Lucie Pinasseau

Laure Wiest
email: laure.wiest@isa-lyon.fr

Laurence Volatier

Florian Mermillod-Blondin

Emmanuelle Vulliet

Emerging polar pollutants in groundwater: potential impact of urban stormwater infiltration practices

Keywords: groundwater, passive sampling, pesticides, pharmaceuticals, stormwater infiltration systems, urban stormwater

The quality of groundwater (GW) resources is decreasing partly due to chemical contaminations from a wide range of activities, such as industrial and agricultural enterprises and changes in land-use. In urban areas, one potential major pathway of GW contamination is associated with urban water management practices based on stormwater runoff infiltration systems (SIS). Data on the performance of the upper layer of soil and the unsaturated zone of infiltration basins to limit the contamination of GW by hydrophilic compounds are lacking.

With this aim, the impact of infiltration practices on GW contamination was assessed for 12 pesticides and 4 pharmaceuticals selected according to their ecotoxicological relevance and their likelihood of being present in urban stormwater and GW. For this purpose, 3 campaigns were conducted at 4 SIS during storm events. For each campaign, passive samplers based on the use of Empore™ disk were deployed in GW wells upstream and downstream of SIS, as well as in the stormwater runoff entering the infiltration basins. Upstream and downstream Capsule: The fate of 16 polar and semi-polar pesticides and pharmaceuticals in groundwater upstream and downstream of stormwater infiltration systems (SIS) was studied.

GW contaminations were compared to evaluate the potential effect of SIS on GW contamination and possible relationships with stormwater runoff composition were examined.

Our results showed two interesting opposite trends: (i) carbendazim, diuron, fluopyram, imidacloprid and lamotrigine had concentrations significantly increasing in GW impacted by infiltration, indicating a contribution of SIS to GW contamination, (ii) atrazine, simazine and 2 transformation products exhibited concentrations significantly decreasing with infiltration due to a probable dilution of historic GW contaminants with infiltrated stormwater runoff. The other 7 contaminants showed no general trend. This study demonstrates that passive samplers deployed in GW wells enabled the capture of emerging polar pollutants present at very low concentrations and allowed the assessment of infiltration practices on GW quality.

New data on GW and urban stormwater are provided for poorly studied hazardous compounds.

Introduction

Groundwater (GW) is the greatest source of freshwater in the world and provides about 75% of drinking water in the European Union [START_REF] Danielopol | Incorporating ecological perspectives in European groundwater management policy[END_REF]European Commission, EC, 2006). Aquifers are also vital for industry, agriculture, and groundwater-dependent aquatic ecosystems such as rivers and lakes. However, GW resources are threatened as they are facing a reduction in their quantity and quality due to increased anthropogenic activities.

Over the past decade some organic pollutants (e.g. lifestyle and personal care products, industrial chemicals, pesticides, pharmaceuticals and their transformation products (TPs)) have been detected in European GW from ng L -1 to several µg L -1 [START_REF] Jurado | Emerging organic contaminants in groundwater in Spain: A review of sources, recent occurrence and fate in a European context[END_REF][START_REF] Lapworth | Emerging organic contaminants in groundwater: A review of sources, fate and occurrence[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Lopez | Screening of French groundwater for regulated and emerging contaminants[END_REF][START_REF] Postigo | Synthetic organic compounds and their transformation products in groundwater: Occurrence, fate and mitigation[END_REF][START_REF] Stuart | Review of risk from potential emerging contaminants in UK groundwater[END_REF][START_REF] Vulliet | Survey regarding the occurrence of selected organic micropollutants in the groundwaters of overseas departments[END_REF]. From the several sources and pathways of GW contamination (e.g., agricultural practices, industrial contamination of soils, etc), stormwater infiltration practices remain poorly studied whereas they can constitute a potential major pathway for GW contamination. Stormwater infiltration systems (SIS) are etc developed in urban areas to mitigate the risk of flooding and offset reduced GW recharge due to road surfacing. Detention/infiltration basins in urban areas receive stormwater runoff collected from impervious surfaces (roads and car parks), which is then infiltrated through a porous medium (i.e. soil and unsaturated zone) [START_REF] Marsalek | International Report: Stormwater management[END_REF][START_REF] Mermillod-Blondin | Dynamics of dissolved organic carbon (DOC) through stormwater basins designed for groundwater recharge in urban area: Assessment of retention efficiency[END_REF][START_REF] Voisin | Aquifer recharge with stormwater runoff in urban areas: Influence of vadose zone thickness on nutrient and bacterial transfers from the surface of infiltration basins to groundwater[END_REF]. However, urban stormwater runoff are often contaminated by heavy metals, polycyclic aromatic hydrocarbons and polar emerging pollutants such as pesticides and more specifically biocides from building materials, pharmaceuticals and personal care products [START_REF] Bollmann | Dynamics of biocide emissions from buildings in a suburban stormwater catchment -Concentrations, mass loads and emission processes[END_REF][START_REF] Fairbairn | Contaminants of emerging concern in urban stormwater: Spatiotemporal patterns and removal by iron-enhanced sand filters (IESFs)[END_REF][START_REF] Gasperi | Micropollutants in urban stormwater: occurrence, concentrations, and atmospheric contributions for a wide range of contaminants in three French catchments[END_REF], 2012;[START_REF] Launay | Organic micropollutants discharged by combined sewer overflows -Characterisation of pollutant sources and stormwater-related processes[END_REF][START_REF] Müller | The pollution conveyed by urban runoff: A review of sources[END_REF][START_REF] Paijens | Biocide emissions from building materials during wet weather: identification of substances, mechanism of release and transfer to the aquatic environment[END_REF][START_REF] Spahr | Hydrophilic trace organic contaminants in urban stormwater: occurrence, toxicological relevance, and the need to enhance green stormwater infrastructure[END_REF][START_REF] Zgheib | Priority pollutants in urban stormwater: Part 1 -Case of separate storm sewers[END_REF]. Pesticides come from the maintenance of urban green surfaces such as public parks, gardens, children's play areas, roadside verges or riverside footpaths. Pharmaceuticals are more rarely investigated in stormwater runoff but several studies reported stormwater contamination by sewage pollutants in separate sewer systems [START_REF] Sidhu | Sewage pollution in urban stormwater runoff as evident from the widespread presence of multiple microbial and chemical source tracking markers[END_REF]Zgheib et al., 2012). Pharmaceuticals in stormwater can also originate from recreational water discharges or outdoor human dejections. Consequently, SIS designed for rapid infiltration of stormwater runoff increase the risks of aquifer contamination. Previous studies demonstrated that the upper layer of soil and the unsaturated zone could act as efficient filters for hydrophobic contaminants [START_REF] Datry | Dynamics of solutes and dissolved oxygen in shallow urban groundwater below a stormwater infiltration basin[END_REF][START_REF] Tedoldi | Impact of runoff infiltration on contaminant accumulation and transport in the soil/filter media of Sustainable Urban Drainage Systems: A literature review[END_REF][START_REF] Winiarski | The impact of stormwater on a soil profile in an infiltration basin[END_REF], with a minor risk of leaching [START_REF] Nogaro | Influence of a stormwater sediment deposit on microbial and biogeochemical processes in infiltration porous media[END_REF]. However, this retention efficiency of the soil and unsaturated zone has not been proven for more hydrophilic compounds such as pesticides and pharmaceuticals that may be present in stormwater runoff. Several studies have already pointed out that SIS are probably responsible for GW contamination by pesticides. By evaluating the influence of detention basins on GW quality, [START_REF] Fischer | Effects of Stormwater Infiltration on Quality of Groundwater Beneath Retention and Detention Basins[END_REF] showed a higher frequency of atrazine, carbaryl, metolachlor and prometon in wells impacted by detention basins than in wells located outside the influence of detention basins. [START_REF] Marmonier | The use of crustaceans as sentinel organisms to evaluate groundwater ecological quality[END_REF] observed similar results for almost 12 pesticides by comparing GW located in aquifer zones non impacted and aquifer zones impacted by urban stormwater infiltration basins. Likewise, [START_REF] Hensen | Entry of biocides and their transformation products into groundwater via urban stormwater infiltration systems[END_REF] measured the transfer of contaminants through a SIS (basin) and showed that stormwater infiltration contaminated GW with 3 biocides applied as preservatives on facades and their TPs. However, it remains several knowledge gaps concerning the assessment of GW contamination by emerging organic contaminants associated with SIS. First, the occurrence and fate of some hazardous pollutants such as certain less-studied polar pesticides, in addition to pharmaceuticals, remain poorly studied. Second, the measurement of organic contaminants in GW is challenging as they occur at low (ng L -1 to pg L -1 ) and variable concentrations. To circumvent this problem, passive sampling approaches have to be deployed to detect contaminants usually found at very low concentrations. Passive sampling also gave a more accurate assessment of groundwater contamination than conventional spot sampling [START_REF] Brack | Towards the review of the European Union Water Framework Directive: Recommendations for more efficient assessment and management of chemical contamination in European surface water resources[END_REF][START_REF] Jones | Emerging priority substances in the aquatic environment: a role for passive sampling in supporting WFD monitoring and compliance[END_REF][START_REF] Miège | Position paper on passive sampling techniques for the monitoring of contaminants in the aquatic environment -Achievements to date and perspectives[END_REF][START_REF] Mills | Measurement of environmental pollutants using passive sampling devices -an updated commentary on the current state of the art[END_REF]. Therefore, passive samplers such as Empore™ disk-based samplers appear as promising tools for the determination of contaminants in the environment as they are easy to use for a wide range of applications [START_REF] Page | Environmental monitoring of selected pesticides and organic chemicals in urban stormwater recycling systems using passive sampling techniques[END_REF][START_REF] Petrie | In Situ Calibration of a New Chemcatcher Configuration for the Determination of Polar Organic Micropollutants in Wastewater Effluent[END_REF][START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF][START_REF] Shaw | Time Integrative Passive Sampling: How Well Do Chemcatchers Integrate Fluctuating Pollutant Concentrations?[END_REF][START_REF] Vermeirssen | Uptake and release kinetics of 22 polar organic chemicals in the Chemcatcher passive sampler[END_REF][START_REF] Vrana | Passive sampling techniques for monitoring pollutants in water[END_REF]. These disks can accumulate analytes on a receiving phase over several days or weeks and the analyte pre-concentration allows for an overall increase of the analytical sensitivity of the procedure. Moreover, changes in average contaminant concentrations over the sampling period are taken into account. Therefore, these devices are very pertinent to assess the occurrence of emerging polar pollutants in GW impacted by urban stormwater infiltration practices that create pulses of stormwater runoff and solutes in GW during rainfall events [START_REF] Datry | Solute dynamics in the bed sediments of a stormwater infiltration basin[END_REF]. Nevertheless, to the best of our knowledge, their deployment in GW is still rare and only one study has reported their use for monitoring organic contaminants in such kind of infiltration system [START_REF] Page | Environmental monitoring of selected pesticides and organic chemicals in urban stormwater recycling systems using passive sampling techniques[END_REF].

