Alexis Martin

Dr Christelle Margoum
email: christelle.margoum@irstea.fr

Antoine Jolivet

Bachir Azziz Assoumani

El Moujahid

Jérôme Randon

Marina Coquery

B El Moujahid

Calibration of silicone rubber rods as passive samplers for pesticides at two different flow velocities: Modeling of sampling rates under water boundary layer and polymer control

Keywords: pesticides, polydimethylsiloxane, freshwaters, mass transfer model, monitoring

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Introduction

The monitoring of trace levels of pesticides with a wide range of physical and chemical properties is a challenge for sampling and analysis to evaluate water resource quality. The variability in the discharge of pesticides into the environment, such as during flood events in small agricultural watersheds, requires the development of suitable methods that take temporal variations into account [1]. Passive sampling offers a promising alternative to classical grab sampling. It allows the determination of representative time-weighted average concentrations of contaminants, such as pesticides in freshwaters, at lower logistical and analytical costs [2].

Passive sampling community experts recently recommended monophasic polymers (e.g. silicone rubber (SR) or low density polyethylene (LDPE) as passive sampler materials of choice for hydrophobic and non-ionised organic compounds [3]. These passive samplers are available in a large variety of shapes: sheets, tubes, rods or coated stir bars named "Passive SBSE" (Stir Bar Sorptive Extraction) [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]. Modelling of contaminant uptake mechanisms in monophasic polymers, such as SR, is now well-documented for passive sampling of hydrophobic contaminants (PAHs and PCBs) [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF][START_REF] Vrana | Calibration of a passive sampler based on stir bar sorptive extraction for the monitoring of hydrophobic organic pollutants in water[END_REF][START_REF] Estoppey | Effect of water velocity on the uptake of polychlorinated biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: An assessment of the efficiency of performance reference. compounds (PRCs) in river-like flow conditions[END_REF][START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF][START_REF] Belles | Thickness and material selection of polymeric passive samplers for polycyclic aromatic hydrocarbons in water: Which more strongly affects sampler properties?[END_REF][START_REF] Jacquet | Comparison of five integrative samplers in laboratory for the monitoring of indicator and dioxin-like polychlorinated biphenyls in water[END_REF][START_REF] Allan | Short-term exposure testing of six different passive samplers for the monitoring of hydrophobic contaminants in water[END_REF]. Although SRs are strongly hydrophobic [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], they can extract organic compounds with a broad range of polarities (log K ow > 1-2) from water but only a few studies have focused on pesticides [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF][START_REF] Heltsley | Novel methods for monitoring chlorinated contaminants in aquatic environments[END_REF][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-co-carbon monoxide) and polydimethylsiloxane for equilibrium sampling of polar organic contaminants in water[END_REF][START_REF] Ahrens | Characterization of five passive sampling devices for monitoring of pesticides in water[END_REF]. Sorption properties of SRs for polar contaminants have mainly been assessed by determining partition coefficients between the SR and water at equilibrium (K sw ), but if equilibrium is not attained, the determination of representative time-weighted average concentrations in water requires determining uptake kinetic parameters such as sampling rates (R s ) or diffusion coefficients (D s ) in the SR for every compound studied [START_REF] Valderrama | Implications of observed PBDE diffusion coefficients in low density polyethylene and silicone rubber[END_REF][START_REF] Pintado-Herrera | Determination of silicone rubber and low-density polyethylene diffusion and polymer/water partition coefficients for emerging contaminants[END_REF][START_REF] Rusina | Diffusion Coefficients of Polychlorinated Biphenyls and Polycyclic Aromatic Hydrocarbons in Polydimethylsiloxane and Low-Density Polylethylene Polymers[END_REF]. The uptake kinetic parameters are dependent on the variation of environmental factors including water flow velocity [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF][START_REF] Estoppey | Effect of water velocity on the uptake of polychlorinated biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: An assessment of the efficiency of performance reference. compounds (PRCs) in river-like flow conditions[END_REF][START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF], temperature, salinity [START_REF] Jonker | Quantifying the Effects of Temperature and Salinity on Partitioning of Hydrophobic Organic Chemicals to Silicone Rubber Passive Samplers[END_REF] and biofouling [START_REF] Booij | Little effect of excessive biofouling on the uptake of organic contaminants by semipermeable membrane devices[END_REF][START_REF] Allan | Effect of sampler material on the uptake of PAHs into passive sampling devices[END_REF]. To correct for environmental variability, and especially water flow velocity, R s can be estimated from the dissipation rates of performance reference compounds (PRCs) with which the passive sampler is spiked beforehand [START_REF] Booij | An Improved Method for Estimating in Situ Sampling Rates of Nonpolar Passive Samplers[END_REF]. A second method, described by Estoppey et al. [START_REF] Estoppey | Effect of water velocity on the uptake of polychlorinated biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: An assessment of the efficiency of performance reference. compounds (PRCs) in river-like flow conditions[END_REF] with SR sheets, is based on correction factors derived from a power function relationship between the compound uptake and water flow velocity. This new method was introduced to correct PCB concentrations among sites with great differences in water flow velocity, where PRC-based R s overcorrected the impact of varying flow velocities. Estoppey et al. [START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF] recently improved their method for PCBs by using a simple linear correlation between R s and flow velocity. For SRs, a close agreement between this second method and PRC correction was reported for flow velocities from 10 cm s -1 to 60 cm s -1 [START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF], and a similar linear correlation was observed by O'Brien et al. [START_REF] O'brien | Determination of deployment specific chemical uptake rates for SPMD and PDMS using a passive flow monitor[END_REF] using a passive flow monitor. This method has the advantages of being accessible by the operator applying passive samplers, less time consuming, less expensive than PRC-based methods and not influenced by uncertainties in log K sw and R s determination [START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF].

The determination of R s requires a calibration experiment with a constant concentration of contaminants and controlled experimental parameters (constant temperature and adjustable flow velocity) [START_REF] Greenwood | Passive Sampling Techniques in Environmental Monitoring, 1st Edition[END_REF]. For SRs, few calibrations have been conducted for pesticides and especially for polar pesticides [START_REF] Vrana | Calibration of a passive sampler based on stir bar sorptive extraction for the monitoring of hydrophobic organic pollutants in water[END_REF][START_REF] Heltsley | Novel methods for monitoring chlorinated contaminants in aquatic environments[END_REF][START_REF] Ahrens | Characterization of five passive sampling devices for monitoring of pesticides in water[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]. These calibration studies highlighted the need for an experimental design adapted to pesticide properties with short exposure monitoring (<15 d). Emelogu et al. [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] quantified urea herbicide in field studies with SR-based passive samplers, but lacked calibration data to convert mass accumulated by the SR into water concentration.

