Keywords: CHCA: alpha-Cyano-4-HydroxyCinnamic Desorption ElectroSpray Ionization DHA: 2, 6-DiHydroxyAcetophenone DHB: 2, 5-DiHydroxyBenzoic acid ElectroSpray Ionization FT ICR: Fourier Transform-Ion Cyclotron Resonance High Resolution Mass Spectrometry IMS: Imagerie par Spectrométrie de Laser Ablation ElectroSpray Ionization LC: Liquid Chromatography LC-HRMS/MS: Liquid Chromatography-tandem High Resolution Mass Spectrometry MALDI: Matrix-Assisted Laser Desorption/Ionization

Summary (in French) of the thesis

La qualité des masses d'eau est devenue une problématique majeure au cours des dernières décennies. En effet, la mise en place de réglementations internationales et nationales pour protéger ces masses d'eau et limiter les risques pour l'environnement et la santé humaine souligne cette importance. Les polluants, et plus particulièrement les micropolluants retrouvés dans l'environnement, peuvent avoir différentes origines, mais les eaux urbaines sont la source la plus mentionnée dans la littérature. Les stations d'épuration des eaux usées ont été mises en place pour traiter ces eaux usées, tout d'abord dans les zones urbaines puis les zones rurales. Or le traitement des eaux usées dans ces zones rurales pose des problèmes spécifiques. Ainsi les filtres plantés de roseaux sont devenus la filière de traitement privilégiée pour répondre à cette problématique dans cet environnement. En effet, ces filtres plantés de roseaux vont utiliser des fonctions naturelles pour traiter les eaux usées (sorption, filtration, biodégradation). En outre, pour compléter ces traitements, des systèmes tels que les zones de rejet végétalisées peuvent être mis en place. Ainsi l'ensemble de cette filière de traitement a prouvé son efficacité pour la dépollution des matières en suspension, des polluants carbonés, azotés et phosphorés, mais n'a pas été conçue en considérant la problématique des micropolluants. Cependant, des études ont montré leur efficacité sur l'abattement d'un certain nombre d'entre eux. Ces études restent néanmoins concentrées sur un nombre restreint de molécules et ne prennent que rarement en considération l'ensemble de l'écosystème. Ainsi l'objectif de l'étude menée vise à décrire et comprendre la distribution et le devenir d'un large spectre de micropolluants en analysant l'ensemble de l'écosystème du filtre planté et de la zone de rejet végétalisée. Pour cela, ce projet de recherche s'est concentré sur trois axes majeurs permettant de répondre à cette problématique :

une analyse non ciblée des micropolluants au sein de l'écosystème en utilisant la spectrométrie de masse à haute résolution couplée à la chromatographie liquide (LC-HRMS/MS),

une localisation spécifique des micropolluants au sein des tissus des plantes à l'aide de l'imagerie par spectrométrie de masse (IMS),

une modélisation numérique de l'écoulement dans les zones de rejet végétalisées par Computational Fluids Dynamic (CFD) couplée à une analyse non ciblée pour comprendre la distribution des micropolluants au sein des différents écoulements.

La première partie de cette thèse est consacrée à l'analyse d'un spectre large des micropolluants dans l'écosystème d'un filtre planté et d'une zone de rejet végétalisée. Ainsi l'eau, les sédiments et les plantes de ce système ont été analysés. Cette analyse s'est tout d'abord concentrée sur une phase de développement. En effet, une méthode d'extraction et d'analyse des micropolluants au sein des différentes matrices a été développée. Cette méthode, basée sur la sublimation et l'extraction liquide, a été optimisée pour l'identification des micropolluants. Ensuite, l'identification des micropolluants a été réalisée par spectrométrie de masse (spectrométrie de masse à haute résolution couplée à la chromatographie liquide (LC-HRMS/MS)) et spectrométrie de masse couplée à la chromatographie en phase gazeuse (GC-MS/MS). L'identification en LC-HRMS/MS ou l'annotation des micropolluants a été effectuée en générant des formules brutes et en suivant la classification mentionnée dans Schymanski et al., 2014. Pour cela, une base de données locale a été produite en collectant les données d'une dizaine de bases de données concernant ces polluants.

Les résultats obtenus montrent que les micropolluants représentent entre 30 % et 50 % du profil métabolique annoté sur les différents compartiments. Cette proportion de micropolluants est également accompagnée par une large distribution des micropolluants au sein de l'écosystème. De plus, une distribution similaire peut être observée entre le filtre planté de roseau et la zone de rejet végétalisé, soulignant cette large diffusion des micropolluants et de leurs produits de dégradation au sein des différents compartiments de l'environnement. En parallèle, cette analyse non ciblée a également été effectuée sur un profil vertical du sol composant le filtre planté, afin d'évaluer la migration potentielle des micropolluants vers les couches inférieures. À travers cette étude, il a ainsi pu être constaté que les micropolluants vont se diffuser au sein des différents horizons de sol. Au sein de cette distribution, un nombre plus conséquent de composés est détecté dans l'horizon supérieur. À l'opposé, l'horizon inférieur contiendra de manière préférentielle des composés hydrophobes. La couche supérieure (10 premiers centimètres) étant celle séquestrant la plus grande partie des micropolluants, celle-ci joue un rôle clé dans les interactions et contaminations potentielles.

Or, cette distribution ne peut être expliquée sans un questionnement sur le devenir des molécules mères. En effet, la transformation des molécules mères par biodégradation lorsqu'elles sont organiques est souvent mentionnée dans la littérature. Or, ces systèmes composés d'organismes vivants tels que les plantes favorisent cette transformation des composés. Ainsi la seconde partie de cette thèse a porté sur la distribution et la dégradation de micropolluants au sein des plantes. En effet, les plantes se développant au sein du filtre planté ou sur les berges d'une zone humide de traitement tertiaire sont exposées de manière chronique aux micropolluants. Ainsi, cette étude a été réalisée en utilisant l'imagerie par spectrométrie de masse (Matrix-Assisted Laser Desorption/Ionization-Imaging (SolariX) (FT ICR)) sur des plantes poussant au sein des ouvrages de dépollution ou se développant à proximité de ceux-ci. Ce mode d'analyse a également entrainé un développement et une optimisation de la préparation d'échantillons (coupe d'échantillons de plantes).

Ainsi cette distribution a été analysée au sein de différentes plantes. Tout d'abord, la distribution des micropolluants a été étudiée dans les feuilles d'un peuplier noir (Populus nigra). En effet, le peuplier est une espèce spontanée qui pousse sur les berges des rivières en Europe. Dans cette étude, une distribution spatiale spécifique des micropolluants au sein des feuilles peut être soulignée. En effet, les micropolluants semblent être stockés préférentiellement dans les régions périphériques des feuilles de peuplier. Ces résultats suggèrent un mécanisme de stockage au sein de la plante pour la gestion et le confinement de ces polluants.

Cette distribution de micropolluants a ensuite été mise en évidence au sein des feuilles de Salix alba (saule, autre plante fréquemment retrouvée au sein des zones de rejet végétalisées). L'objectif de cette seconde étude portait sur le devenir des micropolluants et de leurs métabolites (produits de dégradations et produits conjugués). En effet, un mécanisme de biotransformation semble être observé au sein des feuilles de saules. Ces produits de dégradation n'étant pas retrouvés au sein des échantillons d'eau et de sol, il semble qu'un processus in planta apparait. Ce processus est mentionné dans la littérature sous la dénomination de « green liver » en comparaison de ceux se déroulant au sein du foie chez les animaux.

Après avoir étudié la distribution des micropolluants et leurs métabolites dans des feuilles de plantes qui poussent dans des zones de rejets, l'étude s'est concentrée enfin sur la distribution des micropolluants sur une plante entière. En effet, l'imagerie par spectrométrie de masse a été employée sur le profil d'une plante (Phragmites australis : roseau, plante référence dans les ouvrages de traitement des eaux usées par les plantes) afin de comprendre la répartition de ces micropolluants au sein des différents organes.

La dernière partie de cette thèse a porté sur les corrélations entre les écoulements hydrodynamiques à travers une zone de rejet végétalisée et la distribution des micropolluants. Pour cela, les écoulements des eaux usées dans la zone de rejet végétalisée ont été simulés en utilisant un modèle numérique 3D (OpenFoam) basé sur la CFD (computational fluid dynamics). Ce modèle 3D a été construit à partir des données GPS collectées sur le site de l'étude et permettant de représenter finement le site d'étude. En parallèle, le débit d'eau d'entrée a été mesuré pendant 2 ans afin de simuler les cas les plus représentatifs. Les modèles d'écoulement puis de transport scalaire ont été validés à l'aide d'expériences de traçage in situ réalisées à différentes saisons. La création du modèle numérique et les résultats de ces simulations ont mis en évidence la présence de zones d'écoulement préférentielles ainsi que des régions à écoulements lents, voire stagnants, au sein de la zone de rejet végétalisée. En considérant ces différentes zones, des échantillons de sédiments (vision intégrative de la pollution) ont été collectés afin d'analyser leur contenu en micropolluants. Cette analyse a été effectuée selon la méthode développée dans la première partie de cette thèse. Les résultats obtenus ont mis mettre en évidence l'influence de l'hydraulique sur la distribution et le devenir des micropolluants. ). The analysis shown that chlorophyll a is found in higher concentration in the polluted leaves. ABA and cis-OPDA are found in higher proportion in the control leaves, while BA, SA and GA7 were found in higher amount in polluted leaves from Villette et al., 2019a Figure 59: Distribution of plant metabolites annotated in poplar leaves (polluted the longest leaf and non-polluted the shortest). Different distribution could be observed in the leaves. Salicin (D) and neurine (B) seems to be preferably found in the outer tissue while pheophorbide a (F) are mainly found in the inner tissue. Piperidine (A), adenine (C), lyso PC 16:0 (E) are found in the different tissues. The boxplot (G) suggests that these compounds are found in higher proportion in the polluted leaves (Villette et al., 2019a)

List of figures

List of tables

General Introduction

Anthropogenic activities and more particularly industrial activities, and our lifestyle have fostered the emergence of contaminants in the environment. All compounds released to the environment, which could generate a risk, or an issue could be called contaminants or pollutants. To deal with the increasing discharge of pollutants into the environment, several treatment systems have been set up in recent decades. But the pollutants concern has also been studied in water for centuries. Indeed, water, due to its multiple uses has always been a resource causing tensions. On the other hand, a large part of contaminants has been released to water since the Antiquity. The main objective of these first sewer systems was to remove stagnant and wastewater pollution from residential areas. This evacuation and concentration at local point to release wastewater have also led to large environmental pollution.

In France and in Europe, national and international regulations to protect water resources were only established in the second half of the twentieth century. The wastewater treatment plant (WWTP) setting up began in the 1960s-1970s years and the contaminants removal objectives were only defined in the French water law in 1992. Besides, these WWTPs were first set up in urban areas, which represent the main pollutants flow released to the environment. However, Water Framework Directive (WFD), adopted in 2000 by the European Commission, which determine the water quality objectives to be reached in the countries of the European Union, has led to the setting up of WWTPs in areas. But the rural context has also its drawbacks. Low cost systems should be implemented, and this context has led to the introduction of extensive water treatment systems. Furthermore, all these WWTPs were generally designed to remove carbon, nutrients and suspended solids pollution, which were the main pollutants issues in the twentieth century, but not designed for micropollutants removal.

In France, most of the WWTPs are well adapted to remove these "conventional" pollutants, but these systems are now facing two main challenges: the sustainability and the emergent contaminants. These emergent contaminants or micropollutants cover a wide compounds variety. But they can be defined as compounds that could have adverse effects on the environment or the human health even in low concentration (ppm, ppb). These micropollutants issues and the potential risks generated, have led to new regulations. The example of Watch lists in the European legislation could well illustrate this new regulation.

Micropollutants can reach the environment using several flow paths (runoffs from agriculture or urban areas, accidental releases…). But in developed countries, urban wastewater (household or industry) remains one of the major points where micropollutants were detected [START_REF] Ternes | Human Pharmaceuticals, Hormones and Fragrances[END_REF][START_REF] Michael | Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: A review[END_REF][START_REF] Eggen | Reducing the Discharge of Micropollutants in the Aquatic Environment: The Benefits of Upgrading Wastewater Treatment Plants[END_REF], WWTPs are one of the main release points to aquatic environment. To deal with these micropollutants, several processes could be used such as ozonation processes or activated carbon, etc. Nonetheless, the high costs and energy consumption do not fit with extensive treatment systems developed in rural areas. In the local context, the Agence de l'Eau Rhin-Meuse (AERM) promotes the constructed wetland set up and especially tertiary treatment wetlands at the outlet of the WWTPs. These tertiary treatment wetlands could not be considered as treatment systems according to the French law, but have several benefits:

an alternative to pipes to connect the WWTP to the environment, a buffer effect to reduce peak flow downstream for the environment release, a complementary treatment.

These systems being a sustainable alternative could also be partly efficient to remove some micropollutants from the aqueous phase. Indeed, in a previous PhD project [START_REF] Nuel | Devenir de 81 substances médicamenteuses et de leurs métabolites au sein des Zones de Rejet Végétalisées (ZRV)[END_REF], the system efficiency to remove drugs was studied. It has been demonstrated that this system could partly remove drugs but the efficiency could be impacted by the seasons. On the other hand, drugs distribution was also investigated in the other compartments (plants, macroinvertebrates and sludge) and showed that compounds specificity could be detected. Nevertheless, this study was only performed using LC-MS/MS target analysis with one micropollutant type: the drugs with 86 compounds. The behavior of these 86 compounds do not necessarily provide a global overview of micropollutants fate in this system. A study focused on selected compounds could bias the general results obtained. But in recent years, the technology advances and more precisely in liquid chromatography coupled in high resolution mass spectrometry (LC-HRMS) have led to screening a vast diversity of micropollutants even at very low concentrations. These advances will be helpful to understand the mechanisms involved in this kind of system.

Therefore, this research project has been focused on a large screening of micropollutants in a constructed wetland (CW) and the associated tertiary treatment wetland. This investigation and distribution analysis was conducted considering the following concerns:

micropollutants large screening development in the main environment compartment (water, sludge, plants), micropollutants spatial distribution in the different compartments using MALDI Imaging, micropollutants distribution coupled with water flow modeling using Computational Fluid Dynamics (CFD).

In this way, the manuscript will be structured following these axes. Indeed, the first part of the manuscript will be dedicated to the state of the art. In this first chapter, the urban wastewater context, the micropollutants types, their origins and fate in the environment, and the methods used to find or understand their behavior will be detailed. Then the second chapter will be dedicated to the distribution of a micropollutants large screening within the different compartments of the wetlands. The method developed to detect these micropollutants and all the metabolomic workflow will be introduced and commented. Three main examples will be chosen to illustrate this method. Indeed, the environment compartments (water, sludge, plants) in the constructed wetland and the tertiary wetland have been investigated, as the vertical sludge profile of the constructed wetland. The third chapter will be devoted to the micropollutants spatial distribution using mass spectrometry imaging. Non-target screening could provide a general overview of the contaminants, but spatial distribution could also be crucial to understand some bioprocesses. In this chapter, the main interest will focus on the understanding of spatial distribution in living organisms (plants) but also in sludge.

The use of mass spectrometry imaging could help to understand some biological processes and the mechanisms occurring in plants to deal with this stress. But water remains the major vector of micropollutants to the environment. Therefore, the relationship between micropollutants distribution and water flow process should be investigated. The last chapter is dedicated to this relationship. This chapter will be focused on the prediction of water flow areas in an artificial pond wetland using a CFD model and the understanding of water flow influence on micropollutants distribution (using a non-targeted approach). This manuscript will end with a general conclusion which takes up the main ideas detailed in this thesis. Since the Antiquity, different civilizations tried to remove these feces from the resident areas.

For example, the Minoan civilization [START_REF] Corrigan | SANITATION UNDER THE ANCIENT MINOAN CIVILIZATION[END_REF] or the Harappa cities [START_REF] Jansen | Water supply and sewage disposal at Mohenjo-Daro[END_REF] illustrate this sanitation beginning. But the prime example is the Cloaca Maxima set up in Roma. It was probably the first sewer system in a big city [START_REF] Hopkins | THE CLOACA MAXIMA AND THE MONUMENTAL MANIPULATION OF WATER IN ARCHAIC ROME[END_REF]. These systems will reappear in the nineteenth century after the cholera outbreaks. Indeed, John Snow demonstrated that cholera outbreaks in London were linked to polluted water. Therefore, sewer emergence took place gradually in the big cities (London, Paris with Belgrand and Haussman).

Nevertheless, the wastewater disposal had to be combined with treatment system to prevent the health risks. Therefore, water treatment before the release into the environment, was gradually introduced for wastewater collected. One of the main examples is probably the development of activated sludge process by Arden and Lockett in 1914.

The first urban wastewater concern was focused on sanitation with health issues and therefore has been associated with the wastewater coming from the domestic activities (urines and feces).

But urban wastewater could not be restricted to this aspect. Indeed, domestic activities also generate graywater. This graywater is not directly in contact with feces but could contain some contaminants coming from other domestic activities (shower, washing machines, sink…).

Besides domestic activities are not the only source of urban wastewater. Stormwater could also be considered as a part of urban wastewater. For example, rainfall dripping on roofs, or road could be collected in combined sewers and become a part of this urban wastewater.

Nevertheless, in the recent years the national authorities tried to separate domestic wastewater from stormwater and promote specific treatment for storm water near the areas where they were generated, as shown by the separative sewer networks development in the new city districts.

Finally, urban wastewater could also cover industrial effluents, which contain a wide variety of chemical contaminants. These effluents with new chemical contaminants have led national authorities to set up water treatment before their release in the sewer and the environment.

Nevertheless, these water treatment systems spread widely only in the second half of the twentieth century in Europe. The development of industrial activities has also generated new effluents with chemicals contaminations and led to the water treatment system development. This WWTP setting up has been significantly impacted by these legislation changes. First

Indeed

WWTPs were set up in urban areas with the main pollutants flow. Then, the performance objective introduced in the WFD generated WWTP installations throughout the country and small communities. However, these small communities do not have the same issues and resources than urban areas. In this way, the treatment system used for big cities as activated sludge, most common treatment system, is not well adapted to the rural context. These specific problems and resources should be considered to define the water treatment fitting with the context.

Constructed Wetlands (CWs): wastewater treatment system applied to small communities

The rural specificities have led to the development of wastewater treatment systems adapted to this context. Indeed, low cost and low energy consuming systems have been set up. These extensive systems, as opposed to intensive systems (as activated sludge), were based on natural processes found in the environment.

These systems could be named using the terms of ecological engineering, introduced by Mitsch (Mitsch 2012). One of its definitions mentioned ecological engineering as the use of the surrounding ecosystems to reduce or solve pollution issues. According to this definition, different treatment systems could be set up to clean water without chemical input or high energy consumption (ponds, ditches, bed filters). Kadlec and Wallace define wetlands as "land areas that are wet during part or all of the year because of their location in the landscape" [START_REF] Kadlec | Treatment wetlands[END_REF]. They define these areas as transition land between terrestrial systems and aquatic systems. Constructed wetlands (CWs) are man-made systems designed in non-wetland site to treat stormwater or wastewater using the high biological activities found in their ecosystems to improve the pollutant removal abilities [START_REF] Kadlec | Treatment wetlands[END_REF]. In this way, CWs are fully in line with this approach. As natural wetlands, the CWs are composed of water, plants and a media (soil, gravel…) [START_REF] Kadlec | Treatment wetlands[END_REF].

CWs were created during the 1950s in Germany with the experiments of Käthe Seidel [START_REF] Vymazal | Constructed wetlands for wastewater treatment[END_REF] and the CW set up to clean wastewater began in the 1970s [START_REF] Kickuth | Ökochemische Leistungen höherer Pflanzen[END_REF].

Besides their efficiency to remove pollutants in moderate climates, these systems show their adaptation to all climate types. Indeed, CW are used around the world to treat several effluents (domestics, industrial, stormwater…), as the literature underlines their set up in all the continents [START_REF] García | Contaminant Removal Processes in Subsurface-Flow Constructed Wetlands: A Review[END_REF].

These CWs could be distinguished by the hydraulics modes. The two main CWs types are surface flow and subsurface flow wetlands [START_REF] Kadlec | Treatment wetlands[END_REF]. The surface flow wetlands mimics natural systems such as ponds or marshes and are mainly used as polishing/ and/or tertiary treatment of effluent from previous water treatment systems [START_REF] Kadlec | Treatment wetlands[END_REF]. These surface wetlands could also be clustered according to the plant type growing in the wetland [START_REF] Brix | Use of constructed wetlands in water pollution control: historical development, present status, and future perspectives[END_REF]:

free-floating macrophyte-based systems, emergent macrophyte-based systems, submerged macrophyte-based system.

These surface flow wetlands could be compared to the subsurface wetlands. The subsurface wetlands do not have a surfacing water and wastewater flows beneath the surface of the media around the plants rhizomes [START_REF] Kadlec | Treatment wetlands[END_REF]. This kind of wetlands could be clustered as horizontal subsurface wetlands and vertical flow wetland depending on the hydraulics mode applied. In the horizontal subsurface wetland, a continuous and horizontal water flow is set up. The horizontal subsurface wetland is used as a single-family or small communities water treatment system and it is well adapted for the denitrification processes [START_REF] Wallace | Feasibility, Design Criteria, and O&M Requirements for Small Scale Constructed Wetland Wastewater Treatment Systems[END_REF]. On the other hand, wastewater treatment could also be performed by vertical flow constructed wetlands (VFCWs). This system is widely used in Europe and more particularly in France as a wastewater treatment system for small communities (lower than 2,000 peoples). Therefore, this CW type will be detailed in the next sections. Briefly, water will percolate through the different soil types (organic matter, sand, gravel…) [START_REF] Kadlec | Treatment wetlands[END_REF], and in the rhizosphere area to remove carbon, nutrients or total suspended solids by biological processes or filtration.

Finally, these systems mentioned in the literature as traditional CW could be combined to generate hybrid systems. For example, a combination of two VFCWs with a horizontal flow CWs could be used for effluent denitrification in sensitive areas. Besides, these traditional CWs are distinguished from enhanced CW with facilities (ventilation, baffles…). An overview of the different CWs is proposed in the Figure 1.

Figure 1: CWs types mentioned in the literature [START_REF] Kadlec | Treatment wetlands[END_REF]. The traditional CWs without any input are opposed to enhanced CW using facilities (artificial and baffle). Concerning the traditional CWs the hydraulics mode influence could be visualized with the different CW type (subsurface, free surface and vertical flow).

The pollutants removal in these wetlands are based on a combination of physical, chemical, and biological processes. Thus the main processes are sedimentation, adsorption, plant uptake and biological degradation [START_REF] Brix | Use of constructed wetlands in water pollution control: historical development, present status, and future perspectives[END_REF][START_REF] Reddy | Biogeochemical indicators to evaluate pollutant removal efficiency in constructed wetlands[END_REF].

These mechanisms could be distinguished in two ways. The pollutants transfers and the biotransformation. Physicochemical mechanisms such as sedimentation, adsorption or volatilization underline the transfers of pollutants from the liquid phase to the gas or solid phase.

These transfers could be characterized by several parameters (Henry constant for volatilization, Kow or Kd for sorption, …). A higher log Kow coefficient will indicate a higher hydrophobicity behavior and then a higher ability to be sorbed [START_REF] Rogers | Sources, behaviour and fate of organic contaminants during sewage treatment and in sewage sludges[END_REF]. Nevertheless, the sorption could also be influenced by the media characteristics, and the environmental conditions (pH, redox, …) or organic matter [START_REF] Cottin | Removal of PAHs from laboratory columns simulating the humus upper layer of vertical flow constructed wetlands[END_REF][START_REF] Brunsch | Retention soil filter as post-treatment step to remove micropollutants from sewage treatment plant effluent[END_REF][START_REF] Biel-Maeso | Removal of personal care products (PCPs) in wastewater and sludge treatment and their occurrence in receiving soils[END_REF].

Indeed, biodegradation could be setting up in the different living organisms growing in these systems. The degradation of "conventional" pollutants (carbon, nitrogen or phosphorus) could be performed by the microorganism's metabolism or plant degradation. Nonetheless, the micropollutants could also be degraded by these living organisms. For example, ibuprofen degradation has been shown in reed [START_REF] He | Metabolism of Ibuprofen by Phragmites australis: Uptake and Phytodegradation[END_REF] but also with the combination of microorganisms growing in the media and associated to the plants. Concerning the degradation by microorganisms, micropollutants will be used for co-metabolism due to their low concentration [START_REF] Luo | A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment[END_REF][START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF].

Besides, all these mechanisms could be influenced by several environmental parameters. First to obtain comparable results in the inlet and outlet wastewater and be able to conclude on the system efficiency, the water budget should be established. [START_REF] Kadlec | Treatment wetlands[END_REF]. Indeed, as these systems mimic the natural processes, they are also influenced by the environmental conditions. Therefore, rainfall, evaporation, evapotranspiration, infiltration and solar radiations should be considered to evaluate wetland efficiency. Besides, these environmental parameters could also influence physico-chemical processes (radiation with the photodegradation, pH for the sorption, …) [START_REF] Rühmland | Fate of pharmaceuticals in a subsurface flow constructed wetland and two ponds[END_REF] and biological processes (temperature, pH, dissolved oxygen, redox potential for the microorganisms communities growing in these systems) [START_REF] Hijosa-Valsero | Influence of design, physico-chemical and environmental parameters on pharmaceuticals and fragrances removal by constructed wetlands[END_REF]. On the other hand, the wetland characteristic should also affect these processes. Indeed, hydraulic load rate, water volume (for surface flow wetland) and media characteristics could also alter the mechanisms [START_REF] Kadlec | Treatment wetlands[END_REF]. The media type (porous or impermeable) could impact water flow inside the wetland and different hydraulics equations will govern the water in these two types of media. Furthermore, media size and porosity are also crucial points concerning the subsurface flow as a non-adapted media could generate clogging in these systems.

In France, more than 70% of the cities have fewer than 1,000 inhabitants. Regarding this population distribution, it seems that VFCWs are well adapted to treat wastewater in these cities with small communities. The CW are not inexpensive treatment systems, but comparatively to other technologies (activated sludge, sequencing batch reactors, …), they represent a low-cost solution [START_REF] Kadlec | Treatment wetlands[END_REF] well adapted to the rural context. Besides, the vertical flow constructed wetlands (VFCWs) have become the most popular systems used in France.

Therefore, this state of art will now be focused on VFCW and more precisely on VFCW adapted to the French context.

In France, the environment ministry (ministère de la Transition écologique et solidaire) results

underline that CWs represent 4,200 WWTP in 2018. The increases of CWs set up could be observed in Figure 2 and highlight the impact of WFD and the growing importance of this kind of system in France ("Portail d'informations sur l'assainissement communal -Services en ligne," visited on 01/02/ 2020). On the other hand, these CWs can be clustered according to the hydraulics that manages the water treatment. CWs could distinguish those with a horizontal flow and vertical flow constructed wetlands (VFCW). The flow applied in the CW play a key role in the degradation processes that could occur in the CW.

The horizontal flow CW maintains a constant water level. The CW will be partly saturated with water, and anoxic conditions could be observed. These conditions could for example boost the denitrification. On the other hand, VFCWs are bed filters planted with Phragmites australis.

