S Duret

L Guillier

M Hoang

D Flick

O Laguerre

This study

proposes a new approach combining the deterministic and stochastic modeling (Monte Carlo) to take into account the variability of the logistic supply chain for the last three steps, display cabinet, shopping basket and domestic refrigerator.

Background

Deterministic models describing heat transfer in cold chain, microbial growth and product quality evolution are widely studied. However, it is difficult to apply them in practice because of several random parameters of the logistic supply chain (ambient temperature varying due to season, product residence time in equipment), and of the product characteristics (initial microbial load, lag time, water activity…). These variabilities can lead to different product evolutions (microbial load, weight losses, firmness and colour change) causing product losses and health risks. The itinerary (time-temperature profile) and especially the domestic refrigerator, was previously identified as the most importance factor influencing the final contamination of L.monocytogenes in cooked ham [START_REF] Duret | Impact of cooked ham cold chain variability on safety by sensitivity analysis[END_REF].

Objectives

To predict the contamination of L. monocytogenes in cooked ham at the consumption point taking into account the variability cited previously (logistic supply, chain product characteristics). To assess the impact of consumer behaviors, season and geographical situation, on the exposure of consumers by L.monocytogenes

Assessment of the impact of consumer behaviors on exposure to

Listeria monocytogenes by deterministic and stochastic approaches

Materials and Methods

The exposure of L. monocytogenes in cooked ham could be easily decreased by modifying the consumer behaviors. The most efficient and simple instruction consists to increase the thermostat of the domestic refrigerator of one notch.

The numerical tool developed in this study for simulating variability of food itineraries and temperatures can be applied to other products and quality criteria and used by food business operators to assess the impact of the modification of the cold chain logistic or by public organization to give the most pertinent and concrete instructions to consumers 1

The variability of the itinerary and equipments through the cold chain is taken into account.

Figure 2 shows the probability of the different itinerary for the last three steps.

Figure 1 .

 1 Figure 1. Last three steps of the cold chain, display cabinet, shopping basket, domestic refrigerator.

Figure

  Figure 2. Probability of the different itineraries of the product along the cold.

Figure 3 .Figure 3 .

 33 Figure 3. Effect of the air temperature during transport by consumer on the probability of non-compliance P nc of the product (a) cold scenario (b) warm scenario

Acknowledgements

The research leading to this report was funded by Région Ile de France and the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 245288.

*Standard case


P Cavaillé

B Dumont

K Van Looy

Mathieu Floury

E Tabacchi

A Evette

The influence of riverbank stabilization techniques on taxonomic and functional macrobenthic diversity

www.irstea.fr

The influence of riverbank stabilization techniques on taxonomic and functional macrobenthic diversity Methods -Habitat survey.

-Five Surber samples on the most qualitative habitats.

Context, Objective :

Channelization has increased in piedmont areas with urbanization and other anthropogenic alterations. Changes in riparian structures and in sediment dynamics have affected riparian biodiversity including aquatic macroinvertebrate richness. In this context, riverbank restoration becomes a major issue in preserving stream ecosystem functioning and associated services. As a relevant integrator of environmental changes in river systems, macroinvertebrate communities are likely to readily respond to a combination of anthropogenic disturbances.

We aimed at assessing the response of the taxonomic and functional characteristics of benthic macroinvertebrate communities along a gradient of managed riverbank naturalness.

Paul Cavaillé, Bernard Dumont, Kris Van Looy, Mathieu Floury, Eric Tabacchi, André Evette

Results

Discussion:

• Riverbank stabilisation limits local sedimentary dynamics with the end in a lack of habitat diversity and heterogeneity.

• Embankments were sampled 3 to 7 years after works, the period for the colonization of the new habitats is probably structuring restored communities.

Functional traits analysis Taxonomical analysis

  Paul Cavaillé; paul.cavaille@irstea.fr André Evette; andre.evette@irstea.fr Surber macroinvertebrate sampling on the immersed part of a rip-rap (Vorz, Isère). Plecoptera (protonemura) larvae Methods Biological traits were extracted from Tachet et al. (2010) Seven biological traits were selected to be relevant to colonization dynamics, trophic organisation, and habitat characteristics: -Life history traits with "Maximum potential size," "Life cycle duration" and "Potential number of cycles per year"; -Potential resistance and resilience of organisms with "Dissemination"; -Behavioural aspects of reproduction and nutrition with "Reproduction," "Feeding habits," and "Trophic status." Taxonomic richness for each riverbank type Blueprint of riverbank protection techniques : (r) rip-rap; (m) mixed technique; (c) vegetated cribwall; (f) willow fascine; (n) natural bank. and a comparative analysis of a nearby reference community could be useful to measure resilience (or restoration success) of every bank stabilisation technics.

  on Natural riverbank are smaller and with a shorter life cycle that suggested a greater community resilience on Natural banks compared to protected banks. • A shift in macrobenthic communities with a higher proportion of generalist species welcomed on Natural banks.


Marie M Houdart

Sylvie Cournut

Hélène Rapey

Marie Taverne

Livestock dynamics and sustainable development of a French wet mountain territory: Local stakeholders' points of view

come   L'archive ouverte pluridisciplinaire

Livestock dynamics and sustainable development of a French wet mountain territory: local stakeholders' points of view

Background

The Livradois-Forez is a rural medium mountain territory in the center of France where livestock is a major activity, based on the breeding of ruminants and valorization of grassland and pastoral areas. As in many French mountain regions, considering globalization and local changes, the future of livestock is often questioned, in particular to assess how it contributes and could encourage the sustainable development of the territory.

Questions

How local stakeholders perceive the current and future role of livestock? Do their perceptions similarly include the different dimensions of sustainable development? Which models of agriculture are acknowledged ?

Conclusions/Next steps

More than showing the ways livestock may participate to sustainable development of a territory, the results highlight the diversity of scales (farm, sector, food chain, territory) and the coexistence of models for local-actors involved in this development. These results point out the importance of stakeholders' coordination to promote sustainable development.

Results

Various but complementary regards on the roles of livestock between economic, social and environmental aspects, even if each actor doesn't give the same importance to each of these dimensions of sustainability.

Forms of livestock farming that could fulfill these roles are also very different according to the interviewees.

• Scale of farm and production: divergences of views between -a vision staking on high volumes of product with a little added-value; -a vision staking on little volumes with a high added-value.

• Scale of sector and commercialization: all agree on a need for more valorization of milk and meat products but opposition between advocates of 2 different ways: -through industrialized sector; -through local sector, especially short food supply chains.

• Territory scale: vision much more shared, in favor of -diversified livestock farming (large and small productions) and commercialization (outside and inside territory); -maintaining transformation tools (dairies, slaughterhouses); -restructuring land tenure.