The main goal of this study was to determine R s and K sw for 24 polar and non-polar pesticides (0.6 < log K ow < 5.5) with an experimental calibration, in order to estimate an appropriate Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 deployment period for linear uptake in freshwaters. We also assessed the impact of water flow velocity on the uptake of pesticides. The use of 13 deuterated pesticides as PRC candidates was evaluated to correct R s for differences in flow velocity. The kinetic variables were determined using a home-made calibration system with adjustable water flow velocity and a diffusive source of pesticides. The theoretical model of overall resistance to mass transfer [START_REF] Huckins | Monitors of Organic Chemicals in the Environment[END_REF] was first applied to estimate D s values. In a second step, we then used this model to predict R s of pesticides for other water flow velocities in order to simplify the calculation of timeweighted average concentrations in different field applications.

2 Mass transfer resistance model in silicone rubber-

based passive samplers

The mass transfer of contaminants into a passive sampler depends mainly on both the characteristics of the device and the physical and chemical properties of the sampled analytes, but it depends also on environmental factors. We focused only on the theory for membranefree or monophasic samplers, such as SR plates. In this configuration, the exchange process is driven by different transport mechanisms involving transport of the compound by diffusion through the water, diffusion across the thickness of the receiving phase/ polymer. The transport across the biofilm layer for long exposure in water was neglected in the present work. Each transport mechanism contributes to the resistance to mass transfer (1/k o ), calculated using Eq. 1, with k w and k s as the mass transfer coefficients in the water and the SR polymer respectively, and K sw the silicone-water partition coefficient [START_REF] Greenwood | Passive Sampling Techniques in Environmental Monitoring, 1st Edition[END_REF][START_REF] Tcaciuc | Modeling the transport of organic chemicals between polyethylene passive samplers and water in finite and infinite bath conditions[END_REF][START_REF] Allan | Passive sampling for target and nontarget analyses of moderately polar and nonpolar substances in water[END_REF].

s sw w o k K 1 k 1 k 1   (1)
Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

According to Greenwood et al. [START_REF] Greenwood | Passive Sampling Techniques in Environmental Monitoring, 1st Edition[END_REF] and Huckins et al. [START_REF] Huckins | Monitors of Organic Chemicals in the Environment[END_REF], the individual mass transfer for a specific compound is related to the ratio of the compound's diffusion coefficient (D) and the thickness (δ) of each compartment (water or SR, respectively noted with subscripts w and s)

contributing to k o. The mass-transfer coefficients for each compartment are calculated as follows (Eq. 2 and Eq. 3):

s s s D k   , (2)
w w w D k   . ( 3
)
If diffusion occurs from both sides for a plate configuration of a SR-based passive sampler, the barrier thickness ( S  ) is considered as the half-thickness. Using the very simplified approach of Zhao et al. [START_REF] Zhao | On-rod standardization technique for time-weighted average water sampling with a polydimethylsiloxane rod[END_REF], where a linear concentration profile is assumed within the polymer cylinder or rod configuration, the barrier thickness ( S  ) is considered as the half- diameter or radius. For the water compartment, the notion of water boundary layer (WBL) of thickness δ w was assumed for convenience, considering the complexity of the hydrodynamics.

In this work, we chose to use a mass transfer resistance model with linear concentration profiles in both the WBL and the SR. It is a simple and approximated model, contrary to the two-phase Fickian model [START_REF] Tcaciuc | Modeling the transport of organic chemicals between polyethylene passive samplers and water in finite and infinite bath conditions[END_REF] (considering a time-dependent D s and resulting in non-linear diffusion profiles in the polymer) that would lead to more accurate results.

Assuming that elimination and uptake of compounds obey first-order kinetics (isotropic exchanges), the uptake of compounds in an SR-based passive sampler over time with constant ambient water concentration (and free of compounds at deployment), is described by Eq. 4:

t)) k exp( (1 K C (t) C e sw w s    , (4)
where C s (ng L -1 ) is the concentration of the compound accumulated in the receiving phase;

C w (ng L -1 ) the concentration of the compound in the water phase, and t (d) the duration of exposure. Elimination and uptake rate constants, respectively k e (d -1 ) and k u (d -1 ) are linked by a proportional constant K sw (L L -1 ), the silicone-water partition coefficient. It is described by the ratio of concentration at equilibrium of the compound in the sampler C s,eq (ng L -1 ) to the concentration at equilibrium of the compound in the water phase C w,eq (ng L -1 ). The elimination rate constant k e is expressed by:

s sw o s sw s e V K A k V K R k   , ( 5
)
where V s (L) is the volume of the receiving phase, R s is the sampling rate (L d -1 ) and A (cm²)

is the exposed surface area of the sampler. The calculation of time of sorption halfequilibrium (t 1/2 = ln 2/ k e ) is also used to describe the calibration regime as linear (t < t 1/2 ), curvilinear (t 1/2 < t < 4t 1/2 ) or equilibrium (t > 4t 1/2 ) state for the exposure period (t) [START_REF] Huckins | Monitors of Organic Chemicals in the Environment[END_REF].

Combining Eq. 1, Eq. 2, Eq. 3 and Eq. 5, and neglecting biofilm resistance, R s , based on the properties of the compounds, is estimated by:

s sw s w w s D K D A R     . (6)
3 Materials and methods

Chemicals

The 24 pesticides selected, including two metabolites, covered a broad polarity range (0.6 < log K ow < 5.5) and were listed in (Guyancourt, France). Ultrapure water was obtained using a Millipore water purification system equipped with an LC-Pak cartridge (Billerica, MA, USA). Nitrogen gas (purity 99.995%) used for thermal decontamination was purchased from Messer (Saint-Georgesd'Espéranche, France).

Passive samplers

The passive samplers used in this study were made of SR shaped into small rods. The shape of rod was preferred to the classic sheet because of the convenience of handling and storage, the lower volume of solvent required for back extraction and the possibility to use thermodesorption (TD)-GC/MS analysis. Translucent SR was obtained as flexible cord from Goodfellow (Lille, France). This SR had been selected in an earlier study for its sorption properties for pesticides [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF]. The SR was cut with a clean cutter blade into rods (20 × 3 mm), with a surface area of 2.03 cm² and a volume of 141 µL. Silicone rubber rods (SR rods) were selected by weight (165 mg ± 2.5%, d = 1.2) to ensure repeatability.

