Vincent-Hubert Françoise

Uher Emmanuelle

Di Giorgio Carole

Michel Cécile

De Meo Michel

Françoise Vincent-Hubert

Use of low density polyethylene membranes for assessment of genotoxicity of PAHs in the Seine River

Keywords: Genotoxicity, River water, Passive samplers, PAHs, Ames assay, Umu test

The genotoxicity of river water dissolved contaminants is usually estimated after grab sampling of river water. Water contamination can now be obtained with passive samplers that allow a time-integrated sampling of contaminants. Since it was verified that low density polyethylene membranes (LDPE) accumulate labile hydrophobic compounds, their use was proposed as a passive sampler. This study was designed to test the applicability of passive sampling for combined chemical and genotoxicity measurements. The LDPE extracts were tested with the umu test (TA1535/pSK1002 ± S9) and the Ames assay (TA98, TA100 and YG1041 ± S9). We describe here this new protocol and its application in two field studies on four sites of the Seine River. Field LDPE extracts were negative with the YG1041 and TA100 and weakly positive with the TA98 + S9 and Umu test. Concentrations of labile mutagenic PAHs were higher upstream of Paris than downstream of Paris. Improvement of the method is needed to determine the genotoxicity of low concentrations of labile dissolved organic contaminants.

Introduction

Water pollution by chemicals as a result of industrialization and increasing urbanization is a factor that threatens the preservation of aquatic ecosystems and also human health. Polycyclic Aromatic Hydrocarbons (PAHs) constitute a ubiquitous class of environmental chemical pollutants identified in surface waters and sediments. Many of them are mutagenic and can impact aquatic organisms [START_REF] Ohe | Mutagens in surface waters: A review[END_REF]. Most studies report the mutagenic activity of dissolved and particulate PAHs after grab sampling of large volumes of water. As a consequence, the genotoxicity of river waters or effluents could be either underestimated or overestimated, depending upon the recent discharge of pollutants into the river water.

More representative information of water contamination can now be obtained with passive samplers that allow a time-integrated sampling of contaminants [START_REF] Lebo | Use of semipermeable membrane devices for in situ monitoring of polycyclic aromatic hydrocarbons in aquatic environments[END_REF][START_REF] Vrana | Passive sampling techniques for monitoring pollutants in water[END_REF]. Once immersed, passive sampler devices accumulate contaminants from the water. For dissolved hydrophobic contaminants, a semi-permeable membrane device (SPMD) was developed in the 1990's.

The low density polyethylene (LDPE) membrane has been also proposed as a passive sampler as it has been confirmed that it can accumulate dissolved hydrophobic compounds that are bioavailable to some aquatic organisms [START_REF] Booij | Temperaturedependent uptake rates of nonpolar organic compounds by semipermeable membrane devices and low-density polyethylene membranes[END_REF][START_REF] Carls | Monitoring polynuclear aromatic hydrocarbons in aqueous environments with passive low-density polyethylene membrane devices[END_REF][START_REF] Gourlay | How accurately do semi-permeable membrane devices measure the bioavailability of polycyclic aromatic hydrocarbons to Daphnia magna?[END_REF]. As a single-phase sampler, LDPE has been gaining interest because it avoids the toxicity of triolein, a molecule contained in the lipidic phase of SPMD [START_REF] Sabaliunas | Screening bioavailable hydrophobic toxicants in surface waters with semipermeable membrane devices: Role of inherent oleic acid in toxicity evaluations[END_REF].

Even if passive samplers' coupling to bioassays might be a promising tool, few studies have been performed to determine the limits of this coupling. Regarding SPMDs, it has been shown that oleic acid derived from triolein induces a positive response in bioassays [START_REF] Sabaliunas | Acute toxicity and genotoxicity of aquatic hydrophobic pollutants sampled with semipermeable membrane devices[END_REF].

Presently, no data are available for LDPE membranes and their release of compounds when immersed in organic solvent. They may induce toxicity during bioassays, or reduce the bioavailability of the tested contaminants by adsorbing them during the bioassay, and thus lower the response of the bioassay.

The impact of purification procedures on the test response needs to be evaluated.

In France, evidence of mutagenic activities have been demonstrated in the water column and in the sediment of the Seine estuary. Suspended particulate matter collected downstream of various petrochemical industries was positive with the Ames assay (TA98+S9) and the comet assay (HepG2+S9) and sediment from the estuary was also genotoxic and mutagenic [START_REF] Cachot | Evidence of genotoxicity related to high PAH content of sediments in the upper part of the Seine estuary (Normandy, France)[END_REF][START_REF] Vincent-Hubert | Mutagenicity and genotoxicity of suspended particulate matter in the Seine river estuary[END_REF]. The principal sources of pollution in the Seine River basin come from high industrial and urban activities and intensive agriculture. This area of France (75,000 km2) is inhabited by about 16 million people, concentrated within the Paris area. This population density leads to elevated levels of pollutants in water, including PAHs [START_REF] Blanchard | Persistent toxic substance inputs to the river Seine basin (France) via atmospheric deposition and urban sludge application[END_REF][START_REF] Meybeck | Historical perspective of heavy metals contamination (Cd, Cr, Cu, Hg, Pb, Zn) in the Seine River basin (France) following a DPSIR approach (1950-2005)[END_REF][START_REF] Tusseau-Vuillemin | Dissolved and bioavailable contaminants in the Seine river basin[END_REF]. However, the mutagenicity of surface water contaminants from the Paris area has not been reported.

A large number of different assays have been recommended to assess the presence of genotoxic contaminants in surface water, wastewater and industrial effluents [START_REF] Ohe | Mutagens in surface waters: A review[END_REF]. Most of the published studies employed the Salmonella typhimurium mutagenicity test with strains TA98 and/or TA100, with and without metabolic activation (S9 mix). Data obtained with the Salmonella strains can provide information on the classes of mutagens present in the water. For example, the YG1041 strain is more sensitive to nitroaromatic compounds than the parent strain TA98 (Watanabe et al., 1989). Nitro-PAHs have been detected in the extracts of environmental materials containing diesel emissions and in urban river waters [START_REF] Murahashi | Determination of nitroarenes in precipitation collected in Kanazawa, Japan[END_REF]. The umu test was developed as an alternative to the Ames test by Oda [START_REF] Oda | Evaluation of the new sytem (umu-test) for the detection of environmental mutagens and carcinogens[END_REF]. Activation of the SOS repair system by genotoxic compounds is measured by photometric determination of the β-galactosidase enzyme activity. The umu test is widely used for routine monitoring of water samples as the results are available in a single day with minimal advance preparation. The umu test proved to be very sensitive for the detection of mutagens in surface waters in Germany and Japan [START_REF] Dizer | The cytotoxic and genotoxic potential of surface water and wastewater effluents as determined by bioluminescence, umuassays and selected biomarkers[END_REF][START_REF] Ohe | Evaluation of SOS-inducing activity with an O-acetyltransferaseoverexpressing strain Salmonella typhimurium NM2009 for municipal river water and the identification of 1-nitropyrene[END_REF].

This study was designed to develop a method for coupling the use of LDPE passive samplers with genotoxicity assays. The umu test was chosen as a pre-screening test for its capacity of induction of the SOS-system. For the Salmonella mutagenicity assay, three strains were chosen, the TA 98, TA 100 and YG1041 strains. Two field applications of the developed protocol are presented here. In the urban area of Paris, LDPEs were deployed for three weeks in two different years at four river sites, then genotoxicity testing and chemical analyses of LDPE extracts were performed.

Materials & Methods

Chemicals

Dimethylsulfoxide (DMSO) for molecular biology, 4-nitroquinoline-1-oxide (4NQO), 1-nitropyrene (1-NP), chlorophenol red-β-D-galactopyranoside (CPRG), sodium azide (SA), 2 aminoanthracene, and benzo [a]pyrene (BaP) were purchased from Sigma (France). Rat S9 was from Trinova (Germany).

Heptane Picograde ® for residue analysis was purchased from LGC Promochem (France) and ethyl acetate Suprasolv ® for Gas Chromatography from VWR (France).

Preparation and deployment of LDPE membranes

Pieces (10x30 cm) of LDPE membranes (membrane thickness of 80 µm) were cut from a roll (Manutan, France). A cleaning procedure was performed to avoid the potential effects of impurities contained in LDPE membranes, as preliminary tests revealed a slight toxicity of non-cleaned LDPE. This procedure consisted of immersing LDPE sheets into a heptane/ethyl acetate mix (50/50, v/v) twice for 24h each.