These beds are alternatively feeding by large batches of wastewater [START_REF] Kadlec | Treatment wetlands[END_REF]. Then water will percolate in the different soil layers. This percolation will engender aerobic conditions, promoting organic matter degradation, as the bed filters are drained. The VFCWs could also remove total suspended solids (TSS), by filtration process, but they are less efficient for the denitrification process. This system is not only popular in France but also in several countries in Europe (Austria, Denmark, Germany, United Kingdom) to deal with the pollution sources from small communities [START_REF] Kadlec | Treatment wetlands[END_REF].

In France, the classical wastewater treatment system is based on a two-stage VFCWs, where water percolates through the layers as shown in Figure 3. This system could be completed with a horizontal flow CW or a pond to refine water treatment particularly for nitrates. According to the French law, their expected performances obey the regulation of TSS, biochemical oxygen demand during a period of 5 days (BOD5), chemical oxygen demand (COD) and total Kjeldahl nitrogen (TKN) [START_REF] Ruiz | Optimization of French Vertical Flow Treatment Wetlands applied to domestic wastewater treatment for different levels of performances 166[END_REF]). Nevertheless, these WWTP could be located dozen meters from the environment and pipes should be added to connect WWTP to the environment. Nevertheless, more sustainable alternatives could be set up such as tertiary treatment wetland implementation.

Tertiary treatment wetlands

In the French context, these tertiary treatment wetlands could be considered as surface flow wetlands. Connecting the WWTP to the environment, the tertiary treatment wetland could not be considered as a treatment system according to the regulation at the opposite of VFCWs , 2007). In this way, no regulation defines the removal performance of these systems. Besides, non-conventional design and the lack of procedures have led to different tertiary treatment wetlands. The wetland design is mainly defined by the site constraints [START_REF] Nuel | Devenir de 81 substances médicamenteuses et de leurs métabolites au sein des Zones de Rejet Végétalisées (ZRV)[END_REF]. Nevertheless, three main models could be observed in France. These three main classes are meadows wetlands, pond wetland, ditch wetland.

Wetland using other materials (gravels, geomembrane…) could also be mentioned. The three main classes could be observed in Figure 4. The meadow wetland has large flood areas with a gentle slope to the environment and will infiltrate water. On the other hand, pond wetlands were generated by excavating the site. The digging will generate a natural pond where water could flow. The final cluster, ditches wetlands, are similar to pond wetlands, but the length to width ratio is higher and the water depth lower.

(Prost-boucle and Boutin, 2012).

All the systems were set up to enhance some processes. The CWs and the tertiary treatment wetlands were designed to improve the quality of water released, considering the carbon, nitrogen and phosphorus. Nevertheless, these systems are chronically exposed to new emergent contaminants and were not designed to treat them.

Micropollutants: a large panel of molecules with an increasing interest 1.2.1. A continually evolving definition

Micropollutants or emerging contaminants definition covers a wide variety of compounds and this definition have been modified over the years. Moriarty defined micropollutants as compounds from anthropogenic activities (xenobiotics) detected in higher proportion than naturally occurring amount (Moriarty 1983). Nevertheless, this definition is questionable, as micropollutants remain contaminants with adverse effects, even if they were found naturally in the environment. Therefore, another definition most adapted to our context and widely used in the environmental study introduces micropollutants as all compounds with adverse effects on the environment or human health even in very low concentration (µg/L or ng/L) (Ministère de la transition ecologique, 2015).

The micropollutants could be clustered as inorganic micropollutants (the heavy metals) and organic micropollutants (pesticides, drugs, personal care products, …). According to the European Union, micropollutants could represent more than 110,000 compounds with diverse properties (Ministère de la transition ecologique, 2015).

Compounds found in this definition have changed over the years. Indeed, a better knowledge of the impacts of chemical products and the advances in technology have led the addition of xenobiotics included in this definition. In this way, the different classes were progressively considered over the last decades. The first micropollutants studies were focused on heavy metals, metalloids from industry pollution during the 1970s. Then, pesticides, drugs or personal care products were investigated. This approach has also influenced the scientist literature.

Indeed, heavy metals are still the main micropollutants studied, even if an increase of pesticides or drugs studies could be observed in the environmental field. On the other hand, personal care products, considered as a more recent subject area, is only emerging these recent years. An example of this evolution could be illustrated in the Figure 5 with the progression of published articles focused on the different micropollutants classes in wastewater from 1970 to 2020. The results could be obtained using the keywords "wastewater" + micropollutants classes (for example "drugs" in the title and the keywords) in ScienceDirect.

Micropollutants types

The technology development has led to increase the analytical capabilities. Indeed, the advances in LC-HRMS in the recent years have promoted the screening of a vast diversity of micropollutants. On the other hand, the micropollutants concern has also led to new European or national regulations to consider this issue. As mentioned in the introduction, the WFD has defined strategies to restore the waterbodies quality. [START_REF] Deblonde | Emerging pollutants in wastewater: A review of the literature[END_REF][START_REF] Luo | A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment[END_REF][START_REF] Margot | A review of the fate of micropollutants in wastewater treatment plants[END_REF][START_REF] Wang | Monitoring of 943 organic micropollutants in wastewater from municipal wastewater treatment plants with secondary and advanced treatment processes[END_REF]. In this way, these main classes, i.e., polycyclic aromatic hydrocarbons (PAHs), pesticides, drugs, personal care products and heavy metals will be introduced in this section.

Hydrocarbon micropollutants: PAHs

PAHs are described as a compound class with carbons and hydrogen and at least two aromatic rings [START_REF] Abdel-Shafy | A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation[END_REF]. They are a part of the non-saturated hydrocarbons and belong to the aromatic hydrocarbons class, which could be divided into monocyclic aromatic hydrocarbons (benzene, toluene, ethylbenzene and xylen (BTEX)) and PAHs. These PAHs could be found in different environment compartments due to their physico-chemicals properties. Generally, most of the PAHs found in the environment are those with 2 to 7 rings [START_REF] Neff | Polycyclic aromatic hydrocarbons in the aquatic environment: sources, fates and biological effects[END_REF]. They represent more than 100 chemical persistent compounds which could be naturally found in the environment.

In the literature, PAHs are mainly studied using the priority list defined by the United States environmental protection agency (US EPA) in 1976 [START_REF] Keith | The Source of U.S. EPA's Sixteen PAH Priority Pollutants[END_REF]. The list could be found in the Figure 6. In the literature, this list combined to the total hydrocarbon is widely used to determine the PAHs contamination found in the environment and is used for worldwide comparison.

Nevertheless, these compounds were listed in the 1970s or 1980s [START_REF] Andersson | Time to Say Goodbye to the 16 EPA PAHs? Toward an Up-to-Date Use of PACs for Environmental Purposes[END_REF]. But the analytical methods and studies advances have improved our knowledge of these PAHs, and underline the lacks of the identification of some of the most toxic PAHs [START_REF] Andersson | Time to Say Goodbye to the 16 EPA PAHs? Toward an Up-to-Date Use of PACs for Environmental Purposes[END_REF].

Different classifications using a broader spectrum of PAHs could be found in the literature.

First, the PAHs could be clustered according to their structure (ring numbers). In this way, three main classes are described (Marçais 2017):

-"light" PAHs (fewer than three rings),

-"intermediate" PAHs (four rings),

-"heavy" PAHs (more than four rings).

On the other hand, PAHs could also be grouped according to the pathways [START_REF] Neff | Polycyclic aromatic hydrocarbons in the aquatic environment: sources, fates and biological effects[END_REF]):

pyrogenic, petrogenic biogenic [START_REF] Grice | New insights into the origin of perylene in geological samples[END_REF][START_REF] Wilcke | Carbon Isotope Signature of Polycyclic Aromatic Hydrocarbons (PAHs): Evidence for Different Sources in Tropical and Temperate Environments?[END_REF].

However, most PAHs found in the environment are pyrogenic PAHs, resulting from incomplete burning of organic matter [START_REF] Kouzayha | Développement des méthodes analytiques pour la détection et la quantification de traces des HAP et de pesticides dans l'eau. Application à l'évaluation de la qualité des eaux libanaises[END_REF]. Indeed, PAHs are mainly detected due to anthropogenic activities (for example heating or driving) or the fossil fuels burning [START_REF] Baek | A review of atmospheric polycyclic aromatic hydrocarbons: Sources, fate and behavior[END_REF][START_REF] Besombes | Particulate PAHs observed in the surrounding of a municipal incinerator[END_REF][START_REF] Masclet | Relative decay index and sources of polycyclic aromatic hydrocarbons[END_REF]. Depending on the region studied, natural emissions of PAHs could also be noticed with forest fires or volcanic eruptions [START_REF] Nikolaou | Sources and chemical reactivity of polynuclear aromatic hydrocarbons in the atmosphere -A critical review[END_REF].

Pesticides: substances controlling organisms harmful to crops

According to the World Health Organization (WHO) definition, pesticide is a general term used for chemical compounds intended for controlling any organism considered harmful to crops.

To protect crops, different compounds with diverse pesticides action could be found. This variety of pesticides actions could be illustrated by the examples of isoproturon that inhibits photosynthesis or pendimethalin that blocks the cell division.

As PAHs, the pesticides have also been classified. The international organizations such as European Union or WHO have clustered pesticides following different criteria. For example, pesticides could be clustered as biocide compounds or phytopharmaceutical compounds according to the European regulation (Directive 91/414/CEE du Conseil, du 15 juillet 1991, concernant la mise sur le marché des produits phytopharmaceutiques). The WHO, clustered the pesticides according to the lethal dose (LD50) and then the risk for human health (World Health Organization 2010). Nevertheless, this classification is barely mentioned in the literature. The main clusters used are based on the pesticides target or the chemical structure. The target pesticides classification is focused on the targeted organisms and defines three main classes:

insecticides, herbicides, fungicides.

In these different classes, herbicides represent the highest pesticides sales increases in the last dedaces [START_REF] Carvalho | Pesticides, environment, and food safety[END_REF]. Nevertheless, the pesticides use and widespread is depending on the continent considered. Indeed in North America or in Europe herbicides are widely used, due to intensive agriculture, while in Asia a high use of insecticide could be noticed [START_REF] Carvalho | Pesticides, environment, and food safety[END_REF].

The second classification is based on the pesticides chemical structure. Pesticides could be clustered from the origins (mineral, plant or synthetic compound). This last cluster only used the chemical structure to distinguish compounds. The main classes are properties for human or animal health ("Code de la santé publique | Legifrance"). This common objective is one of the few similarities found for all these compounds [START_REF] Taylor | Human pharmaceutical products in the environment -the "problem" in perspective[END_REF].

Besides, all the legal compounds found had undergone a marketing authorization process. This process is based on three main steps to validate a drug for a commercial use of an active substance [START_REF] Lechat | Pharmacologie niveau DCEM 1[END_REF]):

the preclinical stage to define the toxicity,

the first stage with the pharmaco kinetic study,

the second stage with the dose response and the pharmaco kinetic parameters.

The drugs commercially used could, then, be clustered according to the Anatomic Therapeutic Chemical (ATC) systems and following the WHO recommendations (World Health Organization Collaborating Centre for Drug Statistics Methodology 1996). Nevertheless, this classification is not mainly used by French doctors and the scientist literature. Drugs are mainly classified using the therapeutic classes as anti-inflammatories, analgesics, antidepressants, beta blockers or antibiotics. After their application, drugs will be only partially degraded and excreted in urine, generating a pollution source for wastewater.

On the other hand, domestic activities would also generate a large part of micropollutants found in the environment. Indeed, all these compounds used for the body, beautification or cleaning are found in our daily life and could be gathered in the detergent or personal care products.

Toothpaste, shampoo, soap and all cosmetic products are examples of this class of compounds, which are daily used. But most of them are released in the "gray" water and then in the sewer and the WWTP.

Heavy metals and metalloids

The last category mentioned in this manuscript will be the heavy metals and the metalloids. The heavy metals term is a loose term used to define all the metal compounds with adverse effect on the environment [START_REF] Hodson | Heavy metals-geochemical bogey men?[END_REF]. This loose term has generated different definitions based on the density or other properties of these compounds [START_REF] Hodson | Heavy metals-geochemical bogey men?[END_REF]).

This compounds with density higher than 5,000 kg/m 3 are naturally found in the environment [START_REF] Tchounwou | Heavy Metals Toxicity and the Environment[END_REF]. Heavy metals could also be described as metal elements found between the copper and lead in the Mendeleïev classification. However, these compounds are mainly used in anthropogenic activities such as industry activities or domestic activities. The main activities generating heavy metals are fossil fuels burning, metallurgy, textile, plastic or electronic, electrochemistry, industry and reach the environment through road runoffs or industrial effluents [START_REF] Cotran | Robbins, patología estructural y funcional, cuarta edición[END_REF][START_REF] Fergusson | The heavy elements: chemistry, environmental impact and health effects[END_REF][START_REF] Hong | Greenland Ice Evidence of Hemispheric Lead Pollution Two Millennia Ago by Greek and Roman Civilizations[END_REF][START_REF] Candelone | Post industrial revolution changes in large scale atmospheric pollution of the northern hemisphere for heavy metals as documented in central Greenland snow and ice[END_REF][START_REF] Freyssinet | Chemical changes and leachate mass balance of municipal solid waste bottom ash submitted to weathering[END_REF][START_REF] Callender | Heavy Metals in the Environment-Historical Trends[END_REF][START_REF] Sukandar | Metals leachability from medical waste incinerator fly ash: A case study on particle size comparison[END_REF].

Besides, heavy metals could be found in different compounds used in our daily life (painting, flame retardants, electronics) and could be found in the sewer such as personal care products.

Therefore, this section has underlined that diverse micropollutants could be in the environment.

Besides the diversity and the number of micropollutants are increasing over the years, by the development of anthropogenic activities. This large variety raises the question of fate and origins of micropollutants found in the environment.

Origins and fate of micropollutants in the wastewater treatment systems and the environment 1.3.1. Micropollutants emission and potential degradation process

Micropollutants found in the environment result from diverse processes. Some micropollutants such as heavy metals could result from natural processes (eruption, forest fires, rock alteration) [START_REF] Masindi | Environmental Contamination by Heavy Metals[END_REF] and others such as personal care products are coming from anthropogenic activities. Nevertheless, human activities generate the main metals or organic micropollutants flow to the environment [START_REF] Ravindra | Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation[END_REF][START_REF] Warner | Qualitative and quantitative use of micropollutants as source and process indicators. A review[END_REF]. This activity development has widely impacted the environment quality.

Micropollutants sources

Micropollutants diversity found in the environment could have several origins, due to the variety of classes. For example, volatile compounds can easily disperse in the air and atmospheric transfers and the rainfalls could spread them to the environment. Besides, these volatiles compounds are found in several micropollutants compounds [START_REF] Abdel-Shafy | A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation[END_REF][START_REF] Pimentel | Amounts of pesticides reaching target pests: Environmental impacts and ethics[END_REF], generating a spread to environment. Therefore, the use of this kind of compounds could cause a global environmental issue. The contamination by atmospheric transfers could be described according to three main ways:

the atmospheric dispersion,

the rainfalls,

the rainfall with soil leaching.

The atmospheric dispersion could be illustrated by the example of fuel burning creating hydrocarbons emission or pesticides application in agriculture fields. This transfer to the environment is highly correlated to weather (wind, rain, …) [START_REF] Gil | Emission of pesticides to the air during sprayer application: A bibliographic review[END_REF]. And even if the pesticides should not be applied in windy weather, the contamination by atmospheric transfers should not be neglected. On the other hand, the rainfall could also impact the environment. Indeed, volatile compounds such as PAHs or pesticides will pollute the environment during rainfall events directly or by soil leaching. Rainfall could transfer micropollutants found in the soil after pesticides application or atmospheric deposits to the environment [START_REF] Marçais | Transferts des polluants organiques persistants de l'atmosphère aux milieux aquatiques de montagne[END_REF]. Therefore, the runoffs and the infiltration will contaminate both surface and groundwater.

The leaching could also impact drugs distribution in the environment. Indeed drugs are found in the sewage sludge applied as soil improvement on the agriculture field [START_REF] Ivanová | Pharmaceuticals and illicit drugs -A new threat to the application of sewage sludge in agriculture[END_REF].

Studies have already shown the ability of sludge to sorbe micropollutants [START_REF] Petrović | Liquid chromatography-tandem mass spectrometry for the analysis of pharmaceutical residues in environmental samples: a review[END_REF][START_REF] Verlicchi | Pharmaceuticals and personal care products in untreated and treated sewage sludge: Occurrence and environmental risk in the case of application on soil -A critical review[END_REF][START_REF] Ivanová | Pharmaceuticals and illicit drugs -A new threat to the application of sewage sludge in agriculture[END_REF][START_REF] Lee | Occurrence and emission of phthalates and nonphthalate plasticizers in sludge from wastewater treatment plants in Korea[END_REF][START_REF] Liu | A review on removal of organophosphorus pesticides in constructed wetland: Performance, mechanism and influencing factors[END_REF][START_REF] Pérez-Lemus | Analytical methodologies for the determination of pharmaceuticals and personal care products (PPCPs) in sewage sludge: A critical review[END_REF]. Finally, these micropollutants could be released with the soil leaching.

Leaching could cause runoffs and will diffuse micropollutants to the environment [START_REF] Lapworth | Emerging organic contaminants in groundwater: A review of sources, fate and occurrence[END_REF]. On the other hand, Gros et al., 2019 have underlined that veterinary drugs found in slurry, used as soil improvement, could be infiltrated through different soil layers.

Nevertheless, this pollution characterized as diffuse pollution has generally a lower impact than local micropollutant emission point. The main sources of drugs and all micropollutants from domestic activities are correlated to this local emission point [START_REF] Eggen | Municipal landfill leachates: a significant source for new and emerging pollutants[END_REF].

The specific point could represent several activities and the key examples are landfills, where unused drugs or personal care products could be found. These micropollutants could be released to the environment due to leaching, percolation and other water infiltration. [START_REF] Buszka | Wasteindicator and pharmaceutical compounds in landfill-leachate-affected ground water near Elkhart, Indiana, 2000-2002[END_REF][START_REF] Eggen | Municipal landfill leachates: a significant source for new and emerging pollutants[END_REF][START_REF] Holm | Occurrence and Distribution of Pharmaceutical Organic Compounds in the Groundwater Downgradient of a Landfill (Grindsted, Denmark)[END_REF],

farming or aquaculture where animals could be treated using antibiotics. The use of these drugs could generate direct or indirect environmental contamination near the farms [START_REF] Haya | A Review and Assessment of Environmental Risk of Chemicals Used forthe Treatment of Sea Lice Infestations of Cultured Salmon[END_REF][START_REF] Cabello | Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment[END_REF][START_REF] Burkholder | Impacts of Waste from Concentrated Animal Feeding Operations on Water Quality[END_REF],

accidental spill with the hydrocarbons could be released to the environment (Le Bihanic 2013),

septic tanks or on-site sewage facilities when the system is permeable [START_REF] Blum | Nontarget screening and prioritization of potentially persistent, bioaccumulating and toxic domestic wastewater contaminants and their removal in on-site and large-scale sewage treatment plants[END_REF]Conn et al., 2010;[START_REF] Godfrey | Pharmaceuticals in on-site sewage effluent and ground water, Western Montana[END_REF][START_REF] Gros | Screening and prioritization of micropollutants in wastewaters from on-site sewage treatment facilities[END_REF].

Wastewater treatment systems and urban wastewater which are mentioned as the major point where micropollutants were released to the environment [START_REF] Kasprzyk-Hordern | The removal of pharmaceuticals, personal care products, endocrine disruptors and illicit drugs during wastewater treatment and its impact on the quality of receiving waters[END_REF][START_REF] Luo | A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment[END_REF][START_REF] Petrie | A review on emerging contaminants in wastewaters and the environment: Current knowledge, understudied areas and recommendations for future monitoring[END_REF][START_REF] Phillips | Combined Sewer Overflows: An Environmental Source of Hormones and Wastewater Micropollutants[END_REF][START_REF] Verlicchi | Occurrence of pharmaceutical compounds in urban wastewater: Removal, mass load and environmental risk after a secondary treatment-A review[END_REF]. Indeed, all the domestic activities (cleaning, hygiene, clothes washing, healthcare…) will generate gray water mixed with wastewater and sometimes with storm water, which is released to the WWTP.

Nevertheless, these WWTPs were not designed to treat micropollutants. In this way, WWTP became one of the major points where micropollutants can be released to the environment.

The Figure 7 summarized the various processes that cause the environmental micropollutants contamination. Nevertheless, the micropollutants investigation which only focus on parent compounds underestimate the micropollutants release to the environment. Indeed, the parent compounds could be partly degraded using some metabolization processes generating degradation products called metabolites. These metabolites (catabolite, conjugated or deconjugated compounds) could be found in higher quantity in the environment and could generate a higher toxic risk than the parent compounds [START_REF] Kosjek | Applications of mass spectrometry to identifying pharmaceutical transformation products in water treatment[END_REF][START_REF] Kern | Assessing Exposure to Transformation Products of Soil-Applied Organic Contaminants in Surface Water: Comparison of Model Predictions and Field Data[END_REF][START_REF] Wang | Phototransformation of Cephalosporin Antibiotics in an Aqueous Environment Results in Higher Toxicity[END_REF][START_REF] Herrmann | Identification of phototransformation products of the antiepileptic drug gabapentin: Biodegradability and initial assessment of toxicity[END_REF][START_REF] Yin | Pay special attention to the transformation products of PPCPs in environment[END_REF]. For example, [START_REF] Marsik | Metabolism of ibuprofen in higher plants: A model Arabidopsis thaliana cell suspension culture system[END_REF], have demonstrated that ibuprofen (parent compound) represents less than 1% of all the ibuprofen forms found in plants.

These results highlight the fact that all the different metabolites should be considered to understand the fate of drugs in the environment.

Micropollutants fate

The catabolites and conjugates of organic micropollutants after metabolization cover various compounds coming from transformation processes. Biotic (like digestion) and abiotic (like photodegradation) processes could occur to transform the parent compounds. Furthermore, a spatial and temporal difference could be observed in the metabolites detected. Indeed, this transformation could be performed before the release into the environment, during micropollutants transfers or once it has been caught in an environment compartment. The drugs example could illustrate the transformation before their release. These metabolization processes are highly considered for pharmaceuticals applications. Once the drugs have been absorbed and reach the target organs, it would be removed from the organism. Enzymatic reactions found in the organs and more particularly in liver cells will metabolize the compound [START_REF] Lechat | Pharmacologie niveau DCEM 1[END_REF].

Cytochrome P450 and aryl sulfatase or UDP glucuronyl transferase are the main enzymes involved degrading drugs in the organism. Cytochrome P450 will generate a compound oxidation (Phase I) then a conjugation process (phase II) which could be performed using aryl sulfate or UDP-glucuronyl transferase [START_REF] Meunier | Mechanism of Oxidation Reactions Catalyzed by Cytochrome P450 Enzymes[END_REF]. The main degradation process could be found in Figure 8. On the other hand, these transformations are compound-dependent, and compounds are not always fully degraded in the organisms. Therefore, parent compounds and their metabolites will be excreted in the urine then found in wastewater.

Furthermore, this transformation resulting from human or animal metabolism is only part of the micropollutants transformation, and it could be only related to some micropollutants classes.

Indeed, during the wastewater transport or treatment and release to the environment other metabolization processes could occur. Two main metabolization processes could be distinguished in two groups. First the micropollutants metabolization could be linked to biological activities and then to physico-chemical reactions.

Organic micropollutants degradation using microbial consortium metabolism in the water treatment systems or in the environment has already been studied in the literature [START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF]. Indeed, Singhal et Perez-Garcia indicate that the use of microorganisms with specific metabolic capabilities such as -heterotrophic sulfate reduction [START_REF] Zhang | Organic wastes as carbon sources to promote sulfate reducing bacterial activity for biological remediation of acid mine drainage[END_REF] autotrophic sulfur-oxidizing denitrification [START_REF] Hao | A review of biological sulfate conversions in wastewater treatment[END_REF][START_REF] Pokorna | Sulfur-oxidizing bacteria in environmental technology[END_REF] anaerobic methane-oxidation denitrification [START_REF] Raghoebarsing | A microbial consortium couples anaerobic methane oxidation to denitrification[END_REF], partial nitrite reduction to nitrous oxide for energy generation (D. [START_REF] Scherson | Nitrogen removal with energy recovery through N 2 O decomposition[END_REF] and electron shuttle redox biotransformation (Van der Zee and Cervantes, 2009) -will be helpful to promote micropollutants removal. Besides, Singhal et Perez-Garcia show that two-enzyme classes, cytochromes P450 and laccases are classically used to degrade drugs in the organisms and micropollutants in enzyme assays [START_REF] Harms | Untapped potential: exploiting fungi in bioremediation of hazardous chemicals[END_REF][START_REF] Lah | The versatility of the fungal cytochrome P450 monooxygenase system is instrumental in xenobiotic detoxification[END_REF][START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF]. Nevertheless, these micropollutants are mainly used in the co-metabolization due to their low concentration [START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF]. Besides, this cometabolization is dependent on the bioavailability (complex structure or function group) [START_REF] Luo | A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment[END_REF].

On the other hand, these micropollutants could also be caught by plant in the environment or in wastewater treatment systems using plants such as CW, as it has been described for metals. [START_REF] Verbruggen | Molecular mechanisms of metal hyperaccumulation in plants[END_REF]. Besides plant could also have a key role in this metabolization and the environment detoxification. Indeed, the metabolic processes occurring in plants exposed to micropollutants have already been studied (C. [START_REF] Huber | Metabolism of acetaminophen (paracetamol) in plants--two independent pathways result in the formation of a glutathione and a glucose conjugate[END_REF][START_REF] He | Metabolism of Ibuprofen by Phragmites australis: Uptake and Phytodegradation[END_REF][START_REF] Marsik | Metabolism of ibuprofen in higher plants: A model Arabidopsis thaliana cell suspension culture system[END_REF][START_REF] Klampfl | Metabolization of pharmaceuticals by plants after uptake from water and soil: A review[END_REF]. Furthermore [START_REF] Marsik | Metabolism of ibuprofen in higher plants: A model Arabidopsis thaliana cell suspension culture system[END_REF], have demonstrated that the metabolites represent more than 99% and 18 compounds (considering Phase I metabolites) of the organic micropollutants detected.

This detoxification process with three phases has already been described. The first phase (phase I) introduces functional groups and prepare the conjugation occurring in the phase II by oxidation or hydrolysis [START_REF] Komives | Phase I xenobiotic metabolic systems in plants[END_REF]. The reactions occurring in the phase I are mainly managed by cytochrome P450 in the endoplasmic reticulum [START_REF] He | Metabolism of Ibuprofen by Phragmites australis: Uptake and Phytodegradation[END_REF][START_REF] Klampfl | Metabolization of pharmaceuticals by plants after uptake from water and soil: A review[END_REF]). Then the second phase (phase II) deactivates the micropollutants using some transferases. Finally, compounds are stored in specific plant compartments (part of the phytoextraction). In this way, this process working similarly to animal liver has been described

as "green liver" in the literature [START_REF] Sandermann | Plant Metabolism of Organic Xenobiotics. Status and Prospects of the 'Green Liver' Concept[END_REF]).

On the other hand, physico-chemical reactions could also create micropollutants metabolites.