In this context, the present study aimed to assess the effects of urban stormwater runoff infiltration on GW contamination by polar and semi-polar pesticides and pharmaceuticals using Empore™ disk-based sampler. A list of 16 targeted contaminants was selected according to several criteria: their environmental and ecotoxicological interest, their frequent detection at our study sites based on previous studies [START_REF] Marmonier | The use of crustaceans as sentinel organisms to evaluate groundwater ecological quality[END_REF][START_REF] Pinasseau | Use of passive sampling and high resolution mass spectrometry using a suspect screening approach to characterise emerging pollutants in contaminated groundwater and runoff[END_REF][START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF] and their physico-chemical properties. Less-studied hazardous compounds such as bromacil, fluopyram, imidacloprid and lamotrigine were also investigated to supplement these works.

Three campaigns of 10 days were conducted at 4 SIS during storm events in winter. For each SIS, the potential impact of infiltration practices on GW contamination was assessed by comparing masses accumulated on passive samplers of the 16 pesticides and pharmaceuticals in GW non impacted by urban stormwater runoff infiltration (aquifer zone located upstream of SIS) and in GW impacted by urban stormwater runoff infiltration (aquifer zone located downstream of SIS in the plume of infiltrated stormwater). Qualitative characterization of the stormwater runoff entering the infiltration basins was also undertaken for the same 16 contaminants in order to evaluate the potential relationships between stormwater runoff composition of pollutants and GW contamination by stormwater runoff infiltration.

Material and Methods

Studied sites and experimental design

The experiments were undertaken in 4 SIS located in the eastern metropolitan area of Lyon (France) and monitored in the framework of the field observatory in urban hydrology (OTHU) (further details of the SIS are given in [START_REF] Marmonier | The use of crustaceans as sentinel organisms to evaluate groundwater ecological quality[END_REF], [START_REF] Pinasseau | Use of passive sampling and high resolution mass spectrometry using a suspect screening approach to characterise emerging pollutants in contaminated groundwater and runoff[END_REF], [START_REF] Voisin | Aquifer recharge with stormwater runoff in urban areas: Influence of vadose zone thickness on nutrient and bacterial transfers from the surface of infiltration basins to groundwater[END_REF]. Unsaturated zone thickness, average infiltration time from basin surface to GW, well depth, infiltration surface of the basins, catchment area, and surrounding areas of each SIS are given in Table S1. The 4 SIS (Django Reinhardt (DJR), Chemin de Feyzin (FEY), Grange Blanche (GB) and Campus of the University Lyon 1 (IUT)) collected stormwater runoff from impervious urban areas (mainly roads, car parks and building surface materials) although land use (agricultural, commercial, residential and/or industrial activities) varied among areas surrounding sites. All infiltration basins receive exclusively stormwater runoff. Domestic and industrial wastewaters are conveyed in separate sewer systems and do not reach stormwater infiltration systems. A plastic tank was placed at the bottom of each stormwater infiltration basin (dived in soil) for measurements of contaminants on stormwater runoff entering the basin. Each SIS was equipped with two GW wells: a well located outside the area of influence of the stormwater plume induced by infiltration (SIS-up) which was used as a control well, and a well located in the immediate downstream vicinity of the infiltration basin (SIS-dw) used as a recharge well because it intersected the stormwater plume induced by the infiltration of stormwater runoff (Figs. 1 andS1). As performed for microbial GW assessment using artificial substrates [START_REF] Mermillod-Blondin | Clay beads as artificial trapping matrices for monitoring bacterial distribution among urban stormwater infiltration systems and their connected aquifers[END_REF][START_REF] Voisin | Ecological assessment of groundwater ecosystems disturbed by recharge systems using organic matter quality, biofilm characteristics, and bacterial diversity[END_REF], these wells were used to incubate passive samplers (see below) during 10 days for contaminant assessment of GW by stormwater infiltration [START_REF] Pinasseau | Use of passive sampling and high resolution mass spectrometry using a suspect screening approach to characterise emerging pollutants in contaminated groundwater and runoff[END_REF]. The wells consisted in either polyvinyl chloride or methacrylate tubes (with a minimum internal diameter of 5.5 cm) perforated at their lower end (0.5 m) to allow an efficient hydraulic connection with the aquifer. Previous work has shown that at these 4 SIS, the bulk water properties (conductivity, dissolved oxygen, and temperature) measured in the wells was similar to that of the GW in the surrounding aquifer [START_REF] Foulquier | Vertical change in dissolved organic carbon and oxygen at the water table region of an aquifer recharged with stormwater: biological uptake or mixing?[END_REF][START_REF] Voisin | Aquifer recharge with stormwater runoff in urban areas: Influence of vadose zone thickness on nutrient and bacterial transfers from the surface of infiltration basins to groundwater[END_REF]. Three campaigns were undertaken over a period of ten days in the 4 studied SIS: between 8th -18th of December, 2017, between 26th of October -5th of November, 2018 and between 14th -24th of November, 2019. The three campaigns were performed in winter to obtain 3 replications of the experiment by limiting a potential seasonal effect on the results. During these three seasons, the amounts of precipitation during the 10 days of passive sampler incubation were 30.3 mm in December 2017, 60.9 mm in November 2018 and 66.1 mm in November 2019. For each campaign, passive samplers were used to analyze target contaminants in runoff drained into the basins (surface plastic tanks) and in GW beneath the basins (SIS-up and SIS-dw wells). Passive samplers were based on polystyrene divinylbenzene (SDB-XC) Empore™ disks (EDs) (47 mm diameter, 12 µm particle size, 0.5 mm thick) purchased from 3M (Neuss, Germany). Deployment rigs were designed with the University of Portsmouth (Portsmouth, UK) to fit into GW well dimensions (see details in [START_REF] Pinasseau | Use of passive sampling and high resolution mass spectrometry using a suspect screening approach to characterise emerging pollutants in contaminated groundwater and runoff[END_REF]). Each rig held 9 individual EDs (only 4 individual EDs for the campaign 2017) to obtain replicated analyses per rig (each ED being analyzed independently). Details of EDs preparation before deployment in the field as well as passive sampler adsorption kinetics are given in [START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF]. A total of 12 ED rigs were used (one rig per plastic tank and well) and deployed per campaign. As environmental conditions (i.e. water turbidity, water temperature, flow rate) measured in the plastic tanks differ from conditions found in GW, contaminant results from stormwater runoff ED rigs were assessed for qualitative purposes only.

In addition, continuous measurements of dissolved oxygen concentration (HOBO® U26, Onset, USA), specific conductance and temperature (LTC Levelogger® Junior, Solinst, Canada) were monitored in GW during the field deployment periods (mean values for each well, SIS and field campaign are reported on Table S2). Physico-chemical measurements were also done on water samples collected in wells with a submerged pump (PP36 inox, SDEC, Reignac sur-Indre, France) at 3 dates during each ED rig deployment period (day 0, day 5 and day 10) to document pH values (portable pH-meter HQ20, HACH, Dusseldorf, Germany), N-NO3 -, N-NH4 + and P-PO4 3-concentrations in GW (Table S2).