This work was carried out within the framework of the research project MOUVE. (2010-2014 ; https://www1.clermont.inra.fr/mouve/ ). It is supported by the French Research National Agency (ANR) (project n°ANR-2010-STRA-005-01)

Method

In 2012, individual interviews were carried out with 28 persons involved at different stages of the livestock chain (production, up-and down-stream, territorial, agricultural and environmental).

Economic role of livestock


Jérôme Randon

Marina Coquery

A Martin

C Margoum

Silicone rubber selection for passive sampling of pesticides in water

Keywords: Pesticide, Silicone rubber, Partition coefficient, Passive sampling, Liquid chromatography

Silicone rubber selection for passive sampling of pesticides in water.

Introduction

Intensive agricultural consumption of pesticides requires the accurate monitoring of these contaminants in the aquatic environment to protect aquatic ecosystems and human health. Pesticides are present in trace amounts, and comprise organic compounds with a wide range of properties, raising a challenge for sampling and analytical methods to evaluate water resource quality. A number of sorptive techniques based on silicone rubber (SR) materials have been developed over the past 30 years to extract organic compounds such as pesticides.

Silicone rubbers were developed as sorptive extraction phases in the 1980s, the dominant polymer being polydimethysiloxane (PDMS). The most popular sorptive methods using SR, combining robustness and reproducibility, are solid-phase microextraction (SPME) [1], stir bar sorptive extraction (SBSE) [2] and silicone rods/tubes [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF]. SRs are mechanically resistant, insoluble in the common solvents used for analyte back-extraction, and allow thermal desorption at high temperatures (250-300 °C) [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF]. Silicone rubbers will extract organic compounds with a broad range of polarities (PAHs, PCBs, pesticides, etc., with log K ow >2-3) from aqueous solutions, despite their hydrophobic nature [START_REF] Schellin | Application of a polysiloxane-based extraction method combined with large volume injection-gas chromatography-mass spectrometry of organic compounds in water samples[END_REF]. The characteristics of SRs have also been successfully exploited for in situ passive sampling in surface waters with the membrane-enclosed silicone collector (MESCO) [START_REF] Vrana | Membrane-enclosed sorptive coating. an integrative passive sampler for monitoring organic contaminants in water[END_REF], passive SBSE [START_REF] Assoumani | Use of passive stir bar sorptive extraction as a simple integrative sampling technique of pesticides in freshwaters: determination of sampling rates and lag-phases[END_REF] and silicone rods/sheets/tubes [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF], [START_REF] Emelogu | Identification of selected organic contaminants in streams associated with agricultural activities and comparison between autosampling and silicone rubber passive sampling[END_REF] and [START_REF] Smedes | Guidelines for passive sampling of hydrophobic contaminants in water using silicone rubber samplers[END_REF].

All extraction methods using SR are based on partitioning the analytes between the water sample and the silicone phase, which is governed by the partition coefficient (K sw ), defined as the ratio of the concentration of the analyte in the silicone to its concentration in water in equilibrium conditions. The partition coefficient is one of the key parameters in sorptive methods using SRs, as illustrated by the theoretical calculation of extraction recovery for SBSE in batch experiments (Eq. ( 1), from [START_REF] Prieto | Stir-bar sorptive extraction: A view on method optimisation, novel applications, limitations and potential solutions[END_REF]) and calculation of in situ water concentration (C w(in situ) , µg L -1 ) for passive sampling of hydrophobic contaminants in field applications over a period of exposure (Eq. (2), from [START_REF] Vrana | Membrane-enclosed sorptive coating. an integrative passive sampler for monitoring organic contaminants in water[END_REF]). Other parameters are also required, such as volume of water in batch experiments (V w , L), volume of silicone (V s , L), sampling rate (R s , L d -1 ), and exposure time of a passive sampler (t, d).

The popularity of SRs as sorptive materials has prompted numerous studies using materials of varied origin and from different suppliers, raising concerns about the variability of sorption properties [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF], [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] and [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF]. Some components are present in SR formulations in varying proportions, resulting in physical-chemical properties specific to each material (e.g., density, hardness, elongation) [START_REF] White | Rubber technologist's handbook[END_REF]. Silicone rubber manufacturing requires additional components such as a base and a crosslinking agent (silicone oils) that react by various chemical mechanisms via a catalyst (platinum cure or peroxide activated cure), and often also a filler (amorphous or modified silica, calcium carbonate, metasilicate, etc.) for mechanical strength [START_REF] White | Rubber technologist's handbook[END_REF]. The base and the crosslinking polymer can be pure PDMS or modified polysiloxane with some phenyl, vinyl or fluoro groups (MVQ, PMVQ or FMVQ) of various molecular weights and low functional content. The complex manufacturing process, unfortunately not disclosed by silicone suppliers, also generates non-polymerized residuals such as small polymer chains (oligomers) and other impurities [START_REF] Lipp | The Analytical Chemistry of Silicones[END_REF].

However, only few studies have investigated the impact of this diversity on the sorption of organic compounds [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF], [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF] and [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF]. Rusina et al. [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF] showed that differences in log K sw values for several SRs reached 0.4 log units for four PAHs (log K ow from 3.37 to 6.86), and noted that the presence of fluorinated groups resulted in slightly lower K sw values.

Ter Laak et al. [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF] studied the uptake of polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) (log K ow 4.70-8.30) in PDMS fiber (SPME) and PDMS sheets, and found better performance for PDMS sheet, 0.21±0.08 log units higher for the silicone-water partition coefficient. Yates et al. [START_REF] Yates | Passive sampling: partition coefficients for a silicone rubber reference phase[END_REF] and Smedes et al. [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] used a cosolvent method with methanol to determine the K sw of PAHs and PCBs on SRs obtained from different suppliers, and showed different correlations between log K ow and log K sw for these two kinds of compounds. These results suggest that polymer-water partitioning is not driven exclusively by hydrophobic interactions, but also by compound-specific interactions with the polymer phase (electron-acceptor/donor characteristics of both) [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] and the free volume theory describing diffusion in polymers (size of the compound) [START_REF] Garcia | Study of the relationship between transport properties and free volume based in polyamide blends[END_REF]. Smedes et al. [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] also found that differences in log K sw from different suppliers ranged from 0.16 to 0.58 and might differ by a constant SR-specific factor for PAHs and PCBs. These studies focused on the uptake of several specific classes of hydrophobic compounds with high affinity for SR, but sorption of less hydrophobic compounds with log K ow <3-4, such as pesticides, have been less thoroughly investigated, owing to lower affinity for SR material. Heterogeneity in chemical structures and properties of pesticides could also lead to greater differences than in previous studies on hydrophobic compounds in relation with SR material.