Ozonation process is mainly used to treat micropollutants in water (M. M. [START_REF] Huber | Oxidation of pharmaceuticals during ozonation of municipal wastewater effluents: a pilot study[END_REF][START_REF] Hollender | Elimination of Organic Micropollutants in a Municipal Wastewater Treatment Plant Upgraded with a Full-Scale Post-Ozonation Followed by Sand Filtration[END_REF][START_REF] Nakada | Removal of selected pharmaceuticals and personal care products (PPCPs) and endocrine-disrupting chemicals (EDCs) during sand filtration and ozonation at a municipal sewage treatment plant[END_REF][START_REF] Zimmermann | Kinetic assessment and modeling of an ozonation step for full-scale municipal wastewater treatment: Micropollutant oxidation, by-product formation and disinfection[END_REF]Y. Lee et al., 2013) and transform micropollutants.

This process has shown its efficiency but as any degradation processes, it could generate more toxic by-products [START_REF] Sonntag | Chemistry of Ozone in Water and Wastewater Treatment[END_REF]. Furthermore, this system could not be set up in rural areas due to their high costs. However, other physico-chemical processes as photodegradation could occur in these systems. Indeed, the photodegradation is also efficient to degrade micropollutants in wastewater [START_REF] Mathon | Influence of water depth and season on the photodegradation of micropollutants in a free-water surface constructed wetland receiving treated wastewater[END_REF][START_REF] Rühmland | Fate of pharmaceuticals in a subsurface flow constructed wetland and two ponds[END_REF] and generate some metabolites.

All the processes could help to degrade the parent compounds but even if degradation products are generated, these WWTPs remain the major point of micropollutants releases.

But this degradation process concerns only organic micropollutants and therefore, inorganic micropollutants will only be transferred in other compartments. As mentioned previously, the plant uptake is one transfer way mentioned in the literature. But for conventional wastewater treatment system, sorption is one of the most mentioned mechanisms explaining their fate [START_REF] Batool | Removal of toxic metals from wastewater in constructed wetlands as a green technology; catalyst role of substrates and chelators[END_REF][START_REF] Kumar | Metal removal, partitioning and phase distributions in the wastewater and sludge: Performance evaluation of conventional, upflow anaerobic sludge blanket and downflow hanging sponge treatment systems[END_REF]. These transfers to the solid phase could also be depending on the metals nature as the sorption differs according to the metals considered [START_REF] Karvelas | Occurrence and fate of heavy metals in the wastewater treatment process[END_REF].

Wastewater treatment systems: systems degrading micropollutants but also a release point

Micropollutants found in freshwater have become one of the major concerns in the last decades [START_REF] Schwarzenbach | The Challenge of Micropollutants in Aquatic Systems[END_REF][START_REF] Richardson | Water Analysis: Emerging Contaminants and Current Issues[END_REF][START_REF] Margot | A review of the fate of micropollutants in wastewater treatment plants[END_REF][START_REF] Geissen | Emerging pollutants in the environment: A challenge for water resource management[END_REF][START_REF] Busch | Micropollutants in European rivers: A mode of action survey to support the development of effect-based tools for water monitoring[END_REF]. And the wastewater treatment systems effluent releases have been described as one of the main sources of these micropollutants in surface water [START_REF] Huber | Oxidation of pharmaceuticals during ozonation of municipal wastewater effluents: a pilot study[END_REF][START_REF] Kasprzyk-Hordern | The removal of pharmaceuticals, personal care products, endocrine disruptors and illicit drugs during wastewater treatment and its impact on the quality of receiving waters[END_REF][START_REF] Kolpin | Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999-2000: A national reconnaissance[END_REF]. Therefore, studies investigated micropollutants presence in WWTP. These micropollutants are found in both urban and rural areas [START_REF] Matamoros | Behavior of selected pharmaceuticals in subsurface flow constructed wetlands: a pilot-scale study[END_REF][START_REF] Nakada | Removal of selected pharmaceuticals and personal care products (PPCPs) and endocrine-disrupting chemicals (EDCs) during sand filtration and ozonation at a municipal sewage treatment plant[END_REF] underling this global issue. Indeed, several national studies were carried out in France such as AMPERES (2006AMPERES ( -2009) ) or ECHIBIOTEB ( 2010) and indicate this global issue. For example, the AMPERES project suggests that no micropollutants removal efficiency difference could be observed between WWTP or CW systems, when these systems were designed to treat the same conventional pollutants (carbon or carbon and nutrients). More than 10% of the selected compounds and 90% of drugs were found in the effluents with concentrations higher than 100 ng/L pointing up the impact of water treatment system on the micropollutants release. However, the AMPERES project suggests that less substances were quantified in wastewater from rural areas than in urban areas. Even if these systems have been widely described as micropollutants release sources, processes have been set up to improve the micropollutants removal. This process could for example, be based on advanced oxidation processes (ozonation, UV, photo irradiation, Fenton oxidation…) [START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF]. Nevertheless, these systems could have high costs and would not be adapted to the rural context. The most adapted solution in rural area is to use wetlands with high residence time [START_REF] Singhal | Degrading Organic Micropollutants: The Next Challenge in the Evolution of Biological Wastewater Treatment Processes[END_REF]. Although CW as any water treatment system could release micropollutants, the micropollutant removal study in CW have also highlighted their efficiency to treat some micropollutants [START_REF] Hijosa-Valsero | Optimization of performance assessment and design characteristics in constructed wetlands for the removal of organic matter[END_REF][START_REF] Matamoros | A comparative assessment of intensive and extensive wastewater treatment technologies for removing emerging contaminants in small communities[END_REF][START_REF] Matamoros | Organic micropollutant removal in a full-scale surface flow constructed wetland fed with secondary effluent[END_REF][START_REF] Matamoros | Behavior of selected pharmaceuticals in subsurface flow constructed wetlands: a pilot-scale study[END_REF][START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF]. Besides long sludge residence time and hydraulics residence time would have a positive impact of micropollutants removal [START_REF] Harb | Perspectives on the fate of micropollutants in mainstream anaerobic wastewater treatment[END_REF].

Nevertheless, a wide diversity of micropollutants removal rate or concentration could be found in these systems. [START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF][START_REF] Pereira | Environmental impact of pharmaceuticals from Portuguese wastewaters: geographical and seasonal occurrence, removal and risk assessment[END_REF]. Some micropollutants could be released in higher concentrations in outlet water than inlet water. This phenomenon could be partly explained by drugs deconjugaison that could occur during the water transport [START_REF] Verlicchi | Pharmaceuticals and personal care products in untreated and treated sewage sludge: Occurrence and environmental risk in the case of application on soil -A critical review[END_REF]. On the other hand, the variety of micropollutants removal rate could be influenced by biotic and abiotic parameters. Indeed a seasonal effect could be found for some drugs as described in [START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF]. Temperature (Hijosa-Valsero et al., 2011), sunlight and photodegradation [START_REF] Andreozzi | 'assainissement ainsi qu'à la surveillance de leur fonctionnement et de leur efficacité, et aux dispositifs d'assainissement non collectif recevant une charge brute de pollution organique supérieure à 1[END_REF] could influence the degradation process by enhancing the bacterial activities, increasing indirectly the hydraulic residence time or at the opposite reducing the degradation with less sunlight. Finally, the physicochemical properties (log Know, pka, Henry constant, solubility…) are mentioned explaining micropollutants removal from the liquid phase. Nevertheless, these parameters cannot fully be exampling the micropollutants distribution in the different environment compartment.

Micropollutants distribution in the different CW compartments

Micropollutants initially found in wastewater will be distributed in the different compartments,

during their transport and according to some of their properties. For example, volatile compounds will be found in air compartment too. On the other hand, some of them could be sorbed to solid particles and accumulated in soil/sediment matrices in wetlands or in the environment [START_REF] Azaroff | Priority and emerging micropollutants distribution from coastal to continental slope sediments: A case study of Capbreton Submarine Canyon (North Atlantic Ocean)[END_REF][START_REF] Gruchlik | Removal of organic micropollutants in waste stabilisation ponds: A review[END_REF]. In the literature, it is mentioned that hydrophobic compounds are easily sorbed in solid matrices [START_REF] Gruchlik | Removal of organic micropollutants in waste stabilisation ponds: A review[END_REF]. For example, PAHs properties will improve their sorption in solid particles. This sorption capacity is partly depending on the molecular weight. Indeed, heavy PAHs will be sorbed easily in solid matrices (Le Bihanic 2013). On the other hand, micropollutants could also be absorbed by animals, especially in the lipidic tissues, then be accumulated along the food chain [START_REF] Chevreuil | Contamination of fish from different areas of the river Seine (France) by organic (PCB and pesticides) and metallic (Cd, Cr, Cu, Fe, Mn, Pb and Zn) micropollutants[END_REF]Derco et al., 2013;[START_REF] Yao | Personal care products in wild fish in two main Chinese rivers: Bioaccumulation potential and human health risks[END_REF], generating a potential human health risk.

Besides, in the wetland such as in river, plants are also growing in this polluted environment.

Their capabilities to store micropollutants using different processes have already been mentioned and are described in the Figure 9. Indeed, the use of plants combined to microorganisms to remove, degrade or isolate micropollutants or other toxic compounds from contaminated soil has been described as phytoremediation [START_REF] Chaney | Phytoremediation of soil metals[END_REF][START_REF] Gadd | Phytoremediation of toxic metals; using plants to clean up the environment[END_REF][START_REF] Favas | Phytoremediation of Soils Contaminated with Metals and Metalloids at Mining Areas: Potential of Native Flora[END_REF]. This approach was first focused on metal removal with the use of hyperaccumulating plants. But the role of plants could not be restricted to metal uptake as various compounds could be caught by plants [START_REF] Favas | Phytoremediation of Soils Contaminated with Metals and Metalloids at Mining Areas: Potential of Native Flora[END_REF]. Plants have different strategies to remove micropollutants from contaminated environment. The main strategies mentioned are phytodegradation that metabolizes contaminants inside plant cells using specific enzymes (mentioned in the section 3.1.2),

phytoextraction that stores contaminants in specific parts of plants,

phytostabilization that limits the diffusion of contaminants in soil by trapping and catching them in root cells or humus, phytovolatilization that volatilizes and releases compounds into the atmosphere after their transformation [START_REF] Favas | Phytoremediation of Soils Contaminated with Metals and Metalloids at Mining Areas: Potential of Native Flora[END_REF]. Nevertheless, as mentioned in the previous sections, micropollutants cover a large diversity of compounds. The diversity of compounds analysis has long been a challenge, and the micropollutants detection has involved the analytical tools development. Besides, the increase of this concern has also generated a technological breakthrough to be able to perform non-target screening and obtain an overview of micropollutants in the different environmental matrices.

Phytovolatilization Phytodegradation Phytoextraction Phytostabilization

Large micropollutants screening in the environment compartments

This section will be focused on the screening in the different compartments of the environment and the different analytical instruments used to detect micropollutants, then the non-target screening of micropollutants using high-resolution mass spectrometry will be developed and finally the use of mass spectrometry imaging to obtain spatial distribution in samples will be described.

Micropollutants screening in the different compartments of the environment and wetland

The analyses found in the 1970s and 1980s were mainly focused on river water [START_REF] Literáthy | Study of river pollution caused by micropollutants[END_REF][START_REF] Meijers | The occurrence of organic micropollutants in the river Rhine and the river Maas in 1974[END_REF]. Nevertheless, the large widespread of micropollutants into the other compartments were also considered. Micropollutants analyses were also performed in the different compartments such as sediments [START_REF] Literáthy | Study of river pollution caused by micropollutants[END_REF] or in animals like fish [START_REF] Koli | Comparative analysis of inorganic elements in the muscle and liver of some fish species[END_REF] to understand their toxic effect on the organisms. At the same time, hyperaccumulating plants were studied to remove heavy metals from contaminated soil [START_REF] Reeves | Hyperaccumulation of lead and zinc by two metallophytes from mining areas of Central Europe[END_REF][START_REF] Wither | Hyperaccumulation of nickel by some plants of Southeast Asia[END_REF].

As waste water was identified as one of the major sources, studies were also focused on WWTP to understand the release and the potential removal of micropollutants in this treatment system. [START_REF] Carballa | Behavior of pharmaceuticals, cosmetics and hormones in a sewage treatment plant[END_REF][START_REF] Castiglioni | Removal of Pharmaceuticals in Sewage Treatment Plants in Italy[END_REF][START_REF] Lindqvist | Occurrence of acidic pharmaceuticals in raw and treated sewages and in receiving waters[END_REF][START_REF] Radjenović | Fate and distribution of pharmaceuticals in wastewater and sewage sludge of the conventional activated sludge (CAS) and advanced membrane bioreactor (MBR) treatment[END_REF][START_REF] Ternes | Occurence of drugs in German sewage treatment plants and rivers1[END_REF][START_REF] Vogelsang | Occurrence and removal of selected organic micropollutants at mechanical, chemical and advanced wastewater treatment plants in Norway[END_REF] but also in sewage sludge produced [START_REF] Ivanová | Pharmaceuticals and illicit drugs -A new threat to the application of sewage sludge in agriculture[END_REF][START_REF] Martín | Pharmaceutically active compounds in sludge stabilization treatments: Anaerobic and aerobic digestion, wastewater stabilization ponds and composting[END_REF]. Indeed, this sludge used as soil improvement could also be considered as a potential pollutant source. In the same vein, this approach was also conducted for water treatment adapted to the rural context. Therefore, natural wetlands and constructed wetlands were also investigated to understand their removal performance [START_REF] Chen | Occurrence, removal and environmental risk assessment of pharmaceuticals and personal care products in rural wastewater treatment wetlands[END_REF][START_REF] Dordio | Removal of pharmaceuticals in microcosm constructed wetlands using Typha spp. and LECA[END_REF][START_REF] Hijosa-Valsero | Influence of design, physico-chemical and environmental parameters on pharmaceuticals and fragrances removal by constructed wetlands[END_REF][START_REF] Matamoros | Organic micropollutant removal in a full-scale surface flow constructed wetland fed with secondary effluent[END_REF][START_REF] Matamoros | Elimination of Pharmaceuticals and Personal Care Products in Subsurface Flow Constructed Wetlands[END_REF][START_REF] Petrie | Biotic phase micropollutant distribution in horizontal sub-surface flow constructed wetlands[END_REF][START_REF] Rühmland | Fate of pharmaceuticals in a subsurface flow constructed wetland and two ponds[END_REF].

On the other hand, over the last decades, the advances in technology have generated a breakthrough in micropollutants detection. Several analytical tools could be used to detect emergent contaminants in samples (UV, fluorescence detectors…). Nevertheless, the main technology used is mass spectrometry (MS). In environmental studies MS in tandem with gas chromatography (GC) or with liquid chromatography (LC) is widely used to identify them.

Indeed, the tandem chromatography and MS based on the separation and measurement of massto-charge ratio (m/z) of individuals and ionized molecules and their fragmentation products provide high sensitivity and selectivity to detect this kind of compound. Mass spectrometer is characterized by three main elements: the ion sources, analyzer and the detectors as shown in In the environmental studies, GC-MS (or GC-MS/MS) or LC-MS (MS/MS) were mainly used to analyze micropollutants. First the analyses were performed using GC-MS, which is well adapted to detect volatile, apolar compounds, PCB (polychlorinated biphenyl), PAHs or some pesticides. Nevertheless, the challenge of pesticides and drugs analysis, with more polar compounds have modified analytical methods. Indeed, the LC-MS using an ESI source is better suited to this kind of compounds. Besides, to obtain a wider range of compounds, the literature mentioned the combination of different sources as ESI and APPI (Chiaia-Hernandez et al., 2013).

In this way, micropollutants could be detected in high precision and with a lower limit of detection threshold thanks to the development of mass spectrometry and the combination of different analyzers (for example triple quadrupole…). Nonetheless, a large part of the environmental study are mainly focused on few numbers of micropollutants or some classes, as TQ are not well adapted for large screening and the detection of unknowns [START_REF] Chiaia-Hernandez | Screening of Lake Sediments for Emerging Contaminants by Liquid Chromatography Atmospheric Pressure Photoionization and Electrospray Ionization Coupled to High Resolution Mass Spectrometry[END_REF][START_REF] Krauss | LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns[END_REF]. However, the analysis of a restricted number of molecules bias the vision of the contamination and the potential impact on the environment. Indeed, a large part of persistent micropollutants are not eliminated by conventional water treatment systems [START_REF] Clara | Removal of selected pharmaceuticals, fragrances and endocrine disrupting compounds in a membrane bioreactor and conventional wastewater treatment plants[END_REF][START_REF] Hug | Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening[END_REF]. Therefore, non-target screening of micropollutants was performed using high-resolution mass spectrometry to obtain a large overview of the contaminations found in the compartment considered.

High resolution mass spectrometry and large screening of micropollutants

The high resolution mass spectrometry (HRMS) improvement is based on two technological improvements, the multidimensional chromatography (GCxGC or LCxLC) and the trapping method (TOF, FT ICR, orbitrap…). [START_REF] Pleil | High-resolution mass spectrometry: basic principles for using exact mass and mass defect for discovery analysis of organic molecules in blood, breath, urine and environmental media[END_REF]. The capability to detect a large scale of compounds has been improved by this technological change. Indeed, thousands of features could be currently detected in biological or environmental samples [START_REF] Gago-Ferrero | Nontarget Analysis of Environmental Samples Based on Liquid Chromatography Coupled to High Resolution Mass Spectrometry (LC-HRMS)[END_REF][START_REF] Pleil | High-resolution mass spectrometry: basic principles for using exact mass and mass defect for discovery analysis of organic molecules in blood, breath, urine and environmental media[END_REF]. The results obtained using HRMS will generate large data then different strategy of data interpretation should be performed [START_REF] Krauss | LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns[END_REF][START_REF] Schymanski | Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis[END_REF][START_REF] Schymanski | Strategies to Characterize Polar Organic Contamination in Wastewater: Exploring the Capability of High Resolution Mass Spectrometry[END_REF]. In their study Schymanski et al., have mentioned the different strategy applied to identify compounds:

target screening,

suspect screening,

non-target screening.

The target screening used reference standards to confirm compounds detection using retention time, exact mass and MS/MS. This method is well adapted for the analysis of a limited number of compounds.

To analyze a broader spectrum of compounds, suspect and non-target screening could be performed using LC-HRMS. The difference of these two analyses is based on the potential prior information obtained. Indeed, for suspect screening compounds information are available (molecular formula, structure…) and exact mass could be calculated and compared with data found in the spectra. The suspect screening procedure is described in Figure 11.

Figure 11: Suspect screening procedure using compound information to identify the analyte of interest as described in [START_REF] Hug | Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening[END_REF] On the other hand, when no prior data are available and the identification starts from the exact mass, isotopic pattern, adduct [START_REF] Schymanski | Strategies to Characterize Polar Organic Contamination in Wastewater: Exploring the Capability of High Resolution Mass Spectrometry[END_REF], non-target screening approach is applied using automated peak detection and spectra deconvolution algorithms [START_REF] Hug | Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening[END_REF]. Then molecular formula are generated from the exact mass and isotope pattern and several candidate structure could be found [START_REF] Hug | Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening[END_REF]. Therefore, several strategies with list of compounds (confirmed by standards, spectral libraries…) could be carried out to identify the compounds [START_REF] Hug | Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening[END_REF]. The non-target screening procedure is described in This development has led to publications using this non-target screening in the environmental field to obtain the micropollutants distribution overview and understand their fate [START_REF] Albergamo | Nontarget Screening Reveals Time Trends of Polar Micropollutants in a Riverbank Filtration System[END_REF][START_REF] Schollée | Statistical Approaches for LC-HRMS Data To Characterize, Prioritize, and Identify Transformation Products from Water Treatment Processes[END_REF]. Therefore, the fate of micropollutants is now one of the major concerns. Indeed, this fate with transformation products has long been underestimated due to the lack of reference material for transformation products, analytical tools not well adapted and most transformation products are unknown [START_REF] Kiefer | New relevant pesticide transformation products in groundwater detected using target and suspect screening for agricultural and urban micropollutants with LC-HRMS[END_REF]. Therefore, conjugates or degradation products were poorly studied in the different matrices. However, some degradation products have already been detected in the environment and have serious toxic effect. The use of non-target screening will be very helpful to detect these degradation products in aquatic environment or water treatment. Indeed, several studies mentioned non-target screening to find transformation products or unknowns in the environment or after water treatment (ozonation) [START_REF] Chiaia-Hernandez | Suspect and nontarget screening approaches to identify organic contaminant records in lake sediments[END_REF][START_REF] Bletsou | Targeted and nontargeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment[END_REF][START_REF] Schollée | Non-target screening to trace ozonation transformation products in a wastewater treatment train including different post-treatments[END_REF].

This non-target approach could provide a broader overview of the micropollutants found in a sample. Nevertheless, these micropollutants are extracted by solvent from their original matrix.

This extraction could generate some dilution and no spatial distribution could be observed for biological (plants, animals, …) samples. Yet, as it has been applied for medical approach, spatial distribution could be crucial to understand bioprocesses and reactions occurring in organisms that have absorbed these pollutants. Therefore, the use of mass spectrometry imaging (MSI) will be crucial to understand the reactions of organisms exposed to these micropollutants.

MALDI Mass spectrometry imaging: The in situ location of pollutants in tissues

MSI could provide spatial distribution of compounds in samples surfaces, based on desorptionionization phenomena [START_REF] Pól | Molecular mass spectrometry imaging in biomedical and life science research[END_REF]. This method is becoming increasingly popular as

shown by the number of articles on this topic. This method combines molecular analysis and spatial information and thus has a definite interest for biological applications. Every time the laser beam hits the sample, a mass spectrum will be recorded at the impact point and define the spatial resolution (pixel). MALDI principle is described in Figure 14. Generally, the spatial resolution found is between 10 and 500 µm (and can reach the 5 µm resolution). But some instruments could have a higher resolution and work on the single cell.

Finally, each pixel could be converted to obtain the mass spectra and find the distribution of compounds of interest in the samples. Several techniques are applied to perform MSI. In the literature, the most mentioned are -Secondary Ion Mass Spectrometry (SIMS),

-Matrix Assisted Laser Desorption Ionization (MALDI),

-Laser desorption ionization,

-Desorption ElectroSpray Ionization (DESI),

-Laser Ablation ElectroSpray Ionization (LAESI).

The different techniques each have their benefits and drawbacks, but this section will only be focused on the MALDI-Imaging. This method was developed in the 1990s based on the works of Caprioli et al., [START_REF] Caprioli | Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS[END_REF] and facilitates the MSI use for life science [START_REF] Pól | Molecular mass spectrometry imaging in biomedical and life science research[END_REF]. This method based on soft ionization use was initially developed for proteins and lipids analysis [START_REF] Caprioli | Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS[END_REF] but now a wide range of applications could be mentioned such as drugs distribution [START_REF] Cornett | MALDI-FTICR Imaging Mass Spectrometry of Drugs and Metabolites in Tissue[END_REF][START_REF] Hsieh | Mapping pharmaceuticals in tissues using MALDI imaging mass spectrometry[END_REF], plant metabolites [START_REF] Jung | Direct analysis of cellulose in poplar stem by matrix-assisted laser desorption/ionization imaging mass spectrometry[END_REF].

On the other hand, the analysis performed using MALDI-MSI is really dependent of the sample's quality. Therefore, the most crucial part of the analysis is probably the sample preparation. Indeed, to preserve the spatial location, the tissue integrity should also be protected.

Thus, the samples are generally snap-frozen after the collection using liquid nitrogen then stored at -80 °C. [START_REF] Römpp | Mass spectrometry imaging with high resolution in mass and space[END_REF]. To protect their integrity, the samples are then cryosectioned. In this crucial step, embedding materials which are compatible with MSI as described in Römpp et Spengler 2013, could be used. Then the slices are deposited on a microscope slide coated with indium-tin oxide (ITO) and MALDI matrix could be applied. This matrix will be used to extract the analytes of interest without impacting their spatial distribution. Besides, the matrix will absorb the laser wavelength and induce electronic transitions, and then generate ions. Several matrices and their deposition (robotic spotting, Electrospray deposition, spray coating, sublimation) methods are mentioned in the literature and the main matrices used are alpha-Cyano-4-HydroxyCinnamic Acid (CHCA),

-2,6-DihHdroxyAcetophenone (DHA),

-2,5-DiHydroxyBenzoic acid (DHB),

-Sinapic Acid (SA).

The matrix choice is depending on the analyte nature. For example, DHB is more adapted for compounds with high molecular weight and CHCA for small molecules. The matrix deposit is also a crucial part of the sample preparation. Indeed, the spatial resolution (depending on the droplets size or the analytes extraction are depending on the matrix choice and deposit (heterogeneity). Besides the deposition methods used (manual, spraying or sublimation) could also influence the spatial distribution.

Once the matrix applied, the sample and its matrix will be irradiated by a laser beam to desorbe and ionize compounds caught in the matrix. This ionization could be considered as a soft ionization and will generate mainly [M+H] + , [M+Na] + , [M+K] + in positive mode and [M-H] - in negative mode. The spatial resolution found in the tissue is depending on the laser spot size and then pixel size generated.

MALDI imaging is often used with TOF detectors. However, the mass resolution and accuracy could be improved using a FT ICR mass spectrometer. Indeed, FT ICR is based on the principle of charged particles circular movement (due to the Lorentz law) in a magnetic field. The cyclotron frequency will be proportional to the mass-to-charge ratio (m/z) and will be independent from the initial kinetic energy [1].

𝜔 = 𝑧𝐵 𝑚 [1]
Where ω the frequency, z the charge, m the mass and B the magnetic.

The ions will be trapped in an ICR (ion cyclotron resonance) cell radially by the magnetic field and axially by an electric field. To detect the different m/z, an excitation step followed by a detection step is necessary to measure the cyclotron frequencies. These steps will generate a sinusoidal signal that could be digitalized and amplified. To find the m/z in our samples this signal will be transformed using the Fourier Transform.

As it has been mentioned previously, MSI has several applications. A large part of the literature is focused on the clinical applications and pharmacology applications but few studies are focused on environmental issues. The few studies focused on this topic evaluated the impacts of micropollutants on human health or in animals/plants distribution as environmental applications are an emerging topic [START_REF] Lagarrigue | Potential of MALDI imaging for the toxicological evaluation of environmental pollutants[END_REF].

This distribution could also be investigated with a broader view. Indeed, in environmental systems as well as water treatment systems (and CWs), the water distribution could influence the potential contamination in the environment of wetland systems. Therefore, a detailed knowledge of hydraulics will be very helpful to predict the potential area mainly contaminated.

Computational Fluid Dynamics for water flow process understanding in treatment systems

Numerical modeling could be very helpful to understand the WWTP processes. Indeed, physical, chemical, or biological processes but also hydraulics and their combination could be modeled to better understand the WWTP performances [START_REF] Wicklein | Good modelling practice in applying computational fluid dynamics for WWTP modelling[END_REF]. Several approaches have been used to model treatment systems processes. Among these different approaches, the development of Computational Fluid Dynamics (CFD) over the last 50 years [START_REF] Blocken | Ten iterative steps for model development and evaluation applied to Computational Fluid Dynamics for Environmental Fluid Mechanics[END_REF] has improved the capabilities to solve complex flow problems.

Even if the governing equations are known since the 19 th century, they required the development of numerical solutions to be solved on complex geometry using numerical schemes to find numerical solutions to partial derivatives that have no analytical solution [START_REF] López-Jiménez | Application of CFD methods to an anaerobic digester: The case of Ontinyent WWTP, Valencia, Spain[END_REF][START_REF] Samstag | CFD for wastewater treatment: an overview[END_REF]. Therefore, CFD has been increasingly used to solve water treatment problems in the recent years [START_REF] Wicklein | Good modelling practice in applying computational fluid dynamics for WWTP modelling[END_REF]. The main applications of CFD models in water treatment systems are designed for treatment systems:

position of key structures (agitators, baffles…),

diagnosis of existing system, optimization of the existing system (impact of system modification) [START_REF] Laurent | Modélisation du couplage de la dynamique des écoulements et des cinétiques réactionnelles : démarches appliquée aux bioprocédés de traitements des effluents[END_REF]).