Studied contaminants

Compounds selected for the study were chosen according to their ecotoxicological relevance and their presence in urban stormwater (biocides from building materials) and GW according to literature (key compounds including key pharmaceuticals and pesticides TPs) [START_REF] Gasperi | Micropollutants in urban stormwater: occurrence, concentrations, and atmospheric contributions for a wide range of contaminants in three French catchments[END_REF][START_REF] Gasperi | Priority pollutants in urban stormwater: Part 2 -Case of combined sewers[END_REF][START_REF] Jurado | Emerging organic contaminants in groundwater in Spain: A review of sources, recent occurrence and fate in a European context[END_REF][START_REF] Lapworth | Emerging organic contaminants in groundwater: A review of sources, fate and occurrence[END_REF][START_REF] Launay | Organic micropollutants discharged by combined sewer overflows -Characterisation of pollutant sources and stormwater-related processes[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Paijens | Biocide emissions from building materials during wet weather: identification of substances, mechanism of release and transfer to the aquatic environment[END_REF][START_REF] Postigo | Synthetic organic compounds and their transformation products in groundwater: Occurrence, fate and mitigation[END_REF][START_REF] Stuart | Review of risk from potential emerging contaminants in UK groundwater[END_REF]. Most of them were also frequently detected at our study sites and are presumably indicators of the SIS catchments pollution [START_REF] Marmonier | The use of crustaceans as sentinel organisms to evaluate groundwater ecological quality[END_REF][START_REF] Pinasseau | Use of passive sampling and high resolution mass spectrometry using a suspect screening approach to characterise emerging pollutants in contaminated groundwater and runoff[END_REF][START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF]. Their physicochemical properties (polar and semi-polar with probability of reaching GW) was also a selection criterion. To supplement these works, little studied hazardous compounds such as bromacil, fluopyram, imidacloprid and lamotrigine, for which there are still few or no GW data in the literature, were also investigated (Table 1).

Sampling and analysis

After passive sampler deployment and collection, contaminant extraction from EDs were performed according to [START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF]. Briefly, EDs were extracted using 10 mL of acetone/methanol (50/50; v/v). With this extraction protocol, more than 81% of the compounds had a recovery between 70% and 120% [START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF]. Then, triplicate extracts were pooled and evaporated to dryness. Finally, samples were reconstituted in 500 µL of water/methanol (95/5; v/v) and diluted 30 times.

Analysis was performed using an Agilent Series 1200 HPLC system (Agilent Technologies, Massy, France) (binary pump) coupled to a triple quadrupole mass spectrometer (MS) (5500 QTrap, AB Sciex, Les Ulis, France) equipped with an electrospray ion (ESI) source working in positive ionization mode. The chromatographic separation was performed with a Poroshell 120 EC-C18 column (2.1×50 mm, 2.7 μm particle size) (Agilent Technologies). The column oven temperature was 30 °C, the injection volume was 20 μL and the flow rate was 500 µL min -1 . Mobile phase (A) was water and mobile phase (B) was methanol with 0.1% formic acid in both phases (see [START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF] for the gradient elution details). MS detection was performed in the multiple reaction monitoring (MRM) mode (see [START_REF] Pinasseau | Calibration and field application of an innovative passive sampler for monitoring groundwater quality[END_REF]). Method detection and quantification limits are reported in Table S3.

Statistical analysis

The influence of SIS on GW contamination was visualized using a principal component analysis (PCA) performed with contents of the 16 contaminants measured after deployment in 8 wells (1 SIS-up well and 1 SIS-dw well per SIS, 4 SIS) for the 3 campaigns (2017,2018,2019). This analysis allowed to compare samples by transforming pollutant contents to a new set of variables, the principal components (PCs), which were uncorrelated, and which were ordered so that the 2 first PCs retain most of the variation present in all of the original variables. Scores of the samples (24 = 4 SIS * 2 wells * 3 dates) were displayed on a factorial map for comparisons. The variables (pollutants) were represented on a correlation circle indicating the contribution of each variable on PC1 and PC2. Thus, this analysis permitted to determine group of contaminants presenting the same patterns of sample distribution and which contaminants tended to differ between SIS-up and SIS-dw GW extracts. Following this analysis, contents of specific contaminants were compared between SIS-up and SIS-dw wells of each site using repeated-measures ANOVAs (RM-ANOVAs) with site as fixed factor and recharge as repeated factor to take into account the non-independence of SIS-up and SIS-dw wells in each site for each campaign (the 3 campaigns acted as replicates). The Mauchly's sphericity test, the Shapiro-Wilk's test, and the Levene's test, were used to verify the statistical assumptions of sphericity, normality, and homoscedasticity, respectively. When these assumptions were not proved, the variables (contaminant content) were log (x+1) transformed before RM-ANOVA tests. Statistical analyses were carried out using R version 3.2.3 software (R Development Core Team, 2015). Significance for statistical tests (RM-ANOVA) was accepted for a first species risk set at α < 0.05.

Results and discussion

Stormwater runoff characterization

After each campaign, the deployment rigs placed in the plastic tanks were retrieved and the stormwater runoff extracts were analyzed qualitatively. For each site, results were displayed in a Venn diagram showing the percentage of contaminants that were detected during one, two or three campaigns (Fig. 2). At least, 12 out of the 16 selected contaminants were found in the stormwater runoff extracts of each site by considering all campaigns. Carbendazim, DEET, diclofenac and diuron were detected in all extracts and 50, 54, 62 and 71% of the contaminants were recovered from passive samplers during the 3 campaigns for IUT, FEY, GB and DJR, respectively. In addition, there were only few cases (4.3% in the dataset) where a contaminant was only present in one of the 3 campaigns. This means that the contaminant compositions of stormwater runoff entering each infiltration basin were comparable among the 3 campaigns, suggesting no drastic change in contaminant sources from 2017 to 2019 during winter. Moreover, the contaminants detected in urban stormwater runoff of the 4 SIS were classically observed in urban drainage systems [START_REF] Fairbairn | Contaminants of emerging concern in urban stormwater: Spatiotemporal patterns and removal by iron-enhanced sand filters (IESFs)[END_REF][START_REF] Spahr | Hydrophilic trace organic contaminants in urban stormwater: occurrence, toxicological relevance, and the need to enhance green stormwater infrastructure[END_REF].

Consequences of infiltration practices on groundwater contamination by pesticides and pharmaceuticals

For each campaign, contaminant masses accumulated on disks (Table S3) were used to evaluate the impact of SIS on GW contamination by comparing results obtained from nonimpacted SIS-up well and SIS-dw well for each SIS. This influence of SIS on GW contaminants was visualized using a PCA (Fig. 3). The PC1 and PC2 axes of the PCA explained 57% of the variability contained in the data matrix, with 32% on the first axis (Fig. 3a). The correlation circle (Fig. 3b) indicated that groups of contaminants having the same distributions among samples. For example, the three pesticides DEA, simazine and atrazine presented the same distribution among samples with the highest concentrations obtained for samples having a negative score on PC1 and a positive score on PC2 like the sample performed in SIS-up well of Django-Reinhardt in 2017 (left bottom panel in Fig. 3c). To evaluate the impact of stormwater runoff infiltration on GW quality, we observed that extracts obtained from SIS-dw wells tended to have lower scores along axis PC2 than extracts from SIS-up wells for the 4 sites and the 3 campaigns (Fig. 3c). This partitioning was associated with two opposite trends when comparing extracts from SIS-up wells with extracts from SIS-down wells: a decrease in the contents of atrazine, BAM, DEA and simazine and an increase in the contents of carbendazim, diuron, fluopyram, imidacloprid and lamotrigine, and sulfamethoxazole only for FEY site (Fig. 3b). For FEY site, SIS-dw GW extracts also presented lower scores than SIS-up GW extracts along axis PC1, indicating an increase in carbamazepine and hexazinone in SIS-dw GW for the 3 campaigns. For the other 3 sites, we did not observe clear trends along PC1 axis, except for the campaign of 2017 in the SIS-up GW of DJR site characterized by high contents of atrazine, DEA and simazine. This PCA analysis allowed to classify the 16 contaminants into 3 main groups depending on their distributions: contaminants with concentrations increasing with infiltration, suggesting a contribution of the SIS on GW contamination (carbendazim, diuron, fluopyram, imidacloprid and lamotrigine), contaminants with concentrations decreasing with infiltration, suggesting a dilution of GW contamination by SIS (atrazine, BAM, DEA and simazine), and contaminants which were apparently not influenced by infiltration in all sites or whose influence differed among sites (bromacil, carbamazepine, DEET, diclofenac, hexazinone, metolachlor and sulfamethoxazole).