In this context, we investigated the sorption of pesticides over a broad polarity range by several SRs to select the best one for passive sampling in aquatic environments. Other economic factors and ease of applicability by laboratories were considered in the selection.

The impact of SR formulation was tested using SRs from various suppliers, covering a broad diversity of silicone materials with different curing reactions, formulations and fields of application. We also highlighted the differences in material structure of the selected SRs by analyzing their functional groups, oligomer content and thermal resistivity. We performed batch kinetic experiments to determine experimental extraction recovery as an evaluation criterion for pesticide uptake capacity. We then measured pesticide partition coefficients K sw , poorly documented in the literature, for the selected SR with a sorption isotherm in five experiments. Finally, these data were related to their log K ow values with relevant literature studies for various classes of organic compounds to enable a wider use of SR as a sorptive material.

Materials and methods

Experimental section

Chemicals

The 23 pesticides selected, including two metabolites, covering a broad polarity range (-0.06 < log K ow < 5.51), are listed in Table 1. Pesticides and one internal standard (diuron-d6) were purchased from Dr Ehrenstorfer (Augsburg, Germany) and Sigma Aldrich (Saint-Quentin Fallavier, France). Acetone and dichloromethane for pesticide residue analysis, UHPLC grade acetonitrile and methanol were purchased from Biosolve (Dieuze, France). LC-MS grade formic acid (purity 98%) was supplied by Fischer Scientific (Illkirch, France).

Ultrapure water was obtained using a Millipore water purification system equipped with an LC-Pak cartridge to remove remaining organic contaminants present in trace amounts (Billerica, MA, USA). Nitrogen gas (purity 99.995%) used for thermal decontamination was purchased from Air Liquide (Juhem, France).

Selection of silicone rubber materials and conditioning steps

The most widely used SR, commercialized by Gerstel under the name Twister® (TW)

for analytical SBSE and recently used as a passive sampler (MESCO/Passive SBSE), was taken as a reference SR in this study. Six other SRs were selected to cover the diversity of silicone rubber formulations (Table 2). They were purchased from different manufacturers, either as silicone oils ("Polymerized SR kits": one-component or two-component kits ready for polymerization) or as SR already polymerized by the manufacturer ("Manufactured SRs").

For "Polymerized SR kits", we followed the suppliers' instructions to produce polymerized sheets of SR approximately 3 mm thick in plastic Petri dishes, with heat for SY, ultraviolet light for UV and water for SB to initiate cross-linking. Five of the selected SRs had already been studied as extractive materials: TW [START_REF] Prieto | Stir-bar sorptive extraction: A view on method optimisation, novel applications, limitations and potential solutions[END_REF] and SL [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF] for water extraction in the laboratory; AL, ST and SL for passive sampling of hydrophobic compounds (organochlorine pesticides, PAHs, PCBs [2] and [START_REF] Vrana | Membrane-enclosed sorptive coating. an integrative passive sampler for monitoring organic contaminants in water[END_REF]), and SY for passive sampling of pesticides [START_REF] Assoumani | Polydimethylsiloxane Rods for the Passive Sampling of Pesticides in Surface Waters[END_REF].

To facilitate the comparison of SRs, raw materials from different suppliers were shaped into closely similar volumes. Silicone sheets were cut with a clean cutter blade into rectangular cuboids measuring approximately 20×3×3 mm, and silicone thread into rods 20×3 mm. Twister® was purchased in 20×1 mm thick PDMS film (V=126 µL), and the magnetic stirring bar was removed before use. Rectangular cuboid SR samples were selected by weight (170 mg±2.5%), and their exact volume was then calculated from their density (Table 2). Before use, SRs were cleaned by chemical and thermal conditioning to eliminate most residues, such as oligomers that can interfere with the instrumental analysis (particularly for thermodesorption (TD)-GC/MS analysis). First, SRs were chemically conditioned in a solvent mixture (dichloromethane: methanol, 50:50, v/v) under sonication for 15 min. Pieces of SRs were then wiped with lint-free tissue and dried at 70 °C for 1 h. Lastly, SRs were thermally treated in a Gerstel tube conditioner TC (Mülheim a/d Ruhr, Germany) under a nitrogen stream of 75 mL min -1 with a temperature ramp of 10 °C min -1 to 300 °C for 6 h.

Kinetic study

A kinetic study of the selected SRs was performed to compare extraction recoveries, and determine the equilibrium time of the 23 pesticides. One piece of each SR was inserted into a 60 mL amber glass vial containing 50 mL of Evian® water spiked with the pesticides studied (pH=7.2). Acetone content, for water spiking, was calculated so as not to exceed 0.1 vol% in the aqueous phase. The initial spiked concentration of each pesticide was adjusted from 0.4 µg L -1 to 15 µg L -1 , depending on the analytical limit of quantification (Table 1), and affinity with SR materials already reported with SBSE in water sample laboratory extraction [START_REF] Margoum | Stir bar sorptive extraction coupled to liquid chromatography-tandem mass spectrometry for the determination of pesticides in water samples: method validation and measurement uncertainty[END_REF]. Stirring (800 rpm) was done with a glass magnetic stir bar on a Variomag magnetic stirrer purchased from Thermo (Waltham, MA, USA). The experiment ran for 48 h in batches with one piece of SR for each sampling point at 15 min, 1 h, 3 h (triplicate), 8 h, 24 h (triplicate) and 48 h, with seven experimental blanks without pesticides and three control batches without SR.

The extraction recovery is expressed as the ratio of the mass of analyte in the SR phase (m s ) to the initial mass of analyte in the water phase (m w,0 ) corrected with the control batch.

For hydrophobic pesticides (log K ow ≥3), m s was computed by the difference in concentration of pesticides measured in water (C w ) between the control batch and the final state of the experiment. For hydrophilic pesticides (log K ow <3), a direct determination by solvent back-extraction of the SRs (see Section 2.2.2) was preferred. For hydrophilic pesticides, differences in concentrations in water between initial and final states were too low to determine extraction recovery, resulting in high uncertainties in final results.