Nevertheless, few documents as guides for users detailing the CFD in water treatment applications are available, even if many works on the CFD fundamentals [START_REF] Andersson | Computational fluid dynamics for engineers[END_REF] could be found in the literature (Laurent 2016).

Water flow in complex geometry numerical modeling: the key steps

CFD could be used to solve the fundamental equations of fluid dynamics: the continuity and momentum equations (Navier-Stokes equations) using numerical schemes [START_REF] Samstag | CFD for wastewater treatment: an overview[END_REF]. These equations do not have any analytical solutions and need hypothesis to be solved.

Therefore, this section will introduce the key steps of the CFD model, mentioned in Blocken et Gualtieri 2012.

All CFD projects should start with the definition of the purposes and the context of modeling.

Then the model must be conceptualized and will be structured using several hypotheses and science knowledge [START_REF] Blocken | Ten iterative steps for model development and evaluation applied to Computational Fluid Dynamics for Environmental Fluid Mechanics[END_REF]. This hypothesis concerns the dimension (2 or 3D), the water flow regimes (steady state or transient), the phases considered (air, water, particles) and their properties [START_REF] Laurent | Modélisation du couplage de la dynamique des écoulements et des cinétiques réactionnelles : démarches appliquée aux bioprocédés de traitements des effluents[END_REF]. For example, a 3D model could be useful for complex, heterogenous geometry without symmetries but will be more time consuming. On the other hand, water flow regime is depending on the boundary conditions. Indeed, for constant boundary conditions, a steady state simulation seems to be appropriated, at the opposite if the boundary conditions are not constant a transient simulate fits better [START_REF] Laurent | Modélisation du couplage de la dynamique des écoulements et des cinétiques réactionnelles : démarches appliquée aux bioprocédés de traitements des effluents[END_REF]. Then depending on the project, several phases could be considered, and multiphasic models could be set up for example for sedimentation process simulation. In this research project only, monophasic simulation will be considered.

Then the system geometry of the CFD model should be created. This geometry should include the details impacting the water flow process. Nevertheless, the details should be balanced with the calculation time consumption. Then this geometry should be meshed to solve the equations.

In the same way, an accurate meshing should be performed in the region of interest and loose meshes should be done in other regions to limit calculation time consumptions. The meshing will be developed in the next section. Then the turbulence model and the boundary conditions could be set up. Next the equations could be solved using iterative methods to find a converged and stable solution. To evaluate this convergence, the difference in the sum of the errors between two successive iterations called residuals could be used [START_REF] Laurent | Modélisation du couplage de la dynamique des écoulements et des cinétiques réactionnelles : démarches appliquée aux bioprocédés de traitements des effluents[END_REF]. The final steps are focused on the calibration and the validation of the model to verify the solution quality with experimental data. The Figure 15 introduces the different step used for a CFD model building.

Numerical model building

Once the general scheme introduced, some steps could be developed to introduce the numerical model development.

Governing equations in complex geometry for transient incompressible water flow

A numerical model is set up to bring a numerical solution to a physical problem. Therefore, the engineering problem should be transposed to a mathematical problem. In this way, the hypothesis as well as the equations governing the phenomena to solve should be identified, after the system limits definition (for example coordinates collected on field). In our case, the main object of the study concerns the water flow in the pond system. As water is considered as a Newtonian fluid (its viscosity is stressing independent), the Navier-Stokes equations could be used to simulate the flow. These equations are based on two equations: the continuity and

momentum equations [2] [3]. 𝜕𝑝 𝜕𝑡 + ∇. (𝑝𝑢) = 0 [2] 𝜕𝑝𝑢 𝜕𝑡 + ∇ (𝑝𝑢𝑢) + ∇. 𝜏 = -∇𝑝 + 𝑔 [3]
Considering water as an incompressible fluid (density independent of the pressure changes).

These equations can be used according to the following formulations [4] [5].

∇. 𝑢 = 0 [4] 𝜕𝑢 𝜕𝑡 + 𝑢. ∇ 𝑢 = -∇𝑝 + 𝜈∆𝑢 [5]
These equations well known in the literature are detailed in several articles or books such as in Versteeg and Malalasekera 2007.

Turbulence

These equations applied in sewer systems, or WWTP generally used turbulent conditions. In this way, the fluid motion will be characterized by velocity and pressure changes. The turbulent condition could be characterized using Reynolds criteria described in the following equation [6].

𝑅𝑒 = 𝑝𝑈𝐿 𝜇 [6]
ρ water density (kg/m 3 ), U: velocity (m/s), L: length (m) and µ dynamic viscosity.

When the Re is lower than 2000, a laminar flow could be noticed (no disruption or change in velocity and pressure) at the opposite a turbulent flow is observed when the Re is higher than 4000. The current IT technology could not resolve these equations for real applications using a Direct Numerical Simulation. Therefore, the turbulent model should be simplified using different hypotheses [START_REF] Andersson | Computational fluid dynamics for engineers[END_REF]. The large-Eddy simulation (LES) could resolve the turbulence at large scale. But the most popular method for engineering issues is the one based on Reynolds Average Navier-Stokes (RANS) mentioned in Figure 16. This method calculates the turbulent fluctuations as time-averaged [START_REF] Andersson | Computational fluid dynamics for engineers[END_REF]. This property reduces the calculation time, but some flow properties could not be solved. Besides different models could be used to represent the turbulence in a system. The main models found in the literature are k-Ɛ, k-ω or k-ω SST. Diverse articles and works have introduced these different models, but in CFD, the most conventional method used to consider the turbulence is the one based on RANS and k-Ɛ model [START_REF] Samstag | CFD for wastewater treatment: an overview[END_REF].

Boundary conditions

Boundary conditions should be implemented in the model to solve the different equations.

These conditions could be clustered following two clusters:

the Dirichlet conditions with a fixed value,

the Neumann conditions with a gradient [START_REF] Isenmann | Approche Euler-Lagrange pour la modélisation du transport solide dans les ouvrages de décantation 214[END_REF].

The main conditions applied on the system's limits (inlet, outlet, walls) concern velocity, k-Ɛ parameters, and pressure. Velocity and turbulence parameters are generally filled for the inlet, an atmospheric pressure could be applied at the outlet [START_REF] Isenmann | Approche Euler-Lagrange pour la modélisation du transport solide dans les ouvrages de décantation 214[END_REF]. Then concerning the walls, a zero velocity (no slip) could be applied or constrained velocity with the absence of roughness (slip). Finally, a free surface could be considered using a symmetry (no vertical flow) in the area considered.

Discretization

To solve the Navier-Stokes equation, the partial derivatives should be discretized. This discretization will transform the partial derivation into algebraic equation. The main method used in CFD is the finite volume method [START_REF] Andersson | Computational fluid dynamics for engineers[END_REF]). The CFD model is split in "small volume" called cells and considered small enough to calculate the fluid movement. This discretization could be performed using the Gauss theorem. Indeed Navier-Stokes equation could be written using a generic transport equation of a variable ∅ (Versteeg et Malalasekera 2007). The equation is described below [7].

𝜕𝜌∅ 𝜕𝑡 + ∇. (𝜌∅𝑢) -∇. [𝜌Γ ∅ ∇∅] = 𝑆 ∅ (∅) [7]
The equation could be written using the integral form [8].

∫ 𝜌 𝜕∅ 𝜕𝑡 𝑑𝑉 + ∫ 𝜌 𝜕𝑈∅ 𝜕𝑥 𝑑𝑉 = 𝑉𝑝 ∫ 𝜕 𝜕𝑥 (Γ 𝜕∅ 𝜕𝑥 ) 𝑑𝑉 𝑉𝑝 + ∫ 𝑆 ∅ 𝑑𝑉 𝑉𝑝 𝑉𝑝 [8]
Advection Diffusion

Then using the Gauss theorem, the volume integral could be transformed into surface integral

[9]. ∫ ∇. 𝐴𝑑𝑉 = ∫ 𝐴𝑑𝑆 𝑆 𝑉𝑝 [9]
And finally these equations could be linearized [START_REF] Isenmann | Approche Euler-Lagrange pour la modélisation du transport solide dans les ouvrages de décantation 214[END_REF]).

On the other hand, several schemes could be used to discretize the CFD systems. A first-order scheme has been applied for the spatial discretization and concerning the temporal discretization 3 main schemes are generally used in the literature Implict Euler, Crank-Nicolson and Explicit Euler. In this study the numerical scheme applied has the first order scheme Implicit Euler described in the following equation.

Convergence calibration and validation

The final step of the CFD concerns the result verification. The first step is dedicated to the result convergence. Indeed, a result could be considered as converged if no changes could be observed in the velocity or a scalar field. To evaluate this change, it is recommended to monitor the residuals and check that differences is lower than the threshold. Finally, to ensure that the model represents the desired flow, it is necessary to compare the numerical results with experimental data, such as the residence time distribution found in [START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF].

CFD applied to water treatment systems

The final section concerning the CFD will be focused on its use on water treatment systems and more specifically in pond systems, which are similar to our study site. Indeed, CFD has widely been used to optimize WWTP units or in sewer systems [START_REF] Dufresne | Experimental investigation and CFD modelling of flow, sedimentation, and solids separation in a combined sewer detention tank[END_REF][START_REF] Mohanarangam | CFD MODELLING OF FLOATING AND SETTLING PHASES IN SETTLING TANKS[END_REF] but also in more extensive systems like waste stabilization pond [START_REF] Alvarado | CFD study to determine the optimal configuration of aerators in a full-scale waste stabilization pond[END_REF][START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF][START_REF] Ouedraogo | Impact of sludge layer geometry on the hydraulic performance of a waste stabilization pond[END_REF]. Concerning this last cluster, the first studies were focused on the pond design parameters [START_REF] Persson | The hydraulic performance of ponds of various layouts 2[END_REF][START_REF] Sweeney | The spatial significance of water quality indicators in waste stabilization ponds--limitations of residence time distribution analysis in predicting treatment efficiency[END_REF][START_REF] Shilton | Guidelines for the hydraulic design of waste stabilisation ponds[END_REF]. The main objectives were to understand the water flow process through some complex geometry and then to optimize the pond design to improve the hydraulic performance.

Then studies were more generally focused on the processes optimization with some facilities (baffle for example) to improve this hydraulic performance in the systems [START_REF] Rengers | Hydraulic performance of a modified constructed wetland system through a CFD-based approach[END_REF]. On the other hand, some studies are interested in the pollutant removal rate improvement. To improve the removal, it has been suggested adding complementary aerators.

For example, [START_REF] Alvarado | CFD study to determine the optimal configuration of aerators in a full-scale waste stabilization pond[END_REF], have studied the implementation of aerators in ponds.

But several studies have already used CFD in pond systems to evaluate their performance and the impact of ageing in this system. Indeed, ponds are naturally subject to sedimentation. This sedimentation could highly impact the hydraulic performance by reducing the residence time and create short circuits. Therefore, this issue has been studied in the recent years. For example, [START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF] have studied the impact of sludge accumulation on hydraulic performance for a pond system. Their study shows the impact of sludge accumulated over the months on the residence time. The area accumulating the sludge will generate a short circuit then reduce the residence and the hydraulic performance. According to their results, the residence time reduction is proportional to the sludge accumulated. Besides the sludge accumulation then the impact of hydraulic performances is linked to the system configuration. In the same way, other studies like the one performed by [START_REF] Ouedraogo | Impact of sludge layer geometry on the hydraulic performance of a waste stabilization pond[END_REF] have also investigated the impact of sludge accumulation on hydraulic performance [START_REF] Ouedraogo | Impact of sludge layer geometry on the hydraulic performance of a waste stabilization pond[END_REF]. This section has shown that the CFD approach could be very helpful to understand the water flow and then to optimize some treatment system performance. Nevertheless, these performances are mainly focused on the hydraulic, carbon or nitrogen removal. But the link with micropollutants distribution seems to be less obvious.

Objectives of the thesis

As it has been mentioned in this state of art, the micropollutants distribution in the environment has become a major concern. This micropollutants analysis is an interesting challenge for the mud or sediments accumulated over the years in the environment or constructed wetlands. It has been demonstrated that this compartment could accumulate micropollutants. Nevertheless, a study focused only on this compartment would ignore the interactions. The wastewater must be investigated to understand the potential origins of micropollutants in this system. Furthermore, these wetlands are systems containing living organisms and it is well known that micropollutants could be transformed or transported by these organisms. Therefore, the plants growing in these areas were also investigated to understand the fate of the micropollutants. In this way, the main objective of this PhD project is focused on the large scale micropollutants screening in the constructed wetland ecosystems and aims to understand their distribution and their fate. Therefore, this research project was structured in three specific main objectives:

non-target micropollutants screening in LC-HRMS/MS in the different compartments of the constructed wetland, specific location of micropollutants in planta using mass spectrometry imaging, numerical modeling of water flow in wetland ecosystems and the correlation with micropollutants distribution.

After the investigation of the state of art, the manuscript will be structured following three main axes. The first part of this PhD project, presented in the chapter 2, investigated the micropollutants large scale screening in wetland ecosystem. In this way, water, sediments and plants were analyzed in constructed wetland and in the associated tertiary treatment wetland to obtain an overview of the metabolic profile of each compartment. This large screening will help to understand the wide spread of micropollutants in the ecosystem and the potential interactions between these compartments.

To obtain this large screening, a method has been developed from sample preparation to data analysis. However, the fate of micropollutants could not be restricted to their transfer to the other compartments. Indeed, the micropollutants detected in mud or water could also be biodegraded, bio transformed, or transformed by physico-chemical reactions (oxidations).

These phenomena are mainly due to the living organisms. Therefore, they were studied in plants growing in these polluted areas. Nevertheless, to understand these bioprocesses, a spatial distribution should be performed. This spatial distribution could be crucial to understand the mechanisms occurring in the organisms and then to understand the fate of these micropollutants.

Therefore, MSI will be used in this PhD project and developed in the third chapter. Indeed, the spatial distribution in sediment samples could also be studied using MSI. On the other hand, plants growing in constructed wetland or on the banks of a tertiary treatment wetland are chronically exposed to micropollutants. Therefore, micropollutants investigation was performed using mass spectrometry imaging (MALDI-Imaging (SolariX) (FT ICR)) on plants growing near the wastewater. In this section, all the MSI method development will be mentioned and then will be applied in three plants growing near wastewater. First, micropollutants distribution was studied in a black poplar (Populus nigra) leaves, then the fate of micropollutants and their metabolites was investigated in willow leaves (Salix alba). Finally, a whole plant (reed, Phragmites australis) has been analyzed using mass spectrometry imaging to study the distribution in the different organs.

Nevertheless, in the constructed wetland, water flow could influence micropollutants distribution and then their fate. Therefore, the last part of this PhD project investigated waterflow influence on micropollutants distribution in a wetland system. The aim of this study was to link micropollutants distribution and water flow modeling. This section will be dedicated to the development of a numerical modeling for a study site. The results from water flow modeling will be used to define a sampling strategy and analyze micropollutants in sediments to visualize if a link between micropollutants distribution and water flow is existing. The results could underline the influence of hydraulics on micropollutants distribution.

Finally, this manuscript will end with a general conclusion containing the main results and ideas developed in this PhD project.

Chapter 2: Non-target screening of micropollutants in the constructed wetlands' ecosystems This thesis aimed at understanding micropollutants distribution in a constructed wetland ecosystem to optimize their management. Therefore, different tools and approaches have been used as it has been described in the objectives of the thesis. Micropollutants represent a wide diversity of compounds, as it has been mentioned in the state of art. Besides, this micropollutants diversity generates diverse behaviors (removal, adsorption, degradation). In this way, target analysis on micropollutants cannot provide a global overview of the potential contamination, distribution and spreading into the environment. Therefore, this global overview could only be obtained using a non-target approach, without any bias introduced by compounds selected. One of the major concerns in water treatment is focused on the fate of sludge accumulated over the years, in WWTP but also for the small communities. Indeed, their treatment could generate high costs. To reduce their costs and find the most adapted treatment systems, their content must be analyzed. Micropollutants content analysis will help to understand their potential toxicity, their potential release to the environment, and the potential reuse of sludge as agriculture soil improvement. However, as mentioned in the objectives of the thesis, the analysis of sludge could not be dissociated from the other compartments. Indeed, wastewater should be analyzed to understand the potential origins of these micropollutants. On the other hand, as plants are growing in these water treatment systems, they should be investigated to understand the fate of the micropollutants (uptake and transformation). This chapter will thus be focused on this non-target screening in the three compartments mentioned.

First, the material and the methods used and developed to obtain this large screening of micropollutants will be described. This section will introduce the sampling strategy defined, the extraction method optimization, the non-target analysis method and then the data treatment optimization. Then this large scale micropollutants screening will be used for two applications in the environment ecosystem. First, a large-scale micropollutants screening in wetlands (tertiary treatment wetland and constructed wetlands) ecosystems then a large screening in a vertical profile of sediment layers in a constructed wetland.

Materials and Methods

In this materials and methods section, the environmental metabolomic workflow applied to this study is developed. This environmental metabolomic workflow is described in Figure 17 with its key steps. This material and methods will also be structured following this metabolomic workflow.

Indeed, this section will introduce first the study site, environment compartments analyzed, and the sampling strategy defined. Then the micropollutants extraction methods development in the different compartments will be mentioned. The non-target micropollutants screening method, using LC-HRMS/MS and GC-MS/MS and carried out the samples will also be specified.

Finally, the data treatment to identify micropollutants will be brought up.

Study site description

All the samples on which the micropollutants non-target screening has been performed in this section were collected on the Falkwiller study site. Falkwiller is a small village located around 150 km south of Strasbourg as described in Figure 18. On the other hand, tertiary treatment wetland has been set up to obtain a complementary treatment. These complementary treatment systems are built, when the environment (river that collects the treated wastewater) is highly sensitive to nitrogen or phosphorus issues.

Furthermore, these systems could be an alternative to pipes, and connect WWTP to the environment. Nevertheless, no rules have been defined to create these wetlands. In this way, wetlands were designed depending on the field constraints and the operators' sensitivity. The Falkwiller wetland could be described as a ditch wetland with a length of 95 m in Figure 21. All the tertiary treatment wetland characteristics could be described in the Table 2. The waterbodies quality defined in the Water framework is checked in the measurement station located downstream in Spechbach-Le-Bas on the Largue river. The results highlight a medium waterbodies quality found in the supplementary data (1). In this way, considering the water framework, the waterbody quality should be improved.

Environment compartments analyzed and sampling strategy

As it has been described in the previous section, the study site is composed of two main system compartments, the 2 stage VFCW as secondary treatment and a vegetated ditch tertiary treatment wetland. These two treatment systems are based on the ecological engineering principles mentioned by Mitsch 2012. Thus, the non-target screening of micropollutants has been performed on the compartments composing the treatment system, i.e., wastewater, sediments and plants growing near or in these treatment systems. These compartments were chosen to visualize distribution in the ecosystems. Indeed, this distribution could be highlighted by the direct interactions processes such as sorption between micropollutants and solid matrix or micropollutants release from one compartment to another.

Concerning the sampling strategy, the methods used to collect the samples could be developed for each compartment.

Wastewater sampling strategy

The wastewater was sampled in two ways depending on the aims of the study. Except for one case, wastewater was collected by automatic samplers, as it has been described in [START_REF] Nuel | Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms[END_REF]. ) . (2. 𝑔. ℎ 5 )

1 2

[12]

𝐶𝑑 = 1 √3 . [1 + ( ℎ 2 .tan( ∝ 2 ) 3.𝐵.(ℎ+𝑤) )] . [1 + ( 0.66 ℎ 3 2.tan( ∝ 2
)

)]

[13]

Where B: weir width (m), α: weir opening angle (rad), h: water level (m)

Control loop of the samplers will also be adjusted using the flow volume measured. Indeed, it was defined according to the methods introduced in Nuel 2017. Briefly, the sampling starts with a sampler cleaning (pipes and glass) using wastewater collected to limit the crosscontamination. On the other hand, an instantaneous flow rate is measured and has been used to calculate the daily flow rate in the sampling point. Then 150 samplings of 50 mL (to obtain the most representative average samples) will be selected to perform the sampling. The sampling will be induced by pulse generated at regular flow intervals. Finally, these intervals were calculated using the samplings number and the flow rate measured.

On the other hand, wastewater was just collected specifically for one case. In order to visualize the distribution concerning the poplar study, wastewater was collected near the poplar roots.

Therefore, in this study case, surface wastewater (0-15 cm) was directly collected in glass bottles (2L) near the poplar roots, as the samplers could not be placed in these areas.

Solids samples (sludge and plants) sampling strategy

Concerning the solid samples (sediments and plants), all samples were collected in the same square meter to visualize the potential distribution and interactions between the different compartments as described in Figure 25. Reed roots were collected in triplicates. Concerning the sediments, a composite sample was collected in the area around the water inlet and biological replicates of these samples were performed. Two strategies have been defined for each system, according to the different objectives defined for each study case. Therefore, in the tertiary treatment wetland sediments were collected in the square meter around the plant to visualize the potential micropollutants releases or sources for each plant as described in Figure 26. For the plant growing in the tertiary treatment wetland, only the leaves were analyzed, as these tissues will also be used for MSI ant the plants were not sacrificed for further experiments.

Sludge vertical profile sampling strategy

On the other hand, the distribution in the different compartments is helpful to understand the fate of micropollutants, but it cannot be restricted to the sludge surface layer. Therefore, the study was also focused on the distribution of a sediment vertical profile. A coring in the CW was performed with three replicates using a manual auger as shown in Figure 27. The points selected for the coring were located around one meter near the wastewater inlet in the CW. And the different layers analyzed could be visualized in Figure 28. Finally, the different sieves were weighted, and results of each sieves weight will be used to create a granulometry curve. Thus, distribution of the grain size for each layer could be characterized, effective diameter and uniformity coefficient are estimated.

Bulk density and porosity, for their parts were obtained by following the French standard NF P94-410-3. The porosity measurement was performed using a water pycometer.

Micropollutants extraction optimization

Once the sampling strategy defined and the samples collected, a micropollutants extraction must be performed. Concerning sediment and water samples, several protocols and methods described the micropollutants extraction. The major method used is based on an SPE extraction [START_REF] Gros | Screening and prioritization of micropollutants in wastewaters from on-site sewage treatment facilities[END_REF][START_REF] Petrie | Multi-residue determination of micropollutants in Phragmites australis from constructed wetlands using microwave assisted extraction and ultra-high-performance liquid chromatography tandem mass spectrometry[END_REF][START_REF] Hebert | Bioanalytical assessment of adaptive stress responses in drinking water: A predictive tool to differentiate between micropollutants and disinfection by-products[END_REF][START_REF] Boonnorat | Effect of hydraulic retention time on micropollutant biodegradation in activated sludge system augmented with acclimatized sludge treating low-micropollutants wastewater[END_REF][START_REF] Tröger | A case study of organic micropollutants in a major Swedish water source -Removal efficiency in seven drinking water treatment plants and influence of operational age of granulated active carbon filters[END_REF]. Nevertheless, in the context of this PhD project with the micropollutants non-target screening several extraction methods with different conditions were compared to select the most adapted to the study case. The critical points of this optimization were focused on the sample quantity, the extraction solvent, and the data acquisition. The main criteria used to select the most appropriate method were the reproducibility, the speed, the solvent dangers.

Initial weight or volume sample optimization

In this way, the first step of this optimization was to find the appropriate initial weight (for sediment) or volume (for wastewater). Therefore 2 sediment initial weights (10 g and 20 g) and 2 wastewater volumes were selected (10 mL and 50 mL) based on literature reviews [START_REF] Bergé | Non-target strategies by HRMS to evaluate fluidized micro-grain activated carbon as a tertiary treatment of wastewater[END_REF][START_REF] Cotton | Development and validation of a multiresidue method for the analysis of more than 500 pesticides and drugs in water based on on-line and liquid chromatography coupled to high resolution mass spectrometry[END_REF].

Wastewater was previously filtered with paper filters to remove coarse particles, and sediments were also filtered to eliminate the aqueous phase (in excess). This first comparison was carried out using a single extraction method based on double extraction (for sediment only) and freeze drying. Then samples were solubilized in 1 mL of solvents and analyzed in positive mode using TASQ software and its Pesticides & ToxScreener database (Bruker). The details of the method, the acquisition and the data treatment will be introduced in the next section.

The results of this optimization could be described in the Tables 3 and4.

g 10 g

Micropollutants found in sludge sample 76 82

According to the results found in this optimization, the sediment weight used for the rest of the studies will be 10 g and the wastewater volume will be 50 mL.

Micropollutants extraction methods optimization

Once the initial weight or volume defined, the optimization could be focused on the extraction method. In this way, three methods have The second method compared was based on the SPE cartridge, which is widely used in the literature. The extraction was adapted from the method presented in Nuel, 2017. First the liquid extraction was carried out as it has been described in the previous method, followed by a cartridge elution phase. Briefly, a silica cartridge and C18 coupled with an HLB cartridge (mounted in tandem) have been used respectively for sediment and water samples extraction.

The cartridges were activated using 5 mL of methanol, then they were equilibrated with 10 mL of water (MilliQ) with 0.5% of acetic acid for water samples and 10 mL of methanol: water (95:5) with 0.5% of acetic acid for sediment samples. Next, the sample extraction is added to the cartridge. The cartridges are eluted by 10 mL of methanol: water (80:20). Finally, the eluate is freeze-dried and the pellet is solubilized in 1 mL as it has been described previously. This SPE system could be described in the Figure 33. The last method used in this comparison is the [START_REF] Bligh | A Rapid Method of Total Lipid Extraction and Purification[END_REF] method, that was initially used for lipids extraction. This method could be well adapted for hydrophobic xenobiotics but also to identify the sludge metabolome, according to the method developed in Bligh et Dyer 1959. 10 g of sediment were extracted using 37.5 mL of chloroform: methanol (2:1). Then the sample is stirring for five minutes and the extraction is performed overnight. Finally, liquid phases are found: the aqueous and the organic phase, which were both collected and analyzed separately. A scheme of the Bligh and Dyer principles could be described in the Figure 34. The results comparison was carried out in the same way, as mentioned for the weight or volume selection. The micropollutants identified in each method could be found in the Tables 5 and6. In this way, a higher number of compounds could be found in the liquid extraction method without SPE. Furthermore, this method is faster than the SPE method and uses less toxic solvent than the Bligh & Dyer. Following these criteria, this method will be selected for the different studies, as the optimized method used to extract micropollutants.