Before use, SR rods were chemically and thermally cleaned to remove most residues such as oligomers that could interfere with the instrumental analysis [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF]. First the SR rods were chemically conditioned by immersion in dichloromethane/methanol (50/50, v/v) under sonication for 15 min. They were then wiped with a lint-free tissue and dried at 70 °C for 1 h, and thermally treated in a Gerstel tube conditioner TC (Mülheim a/d Ruhr, Germany) under a nitrogen flow (75 mL min -1 ) with a temperature ramp of 10 °C min -1 to 300 °C maintained constant for 1 h.

Cleaned SR rods (n = 34) were preloaded with PRCs by agitation at 600 rpm for 48 h in 650 mL of ultrapure water spiked with a mixture of 13 PRCs (water concentrations ranging from 150 to 220 µg L -1 ). The SR rods were then gently rinsed with ultrapure water, dried with a lint-free tissue and stored at -18 °C until deployed in the calibration system. Before

Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 deployment each SR rod was inserted into a stainless steel spring (30 × 5 mm, wire diameter 0.4 mm) to simplify handling.

Characteristics of the calibration system

The calibration system was custom-made (Colas & Gire, Saint-Genis-les-Ollières, France). It consisted of a stainless steel cylindrical tank (diameter 40 cm, height 35 cm), with a stirring system and hooks fixed on the inner wall of the tank at 4 levels, each level enabling the experimenter to expose 8 SR rods or 8 SR dosing sheets (Figure 1). Two copper wire hoops were fitted in the middle of the tank. Eight magnetic stir bars, wrapped in aluminium foil, were fixed on the copper wire between each hook; in this way the SR rods in their springs were retained magnetically at each position. The stainless steel tank was filled with 31.4 L of tap water and placed in a thermostatic bath at 20 °C. The tank was covered with an aluminium plate to prevent evaporation and photo-degradation in the water compartment. A Tinytag selfrecording thermometer (Gemini Data Loggers Ltd, Chichester, United Kingdom) was immersed in the thermostatic bath to continuously follow the water temperature during the experiment.

Stirring was done by a four-blade motor-driven propeller composed of an agitator motor (RZR 2020 Control from Heidolph Instrument, Schwabach, Germany) and a propeller with a diameter of 10 cm (type R1345 from IKA, Staufen, Germany). The rotation generated a water flow velocity near the SR rod positions (set at 5 and 20 cm s -1 ). These flow velocities were measured with a propeller-type current meter (Streamflo430, Nixon, Cheltenham, United Kingdom) in the same way for all positions in the tank. All dimensions of the calibration system are availables in SI-2.

Constant concentrations of pesticides of about 1 µg L -1 in the water were ensured by two methods. Some polar pesticides (CBZ, CTU, DIU, IMD, IPU, NFZ) (Table 1) were directly

Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 added to the water using 5 mL of a highly concentrated solution in acetone. For more apolar pesticides, spiked SR sheets acted as diffusive contaminant sources in the water [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF][START_REF] Jacquet | Comparison of five integrative samplers in laboratory for the monitoring of indicator and dioxin-like polychlorinated biphenyls in water[END_REF]. Eight SR sheets (2 × 7.5 × 0.3 cm, Goodfellow) were fixed with hooks at the bottom of the tank.

The total volume of the dosing sheets was 20 times that of the SR rods, ensuring that depletion caused by uptake would be negligible. Before being spiked, the sheets were cleaned by shaking in ethyl acetate and then rinsing in ultrapure water [START_REF] Rusina | Polymer selection for passive sampling: a comparison of critical properties[END_REF]. The quantity of pesticides absorbed by the sheets was assessed with partition coefficients determined by Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF]. For the spiking step, the SR sheets were placed in a bottle containing 2.5 L of ultrapure water contaminated with 5 mL of highly concentrated pesticide solutions (concentrations ranging from 56 to 923 mg L -1 in acetone) and the bottle was shaken for 72 h at 300 rpm.

Before the exposure of PRC preloaded passive samplers, the system was run for 72 h to allow the stabilization of the pesticide concentrations by reaching equilibrium between water and the spiked sheets for all the compounds (including added polar pesticides).

Experimental design

Two calibration experiments lasting 14 days were performed with SR rods spiked with PRCs to simultaneously follow uptake and elimination of compounds at an effective water flow velocity of 5.5 ± 2.3 cm s -1 (n = 14) and 19.9 ± 3.5 cm s -1 (n = 19). A total of 20 SR rods were exposed for each experiment. Three SR rods spiked with PRCs were analysed to determine the initial concentrations of PRCs. The first SR rod was removed from the tank 30 min after the beginning of the experiment and then after 1, 2, 4, 8 h, and 1, 2, 3, 4, 5, 6, 7, 10, 14 days.

Additional triplicates of SR rods were also exposed and analysed to evaluate overall repeatability for both uptake of pesticides and elimination of PRCs every time a SR rod was collected from the tank to measure concentrations of pesticides. All the SR rods and water samples were stored at -18 °C before analysis. Moreover, total organic carbon (TOC) was measured at the start and the end of each experiment in acidified water samples (HCl 2N, 1%, v/v) with TOC analyser (multi N/C® 3100, Analytik Jena) using thermal oxidation (850°C) and NDIR (Non-Dispersive Infrared) sensor.

Analytical procedure

Pesticides were desorbed from SR rods by chemical extraction according to the protocol developed by Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF]. The solvent back-extraction recoveries of this step were between 60 and 98% depending on the compounds (listed in Table 1). Briefly, SR rods were rinsed with ultrapure water, wiped with lint-free tissue and stored at -18 °C. Before analysis the pesticides absorbed in the SR rods were desorbed in 200 µL of acetonitrile/methanol (50/50) for 15 minutes under sonication. Finally, 40 µL of the desorbate was added to 150 µL of ultrapure water and 10 µL of a diuron-d6 solution (200 µg L -1 ) to prepare the extract for analysis by UHPLC-MS/MS (Nexera X2 UHPLC system, Shimadzu and API 4000, AB Sciex). For calibration experiments, the analysis of the water samples was performed by direct

injection. An aliquot of water of 1 mL was spiked with diuron-d6 at a concentration of 10 µg L -1 as for the SR rod extract analysis. Chromatographic parameters, limit of quantification (LOQ) and settings for MS/MS (SI-1) analysis are reported elsewhere by Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF].