Partition coefficients

Following on from the kinetic study, we investigated sorption capacity of the preferred SR, SL, and TW as reference material (Table 2). With this aim, partition coefficients (K sw ) were calculated in an isothermal experiment with five levels of concentration (C1, C5, C10, C25 and C50) expressed relative to C1. Initial spiked concentrations were adjusted for each pesticide to allow a quantitative analysis of concentration in equilibrium conditions: C1 concentration level corresponded to spiking at 0.5 µg L -1 , for pesticides with log K ow <3 and at 10 µg L -1 for those with a higher log K ow . Like in the kinetic study, batch experiments were performed in triplicate, at an ambient temperature of 20.5 °C with an equilibrium time of 48 h. Partition coefficients were calculated as the slope of the unweighted linear regression of analyte concentration in SR (C s, eq in µg L -1 ) plotted against concentration of pesticides in water in batches (C w,eq in µg L -1 ) at equilibrium. Linear regressions were also performed with errors in x and y axes by the Williamson-York bivariate method (in a Microsoft Excel spreadsheet adapted from Cantrell [START_REF] Cantrell | Technical Note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems[END_REF]). Linear regressions were forced through the origin, for non-detection of pesticides in SR blanks. The errors in x and y were specified as a confidence interval (α=0.05, n=3) taking into account uncertainties of SR and water volumes for each experimental point. Slope uncertainty (u(K sw )) was also expressed as a confidence interval.

Analytical methods

Physical-chemical properties of silicone rubbers

To explain similarity or differences in the pesticide sorption properties of the SRs studied, we investigated the silicone chemical structure of raw SRs by infrared spectroscopy. Attenuated Total Reflectance spectroscopy (ATR-FTIR) is a rapid and easy technique for the identification of organic groups in cured organosilicon materials [START_REF] Lipp | The Analytical Chemistry of Silicones[END_REF]. A spectrum Two with UATR accessory (Single Reflection Diamond) from Perkin Elmer (Roissy-en-France, France) was used with a resolution of 4 cm -1 and 16 acquisitions per spectrum in the range 4000-520 cm -1 . Thermal gravimetric analysis (TGA) was carried out to obtain the residual content of SRs (expressed in %) at high temperature and to determine the SR degradation temperature on raw and conditioned materials. First, TGAs were performed on a TA Instruments Q50 (New Castle, USA) under nitrogen and air stream up to 800 °C (90 mL min -1 and 50 °C min -1 ), maintaining the target temperature for 30 min. Second, TGAs were performed under a helium stream of 45 mL min -1 with a temperature ramp of 10 °C min -1 up to 500 °C (raw and conditioned SRs). The release of residual oligomers by chemical and thermal conditioning steps of SRs was also estimated by measuring weight loss between raw material mass and conditioned SR. Scan chromatograms were also acquired by TD-GC/MS analysis in full scan mode (TD: 300 °C, 15 min, helium at 75 mL min -1 ), as detailed in Assoumani et al. [START_REF] Assoumani | Use of experimental designs for the optimization of stir bar sorptive extraction coupled to GC-MS/MS and comprehensive validation for the quantification of pesticides in freshwaters[END_REF], to identify some oligomers and validate the usefulness of the conditioning steps.

Pesticide solvent back-extraction and sample preparation

Pesticides were desorbed from SRs by chemical extraction according to the protocol developed by Margoum et al. [START_REF] Margoum | Stir bar sorptive extraction coupled to liquid chromatography-tandem mass spectrometry for the determination of pesticides in water samples: method validation and measurement uncertainty[END_REF] for SBSE application. To compare SRs, optimization of the solvent back-extraction step was not required. Briefly, silicone rods were removed with clean tweezers from water, cleaned with ultrapure water and dried with a lint-free tissue. After storage for at least 24 h at -18 °C, SRs were desorbed with 200 µL of methanol/acetonitrile (50:50, v/v) in a 250 µL flat-bottom glass insert under sonication in an ultrasonic bath (FB11014, Fisher Scientific, UK) for 15 min; 150 µL of ultrapure water and 10 µL of internal standard solution (diuron-d6 at a concentration of 200 mg L -1 ) in acetone were then added to 40 µL of the desorbate to make up the extracted sample for analysis. Four steps of solvent back-extraction on the same silicone rod confirmed that the proportion of hydrophilic pesticides (log K ow <3) extracted with the first step exceeded 80% (SI-1) and was not statistically different from 100% (Student t-test, n=3, CL=95%). For experiments, analysis of water samples was performed by direct injection. A volume of 10 µL of diuron-d6 at a concentration of 1000 mg L -1 was added to 990 µL of an aliquot of water to obtain a concentration of diuron-d6 of 10 µg L -1 as for SR extract analysis.

Analysis by liquid chromatography

Ultra-high-performance liquid chromatography (UHPLC) was performed on a Shimadzu series Nexera X2 UHPLC system (Marne-la-Vallée, France). Analytes were separated on an Acquity UPLC® HSS T3 1.8 µm 2.1×100 mm column. Chromatographic separation of analytes was carried out with acetonitrile and ultrapure water, both with formic acid (0.1%) in an analytical gradient (from 10% to 90% acetonitrile) lasting 15 min at 0.4 mL min -1 . The UHPLC system was coupled to a triple-quadrupole mass spectrometer (MS/MS) API 4000 from AB Sciex (Les Ulis, France). The electrospray ion source (Turbo-Ionspray from AB Sciex) was set to positive mode at 600 °C. Quantification was performed with deuterated diuron-d6, used as injection internal standard. Settings for MS/MS analysis (SI-2) were from Margoum et al. [START_REF] Margoum | Stir bar sorptive extraction coupled to liquid chromatography-tandem mass spectrometry for the determination of pesticides in water samples: method validation and measurement uncertainty[END_REF] with additional pesticides optimized and validated later.

Results and discussion

Silicone rubber characteristics

The main readily available macroscopic SR parameter is density, which generally ranges from 1.05 to 1.60 [START_REF] Lipp | The Analytical Chemistry of Silicones[END_REF]. In the data shown in Table 2, the densities of "Polymerized SR kits" sheets (SB, SY and UV) were lower than those of "Manufactured SRs" by 0.2 units. A higher density of SR reflects an increase in cross-link density or silica filler content, or substitution of methyl groups by other functional groups [START_REF] White | Rubber technologist's handbook[END_REF]. To our knowledge, few SRs with densities lower than 1.15 have been tested for compound sorption in water [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] and [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF], but the density was not always known.

TGA at 800 °C under nitrogen ranked the SRs from lowest to highest content: SB(17.3%)>AL(31.0%)>TW(32.5%)>SL(35.5%)>SY(51.0%)>UV(60.4%)>ST(76.3%).

More abundant residues could not be directly linked to higher filler content owing to the complex thermal degradation of crosslinked PDMS [START_REF] Hamdani | Flame retardancy of silicone-based materials[END_REF]. The equivalent final amount of residues and the similar nitrogen/air ratio (SY and UV≈0.90 and TW, AL and SL≈0.75) observed between SRs can be explained by their similar components and formulations.