Micropollutants extraction from plant samples

Concerning the plant extraction, the method used was developed in the laboratory and mentioned in Villette et al., 2019. 300

Method limits characterization

The final step of this optimization is focused on the characteristic of this method. This method has been characterized by the reproducibility and the identification of limits of detection (LOD), limit of quantification (LOQ) and finally the matrix effect. Deuterated compounds have been used to figure out the limits and the reproducibility. The compounds used were acetaminophen-d4, sulfamethoxazole-d4, N-Desmethyl sildenafil d8, gemfibrozil-d6, diclofenac-d4, bezafibrate-d4, as mentioned in [START_REF] Boleda | Validation and uncertainty estimation of a multiresidue method for pharmaceuticals in surface and treated waters by liquid chromatography-tandem mass spectrometry[END_REF]. Indeed, water and sludge samples were spiked with these standards from 1 ng/mL to 10 µg/mL. The signal-to-noise ratio (S/N) has been used to determine the LOD (S/N lower than 3) and LOQ (S/N lower than 5). According to the results found in the supplementary datas (2), LOD could be fixed at 1 µg/mL. As the extraction was performed on 10 g of sediments and 50 mL of wastewater, LOD could be 100 ng/g in the sediment samples and 20 ng/mL in the water samples. On the other hand, the matrix effects in water and sludge could also be calculated as described in Alygizakis et al., 2016. [14] 𝑀𝑎𝑡𝑟𝑖𝑐𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 = 1 -𝐴𝑟𝑒𝑎 𝑜𝑓 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑎𝑡𝑟𝑖𝑥 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝑖𝑛 𝑚𝑒𝑡ℎ𝑎𝑛𝑜𝑙

[14]

The results for matrix effects and LOD and LOQ could be described in the Table 7. Matrix effect results underline the difficulties to identify compounds in complex samples as water or sludge, due to the extinction coefficient around 95%. These results suggest that a part of the micropollutants are hardly detected and highlights the importance to consider this matrix effect.

The last parameter studied in this optimization concerns the repeatability of the extraction process. To qualify this repeatability, three technical replicates for each matrix were performed and the standard deviation was studied. A summary could be found in the Table 8 and the area details are found in the supplementary datas (3). The coefficient of variation observed is in agreement with the range defined by [START_REF] Boleda | Validation and uncertainty estimation of a multiresidue method for pharmaceuticals in surface and treated waters by liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Cotton | Development and validation of a multiresidue method for the analysis of more than 500 pesticides and drugs in water based on on-line and liquid chromatography coupled to high resolution mass spectrometry[END_REF] (2 to 18%), except for acetaminophen-d4 in sediment and Ndesmethyl-sildenafil-d8 in water. As deviation seems to agree with the results mentioned in the literature, this method could be validated to extract the micropollutants.

The extraction process has been optimized considering the sample quantity, and the development of a new method adapted to extract micropollutant in this study. Furthermore, repeatability, LOD, LOQ and matrix effect have been determined for this extraction. In this way, once the extraction process has been determined, the sample analysis in mass spectrometry must be introduced.

The final step of the extraction optimization was focused on the dilution rate used to inject the samples. Indeed, the concentration process performed to identify micropollutants in the samples can lead to the spectrometer saturation. To deal with this saturation but also with the lowintensity compounds, the samples collected before the analysis were diluted in different dilution rates (2; 10; 20; 100; 200; 1000) or concentrated (10 fold) to obtain the most global overview of compounds content.

Sample analysis in mass spectrometry

Micropollutants class is a heterogeneous class of compounds. Polar, apolar, volatile, nonvolatile compounds, compounds with diverse chemical structures or molecular weight could be found in the micropollutants. But this micropollutants non-target screening was achieved using mass spectrometry. Yet, to detect the most global overview of compounds, GC-MS and LC-HRMS/MS analyses were combined. Indeed, GC-MS is well adapted to analyze volatile, apolar compounds such as PAHS or some pesticides. Besides, GC-MS method is widely used to analyze sludge or sediment samples. On the other hand, LC-HRMS/MS is better suited for polar organic compounds such as drugs. Therefore, samples were analyzed using these two types of analytical tools.

The samples were first analyzed in GC-MS/MS coupled to triple quadrupole (SCION TQ, Bruker; 70eV, mass to charge ratio 50 to 700 Da). The gas chromatography is carried out on a HP-5-MS column (30-m, 0.25-mm, 0.25 µm; 436-GC, Bruker) and uses helium as a carrier gas.

The injector temperature was set at 230 °C. Samples injected were kept at 60 °C for the first minute, then a temperature ramp (5 °C/min) has been programed and raised to 320 °C. This temperature is kept for 10 min. On the other hand, a constant flow of 1 mL/min with a pressure pulse of 30 psi during 1 min has been set up.

Furthermore, samples were also analyzed using LC-HRMS/MS on a Dionex Ultimate 3000 (Thermo) coupled to a Q-TOF (Impact II, Bruker), using the TargetScreener method developed by Bruker (Bremen, Germany) and mentioned in This gradient program is also found in the supplementary datas (4). All the samples were analyzed using the positive ion mode of the spectrometer with a spectra rate of 2 Hz and a mass range from 30 to 1000 Da. The capillary voltage was set at 2500 V, the nebulizer at 2 Bars and the dry gas at 8 L/min, with a dry temperature of 200 °C. Fragments were obtained via broadband collision-induced dissociation (bbCID) with an MS/MS collision energy set at 30 eV.

Data processing

Analyte of interest extraction

The final step of this method optimization is focused on the data processing. Indeed, non-target screening generates a huge number of data, which need to be processed to obtain micropollutants identification. As it has been described in the state of art, the identifications were performed following the method mentioned in Schymanski et al., 2014 and the 5 levels defined in this study.

First the compounds annotated in GC-MS/MS were obtained using MS DataReview. The compounds identification is based on the comparison with spectral libraries. In this PhD project, large spectral databases were used to obtain a global overview with NIST 17 or Wiley 14 but also specific databases for toxic compounds with the example of Maurer, Pfleger, Weber 2011.

These spectral libraries are useful to reach the level 3 (molecules names) in the Schymanski classification. To perform this annotation only compounds with the following criteria were extracted from the mass spectra, as recommended in [START_REF] Sumner | Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)[END_REF] peak width lower than two seconds, slope sensitivity higher than three, peak intensity higher than 1000 cps.

Finally, only compounds with a RMatch higher than 800 were considered as compounds annotated (level 3) (Lange et al., 2019) and used in the study. The confirmation of compounds annotations requires the analysis and the match with an analytical standard. In this way, the identification reaches to the level 1.

On the other hand, the samples were also analyzed in LC-HRMS/MS. A similar method using spectral libraries could be used to identify compounds. Nevertheless, spectral libraries are less suited for LC-HRMS/MS. GC-MS/MS used an electron ionization (EI) as ion sources. Identical operating conditions were applied for all EI sources and thus facilitate the comparison with spectral libraries. However, EI sources are not coupled with LC-MS systems. In our case, the ion source used in the LC-HRMS is electrospray ionization (ESI). Nevertheless, this ion sources could have different operating conditions, depending on the constructors or the applications. In this way, the use of spectral libraries does not seem to be the most appropriate to identify compounds in LC-HRMS or would be high time consuming. To solve this issue, another method could be used. This method is based on the raw formula generation from exact mass, isotopic pattern but also of the fragment ions. To achieve these annotations, the software Metaboscape 3.0 (Bruker) and SmartFormula 3D from Bruker were used. Therefore, the samples injected for a study could be imported in the Metaboscape software. The data selected (with the corresponding ionization mode, positive in our case) will be used to create groups and define the most representative value. To obtain this group, first an intensity threshold as well as the value count of each group should be defined. The optimization of intensity threshold, described in the next section, has suggested setting the threshold at 1000 and only compounds found in 80% of the individuals from at least one group sample were selected as mentioned in [START_REF] Villette | Non-targeted metabolic profiling of BW312 Hordeum vulgare semi dwarf mutant using UHPLC coupled to QTOF high resolution mass spectrometry[END_REF] Then the compounds of interest were selected based on the non-parametric Wilcoxon rank sum test (p-value <0.05) and the fold changes ≥ 2 (absolute value).

Metaboscape detects all the adducts forms for a mass of interest. Then it creates a pool of these adducts, called "buckets", which is representing a single metabolite feature (Villette et al., 2019). Then all the buckets could be found in the software and be annotated as shown in

Molecular formula generation and tentative annotations

Regarding the results found in Figure 37, it seems that intensity threshold should be introduced at 1000 as it represents the higher number of mass of interest (level 5). Once the bucket table created and all the features found, identifications could be performed. The first step is dedicated to the raw formula generation. As it has been described in the previous section, SmartFormula will be used to obtain this raw formula. SmartFormula is based on the exact mass and the isotopic patterns of fragment ions to generate a formula. The software will combine the data from MS and MS/MS data to find a restricted list of formulae. The most elements found in the micropollutants were selected, i.e., C, H, N, O, P, S, Cl, Br, F and I. The following criteria have been set up to generate raw formulae:

a mass deviation (Δm/z) under 3ppm, a mSigma (fitting between measured and theoretical isotope patterns) under 30.

Once the raw formula generated, the following objectives are to identify the compounds.

Therefore, these raw formulae must be compared with data included in several databases. The classical way could be the interrogations of each database. Indeed, in metabolomics, no single database containing all the micropollutants or all the plant metabolites is existing. Therefore, each database of interest should be used separately. Nevertheless, this option has several drawbacks. Due to the lack of a single database for micropollutants or plant metabolites, this option will be time consuming. Furthermore, the use of online databases could cause several issues. The access is depending on the servers and databases could disappear and working on an online database with sensitive data would not be safe. Therefore, a home-made database has been created by importing the raw formula found in the online database. This home-made database could be used in Metaboscape 3.0. Indeed, the software has serval options to annotate compounds, and the most appropriate option in this case is called "Analyte list". These "Analyte list" can contain a list of compounds that can be compared with the buckets found in the samples. The compound lists (restricted to 60,000 compounds) could be imported in Mestaboscape and raw formula to calculate the exact mass and compared with the data contained in the bucket. The identification fitting could be checked with the same criteria as those applied for SmartFormula (Δm/z, mSigma). An example of Analyte list will be found in the supplementary datas (5). Then if the raw formula generated by Smart Formula 3D and the raw formula containing in the Analyte list match, in this way, the compounds could be annotated and reach the level 3 in the Schymanski classification. Finally, the different annotations found for a feature were compared, thanks to a home-made program developed in Visual Basic. In the same way as GC-MS annotation, the confirmation of compounds annotations requires the analysis and the match with an analytical standard. The databases used for the identification and the compounds number contained in the databases could be observed in the Table 9 and were available online in July 2018.

1,652 compounds 3,722 compounds

Finally, the buckets of interest were selected based on the non-parametric Wilcoxon rank sum test (p-value <0.05) and the fold changes set at 2. Furthermore, all these databases contained parent molecules and few micropollutants metabolites. Nevertheless, the distribution of micropollutants in the environment must also focus on the fate of these micropolluants, and thus of micropollutants metabolites. In this way, the software Metabolite Predict 2.0 (Bruker, Germany) has been used to generate drugs metabolites (catabolites and conjugates) from drugs detected in the samples. The drugs structure is drawn or imported using the SDF file from Pubchem in the software then 79 biotransformation rules found in the supplementary datas (6) were selected for in silico prediction. A mass list is then generated based on the raw formulae found, and this list could be imported as an "Analyte list" described in the previous section. This material and method section have defined the context of the study with the main objectives and the study site. Furthermore, all the sampling strategy and the different compartments analyzed were introduced. Then the optimization of micropollutant has been introduced. This optimization suggests the use of 10 g of sediment, 50 mL of water and a method based on freeze-drying and liquid extraction without SPE. On the other hand, the method limits have also been defined with the LOD, LOQ, the matrix effect and the reproducibility. To obtain the largest view of micropollutants, samples were both analyzed in GC-MS/MS and LC-HRMS/MS. Finally, this section was focused on data treatment with the use of spectral libraries for the GC-MS/MS analysis. On the other hand, compounds identified in LC-HRMS/MS were identified thanks to a method using raw formula and a home-made annotation based on several online databases imported. These methods could now be applied on the different studies to obtain the micropollutants distribution in the tertiary treatment wetland ecosystems and the constructed wetland.

Results and discussions

Large scale micropollutants screening in the wetland ecosystem

The first part of the results will be dedicated to the large screening of micropollutants in the wetland (vegetated ditch) ecosystem. The three main compartments composing this ecosystem were wastewater, plants and sludge. In the tertiary treatment wetland, poplar leaves were studied, while reed roots were analyzed in the constructed wetland.

Concerning the results, the non-target screening was first performed on the tertiary treatment wetland located at the outlet of the CW. Results found in this wetland could represent the micropollutants widespread in the environment near an urban wastewater release. The First, a wide diversity of metabolites could be noticed in the different compartments. The compounds could be distinguished as compounds found specifically in a compartment and those detected at least in two compartments. All the details of the compounds are mentioned in the supplementary data from the publication Villette et al.,2019 a. Concerning the specific compounds, they are mainly found in the polluted poplar (500 annotations) then in wastewater (235 annotations). The interface role of sludge could partly explain that few compounds (41 annotations) are specific to sludge. Indeed, sludge analyzed was not dried so water could remain in sludge pores. Therefore, sludge clearly contains compounds found in the wastewater, as underlined by the huge number of compounds found both in these two compartments. It is well known that micropollutants taken up by plants were mainly found in the water pores from the soil [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF][START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF]. Besides, even if a large part of compounds could be sorbed, most of them could be detected in equilibrium in the water and solid phase. Therefore, it is not surprising to find a large number of compounds in the interface of the three compartments. About half of the annotations (1048 annotations on 1824 total annotations) were found at least in two compartments and almost one third (505 annotations) could be detected in all the compartments. Indeed, 144 common annotations are detected for the poplar and wastewater, 253 for sludge and wastewater and finally, 146 for sludge and poplar. The comparison with the constructed wetland suggests a similar trend with strong interactions between the different compartments. However, a higher compartment specificity could be observed, only 20% of the compounds are found at least in two compartments ( 275 Concerning the compounds classes, the classes naturally found in the environment are predictably detected in each compartment. Indeed, the plant and microorganisms growing in this system, the animals living there (worms, tadpoles, birds) will generate some metabolites during their development (growth and death). These metabolites are not surprisingly found in a system based on natural processes. But the interesting result is that drugs, industry toxics, pesticides, phthalates or personal care products are also detected in the different compartments.

Besides, the xenobiotics fraction in the metabolites profile observed in each compartment could not be neglected. These xenobiotics represent between 28% and 46% of the metabolic profile, depending on the compartment considered. Among these xenobiotics, drugs, other toxics and personal care products are the main classes detected. Besides, the same classes are detected in each compartment, suggesting the wide spread of each xenobiotics class in all the compartments, as it has already been demonstrated in the literature [START_REF] Carvalho | A review of plant-pharmaceutical interactions: from uptake and effects in crop plants to phytoremediation in constructed wetlands[END_REF]Li et al., 2019). Results underline the interactions that can occur between the different compartments [START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF][START_REF] Petrie | Biotic phase micropollutant distribution in horizontal sub-surface flow constructed wetlands[END_REF]. As described in several studies, the physicochemical properties are not the driving force governing the distribution. The distribution of the most intense compounds detected in each compartment has been studied using the distribution factor (Kd, BCF factor…) but also their physico-chemical properties as found in the supplementary datas (7). This observation has already been made for selected compounds to understand the transport in soil layers (Gros et al., 2019) or in different compartments [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF][START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF]. Nevertheless, the literature mentions that ionic fraction could be used to find compounds preferably found in plants [START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF][START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF], but the link is not clearly obvious in this study. Indeed, only the compounds detected in the three compartments seem to have a higher ionic proportion. This result suggests that compounds transferred and found in equilibrium in the environment compartment may be influenced by these parameters.

To illustrate this distribution, venlafaxine was quantified in the three compartments (with concentration about 860 µg/kg in the mud, 531 µg/kg in the plant and 24 µg/L in wastewater) and Kd = 35.19 (partage coefficient) and BCF (bioconcentration factor) = 22.15 were calculated. The Kd has also been calculated for acebutolol also observed in raw wastewater (Kd=1.55). Nevertheless, these parameters, as described in Li et al., 2019, combined with predicted Henry law parameters (for photovolatilization), could not be related to the distribution.

On the other hand, the most surprising result is that a plant growing in a polluted area could survive with almost one third of its metabolic profile that is composed of xenobiotics. The different micropollutants taken up by plants is widely discussed in the literature and could explain a part of their fate [START_REF] Picó | Analysis of emerging contaminants and nanomaterials in plant materials following uptake from soils[END_REF]. This transport from water and soil to plant could be influenced by the sorption of micropollutants in mud and by bioprocesses that can transform the micropollutants. [START_REF] Miller | Root Uptake of Pharmaceuticals and Personal Care Product Ingredients[END_REF][START_REF] Uddin | Insight into dynamics and bioavailability of antibiotics in paddy soils by in situ soil moisture sampler[END_REF].

As mentioned previously, this uptake is mainly related to the compounds found in pore water [START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF] and water and sludge content mainly influence the micropollutants detected in plants [START_REF] Carvalho | A review of plant-pharmaceutical interactions: from uptake and effects in crop plants to phytoremediation in constructed wetlands[END_REF][START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF][START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF]. But the use of single physicochemical or fate parameters could not by itself explain the distribution. On the other hand, the molecule size is also mentioned as a discriminative parameter for compounds found in planta. Nevertheless, in their study [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF] mentioned that large size molecules could be transferred using both apoplastic and symplastic ways. The symplastic pathway is just slower [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF]. However, as we investigated plant chronically exposed to micropollutants, the results could provide an integrative view of the contamination and the impact of pharmaceuticals size seems to be limited. This transfer using several ways could also explain the difference of xenobiotics found in poplar leaves or in reed roots and the other compartments. The detection in the aerial parts has for example been performed by Bartrons and Peñeulas who described the micropollutants detection in stem or leaves [START_REF] Bartrons | Pharmaceuticals and Personal-Care Products in Plants[END_REF]. Indeed, the detection of micropollutants in the shoots and this transport could be explained by the transpiration stream, generating the micropollutants movements [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF][START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF]. The translocation factor (TF) could estimate the distribution (Li et al., 2019;[START_REF] Gong | Uptake and dissipation of metalaxyl-M, fludioxonil, cyantraniliprole and thiamethoxam in greenhouse chrysanthemum[END_REF][START_REF] Liu | A review on removal of organophosphorus pesticides in constructed wetland: Performance, mechanism and influencing factors[END_REF]W. Wang et al., 2019) but it has not been investigated in this study as only specific organs were analyzed. An estimation of this parameter will be done in the chapter dedicated to the mass spectrometry imaging. Besides these transfer processes are not class dependent as Pullagurala et al., 2018 underline in their review that different classes of emerging contaminants could be found. Nevertheless, no single xenobiotics behavior concerning this translocation could be highlighted.

Nevertheless, this micropollutants accumulation could create a stress response from the plants, as described in Figure 40. Indeed, this plant stress is underlined with the pigment degradation and plant metabolite involved in tissues degradation found in the poplar leaves. The chlorophyll a was observed in higher amounts in the polluted plant. This result could highlight the process set up by the plants to deal with the contamination. Indeed, precursor production is coupled to catabolites production. Therefore, the plant should activate a rapid turnover to counter the loss of chlorophyll and produce biomass, as the gibberellin A7 (GA7) (plant hormone promoting growth and elongation cells) seems to indicate, to prevent biomass loss potentially induced by the micropollutants stress. In addition to this turnover, plant defense mechanisms are also set up. These defense mechanisms could be supported by the detection of IBA (indole-butyric acid) or azelaic acid (AzA) in the polluted plant. IBA is involved in the abiotic stress response [START_REF] Tognetti | Perturbation of Indole-3-Butyric Acid Homeostasis by the UDP-Glucosyltransferase UGT74E2 Modulates Arabidopsis Architecture and Water Stress Tolerance[END_REF] and AzA in the systemic acquired resistance. This stress metabolites found should indicate that plants set a defense mechanism against this chronic exposure (Villette et al., 2019,a). The analysis showed that chlorophyll a is found in higher concentration in the polluted leaves. ABA and cis-OPDA are found in higher proportion in the control leaves, while BA, SA and GA7 were found in higher amount in polluted leaves from Villette et al., 2019a. The plant sets up mechanisms as stress responses, storage (as shown in the next chapter) or biodegradation to deal with these micropollutants. Using the plants defense mechanism combined with storage processes and biotransformation the plant could deal with these micropollutants. This biotransformation could also explain why some parent compounds are not detected. Indeed, enzymes present inside the plants could generate micropollutants biotransformation as mentioned by Klampfl 2019. These biotransformations will be mentioned in the next chapter.

The results found in this section showed that the micropollutants detected in this ecosystem are a global issue for this system and for the environment located in the outlet of the CW. The results found here highlight that the micropollutants issue could not be restricted to some classes and the non-target approach should be performed to obtain an overview of this contamination.

Moreover, this contamination is not restricted to the wastewater but is widespread in all the compartment, where plants could play a key role. Nevertheless, this first section was only focusing on compartments with direct interactions with wastewater. But the water infiltration could also contaminate lower soil layers. Therefore, a vertical profile of soil in the constructed wetland was also investigated, using the same approach.

Large scale micropollutants screening in the sludge layers in a constructed wetland

The last part of this non-target screening in the ecosystem will investigate the vertical profile of soil layers in the CW. Indeed, wastewater could contaminate the soil at various depths. To 73 drugs, 79 toxics, 7 personal care products, 16 pesticides are found in the top layer, while 1 drug, personal care product, pesticide and 10 toxics were detected in the bottom layer. The drugs and pesticides are mainly polar compounds, and could diffuse in the entire depth or will be stored in the top layer as mentioned for pharmaceuticals in the study from Gros et al., (2019) based on soil contaminated by slurry. Indeed, in our micropollutants study these two xenobiotics behaviors has been observed. In general terms, all the micropollutants quantified were found in higher concentrations in the top layer.

This distribution through the different layers could be linked with water transport through the sludge layers. Nevertheless, some sludge physical properties and their content in carbon, nitrogen and phosphorus should be investigated to understand the specificity observed in each layer. The texture layers analysis indicates a sandy-like texture with fine grain size promoting the water infiltration through the layers. Nevertheless, the top layer seems to have a higher This finer grain size could lead to slow water flow and increase the residence time and the interactions with sludge or rhizomes [START_REF] Stefanakis | Effects of loading, resting period, temperature, porous media, vegetation and aeration on performance of pilot-scale vertical flow constructed wetlands[END_REF]. Besides, the other characteristics such as porosity analysis could highlight a higher soil sorption ability, by increasing specific surface per unit of volume and enhance the water storage (Ávila et al., 2014).

In our study, a high porosity has been found in both layers (92% in the top layer and 70% in the bottom layer), suggesting the high pollutants storage abilities in accordance with the micropollutants results found in the different layers. But physical sludge properties and structure should also be combined with their carbon, nitrogen and phosphorus content. Indeed, organic matter could improve sorption capacity in this layer [START_REF] Cottin | Removal of PAHs from laboratory columns simulating the humus upper layer of vertical flow constructed wetlands[END_REF][START_REF] Brunsch | Retention soil filter as post-treatment step to remove micropollutants from sewage treatment plant effluent[END_REF][START_REF] Biel-Maeso | Removal of personal care products (PCPs) in wastewater and sludge treatment and their occurrence in receiving soils[END_REF]. Therefore, the higher total organic carbon (TOC) content (94.93 ± 23.24 mg/kg in the top layer versus 57.03 ± 10,92 mg/kg in the bottom layer) in the top layer could also partly explain this higher micropollutants content in the top layer. The literature mentioned that a higher organic matter content could promote sorption capacity [START_REF] Cottin | Removal of PAHs from laboratory columns simulating the humus upper layer of vertical flow constructed wetlands[END_REF][START_REF] Brunsch | Retention soil filter as post-treatment step to remove micropollutants from sewage treatment plant effluent[END_REF]) [START_REF] Macht | Specific surface area of clay minerals: Comparison between atomic force microscopy measurements and bulk-gas (N2) and -liquid (EGME) adsorption methods[END_REF][START_REF] Liu | A review on removal of organophosphorus pesticides in constructed wetland: Performance, mechanism and influencing factors[END_REF] and will enhance the interactions with wastewater. This high organic matter content will also promote the development of more animal and bacterial life or biofilms. Therefore, the sludge should also be investigated as a biological compartment. In this way, the different classes of lipids have been described and could be observed in the Figure 43. The lipids classes investigation underlines that cellular lipids such as ceramides, sphingolipids and lysophosphatidylcholines could be specifically found in the bottom layer. At the opposite, carnitines, and saturated fatty acids (FA) were mainly found in the top layer. Physico chemical properties could partly explain the lipids distribution. By definition lipids are hydrophobic compounds and could be easily sorbed to sludge, but fatty acids and carnitines are very hydrophobic compounds and could easily be caught in the top sludge layers and cannot reach the lower layers. On the contrary, sphingolipids or lysophosphatidylcholines are amphiphilic compounds, promoting their transport to the bottom layer. Their amphiphilic characteristics will enhance the water affinity and help them to reach the lower layers. However, their distribution could also be investigated using their biological function. Indeed, the fatty acids are considered as a substrate for microorganisms [START_REF] Zhu | Lipid profiling in sewage sludge[END_REF]. Fatty acids are widely detected in raw wastewater which leads to their detection in the top layer then support the microorganisms' growth. Therefore, according to these results the main microbial activity seems to be found in the top layer as mentioned in [START_REF] Tietz | Bacterial carbon utilization in vertical subsurface flow constructed wetlands[END_REF] and contributes to the pollutants removal. This layer will have a key role in this process. This microbial activity could also explain the few xenobiotics specifically found in the bottom layer. Indeed, a part of xenobiotics could be degraded thanks to these microorganisms and the other will be distributed in the whole vertical profile. Besides, ceramides which have been found in the bottom layer are also detected in activated sludge system [START_REF] Tsuge | Glycosphingolipids as a Possible Signature of Microbial Communities in Activated Sludge and the Potential Contribution of Fungi to Wastewater Treatment under Cold Conditions[END_REF]. But bacteria producing this kind of lipids in activated sludge can degrade emergent pollutants [START_REF] Tsuge | Glycosphingolipids as a Possible Signature of Microbial Communities in Activated Sludge and the Potential Contribution of Fungi to Wastewater Treatment under Cold Conditions[END_REF][START_REF] White | The genus Sphingomonas: physiology and ecology[END_REF]. The other classes detected in the bottom layer could also be linked to the variety of origins (animal, plant microbes degradation products) [START_REF] Zhu | Lipid profiling in sewage sludge[END_REF].

All these results suggest that micropollutants only found in the top layer could be degraded or sorbed by these organisms [START_REF] Ávila | Emerging organic contaminants in vertical subsurface flow constructed wetlands: Influence of media size, loading frequency and use of active aeration[END_REF][START_REF] Falås | Transformation, CO 2 formation and uptake of four organic micropollutants by carrierattached microorganisms[END_REF][START_REF] Liu | A review on removal of organophosphorus pesticides in constructed wetland: Performance, mechanism and influencing factors[END_REF], degraded by photodegradation in surface water [START_REF] Rühmland | Fate of pharmaceuticals in a subsurface flow constructed wetland and two ponds[END_REF][START_REF] Mathon | Influence of water depth and season on the photodegradation of micropollutants in a free-water surface constructed wetland receiving treated wastewater[END_REF] or transport in the other environment compartments.

Chapter conclusion

This second chapter dedicated to the non-target screening of micropollutants in wetland ecosystem has highlighted the large spreading of micropollutants. Indeed, the method developed in this section underlines that micropollutants are found in each compartment from the constructed wetlands. Besides even if different trends could be observed in the different compartment, the same classes are detected in each compartment. Yet the physicochemical properties and the transfer factors such as translocation factor, bioconcentration factor could not fully explain the distribution between the different compartments. The analyses made on the wetland systems underline that micropollutants are both found in a CW and the environment located at its outlet. Besides these micropollutants will also diffuse in a mud vertical profile and contaminate the lower layers. In this way, these results could provide an overview of micropollutants contamination in a polluted area. This sludge contamination in the vertical profile but also in the treated wastewater could create a risk for living organisms. As it has been mentioned in the literature, these micropollutants could contaminate living organisms and these organisms must deal with them. Then living organisms chronically exposed to micropollutants will take up and transform them from the environment. Besides as wetlands are plant-based systems, the analysis of plants could help to understand the fate of micropollutants. However, the non-target analysis as performed in this section could not provide spatial information in plant tissues. But this spatial informational could be crucial to understand bioprocesses.