4 Results and discussion

Monitoring of experimental parameters

During the two 14-day exposure experiments, the mean water temperature recorded was 19.7 ± 0.5 °C. Considering two measures per experiment and the results of both experiments, we observed a slight variation of pH, from 7.9 ± 0.6 at the start of the calibration to 8.6 ± 0. the end. No influence of pH on the properties of the selected pesticides or the range of fluctuation was expected. A mean value of TOC for the two experiments was measured at 3.5 ± 1.1 mg L -1 (n = 4).

The mean pesticide concentrations in the water of the tank ranged from 0.4 µg L -1 to 3.1 µg L - 1 (Table 1). We opted to work at higher concentrations for some pesticides (TBZ at 23.9 µg L - 1 , FNT at 24.1 µg L -1 and PCM at 9.6 µg L -1 ) that had a higher LOQ or could be partly degraded or absorbed by our experimental calibration system. The concentrations of the 23 pesticides remained stable (RSD between 8% and 27%, n = 30). By contrast, a higher RSD was obtained for PCM (41%), which may have been partly degraded (aqueous hydrolysis DT 50 of PCM: 25 d). Nevertheless, the SR sheets added into the tank acted as a contaminant source as expected for the other pesticides. The PRC concentrations in the water of the tank at the end of either experiment were below LOQ.

Determination of kinetic parameters

Considering the two experiments, repeatability of sorbed mass on triplicates of SR rods sampled in experiments, expressed as RSD (n = 3), ranged from an average value of 3.6% for dimethomorph (DMM) to 14.5% for spiroxamine (SPX), except for PCM (21.5 %). These RSD values take into account the accumulation of target pesticides in the SR rods, the liquid desorption process and the UHPLC-MS/MS analysis. We therefore excluded PCM from this study owing to high variability in uptake into SRs and in the water of the tank (cf. §4.1). All calibration results are detailed in an additional spreadsheet (SI-7).

For each pesticide, the concentration factor (C s /C w ) was calculated according to the ratio between the concentration in the SR rod (ng L -1 ) and the mean concentration in water (ng L -1 ) from the beginning of the experiment to the same sampling time. The concentration factors of the pesticides for various time periods were fitted to a first order uptake model (Eq. 7 derived

Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 from Eq. 4 and Eq. 5) using R s and K sw as adjustable variables. We optimised the sum of the squared differences between the experimental and calculated values to a minimum using nonlinear regression with XLSTAT software (version 2015. 3.01.19703). The RSD values of the kinetic parameters were calculated using the derivative of the function for each parameter.

We obtained satisfactory coefficients of determination (r²) of fitted data with the first order uptake model, ranging from 0.938 to 0.993. The calculation of parameters (R s , K sw, t 1/2 ) took into account solvent back extraction recoveries determined in Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF]. The calculated parameters are detailed in Table 1, and the fitted curves are illustrated in SI-3. The constants of elimination of pesticides in uptake experiments (k e up ) were calculated using their associated R s and K sw values according to Eq. 5.

The times of sorption half-equilibrium (t 1/2 ) ranged from 0.2 d for DCA, reaching equilibrium very quickly in SR rods, up to nearly 15.2 d for CPE at 5 cm s -1 . Comparing DCA properties with DMM, which has an equivalent log K ow , the fast uptake kinetic of DCA may be related to its low molecular volume (121 Å 3 and log K ow of 2.69). DMM has a larger molecular volume (342 Å 3 and log K ow of 2.68), and we observed a longer t 1/2 (6.4 d) at 5 cm s -1 . Thus, mainly curvilinear and equilibrium patterns were observed for the uptake of pesticides in SR rods at 5 cm s -1 for 14 d exposure. Equilibrium state was reached more frequently in the calibration experiment at 20 cm s -1 . The impact of flow velocity is discussed in section 4.3.

According to low flow velocities measured near passive samplers in freshwaters (close to 5 cm s -1 with a deployment system), we found that an in situ exposure duration of 7 d was a good compromise in this SR rod passive sampler configuration to stay within the linear or curvilinear uptake period for 19 out of 23 pesticides (4t 1/2 > 7 d except for DCA, FNT, MTC and TBZ).

)) V K t R exp( (1 K (t) C C s sw s sw w s    . ( 7
)
Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

Those t 1/2 values are greater than those previously reported for a selection of 19 pesticides with "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]. The authors obtained a t 1/2 ranging from 1.5 to 4 d in an experiment with flow velocity at 2.5 cm s -1 . This difference is because of the use of different geometries (area and volume) between the two passive samplers (A/V =17.0 cm -1 and 14.3 cm -1 respectively for Passive SBSE and SR rod) [START_REF] Belles | Thickness and material selection of polymeric passive samplers for polycyclic aromatic hydrocarbons in water: Which more strongly affects sampler properties?[END_REF] or differences in diffusion coefficients (D s ).

Pesticides potentially diffuse into the half-diameter of SR rods (1.5 mm), whereas the thickness of the SBSE coating is only 1 mm.

Calculated sampling rates (R s ) for the two experiments are reported in Table 1 with their relative standard deviations (RSD). Sampling rates ranged between 0.039 mL d -1 (IMD) and 812 mL d -1 (CPE), with RSD ranging from 8% to 27%. In comparison, "Passive SBSE" exhibited lower R s values by a mean factor of 4.7 for 19 pesticides studied in common, excluding DCA, which differed by a factor of 68 [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]. Differences in D s due to a specific SR formulation could explain this difference for DCA, as no differences in sorption at equilibrium were found for this compound by Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF].

The partition coefficients calculated in both experiments were compared with results obtained on the same SR in an earlier study by sorption isotherm experiments reported by Martin et al.

[13] (SI-4). For pesticides with log K sw < 3, K sw values obtained in the present study were 1.8 to 8.9 times greater than K sw previously determined. Martin et al. [START_REF] Martin | Silicone rubber selection for passive sampling of pesticides in water[END_REF] suggested a possible adsorption of the pesticides on the SR following a non-linear sorption isotherm depending on water concentration (Freundlich model). However, K sw Freundlich calculated with the mean concentration in water does not match the K sw values obtained in the present study. This difference could be due to different experimental approaches and calculation methods, or to the influence of water characteristics on adsorption (Evian® water vs. tap water). We adopted K sw values derived from Eq. 7 in the experiment at 20 cm -1 as reference values ( than at 5 cm s -1 for most pesticides after 14-d exposure (Cf. § 4.3), leading to more accurate values.