The amount of oligomers eliminated by the conditioning steps was also studied to highlight differences in SR formulations. The mean weight loss recorded for the SRs studied after chemical and thermal conditioning was 3.2%. Soxhlet extraction with the same solvent for a further 3 days. This loss could be attributed to the release of small residue molecules (oligomers) from raw polymers or residues from thermal degradation of cross-linked PDMS chains. PDMS undergoes thermal rearrangement at high temperature (250-350 °C) with cleavage of the siloxane chains to form products that are predominantly cyclic siloxanes of low molecular weight [START_REF] Hamdani | Flame retardancy of silicone-based materials[END_REF]. These results were completed by the analysis of chemical organic groups of the SR backbone by ATR-FTIR (SI-3), and TGA analysis of SY and SL after each conditioning step (SI-4). We concluded that "Polymerized SR kits" contained higher amounts of residues than "Manufactured SRs", owing to a non-stoichiometric ratio in the curing process. These findings also demonstrate the efficiency of our selected conditioning steps for eliminating residuals in SRs. Additional investigation of the nature of residues in SRs by TD-GC/MS analysis is reported in SI-6.

Mainly cyclic PDMS chains were found from cycloterasiloxane (octamethyl-), (M=296.6 Da)

to cyclodecasiloxane (eicosamethyl-) (M=741.5 Da), and small linear PDMS chains (approximately 500 Da) with seven and eight dimethyl siloxane groups. These results agree with those of Lykissa et al. [START_REF] Lykissa | Release of low molecular weight silicones and platinum from silicone breast implants[END_REF], who identified the main oligomers released in SRs as cyclic PDMS of 200-1500 Da, along with smaller amounts of linear PDMS of 500-1500 Da.

Kinetic study and determination of extraction recoveries

No pesticide degradation or adsorption was observed for the control batch during the 48 h of experimentation, except for PCM, with a mean decrease in concentration of 50%. The half-time of hydrolysis in water of PCM is about 25 days (University of Hertfordshire, Pesticide Properties DataBase), so this decrease was probably due to adsorption on glass in the experimental system. The most hydrophilic pesticide studied, FLZ (log K ow =-0.06), was not sorbed in any of the SRs studied; no data are therefore presented for this compound.

Repeatability of results between batches tested at 3 h and 24 h, expressed as RSD (n=3), was less than 15% for every SR.

For all the compounds, a first-order kinetic model of accumulation [START_REF] Prieto | Stir-bar sorptive extraction: A view on method optimisation, novel applications, limitations and potential solutions[END_REF] was applied according to the SBSE theory, and a satisfactory correlation coefficient was obtained (r>0.87). For hydrophobic pesticides, the mass of pesticides sorbed remained stable after 24 h, as shown for DFF (Fig. 1a). For hydrophilic compounds, such as SMZ (Fig. 1b), equilibrium was reached more slowly (after 24-48 h). Uptake of hydrophilic compounds in SRs was lower in mass, consistent with the lower affinity for the SR phase. Differences between SRs in the uptake kinetics of hydrophilic compounds could be due to variations in the volume or apparent surface area of each silicone rod. The surface area of silicone rods can affect uptake kinetics but not equilibrium state. Different polymer diffusion coefficients can also influence kinetic parameters. Considering SR as an absorbent material [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF], a strict comparison and selection of the best SR was required to normalize extraction recoveries to the same volume for all SRs. (1 )

V R V R VV RR VV   . (Eq. 3)
The normalized R NV took into account uncertainties in SR volume (2.5% for weight measurement and 5% for density). Overall, for all the SRs, pesticides with log K ow ≥3 had higher affinities, with R NV >20% (Fig. 2), in line with chemical properties of SRs [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF] and [START_REF] Prieto | Stir-bar sorptive extraction: A view on method optimisation, novel applications, limitations and potential solutions[END_REF].

However, the high affinity of AZS (log K ow =2.5, R NV =17%) toward SRs could be explained by stronger interactions with SRs, related to specific molecular structure and properties.

Comparison of sorption capacities and selection of the best SR

The extraction recovery normalized by volume, R NV , was used as a discriminating criterion for comparison of SR sorption capacities for pesticides. One-way ANOVA (ANalysis Of VAriance) with a post hoc Tukey HSD (Honest Significant Differences) (XLStat software, 2015.4.1) was used to find SRs that were significantly different from each other, for each pesticide. Four pesticides (CPE, CPM, DFF and SPX) showed no significantly different extraction recoveries for the SRs studied. During the experiment, pesticide concentrations in water after 24 h lay below the limit of quantification (LOQ) (Table 1) in three cases (CPM, CPE and SPX). Consequently, differences in extraction recoveries, all near 100%, could not be deduced from the experimental data. Significant differences between the different types of SRs were found for the other 19 pesticides, as illustrated by CFV, with R NV ranging from 49% to 95% depending on the SR (Fig. 2). To our knowledge, this is the first time that such differences in sorption have been demonstrated between different formulations of SR for a broad range of pesticides. The earlier study of Rusina et al. [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF], which compared sorption of PAHs and PCBs by several SRs, found differences in log K sw below 0.4 log units Using the results of the comparison of SR sorption capacities for pesticides, we then chose an SR from the group of "Manufactured SRs" with the best sorption properties for pesticides. Additional variables such as cost, commercial availability, available shapes (rods, sheets, etc.) and results of previous studies, were also taken into account. The production of AL was recently discontinued by the manufacturer. We selected the SR called SL (Table 2).

This SR was also the one previously chosen to replace commercial Twister® in a MESCO passive sampler [START_REF] Paschke | Rapid semicontinuous calibration and field test of membrane-enclosed silicone collector as passive water sampler[END_REF]. Further investigation is needed for other hydrophobic compounds to replace AL (no longer available) by SL.

Partition coefficients

To use SL as an extractive material for laboratory applications or as a passive sampler, the K sw of pesticides with SL and TW (as reference) were assessed by an isothermal experiment at five concentration levels. In this experiment, the mass balance of control batches (i.e. without SR) was verified for the five spiked levels with all the pesticides, except for PCM (owing to its adsorption, see 3.2) and for DFF for the two highest spiked levels, because the initial water concentrations (see 2.1.4) exceeded solubility in water for this compound (50 µg L -1 at 20 °C).

Linear sorption isotherms were obtained for 13 pesticides (ATZ, CBZ, CPE, DCA, DCPMU, DFF, DIU, IPU, NFZ, SMZ and SPX) with a satisfactory correlation (r 2 >0.95) (all sorption isotherms are available in SI-7) as predicted by absorption theory [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF]. However, sorption isotherms of 10 other pesticides (ATC, AZS, CFV, CPM, CTU, DMM, FNT, LINU, MTC, and TBZ) deviated from linear to curvilinear sorption isotherms at high concentration levels (C25 and C50). Equilibrium state at 48 h for C50 level was confirmed by further kinetic experiments over one week. Thus the Freundlich empirical model was applied for these compounds C s =K F C w 1/n (Eq. ( 3)), with K F and n dimensionless Freundlich parameters.