Therefore, the next section will be dedicated to the spatial distribution and metabolization of micropollutants using mass spectrometry imaging (MSI).

Chapter 3:

Spatial distribution and metabolization of micropollutants

The previous section has provided an overview of the contamination found in the wetland ecosystem using the non-target approach in LC-HRMS/MS. The distribution has shown that plant could take up micropollutants from the environment. And literature underline that plants set up mechanisms to deal with these pollutants. Yet, this LC-HRMS/MS method could be combined with MSI to visualize the micropollutants distribution in organisms living in this polluted environment. This method will be helpful to understand the stress factors detected in plants. On the other hand, the mechanisms induced in the plant (storage, biodegradation) to deal with these pollutants should be investigated. This approach will be developed in this chapter and applied on two compartments. First, MSI will be used on plants growing in the tertiary treatment wetland (vegetated ditch) and constructed wetlands (two stage VFCWs) to understand the mechanisms (storage, biotic and abiotic transformations) that could occur in these plants.

Then the MSI will also be used to visualize spatial distribution in sludge, as it has been done in the previous chapter with the non-target screening in the different sludge layer. Therefore, this chapter will be structured as follows. First, the material and the methods used and developed will be described. This section will be dedicated to the sampling strategy, the samples preparation for MSI, the MSI acquisition and finally the data processing. Then these methods developed will be applied on three examples. First, the leaves of plant growing in the tertiary treatment wetland will be investigated to understand the fate and distribution of micropollutants in plants. Then a whole plant (Phragmites australis the common reed) will be studied to visualize the micropollutants distribution in the whole plant and its different organs. Finally, MSI will be used to visualize the distribution of micropollutants in different sludge samples.

Materials and Methods

Samples and sampling strategy

All the samples were collected in the same study site mentioned in the previous chapter. Indeed, the micropollutants distribution was investigated in plants growing in the constructed wetland and the tertiary treatment wetland. These areas are considered as polluted area in the Falkwiller study site.

Plants from vegetated ditch

First, the analysis was focused on plants growing in the tertiary treatment wetland, to visualize the impact of micropollutants release on plants located at the outlet of CWs. Therefore, the leaves of two plants growing in the wetland bank were collected. These two plants were Salix alba (willow) and Populus nigra (poplar) are described in Figures 44 and45. Concerning the poplar, two poplars were planted in the study site. The first one is located on the tertiary treatment wetland banks considering as polluted area and another one in the field were no wastewater could be collected. Therefore, this second poplar could be used as a control.

In these poplars, leaves were collected and immediately store at 4 °C. Then, once the samples are brought back to the laboratory, they have been frozen at -20 °C before the analysis. In the same vein, a willow has been growing in the bank and leaves were collected, stored at 4 °C before being frozen at-20 °C. The leaves collected and used for MSI analysis could be observed in Figure 46.

willow

3.1.1.2.Phragmites australis from VFCWs

On the other hand, plants growing in the CW were also sampled. As the CW were planted with

Phragmites australis (common reed), this plant was selected. Indeed, the Phragmites australis is a plant used worldwide in wastewater treatment. In the study focused on the reed, the whole plant has been collected and analyzed. Therefore, three reeds replicates were selected with similar age and size as shown in Figure 47 in the first stage of the VFCWs. Besides replicates were collected in the same area, near the water inlet (around 50 cm) as shown in the Figure 48.

But the manuscript will illustrate the distribution using only the first reed replicates.

Plant samples preparation

Once the samples are brought back to the laboratory, two strategies have been defined. For the study focused on the leaves of plants growing in the tertiary treatment wetland, the samples were immediately frozen at -20 °C before the cutting and the analyzing. Concerning the study focused on the whole reed, another approach has been set up. Indeed, the analysis of a whole plant has generated other constraints (for example samples storage in the freezer) then the plant samples preparation strategy has been adapted. The first step has been to separate the different organs of plants as shown in the Figure 49. The following step will be the samples cut and deposit in slides to perform the MALDI Imaging acquisition. After an optimization stage on the cryomycrotome, the optimal cut parameters for these samples were defined as a thickness about 30 µm at -20 °C as shown in Then, the slides are immediately sprayed with α-cyano-4-hydroxycinnamic acid matrix (HCCA, Sigma Aldrich, USA) at 7 g L-1 in 50/50 MeOH/H2O, 0.2% TFA (Sigma Aldrich, USA) using the ImagePrep spotter (Bruker, Germany) found in the supplementary datas (8).

Sludge samples preparation

As mentioned in the previous chapter with the sludge layers, a spatial distribution could be observed in the sludge compartments. Therefore, the MALDI Imaging approach has also been applied in this compartment. In this study the sludge from constructed wetlands were analyzed. Then, once the samples have brought back to the laboratory, a small cube of sludge about 1 cm 3 has been frozen at -20 °C.

The samples were placed in a small bucket with similar size in order to repeat the same sample Then, as it has been done for the plant samples, an optimization stage was carried out to obtain the optimal parameters. The parameters obtained were a thickness about 15 µm and at -20 °C.

An example of sludge cut could be observed on Figure 55.

Mass spectrometry imaging acquisition

All the plants and sludge section deposited on ITO slides were loaded in the dual source of a Then MSI acquisition was performed following the method mentioned in Villette et al., 2019a.

A 50 µm x-y raster width was set using Smartbeam-II laser optics with the laser focus set to small (20-30 μm) and laser power at 18%. The region of interest and the teaching used to the sections on slide and the target position in the instruments were defined in flexImaging 5.0 (Bruker, Germany). Each pixel provides a spectrum accumulated from 50 laser shots. This laser was run at 1000 Hz and the ions were accumulated externally (hexapole) before being transferred into the ICR cell for a single scan.

The spectra were internally calibrated using a matrix cluster of HCCA (C20H14N2O6 + H+, m/z = 379.0925) with a maximum deviation of 0.200 ppm. Besides, the mass range used for data acquisition was 100 <m/z <2000 and all the samples were analyzed using the positive ion mode with a transient size of 2 M. Online feature reduction was performed in ftmsControl software 2.1.0 (Bruker, Germany) to return only the peak centroids and intensities.

Data processing

The final step of this Material and methods section will be dedicated to the data processing and was based on the method mentioned in Villette et al., 2019a, b. SQLite data were imported in raw data convolution mode and the peak list data were used to create the dataset. The data were treated using the SCiLS Lab2016b software. The segmentation process could provide the compounds specifically found in a sample. Then, the dataset was exported in.csv file.

Concerning the reed cuts samples, the whole dataset containing was exported then treated using Microsoft Access to visualize all the data points found in the spectra (approximately 2 million data points). This file was separated in csv containing 60,000 data points to annotate the all data observed in the datasets.

Then the annotations were carried out in two ways:

using the Metaspace platform with HMDB, CHEBI, Swiss Lipids databases, using the Metaboscape software (3.0. for Salix alba and Populus nigra and 4.0 for

Phragmites australis).

The second method was based on the method using analyte list and SmartFormula that have been mentioned in the chapter 1. After the peak list extraction, all the features obtained were annotated with these analyte lists using the same criteria of mass deviation (Δm/z under 3ppm).

Then the peak fitting has been checked to ensure that annotations correspond to the peak identified.

Finally, the drug metabolites (conjugated and catabolites) were also generated using Metabolites Predict 2.0 (Bruker, Germany) using in silico prediction. These in silico predictions from the chemical structures of the parent compounds were based on 79 biotransformation rules found in supplementary data (6). These metabolites generation was performed in two generations. The biotransformation rules were first applied on the parent compounds and the results will provide the first-generation metabolites. Then, the same method has been applied on the first-generation metabolites to obtain the second-generation metabolites. The results could be exported in a mass list and used as analyte list in Metaboscape as mentioned in the chapter 1.

On the other hand, to compare the results found in the different cut of the reed samples, the intensities were normalized using the following equation [15]:

∑ 𝐼 𝐶 𝑝𝑖𝑥𝑒𝑙 𝑐 𝐼 𝑡 𝑝𝑖𝑥𝑒𝑙 𝑡 [15]
Where Ic sum of compound intensities in a single cut, pixelc pixels containing the compound in the single cut; It sum of compound intensities in the whole plant; pixel t pixels containing the compound in the whole plant.

In this way, all the cut intensities could be compared.

Methods limits identification

Then the acquisition method has been evaluated using the limits of detection (LOD), quantification (LOQ) and the matrix effect on the samples annalyzed in MSI. Therefore, sildenafil-d3 was used as model compounds to obtain the order of magnitude for the micropollutants detected. The LOQ defined at 100 µg/mL and the LOD at 10 µg/mL in the reeds, and 100 ng/mL when the sildenafil is spiked in the slides. The matrix effect in reed was about 99.5% and 98.5% in the sludge samples the details could be found in the supplementary datas (10).

Tracer experiment: a model for micropollutants movement

Finally, to understand the micropollutants distribution in the whole plants, model compounds were used to simulate this distribution. Therefore, reeds were collected in the study site with some sludge surrounding the reed rhizome in the same conditions from those collected for micropollutants distribution. In the laboratory, the reed was potted and 1 g/L of fluorescein (also called uranine) and sulforhodamine solution have been added. The combination of fluorescein (photosensitive) and sulforhodamine (sorptive compound) could model several micropollutants behavior and then those taken up by plants. Indeed, these fluorescent dyes were used in the literature as model [START_REF] Vanderborght | Identification of Transport Processes in Soil Cores Using Fluorescent Tracers[END_REF][START_REF] Schuetz | Multitracer assessment of wetland succession: Effects on conservative and nonconservative transport processes[END_REF] to understand the distribution or fate of herbicides in several constructed wetlands [START_REF] Lange | Multi-tracer experiments to characterise contaminant mitigation capacities for different types of artificial wetlands[END_REF][START_REF] Durst | Transport of pesticides and artificial tracers in vertical-flow labscale wetlands[END_REF][START_REF] Maillard | Dissipation of hydrological tracers and the herbicide S-metolachlor in batch and continuousflow wetlands[END_REF]. Besides, these dyes have already been used to mimic herbicides sorption or plant uptake [START_REF] Dollinger | Using fluorescent dyes as proxies to study herbicide removal by sorption in buffer zones[END_REF]. [START_REF] Dollinger | Using fluorescent dyes as proxies to study herbicide removal by sorption in buffer zones[END_REF] have underlined that fluorescein could be used as model for hydrophobic herbicides and sulforhodamine for hydrophilic herbicides.

The reed has been sacrificed ten days after the injection of the solution. The Figure 58 described the cuts, which have been selected to visualize the tracer distribution inside the whole plant. The different cuts were analyzed using a confocal microscope to visualize the fluorescent compounds.

Results and discussions

Organic micropollutants and plant metabolites in situ location and metabolization in a poplar and willow growing in the tertiary treatment wetland

First results were focused on the distribution of compounds in plant leaves. As mentioned in chapter 2, two main compounds classes could be distinguished in plant samples, the endogenous compounds and the xenobiotics. Therefore, these two classes were investigated in this section with the plant metabolites (all plant small molecules which could be intermediate or end product of the metabolism). Chronic exposure to micropollutants has led to the transfers of some micropollutants in the shoot part as mentioned in the previous chapter. But leaves have also been studied according to technical reasons. First, plants (poplar and willow) were not sacrificed to use for potential further experiments. Besides, the leaves samples are composed of soft tissue that are better suited to MSI than stem or roots with "wood section." Therefore, the first results will be focused on leaves, and further optimization should be done to optimize the stem or root analysis.

First results will be focused on the compounds distribution inside the leaves of the two plants reproduction, signaling, enhancing, inhibition, defense or other interactions) and for some of them, these functions classification is unclear. In these results, this ambiguous classification could be illustrated by the example of salicin. Indeed, salicin is naturally detected in poplar but it is also well known that these compounds are used as anti-inflammatory [START_REF] Mahdi | Medicinal potential of willow: A chemical perspective of aspirin discovery[END_REF], and therefore could have a similar distribution than xenobiotics found in the leaves. Concerning pheophorbide a, a chlorophyll degradation product, the compound is mainly found in the inner tissue. But the literature mentioned that pheophoride screening could be used to detect chloroplast in the plant tissues (Jun, 2012.). And chloroplasts are mainly found in the mesophyll then in the inner tissue. Other plant metabolites distribution is less surprising as plant metabolites could be detected in the different tissues. Different distribution could be observed in the leaves. Salicin (D) and neurine (B) seems to be preferably found in the outer tissue while pheophorbide a (F) are mainly found in the inner tissue. Piperidine (A), adenine (C), lyso PC 16:0 (E) are found in the different tissues. The boxplot (G) suggests that these compounds are found in higher proportion in the polluted leaves (Villette et al., 2019a).

Micropollutants distribution observed in Figure 60 underline that they were mostly detected in the outer tissue. The micropollutants found in these leaves have hydrophobicity characteristic that can generate their uptake (log between 0.5 and 3) [START_REF] Zhang | Organic wastes as carbon sources to promote sulfate reducing bacterial activity for biological remediation of acid mine drainage[END_REF]. Nonetheless, their physicochemical properties found in the supplementary datas (11), could not but themselves explained the distribution differences observed. Nevertheless, this specific distribution found for different micropollutants classes could suggest a mechanism set up by the plant to isolate these toxic compounds. An active transport to the peripheric tissues should be adopted to confine compounds in less sensitive areas for plants or to optimize the degradation processes that could occur in planta. On the other hand, these micropollutants will impact the plant health and induce stress factors, as discussed in the previous chapter, and then promote some plant metabolites detection in polluted leaves. In MSI data, Lyso PC 16:0 and phephorbide a detection could illustrate this stress factor. The pheophorbide a mentioned in the previous section as chlorophyll degradation products could highlight a chlorophyll disruption. And [START_REF] Parween | Differential Response of Vigna Radiata L. to Varied Doses of Chlorpyrifos[END_REF] have shown that this disruption could occur in plant exposed to pesticides. Besides, phosphatidylcholine (Lyso PC 16:0) detected in plant during their stress phase [START_REF] Sarabia | High-mass-resolution MALDI mass spectrometry imaging reveals detailed spatial distribution of metabolites and lipids in roots of barley seedlings in response to salinity stress[END_REF] have also been found in these leaves.

Furthermore, this micropollutants distribution observed in plant growing in the tertiary treatment wetland is not specific to the poplar. Indeed, [START_REF] Nuel | Seasonal and ageing effect on the behaviour of 86 drugs in a full-scale surface treatment wetland: Removal efficiencies and distribution in plants and sediments[END_REF] have shown that micropollutants could be widespread in different plants growing in this tertiary treatment wetland. The willow located in the tertiary treatment wetland has also accumulated micropollutants which were different from those detected in the poplar. Therefore, the micropollutants distribution was also investigated in the willow as found in the Figure 61.

. Micropollutants distribution observed in the willow leaves suggests that no preferential transport way is occurring in this plant. This different transport ways could be illustrated by the micropollutants localization found in leaves tissues. Quinestrol is found in inner tissue and iodosulfuron is detected in the vascular tissue. Nevertheless, in planta transport of micropollutants is mainly described by the transpiration stream [START_REF] Bartrons | Pharmaceuticals and Personal-Care Products in Plants[END_REF][START_REF] Petrie | Biotic phase micropollutant distribution in horizontal sub-surface flow constructed wetlands[END_REF][START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF] then, it is not surprising to find iodosulfuron in the vascular tissue. But Chuang et al., mentioned that this preferentially flow is not the single way to found micropollutants in plant tissues. By studying their translocations, they described their movement in two ways; the symplastic and the apoplastic way. This apoplastic way could therefore, partly explain their detection in inner tissues. On the other hand, quinestrol with log P = 6.13 should not be found in the plants according to the criteria mentioned in [START_REF] Zhang | Organic wastes as carbon sources to promote sulfate reducing bacterial activity for biological remediation of acid mine drainage[END_REF] Nevertheless, this detection in leaves could be explained by several hypotheses. Indeed, growing in this environment, micropollutants, microorganisms and plants have interactions, that could lead to the uptake by plants (W. C. [START_REF] Li | Interaction of Cd/Zn hyperaccumulating plant (Sedum alfredii) and rhizosphere bacteria on metal uptake and removal of phenanthrene[END_REF][START_REF] Zeilinger | Friends or foes? Emerging insights from fungal interactions with plants[END_REF]. Besides, the similar structure with plants metabolites could also promote their uptake [START_REF] Bucheli | Phytotoxins: Environmental Micropollutants of Concern?[END_REF]).

On the other hand, it cannot be neglected that these compounds could also be sorbed as micropollutants metabolites then a back conversion [START_REF] Fu | Back Conversion from Product to Parent: Methyl Triclosan to Triclosan in Plants[END_REF] could occur as a demethylation.

This back conversion could also suggest that micropollutants storage is only a part of the processes occurring in the different plants. As mentioned in the previous chapter, these plants should deal with this chronic exposure of organic pollutants. Therefore, plants have set up mechanisms to degrade or transform the micropollutants [START_REF] Marsik | Metabolism of ibuprofen in higher plants: A model Arabidopsis thaliana cell suspension culture system[END_REF] by their own metabolism.

Therefore, two processes could explain micropollutants metabolites found in the willow leaves could be explained by two main processes. Indeed, metabolites generated in the environment by several processes such as metabolites generated by animal digestion, biodegradation by microorganisms, photodegradation or other oxidation processes could generate these metabolites that could be directly taken up by the plants.

On the other hand, other mechanisms will concern the metabolites only detected in plant tissues.

Concerning these compounds, the "green liver" concept [START_REF] Sandermann | Plant Metabolism of Organic Xenobiotics. Status and Prospects of the 'Green Liver' Concept[END_REF]) could be mentioned explaining the metabolite generated in planta. This concept is close the one occurring in mammals liver to degrade compounds. As liver, plant will use diverse enzymes to degrade organic micropollutants. Micropollutants transformation in willow leaves is mainly due to cytochrome P450 for the first generation. Previous results found in the literature have already highlighted the main role of cytochrome P450. Indeed, it is well documented that cytochromes P450 have a key role in the phase I metabolism for several herbicides [START_REF] Cañameras | Bioavailability and Uptake of Organic Micropollutants During Crop Irrigation with Reclaimed Wastewater: Introduction to Current Issues and Research Needs[END_REF]. Besides, these cytochromes P450, found in wide diversity of organisms, can degrade a wide variety of micropollutants due to their low specificity [START_REF] Cañameras | Bioavailability and Uptake of Organic Micropollutants During Crop Irrigation with Reclaimed Wastewater: Introduction to Current Issues and Research Needs[END_REF].

Then, the second generation of metabolites that can be related to phase II degradation, are linked to large diversity of enzymes found as described in Claire Villette et al., 2019b. This compounds metabolization could be illustrated using the example of telmisartan found in willow leaves. Metabolites generated and detected in the willow were described in Figure 62 and their spatial distribution in willow leaves are shown in Figure 63. Green liver concept could be well illustrated using the telmisartan example. Indeed, most of its metabolites detected in MSI were only found in leaves and not in sludge or water samples analyzed in LC-HRMS/MS (except for C33H30N4O3 and C33H32N4O4). Water samples are for sure not an integrative sample, then the interpretation concerning metabolites should be tempered. Nevertheless, it seems that telmisartan is mostly degraded by enzymatic reaction and not by chemical oxidation or UV radiation in accordance with the green liver concept. Concerning the telmisartan and its metabolites distribution in the different leaves, few intensity differences have been observed between old and young leaves. The chronic exposure could probably renew the micropollutant content in plants.

On the other hand, the comparison between the telmisartan and its metabolites show that its metabolites are found in higher intensity. These results agree with those mentioned in the study from Marsik et al., 2017. Indeed Marsik et al., Besides these metabolites could also have a different location, impacting by the enzyme involved. For example, in these results, compounds obtained after a beta oxidation were mainly found in inner tissue. Beta oxidation will generate more hydrophilic compounds. These compounds could be stored in inner tissues instead being stored in outer tissue where more hydrophobic compounds could be located (lipid barriers).

All these results have underlined the different processes occurring in the plants (storage, degradation) using the shoot parts. In these processes, it has been shown that specific distribution could be observed in the different tissues, suggesting an active process set up by the plant to deal with these pollutants. This accumulation and distribution in shoots should also be related to other mechanisms non investigated in this section. Indeed, plants have interactions with the water, soil but also air compartment. Besides the final step of compounds degradation is the volatilization. Therefore, further analysis (with volatile compounds for example) should be investigated to understand their fate in the air compartment. Nevertheless, these observations are only focused on a single organ of plants: the leaves. And distribution in the leaves is not necessarily relevant to the one detected in a whole plant. As mentioned in the previous chapter, most of the compounds found in a plant coming from water or soil then are transferred to the shoots part, following several mechanisms. Therefore, to investigate the distribution in the different organs and evaluated the translocation, the micropollutants distribution has been studied in a whole plant (a reed). The two drugs investigated, are detected in higher intensity in leaves. Indeed, the intensity maps show this higher detection in the leaves. Regarding the results of carbamazepine, it seems that this drug is almost only found in the leaves. The metoprolol is mainly observed in the leaves, but a more homogeneous distribution seems to appear between the top part of the stem and the leaf apex. Therefore, as mentioned in the previous chapter, this distribution in the shoots parts is related to the transpiration stream then the water flow found in the plant [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF][START_REF] Li | Insight into the distribution of pharmaceuticals in soil-water-plant systems[END_REF], as drugs should mainly come from the wastewater. Distribution difference could be partly explained by a temporal difference. As the plant is chronically exposed to micropollutants and have accumulated micropollutants for eight months, the distribution could be influenced by this temporal difference. The plant sacrificed has stopped transpiration stream and downward flow (from the leaves to roots), and an instantaneous view is obtained. On the other hand, the affinity with plants could also be discussed, but for example the ionic fraction of the two compounds are less than 0.1%. Besides, the translocation factor was calculated for these two drugs using the mean intensity found in the cuts. Roots cuts, leaves cut, stem cuts ( 6top stem cuts and 6 bottom stem cuts) were compared. Results found in Table 11 can quantify the transfer. These results suggest the high transfers from roots to the leaves (TF = 468 for carbamazepine and TF = 317 for metoprolol). Therefore, the different organs should be studied and as mentioned previously, the transpiration stream seems to guide mainly compounds to the shoots (the leaves for carbamazepine and the leaves and the top stem for metoprolol). Nevertheless, compounds structure was also compared to structure of compounds found in plant database (Knapsack). The higher structure similarity was about 74% for the carbamazepine and 56% for metoprolol. Besides the carbamazepine have a structure similarity higher than 50%

with 140 compounds contained in Knapsack, while metoprolol with only 25.

Finally, using the Kegg pathway, the carbamazepine could be found in the sodium pump pathways, which is also present in planta. At the opposite metoprolol were not detected in any pathways in plants. In addition to the temporal difference, these structure differences but also the pathways found could probably be a part of the few differences observed. On the other hand, translocation is only a part of the fate of this micropollutants. As mentioned for the willow, biotransformation could also occur in planta (phytodegradation or phytovolatilization). Indeed, as the drugs were mainly detected in shoots parts. This location is probably related to the potential final step of their degradation. Indeed, these compounds should be volatilized and then excrete them to the atmosphere by stomates. To visualize degradation processes occurring in planta, the metabolites of these drugs were investigated in Figure 65.

Nevertheless, their intensities were lower than the parent compounds, at the opposite of results found in the willow leaves. Their distribution was also not fully related to the transpiration stream of water flow, as no clear gradient (top bottom) for all compounds could be distinguished.

Besides, these two drugs were also compared to pesticides found in the reed as shown in the This could probably be linked to the upward and downward flow occurring in plants. At the opposite of drugs which are coming almost exclusively from wastewater, pesticides could also be found in the air compartments. Then the downward flow could probably influence the transfers to the bottom stem or the roots.

Propachlor and fuberidazole seem to have a similar behavior that the one mentioned in the previous section. Parathion ethyl was poorly detected in leaves, but a similar trend seems to appear with a main flow in the top part of leaves. As mentioned for metoprolol, a temporal difference could probably explain this distribution. However, mecarbam and flamprop methyl seems to more evenly distributed in plants and hotspots of dodemorph could be detected in the stem near stem-leaf interface. Therefore, this distribution is fully not related to the pesticides classes or of its physicochemical properties, according to the data found in supplementary data (12). The chronic exposure limits the impacts of compounds size [START_REF] Chuang | Mechanistic study on uptake and transport of pharmaceuticals in lettuce from water[END_REF]. On the other hand, different tissue distribution could also be observed. For example, parathion ethyl is mainly detected in the pith while mecarbam or fuberidazole could be observed in the peripheral tissues. The main process seems to be linked to transpiration streams and therefore a large part of them could be detected in the shoots or in the lateral leaves. Therefore, the distribution does not seem to be classes dependent, even if pesticides are compounds designed to protect plants and must have a specific process. But these compounds could also be degraded and then their metabolites should be investigated as performed for the drugs. The Figure 67 described the pesticides metabolites detected in the reed. Nevertheless, as mentioned for drugs metabolites their intensities were lower than the parent compounds. Only a flamprop methyl metabolite (C16H13NO3FCl) was detected in higher intensity in some cuts. But as mentioned with the drugs metabolites, their distribution also was not related by the transpiration stream of water flow, as no clear gradient (top bottom) for all compounds could be distinguished.

. On the other hand, in the literature, micropollutants or their metabolites are widely studied in roots. But the results found in this study underline that few compounds are detected in roots.

The compounds found in roots tissue, where analyzed with low intensity. The rhizodegradation occurring in the rhizosphere could partly explain this low intensity. Indeed, the pollutants store in these areas where microbial activity is enhanced [START_REF] Ouvrard | PAH Phytoremediation: Rhizodegradation or Rhizoattenuation?[END_REF]) could be easily degraded (rhizodegradation). But chronic exposure and reed age could have a benefit impact on the micropollutants translocation and then partly explain this low distribution. Indeed, these results could not be compared by mesocosm experiments as few studies are focused on a daily exposure of micropollutants as it seems to appear in the full-scale study site. These conditions could promote the translocation and then compounds mainly detected in the shoots part.