Contaminants with concentrations increasing with stormwater infiltration

For imidacloprid, carbendazim, lamotrigine, diuron and fluopyram, concentrations measured in SIS-dw GW were significantly higher than those measured in SIS-up GW (Fig. 4a, RM-ANOVAs, "SIS-up GW versus SIS-dw GW" effet, F(1,8) > 19, p < 0.01 for the 5 contaminants), indicating a contribution of SIS to GW pollution by these contaminants. This conclusion was confirmed by the detection of these 5 compounds in runoff collected from the 4 SIS (for at least 2 or 3 dates), at the exception of imidacloprid which was not detected in runoff from IUT site (Fig. 2). Diuron was quantified in 96% of GW extracts, with a maximum concentration reaching 34.8 ng disk -1 (Fig. 4a, Table S3). First used in 1951, it is still authorized in some products for the protection of building materials, despite being banned for agricultural uses since 2003 (Table 1). These diuron usages could explain its occurrence in urban runoff (drainage of built areas) and its impact on GW quality because the aquifer was less contaminated by agricultural practices occurring upstream in its catchment area. Our results also confirmed previous observations reporting the presence of diuron in all environmental compartments and in GW due to its wide spreading and persistence in the environment [START_REF] Cabeza | Monitoring the occurrence of emerging contaminants in treated wastewater and groundwater between 2008 and 2010[END_REF][START_REF] Hensen | Entry of biocides and their transformation products into groundwater via urban stormwater infiltration systems[END_REF][START_REF] Lapworth | Persistent and emerging microorganic contaminants in Chalk groundwater of England and France[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Reh | Occurrence and spatial distribution of organic micro-pollutants in a complex hydrogeological karst system during low flow and high flow periods, results of a two-year study[END_REF]. In comparison with diuron, imidacloprid, carbendazim, lamotrigine and fluopyram exhibited very low concentrations or, in most cases, no detection at all in SISup GW located outside the influence of stormwater infiltration. This observation suggests that stormwater infiltration was almost exclusively responsible for the presence of these contaminants in SIS-dw GW. These 4 contaminants are in use today (Table 1). Carbendazim, found in 96% of GW extracts with a maximum concentration of 4.2 ng disk -1 (Fig. 4a, Table S3), has been banned for agricultural uses since 2009 but is still authorized in some products for the protection of building materials. It has been already detected in European GW by [START_REF] Hernández | Pesticide residues and transformation products in groundwater from a Spanish agricultural region on the Mediterranean Coast[END_REF], [START_REF] Reemtsma | Emerging pesticide metabolites in groundwater and surface water as determined by the application of a multimethod for 150 pesticide metabolites[END_REF] and [START_REF] Vulliet | Survey regarding the occurrence of selected organic micropollutants in the groundwaters of overseas departments[END_REF]. Lamotrigine and fluopyram, quantified in 79 and 67% of GW extracts with maximum concentrations of 11.3 and 1.4 ng disk -1 , respectively (Fig. 4a, Table S3), are the most recent contaminants among the 16 investigated. They were used for the first time in the 2000s (Table 1). It could explain the lack of study reporting the presence of fluopyram in GW and the presence in literature of only one study reporting lamotrigine [START_REF] Bollmann | Occurrence and fate of amisulpride, sulpiride, and lamotrigine in municipal wastewater treatment plants with biological treatment and ozonation[END_REF]. Despite its introduction in 1990s, imidacloprid, an insecticide detected in 71% of GW extracts with a maximum concentration of 5.6 ng disk -1 (Fig. 4a, Table S3), has been poorly investigated in European GW and, consequently, only one study in literature reported the occurrence of this compound in GW [START_REF] Hernández | Pesticide residues and transformation products in groundwater from a Spanish agricultural region on the Mediterranean Coast[END_REF].

In the present study, imidacloprid presented singular results: its concentration was higher in SIS-dw GW than in SIS-up GW of IUT site (see campaign 2017 on Fig. 4a) despite its absence in stormwater runoff of IUT site (Fig. 2). This SIS-associated contamination of GW with a compound which was not detected in stormwater runoff might have been due to the leaching of this compound from soil and sediment particles on which it has been previously trapped and accumulated during several years of infiltration basin operation. This adsorptiondesorption mechanism has been already observed with carbendazim which can be temporarily trapped on soil and thereafter partly remobilized in water [START_REF] Sébastian | Event-based quantification of emerging pollutant removal for an open stormwater retention basin -Loads, efficiency and importance of uncertainties[END_REF].

As described by [START_REF] Hensen | Entry of biocides and their transformation products into groundwater via urban stormwater infiltration systems[END_REF], each stormwater infiltration event could have led to the leaching of some contaminants absorbed in the soil and/or the unsaturated zone. However, the role of the infiltration medium to trap hydrophilic compounds is still unclear, even if the soil and the unsaturated zone have the potential to efficiently retain some hydrophobic contaminants like hydrocarbons or heavy metals [START_REF] Datry | Dynamics of solutes and dissolved oxygen in shallow urban groundwater below a stormwater infiltration basin[END_REF][START_REF] Tedoldi | Impact of runoff infiltration on contaminant accumulation and transport in the soil/filter media of Sustainable Urban Drainage Systems: A literature review[END_REF][START_REF] Winiarski | The impact of stormwater on a soil profile in an infiltration basin[END_REF]. Thus, more research is needed to confirm the hypothesis that adsorption-desorption mechanisms could explain the GW enrichment in imidacloprid by SIS.

Contaminants with concentrations decreasing with stormwater infiltration

For DEA, simazine, BAM, and atrazine, concentrations observed in SIS-dw GW were significantly lower than those observed in SIS-up GW (Fig. 4b, RM-ANOVAs, "SIS-up GW versus SIS-dw GW" effet, F(1,8) > 8.5, p < 0.05 for the 4 contaminants), indicating that stormwater runoff infiltration reduced the concentrations of these 4 contaminants in GW.

Although these compounds were detected in stormwater runoff (Fig. 2), they were probably present at lower concentrations in stormwater runoff than in SIS-up GW because infiltrated stormwater probably reduced their concentrations in SIS-dw GW by dilution. Such interpretation assumes that DEA, simazine, BAM, and atrazine were mainly found in GW and this was probably the case. Indeed, DEA, simazine, BAM and atrazine were detected in 100, 96, 88 and 100% of GW extracts with maximum concentrations of 46.9, 29.3, 13.1 and 87.9 ng disk -1 , respectively (Fig. 4b, Table S3). In comparison, DEA, simazine, BAM and atrazine were detected only in 58, 58, 41 and 66% of runoff extracts, respectively (Fig. 2). This widespread occurrence of atrazine, BAM, DEA and simazine in GW have been largely reported in literature [START_REF] Lapworth | Persistent and emerging microorganic contaminants in Chalk groundwater of England and France[END_REF]. Atrazine and simazine have been banned since 2003 but, like DEA, they have already reached GW for many years [START_REF] Bono-Blay | Survey of phthalates, alkylphenols, bisphenol A and herbicides in Spanish source waters intended for bottling[END_REF][START_REF] Kuster | Fate of selected pesticides, estrogens, progestogens and volatile organic compounds during artificial aquifer recharge using surface waters[END_REF][START_REF] Lapworth | Persistent and emerging microorganic contaminants in Chalk groundwater of England and France[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Lopez | Screening of French groundwater for regulated and emerging contaminants[END_REF]. BAM, a common degradation product of dichlobenil (herbicide banned in 2009) and fluopicolide (fungicide still authorized), has been also frequently detected in European GW (Lapworth et al., 2015;[START_REF] Lopez | Screening of French groundwater for regulated and emerging contaminants[END_REF][START_REF] Postigo | Synthetic organic compounds and their transformation products in groundwater: Occurrence, fate and mitigation[END_REF]. Although some of these contaminants have been banned, the renewal time of aquifers, which includes the transfer time in the unsaturated zone, may take years to decades and could therefore explain the persistence of these contaminants in GW (Chen et al., 2019a;[START_REF] Gleeson | Water balance of global aquifers revealed by groundwater footprint[END_REF][START_REF] Gutierrez | Long-term transfer of diffuse pollution at catchment scale: Respective roles of soil, and the unsaturated and saturated zones (Brévilles, France)[END_REF][START_REF] Swartjes | Measures to reduce pesticides leaching into groundwaterbased drinking water resources: An appeal to national and local governments, water boards and farmers[END_REF][START_REF] Wang | The changing trend in nitrate concentrations in major aquifers due to historical nitrate loading from agricultural land across England and Wales from 1925 to 2150[END_REF]. Moreover, it has been reported that pesticide degradation within GW aquifers was low [START_REF] Baran | Hydrodynamic and geochemical constraints on pesticide concentrations in the groundwater of an agricultural catchment (Brévilles, France)[END_REF]. Under these conditions, even if a nil or negligible part of the contamination was provided by soil leaching (explaining the occurrence of atrazine or simazine in stormwater runoff), stormwater runoff infiltration reduced the concentrations of these contaminants in GW by dilution of contaminated GW (SIS-up GW) with less contaminated stormwater runoff.