These curvilinear sorption isotherms conflict with linear absorption isotherms generally accepted for SR with hydrophobic compounds [START_REF] Van Pinxteren | Silicone rod and silicone tube sorptive extraction[END_REF]. This can be explained by double sorption mechanisms: absorption in the PDMS bulk, and adsorption onto other SR constituents such as amorphous or treated silica used as filler. In such adsorbent material, competition for pesticide adsorption can occur, and the material can be saturated, these two processes leading to a curvilinear sorption isotherm [START_REF] Parida | Adsorption of organic molecules on silica surface[END_REF].

Sorption isotherms at low concentrations tested in our batch experiments, C1, C5 and C10 (0.5 µg L -1 to 100 µg L -1 ), are the most relevant part of SR sorption isotherms, as these concentrations lie in the range of those generally found in environmental waters and assessed by passive sampling. Partition coefficients (K sw ) with SL silicone rubbers could thus be deduced satisfactorily for all the pesticides studied by a linear regression over the whole concentration range with the Williamson-York bivariate method, which gives more weight to low concentration levels due to reduced experimental errors (SI-7).

We compared K sw values determined for SL to those of TW determined in the same experimental conditions. A linear correlation was found, described by log K sw (SL)=0.93og K sw (TW)+0.44(r 2 =0.96). Thus, SL showed better extraction properties than TW, with an increase of 0.44 log K sw unit for the pesticides. This difference between SRs was in line with the conclusions of Smedes et al. [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF], who suggested a constant difference factor (from 0.16 to 0.58 log unit) for SR from five different suppliers with PAHs and PCB. These results also confirm previous conclusions drawn from sorption capacity comparisons (SL>TW) as demonstrated by PCA analysis (Fig. 3).

Correlation between log K sw and log K ow

As shown by other authors [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF], the estimation of K sw as a function of the polarity of organic compounds, expressed as K ow , is expected to follow a linear correlation. We therefore compiled K sw and K ow from various compound families and SRs (only "Manufactured SRs") obtained by laboratory studies in the literature (anilines: this study (DCA) and [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF]; chlorobenzenes: [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF]; PAHs: [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF], [START_REF] Yates | Passive sampling: partition coefficients for a silicone rubber reference phase[END_REF], [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF] and [START_REF] Rusina | Calibration of silicone rubber passive samplers: experimental and modeled relations between sampling rate and compound properties[END_REF]; PCBs: [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF], [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF], [START_REF] Yates | Passive sampling: partition coefficients for a silicone rubber reference phase[END_REF], [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF] and [START_REF] Rusina | Calibration of silicone rubber passive samplers: experimental and modeled relations between sampling rate and compound properties[END_REF];

pharmaceuticals: [START_REF] Paschke | Silicone rod extraction of pharmaceuticals from water[END_REF] and [START_REF] Magner | Evaluation of poly(ethylene-co-ninyl acetate-cocarbon monoxide) and polydimethylsiloxane for equilibrium sampling of polar organic contaminants in water[END_REF]; phenols: [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF]; pesticides: this study and [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF], [START_REF] Magner | Evaluation of poly(ethylene-co-ninyl acetate-cocarbon monoxide) and polydimethylsiloxane for equilibrium sampling of polar organic contaminants in water[END_REF] and [START_REF] Heltsley | Novel methods for monitoring chlorinated contaminants in aquatic environments[END_REF])

Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ (Fig. 4). Data with log K ow >3.5 (chlorobenzenes, PAHs, PCBs and pesticides) are well represented in numerous studies, whereas data for more hydrophilic compounds (anilines, pesticides, pharmaceuticals and phenols) come from few studies. Previously studied pesticides were mainly organochlorine pesticides (n=30) with log K ow >3 from Hestley [START_REF] Heltsley | Novel methods for monitoring chlorinated contaminants in aquatic environments[END_REF] with "Virgin PDMS" (d≈1, Diversified Silicone Products Inc.); and a few polar pesticides from Kwon et al. [START_REF] Kwon | Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly(dimethylsiloxane) and water[END_REF] (n = 5), with medical grade SR (d=1.17, Specialty Silicone Products), and from Magner et al. [START_REF] Magner | Evaluation of poly(ethylene-co-ninyl acetate-cocarbon monoxide) and polydimethylsiloxane for equilibrium sampling of polar organic contaminants in water[END_REF] (n=5) with SR tubing (unknown density, Noax Lab). Recent work on passive sampling of 124 pesticides using SR in river water [START_REF] Ahrens | Characterization of five passive sampling devices for monitoring of pesticides in water[END_REF] was not taken into account in this study because uptake kinetic of pesticides were insufficiently described (four points in an experiment over 25 days) and highly variable over time (no confirmation of the equilibrium state for several compounds). Thus data from our study on pesticides covering a broad range of log K ow (1.50-5.51), fill an important gap in the literature, enabling wider use of SR as a sorptive material (Fig. 4). A single linear relationship of data for all compounds (log K sw =0.94log K ow -0.47; n=253; r 2 =0.88; s y/x =0.54) (Fig. 4), resulted in a high standard error of estimate of K sw (s y/x ). This relationship extends the conclusions of Smedes et al. [START_REF] Smedes | Polymer-water partition coefficients of hydrophobic compounds for passive sampling: application of cosolvent models for validation[END_REF] for differences of sorption between SRs to compounds other than PAHs and PCBs. However, we restrict these conclusions to "Manufactured SRs" (i.e. polymerized by manufacturers), given the very large differences observed with "Polymerized SR kits". A better standard error of the estimate of K sw could be obtained with this linear model by restricting the diversity in the formulation of Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ the SRs used, and by plotting data for compounds with closely similar molecular structures.

Other predictive models for K sw have also been developed on PDMS fiber (SPME) data, such as multiparameter linear solvation energy relationships (LSER) [START_REF] Sprunger | Characterization of the sorption of gaseous and organic solutes onto polydimethyl siloxane solid-phase microextraction surfaces using the Abraham model[END_REF]. However, they have not been validated on SR, and require physiochemical parameters that are not readily available.

Conclusion

The selection procedure used in this study discriminated seven SRs from various suppliers for the extraction of pesticides in water. We found very large differences in affinity for pesticides between "Polymerized SR kits" and "Manufactured SRs", never demonstrated before. These differences were linked to the broad diversity of the SR formulations studied.