The solvents were refreshed after the first 24-h period. LDPE sheets were then rinsed by immersion in ultrapure water for 12 days to remove solvent traces. Water was changed every four days. A preliminary three weeks field deployment study, on contaminated river sites, revealed that LDPE extracts were genotoxic in the umu test in these conditions (data not shown).

Field studies

Four river sites were selected for the deployment of four LDPE membranes per site at 1.50 m depth (Fig. 1). One river site was not impacted by the urban activity of Paris (Marnay-sur-Seine) and the three others were highly impacted by the urban area of Paris: Saint-Maurice, Triel-sur-Seine and Bougival.

Bougival is also impacted by the Seine-Aval wastewater treatment plant [START_REF] Blanchard | Persistent toxic substance inputs to the river Seine basin (France) via atmospheric deposition and urban sludge application[END_REF][START_REF] Meybeck | Historical perspective of heavy metals contamination (Cd, Cr, Cu, Hg, Pb, Zn) in the Seine River basin (France) following a DPSIR approach (1950-2005)[END_REF][START_REF] Tusseau-Vuillemin | Dissolved and bioavailable contaminants in the Seine river basin[END_REF]. Two field studies were performed, the first one, in spring 2009, was limited to Marnay-sur-Seine and Bougival, and the second one was conducted on the four sites during the spring and autumn 2010.

Extraction and purification of the PAHs

After field deployment, membranes were thoroughly cleaned of biofilm. They were then immersed in 250 ml heptane/ethylacetate mix (50/50) in closed amber glass bottles. Bottles were stirred on an orbital shaker at 100 rpm for 48h. A preliminary study showed that 94 to 99% of the compounds were removed from the membranes with this procedure. Membranes were then removed from the solvent extract. The solvent mix was reduced to 1 mL with a rotary evaporator. The extract was purified with a florisil column (Phenomenex, France) pre-conditioned with 5 mL of 94/6 heptane/ethylacetate mix.

Elution was performed by transferring 4x5 mL of a 94/6 heptane/ethylacetate mix. Solvent was evaporated under a gentle steam of N2. The evaporation was stopped when a 1 mL volume was reached: 200 µL were reserved and spiked with the internal standards mix (Naphtalene-d8; Acenaphtalene-d10; Phenanthrene-d10; Chrysene-d12; Perylene-d12) and stored at -20°C for chemical analysis. The last 800 µL were evaporated until near dryness (5 µl) and transferred into 200 µL of DMSO for genotoxicity testing.

Chemical analysis of LDPE

PAH analyses were performed on a GC/MS (Thermo electron, Les Ulis, France) operating in Selected Ion Monitoring mode. The ionisation mode was electronic impact and the analyser was a simple quadrupole. It was equipped with a Zebron ZB-5MS capillary GC column (60 m length x 0,25 mm ID x 0.2 µm film thickness). PAHs were quantified with internal calibration. The target PAHs were those on the US EPA list (fluoranthene, fluorene, anthracene, benzo(b)fluoranthene, benz(a)anthracene, indeno(1,2,3-cd)pyrene, benzo(a)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene, phenanthrene, benzo(k)fluorothene, pyrene and chrysene) excluding the three with lowest molecular weights (naphthalene, acenaphtylene, acenaphtene). Detection limits were 10 ng.L -1 in the final extract for each PAH.

Matrix effect

Non-deployed LDPE membranes were extracted as blank extracts. These blank extracts were spiked with the reference genotoxic components 1-NP and BaP. The umu test and Ames test were performed with these spiked extracts.

umu test

The genotoxicity of PAHs accumulated on LDPE membranes was assessed with the umu-microplate test using the bacterial strain TA1535/pSK1002 according to [START_REF] Oda | Use of a High-Throughput umu-Microplate Test System for Rapid Detection of Genotoxicity Produced by Mutagenic Carcinogens and Airborne Particulate Matter[END_REF] and slightly modified by using Chlorophenol red-D-galactopyranoside (CPRG (4 mg/ml) instead of O-nitrophenyl-β-D galactopyranoside [START_REF] Oda | Use of a High-Throughput umu-Microplate Test System for Rapid Detection of Genotoxicity Produced by Mutagenic Carcinogens and Airborne Particulate Matter[END_REF]. The cytotoxic effect of the samples was determined by measuring OD600 nm changes and the genotoxic effects were evaluated by OD570 nm changes. Samples with a growth ratio (G) < 0.8 were considered cytotoxic and samples with an induction ratio (IR) >1.5 were considered to be genotoxic [START_REF] Dizer | The cytotoxic and genotoxic potential of surface water and wastewater effluents as determined by bioluminescence, umuassays and selected biomarkers[END_REF]. Determination of growth factor G, relative enzyme activity (REA)

and induction rate (IR) were calculated as described in Oda et al (.2004). The results are presented as the mean ± SD of two or three independent experiments. Because most PAHs required metabolic activation to be genotoxic, an activation system was used containing S9 of rats (4% S9, 0.4 M MgCl2,

1.65 M KCL, 1.0 M Glucose 6-Phosphate, 0.1 M NADPH, 0.1 M NADH, and 0.2 M Na-Phosphate buffer pH 7.4). Final concentrations of positive controls were: 4 NQO (0.132 µM), 1 NP (0.8µM) and BaP (5 µM) with S9 mix.

Ames test on TA98, TA100 and YG1041 ± S9 mix

The mutagenicity of PAHs extracted from LDPE membranes was assessed by the microsuspension version of the Salmonella mutagenicity test [START_REF] De Méo | Optimization of the Salmonella/mammalian microsome assay for urine mutagenesis by experimental designs[END_REF] using three strains of Salmonella typhimurium, strains TA98, TA100 and YG1041. TA98 has been shown to detect PAHs in the presence of S9 mix [START_REF] De Méo | Optimization of the Salmonella/mammalian microsome assay for urine mutagenesis by experimental designs[END_REF] [START_REF] Nikoyan | Evaluation of a battery of Salmonella typhimurium tester strains for biomonitoring of mutagenic polycyclic aromatic hydrocarbons, nitroarenes and aromatic amines[END_REF]. N-heterocyclic or aromatic amines have shown elevated mutagenic activities in the presence of S9 mix on the metabolically enhanced YG1041 that expresses O-acetyl transferase [START_REF] Hagiwara | Specificity and sensitivity of salmonella typhimurium YG1041 and YG1042 strains possessing elevated levels of both nitroreductase and acetyltransferase activity[END_REF]. Nitroarenes have displayed a high mutagenic activity without metabolic activation in the nitroreductase-enhanced YG1041 strain [START_REF] Nikoyan | Evaluation of a battery of Salmonella typhimurium tester strains for biomonitoring of mutagenic polycyclic aromatic hydrocarbons, nitroarenes and aromatic amines[END_REF].

Each experiment included triplicate plates of four tested doses (4, 6, 8 and 10 µL). A solvent (DMSO) control was added to detected the spontaneous frequency of revertants (quadruplate plates). Two positive controls were also included to ensure the performance of the tester strains and the S9 Mix: 20 ng/plate 2,4,7-trinitrofluorenone (TNFone) for TA98 and YG1041 and 5 µg/plate sodium azide for TA100, 0.5 and 1.0 µg/plate of BaP for TA98 and YG1041 with S9 Mix.

A two-step analysis was performed to interpret data. The Dunnett test [START_REF] Wahrendorf | A non parametric approach to the statistical analysis of mutagenicity data[END_REF] was performed to determine the significance of differences between the mean number of induced revertants and the mean number of spontaneous revertants. If the Dunnett test was positive for at least one tested concentration, non-linear regression analysis was carried out using two arbitrary models as described previously [START_REF] Kim | Statistical methods for the Ames Salmonella assay: a review[END_REF] with Table Curve 2D

Results

Preliminary tests performed to develop the coupling of mutagenicity tests and passive sampling. No matrix effect of LDPE membranes was detected in our experimental conditions (data not shown)

indicating that molecules from LDPE membranes did not modify the genotoxity of BaP.

Two field studies were conducted, the first one in 2009 and the second in 2010. Samples obtained in 2009 did not present any genotoxicity in the Umu test, with or without metabolic activation (Table 1).