Contaminants not consistently influenced by stormwater infiltration

No significant influence of stormwater runoff infiltration was observed on concentrations of bromacil, carbamazepine, DEET, diclofenac, hexazinone, metolachlor and sulfamethoxazole over the 4 SIS and 3 campaigns (Fig. 5, RM-ANOVAs, "SIS-up GW versus SIS-dw GW" effet, F(1,8) < 2.1, p > 0.15 for the 7 contaminants). As observed for atrazine or simazine, we could expect a dilution effect in GW for bromacil and hexazinone because (1) these compounds have been banned in 2001 and 2007 respectively (Table 1), (2) they were not detected in stormwater runoff (Fig. 2) and (3) they were present in GW (63 and 83% of GW extracts with maximum concentrations of 8.9 and 9.1 ng disk -1 for bromacil and hexazinone, respectively, Fig. 5, Table S3). However, the opposite results were observed on these compounds in FEY and DJR sites with higher concentrations obtained in SIS-dw GW than in SIS-up GW (Fig. 5). As these results cannot be exclusively explained by the compositions of infiltrated stormwater runoff and SISs-up GW, it can be assumed that two mechanisms (desorption and dilution) are involved in the fate of these contaminants in SIS: each stormwater runoff infiltration event would both leach these compounds from the soil and unsaturated zone, and dilute those already present in the GW in variable proportions depending on SIS. The same mechanisms probably occurred for the 5 other contaminants which did not exhibit consistent changes in concentration with stormwater infiltration. Indeed, stormwater infiltration at FEY site significantly contributed to GW deterioration by sulfamethoxazole and carbamazepine, while these same compounds appear to be diluted at GB site (Fig. 5). In the same way, stormwater infiltration at FEY site tended to increase sulfamethoxazole and carbamazepine concentrations in GW whereas no trend was observed for the same site for diclofenac, DEET and metolachlor concentrations (Fig. 5).

The lack of clear stormwater infiltration effect on bromacil, carbamazepine, DEET, diclofenac, hexazinone, metolachlor and sulfamethoxazole concentrations among sites could be also due to the background contamination in the environment and GW that reduced our ability to detect changes associated with SIS. Indeed, carbamazepine, an antiepileptic known to be persistent and frequently detected in GW [START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Stuart | Fingerprinting groundwater pollution in catchments with contrasting contaminant sources using microorganic compounds[END_REF][START_REF] Vulliet | Survey regarding the occurrence of selected organic micropollutants in the groundwaters of overseas departments[END_REF][START_REF] Vulliet | Screening of pharmaceuticals and hormones at the regional scale, in surface and groundwaters intended to human consumption[END_REF],was detected in 100% of GW extracts with a maximum concentration of 47.7 ng disk -1 (Fig. 5, Table S3). Sulfamethoxazole and diclofenac, pharmaceuticals used in human and veterinary medicine and commonly found in European GW [START_REF] Cabeza | Monitoring the occurrence of emerging contaminants in treated wastewater and groundwater between 2008 and 2010[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Reh | Occurrence and spatial distribution of organic micro-pollutants in a complex hydrogeological karst system during low flow and high flow periods, results of a two-year study[END_REF][START_REF] Vulliet | Survey regarding the occurrence of selected organic micropollutants in the groundwaters of overseas departments[END_REF][START_REF] Vulliet | Screening of pharmaceuticals and hormones at the regional scale, in surface and groundwaters intended to human consumption[END_REF], were also quantified in 88 and 58% of GW extracts with maximum concentrations of 4.2 and 0.8 ng disk -1 , respectively (Fig. 5, Table S3). DEET, a domestic insect repellent widely used across the world [START_REF] Lapworth | Persistent and emerging microorganic contaminants in Chalk groundwater of England and France[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Marques Dos Santos | DEET occurrence in wastewaters: Seasonal, spatial and diurnal variability -mismatches between consumption data and environmental detection[END_REF][START_REF] Sorensen | Emerging contaminants in urban groundwater sources in Africa[END_REF] was also found in 100% of GW extracts with a maximum concentration of 7.1 ng disk -1 (Fig. 5, Table S3). Metolachlor, detected in 100% of GW extracts with a maximum concentration of 16.6 ng disk -1 (Fig. 5, Table S3) is also ubiquitous in European GW under its both racemic mixture form (forbidden in 2003) and S enantiomer form [START_REF] Hildebrandt | Impact of pesticides used in agriculture and vineyards to surface and groundwater quality (North Spain)[END_REF][START_REF] Kuster | Fate of selected pesticides, estrogens, progestogens and volatile organic compounds during artificial aquifer recharge using surface waters[END_REF][START_REF] Loos | Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water[END_REF][START_REF] Lopez | Screening of French groundwater for regulated and emerging contaminants[END_REF].

To better understand the mechanisms involved in the dynamics of these 7 contaminants, we searched for potential relationships between our contaminant results, the SIS design (Table S1) and the targeted compounds chemical properties (Table 1). Neither the average transit time of stormwater from the basin to GW nor the physico-chemical properties of compounds (i.e. pKa, logDow, GUS index) can be directly related to the significance of SIS-dw GW contamination, as previously concluded by [START_REF] Baran | Hydrodynamic and geochemical constraints on pesticide concentrations in the groundwater of an agricultural catchment (Brévilles, France)[END_REF] and [START_REF] Manamsa | Temporal variability of micro-organic contaminants in lowland chalk catchments: New insights into contaminant sources and hydrological processes[END_REF].

For example, contamination of GW by infiltration practice (SIS-up GW versus SIS-dw GW) was the highest for FEY site (Figs. 3, 4 and5) whereas the unsaturated zone of this site would be the most efficient to retain contaminants as the average transit time of stormwater from surface to GW was more than 2-fold higher in this site (211.8 ± 90.5 h) than in other sites (< 86.0 ± 12.4 h, Table S1). The results mean that some additional parameters and mechanisms are involved in the fate of contaminants in SIS during a stormwater runoff infiltration event: soil composition and physico-chemical properties [START_REF] Regnery | Trace organic chemical attenuation during managed aquifer recharge: Insights from a variably saturated 2D tank experiment[END_REF], the presence of preferential pathways and/or retention of compounds due to capillary tension [START_REF] Wang | The changing trend in nitrate concentrations in major aquifers due to historical nitrate loading from agricultural land across England and Wales from 1925 to 2150[END_REF], and the contaminant degradation by microbial communities [START_REF] Pitt | Groundwater contamination potential from stormwater infiltration practices[END_REF].

Concerning soil composition, the high concentrations of organic matter (more than 10% of organic carbon per soil dry mass, [START_REF] Pigneret | Urban pollution of sediments: impact on the physiology and burrowing activity of tubificid worms and consequences on biogeochemical processes[END_REF]) found in soil of infiltration basins may play a key role on contaminant dynamics. Indeed, organic matter is likely to sorb neutral or non-ionizable compounds, whereas ionizable ones, such as pharmaceuticals, can form electrostatic interactions with clay minerals [START_REF] Gutierrez | Long-term transfer of diffuse pollution at catchment scale: Respective roles of soil, and the unsaturated and saturated zones (Brévilles, France)[END_REF][START_REF] Kodešová | Pharmaceuticals' sorptions relative to properties of thirteen different soils[END_REF][START_REF] Regnery | Trace organic chemical attenuation during managed aquifer recharge: Insights from a variably saturated 2D tank experiment[END_REF][START_REF] Valhondo | Evaluation of EOC removal processes during artificial recharge through a reactive barrier[END_REF]. Moreover, sorption processes can be complicated due to natural heterogeneity, competition for sorption sites and preferential pathways through fractures [START_REF] Baran | Hydrodynamic and geochemical constraints on pesticide concentrations in the groundwater of an agricultural catchment (Brévilles, France)[END_REF]Chen et al., 2019b), and are most certainly reversible [START_REF] Greskowiak | The uncertainty of biodegradation rate constants of emerging organic compounds in soil and groundwater -A compilation of literature values for 82 substances[END_REF]. Consequently, it can be hypothesized that the transport of some contaminants to GW was delayed and/or progressive over several years and that each stormwater runoff infiltration event produced the release of trapped contaminants [START_REF] Baran | Hydrodynamic and geochemical constraints on pesticide concentrations in the groundwater of an agricultural catchment (Brévilles, France)[END_REF][START_REF] Swartjes | Measures to reduce pesticides leaching into groundwaterbased drinking water resources: An appeal to national and local governments, water boards and farmers[END_REF].

It is worth noting that the choices of targeted contaminants together with the sampling campaign periods and the period of GW quality assessment had major influences on our results. Associated with seasons and meteorological conditions, dry periods followed by intense rainfall events are known to have direct impacts on the number of contaminants and their concentrations in stormwater runoff [START_REF] Bollmann | Dynamics of biocide emissions from buildings in a suburban stormwater catchment -Concentrations, mass loads and emission processes[END_REF]Burkhardt et al., 2011;[START_REF] Hensen | Entry of biocides and their transformation products into groundwater via urban stormwater infiltration systems[END_REF][START_REF] Paijens | Biocide emissions from building materials during wet weather: identification of substances, mechanism of release and transfer to the aquatic environment[END_REF]. Indeed, urban pollutants such as herbicides are more likely washed off from building materials and green areas after a long dry period (Burkhardt et al., 2011;[START_REF] Spahr | Hydrophilic trace organic contaminants in urban stormwater: occurrence, toxicological relevance, and the need to enhance green stormwater infrastructure[END_REF]. For pharmaceuticals, it has been reported that they are more transported by stormwater discharge overflow during high volume rainfall events than during low volume rainfall events [START_REF] Launay | Organic micropollutants discharged by combined sewer overflows -Characterisation of pollutant sources and stormwater-related processes[END_REF][START_REF] Spahr | Hydrophilic trace organic contaminants in urban stormwater: occurrence, toxicological relevance, and the need to enhance green stormwater infrastructure[END_REF].

