarios, one must first describe all of the elementary attacks or failures. In this paper we will present two elementary events in figures 2 and 3 : an intruder gaining access to the OCNet and an attacker compromising the communication link between the Common PLC and the Pump.

After defining all of the events identified by the safety and security experts, they are fed to the correlation engine that outputs scenarios. Several scenarios can lead to the failure of the upper reservoir. For the sake of this article, we have selected one displayed on figure 4. The graph output by CRIM corresponding to the selected scenario is displayed in figure 4. It describes that one can cause the overtopping by intercepting all stop orders sent to the pump while it is active. Three sources can produce the stop orders : the Common PLC, the Upper Reservoir PLC and the Operator Control Center. All of the orders can be intercepted if one compromises the communication links between the order sources and the pump. Finally, all of the communication links can be compromised if one has We will consider various cases where several alerts have been raised. The cases and the probabilistic results are displayed in Table 1. Only the MTTS is shown but, as in the precedent paragraph, the probability of occurrence after any duration can be computed. As a matter of fact, we have plotted the probability that event H happens for case 6 in Figure 6. All of the computations can be done beforehand or in real-time. Having an evaluation of the MTTS depending on the raising of various alerts is a precious asset for a decision-maker. First, it is a metric that can be recombined with others, like the impact, in order to have an accurate assessment of the risk. Moreover, a safety/security supervisor might want to define a threshold under which the deployed countermeasures are different. Indeed, an impending undesired event may call for a response more effective, but with more negative side-effects. With several different cases over one scenario, we have shown that the model adapts to real-time scenarios: the probabilities of the undesired events evolve over the realisation of subsequent ones. This is important because, for example, the choices of the attacker may change which are the most probable scenarios. And the appropriate response may differ depending on what parts of the system are at risk. Safety and security supervisors obtain precious information from the model in the form of probabilistic evaluations of the evolution of the failure/compromise rate.

Related work

FIGARO [START_REF] Bouissou | Knowledge modelling and reliability processing: presentation of the figaro language and associated tools[END_REF] is a versatile object language used to populate knowledge bases, later on used to model safety and security events through two kinds of rules: occurrence and interaction rules.

After the model has been defined using the FIGARO language, it is fed to the KB3 platform: a calculus engine. KB3 can not be used in real time to monitor the evolution and propagation of ongoing incidents. By using CRIM, our model is able to track which events have occurred. Their realisation changing the probabilities of possible undesired events, it is an important part of diagnosis.

[17] proposed a model based on Markov chains to compute MTTS (named MTTF in the paper). This model can only consider exponential distributions, which is understandable for security but not for safety. Moreover, with a model based on Markov chains, it can only consider transitions present in the model.

It is therefore impossible of analysing interleaved scenarios, since the scenarios will be modelled as disjoint Markov chains.

Bayesian networks is a popular model for safety modelling [START_REF] Weber | Overview on bayesian networks applications for dependability, risk analysis and maintenance areas[END_REF]. Notably, [START_REF] Boudali | A new Bayesian network approach to solve dynamic fault trees[END_REF] had an interesting take on the matter. Like in our approach, they associated a random variable to each node of their Bayesian network. The random variable models the probability of failure of a component for a given duration. They also use the distributions associated with the random variable to compute failure rates for a scenario. However, they have to discretize the timespan they consider for their scenario, when we have a purely continuous approach, providing more accurate results.

Conclusions

In this paper, we presented an incident model responding to the needs and respecting the constraints of diagnosis. We proposed a graphical model of safety and security incidents, usable in real-time if need be, and able to track the realisation of events and updating itself accordingly. The model, considering concurrent events, is thus able to get the context in which alerts are raised, locating the problem. Through PDF embedded in each event, the model allows for a probabilistic evaluation of possible future events, as well as an estimation of the likelihood of occurrence of an event. This feature gives us both the possible reach of the incident and a way to measure the appropriateness of the model to the situation. We have illustrated the capacities of the model on an industrial use case.

With this usecase we have presented that it is possible to compute the likelihood of one scenario. Since the model acknowledges for several scenarios, one can compute which is the most probable one, given a set of alerts. Or which are the five most probable one, with the information that the undesired events associated with these scenarios are safety or security events. Moreover, with the model keeping track of past events, it is able to give the origin of a scenario, and whether it was caused by failures, attacks, or both. The model is also able to evaluate the likelihood of an event by comparing its realisation time with references values in the form of a PDF that describes the realisation time. All of this is valuable information that helps to the diagnosis of an event, as well as represents a precious aid when deciding of the appropriate response to an incident.

The model is modular and easily extendible. That will incidentally be the object of future works in order to add a measure of the impact of an incident and its propagation to be able to assess the risk, as well as countermeasures.

Fig. 1 :

 1 Fig. 1: Hard-drive failure

Fig. 2 :Fig. 3 :Fig. 4 :

 234 Fig. 2: Access to the Operator Control Network

Fig. 5 : 1 Fig. 6 :

 516 Fig. 5: Evolution of the probabilities in case 1 Fig. 6: Evolution of the probabilities in case 6

Table 1 :

 1 MTTS associated with various cases

	Case Alerts raised	MTTS
	1 ∅	3y 23min 27sec
	2 A	23min 27sec
	3 A, B	21min 13sec
	4 A, B, E	20min 54sec
	5 A, C, D	14min 54sec
	6 A, B, C, D, G	7min 30sec

C is not a LAMBDA event. It is just an abstraction used to compute the PDF associated to a set of events. It has no meaning outside of the scope of the probabilistic calculations.

Acknowledgements

The Cyber CNI Chair of Institute Mines TELECOM is held by IMT Atlantique and supported by Airbus Defence and Space, Amossys, BNP Paribas, EDF, Orange, La Poste, Nokia, the Regional Council of Brittany and Société Générale. It has been acknowledged by the Center of excellence in Cybersecurity.