As we did not have enough LDPE extracts, we tested them on TA98+S9 and YG1041+S9 only, which are more sensitive to mutagenic activity of PAHs, a class of contaminants predominant in urban areas. The sample from Marnay-sur-Seine site was statistically mutagenic in the Ames test (TA 98+S9) (Table 1),

indicating the presence of frameshift mutagens. No mutagenic activity was detected with YG1041, indicating that no nitroaromatic compounds were present in these extracts.

For samples obtained in 2010, only one sample, Bougival, was positive in the umu test, indicating the presence of direct acting genotoxic compounds in the river water there (Table 2). All samples displayed statistically significant mutagenic activity in the Ames test (TA98 with S9 mix) (Table 2) indicating the presence of frameshift mutagens in the river water at the four sites and for both seasons; two out of eight samples were statistically mutagenic (TA98 without S9 mix) indicating the presence of direct frameshift mutagens at Marnay-sur-Seine and Saint-Maurice. No mutagenic activity was detected with the TA100 with or without S9 mix indicating the absence of mutagens inducing base substitutions.

The concentrations of total labile PAHs in LDPE measured in the 2010 samples shown that the Marnaysur-Seine site was the least contaminated and the Bougival and Triel-sur-Seine sites were the most contaminated (Table 3). The highest concentrations of labile mutagenic and carcinogenic PAHs were

Discussion

This study evaluated the coupling of mutagenicity bioassays and with LDPE membranes in evaluating the genotoxicity of Seine River water. Investigation of the genotoxicity of water contaminants is generally limited to dissolved contaminants collected after spot sampling [START_REF] Houk | The genotoxicity of industrial wastes and effluents: A review[END_REF][START_REF] Umbuzeiro | The Salmonella mutagenicity assay in a surface water quality monitoring program based on a 20year survey[END_REF] or to particulate contaminants which represent the greatest fraction of the hydrophobic contaminants. Despite low genotoxicity, our data confirmed the predominance of direct and S9activated frameshift-type mutagens in surface water of the Seine River as frequently reported after spot sampling [START_REF] Ohe | Mutagens in surface waters: A review[END_REF][START_REF] Vincent-Hubert | Mutagenicity and genotoxicity of suspended particulate matter in the Seine river estuary[END_REF].

Mutagenic PAHs sampled with passive samplers may inform on the level of bioavailable compounds to some aquatic organisms. However, the mutagenicity of bioavailable compounds is still difficult to estimate probably for two main reasons, the very low concentration, compared with concentrations usually used in standard genotoxicity assays, and the mixture of contaminants sampled with passive samplers. Very few studies have reported on the genotoxicity and the mutagenicity of river water contaminants collected with passive samplers even though their toxicity has been reported [START_REF] Allan | Bridging environmental mixtures and toxic effects[END_REF][START_REF] Liscio | Methodology for profiling anti-androgen mixtures in river water using multiple passive samplers and bioassay-directed analyses[END_REF]. Gilli et al. reported that SPMD extracts from the Po River were not mutagenic [START_REF] Gilli | Application of semipermeable membrane device for assessing toxicity in drinking water[END_REF]. Sabaliunas et al. tested the genotoxicity of river water in Lithuania with SPMD coupled to the Mutatox assay and found a positive response only without S9 metabolic activation, which indicated the presence of direct acting mutagens [START_REF] Sabaliunas | Acute toxicity and genotoxicity of aquatic hydrophobic pollutants sampled with semipermeable membrane devices[END_REF].

The low level of mutagenicity of LDPE extract in our study can be explained by the low concentration of labile mutagenic PAHs, ranging from 13 to 126 ng/LDPE extract. For example for BaP, which is among the more mutagenic PAHs, the highest concentration of BaP was 62-fold below the lower BaP concentration reported positive in the Ames test (1 µg/plate). Improvement of the detection could be obtained in future studies by increasing the number of deployed membranes and the volume of sample to be tested.

The concentrations of labile PAHs measured here are consistent with previous published data showing that the Seine River sites under the influence of Paris area are more polluted than sites upstream of Paris [START_REF] Bourgeault | Monitoring PAH contamination in water: Comparison of biological and physico-chemical tools[END_REF][START_REF] Michel | Seasonal and PAH impact on DNA strand-break levels in gills of transplanted zebra mussels[END_REF].

The umu-assay is recognized as the most sensitive standard method for estimating the genotoxicity of polluted waters such as industrial wastewater, suspended matter and surface water of rivers [START_REF] Dizer | The cytotoxic and genotoxic potential of surface water and wastewater effluents as determined by bioluminescence, umuassays and selected biomarkers[END_REF][START_REF] Rao | Assessment of genotoxic potential of pulp mill effluent and an effluent fraction using Ames mutagenicity and Umu-C genotoxicity assays[END_REF][START_REF] Vahl | Genotoxicity assessment of suspended particulate matter in the Elbe river: comparison of Salmonella microsome test, arabinose resistance test, and umu-test[END_REF]. For the 2010 samples, the SOS induction gave a different aspect of the genotoxicity than the Ames assay, as only one sample was positive in the Umu test (Bougival) while all samples were mutagenic in the Ames assay (TA98+S9 mix). The umuC gene is induced by DNA lesions but this does not lead necessarily to mutations which may explain the absence of correlation between the two tests. The low reaction of the umu-test may be ascribed to the presence of compounds that inhibit components of the SOS-system or the indicative enzyme, bgalactosidase. The complexity of the mixtures makes the presence of SOS-inhibiting compounds possible as reported in the literature [START_REF] Vahl | Genotoxicity assessment of suspended particulate matter in the Elbe river: comparison of Salmonella microsome test, arabinose resistance test, and umu-test[END_REF], even though the low concentration of PAHs might be the major explanation of the low genotoxicity of LDPE extracts.

In conclusion, we developed a method for coupling the use of LDPE passive samplers with genotoxicity assays. Field application of this new method revealed that labile dissolved PAHs extracted from passive samplers were weakly positive with the TA98+S9 and Umu test. Concentrations of labile mutagenic PAHs were higher upstream of Paris than downstream of Paris. Improvement of the method is needed to determine the genotoxicity of low concentrations of labile dissolved organic contaminants.

Watanabe, M., Nohmi, T., and Ishidate, M. (1989). Establishment of new strains of Styphimurium highly sensitive to nitroarenes and amine aromatic-TA98 and TA100 derivatives with high levels of nitroreductase or acetyltransferase activities Mutat Res 216, 383-383.

  observed at Bougival at both seasons, followed by Triel-sur-Seine, while Marnay-sur-Seine and St-Maurice presented the lowest concentrations. The highest concentrations of PAHs were for Pyrene at St-Maurice (autumn), Bougival and Triel-sur-Seine and for Fluoranthene at Marnay-sur-Seine and St-Maurice (spring). Benzo(a)pyrene and dibenz(a,h)anthracene, which are considered as the more mutagenic PAHs, had the highest concentrations at Bougival. No correlation was noted between PAHs concentrations and Ames assay data (Spearman correlation).

Figure 1 :

 1 Figure 1: Localisation of the study sites in the Seine River Basin

Table 1 :

 1 Genotoxicity and Mutagenicity of LDPE sampler deployed in the Seine River water in 2009

	Sample	Dilution	Umu test	Dose	Ames-test	Ames test
			(TA1535/pSK1002)	(µl)	(TA 98 +S9)	(YG 1041+S9)
			-S9 mix	+ S9 mix
		1/8	1.2 ± 0.2	1.4 ± 0.1	4	42 ± 10	120 ± 24
	Marnay	1/4	1.3 ± 0.2	1.35 ± 0.1	6	51 ± 6	125 ± 6
		1/2	0.9 ± 0.4	-	8	61 ± 7	nd
					10	49 ± 4	nd
		1/8	1.21 ± 0.2 1.33 ± 0.2	4	29 ± 4	107 ± 9
	Bougival	1/4	1.42 ±0.41 1.33 ± 0.2	6	36 ± 5	100 ± 4
		1/2	0.94 ±0.33	-	8	34 ± 2	NC
				-	10	33 ± 6	97 ± 11
	BaP			1.97 ±0.4		654 ± 8	161 ± 6
	DMSO 1%		1.22 ±0.1	1.1	10	31 ± 6	106 ± 11
	LDPE control		0.94±0.5	1.07±0.07		35 ± 7	130 ± 24
	4NQO		3.08±0.7
	1-NP		2.11±0.4
	Control		1	1
	Umu test: induction rate of β galactosidase activity (mean ± SD). Genotoxic sample = value >1.5
	Ames test: number of revertants/plate (mean±SD). Bold faced values are significantly different from
	control value (DMSO1%). Spontaneous revertants: TA98= 25 ±5 ; YG1048=111±8
	Mean of three replicates.