Beside seasonal effects that have not been addressed in the present study, another limitation of our work concerns the environmental variability that could have affect passive sampler efficiency. Although the potential value of passive sampling has already been proven for monitoring emerging pollutants in water [START_REF] Brack | Towards the review of the European Union Water Framework Directive: Recommendations for more efficient assessment and management of chemical contamination in European surface water resources[END_REF][START_REF] Jones | Emerging priority substances in the aquatic environment: a role for passive sampling in supporting WFD monitoring and compliance[END_REF][START_REF] Miège | Position paper on passive sampling techniques for the monitoring of contaminants in the aquatic environment -Achievements to date and perspectives[END_REF][START_REF] Mills | Measurement of environmental pollutants using passive sampling devices -an updated commentary on the current state of the art[END_REF], the uptake mechanisms of polar compounds on EDs are still not fully understood [START_REF] Vrana | An interlaboratory study on passive sampling of emerging water pollutants[END_REF]. Environmental variabilities (flow rate, water temperature, turbidity, etc) can affect uptake mechanisms and contribute to variability of water concentration estimates when calibration data are applied [START_REF] Lissalde | Overview of the Chemcatcher® for the passive sampling of various pollutants in aquatic environments Part B: Field handling and environmental applications for the monitoring of pollutants and their biological effects[END_REF].

Therefore, using ng disk -1 values instead of ng L -1 values helps to improve the reliability of the results, as we did in this study. Working on three autumn/winter rainfall events enabled us to obtain experimental replications under comparable climatic conditions but, in future investigations, it would be necessary to undertake additional sampling campaigns during spring/summer in order to evaluate how seasonal effects (climatic conditions, contaminant composition of stormwater runoff) influence the role of SIS on GW quality.

Conclusions

This study was the first to assess the effects of urban stormwater runoff infiltration on GW contamination by 16 polar and semi-polar pesticides and pharmaceuticals using Empore™ disk-based sampler. To our knowledge, it was the first time that such passive samplers were deployed in GW wells to monitor the infiltration of emerging polar pollutants. This work demonstrates that they enable the capture of contaminants at traces level efficiently.

Moreover, new data on GW are provided for recent and/or understudied hazardous contaminants like bromacil, fluopyram, imidacloprid and lamotrigine.

Obtained results highlighted two interesting opposite trends depending on studied contaminants: 5 contaminants (carbendazim, diuron, fluopyram, imidacloprid and lamotrigine) had concentrations significantly increasing with stormwater runoff infiltration, indicating a contribution of infiltration to groundwater contamination, and 4 contaminants (atrazine, BAM, DEA and simazine) exhibited concentrations significantly decreasing with infiltration due to a probable dilution with stormwater runoff of historic GW contaminants such as atrazine. The fact that the remaining 7 contaminants showed no general trend with site-dependent infiltration effect on their concentrations might be due to: (i) the differences among SIS in historical contaminations of all environmental compartments (urban watershed, soil, unsaturated zone and GW) and (ii) the differences among SIS in soil properties, hydrological transport and microbial processes that would be responsible for the variability observed in contaminant dynamics among SIS.

In further studies, it will be therefore necessary to quantify the processes occurring in the soil and unsaturated zone (sorption and desorption processes, microbial degradation of contaminants) of each SIS. Use of proxy for soil/sediment characteristics (organic matter content, clay percentage), hydrogeological functioning (precise measurements of infiltration rates in each infiltration basin) and microbial activity would help to decrypt the main variables influencing contaminant dynamics during their transfer from infiltration basin to GW. As discussed above, monitoring several rainfall events at different seasons would be necessary to take into account the inter-event variability and the seasonal effects on contaminant transfers. Finally, measurements performed during more than 10 years could be also very promising to evaluate the long time-lag between the use of some contaminants, their dynamics in environmental compartments and the resulting changes on GW quality.

Acknowledgements

This work was supported by ANR FROG-ANR-16-CE32-0006 project. The authors thank Simon Navel, Ludovic Guillard, Félix Vallier and Jérémy Voisin for assisting with the field work. We also acknowledge the Greater Lyon Water Agency, the Rhone Mediterranean

Corsica Water Agency and the Auvergne Rhône Alpes Region for logistic and financial support within the framework of the field observatory for urban water management (OTHUwww.othu.org/ -FED 4161). This work was performed within the framework of the EUR H2O'Lyon (ANR-17-EURE-0018) of Université de Lyon (UdL), within the program "Investissements d'Avenir" operated by the French National Research Agency (ANR).


Clément Zanolli

Florent Détroit

Armand Salvador Mijares

Julien Corny

Guillaume Daver

Eusebio Dizon

Emil Robles

Rainer Grün

Philip J Piper

The teeth of H. luzonensis: a diagnostic taxonomic element

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The new species Homo luzonensis has recently been described based on a set of dental and post-cranial elements found at Callao Cave (Northern Luzon, Philippines) and dated to at least 50 ka. Seven post-canine maxillary teeth are attributed to this taxon, five of them belonging to the same individual (CCH6) and representing the holotype of H. luzonensis. All these teeth are characterized by their small dimensions (in the inferior range or below the modern human variation) and particularly large premolars relative to the molars. A small dentition is also found in H. floresiensis, another hominin having evolved in an insular environment of Southeast Asia, while large premolars (even if to a lesser extent than in H. luzonensis) are also found in H. erectus. The post-canine teeth from Callao Cave display a blend of modern-like features in the crown (with a simplified occlusal morphology) and more archaic traits in the roots (three-rooted premolars, large root volume). The internal tooth structural signature also supports an attribution to another species than those already recognized in the fossil record. The enamel-dentine junction (EDJ), demonstrated to be a reliable proxy to distinguish between taxa up to the sub-specific level, shows that the premolars and molars of H. luzonensis are intermediate between those of H. erectus and H. floresiensis, while they substantially differ from the modern human condition. Further analyses of the internal tooth structure, notably of the enamel thickness and pulp morphology, and comparisons with larger comparative samples will likely shed more lights on the phylogenetic relationship of this mysterious hominin. Altogether, the external and internal organization of the dental tissues of H. luzonensis teeth attest of the existence of this taxon and highlight the taxonomic diversity of Pleistocene hominins in Southeast Asia.


Pietro Astolfi
email: pastolfi@fbk.euavesani@fbk.eu

Ruben Verhagen

Laurent Petit

Emanuele Olivetti

Jonathan Masci

Davide Boscaini

Paolo Avesani

Tractogram filtering of anatomically non-plausible fibers with geometric deep learning

Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplasticity or brain disorders. Each tractogram is composed of millions of fibers encoded as 3D polylines. Unfortunately, a large portion of those fibers are not anatomically plausible and can be considered artifacts of the tracking algorithms. Common methods for tractogram filtering are based on signal reconstruction, a principled approach, but unable to consider the knowledge of brain anatomy. In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties. The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties. Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers.

Introduction

Tractography represents a powerful method to reconstruct the white matter fibers from diffusion magnetic resonance (MR) recordings [START_REF] Basser | In vivo fiber tractography using DT-MRI data[END_REF]. While this method provides a good approximation of the brain connectivity structure, there is an open issue of artifactual fibers [START_REF] Maier-Hein | The challenge of mapping the human connectome based on diffusion tractography[END_REF][START_REF] Maier-Hein | Tractography-based connectomes are dominated by falsepositive connections[END_REF], i.e. anatomically non-plausible pathways. We address the task of filtering out such artifactual fibers using a deep learning model.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

The structural connectivity of the brain can be reconstructed from diffusion MR signal by a step of local estimation of the diffusivity model [START_REF] Tournier | Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution[END_REF] and a subsequent step of fiber tracking [START_REF] Girard | Towards quantitative connectivity analysis: reducing tractography biases[END_REF]. The outcome is a tractogram, a virtual representation of the axonal pathways in the white matter, where each fiber is encoded as 3D polylines, commonly referred to as streamlines. Typically, a whole brain tractogram is composed of millions of streamlines.

Tractograms are providing valuable contributions to critical tasks like presurgical intervention planning, the detection of biomarkers for brain disorders and the investigation of neuroplasticity. For these purposes, the accuracy of fiber tracking is of paramount importance. While in the preprocessing of diffusion data it is common practice to denoise the signal before estimating the diffusivity model [START_REF] Glasser | The minimal preprocessing pipelines for the Human Connectome Project[END_REF][START_REF] Zhuang | Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients[END_REF], there is no similar step after fiber tracking to filter out noisy streamlines.