Nevertheless, to understand and visualize the uptake and the potential role of transpiration stream, the tracer experiment performed in laboratory conditions could simulate the micropollutants uptake. Indeed, two fluorescent tracers (sulforhodamine and fluorescein), considered thus as xenobiotics, used in process engineering to determine residence time distribution were injected in the water feeding the plants. Then plant was feeding for ten days then it has been sacrificed. The analysis of the different sections using a confocal microscope as mentioned in the materials and methods section will provide the distribution of micropollutants over a shorter period of exposition. The results are observed in Figure 68. These results, sulforhodamine experiment could underline the transpiration stream role in micropollutants transport. Sulforhodamine is highly detected in the roots but a transfer to the stem is observed, especially in the intermediate cut of the stem. The high photosensitivity of fluorescein could partly explain the poor detection of these compounds in plant. Besides, as fluorescein should represent hydrophobic compounds, it is not really surprising to find fluorescein in the roots. As fluorescein, sulforhodamine have high sorptive properties [START_REF] Dollinger | Using fluorescent dyes as proxies to study herbicide removal by sorption in buffer zones[END_REF]; therefore this compound is obviously found at the interface between plants and the solution; the roots. But sulfhorhodamine should represent more hydrophilic compounds [START_REF] Dollinger | Using fluorescent dyes as proxies to study herbicide removal by sorption in buffer zones[END_REF]. In this way, sulforhodamine could be easily transported in Results found in the Metaspace platform underline the presence of two lipids evenly distributed in the whole slides (ceramide (iso:d17:1 (4E)/25:0:20H) and ceramide (iso:d17:1 (4E)/30:0:20H)), as described in Figure 70. The homogenous distribution is not surprising as sludge is mainly composed of lipids. But as it has been mentioned for plant samples, these annotations should not represent the distribution of the different compounds detected in the samples. Indeed, the use of Metascape could only investigate some databases available in the platform and a large part of micropollutants will not be annotated. Therefore, another method should be used to annotate the sludge samples and visualized the different analytes distribution in order to obtain a broader view. To overcome this lack of annotations, our current works are focused on the annotations of the whole spectra using Data Analysis 5.0., SmartFormula and the different analyte lists, mentioned in the previous chapter to obtain a non-target approach on the sludge samples. But the main issues was to obtain the general mass list coming from the MSI acquisition.

Therefore, the MALDI Imaging data could be analyzed using Data Analysis. The raw datas will be represented as a chromatogram and from these raw datas, a mass list could be generated. as shown in Figure 71. Then a similar process as mentioned in the first chapter could be applied. Indeed, the mass list generated in Data Analysis could also be annotated with Smart Formula using the same parameters as mentioned in the first chapter. Then using the analyte list developed, a putative annotations could be obtained by associating a single name to the formula generated.

Chapter conclusion

After underlining the micropollutants widespread in the environment in the previous chapter, this chapter was focused on the micropollutants uptake in plant compartments. This plants chronically exposed to micropollutants must deal with them, as the plant response detected seems to indicate. To survive and limit micropollutants impacts, specific mechanisms seems to be set up inside the plant. For example, a specific storage in outer tissue has been observed in the poplar leaves. This storage suggests that plants guide the micropollutants in area to confine them and limit their impacts. On the other hand, some biodegradation processes have also been investigated in willow leaves. Indeed, drugs metabolism was studied and for two of them, the metabolites were only detected in leaves suggesting enzymatic reactions occurring in plant tissue. In these two plants only, the leaves were studied. Nevertheless, to understand the mechanisms which could occur in plants, a whole plant should be investigated. The reed was used as a model. The chronic exposure to micropollutants led to different micropollutant behavior in the plants. As plant grew for 10 months in the polluted area, most of the compounds have already migrated to the top part of the plants and thus the leaves. The other observation could also be linked to different temporal distribution, as the compounds classes could not explain the specific distribution.

Nevertheless, micropollutants found inside the plants come mainly from sludge and water compartments. Besides compounds detected in sludge are directly linked with the micropollutants water content. Nonetheless, the water distribution is not homogeneous due to the water introduction and its velocity field in this kind of system. This heterogeneity of velocity could also generate areas with heterogeneous micropollutants content. Therefore, to visualize the heterogeneity, found the area with higher micropollutants concentration then optimize the process, water flow inside wetlands must be studied. Thus, the water flow heterogeneity will be used to distinguished area where sludge could be samples. Finally, the micropollutants analyzed in sludge samples will indicate if water flow could influence the micropollutants distribution in this system.

The two previous sections have introduced the distribution of micropollutants in the wetland ecosystem but also a spatial distribution in plants growing in these polluted areas. As mentioned in this chapter, the micropollutants found in these compartments come mainly from wastewater.

Therefore, the influence of wastewater distribution should be investigated to understand the potential origins of micropollutants found in other compartments. Indeed, it is well known that long residence time could improve nutrient and carbon removal in this kind of system. But the influence is not so obvious concerning the micropollutants. Nevertheless, residence time distribution is a global indicator and cannot distinguish an area, with specific low velocity field in a whole system. But a detailed knowledge of water flow distribution could also be helpful to understand the micropollutants distribution inside the wetland. Therefore, a water flow process modeling should be performed to distinguish the different water flow areas in the wetland and obtain the detailed knowledge of water flow, as the velocity field, the residence time distribution, the dead and recirculation zones. To represent the most precisely the study site, the model was based on GPS coordinates and then the water flow was simulated using a 3D model based on computational fluid dynamics and the open-source software OpenFoam. The results from this hydrodynamics model could provide areas where the samples should be collected to visualize the different water flow. This model will be used to define a sample strategy where sludge will be collected. The sludge could provide an integrative view and visualize micropollutants found chronically in this wetland. And finally, this sludge samples will be analyzed using the non-target approach in LC-HRMS/MS, to find if a relationship between the micropollutants distribution and the water flow process is existing.

Materials and methods

Study site: the artificial pond description

This last part of this PhD project will be focused on another study site. The artificial pond is located in Lutter (Grand Est France) around 150 km from Strasbourg.as described in Figure 72.

As the Faklwiller study site, a full-scale two stages VFCWs is coupled with a tertiary treatment wetland, an artificial pond. VFCW is working under actual conditions with a capacity around 950 people equivalent. The artificial pond wetland located in the study site is the first tertiary treatment wetland set up in Alsace in 2009. For further details, characteristics of the VFCW, which has not been studied here could be found in the Maximilien Nuel thesis ( 2017). This artificial pond wetland was set up by excavating area available at the outlet of the VFCWs. In this chapter, only the hydraulic behavior of the tertiary treatment wetland and the potential link with micropollutants distribution have been investigated. Indeed, as mentioned in the first chapter, these systems are directly connected to the secondary treatment wetland and release wastewater to the environment. But micropollutants have already been detected in this wetland [START_REF] Nuel | Devenir de 81 substances médicamenteuses et de leurs métabolites au sein des Zones de Rejet Végétalisées (ZRV)[END_REF]. As an affinage treatment, the influence of hydraulics could probably impact the micropollutants distribution. This artificial pond is found on Figure 73 and have a maximum length about 40 m and a maximum width about 20 m. Detailed characteristics are described in Table 12. The water treatment system global scheme is similar to the one described in the Falkwiller study site in Figure 20.

Wetland type Pond

Bank slope 1 by 4

Maximum length (m) 40

Maximum width (m) 20

Depth (m) 0.1-0.9 Surface (m²) 750

Volume (m 3 ) 425

Once the wastewater has passed through the system. The treated wastewater is released in a small river called Le Briquelée which flows into the Lutterbach then into Ill.

Geometry building and meshing

The current paragraph aims to represent the most precisely the artificial pond to understand its hydraulics behavior. Therefore, the geometry data points should be measured to create a geometry model. The study site has been bounded as shown in Figure 74 and GPS coordinates was collected. Once the geometry limit of the pond has been defined, the pond depth could be investigated. To measure this depth, a bathymetry campaign was carried out using an inflatable boat found in the Figure 74. Then to investigate the depth inside the whole pond, a virtual surface grid has been set up and the domain was split in one square meter subdomains. Then using the inflatable boat and a graduated stick the depth was measured manually each meter with the virtual grid as shown in Once the data collected, they were extracted and imported in a design software (SALOME) to create the geometry model. The coordinates were linked to build the whole geometry. Indeed, using the coordinates found in Figure 76A, the general shape has been built as found Nevertheless, after several simulations, the result simulated was not fitting with the experimental data. This fitting will be developed in a following section. To reduce this issue, generate a meshing and obtain a convergent result, a geometry specificity was also considered.

Indeed, a check valve has been set up in the inlet of the wetland. But this check valve has an inclination that can influence the inlet water flow. In the study site, the average inclination was measured at 25°. But this inclination is not well adapted with meshing methods applied (developed in the following section). To overcome the meshing issue, the water depth in the inlet pipe was determined using hydraulics diameter. This hydraulics diameter will simulate this check valve. Therefore, water depth was calculated using the following equation [16] and considering the check valve as the pipe as circular:

𝐷 ℎ = 𝑅 (1 -cos 𝛿) [16]
Where Dh: hydraulics diameter, R: pipe radius (50 mm) and 𝛿 angle Therefore, this check valve simulation will be carried out using a water depth translation in the valve location. The 25° valve opening will generate a water depth under the valve that could be calculated using the trigonometric formula. This valve issue could be illustrated in Figure 77. This water depth found in the pipe was calculated correspond to h = 9.37 mm. This water depth in the pipe will be used to estimate the valve translation. Translation could be illustrated in the In the Figure 78, the red ring will represent the valve and the blue one the inlet pipe. This opening area in the pipe (only the blue part of the ring) will be calculated using this translation.

This opening area could be estimated by the difference between the pipe area, and the ellipse generated by the intersection valve pipes. Therefore, ellipse center must be defined using the circle equation [17] and by resolving the second-degree polynomial [18][19][20][21]. The following equations were used:

(𝑥 0 -𝑥 ′ ) 2 +(𝑦 0 -𝑦 ′ ) 2 = 𝑅 2 [17] 𝐴 = ( 𝑥 0 -𝑥 1 𝑦 0 -𝑦 1 ) 2 +1 [18] 𝐵 = 2𝑦 0 ( 𝑥 0 -𝑥 1 𝑦 0 -𝑦 1 ) -2𝑁 ( 𝑥 0 -𝑥 1 𝑦 0 -𝑦 1 ) -2𝑥 0 [19] 𝐶 = 𝑦 0 2 + 𝑥 0 2 + 𝑁 2 -𝑅 0 2 -2𝑦 0 𝑁 [20] 𝑁 = 𝑅 1 2 -𝑅 0 2 -𝑥 1 2 +𝑥 0 2 -𝑦 1 2 +𝑦 0 2 2(𝑦 0 -𝑦 1 )
[21]

The resolution has led to find the ellipse center placed at a height about 54.7 mm from the pipe bottom. Then the opening area could be calculated and will be considered as the wetted perimeter. Assuming that the area corresponds to a circular pipe, the wetted perimeter could be found using the following equation [22].

𝑃 𝑤𝑒𝑡 = 𝐷 2 4 (𝛿 -sin 𝛿 cos 𝛿) [22]
Using this equation, 𝛿 was found (𝛿 =0.8) and water depth in the pipes correspond to 15 mm, according to the hydraulics diameter equation.

Once the geometry has been built, all this geometry should be meshed. To resolve Navier-Stokes equations, all the domain should be divided in subunits. This meshing step is one of the most crucial part in the modeling. This meshing optimization was performed using two ways.

First, the meshing was created in Salome and then be imported in OpenFoam 4.0. Nevertheless, the meshing quality was not adequate to perform the simulation. Therefore, snappyHexMesh (the mesh generator found in Open Foam) was used. Indeed, snappyHexMesh is well adapted to create high quality three-dimensional meshes and has the advantage being included in OpenFoam. OpenFoam is a free CFD software that uses the finite volume method to resolve the partial differential equations. This software could be used for pre-processing (meshing), solving (mathematical equations) and post processing (ParaView) to visualize the results as underline in Figure 79. Then the first part concerning the meshing was carried out in three main steps:

-BlockMesh: The model is split in small blocks (preferably cubic or rectangular),

-Castellation: The BlockMesh is refinement locally in smaller blocks and the refinement is based on the features, surfaces…, -Snapping: The mesh is snapped to the surface or edges.

Therefore, several mesh sizes were tested to find the most adapted model. Besides the meshing was refined in critical points of the model (the inlet/outlet pipe, the check valve and the pond border).

The final mesh model was chosen using the Y+ criterion following the standard k-Ɛ model.

Indeed, these criteria calculated following the equation Where u is the averaged velocity m/s, ρ the water density (kg.m-3), µ the dynamic viscosity (kg.m-1.s-1), L the average water depth (m) and Δs the mesh size in cm.

Using this criterion, the mesh size must be between 1.1 cm and 11 cm. Considering the calculation costs, the mesh size was defined at 10 cm. Using this mesh size, the model has 1.5 million grid cells. Finally, the mesh quality was checked using the checkMesh solver found in OpenFoam.

maximum and minimum flow rate as described in the results found in [START_REF] Nuel | Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms[END_REF] On the other hand, a zero gradient in pressure has been applied.

Outlet pressure: An atmospheric pressure and zero gradient concerning the velocity were set up to impact the water flow direction.

Water free surface: A symmetry condition has been applied. This condition will consider the normal component at zero, and no exchange with external conditions could be done.

Pond banks and bed:

The velocity was set up at zero and a zero gradient in pressure has been applied.

Water model validation by tracer experiments

Results obtain by the simulation could be observed using the post-processing software included in OpenFoam and called Paraview. Nevertheless, the results do not necessarily represent the real case. Therefore, the water flow model must be validated. This validation was performed by comparing experimental data from full-scale tracer experiment with numerical results

simulating water flows and passive scalar transport processes. Indeed, a sulforhodamine was injected in the wetland to determine the residence time distribution as mentioned in [START_REF] Nuel | Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms[END_REF] Tracer experiments performed in the study site could be found in Figure 80. Tracer intensity was detected in the wetland outlet using a fluorimeter and used to determine the residence time distribution [START_REF] Nuel | Devenir de 81 substances médicamenteuses et de leurs métabolites au sein des Zones de Rejet Végétalisées (ZRV)[END_REF].

Therefore, tracer experiment should also be simulated. The velocity field from the converged water flow model was used for the simulation of the conservative solute transport largely known as passive scalar transport. In this way, an inlet mass flow rate (100 mg/s) was defined and injected as done in the study site. Then a passive scalar has been applied on this velocity field.

The equation [26] governing the passive scalar transport found below will be used to simulate a conservation experiment movement.

𝜕𝐶 𝜕𝑡 + 𝛻. (𝑢𝐶) -𝛻 2 (𝐷 𝑚 𝐶) = 𝑞 𝑚 [26] Where C is tracer concentration, u velocity, t time and Dm tracker diffusion coefficient (sulforhodamine Dm = 3.6 10 -6 m²/s), qm pollutant source term. To solve these equations a

Reynolds-Averaged Navier-Stokes (RANS) method was used.

Then residence time distribution could be calculated by integrated the results found in the outlet of the wetland. These two-residence time distribution (modeled and experimental) will be compared to validate the water flow model. Indeed, Alvarado et al., [START_REF] Alvarado | CFD study to determine the optimal configuration of aerators in a full-scale waste stabilization pond[END_REF] and [START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF] [27]

The validated model will be finally used to define areas were sludge will be collected. The aims of this study were to have a full understanding of water flow dynamics through the artificial pond wetland,

to determine low and fast water flow regions, to understand if hydraulics could influence micropollutants distribution in sludge (integrative view).

Therefore, using velocity field distribution, areas where sludge will be collected has been defined. Then, the micropollutants will be analyzed in the areas defined.

Micropollutants analysis

Micropollutants found in sludge samples were analyzed using the LC-HRMS/MS, on a Dionex Ultimate 3000 (Thermo) coupled to a Q-TOF (Impact II, Bruker), with the Targetscreener method developed by Bruker (Bremen, Germany). The details of micropollutants extraction and analysis are described in the sections micropollutants extraction optimization and sample analysis in mass spectrometry from the chapter 1 and in the supplementary data. Then the data analyses were carried out in two ways. The study was first focused on micropollutants found in the TargetScreener database (2200 drugs and pesticides). Therefore, all the samples were analyzed using TASQ 1.4. (Bruker Daltonics) software, to identify these micropollutants. Using standards analyzed in the same operating condition, the identification can reach the level 1 of the Schymanski classification [START_REF] Schymanski | Strategies to Characterize Polar Organic Contamination in Wastewater: Exploring the Capability of High Resolution Mass Spectrometry[END_REF]. The compounds were selected following the criteria found below:

exact mass variation lower than 3 ppm, retention time variation lower than 0.3 min,

signal-to-noise ratio higher than three.

Besides only compounds detected in the three replicates were considered. Then non-target screening approach was performed to obtain a boarder spectrum of compounds found in these samples and identify some metabolites. The method applied is similar as the one mentioned in the data processing from Chapters 1 and 2.

Results and discussions

Water flow simulation and velocity field in the tertiary treatment wetland

Thanks to the previous PhD thesis in this study site, the residence time distribution has been determined in this system [START_REF] Nuel | Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms[END_REF]. Nevertheless, these results could only define general parameters about hydraulics efficiency found in the wetland and did not provide a detailed knowledge of water flow distribution. Results described in this previous study will be [START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF].

Nevertheless, the flow rate observed in the two simulation cases suggest different hydraulic behavior in the wetland. In both cases, a high velocity field is observed near the boundary conditions, probably due to the constrictions. Then, very low velocity field is detected near the bank, where wastewater hardly flows. Therefore, these areas should be distinguished from other areas where wastewater is constantly flowing. The lowest velocity areas could be helpful for micropollutants removal. Indeed, lowest velocity field will generate an increasing residence time. The impact of higher hydraulic residence time has already been underlined in the literature for nutrients removal [START_REF] Akratos | Effect of temperature, HRT, vegetation and porous media on removal efficiency of pilot-scale horizontal subsurface flow constructed wetlands[END_REF][START_REF] Huang | Nitrogen removal in constructed wetlands employed to treat domestic wastewater[END_REF].

Despite global similarities found in the two simulations, the results also underline that a higher inlet flow rate could influence the velocity field inside the wetland. Regarding the results found in the lowest simulation, the inlet and outlet are directly connected by a preferential flow. This connection is less obvious with an increase of water flow. The results of the highest flow rate suggest the vortices presence inside the wetland. Results found in the Figure 81 could suggest an impact of inlet water on its distribution in the wetland. However, it should be noticed that the velocity fields detected in the wetland are composed of very low velocities. Therefore, the influence of inlet flow rate is to be tempered. Besides, in general terms four main areas could be distinguished in both cases:

the two areas near the boundary conditions (inlet and outlet) with a high velocity field,

the areas near the banks where water hardly flows,

areas where flow constantly flow inside the wetland.

Besides, the model has been validated using the residence time distribution as mentioned in the materials and methods section and found in [START_REF] Alvarado | CFD study to determine the optimal configuration of aerators in a full-scale waste stabilization pond[END_REF][START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF] The comparison between the experimental and simulated of residence time could be observed in The general trends of the two curves indicated a good fitting by comparing experimental and modeled curve. In addition, to obtain a quantitative evaluation of the curve difference.

Difference was calculated using the NAE as mentioned in the material and methods section.

Results concerning the NAE indicate an average NAE about 29%. This NAE could be impacted that differences found at the end of the simulation. Nevertheless, regarding this result combined with the good fitting of the two curves, the model could be validated.

Sludge sampling areas

The validated model could now be used to define a sampling strategy. Indeed, the main aim of this study was to investigate if a link between water flow and micropollutants distribution is existing. To visualize this potential link, sludge sampling strategy has been defined using the numerical modeling. The four areas mentioned in the previous section (area 1: inlet, area 4: outlet, area 3: areas where water hardly flows and the one where water constantly flows; area 2) were selected. The areas were sampled to create a composite sample could be found in Finally, area 4 could be referred as the outlet sludge area.

Compounds and micropollutants distribution in wetlands

The areas defined by numerical modeling were used to collect sludge samples during four campaigns on the study site. A sampling campaign was carried out each season on the study site in the different water flow areas. The results from each season could be found in the and 35 micropollutants. Therefore, seasons have a limited impact on micropollutants number found in the different samples, especially in the areas 3 and 4, where only few decreases could be observed in Spring. This micropollutants decreased could be partly related to the microorganisms' productivity. Indeed, the environmental conditions in this season could promote the microorganism development. On the other hand, in the areas 1 and 2, the autumn simulation provides surprising results. Very few compounds were detected in the area 1 and at the opposite, this season sampling has the high number of micropollutants in the area 2. These two areas are probably linked but unclear reasons could explain the translation to the area 2.

The leaching from area 1 to area 2 in this autumn could probably explain this movement.

Nevertheless, the analysis performed on different seasons could create heterogeneous results, due to the potential impact of environmental conditions. To overcome this influence, only the compounds found in each season were considered. Indeed, these results could provide a most adapted and general view of sludge content and sludge quality. Results could be found in the Table 13. These global results underline that micropollutants distribution does not seem related to the water flow. Indeed, 4 micropollutants were detected in area 1 (inlet), 6 in area 3 (low flow rate) and 7 in areas 2 (intermediate flow) and 4 (outlet) each season. Therefore, the difference does not seem significant. On the other hand, a large part of them are detected at least in two sampling areas, as found in Figure 85. Indeed, the Figure 85 and Table 13 highlight that a large part of micropollutants detected each season were also found at least in two areas. These compounds represent more than a half in each area (four out four in area 1, five out seven in areas 2 and 4 and four out six in area 3).

Therefore, micropollutants will be diffused in the whole wetland and only few compounds were detected in a specific area. Nevertheless, micropollutants distribution in the wetland could not be restricted to micropollutants number found in these areas. Indeed, the water flow could probably generate a distribution of micropollutants in the different areas, but the compounds number cannot indicate if the distribution is homogeneous or not. Therefore, the micropollutants concentration should also be investigated. This investigation was carried out on compounds detected at least in two areas and could be observed in the Figure 86. A lower flow rate will usually enhance general a higher sedimentation and micropollutants will be sequestered in this phase [START_REF] Montiel-León | Widespread occurrence and spatial distribution of glyphosate, atrazine, and neonicotinoids pesticides in the St. Lawrence and tributary rivers[END_REF]. Therefore, it seems that water flow is not the key parameters that influence micropollutants distribution in the system. Several hypotheses could be done to explain this lack of differences observed in the results. First, the general velocity field is very low velocity (≤0.1 m/s). These low velocities could reduce the differences. This low difference will therefore impact the low difference in micropollutants content. The studies from the literature that shown an impact of water flow in micropollutants distribution was based on system using higher velocity, for example in a port as found in [START_REF] Mali | Influence of hydrodynamic features in the transport and fate of hazard contaminants within touristic ports. Case study: Torre a Mare (Italy)[END_REF]. This higher velocity could, therefore, probably impact the distribution. A higher velocity field will generate higher differences and help to distinguish micropollutants samples composition.

Nevertheless, this correlation could also be impacted by the fact that only water flow process was considered at this stage in the model. Indeed, solid transport including conservative and non-conservative processes such as sediment or sludge deposit, sorption and other biotic and abiotic phenomena. But this solid transport could also impact the micropollutants content. The sludge deposit then its renewal is affected by sedimentation (of plant degradation products or solid particles from wastewater) and thus by the solid transport. The solid transport would help to estimate the sludge renew and its impact of micropollutants content. The lack of this parameter consideration in this study could also bias the areas selection.

On the other hand, this sludge samples should also be considered as a compartment where living organisms are growing. Indeed, worms have been found in this sludge and microorganisms are living in these compartments. All these living organisms could transform micropollutant into degradation products and the parent compounds found in the areas were not degraded. In this study, The example of permethrim could illustrate this comment. Permethrim is an insecticide poorly soluble. Therefore, it will be difficult for microorganisms to assimilate it and will be found in the sludge. Besides permethrim could have a negative impact on microorganisms living in aquatic systems [START_REF] Muturi | Effect of pesticides on microbial communities in container aquatic habitats[END_REF]. Therefore, degradation processes are reduced, and it is not surprising to find these compounds in these areas.

Nevertheless, the lack of difference could be hidden by the generation of transformation products (catabolites or conjugated). In this study, the example of irbesartan found in the Finally, the analysis of micropollutants should also be compared with a broader analysis focused on the general identification detected in the non-target analysis. As mentioned in the chapter 1, these putative identification covers both micropollutants but also lipids or plants metabolites. Therefore, the raw formulae (level 4) and putative identifications (level 3) distribution in the different areas were also investigated. The results could be found in The distribution of these putative identification underlines a higher identification number in the areas with lower flow rate. Therefore, in general terms the lower water flow seems to promote the sedimentation and deposits of compounds in these areas in accordance with results found in the literature, at the opposite of the observation made for micropollutants. Nevertheless, in this study, only the parent compounds were considered in this study. But micropollutants could be complex compounds that can be degraded and the absence of a global metabolites investigation could also bias the study. Therefore, their fate should be further investigated to understand if a correlation is existing between the micropollutants distribution, degradation and the water flow areas. This investigation should study both the biodegradation and the physico-chemical transformation that can be induced by oxidation or photodegradation.

Besides the study was only conducted on a single study site. Therefore, the results should be compared with different systems operating in different geographical areas and different flow rates to strengthen the results.

Chapter conclusion

The two previous chapters have underlined the widespread of micropollutants in the environment and the spatial distribution in specific compartment. In both cases, the results highlight the key role of wastewater as vectors of micropollutants in the system. Therefore, this chapter has investigated its distribution in the wetland to detect a potential correlation between the water flow and micropollutants distribution. The use of numerical modeling could predict the areas with different velocity fields and define a sampling strategy. Suspect screening of micropollutants in these different areas did not lead to correlate the micropollutants distribution and the water flow areas. Regarding the results concerning the number of micropollutants or their concentration, water flow process seems not be in this case a sufficient key parameter governing the distribution. This lack of correlation could be partly explained by several hypotheses. First it can be noticed that low velocities are found in the wetland. These low velocities could generate few differences between the different areas. Besides, the model does not consider the solid transport. And this solid transport could influence the sludge deposit.

Finally, to understand the global distribution, the drugs metabolites should also be considered.

Indeed, organic micropollutants could be degraded and their metabolites could also be transported following the water flow.

This work led to investigate the micropollutants distribution in a wetland ecosystem. This distribution analysis was carried out by combining a metabolomic approach and hydrodynamics. Each part of the approach could provide complementary data useful to understand the distribution.

A global approach was first carried out, using a non-target approach in the wetland ecosystem.

This approach could provide a contamination overview of the different wetland's compartments. Indeed, the compartment investigation has highlighted the widespread of micropollutants in the different compartments. This widespread was not "classes" dependent, as the main xenobiotics classes were found in all the compartments. This distribution could not be fully explained by the physicochemical properties or their fate factor (i.e., translocation factor, Kd, Henry law…). Besides, the analysis performed in the CW and the tertiary treatment wetland suggest the widespread is not restricted to the treatment systems. However, the first analysis was only focused on the surface layer in direct interactions with wastewater and plants growing in these systems. But the distribution of xenobiotics could not be restricted to the analysis of the ecosystem's compartments. Spatial distribution could also be observed in different compartments. Therefore, a vertical profile of sludge has been investigated. The results detected in this sludge coring suggest that micropollutants could diffuse in the entire depth, even if a large part of them are specifically found in the top layer. The analysis of sludge content (physicochemical properties but also lipids analysis) could partly explain this distribution. Indeed, the physicochemical properties in the top layers will promote micropollutants sorption (porosity, dryness, granulometry analysis). However, a complete analysis should combine the structure and the biological component of a sludge samples. In this way, the lipids study suggests that the main microorganisms productivity is found in the top layer. Therefore, this microbial activity combined with the polar characteristics for many micropollutants and the sludge structure will explain the few compounds detected specifically in the bottom layer. All the results described in the first part of this manuscript could therefore provide an overview of the contamination found in these kinds of systems.

On the other hand, the results have suggested that this micropollutants could also affect living organisms and create and environmental but also a health risk. The analysis of hormones has underlined that plant generates a stress response to deal with these pollutants. And living organisms growing in these areas are chronically exposed to micropollutants and could develop strategies to manage these pollutants. Plants will take up and then transform micropollutants from the environment. Nevertheless, the non-target analysis should be combined with spatial information to understand the bioprocesses occurring in the plants. Therefore, the micropollutants distribution and metabolization were investigated using mass spectrometry imaging (MSI).