Table 2 :

 2 Genotoxicity and Mutagenicity of LDPE sampler deployed in the Seine River in 2010Umu test: induction rate of β galactosidase activity (mean ± SD). Genotoxic sample = value >1.5 Ames test: positive samples are two fold the DMSO value (bold letters). Control=DMSO1% Positive controls for Ames test were: 20 ng/plate 2,4,7-trinitrofluorenone for TA98-S9 mix and 5 µg/plate sodium azide for TA100-S9 mix. 0.5 and 1 µg/plate of BaP for TA98 with S9 Mix. 10 µL is the volume of LDPE extract tested with the Ames assay. Mean of three replicates.

			Umu test	Ames test (TA 98)	Ames test (TA100)
	Sites	Season	(TA1535/pSK1002)	(nb/rev/plate)	(nb rev/plate)
				-S9 mix	+ S9 mix	-S9 mix	+ S9 mix
		Spring	1.17 ± 0.29	12 ± 5	39 ± 2	110 ± 7	101 ± 18
	Marnay-sur-Seine	Autumn	1.18 ± 0.3	96 ± 8	24 ± 6	77 ± 18	38 ±
		Spring	1.36 ± 0.27	27 ± 2	33 ± 0.5	121± 13	108 ± 12
	St-Maurice	Autumn	1.38 ± 0.36	164 ± 89	35 ± 14	81 ± 8	35 ±
		Spring	2.07 ± 0.5	17 ± 7	30 ± 2	142 ± 15	131± 33
	Bougival	Autumn	2.18 ± 0.7	14 ± 2	39 ± 4	82 ± 2	96 ±
		Spring	1.13 ± 0.18	16 ± 4	29 ± 15	125 ± 2	85 ±
	Triel-sur-Seine	Autumn	1.53 ± 0.37	12 ± 3	42 ± 3	118 ± 22	153 ± 21
	DMSO 1%		1.1	26 ± 6	10 ± 1	167 ± 16	127 ±
	Positive control		7.85 ± 2	197 ± 21	371± 181	371 ± 295	1671 ± 108
	Spontaneous			13 ± 7	11 ± 0.5	55 ± 14	95 ±
	revertants

Table 3 :

 3 Concentrations of Labile PAHs in LDPE

	PAHs (ng/LDPE extract)	Marnay sur Seine		St-Maurice		Bougival		Triel sur Seine
		Spring	Autumn	Spring	Autumn	Spring	Autumn	Spring
	Acenaphtene	0.42	0.89	0.53	0.57	2.41	2.09	1.42
	Fluorene	0.65	1.37	1.07	0.85	2.63	2.02	2.06
	Phenanthrene	2.20	4.03	3.00	2.23	8.66	6.43	6.84
	Anthracene	0.59	0.96	1.06	1.86	5.08	6.24	2.77
	Fluoranthene	5.04	8.36	6.56	4.39	21.20	13.64	18.77
	Pyrene	1.99	4.62	6.00	7.18	31.05	28.14	27.52
	Benzo(a)anthracene	0.10	0.34	1.01	0.43	5.52	4.93	5.32
	Chrysene	0.46	0.78	1.72	1.75	6.93	4.71	6.83
	Benzo(b)fluoranthene	1.03	2.33	1.84	4.35	13.05	21.04	4.42
	Benzo(k)fluoranthene	0.27	0.50	1.05	1.42	4.97	8.73	1.98
	Benzo(a)pyrene	0.24	0.56	0.80	1.37	9.09	16.04	2.58
	Indeno(1.2.3-cd)pyrene	0.43	0.72	0.81	1.06	3.05	5.77	0.84
	Dibenz(a.h)anthracene	0.08	0.24	0.22	0.49	1.19	1.68	0.51
	Benzo(g.h.i)perylene	0.34	0.65	0.57	1.19	3.13	4.87	1.39
	Σ PAHs

Acknowledgment

We thank Dr. Y. Oda (Japon) who gave the bacterial strain TA1535/pSK1002, Dr. B.N. Ames (Berkeley, CA, USA) who gave the bacterial strains TA98 and TA100 and Dr T. Nohmi (Tokyo, Japan) who gave the strain YG1041.

We thank R. LOUAILECHE and C. VARRET for technical assistance.

Compliance with Ethical Standards

Cécile MICHEL had a Ph.D. Grant from the Ile-de-France Regional Council (R2DS program). This work was supported by the PIREN-Seine research program.

Authors do not have Conflict of interest

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Figure legend:


Uher Emmanuelle
email: emmanuelle.uher@irstea.fr

Compère Chantal

Combe Matthieu

Mazeas Florence

Gourlay-Francé Catherine

Emmanuelle Uher

In situ measurement with diffusive gradients in thin films: effect of biofouling in freshwater

Keywords: DGT, Metals, Biofouling, Seine River, Field deployment, Passive sampler

Concerning in situ passive sampler deployment, several technical priorities must be considered. In particular, deployment time must be sufficiently long not only to allow a significant quantity to be accumulated to facilitate analysis but also to ensure that the signal is above the quantification limit and out of the blank influence. Moreover, regarding the diffusive gradient in thin films (DGT) technique, deployment time must also be sufficiently long (at least 5 days) to avoid the interactions of the solutes with the material diffusion layer of the DGT and for the steady state to be reached in the gel. However, biofouling occurs in situ and modifies the surface of the samplers. In this article, we propose a kinetic model which highlights the biofouling effect. This model was able to describe the mitigation of the flux towards the DGT resin observed on Cd, Co, Mn, Ni and Zn during a 22-day deployment in the Seine River. Over a period of 22 days, biofouling had a significant impact on the DGT concentrations measured, which were decreased twofold to threefold when compared to concentrations measured in unaffected DGTs.

Introduction

Passive samplers are an emerging way of assessing water quality. Their use is increasing in the scientific community. It is claimed that they provide time-integrated concentrations of the species they measure during their deployment in water. Quantification limits are lowered and the matrix effects in the analytical process are reduced.

However, in situ conditions differ significantly from convenient laboratory conditions. Biofouling occurs at the surface of the samplers being immersed in water. In freshwater, physicochemical conditions differ greatly between sampling sites, while deployment time is subjected to many constraints: metal accumulation must be significant, whereas, in relatively uncontaminated sites, this may require a long deployment time, and interactions of the solutes with the material diffusion layer cannot be neglected if the deployment time is too short. Diffusion of metals may be retarded at the beginning of the deployment because of these interactions, and deployment time has to be sufficiently long to ensure the steady state is reached, as is discussed by [START_REF] Davison | Progress in understanding the use of diffusive gradients in thin films (DGT) -back to basics[END_REF] and Garmo et al. (2008b,a).

Previous studies examined what consequences the presence of major ions had on diffusive gradient in thin film (DGT) measurements in marine water [START_REF] Tankere-Muller | Effect of competitive cation binding on the measurement of Mn in marine waters and sediments by diffusive gradients in thin films[END_REF]. They also simulated the limits of the linear accumulation regime of DGT concerning pH, deployment time, and dissolved ligands [START_REF] Mongin | Limits of the Linear Accumulation Regime of DGT Sensors[END_REF]. Others studies showed that biofouling might affect DGT measurement: [START_REF] Pichette | Preventing biofilm development on DGT devices using metals and antibiotics[END_REF] and [START_REF] Feng | Effect of Biofilm on Passive Sampling of Dissolved Orthophosphate Using the Diffusive Gradients in Thin Films Technique[END_REF] studied the effect of biofilm development on phosphate measurement using DGT, respectively in a freshwater aquaculture pond and in freshwater. It has already been observed that biofouling had a strong effect on DGT measurement in raw wastewater [START_REF] Uher | Impact of Biofouling on Diffusive Gradient in Thin Film Measurements in Water[END_REF]. However, Buzier et al. showed that 14-day-biofouling did not affect the diffusion coefficient of the DGT diffusion layer in freshwater [START_REF] Buzier | DGT-labile As, Cd, Cu and Ni monitoring in freshwater: Toward a framework for interpretation of in situ deployment[END_REF].