The assessment of the accuracy of fiber tracking has been approached a few years ago with an open contest 7 involving many [START_REF] Smith | SIFT: Sphericaldeconvolution informed filtering of tractograms[END_REF] research groups. The contest was designed as a task of bundle detection on a dataset composed of simulated diffusion MRI data. The joint effort allowed the evaluation of the quality and limits of the most common tracking methods [START_REF] Maier-Hein | The challenge of mapping the human connectome based on diffusion tractography[END_REF]. The positive outcome has been the lack of false negative streamlines, the critical issue has concerned the many false positive errors, e.g. artifactual streamlines.

The occurrence of false positive streamlines is not surprising. The general strategy of tracking methods is to oversample the possible pathways to preserve the property of coverage of all true positive fibers [START_REF] Rheault | Bundle-specific tractography with incorporated anatomical and orientational priors[END_REF]. The tacit assumption is to postpone the task of filtering false positive streamlines to a subsequent post-processing step. The reason for this strategy is the difficulty of encoding anatomical priors into tracking algorithms.

The most common approach to reduce artifactual streamlines are methods based on the inverse problem of signal reconstruction, e.g. Life [START_REF] Pestilli | Evaluation and statistical inference for human connectomes[END_REF], SIFT [START_REF] Smith | SIFT: Sphericaldeconvolution informed filtering of tractograms[END_REF], Commit [START_REF] Daducci | COMMIT: convex optimization modeling for microstructure informed tractography[END_REF]. The intuitive idea is to estimate how much the orientation of a streamline explains the diffusion signal. Despite the principled criterion, these methods do not take into account the knowledge of brain anatomy, like another method based on the topological properties of streamlines [START_REF] Yeh | Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP)[END_REF].

A recent rule-based method, namely ExTractor [START_REF] Petit | Half of the streamlines built in a whole human brain tractogram is anatomically uninterpretable[END_REF], has been proposed to filter out artifactual streamlines from tractograms by following anatomical principles. The rules that it proposes encode the geometrical and spatial properties of the streamlines with respect to the basics of white matter neuroanatomy in terms of association, projection, and commissural fibers. As output, ExTractor labels streamlines as anatomically plausible or anatomically non-plausible.

In this work, we address for the first time the problem of tractogram filtering as a supervised learning problem. We need to train a binary classifier to discriminate between two classes: anatomically plausible (P) and non-plausible (nP) fibers. The intuitive idea is to exploit the labeling of fibers provided by the rule-based method [START_REF] Petit | Half of the streamlines built in a whole human brain tractogram is anatomically uninterpretable[END_REF] and to adopt deep learning models to learn the features of streamlines underlying the rules. The ultimate goal is to have a fast run-time solution to filter large tractograms and a flexible method to transpose new expert annotations.

Given the sequential structure of a streamline, we have chosen as reference learning model a bidirectional LSTM (bLSTM) neural network [START_REF] Huang | Bidirectional LSTM-CRF models for sequence tagging[END_REF]. Although this model can exploit the sequential information, it requires forcing the streamline representation to a fixed number of points. For this reason, we consider Geometric Deep Learning (GDL) models [START_REF] Masci | Geometric deep learning[END_REF] that support more flexible and appropriate data representations. We investigate a PointNet (PN) model [START_REF] Qi | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation[END_REF], where a streamline can be represented as a point cloud i.e. set of 3D points, and a Dynamic Edge Convolution (DEC) model [START_REF] Wang | Dynamic graph cnn for learning on point clouds[END_REF], which in addition to PN considers the relations between points belonging to the same spherical local context. Our experiments shows that both GDL models provide an improvement with respect to bLSTM.

Despite the better results provided by PN and DEC, these models are invariant to permutations of points in the input point cloud. It means that if we permute the points in a streamline classified as plausible, the model will continue to classify it as plausible albeit the altered sequence of points. To overcome this issue we propose a Sequence Dynamic Edge Convolution (sDEC) model, an extension of the DEC model that introduces the property of being sequence sensitive.

While the sDEC model provides only a modest increase in accuracy with respect to PN in classifying P and nP fibers, the analysis of error distribution shows different behaviours. sDEC is more robust when fibers are long and curved: those are the type of fibers where PN performs worst and produces significantly more false positive errors. In addition, visual inspection of the false negatives errors made by sDEC shows that these fibers look truly anatomically non-plausible. We may argue that this apparent mismatch might be related to the noise of the labelling process.

Materials

Dataset. Our reference dataset is composed of a collection of tractograms of 20 subjects randomly selected from the HCP dataset [START_REF] Van Essen | The WU-Minn Human Connectome Project: An overview[END_REF], which is a publicly available curated MRI dataset. The tractograms were obtained computing (i) the constrained spherical deconvolution (CSD) model [START_REF] Tournier | Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution[END_REF] on the 3T DWI (1.25mm, 270 multishell gradients), and the (ii) Particle Filtering Anatomically Constrained Tractography (PF-ACT) algorithm [START_REF] Girard | Towards quantitative connectivity analysis: reducing tractography biases[END_REF]. Specifically, the tracking generated around ∼ 1M fibers for each tractogram by seeding 16 points for each voxel with step size 0.5mm. The tractogram of each subject has been non linearly coregistered to the MNI standard space, and, for computational purposes, all the fibers have been compressed to the most significant points [START_REF] Presseau | A new compression format for fiber tracking datasets[END_REF].

ExTractor labelling. According to the premise of a supervised learning approach, we created a dataset where for each tractogram we labeled the fiber as either plausible (P) or non-plausible (nP). The procedure of labelling followed the heuristic rules defined by ExTractor [START_REF] Petit | Half of the streamlines built in a whole human brain tractogram is anatomically uninterpretable[END_REF], a tool that encodes the current knowledge on the anatomical pathways of white matter structures. In particular, ExTractor carries out a 2-step procedure. First, fibers are marked as nP when they are either (i) shorter than 20 mm, (ii) contain a loop, or (iii) are truncated, i.e., Fig. 1: The DEC architecture adopted. Green, gray, and red blocks represent input, intermediate, and output tensors, respectively. Parametric layers are colored in blue, while fixed layers in white. they terminate in the deep white matter. The second step is concerned with the labeling of fibers marked as P, by splitting the main pathways into the three macro classes: association, projection, and commissural. This selection of fibers is further refined to filter out non-plausible pathways using a clustering method [START_REF] Côté | Cleaning up the mess: tractography outlier removal using hierarchical QuickBundles clustering[END_REF]. The outcome of the ExTractor procedure is a balanced partition between P and nP fibers, irrespective of the different tracking algorithms and data sources [START_REF] Petit | Half of the streamlines built in a whole human brain tractogram is anatomically uninterpretable[END_REF]. On our dataset, Extractor resulted in 49.7 ± 1.5% of P streamlines on average on the 20 subjects, but more significantly it showed the presence of a massive percentage, 31.8 ± 1.2%, of nP streamlines shorter than 20mm.

Methods

The intuition underlying our work is to treat a streamline as an undirected sequence of 3D points, aiming to learn geometric and spatial features relevant for the tractography filtering task. Currently, the best way to achieve such a goal is by employing GDL models [START_REF] Masci | Geometric deep learning[END_REF], which are designed to learn geometric features of graphs and point clouds. Among the existing GDL approaches, PointNet [START_REF] Qi | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation[END_REF] is the most adopted method both for its simplicity and effectiveness. Nevertheless, such an approach does not consider relations between points. It learns global properties of a point cloud just by encoding all the points separately and then aggregating them in a single descriptor through global pooling. Conversely, Dynamic Edge Convolution (DEC) [START_REF] Wang | Dynamic graph cnn for learning on point clouds[END_REF] considers the points relations, by encoding them as edges of a graph dynamically induced by the point cloud.

Dynamic Edge Convolution. Considering a point cloud X = {x 1 , x 2 , . . . , x n }, x i ∈ R 3 ,
the DEC model first guesses an initial graph structure for the point cloud by retrieving for each point x i the set of k nearest neighbors, knn(x i ) = {x ji 1 , . . . , x ji k }, in terms of Euclidean distance (see Figure 2a). Hence, DEC builds a k-nn graph, G(V, E), where V = X is the set of nodes, and an edge (i, j) ∈ E exists iff x j ∈ knn(x i ). Then, each point representation, x i , is enriched with the representation of each of its neighbors x ji , creating edges features e ij , which are learnt through a neural network h Θ , i.e. e ij = h Θ (x i ⊕(x j -x i )), where ⊕ denotes the concatenation operator. Finally, a new representation of a point, x i ,

x ji 1 x ji 2 x i (a)
x ji 1

x ji 2 is obtained by aggregating all the learned edge features with a pooling operator, i.e. x i = pool(e ij ), j : (i, j) ∈ E, where pool is either max or mean. The sequence of operations that from X produce X define an Edge Convolution (EC) layer, which in the DEC model is repeated multiple times. Referring to our architecture, see Figure 1, two EC layers are stacked in depth to produce new representations X and X with 64 and 128 features respectively. Each of the two EC layers computes its own k-nn graph in order to adjust the local neighborhood of points to its input representation, i.e, X and X . The re-computation of knn is what defines the DEC model as dynamic. Finally, the different learned representations are concatenated, encoded with a learning layer, g Φ , to 1024 features, and pooled to obtain a single descriptor of the point cloud, Z = pool(g Φ (X ⊕ X )), which is classified using a fully connected (FC) network.