Accordingly, partition coefficients (K sw ) of 21 pesticides were determined with SL to fill a gap in the literature for hydrophilic to moderately hydrophobic compounds (1.5<log K ow <5.5). Log K sw from this study and data from the literature plotted against log K ow , showed satisfactory linear correlation for the prediction of K sw for non-studied compounds, with an error in the estimate of 0.54 log units. Finally, given sorption properties, literature data and other economic and technical factors, we suggest using SRs from Goodfellow (SL) in future work to reduce variability of K sw values in the literature. We hope that the use of SR, as an extracting material for a wide range of organic compounds, and especially as a passive sampler, will thereby be simplified. Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ SI-3. Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) spectrum of silicone rubbers (SRs) studied and interpretation (color printing).

Chemical organic groups of the SR backbone were studied by interpreting ATR-FTIR spectra for each SR. The characteristic band for symmetrical CH 3 deformation occurs at 1260 cm -1 (with one or more in the range 865-750 cm -1 ) and with CH stretching absorption around 2910 and 2970 cm -1 for all the SRs, confirming methyl groups are the main components identified in PDMS chains of SRs (Lipp and Lee Smith, 1991;[START_REF] Launer | Silicon Compounds: Silanes & Silicones, Infrared Analysis of organosilico compounds: Spectra-structure correlations[END_REF]. Although infrared spectra are largely similar between SRs, there are some differences. For example, when the characteristic weak band of dimethyl units shifts from 860 cm -1 to 845 cm -1 , as observed for SY and UV, units on siloxane chains are random or alternating, and not block [START_REF] Launer | Silicon Compounds: Silanes & Silicones, Infrared Analysis of organosilico compounds: Spectra-structure correlations[END_REF]. We also observe a broader and more complex Si-O-Si absorption (1130-1000 cm -1 ), showing two overlapping bands, especially for SB, SY and UV, which highlights longer or more branched siloxane chains. Hence SY, SB and UV have more complex internal networks of PDMS chains than other SRs. In addition, for SY and UV SRs, a weak band in the range 2100-2300 cm -1 and at 910 cm -1 confirm a residual content of reactive Si-H groups for raw materials, confirming a non-stoichiometric ratio for the curing process by hydrosilylation. No other functional groups such as phenyl or vinyl are clearly identified with this analytical technique. SI-4. Thermal gravimetric analysis (TGA) of silicone rubbers SY and SL for raw and conditioned materials (CC: chemical conditioning and TC: thermal conditioning)

The degradation temperatures of the two raw SRs from "Polymerized kit" and "Manufactured" materials (SY and SL) were determined by TGA analysis. A first weight loss of 0.05% was recorded at 89 °C for SY and 223 °C for SL. The very large difference underlines differences in SR formulation, and suggests a higher content of oligomers in SY than in SL. Complementary TGA analysis on conditioned SL shows that the thermal conditioning step increases the temperature of the first weight loss (0.05%) from 223 °C to 360 °C. This finding suggests that oligomers are released before the thermal degradation of the material. It also highlights the reliability of conditioning steps for the elimination of residual oligomers in raw SR.

Fig. 1 .

 1 Fig. 1. Uptake kinetics of (a) diflufenican (DFF, log K ow =4.2) and (b) simazine (SMZ, log K ow =2.3) by studied SRs with first-order kinetic model. Triplicate at extraction time of 3 h and 24 h. Accordingly, extraction recoveries (R), calculated at 24 h (n=3), were normalized to a volume of 150 µL (mean volume, V NS) of SR based on Eq. (1), to give a new criterion R NV (extraction recovery normalized by volume) using Eq. (3).

Fig. 2 .

 2 Fig. 2. Extraction recoveries of studied pesticides on selected SRs normalized to volume of 150 µL (R NV ) after 24 h. Pesticides were ranked in ascending order of log K ow with log K ow <3 (a) and log K ow ≥3 (b). Error expressed as standard deviation (n=3).

Fig. 3 .

 3 Fig. 3. Comparison of SRs sorption properties for pesticides with PCA analysis (variables=19, individuals=7).

Fig. 4 .

 4 Fig. 4. Correlation between log K sw and log K ow from this study and literature data sorted by class of compounds: anilines (bars), chlorobenzenes (diamonds), PAHs (crosses), PCBs, (triangles), pharmaceuticals (stars), phenols (squares), and pesticides (circles colored by study).

Table 1 . List of selected pesticides and LOQ obtained by UHPLC-MS/MS analysis.

 1

	Pesticide	Abbreviation Log K ow	a	LOQ (µg L -1 ) b
	Acetochlor	ATC	4.14		0.40
	Atrazine	ATZ	2.70		0.10
	Azoxystrobin	AZS	2.50		0.020
	Carbendazim	CBZ	1.50		0.020
	Chlorfenvinphos	CFV	3.80		0.16
	Chlorpiryphos-ethyl	CPE	4.70		0.40
	Chlorpiryphos-methyl	CPM	4.00		0.40
	Chlortoluron	CTU	2.50		0.40
	3,4-Dichloroaniline	DCA	2.69		0.40
	3-(3,4-Dichlorophenyl)-1-	DCPMU	2.31*		0.16
	methylurea
	Diflufenican	DFF	4.2		0.40
	Diuron	DIU	2.87		0.40
	Dimethomorph	DMM	2.68		0.16
	Flazasulfuron	FLZ	-0.06		0.20
	Fenitrothion	FNT	3.32		4.0
	Isoproturon	IPU	2.50		0.16
	Linuron	LINU	3.00		0.16
	Metolachlor	MTC	3.40		0.040
	Norflurazon	NFZ	2.45		0.40
	Procymidon	PCM	3.30		1.6
	Simazine	SMZ	2.30		0.020
	Spiroxamine	SPX	5.51*		0.16
	Tebuconazole	TBZ	3.70		0.16
	a University of Hertfordshire, Pesticide Properties DataBase:
	http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm
	b Limit of quantification determined for direct analysis by UHPLC-MS/MS in Evian® water
	* Predicted by ChemAxon: http://www.chemicalize.org/structure

Table 2 . Main properties of the selected silicone rubbers (SRs)