Biofilm developing at the surface of DGT has long been suspected to behave as an additional inert diffusion layer, which may reduce the uptake of the species analyzed [START_REF] Booij | Little effect of excessive biofouling on the uptake of organic contaminants by semipermeable membrane devices[END_REF][START_REF] Pichette | Preventing biofilm development on DGT devices using metals and antibiotics[END_REF][START_REF] Schafer | Aquatic passive sampling of a short-term thiacloprid pulse with the Chemcatcher: Impact of biofouling and use of a diffusion-limiting membrane on the sampling rate[END_REF].

Moreover, it has long been known that biofilm interacts with metals in solution through various processes [START_REF] Van Hullebusch | Metal immobilisation by biofilms: Mechanisms and analytical tools[END_REF]. One of these processes, biosorption on biofilms and bacterial cells has been studied in depth as a potential sorbing material for removing metals from waste solutions [START_REF] Ginisty | Biosorption of cobalt by Pseudomonas halodentrificans: influence of cell wall treatment by alkali and alkaline-earth metals and ion-exchange mechanisms[END_REF][START_REF] Kuyucak | Biosorbents for recovery of metals from industrial solutions[END_REF][START_REF] Veglio | Removal of metals by biosorption: A review[END_REF][START_REF] Wase | Biosorbents for Metal Ions[END_REF]. Other interactions of varying importance and reversibility may occur between biofilm and metals, namely: complexation, precipitation of insoluble salts, adsorption on iron and manganese oxides, and reduction, as highlighted both in a comprehensive review by [START_REF] Van Hullebusch | Metal immobilisation by biofilms: Mechanisms and analytical tools[END_REF] of knowledge of the mechanisms of metal immobilization by biofilm and also in several experimental studies conducted under varying conditions and with several metals (Bradac et al. 2009a[START_REF] Bradac | Cadmium speciation and accumulation in periphyton in a small stream with dynamic concentration variations[END_REF][START_REF] Duong | Experimental toxicity and bioaccumulation of cadmium in freshwater periphytic diatoms in relation with biofilm maturity[END_REF][START_REF] Faburé | Impact of an urban multi-metal contamination gradient: Metal bioaccumulation and tolerance of river biofilms collected in different seasons[END_REF][START_REF] Fechner | Linking community tolerance and structure with low metallic contamination: A field study on 13 biofilms sampled across the seine river basin[END_REF][START_REF] Moppert | Investigations into the uptake of copper, iron and selenium by a highly sulphated bacterial exopolysaccharide isolated from microbial mats[END_REF][START_REF] Toner | Zinc sorption by a bacterial biofilm[END_REF][START_REF] White | Copper accumulation by sulfate-reducing bacterial biofilms[END_REF]. It is also assumed that biofilm is a "gateway" between dissolved metals in solution and hydrous metal oxides coating the streambed, and that biofilm plays a role in the diel cycles of dissolved metals [START_REF] Nimick | Diel biogeochemical processes and their effect on the aqueous chemistry of streams: A review[END_REF]. More recent reviews have focused on the role of extracellular polymeric substances (EPSs, secreted by microorganisms), which exhibit abundant binding sites for metals (Li andYu 2014, More et al. 2014). Feng et al. showed that the composition of a biofilm grown at the surface of DGT phosphate-samplers mainly consisting of diatoms, several metal oxides (Fe, Al, Mn) and EPSs [START_REF] Feng | Effect of Biofilm on Passive Sampling of Dissolved Orthophosphate Using the Diffusive Gradients in Thin Films Technique[END_REF]. [START_REF] Buzier | DGT-labile As, Cd, Cu and Ni monitoring in freshwater: Toward a framework for interpretation of in situ deployment[END_REF] also observed biofilm forming at the surface of DGT samplers: biofilm was composed of organic deposits and metallic oxides capable of adsorbing species.

The results of our previous study suggest that biofilms at the surface of DGTs and metal species interact [START_REF] Uher | Impact of Biofouling on Diffusive Gradient in Thin Film Measurements in Water[END_REF]. Different effects were observed depending on the metal being studied (Cd, Cr, Co, Cu, Mn, Ni, Pb, Zn). It was concluded from these results that biofilm exhibits metal-binding properties with varying degrees of specificity and affinity, depending on the metal under scrunity. Furthermore, the literature suggests that metal-binding properties also depend on the bulk solution chemistry and on the physiological state of the biofilm [START_REF] Nimick | Diel biogeochemical processes and their effect on the aqueous chemistry of streams: A review[END_REF]. The biofilm's composition is likely to vary according to the sampling site and deployment conditions. Thus, biofouling can also be expected to vary with the sampling site. A simple kinetic model which highlights the physicochemical interactions between metals and biofilms was proposed to explain biofilm's effect on DGT measurement. However, we need to precisely verify whether the description we proposed is valid in other conditions than in wastewater where the former experiments were conducted. DGTs are more often deployed in freshwater.

Therefore more freshwater data are needed to establish a model of the impact of biofouling on DGT measurement.

The first purpose of this particular research was to precisely describe how biofouling may affect the transfer of metals to the DGT chelating resin by proposing a quantitative model involving physicochemical interactions of metals and biofilm. Its second purpose was to verify whether this hypothesis is valid in freshwater. A study was conducted in the Seine River. Accumulation of metals in the DGT Chelex resin was monitored along with biofouling and biomass growth estimation of the biofilm attached to the protective membrane of the DGT, in order to compare the model with the experimental data. Physicochemical conditions and deployment time were considered while discussing the results.

Theoretical background

DGT principle

The principle of DGT is based on Fick's first law. DGTs are composed of a chelating resin, a diffusive hydrogel, and a protective membrane. A metal diffusion gradient develops between the bulk solution and the resin layer because this latter strongly sequesters cationic metals. Consequently, metal species are transported through the material diffusion layer (MDL), comprising of the gel and the membrane, toward the resin. The flux (J) of metal ions can be expressed by Equation 1:

J = D ∆C ∆ Equation 1
where D MDL is the diffusion coefficient in the material diffusion layer, ∆C is the concentration gradient, and ∆ MDL is the thickness of the MDL. The free metal ions in the diffusion layer are in rapid equilibrium with the resin, so the concentration near the resin is zero. ∆C≈C, where C is the concentration in the bulk solution. Therefore at steady state Equation 1 becomes:

J = D C ∆ Equation 2
The flux of species through an area (A) after a given time (t) is also defined by:

J = m tA Equation 3
where m is the mass of metal accumulated in the chelating resin. It should be noted that J is the mean flux of the metals during the deployment time.

Combining Equation 2 and Equation 3 shown above, the equation giving the concentration in water measured by DGT is as follows:

C = m∆ D tA Equation 4

Metal biofilm DGT interaction model

As soon as a substrate is immersed in water, planktonic cells would attach and, through growth and EPS production, biofilms may develop. This biofilm layer both constitutes an additional diffusion layer for DGT and exhibits abundant interaction sites for metals. However, DGT cannot be considered as just any surface in water because of the affinity of metal for chelating resin thereby creating the diffusion gradient in the gel of the DGT device. Thus metals fate may be driven by two different sinks: the diffusion through the DGT gel because of the resin and the binding within the external biofilm. This is illustrated in Figure 1: whenever metals interact with or within the biofilm matrix, they are temporarily fixed by the biofilm. Metals reversibly retained by the biofilm eventually diffuse through the hydrogel toward the resin. If the dissociation of the metal from the biofilm is the limiting step, metal diffusion in the hydrogel might be severely retarded. If the complexes dissociate readily, accumulation of metal in the resin might occur with no significant effect.

Two parameters are decisive: firstly the nature of the biofilm which in turn may alter the nature of the interactions with the metals and secondly the metal concentration in water which influences the diffusion gradient force.

From Equation 2 and Equation 3 above, we can expect that the flux of metal in DGT is constant if the concentration in water is constant. When a part of the metal is retained by the biofilm, the mean flux J should be reduced to account for that part that does not diffuse because of interactions:

J = J -D C ∆ Equation 5
where J 0 is the flux in the absence of biofilm and C is the mean metal concentration immobilized in the biofilm during the deployment time in ng cm -3 .