Impact of flow velocity on uptake of pesticides

As predicted by the theoretical model (Cf. § 2.0), a correlation of sampling rate, expressed as log R s normalised per 100 cm², with log K sw should highlight the control of uptake kinetics by the WBL or by the polymer. This change in transport mechanism was expected for hydrophobic compounds with log K ow in the range 3.5-5 [START_REF] Allan | Short-term exposure testing of six different passive samplers for the monitoring of hydrophobic contaminants in water[END_REF][START_REF] Allan | Field performance of seven passive sampling devices for monitoring of hydrophobic substances[END_REF]. For hydrophilic compounds theoretically under polymer control (

S sw S K D A R  / S
 ), R s values were expected to increase with increasing K sw . On the contrary, for more hydrophobic compounds, such as PCBs and PAHs (with log K ow > 3.3) not investigated in the present study, R s values were expected to be constant and slowly decrease with increasing K sw [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF], in accordance with WBL control theory

( W AD R  / W S 
) (compounds with high K sw also have a lower D w ).

In our experiments (Figure 2), for polar pesticides with a low affinity for SR, we observed that the R s values increased with increasing values of log K sw up to around 3, whereas for more hydrophobic pesticides, R s remained constant irrespective of the log K sw values.

The plot of log R s at the two velocities (Figure 2) shows similar R s values for polar pesticides, and highlights a significant separation of the two curves at log K sw around 3.3, defined as graphical intersection of tangents from both ends, with higher values of R s for the experiments at 20 cm s -1 . This change in behaviour demonstrates the impact of hydrodynamics on the thickness of the WBL and on R s values. A higher flow velocity leads to a thinner WBL, resulting in an increase in R s proportional to 1/δ w . To our knowledge, this is the first time that the change from polymer control to WBL control for log K sw > 3.3 has been experimentally confirmed with polar and non-polar compounds (0.6 < log K ow < 5.

5) using different flow

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velocities. This result is consistent with those of Assoumani et al. [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], suggesting a WBL control for pesticides with log K ow above 3.3 (FNT) in "Passive SBSE" (coated with SR).

PRCs desorption kinetics: isotropic exchanges?

The elimination of PRC obeys first-order kinetics described by:

t) k exp( m (t) m PRC e 0 s   , (8)
where m s (ng) is the mass of PRC remaining in the receiving phase of the sampler, and m 0 (ng) is the initial mass of spike PRC in the receiving phase. The repeatability of mass measurement on triplicates of SR rods sampled in experiments was similar for the release of 13 PRCs and for the uptake of pesticides (RSD from 4.3% to 12.2%). The release of PRCs from the SR rods was fitted by nonlinear regression of PRCs fractions (f = m S (t) / m 0 ) remaining in the SR rod as a function of exposure time (t) using Eq. 8 (XLSTAT), with the elimination constant (k e PRC ) as an adjustable parameter (fitted curves are available in SI-3).

Unlike in uptake experiments, not all coefficients of determination (r²) were satisfactory (ranging from 0.15 to 0.91). Some PRCs (ATZ-d5, CBZ-d4, IPU-d6, MTC-d6 at 5 cm s -1 and CBZ-d4, FNT-d6 at 20 cm s -1 ) seemed to obey elimination kinetics with an inflexion point (or two slopes) that could not be described by Eq. 8. This was also observed for some compounds with another passive sampler (POCIS) by Morin et al. [START_REF] Morin | Determination of uptake kinetics and sampling rates for 56 organic micropollutants using "pharmaceutical[END_REF]. Accordingly only PRCs obeying first order kinetics were considered for the present study and the data for above mentioned PRC were removed.

Several hypotheses can be ventured to explain these deviations from the first order kinetic model. Adsorption on SR could be involved instead of absorption for these strongly polar PRCs. Another explanation is that the geometry of SR rods with a half-thickness of 1.5 mm makes them thicker than the silicone plates (0.25 mm), for which isotropic exchanges have been demonstrated [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF]. It is possible that the two-phase Fickian model was more adapted in

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this case with a time-dependency of D s [START_REF] Tcaciuc | Modeling the transport of organic chemicals between polyethylene passive samplers and water in finite and infinite bath conditions[END_REF][START_REF] Thompson | Modeling uptake of hydrophobic organic contaminants into polyethylene passive samplers[END_REF]. It may be that the distribution of PRCs within this thicker SR-based passive sampler was not homogeneous because SR rods were preloaded with PRCs without methanol, which allows fast and homogeneous equilibrium by swelling the polymeric structure [START_REF] Booij | Spiking of performance reference compounds in low density polyethylene and silicone passive water samplers[END_REF].

Calculated values of k e PRC for the two experiments are reported in Figure 3. RSD ranged from 7% to 34% similarly to k e up derived from the uptake experiments.

The use of several pairs of deuterated (PRCs) and non-deuterated pesticides allowed direct comparison of the uptake and release processes. We opted to use the graphical comparison of uptake/release instead of checking the equality of elimination constants (k e up and k e PRC ), due to different calculation methods (k e up was calculated from Eq. 5. and k e PRC from Eq. 8. with lower r²). We considered a confidence interval of 10% for the intersection of uptake/release: isotropic exchanges were thus validated for an intercept between 40% and 60%.

As shown in Figure 3, curves describing the uptake of CPM and LINU and the release of CPM-d6 and LINU-d6 at 5 cm s -1 intersect at about 50% of the concentration interval, indicating that the exchange process is isotropic. These profiles were observed for the following deuterated analogues: ATC, CPM, CPE, FNT, and LINU (SI-3). Isotropic exchange of FNT and FNT-d6 with "Passive SBSE" was also confirmed by Assoumani et al. [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] by comparison of k e values determined by elimination experiments for the two compounds.

Therefore, regarding data at 5 cm s -1 , the release of the 5 selected PRCs seems pertinent for the correction of R s in in situ applications.