 2 Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

	Material (other name)	Abbreviation	Supplier (Country)	Application field	Supplier formulation Shape (thickness or size)	Density (rod volume)	Curing reaction a (filler)
	Sylgard®184	SY	Dow Corning (USA)	Electronics	Polymerized SR kits (two components)	Sheet (3 mm)	1.03 a (165 µL)	Hydrosilylation (Vinyl modified silica)
	X-34-4184	UV	Shin Etsu (Japan)	Industrial	Polymerized SR kits (two components)	Sheet (3 mm)	1.03 a (165 µL)	Hydrosilylation (Not specified)
	SILBIONE®MED ADH 4300 RTV	SB	Bluestar Silicones (USA)	Medical	Polymerized SR kits (one component)	Sheet (3 mm)	1.08 a (157 µL)	Condensation (Silica amorphous and/or treated)
	Twister®	TW	Gerstel (Germany)	Analytical	Manufactured SRs	Stir bar (20 × 1 mm)	1.37 b (126 µL)	Not specified (Not specified)
	Altessil® (Versilic® S60V34B)	AL	Altec (UK)	Laboratory	Manufactured SRs	Sheet (3 mm)	1.20 a (142 µL)	Peroxide-cured (Mineral fillers)
	Silastic/Silopren	SL	Goodfellow (UK)	Industrial	Manufactured SRs	Thread/rod (3 mm)	1.20 b (133 µL)	Not specified (Some chalk)
	SIL-TEC®			Medical	Manufactured SRs
	(SILASTIC® BioMEdical Grade	ST	Technical Products (USA)			Sheet (3 mm)	1.20 a (142 µL)	Hydrosilylation (Silica)
	LSR)

a Information was collected from producers and/or internet sources b Experimental values by ratio of weight measured and theoretical volume (n = 3, s = 0.05)

  SI-2. Pesticide parameters for UHPLC-MS/MS analysis.

	Pesticide	Abbre-	Quantification	Declustering	Collision	Collision exit
		viation	transition (m/z)	potential (V)	energy (V)	potential (V)
	Acetochlor	ATC	269.9>224.2;	31	15	16
			269.9>148.1	31	27	14
	Atrazine	ATZ	215.9>174.1;	66	25	16
			215.9>104.1	66	41	8
	Azoxystrobin	AZS	404>372;	61	33	26
			404>344	61	35	28
	Carbendazim	CBZ	192>160;	56	25	16
			192>132	56	41	24
	Chlorfenvinphos	CFV	359>155;	76	17	20
			359>99	76	43	8
	Chlorpiryphos-ethyl	CPE	352>200;	45	30	38
			350>97	61	55	4
	Chlorpiryphos-methyl	CPM	322>125;	71	29	22
			322>290	71	23	54
	Chlortoluron	CTU	213>72;	51	25	12
			213>140	51	37	6
	3,4-Dichloroaniline	DCA	162>127;	51	31	24
			162>74	51	73	14
	3-(3,4-Dichlorophenyl)-1-	DCPM	219>162;	66	21	26
	methylurea	U	219>127	66	37	22
	Diflufenican	DFF	395>266;	86	35	28
			395>246	86	47	40
	Diuron	DIU	233>72;	46	51	6
			233>46	46	37	8
	Diuron-d6	DIU-d6 239>78;	66	43	14
			233>52	66	37	10
	Dimethomorph	DMM	388>301;	76	31	36
			388>165	76	43	28
	Flazasulfuron	FLZ	408>182;	81	59	34
			408>227	81	57	14
	Fenitrothion	FNT	278>125;	71	29	22
			278>109	71	25	16
	Isoproturon	IPU	207>72;	51	37	8
			207>165	51	19	28
	Linuron	LINU	249>160;	61	25	32
			249>182	61	19	12
	Metolachlor	MTC	284.1>252.2;	46	21	20
			284.1>176.2	46	37	4
	Norflurazon	NFZ	304>284;	101	35	26
			304>88	101	61	16
	Procymidon	PCM	284>256	76	25	46
	Simazine	SMZ	202.1>132.2;	56	29	10
			202.1>124.1	56	27	10
	Spiroxamine	SPX	298>144;	51	31	8
			298>100	51	45	18
	Tebuconazole	TBZ	308>70;	76	51	12
			308>125	76	57	12

Acknowledgments

The authors thank G. Raffin and M. Hangouët (ISA, UMR 5280) for TGA analysis and interpretation, and C. Guillemain (Irstea) for analytical support. This work was funded by the French National Agency for Water and Aquatic Environments (ONEMA) and the French National Research Agency (ANR) through the PoToMAC project (ANR 2011 CESA 022 02).

We thank ATT for English language editing.

Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Author-produced version of the article published in Talanta (2016), vol. 160, pp. 306-313 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2016.07.019 ©. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ (corresponding to an R NV variation of 10% according to Eq. ( 1) and approximation for CFV of log K sw ≈ log K ow =3.8).

A principal component analysis (PCA) (α=0.05) was performed to discriminate SRs (n=7) by extraction recoveries of relevant pesticides (n=19) (Fig. 3). The F1 and F2 axes explained respectively 82.9% and 9.2% of the data variability. The 19 variables were mainly positively linked to the F1 axis, which represents the mean value of extraction recoveries for each SR. Some pesticides were correlated positively (DCPMU) or negatively (TBZ) to the F2 axis, reflecting a special affinity of the silicone rubber SB for these compounds. Graphical representation of observations discriminated SRs mainly with regard to the F1 axis. The seven SRs investigated fell into three groups. SY and UV had very similar coordinates on the F1 and F2 axes, and showed the lowest pesticide sorption properties. For SB, we observed a medium affinity for pesticides, with the narrowest range of extraction recoveries. The last group presented the highest sorption properties for pesticides, with increasing effect on F1 axis: ST " SL>AL>TW, and similar F2 coordinates. These groups of SRs were the same as those obtained with the ratio of final residues (TGA under nitrogen and air, see results in SI-5), which identified similar components and formulation of SRs, with the exception of ST, which had a higher sorption capacity for pesticides than SL, AL and TW, and a higher final amount of residues than the other SRs. These results demonstrate that "Polymerized SR kits" (SY, UV and SB) were significantly less efficient for pesticide extraction than "Manufactured SRs" (AL, SL, ST, TW). This difference can be attributed to differences in formulation of the SRs, observed with ATR-FTIR spectroscopy (SI-3), showing a more complex internal network of PDMS chains in "Polymerized SR kits". Macroscopically, the differences seem to result from lower SR densities (Table 2). Specific behavior of SB, compared with SY and UV, could be attributed to different cross-linking reactions (condensation vs. hydrosilylation).

These conclusions are supported by the observations of Ter Laak et al. [START_REF] Ter Laak | Poly(dimethylsiloxane) as passive sampler material for hydrophobic chemicals: effect of chemical properties and sampler characteristics on partitioning and equilibration times[END_REF], who suggested that differences in SR formulations, such as cross-linking and polymer length, might modify the solubility of the chemicals in the SR, and thus modify K sw . An accurate understanding of SR sorption differences for pesticides would require further data on the formulation of the different SRs tested, unfortunately not disclosed by manufacturers. This point was not taken further in this study.

Author-produced version of the article published in Talanta ( 2016