Given the reactions shown in Figure 1, the kinetics of metal in the biofilm can be described by Equation 6:

dC dt = k C -k C Equation 6
where C B is the concentration of metal immobilized in the biofilm and C M is the concentration of metal M interacting with biofilm in water in the vicinity of the sampler. Considering C B = 0 at time t = 0, we deduce Equation 7by integrating Equation 6:

C t = C k k 1 -e Equation 7
where k 1 is the uptake rate of metal in the biofilm (d -1 ), and k 2 is the elimination rate constant (d -1 ). Equation 7corresponds to a two-compartment kinetic model [START_REF] Landrum | Toxicokinetics in aquatic systems -model comparisons and use in hazard assessment[END_REF] where k 1 is considered as a constant under the assumption that the free binding sites concentration is in large excess compared to C M . Former studies used this type of model to describe the accumulation of metal in biofilm (Bradac et al. 2009, Hill and[START_REF] Hill | Growth dilution of metals in microalgal biofilms[END_REF]. C B (t) is rigorously the metal concentration in the biofilm at a given time t. Here the mean metal concentration in the biofilm between 0 and t (C in Equation 5) is approximated to C B (t) for every t.

Combining Equation 5and Equation 7gives the following Equation 8:

J = J -C D ∆ k k 1 -e Equation 8 ! = ! " -α # -$ β% Equation 9
Where α = C '() ∆ '() * and β = k Equation 10

Experimental section

DGT deployment in the Seine River

Twenty-four DGTs equipped with restricted gels (acrylamide with 0.8% bis-acrylamide cross-linker) and protective membranes: polyethersulfone-PES (0.45 µm pore diameter, 2.5 cm diameter, 140 µm thickness, Pall, Port Washington, New York, USA), and twenty-four DGTs equipped with restricted gels, protective membranes PES and Polycarbonate nuclepore membranes PC (0.4 µm pore diameter, 2.5 cm diameter, 10 µm thickness, Whatman, Little Chalfont, UK) were deployed in the Seine River, 40 km upstream of Paris, from 27 th March 2012 to 18 th April 2012.

Accumulation of metals in Chelex resin was followed for 22 days by retrieving 6 DGTs of each type (PES and PC) at t=3, 8, 15, and 22 days (Figure 2). New triplicates of DGT of each type were deployed between: t=3 and t=8, t=8 and t=15, t=15 and t=22.

To measure total dissolved concentrations, two grab samples were collected with a plastic needle and filtered in situ ) and also the dissolved organic carbon (DOC). pH, temperature were measured in situ. The data collected may be examined in the supporting information (Table SI 1).

(

DGT treatment

DGTs retrieved at time 3, 8, 15, and 22 days were dismantled and metals were eluted from the Chelex resin by soaking it in HNO 3 1 mol L -1 . PES and PC membranes were frozen (-80°C).

Total carbon measurements and scanning electron microscopy observations

To estimate the mass of deposits on membranes, the total carbon (TC) was analyzed using a LECO CS 125 analyser (St. Joseph, MI, USA) with a combustion of 900°C. Coupons (1 cm × 1 cm) were cut from protective membranes of DGT after exposure (at least three coupons for each immersion condition). Each coupon was immersed in 20 mL pure sterilized water and placed in an ultrasonic bath for 40 mins to remove the deposit. The sonication was stopped regularly and the water in the bath replaced with cold water to prevent the samples from overheating. Then the solution was filtered on a weighted and precombusted (4 hrs, 450°C) filter in fibreglass (GF/F, Whatman; diameter, 2 cm). The fibreglass filter was dried in a laboratory oven at 37°C overnight and then placed in a ceramic crucible directly in the furnace for combustion; accelerators (iron and tungsten) were required.

The biofilm attached to these membranes was observed by scanning electron microscopy (SEM). After exposure, the protective DGT membranes were rinsed in baths of pure water and then dehydrated under a formalin atmosphere in a fume hood; the surfaces were covered with a thin layer of gold/palladium prior to SEM imaging. All samples were imaged in an FEI Quanta 200 scanning electron microscope.

Metal analysis

Metals from DGTs and grab samples were analyzed using the ICP-MS (X series 2 Thermo Fisher Scientific, Villebon-sur-Yvette, France). Calibration of the ICP-MS was verified by analysis of the certified reference material NIST 1640a (natural water): mean recovery = 98%.

Flux calculation

Equation 3 ( ! = , %-

) was used to calculate the mean flux at time t, with m as the mass of metal accumulated on the resin at t and with A as the exposure area. The calculated flux J was plotted against t. The effective sampling area A e taking into account lateral diffusion was used in the calculations. A e was taken equal to 3.66 cm², according to [START_REF] Knutsson | Estimation of Measurement Uncertainties for the DGT Passive Sampler Used for Determination of Copper in Water[END_REF].

Labile concentration calculation

Previous studies showed that a diffusion boundary layer δ is created in front of the samplers when they are immersed in water [START_REF] Garmo | Estimation of diffusive boundary layer thickness in studies involving diffusive gradients in thin films (DGT)[END_REF][START_REF] Uher | DGT measurement in low flow conditions: diffusive boundary layer and lability considerations[END_REF][START_REF] Warnken | Accuracy of the diffusive gradients in thin-films technique: Diffusive boundary layer and effective sampling area considerations[END_REF]. δ has to be taken into account in the calculations where possible. δ is considered as an additional diffusion layer where the diffusion coefficient of the free metal in water is D W . Equation 4becomes:

C = m tA / 0 ∆ D + δ D 3 4 = J 0 ∆ D + δ D 3 4 Equation 11
When a linear relationship between the mass accumulated on the resin m and the time t exists, the slope of the linear model can be used to calculate a global labile concentration as follows:

C = slope A / 0 ∆ D + δ D 3 4 Equation 12
The diffusion coefficients D MDL used in this study were calculated in a previous study where we showed that protective membranes had no influence on the overall diffusion coefficient of the diffusion layer with restricted gels [START_REF] Uher | Impact of Biofouling on Diffusive Gradient in Thin Film Measurements in Water[END_REF]). D W were taken from [START_REF] Li | Diffusion of ions in sea water and in deep-sea sediments[END_REF]. D MDL and D W were corrected for the in situ temperature according to [START_REF] Zhang | Performance-Characteristics of Diffusion Gradients in Thin-Films for the in-Situ Measurement of Trace-Metals in Aqueous-Solution[END_REF].

The flow rate in the Seine River was high (109 ± 10 m 3 s -1 ). No significant precipitations occurred and the flow rate remained fairly stable during the deployment, so the diffusive boundary layer thickness δ was taken to be constant over the deployment. As we dealt with fast-flowing water δ was set at 0.026 cm, as calculated in our previous study [START_REF] Uher | DGT measurement in low flow conditions: diffusive boundary layer and lability considerations[END_REF].

Model fitting

Fluxes J according to the time were calculated for DGTs deployed at d = 0. The models described in the theoretical background were fitted to the experimental data using nonlinear regression of the XLStat © software. J 0 , α and β are the regression coefficients of the nonlinear model. Limits of the model were calculated with the limit values of the 95% confidence intervals of the parameters: upper limit = J calculated with J 0max , α min , β min , lower limit = J calculated with J 0min , α max , β max .

Results and discussion

Dissolved metal concentration

Total dissolved concentrations in Cr, Co, Cu, Mn, Ni, Pb, and Zn measured from day 0 (first day of the deployment)

to day 22 are represented in Figure SI 1. Total dissolved concentrations were fairly stable over time, except for Cu, which increased at time t = 3 and t = 5 days, and for Zn. A discrepancy between the replicates was also observed:

40% for Cr at d3 and d15, 30% for Mn at d22.

Biofilm growth at the surface of the membranes

The total carbon measured on the membranes' surface over time is represented in Figure 3. The mass of carbon clearly increased with time, indicating that the biofilm grew steadily during the deployment. This result is supported by the SEM images (Figure 4) showing the biomass growing with time. At time t = 22 days, the membranes were colonized by diatoms. These results are consistent with those shown by [START_REF] Feng | Effect of Biofilm on Passive Sampling of Dissolved Orthophosphate Using the Diffusive Gradients in Thin Films Technique[END_REF], who observed that the biofouling area was dominated by diatoms after 15 days deployment.

Figure 3 also shows that the biofilm growth was considerably higher on PES membranes from time t = 15 days. This is explained by the presence of a larger number of diatoms, as seen in Figure 4. Standard deviations were high,

showing that biofilm colonization was heterogeneous depending on the samples.