In Figure 4, the log k e PRC at the two flow velocities are plotted against log K sw of nondeuterated corresponding pesticides. A change of transport mechanism is again highlighted by a global increase and then a decrease in k e PRC with increasing K sw values. However, in contrast to uptake, no differences in the release of hydrophobic PRCs (k e PRC ) (ATC, CFV, CPM, CPE, DFF) were observed between the two flow velocities studied (Figure 4). This is

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surprising considering the impact of flow velocity on uptake for these hydrophobic pesticides.

The cause of this discrepancy might arise from (i) a non-isotropic exchange for the two velocities or (ii) ill-suited design of the calibration system for the elimination experiments.

The graphical comparison of uptake/release of PRCs at 20 cm s -1 showed non-isotropic exchange for all pesticides; the uptake was faster than the release of PRCs (SI-3). The alternative explanation is that the static calibration system design (without continuous renewal of water) was not suited to following elimination kinetics.

Hence it is difficult to conclude on the use of selected deuterated pesticides as PRC candidates in this study to take into account variation of flow velocity for R s correction. Other experiments in continuous flow calibration systems will be needed to validate this statement.

Considering these results from PRCs, we propose an alternative method based on an overall resistance to mass transfer model to adjust R s from laboratory experiments to other hydrodynamic conditions by measuring in situ flow velocity.

Application of the overall resistance to mass transfer model

We first applied the overall resistance to mass transfer model on our experimental results. The application of Eq. 6 required knowledge of diffusion parameters (D w and D s ) for the pesticides studied. Diffusion coefficients in water (D w ) were calculated using the method of Hayduk and Laudie [START_REF] Hayduk | Prediction of diffusion coefficients for nonelectrolytes in dilute aqueous solutions[END_REF] from the molar volume of pesticides (SI-5). We chose this method according to the recommendation of the US Environmental Protection Agency and the conclusions of Pintado-Herrera et al. [START_REF] Pintado-Herrera | Determination of silicone rubber and low-density polyethylene diffusion and polymer/water partition coefficients for emerging contaminants[END_REF]. Experimental D s values in SR are scarce in the literature, and have been estimated only for a few classes of contaminants (PCBs and PAHs [START_REF] Rusina | Diffusion Coefficients of Polychlorinated Biphenyls and Polycyclic Aromatic Hydrocarbons in Polydimethylsiloxane and Low-Density Polylethylene Polymers[END_REF], PBDEs [START_REF] Valderrama | Implications of observed PBDE diffusion coefficients in low density polyethylene and silicone rubber[END_REF] and emerging contaminants [START_REF] Pintado-Herrera | Determination of silicone rubber and low-density polyethylene diffusion and polymer/water partition coefficients for emerging contaminants[END_REF]) and unfortunately not determined for pesticides. We therefore opted to estimate D s values for pesticides by simultaneously fitting the data at the two flow

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velocities to the overall resistance to mass transfer model. The WBL thickness (δ w ), not available by direct measurement, was defined as an adjustment factor for the model.

To estimate the initial δ w parameters for the model, we used the approximation of a theoretical WBL control model (

w w s AD R  / 
) with six pesticides that had log K sw > 3.3 and were considered in WBL control (FNT, CFV, CPM, SPX, DFF, CPE) for both experiments. We obtained δ w values of 60 µm at 5 cm s -1 (δ w5 ini ) and 19 µm at 20 cm s -1 (δ w20 ini ). These values are in the range of typical values (from a few µm to 1 mm) reported by Huckins et al. [START_REF] Huckins | Monitors of Organic Chemicals in the Environment[END_REF] for SPMD.

We then used R s and K sw for all pesticides at the two flow velocities (n = 48) to calculate more accurate δ w values (δ w 5 and δ w 20 ) and D s values for 23 pesticides according to Eq. 6. Predicted R s values were fitted simultaneously to the experimental R s values with adjustable parameters (δ w values and D s values) by minimising the sum of the squares of the residuals weighted by the average of the residuals with the "optim" function of R software (version 2.15.2) and a limited memory algorithm for bound constrained optimisation (L-BFGS-B). The initial values of adjustable parameters were δ w ini values and a log D s value of -11 m 2 s -1 for all pesticides (approximated mean value for PAHs and PCBs as initial parameters), and lower and upper bounds were respectively 0 and 1000 mm for δ w , and -10 and -14 for log D s .

Predicted R s values (normalised to 100 cm²) adjusted to our experimental data were plotted against log K sw (Figure 5). Estimated δ w values (δ w 5 = 43 µm and δ w 20 = 10 µm) were slightly lower than those evaluated initially. Estimated log D s for pesticides are given in Table 1. Estimated log D s values in the present study were in the range -12.13 (IMD) to -10.07 (CPE) m 2 s -1 . The lowest log D s for pesticides (-12.13 m 2 s -1 ) was for IMD, the most hydrophilic compound studied (log K ow = 0.6) with the lowest affinity for SR (log K sw = 0.71). By contrast, the highest log D s was for CPE, the second most hydrophobic pesticide (log K ow = Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 4.7) with the highest affinity for SR (log K sw = 4.60). The most hydrophobic pesticide, SPX (log K ow = 5.5), also had a high molar volume reported to influence the diffusion of compounds in polymers [START_REF] Rusina | Diffusion Coefficients of Polychlorinated Biphenyls and Polycyclic Aromatic Hydrocarbons in Polydimethylsiloxane and Low-Density Polylethylene Polymers[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] and resulting in a decreased diffusivity in SR. Other trends of the diffusion coefficients related to chemical structures are illustrated in SI-6 with log D s plotted to molar volumes and molar mass. This is a first evaluation of D s values for pesticides using calibration data instead of direct measurement. They therefore need to be validated with further experiments. In regard to other experimental values for PAHs and PCBs for another SR formulation (supplied by Altec) [START_REF] Rusina | Diffusion Coefficients of Polychlorinated Biphenyls and Polycyclic Aromatic Hydrocarbons in Polydimethylsiloxane and Low-Density Polylethylene Polymers[END_REF],

ranging from -9.8 to -11.0 m 2 s -1 ; our data seem reasonable. In comparison, diffusion coefficient values for the studied pesticides were lower and with a wider range (2 orders of magnitude) than for PCBs and PAHs (1 order of magnitude).