Dynamic Edge Convolution of a Sequence. A remarkable property of the DEC model (also shared by PN) is the invariance to the permutation of the points in the input point cloud. Indeed, such models make use only of operators invariant to the order e.g., FC layers, max / mean pooling layers. Although this property is fundamental in the point cloud domain, it becomes undesired if the input is a sequence as in our case. To solve this issue, we propose a simple but wellmotivated modification of the DEC model, in which we impose the input point cloud to have a graph structure, without needing a Euclidean k-nn to induce it. According to the streamline structure, we impose the input to be a bidirectional sequence graph where each non-terminal point, x i =0,n , has two neighbors: the previous and the next point in the sequence, while the terminal points, x 0 , x n , have just one neighbor (see Figure 2b):

G(V, E ), e ij ∈ E : x i → x j , j = i + 1 ∨ j = i -1.
It is important to remark that we impose this structure only in the first sDEC layer, which is enough to lose the invariance with respect to the input permutations while maintaining the invariance with respect to the flipping of the sequence, thanks to the bidirectionality of the sequence graph. Also, imposing this structure only in the first layer preserves the dynamicity of the model, which remains able to re-organize the structure of the point cloud in the latent space according to the task at hand.

Experiments and Results

The main purpose of the experiments is to perform an empirical assessment of the behaviour of different models for the task of tractogram filtering. In our comparison, we consider a reference model, bLSTM, and three competing models based on geometric deep learning: PN, DEC, and sDEC. Before setting the training of learning models, we carried out a few preprocessing steps. The first step was the pruning of fibers. An analysis of the distribution of fibers between P and nP according to the ExTractors rules, highlighted a massive percentage of very short streamlines, i.e. length below 20 mm. All of them were labeled as nP. To reduce this potential bias for the learning models we removed such streamlines from the tractograms. After the pruning, the average distribution of P and nP classes was 68% and 32% respectively. The second preprocessing step concerned the resampling of the points of streamlines. Traditional learning models, e.g. bLSTM, require input to be represented as fixed vectorial representations. Therefore, despite GDL models can deal with a varying number of points, we have to resample all streamlines to have the same number of points [START_REF] Garyfallidis | Quick-Bundles, a Method for Tractography Simplification[END_REF][START_REF] O'donnell | Automatic tractography segmentation using a highdimensional white matter atlas[END_REF][START_REF] Gupta | FiberNET: An Ensemble Deep Learning Framework for Clustering White Matter Fibers[END_REF]. We computed the resampling using a cubic B-spline interpolation and empirically compared different representations based on 12, 16 and 20 points. No meaningful performance differences were noticed. In Table 1 we report the results for streamlines resampled to 16 points.

To perform a fair comparison of the four models, we have configured all of them to have a similar number of parameters, approximately around 900K. While the architecture of DEC and sDEC models is reported in Figure 1, the configuration of the remaining models is: bLSTM:

MLP(128) → LSTM(256) ⊕ LSTM -1 (256) → MLP(256, 128) → FC(2); PN: MLP(64,64,64,128,1024) → MAX → MLP(512,256,40) → FC(2),
where FC is a Fully Connected layer, and MLP contains sequences of (FC, BatchNorm, ReLU). For the implementation of the models, we adopted the PyTorch library [START_REF] Paszke | PyTorch: An imperative style, high-performance deep learning library[END_REF] with the PyTorch Geometric extension [START_REF] Fey | Fast Graph Representation Learning with PyTorch Geometric[END_REF].

For all the experiments we considered a dataset of 20 tractograms from 20 different subjects. We partitioned the data into three sets: 12 tractograms for training the models, 4 tractograms for hyperparameters tuning and model selection, and 4 tractograms for testing. The training was designed as follows: 1000 epochs with evaluation step every 20 epochs; cross-entropy loss; Adam optimizer with default alfa and beta momentum (0.9, 0.99); initial learning rate of 10 -3 multiplied by a factor of 0.7 every 90 epochs until a minimum value of 5 • 10 -5 is reached. In the training procedure, we used mini-batches composed of 16K streamlines randomly sampled from two subjects, 8K from each of them. A subject is sampled only once for each epoch. On the test set, we computed the following evaluation measures: accuracy, precision, recall, and DSC. In Table 1 the values refer to the average for each subject. Notice that the classification task is single-streamline, therefore the size of our test set is composed of ∼ 3 million streamlines.

An additional experiment was designed to test the invariance of DEC and sensitivity of sDEC with respect to the order of points a streamline. To this end, we tested the two models on a version of the test set where the the sequence of points of streamlines were randomly permuted. In agreement with the working hypothesis, the accuracy for DEC remained the same, 94.3%, while there was a drop for sDEC to 30.0%.

A further analysis concerned the examination of the error distribution. We restricted the analysis to PN and sDEC only. Our interest was to characterize whether and how the error differs between the two models. We considered a couple of features in the analysis: length and curvature of streamlines. All streamlines were partitioned into three categories with respect to length (short [0, 50] mm, medium [50, 100] mm, long [100, 300] mm) and curvature (straight [0.0, 0.05], curved [0.05, 0.10], very curved [0.10, 0.20] ), where each of them contained at least a portion of 15% of streamlines. Combining the partitions we obtained 9 categories. In Figure 3A we depict how the accuracy score varies across the categories. The task becomes more difficult when both length and curvature are greater. A similar analysis was carried out focusing on the distribution of false positive error, i.e. streamlines mistakenly classified as anatomically plausible. The results are reported in Figure 3B. In this case, we observe a major difference between the two models in the most critical categories, namely long and curved streamlines. In Figure 3C we illustrate a qualitative example of false negative errors, when streamlines should be recognized as anatomically plausible while they are classified as anatomically non-plausible. Visual inspection by an expert anatomist confirms that the classifier was indeed correct in those cases and that the labelling process was noisy.

Discussion and Conclusions

The results reported in Table 1 confirm that we may successfully approach the task of tractogram filtering as a supervised learning problem. The best accuracy achieved by sDEC is beyond 95% with low standard deviation across subjects. This performance is obtained considering only the representation of streamlines as a sequence of 3D points. The runtime application of sDEC model is very fast. We may filter a whole tractogram with ∼ 1M streamlines in less than one minute (46.2 sec using a gpu NVIDIA Titan Xp 12Gb).

We argue that the accuracy is underestimated and the true score may be even higher. If we look at the portion of false negative error, as reported in Figure 3C, we may agree that some of those streamline trajectories can be considered anatomically non-plausible, even though the true label states the contrary. Therefore, the computation of accuracy is biased by this apparent mismatch, which we can consider as part of the noise in the labelling process. Moreover, we may claim that the sDEC model has a good generalization capability and behaves accurately even in the presence of misleading labels.

The geometric deep learning models seem to provide only a small, even though meaningful (t-test with p-value < 0.001), improvement in terms of accuracy compared to bLSTM. Nevertheless PN and sDEC support higher flexibility in the streamlines representation, both as points clouds or graphs. The sDEC model seems the most appropriate for tractogram filtering because it is robust with respect to the order of points in the streamlines while preserving the highest accuracy in the discrimination of anatomical plausibility.

The analysis of error distribution allows a better understanding of the difference between PN and sDEC models. Around 40% of the misclassification error concerns different streamlines, indicating that the two models behave differently: in Figure 3A the most critical streamlines are those longer and more curved. Nevertheless, sDEC is more robust and the drop in accuracy is lower than PN. We may explain this difference with the property of sDEC to capture the relations among the points because it is considering also the edges of streamlines. When streamlines are long and curved, edges become more informative and provide a competitive advantage. As illustrated in Figure 3B, PN has the bias to classify long and curved streamlines as anatomically plausible doing more false positive error.

We believe that a fast and accurate filtering of tractograms, like the one supported by the sDEC model, is the premise for further investigations on how tasks like bundle segmentation may take advantage of the removal of artifactual streamlines.

Fig. 2 :

 2 Fig. 2: Comparison between Euclidean k-nn (a), and k-nn based on the sequence graph (b).

Fig. 3 :

 3 Fig. 3: Analysis of the error distribution for PN and sDEC models with respect to streamlines length and curvature: (A) accuracy, (B) false positive rate. (C) Sample of false negative streamlines, mistakenly classified as anatomically non-plausible.

Table 1 :

 1 Average scores for the 4 HCP subjects of test set. Standard deviation among the 4 subjects is reported between brackets.

	Method Accuracy	Precision	Recall	DSC
	bLSTM 92.8 (±0.3)	93.7 (±0.5)	96.1 (±0.2)	94.9 (±0.3)
	PN	94.5 (±0.1)	95.4 (±0.2)	96.8 (±0.2)	96.1 (±0.2)
	DEC	94.3 (±0.1)	95.4 (±0.3)	96.5 (±0.2)	95.9 (±0.2)
	sDEC	95.2 (±0.1) 96.2 (±0.3) 96.9 (±0.1) 96.6 (±0.2)

tractometer.org/ismrm_2015_challenge