Metal accumulation in the DGT

The amount of metals accumulated on the resin of the DGTs was monitored throughout the deployment. The metal accumulation patterns are shown in Figure SI-2 in the supporting information. Despite the higher biofilm growth on PES membranes, no significant difference was observed between those DGTs equipped with both PES and PC membranes and DGTs equipped with PES membranes. Only the Pb accumulation pattern suggests a trend towards greater accumulation when DGTs were covered with PES membrane only (not statistically significant). Diatoms, which were more present on PES membranes, are phototrophic organisms that may lead to elevated pH inside the photosyntetically active biofilms [START_REF] Liehr | Metals removal by algal biofilms[END_REF][START_REF] Roeselers | Phototrophic biofilms and their potential applications[END_REF]. This may favor removal of metals by precipitation. Here the metal accumulation by DGTs was not influenced by the phototrophic nature of the biofilms, except for Pb for which accumulation might be enhanced when diatoms are present.

Cd, Co, Cu, Mn, and Ni accumulations show a globally increasing trend between time t = 0 and time t = 22 (Spearman's correlation tests between m and t: p-values were respectively 5.10 -6 , 3.10 -8 , 7.10 -11 , 3.10 -8 , 7.10 -15 ).

However, accumulation of Cr and Pb was less clear: the signal seems to remain stable because of the great variability of the experimental points, even if they are all above the limit of detection LD (LD = average value of the blanks + 3 × standard deviation on the blanks, n=8). Zn accumulation increased between day 0 and day 3, then seemed to increase from day 8 but the difference between day 8 and day 22 was not significant (Wilcoxon test, p = 0.09).

Replicates exhibited great variability (around 300%). Several sources of uncertainty were highlighted by [START_REF] Knutsson | Estimation of Measurement Uncertainties for the DGT Passive Sampler Used for Determination of Copper in Water[END_REF] such as preparation, handling of the samplers, and the diffusional pathway. Here, after a long deployment time (22 days), the variability of the replicates remained unchanged. After such a long period of time, the influence of blanks decreased significantly because of greater mass accumulation of metals. We thus assume that in situ conditions may play a role in the variability of the replicates, such as, for instance the position in the water column. We also noted that biomass greatly varied from one sample to another (Figure 3). Heterogeneous biofilm colonization may also explain the variability of the replicates.

The accumulation kinetics of DGTs deployed at time t = 0 were compared to accumulation kinetics built from the renewed DGTs, computed as follows: to calculate the average mass of metal at time t = 8 days, the average mass of metal accumulated by DGTs between time t = 3 and time t = 8 days was added to the average mass of metal accumulated at time t = 3, and so on until time t = 22 days. Because no significant difference was observed between DGTs equipped with both PES and PC membranes and DGTs equipped with PES membranes only, accumulation kinetics were represented by the mean of all DGT replicates. Examples of Co, Cu, and Zn are presented in Figure 5 while other metals are presented in the supporting information (see Figure . The kinetics built from renewed DGTs clearly increased more linearly than the kinetics from DGTs deployed at time t = 0 and are significantly higher. A plateau was reached for all metals except Ni for DGTs deployed at time t = 0. There was a substantial difference between the cumulated mass of renewed DGTs and the DGTs deployed for 22 days at the end of the deployment (on average 67%).

Deployment conditions clearly affected the DGT measurement. We will now try to discuss what factors led to this difference between renewed and initial DGTs.

Two studies in the recent literature provide useful indications. Firstly, [START_REF] Mongin | Limits of the Linear Accumulation Regime of DGT Sensors[END_REF] studied the limits of the linear accumulation regime of DGTs and concluded that a low pH (<5), a high metal concentration, a long time, or a high concentration of ligands can affect the linear regime of the DGTs. In the experiment reported in this article, pH was around 8.46 and in favor of a linear regime. The metal concentrations in the Seine River were lower than 5.10 -8 mol L -1 for each metal while concentrations leading to a divergence of the linear regime in the study reported by [START_REF] Mongin | Limits of the Linear Accumulation Regime of DGT Sensors[END_REF] were in the order of 10 -3 mol L -1 . Not more than 8 days were tested in the study of [START_REF] Mongin | Limits of the Linear Accumulation Regime of DGT Sensors[END_REF], so we are unable to draw conclusions on the time deployment. That being said if time affects the DGT measurement the pH conditions in the Seine River would argue more in favour of a linear regime.

Secondly, one significant characteristic of the Seine River is the calcium concentration (around 117 mg L -1 ≈ 2.9 mmol L -1 ). Tankéré-Muller et al. (2012) studied the effect of the competitive cation binding of metals by DGT in marine waters. They concluded that measurement of Mn, which has a weak affinity for Chelex 100 resin, was strongly affected by the competition with Ca 2+ at 10 mmol L -1 (approximately a 25% decrease). However, Co, which was included as a control metal having a higher affinity for Chelex 100 than Mn, was much less affected (with a deviation less than 10%). In our study deviations exist for all metals including those having the best affinity for the resin (Cu, Pb, Co) and are above 25%. If the presence of relatively high concentrations of Ca 2+ affect the DGT measurement, especially for Mn, this does not fully explain the difference we observed in the Seine River. As well as these parameters we suggest that biofouling may play a role in the decrease of the DGT measurement with respect to time.

Flux in the DGTs and biofilm effect

The plot of flux in the DGTs with time shows that flux decreases for all metals (Figure 6 and Figure SI 4). If we suppose that metal concentrations in water are relatively constant (except for Cu), the flux should be constant. Here we observe a sharp decrease during the first days of deployment, followed by a plateau at the end (Co, Zn), probably related to the plateaus observed for the metal accumulation in DGTs deployed at time t = 0 (Figure 5). To verify the hypothesis that biofouling can affect the DGT measurement, we tried to fit the experimental data with the models described in the theoretical background. As no significant difference was observed between DGTs equipped with PES and PC membranes and DGTs equipped with PES membranes only, all the DGT replicates deployed at t = 0 were used to fit the model in order to improve the statistical power of the model.

The interaction model described by Equation 8 fits the data of DGTs deployed at t = 0 for Co and Zn very well with, respectively, R²=0.78 and 0.79 (Figure 6). The same pattern was observed for Cd, Mn, and Ni (R²=0.41, 0.51, 0.79; see SI). When fluxes of DGTs deployed later (at time t = 3, 8, and 15 days) are added to the graphs according to the deployment time, they fit the model for Co and Zn. However, they fit the model in a lesser extent for Cd, Mn and Ni although replicates exhibit a great variability for these metals. This is in agreement with the hypothesis that concentrations were relatively constant for these metals during deployment. For them the fluxes depend more on the number of days DGTs were deployed rather than the moment where they were deployed. Regarding Cu, the flux seems to decrease linearly with time and does not follow the nonlinear Equation 8. However, as we know, the Cu concentrations were not constant during the deployment and thus did not meet one of the assumptions of the model of Equation 8. We cannot therefore conclude about Cu.

Moreover, Cr and Pb (see Figure SI 4) do not follow the model either. This could be related to the fact that accumulation of Cr was not significant enough for these elements (see Figure SI 2 and the section on metal accumulation), or else to the great variability of Pb replicates. Furthermore, other processes not taken into account in the model may occur.

In conclusion, the decrease of the metal flux in DGTs during deployment seems to be correctly defined by the metal-biofilm interaction model for Cd, Co, Mn, Ni, and Zn. This model gives a suitable explanation as to why the biofouling effect on the measurement may depend on the metal and highlights the kinetic aspect of the biofouling effect. However, other processes may occur. For instance we choose to neglect MDL increase in our model. Models involving MDL increase tested with our data (data not shown) were unsuccessful. A model combining both MDL increase and metal-biofilm interactions would be an issue, but requires more data than we had to correctly fit such a model.