Modelling of the influence of water flow velocity on sampling rates

The knowledge of D s for each pesticide and δ w values related to flow velocity enabled us to estimate R s for other hydrodynamic conditions. Also, the change from the polymer control to the WBL control could be predicted. This model aims at estimating δ w by a correlation related to a hydrodynamic parameter such as water flow velocity. Such a correlation was proposed by Estoppey et al. [START_REF] Estoppey | Effect of water velocity on the uptake of polychlorinated biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: An assessment of the efficiency of performance reference. compounds (PRCs) in river-like flow conditions[END_REF][START_REF] Estoppey | An in-situ assessment of low-density polyethylene and silicone rubber passive samplers using methods with and without performance reference compounds in the context of investigation of polychlorinated biphenyl sources in rivers[END_REF] for PCBs by in situ passive sampler exposures using LDPE and SR plates.

Our estimated δ w values for two different measured flow velocities were insufficient to demonstrate such a correlation; we therefore used calibration data for which water flow velocities were only estimated to determine complementary δ w values. The study of Rusina et al. [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF] describes calibration experiments for PAHs and PCBs with SR flat plate passive samplers at two calculated flow velocities (Exp A = 0.14 cm s -1 and Exp B = 9 cm s -1 ), assuming that uptake was controlled by diffusion in the WBL in both cases. We applied the

Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

overall resistance to mass transfer model with the available experimental values of D s for PAHs and PCBs [START_REF] Rusina | Diffusion Coefficients of Polychlorinated Biphenyls and Polycyclic Aromatic Hydrocarbons in Polydimethylsiloxane and Low-Density Polylethylene Polymers[END_REF]. The estimated δ w values were δ w 0.1 = 412 µm and δ w 9 = 27 µm. In quasi-static hydrodynamics conditions (0.14 cm s -1 ), the WBL thickness was more than 7

times greater than at 5 cm s -1 in our experiment.

The estimated δ w values from the two studies (n = 4) were plotted against the flow velocities (v) reported in experiments in Figure 6. A power function correlation was observed between δ w values and v (n = 4, δ w = 111.6 v -0.71 , r² = 0.98). Such a correlation was indirectly demonstrated by Estoppey et al. [START_REF] Estoppey | Effect of water velocity on the uptake of polychlorinated biphenyls (PCBs) by silicone rubber (SR) and low-density polyethylene (LDPE) passive samplers: An assessment of the efficiency of performance reference. compounds (PRCs) in river-like flow conditions[END_REF] with power function relations between amounts of PCB accumulated and flow velocity in the range 1.9 to 37.7 cm s -1 . They then provided individual linear correlations for 6 PCBs between PRC-based R s and water flow velocities above 10 cm s -1 for SR: assuming that PCB uptake was under WBL control, the R s is proportional to 1/δ w , which was indirectly linked to v. Our results confirm that this relationship could also be approximated by a linear correlation for flow velocities above 5 cm s -1 (n = 3, δ w = -2.1 v + 51, r² = 0.93).

These results acquired using different SR based passive sampler designs (plate or cylinder)

suggest that the effect of flow velocity on the water boundary layer thickness calculated from Eq.6 could be weakly influenced by the geometry of the passive samplers used. Considering the complexity of hydrodynamic conditions near a passive sampler, and the different positions of the passive sampler in the fluid, this approximation yields a simple model that will need to be validated with further data including measurement of water flow velocities.

In Figure 5, we calculated R s values for pesticides with SR rods at flow velocities recorded by Rusina et al. [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF]. At low flow velocity (v = 0.14 cm s -1 ), the estimated R s values for WBL controlled pesticides (log K sw > 2.7) were lower by a mean factor of 10 than those experimentally determined at 5 cm s -1 , and the change in transport mechanism occurred at log K sw around 2.7 instead of 3.3. These extrapolated data could be used to calculate time-

Conclusion

The calibration data for SR rods, R s and K sw were determined for 23 pesticides and 13

candidate PRCs in a laboratory calibration system over 14 d at two flow velocities. The results show that an in situ exposure duration of 7 d kept the SR rod passive sampler configuration in the linear or curvilinear uptake period for 19 pesticides. A change in transport mechanism from polymer control to WBL control was observed for a log K sw of around 3.3.

The isotropic exchange of candidate PRCs was demonstrated for 5 PRCs at 5 cm s -1 .

However, the influence of water flow velocity on elimination was not demonstrated. Hence, the use of these PRCs was not validated to correct R s for pesticides with WBL-controlled uptake kinetics.

Considering the PRC results, we propose an alternative method based on the overall resistance to mass transfer model to adjust R s from laboratory experiments to other hydrodynamic conditions by measuring in situ flow velocities. The theoretical model of overall resistance to mass transfer was applied to our experimental data using log D s values and δ w as adjustable parameters. The estimated log D s values ranging from -12.13 to -10.07 m 2 s -1 for pesticides were consistent with values previously determined for PAHs and PCBs.

Nonetheless, the application of a two-phase Fickian model could lead to more accurate results; for this purpose, calibration results are detailed in an additional spreadsheet (SI-7).

Moreover, we demonstrated a power function correlation between δ w and water velocity using a compilation of data from the present study and from Rusina et al. [START_REF] Rusina | Calibration of Silicone Rubber Passive Samplers: Experimental and Modeled Relations between Sampling Rate and Compound Properties[END_REF]. This correlation is Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

  . The triplicates of SR rods exposed are detailed in the spreadsheet file (SI-7) compiling results of the calibration experiments. During the experiments, a 10 mL water sample was taken in an amber flask Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

Figure 1 .

 1 Figure 1. Schematic diagram of the laboratory static calibration system (a), depiction of the

Figure 2 . 29 Figure 3 .

 2293 Figure 2. Plot of log R s normalised per 100 cm² versus log K sw in our experiment at two flow

Figure 4 .Figure 5 .

 45 Figure 4. Plot of log k e PRC versus log K sw in experiments at two flow velocities (5 and 20 cm s -1 ).

Table 1

 1

	. Among them, 13 additional deuterated pesticides

  1 at Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

Table 1

 1 Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

), because equilibrium state was reached more frequently in calibration experiments at 20 cm s -1

  Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 weighted average concentrations of pesticides in freshwaters with only a representative field measurement of water flow velocity. Another way to calculate δ w could be to use an adaptation of the PRCs model with deuterated PAHs or PCBs as described by Booij et al.

	[36].

  Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050apparently not influenced by passive sampler geometry. Thus, the estimation of new R s requires only direct measurements of flow velocity in the deployment location of the SRbased passive sampler. Considering isotropic exchanges, δ w values could also be estimated with WBL-controlled PRCs (PAHs, PCBs). However, further experiments are required to address isotropic exchange and significant dissipation of PRCs with the SR rod configuration.

Table 1 . Physico-chemical properties, kinetic parameters at two flow velocities and estimated diffusion coefficients of the 669 studied pesticides. 670

 1 Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 Figure 6. Plot of water boundary layer thickness (δ w ) estimated by theoretical model (Eq. 6) versus water flow velocity (v) from this present study (circles) and Rusina et al. [5] (crosses). -produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

	Author

SI-1.

Pesticide parameters for UHPLC-MS/MS analysis.

  Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

	Compound ATC Compound ATZ ATC-d11 AZS ATZ-d5 BOS CBZ-d4 CBZ CFV-d10 CFV CPE-d10 CPE CPM-d6 CPM DFF-d3 CTU DIU d6 DCA FNT-d6 DCPMU IPU-d6 DFF LINU-d6 DIU MTC-d6 DMM SMZ-d5 FNT TBZ-d6	LOQ (µg L -1 ) 0.40 LOQ (µg L -1 ) 0.10 0.40 0.02 0.10 0.20 0.02 0.02 0.16 0.16 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 4.00 0.16 0.16 0.40 0.16 0.40 0.04 0.16 0.02 4.00 0.16	Quantification transitions (m/z) 269.9 > 224.2 ; 269.9 > 148.1 Quantification transitions (m/z) 215.9 > 174.1 ; 215.9 > 104.1 281.1 > 235.1 ; 281.1 > 159.1 404 > 372 ; 404 > 344 221.06 > 179.1 ; 221.06 > 101.1 343 > 307 ; 343 > 140 196 > 164.1 ; 196 > 138.1 192 > 160 ; 192 > 132 369.1 > 165.2 ; 369.1 > 101.2 359 > 155 ; 359 > 99 360 > 199 ; 360 > 163 328 > 131.2 ; 328 > 292.9 352 > 200 ; 350 > 97 322 > 125 ; 322 > 290 398 > 268 ; 398 > 248 213 > 72 ; 213 > 140 239 > 78 ; 239 > 52 162 > 127 ; 162 > 74 284 > 131 ; 284 > 249 219 > 162 ; 219 > 127 213.1 > 78.3 ; 213.1 > 171.2 395 > 266 ; 395 > 246 255 > 159.9 ; 255 > 185 233 > 72 ; 233 > 46 290.1 > 258.1 ; 290.1 > 182.2 388 > 301 ; 388 > 165 207 > 129.1 ; 207 > 137.1 278 > 109 278 > 125 ; 314 > 72 ; 314 > 125	Declustering potential (V) 31 31 Declustering potential (V) 66 66 31 31 61 61 46 46 81 81 61 61 56 56 56 56 76 76 66 66 45 66 66 61 71 71 76 76 51 51 66 66 51 51 61 61 66 66 66 66 86 86 56 56 46 46 65 65 76 76 71 71 71 71 81 81	Collision energy (V) Collision energy (V)	Collision exit potential (V) Collision exit potential (V) 16 14 20 12 16 8 18 18 26 28 12 8 28 8 10 10 16 24 14 14 20 8 38 8 22 4 16 20 22 54 14 10 12 6 18 12 24 14 24 10 26 22 8 14 28 40 22 32 6 8 6 8 36 28 16 22 12 10
	IMD	0.10	256 > 209.1 ; 256 > 175.1	61 61		38 12
	IPU	0.16	207 > 72 ; 207 > 165	51 51		8 28
	LINU	0.16	249 > 160 ; 249 > 182	61 61		32 12
	MTC	0.04	284.1 > 252.2 ; 284.1 > 176.2	46 46		20 4
	NFZ	0.40	304 > 284 ; 304 > 88	101 101		26 16
	PCM	1.60	284 > 256	76		46
	SMZ	0.02	202.1 > 132.2 ; 202.1 > 124.1	56 56		10 10
	SPX	0.16	298 > 144 ; 298 > 100	51 51		8 18
	TBZ	0.16	308 > 70 ; 308 > 125	76 76		12 12

SI-2. Schematic diagrams (top and side view) of the laboratory static calibration system with dimensions in mm.

  Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050 Author-produced version of the article published in Environmental Toxicology and Chemistry, 2017. The original publication is available at http://setac.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1552-8618/ doi: 10.1002/etc.4050

	Pesticide	M a (g mol -1 )	V m a (cm 3 mol -1 )	b Log D w (m² s -1 )
	ATC	269.8	240.9	-9.28
	ATZ	215.7	163.1	-9.18
	AZS	403.4	300.1	-9.34
	BOS	342.2	262.1	-9.3
	CBZ	191.2	137.5	-9.14
	CFV	359.6	261.8	-9.30
	CPE	350.6	236.8	-9.28
	CPM	322.5	203.8	-9.24
	CTU	212.7	184.5	-9.21
	DCA	162.0	115.6	-9.09
	DCPMU	219.1	152.1	-9.16
	DFF	394.3	274.0	-9.31
	DIU	233.1	178.6	-9.21
	DMM	387.9	315.1	-9.35
	FNT	277.2	202.8	-9.24
	IMD	255.7	160.1	-9.18
	IPU	206.3	196.3	-9.23
	LINU	249.1	185.2	-9.21
	MTC	283.8	257.8	-9.30
	NFZ	303.7	208.8	-9.24
	PCM	284.1	189.1	-9.22
	SMZ	201.7	147.9	-9.16
	SPX	297.5	308.2	-9.34
	TBZ	307.8	268.1	-9.31

SI-5. Molecular properties (M, V m , Log D w ) of pesticides.

a Predicted by ACD/Labs: http://www.chemspider.coml b Calculated from

[START_REF] Hayduk | Prediction of diffusion coefficients for nonelectrolytes in dilute aqueous solutions[END_REF]

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Acknowledgments

The authors thank Claire Lauvernet (Irstea) for help in R script programing and Matthieu Masson's team (Irstea) for analysis of water. This work was funded by the French Agency for Biodiversity (AFB) and the French National Research Agency (ANR) through the PoToMAC project (ANR 2011 CESA 022 02). We thank ATT for English language editing. The authors declare no conflict of interest.

Calibration of silicone rubber rods as passive samplers for pesticides at two different flow velocities: Modelling of sampling rates under water boundary layer and polymer control

 1). Concentrations of pesticides in water (C w ) was expressed in µg L -1 .