Kinetic constants and labile concentrations

For Cd, Co, Mn, Ni, and Zn, the parameters of the regression J 0 , α and β were estimated. From the latter, uptake and elimination rate constants k 1 and k 2 were calculated in s -1 and d -1 using Equation 10and may be seen in Table 1, except k 1 for Cd for which C W was under the limit of quantification. The characteristic time t 1/2 corresponding to the time where the flux is equal to 50% of the initial flux was also calculated with

t / = - 1 β ln 01 - J 2α 4 Equation 13
The initial labile concentration, which was not affected by biofouling, was calculated from J 0 following Equation 11: k 2 was in the same order of magnitude as k 1 . It illustrates that the biosorption mechanism is based on a number of metal-binding processes taking place with components of the biofilm components' cell wall. The cell walls can reversibly biosorb metals and thus function in a similar way to an ion-exchange resin [START_REF] Wase | Biosorbents for Metal Ions[END_REF]. k 2 represents the dissociation of the metal from the biofilm, which is driven by the DGT gradient strength and must be higher to allow the accumulation of metal by Chelex resin (Co and Ni). The values of k 1 calculated here are in the same order of magnitude as the water rate constant of Cr 3+ (k w =5.10 -7 s -1 ), which is considered to be very slow in comparison to the water rate constants of other metals (k w (Cd 2+ )=3.10 8 s -1 , k w (Co 2+ )=2.10 6 s -1 ) [START_REF] Stumm | Aquatic geochemistry. Chemical Equilibria and Rates in Natural Waters[END_REF]. The association of metals with the biofilm grown at the surface of DGTs is therefore a slow reaction because of the predominant DGT gradient strength. However, some of the metal might be trapped. This is highlighted by the calculation of the characteristic time t 1/2 , presented in Table 1, which shows that the flux is very quickly affected during the deployment and decreases within the first few days: in the case of Co, the flux was decreased by 50% in just 4 days.

C 0 = J < ∆= + > ? Equation
To obtain the C DGT-m , that is, the mean labile concentration of the metals in the Seine River that were the least affected by in situ and physicochemical conditions, C DGT was derived from the slope of the accumulation kinetics of the renewed DGTs (See Figure 5) using Equation 12 The C DGT-T22 was underestimated two-to sevenfold when compared with C DGT-m . This highlights the difference between that of a long deployment time affected as it is by environmental and physicochemical conditions and that of a shorter deployment time.

Conclusion

The quantitative model that has been proposed to highlight the biofouling effect was able to explain the decrease observed on the flux toward the DGT resin of Cd, Co, Mn, Ni, and Zn on the presented data. Although other processes not examined in this model may occur, the hypothesis that metals would be temporarily retained by the biofilm at the surface of the DGTs because of interactions within the biofilm is credible in the conditions of our study. In the conditions we studied we would recommend a deployment time of 5 to 8 days to minimize the biofouling effect. However, biofouling is inevitable. The biofouling effect should certainly be considered as being a part of the in situ DGT response. Therefore in situ speciation results should be considered with care.

However these kinetic processes may be dependent on the metal and the sampling site. Some strong effects observed in our study may not happen in different conditions. More data in different conditions are needed to document biofouling effect.

ASSOCIATED CONTENT Supporting Information. Physicochemical parameters of the Seine River, the total dissolved concentration of metal in water and metal accumulation patterns in the DGTs during the deployment, accumulation kinetics in the DGTs, and the metal flux in the DGTs during the deployment are available as supporting information.

Figures and tables

14 C 0

 140 was compared with the mean dissolved concentration measured in water C W by calculating the C 0 /C W ratio. C 0 was consistent for Cd and was lower than the Cd labile concentrations found by Tusseau-Vuillemin et al. (2007) in the Seine River. For Co and Ni, C 0 was lower than C W , as can be expected from a labile concentration, and in the range of values found by Tusseau-Vuillemin et al. The C 0 /C W ratio was also the same as in the Tusseau-Vuillemin et al. study for Ni, but larger for Co. In the case of Mn and Zn, C 0 was overestimated but was on the same order of magnitude as C W .

  [START_REF] Tusseau-Vuillemin | Dissolved and bioavailable contaminants in the Seine river basin[END_REF]). The resulting labile concentrations are given in Table2. The labile concentration C DGT-T22 calculated from the mass accumulated in DGTs at time t = 22 days using Equation 11 are also presented.Labile concentrations from the renewed DGTs were in the same order of magnitude as the concentrations measured in the Seine River basin reported by[START_REF] Tusseau-Vuillemin | Dissolved and bioavailable contaminants in the Seine river basin[END_REF] in which the deployment took 8 days. Labile percentages ranged from 21 to 202%. It would be interesting to investigate Mn, Pb, and Zn in order to determine if the high value of C DGT-m stems from possible contamination peaks that eluded the grab samples or raises questions regarding the technique and the calculations themselves.

Figure 1 -Figure 2 -FigureFigure 5 -

 125 Figure 1-Schematic representation of the role of the biofilm in the accumulation of metal by DGT (adapted from Uher et al. 2011)

Figure 6 -

 6 Figure 6 -Metal flux in the DGTs during the deployment and model fitting the data of DGTs deployed at d = 0 and including PES and PC. DGTs deployed at t=0 are represented by diamonds. Open symbols represent DGTs equipped with PES membranes, and filled symbols represent DGTs equipped with PC membranes. Upper and lower limits were calculated with the minima and maxima values of the parameters confidence intervals.Deployment time (d)

Table 2 .

 2 Labile concentration calculated from accumulation kinetics of the renewed DGTs and labile concentrations calculated from mass accumulated at t=22 days. Labile percentage in relation to

-Figure

  Figure SI 1 -Total dissolved concentration in µg L -1 during the deployment

  Moreover, grab samples were collected and filtered in situ to measure major ions (Ca 2+ , Mg 2+ , K + , Na + , NO 3

				-, Cl -,
	SO 4	2-, PO 4	3-, CO 3	2-

Minisart syringe filters with PES membranes, 0.45 µm, Sartorius, Göttingen, Germany) at time 3, 8, 15, and 22 days. Samples were acidified 1% vol. using suprapur HNO 3 (65% suprapur, Merck, Darmstadt, Germany) in the laboratory.

Table 1 . Initial flux, initial labile concentration, uptake, and elimination rate of the metal in the biofilm and half-time of the flux calculated from the parameters of the nonlinear regression.

 1

		-7	2.67x10 -6	1.70x10 -4	2.27x10 -5	2.00x10 -4
	C 0 (µg L -1 )	0.0045		0.11	6.8		0.93		6.8
	(% C 0 /C W )	n.c		(60%)	(319%)	(46%)	(308%)
	k 1 (s -1 ) (d -1 )	n.c n.c		1.11x10 -6 0.096	4.06x10 -6 0.35	7.92x10 -7 0.068	8.29x10 -6 0.72
	k 2 (s -1 ) (d -1 )	1.33x10 -6 0.11	2.45x10 -6 0.21	1.84x10 -6 0.16	2.46x10 -6 0.21	3.43x10 -6 0.30
	t 1/2 (d)	9.0		4.1	6.4		4.5		2.7
		Cd	Cr	Co	Cu	Mn	Ni	Pb	Zn
	C DGT-m (µg L -1 )	0.0049	0.21	0.048	0.34	4.3	0.44	0.25	2.4
	(% C DGT /C W )	n.c	(33%)	(27%)	(29%)	(202%)	(21%)	(183%)	(108%)
	C DGT-T22 (µg L -1 )	0.0020	0.058	0.016	0.15	1.6	0.19	0.034	0.68
	(% C DGT /C W )	n.c	(9%)	(9%)	(13%)	(74%)	(10%)	(25%)	(31%)

Table SI 1 -Physicochemical parameters of the Seine River during the deployment. Concentrations are the mean of

 SI

														-1
	Mean		8.46	13.09 117.0 2.356	2.33	10.5	19.1	21.85	n.d	22.48	266	2.48
	CI 95%	0.13	0.79	9.2	0.087	0.17	1.7	2.8	0.51	n.d	0.92	20	0.31
	(Ca	2+ , K	+ , Mg	2+ , Na	+ , Cl								6

Acknowledgments

Sophie Ayrault (Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France) is acknowledged for providing access to ICP-MS, as is Nicolas Gayet (Laboratoire Environnement Profond, Ifremer, Brest) for his helpful technical support and expertise in total carbon measurements and SEM observations.

Funding Sources

This study is part of the EMESTox project, funded by the French National Research Agency.

AUTHOR INFORMATION

Corresponding Author

* E-mail: emmanuelle.uher@irstea.fr; Tel: +33 1 40 96 65 39; Fax: +33 1 40 96 61 99.


Laura Khatib

Le-Duy Do

Marie Benaiteau

Macarena Villagrán-García

Pierre Meyer

Lydia Abou Haidar

Sophie Demeret

Jérôme Honnorat
email: jerome.honnorat@chu-lyon.fr

Laura Khatib1

• Le-Duy

Do

Macarena Villagran-Garcia

Madeleine Scharf

Lydia Abou Haida

Haidar La

Autoimmune Cerebellar Ataxia Associated with Anti-Glutamate Receptor δ2 Antibodies: a Rare but Treatable Entity

teaching and research institutions in France or abroad, or from public or private research centers.