The MSI results have underlined that specific mechanisms have been established in the plants to deal with the micropollutants. For example, a storage in outer tissue, to confine micropollutants, in poplar leaves have been described. In addition to this active storage, biodegradation processes were also found in plants leaves. Indeed, metabolites from two drugs were only found in poplar leaves with a higher intensity that the parent compounds. The results described in this study underline that enzymatic reactions are occurring in plants and the "green liver" process is set up to degraded micropollutants. Nevertheless, the analysis of leaves in MSI or roots in LC non-target screening is not sufficient to understand the bioprocesses that can be set up. The literature mentioned that micropollutants are transfers from roots to leaves using the transpiration stream. This transpiration stream could therefore also investigate using MSI in a whole plant. Therefore, a reed mainly used in CW was considered as a model plant chronically exposed to micropollutants. The pollutants migration has been highlighted by the different location of micropollutants inside the reed stem. This location difference could be related to a temporal distribution. As the plant grew for 10 months in the polluted area, and plant sacrifice has stopped the transfer process. Even if a large part of micropollutants are already detected in the top parts, specific distribution could also be found describing the different step of the transport linked to the transpiration stream.

Nevertheless, micropollutants found inside the plants come from sludge and water compartments. As mentioned in the previous section, the water flow could directly impact the micropollutants distribution. The observations described in a living organism could also be applied to the treatment system. Indeed, water distribution in the wetland is not homogeneous and the heterogeneous velocity field could impact the micropollutants distribution. Therefore, the last part of this manuscript has been dedicated to investigate if a correlation between the micropollutants distribution and the velocity field is existing. The velocity field has been obtained by numerical modeling based on study site measurement. These simulations have predicted different areas according to the velocity field. Then a sampling strategy has been defined to select the lower and higher velocity field area. The suspect analysis performed on the sludge collected in these areas do not provide a correlation between micropollutants distribution and water flow. Regarding the results concerning the number of micropollutants or their concentration, water flow seems not be in this case a key parameter governing the distribution. The lack of correlations could partly be explained by the hypothesis done in this study (no transport solid simulation, general low velocity found in the systems). Besides, only the parent compounds were considered in this study that can bias the general conclusion.

To conclude, this work has highlighted the distribution of micropollutants in wetland ecosystems using several methods. The micropollutants from wastewater reach each compartment of the wetland ecosystem. But the fate of micropollutant could not be limited to the study of their distribution. Indeed, these systems based on natural processes could degrade micropollutants using oxidation or living organisms. Therefore, the micropollutants metabolites should also be investigated to understand the micropollutants fate. Some metabolites have been investigated in this study using biodegradation rules. Nevertheless, they do not fully represent the micropollutants degradation products. As perspectives, the metabolites generated by oxidation but also those found in the gas phase should be studied. Indeed, the final stage of degradation products will produce volatile compounds. Therefore, it would be interesting to study compounds found in the gas compartment (atmosphere) in this kind of system. Indeed, the final step of compounds degradation lead to generate volatile compounds. Therefore, the analysis of volatile compounds excreted by the organisms could probably help us to understand the mechanisms set up to deal with micropollutants. On the other, the combination between modeling and micropollutants distribution should probably improve by simulated the solid transport in the pond and considering all the micropollutants metabolites.

Introduction Générale

Le développement d'activités anthropiques accompagné par l'accroissement de l'industrie de synthèse a favorisé l'émergence de contaminants au sein des différents milieux de l'environnement. Ces résultats montrent bien une contamination globale des compartiments présents à la surface.

Mais ces micropolluants peuvent également contaminer des éléments se situant dans des horizons plus profonds. C'est pourquoi un profil vertical de boues a également été analysé.

Cette analyse a porté à la fois sur les micropolluants, la macropollution mais également la

Conclusion

Ces travaux ont donc permis de visualiser la distribution d'un spectre large de micropolluants dans l'écosystème de filière de traitement extensive. Ils mettent en évidence la contamination de l'ensemble des compartiments, ce qui pourrait générer des risques environnementaux, mais également pour la santé humaine. Les organismes présents dans ces systèmes et en état de stress mettent en place des mécanismes pour faire face à cette pollution. Une compartimentation ou encore des phénomènes de dégradation peuvent néanmoins être mis en place par ces organismes vivants. Enfin, la distribution des micropolluants au sein des filières extensives ne peut être résumée aux processus hydrauliques qui gouvernent le dimensionnement de filières extensives.

En effet, les faibles vitesses observées dans ces ZRVs ne permettent pas une répartition préférentielle des micropolluants au sein de ces ouvrages.

Résumé

Les filtres plantés de roseaux, filière de traitement privilégiée en France pour traiter les eaux usées en milieu rural, fait face à la problématique des micropolluants. L'étude menée vise à comprendre la distribution et le devenir d'un large spectre de micropolluants dans l'écosystème de ces filières extensives. Pour cela, le profil des micropolluants et du métabolome au sein des différents compartiments (eau, boues, plantes) a été établi par une analyse LC-MS/MS en spectre large. D'autre part pour évaluer, l'infiltration de ces polluants un profil métabolique a été décrit sur la profondeur des boues des filtres plantés. Mais cette distribution spatiale peut également être obtenue à l'échelle de l'organisme afin de comprendre certains bioprocédés. L'utilisation de l'imagerie par spectrométrie de masse a pu mettre en évidence ces localisations et les mécanismes mise en oeuvre par les plantes pour faire face à ces polluants. Enfin l'eau demeure le principal vecteur des micropolluants dans l'environnement. Ainsi les données de micropolluants ont été couplées à une modélisation numérique d'écoulement pour comprendre leur distribution au sein de ces systèmes.

LC-HRMS/MS, Imagerie par spectrométrie de masses, ZRV, sludge, micropolluants, CFD, Analyse non ciblée, écosystèmes

Résumé en anglais

Constructed wetland are popular systems used to treat wastewater in rural areas in France. But these systems are facing to the micropollutants issues. This study aims to understand the micropollutants distribution in the wetland ecosystems. Therefore, micropollutants, lipids and plants large scale screening were performed in the wetland ecosystem, as well as in the different sludge layers from the constructed wetland. Nevertheless, the spatial distribution should also be investigated inside the organisms to understand the bioprocesses. The storage and degradation mechanisms to deal with micropollutants has been unveiled using mass spectrometry imaging. Nonetheless, water remains the key vector of micropollutants in the organisms or in the environment. The water distribution should in this way be investigated to explore the micropollutants distribution. Therefore, hydraulics numerical modelling was combined with micropollutants large scale screening to investigate their distribution.

LC-HRMS/MS, MSI, wetland, sludge, micropollutants, CFD, large-scale screening, ecosystems

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :Figure 7 :Figure 8 :Figure 9 :Figure 10 :Figure 11 :Figure 12 :Figure 13 :Figure 14 :Figure 15 :Figure 16 :Figure 17 :Figure 18 :Figure 19 :Figure 20 :Figure 21 :Figure 22 :Figure 23 :Figure 24 :Figure 25 :Figure 26 :Figure 27 :Figure 28 :Figure 29 :Figure 30 :Figure 31 :Figure 32 :Figure 33 :Figure 34 :Figure 35 :Figure 36 :Figure 37 :Figure 38 :Figure 39 :Figure 40 :

 12345678910111213141516171819202122232425262728293031323334353637383940 Figure 1: CWs types mentioned in the literature[START_REF] Kadlec | Treatment wetlands[END_REF]. The traditional CW without any input are opposed to enhanced CW using facilities (artificial and baffle). Concerning the traditional CWs the hydraulics mode influence could be visualized with the different CW type (subsurface, free surface and vertical flow). ......................................................................................... 27 Figure 2: CWs operating in France between 1970 and 2018 (("Portail d'informations sur l'assainissement communal -Services en ligne," visited on 01/02/ 2020)). ......................................... 29 Figure 3: Two stage VFCW system. The main system used in France to treat wastewater. ................ 30 Figure 4: The main wetland classes used as tertiary treatment in France A) Ditches wetland, B) Ponds wetland, C) Meadows wetland. ............................................................................................................. 31 Figure 5: Articles published from 1970 to 2020 with the keywords wastewater and heavy metals or pesticides or drugs or personal products according to the results found in ScienceDirect. ................... 33 Figure 6: PAHs priority list from the US EPA (Yan, et al., 2014). ...................................................... 35 Figure 7: Potential sources of micropollutants found in the environment. The diffuse and the local emission release point are mentioned (WWTP, agriculture activities, domestic activities, leaching…). Besides the different mechanisms occurring in micropollutants transfers could be found (runoffs, volatilization, atmospheric deposits, rainfalls…). ................................................................................. 41 Figure 8: Drugs enzymatic degradation in human body adapted from Meunier et al., 2004 (Meunier, de Visser, et Shaik 2004). .......................................................................................................................... 42 Figure 9: Phytoremediation strategies found in the environment adapted from (Favas et al., 2014). Phytovolatilization, phytostabilization, phytodegradation and phytoextraction are the main mechanisms occurring in plants. ................................................................................................................................ 47 Figure 10: Mass spectrometer composition with the three main components. .................................... 49 Figure 11: Suspect screening procedure using compound information to identify the analyte of interest as described in Hug et al.,2014. ............................................................................................................. 51 Figure 12: Non target screening procedure using compound information to identify the analyte of interest as described in Hug et al.,2014. ................................................................................................ 52 Figure 13: Compounds identification strategy and identification level of confidence (adapted from Schymanski et al., 2014). The green arrows indicate the start point of the screening approach considered. The blue arrows indicate the increasing confidence identification after each level confirmation. ....... 53 Figure 14: MALDI source principles adapted from Mendis, 2016. ..................................................... 54 Figure 15: Following steps used for CFD model building (Wicklein et al. 2016). .............................. 59 Figure 16: Turbulence models found to solve Navier-Stokes equations in CFD (Andersson 2012). Direct Numerical Simulation, Large Eddy Simulation and RANS are the different models used to solve Navier Stokes equation. ......................................................................................................................... 62

Figure 41 :Figure 42 :Figure 43 :Figure 44 :Figure 45 :Figure 46 :Figure 47 :Figure 48 :Figure 49 :Figure 50 :Figure 51 :Figure 52 :Figure 53 :Figure 54 :Figure 55 :Figure 56 :Figure 57 :Figure 58 :

 414243444546474849505152535455565758 Figure40: Pigments concentration and hormones and azelaic acid found in the poplar leaves (control and polluted). The analysis shown that chlorophyll a is found in higher concentration in the polluted leaves. ABA and cis-OPDA are found in higher proportion in the control leaves, while BA, SA and GA7 were found in higher amount in polluted leaves fromVillette et al., 2019a. ...................................... 103 Figure 41: Xenobiotics and lipids distribution in the sludge layers. .................................................. 105 Figure 42: Granulometry curve from the two CW's sludge layers. A) top layer granulometry curve. B) Bottom layer granulometry curve adapted from Maurer et al.,2020. .................................................. 106 Figure 43: Profile of lipids found in the CW's sludge layers. ............................................................ 108 Figure 44: Poplars where leaves were collected to MSI analysis. A control plant was selected in the area considered in the non-polluted area and a poplar growing in wetland bank considered as polluted area. ..................................................................................................................................................... 114 Figure 45: Willow where leaves were collected for MSI analysis. .................................................... 115 Figure 46: Leaves collected from plant growing in the tertiary treatment wetland. A) poplar leaves B) Willow leaves. ..................................................................................................................................... 116 Figure 47: Sample strategy to collect the three reeds replicates in the first stage of the VFCW located in Falkwiller. These replicates were all collected in 50 cm radius of the water inlet. ......................... 117 Figure 48: The three reeds replicates collected in the study site. ....................................................... 118 Figure 49: Reed (Phragmites australis) with its different organs before the analysis. ....................... 119 Figure 50: Plant sample preparation for cryosectionning. A) Stem cut used for the analysis. B) Flash freezing of the sample. C) Sample embedding and placed in the cryomycrotome for the cryosectionning. ............................................................................................................................................................. 120 Figure 51: Samples cuts using the cryomycrotome and its parameters. A) Parameters optimized for the plants samples cutting. B) Sample embedding and placed in the cryomycrotome for the cryosectionning. ............................................................................................................................................................. 120 Figure 52: Cut of samples before matrix application. A) Willow leaves cuts. B) Reed stem cuts. ... 121 Figure 53: Sludge samples placed in bucket with similar size used for MSI experiments. ............... 121 Figure 54: Sludge samples cut using the cryomycrotome. ................................................................. 122 Figure 55: Sludge cut after the optimization stage. ............................................................................ 122 Figure 56: ITO slide containing sludge samples before the MSI analysis. ........................................ 123 Figure 57: FT-ICR-MS (SolariX XR (Bruker, Germany)). ............................................................... 123 Figure 58: Reed cuts analyzed to investigate the fluorescein and sulforhodamine distribution in the whole plant. ......................................................................................................................................... 127

Figure 60 :Figure 61 :Figure 62 :Figure 63 :Figure 64 :Figure 65 :Figure 66 :Figure 67 :Figure 68 :Figure 69 :Figure 70 :Figure 71 :Figure 72 :Figure 73 :Figure 74 :Figure 75 :

 60616263646566676869707172737475 Figure59: Distribution of plant metabolites annotated in poplar leaves (polluted the longest leaf and non-polluted the shortest). Different distribution could be observed in the leaves. Salicin (D) and neurine (B) seems to be preferably found in the outer tissue while pheophorbide a (F) are mainly found in the inner tissue. Piperidine (A), adenine (C), lyso PC 16:0 (E) are found in the different tissues. The boxplot (G) suggests that these compounds are found in higher proportion in the polluted leaves(Villette et al., 2019a). ................................................................................................................................................. 129

Figure 76 :Figure 77 :Figure 78 :Figure 79 :Figure 80 :Figure 81 :Figure 82 :Figure 83 :Figure 84 :Figure 85 :Figure 86 :Figure 87 :Figure 88 :

 76777879808182838485868788 Figure 76: Data used to build the numerical geometry. A) Coordinates imported from the data collected in the study site. B) General shape of the numerical model. ............................................................... 156 Figure 77: Scheme of the check valve inclination issue..................................................................... 157 Figure 78: Scheme of the check valve translation used to determine the hydraulics perimeter. Blue ring represents the pipes and the red ring represent the check valve. ......................................................... 157 Figure 79: OpenFoam structure ("OpenFoam User Guide" 2007). ................................................... 159 Figure 80: Tracer experiment using sulforhodamine in the artificial pond. ....................................... 161 Figure 81: Velocity field in the tertiary treatment wetland obtained by CFD modeling. A) lowest inlet flowrate (4.1 m 3 /h) simulation. B) Highest inlet flowrate simulation (21.1 m 3 /h). ............................. 164 Figure 82: Experimental and modeled residence time distribution comparison to validate the model. ............................................................................................................................................................. 166 Figure 83: Sludge sampling area defined according to the velocity field simulated. Four areas were selected to represent fast and low flow rates but also the areas near the boundary conditions. .......... 167 Figure 84: Micropollutants number found in the different water flow areas depending on the seasons. ............................................................................................................................................................. 168 Figure 85: Distribution of micropollutants found specifically or in different areas. .......................... 170 Figure 86: Concentration of compounds found at least in 2 areas, celiprolol was removed from the figure due to scale issues. .................................................................................................................... 171 Figure 87: Irbesartan concentration found in the different water flow areas. .................................... 171 Figure 88: Raw formula and putative identification found in the different water flow areas. ........... 174

  wastewater treatment: a gradual improvement to limit the impact of human activities While being one of the most crucial resources for mankind, water has been one of the first resources polluted by the human activities. The sedentary lifestyle development has raised the fate of waste and specifically the feces.

  , the European directive in 1991 (European Directive 91/271/C.E.E. May 21th 1991) defines the resource objectives and then WWTPs were introduced in the main urban areas. But the performances results were only established in 2000 with the WFD (Directive: 2000/60/C.E.E. October 23 rd 2000). The WFD aims for good water quality (surface and ground water) in the European Union and have promoted the WWTPs setting up.

Figure 2 :

 2 Figure 2: CWs operating in France between 1970 and 2018 ("Portail d'informations sur l'assainissement communal -Services en ligne," visited on 01/02/ 2020).

Figure 3 :

 3 Figure 3: Two stage VFCW system. The main system used in France to treat wastewater.

Figure 4 :

 4 Figure 4: The main wetland classes used as tertiary treatment in France A) Ditches wetland, B) Ponds wetland, C) Meadows wetland.

Figure 5 :

 5 Figure 5: Articles published from 1970 to 2020 with the keywords wastewater and heavy metals or pesticides or drugs or personal products according to the results found in ScienceDirect.

Figure 6 :

 6 Figure 6: PAHs priority list from the US EPA (Yan, et al., 2014).

Figure 7 :

 7 Figure 7: Potential sources of micropollutants found in the environment. The diffuse and the local emission release point are mentioned (WWTP, agriculture activities, domestic activities, leaching…). Besides the different mechanisms occurring in micropollutants transfers could be found (runoffs, volatilization, atmospheric deposits, rainfalls…).

Figure 8 :

 8 Figure 8: Drugs enzymatic degradation in human body adapted from[START_REF] Meunier | Mechanism of Oxidation Reactions Catalyzed by Cytochrome P450 Enzymes[END_REF][START_REF] Meunier | Mechanism of Oxidation Reactions Catalyzed by Cytochrome P450 Enzymes[END_REF].

Figure 9 :

 9 Figure9: Phytoremediation strategies found in the environment adapted from[START_REF] Favas | Phytoremediation of Soils Contaminated with Metals and Metalloids at Mining Areas: Potential of Native Flora[END_REF]. Phytovolatilization, phytostabilization, phytodegradation and phytoextraction are the main mechanisms occurring in plants.

  Figure 10.

Figure 10 :

 10 Figure 10: Mass spectrometer composition with the three main components.The ion source is used to ionize and volatilize compounds. The types of source is based on a different physical principle and the operator should choose the most adapted source according to the characteristics of the molecule to be analyzed (thermal stability, chemical lability, volatile compound, chemical function, molecules size, MS in tandem with GC or LC). In the literature, several sources are mentioned, but the main representatives are

  Figure 12.

Figure 12 :

 12 Figure 12: Non target screening procedure using compound information to identify the analyte of interest as described in Hug et al., 2014.The Figure13highlights the different strategies.

Figure 13 :

 13 Figure 13: Compounds identification strategy and identification level of confidence (adapted from Schymanski et al., 2014). The green arrows indicate the start point of the screening approach considered. The blue arrows indicate the increasing confidence identification after each level confirmation.

Figure 14 :

 14 Figure 14: MALDI source principles adapted from Mendis, 2016.

Figure 15 :

 15 Figure 15: Following steps used for CFD model building (Wicklein et al. 2016).

Figure 16 :

 16 Figure 16: Turbulence models found to solve Navier-Stokes equations in CFD (Andersson 2012). Direct Numerical Simulation, Large Eddy Simulation and RANS are the different models used to solve Navier Stokes equation.

  [10] 𝐹(𝑢 𝑥𝑖,𝑡𝑗+1 ) = 𝑢 𝑥𝑖,𝑡𝑗+1 +𝑢 𝑥𝑖,𝑡𝑗 ∆𝑡[10]

Figure 17 :

 17 Figure 17: Environmental metabolomic workflow applied in this study with its key steps (sampling, sample preparation, nontarget analysis and data analysis).

Figure 18 :

 18 Figure 18: Study site location in Falkwiller village (from Google maps). This study site is a full-scale CW coupled to a tertiary treatment wetland. This CW collects wastewater from Falkwiller, Gildwiller and Hecken and has a capacity around 1450 people equivalent and has been set up in 2010. The CWs designed in Falkwiller is a two-stage vertical flow constructed wetlands. (VFCW) described in Figure 19. The location of this VFCW in the treatment system could be found in the water treatment global scheme represented in Figure 20.

Figure 19 :

 19 Figure 19: Bed filter planted with reeds from the 2 nd stage Constructed wetland in Falkwiller (Grand Est, France).

Figure 20 :

 20 Figure 20: Falkwiller's water treatment system global scheme.

Table 1 :

 1 Design parameters for the Falkwiller CW based on the hydraulics and pollutant flows. Where BOD5: Biochemical Oxygen Demand 5 days, COD: Chemical Oxygen Demand, TSS: Total suspended solids, TKN: Total Kjedhal Nitrogen, TP:

Figure 21 :

 21 Figure 21: Tertiary treatment wetland (ditch) in Falkwiller (Grand Est, France).

  is released in a small river called the Soultzbach which flows into the Largue and then into the Ill as shown in Figure 22.

Figure 22 :

 22 Figure 22: Rivers maps where the constructed wetlands releases the effluent (Google maps).

Figure 23 :

 23 Figure 23: Automatic samplers (Avalanche Teledyne ISCO) used to collect wastewater.Briefly, a flow rate-controlled sampling was performed using refrigerated samplers (Avalanche TELEDYNE ISCO, Lincoln, Nebraska) found in Figure23. The pooled samples are collected in a glass bottle and all the sampler elements (pipes and glass) were adapted for micropollutants sampling as mentioned in Nuel 2017. Indeed, these samples will provide an overview of the daily water quality integrated peaks and troughs of wastewater releases. This refrigerated samples will be kept at 4 °C before the sample preparation and limit the degradation of heatsensitive xenobiotics. This sampling was controlled by flow rate using ultrasonic sensors (IJINUS, Mellac, France). Venturi flumes, described in Figure24were located at the inlet and the outlet of the CW and the tertiary treatment wetland. Thus, ultrasonic sensors could measure the water depth. Furthermore, each Venturi flume is characterized by a law linking the water depth to flow rates. In our study case, the Venturi flume found at the inlet and outlet of the CW has the following law[11] provided by the constructors (Venturi flume with exponential opening type III ISMA, Forbach, France).

Figure 24 :

 24 Figure 24: Venturi flume found at the outlet of Falkwiller's CW.

Figure 25 :

 25 Figure 25: Sampling of the ecosystem compartment (water, reed roots and mud) in the CW.

Figure 26 :

 26 Figure 26: Compartment sampled in the tertiary treatment wetland.

Figure 27 :

 27 Figure 27: Coring performed using a manual auger.

Figure 28 :

 28 Figure 28: Coring performed in the CW. A) Manual auger used for the coring. B) CW mud vertical profile reconstructed after coring. The coring was performed until 60 cm depth when the auger reached the gravels. In this coring sample, two layers were analyzed. The top layer which corresponds to the ten first cms and the bottom layer which correspond the last ten cms (50-60 cms) as shown in the Figure 29.

Figure 30 :

 30 Figure 30: Sludge samples passed through different sized mesh under mechanical stirring.

  been compared. The most quoted method for micropollutants extraction (liquid extraction coupled with SPE), then a liquid extraction without SPE and finally the Bligh & Dyer extraction. Indeed, SPE cartridges are specific for some micropollutants (cartridge affinity). The comparison with the liquid extraction without SPE cartridges will highlight the potential micropollutants lost due to the cartridge specificity. On the other hand, the Bligh & Dyer method is a common method used in lipids extraction. As sludge are composed of greasy substances, this extraction could also be interesting in this way.The liquid extraction will be the first method developed in this manuscript. A double extraction is performed on the 10 g of sediment samples. The first extraction used 40 mL of acetonitrile: water (90:10) with 1% acetic acid (on the total volume). Samples are shaking overnight at 4 °C using a magnetic stirrer. Then samples are centrifuged at 5500 rpm during 15 min. The supernatant is collected and a second extraction is carried out using 20 mL of isopropanol: acetonitrile (90:10) and shaking during 15 min. Samples are centrifuged again using the same conditions as those mentioned previously. The supernatant is collected and pooled with the first supernatant. Then, the supernatant is freeze-dried and the pellet is solubilized in 1 mL of acetonitrile: isopropanol: water (50:45:5). This process could be observed on Figure31.

Figure 31 :

 31 Figure 31: Micropollutants extraction from solid matrix using the liquid extraction method A) Overnight extraction using isopropanol: acetonitrile then isopropanol: water. B) Supernatant collected before the freeze-drying step. C) Pellet collected after the freeze-drying. D) Samples solubilized in 1 mL acetonitrile: isopropanol: water (50:45:5) before MS analysis. Concerning the water samples, 50 mL are collected and directly freeze-dried. Then the pellet is solubilized in 1 mL of methanol: water (90: 10), as shown in Figure 32.

Figure 32 :

 32 Figure 32: Micropollutants extraction in wastewater based on freeze drying and extracted by methanol: water (90:10). A) Wastewater samples. B) Freeze-drying step. C) Pellet collected after the freeze-drying. D) Sample solubilized in 1 mL methanol: water (90:10).

Figure 33 :

 33 Figure 33: Scheme of the experimental set-up using SPE method for micropollutants extraction. Blue sample corresponds to water and orange sample corresponds to sludge.

Figure 34 :

 34 Figure 34: Scheme of the Bligh & Dyer method.

  mg fresh weight of plant samples are collected and ground in liquid nitrogen as shown in Figure35. Then a first extraction is performed using 1.5 mL cold MeOH containing 2 H6 abscisic acid (Olchemim) as an internal standard (10µg/mL). Then this solution is vortexed and the extraction is carried out overnight (16h) at -20 °C. Then the solution is centrifuged for 15 min at 13 000 rpm. The supernatant is collected and freeze dried using a speed vac concentrator (Savant SPD121P, Thermo Fisher). All the extraction is repeated once, and the supernatants are pooled in the same glass. Finally, the dried material is recovered in 300 µL of MeOH.

Figure 35 :

 35 Figure 35: Reed rhizome extraction. A) reed rhizome samples. B) reed rhizome ground using liquid nitrogen.

  Figure 36.

Figure 36 :

 36 Figure 36: Bucket table from Metaboscape representing the different buckets found in a sample. This pool corresponds to the level 5 (exact mass) in the Schymanski classification. An intensity threshold has been optimized to obtain a global overview. Then the intensity threshold for features extractions parameters should be chosen. Therefore, three thresholds have been tested to visualize the impact on the extraction and compared to the peaks detected. The results from this optimization could be observed in Figure 37.

Figure 37 :

 37 Figure 37: Intensity threshold sensitivity optimization for compounds annotations.

Figure 38

 38 Figure38represents the metabolites distribution found in the tertiary treatment wetland.

Figure 38 :

 38 Figure 38: Metabolites distribution in the ecosystem of the tertiary treatment wetland (wastewater, sludge and polluted poplar) adapted from Villette et al., 2019a.

  annotations on 1392 total annotations) and the results are detailed in the article Maurer et al., 2020. These results could highlight the compounds wide spreading in the different compartments of this ecosystem. Nevertheless, this distribution, based only on the metabolites number, could hide some diversity as only the metabolites annotations are shown in this Figure 38. The metabolites cover compounds naturally present in the environment such as plant, animal, bacteria metabolites but also xenobiotics and lipids. Therefore, the metabolites classes were investigated in each compartment. The results are shown in Figure 39.

Figure 39 :

 39 Figure 39: Distribution of metabolite classes observed in the different wetland ecosystem compartments (water, plants and sludge, adapted from Villette et al., 2019a).

Figure 40 :

 40 Figure40: Pigments concentration and hormones and azelaic acid found in the poplar leaves (control and polluted). The analysis showed that chlorophyll a is found in higher concentration in the polluted leaves. ABA and cis-OPDA are found in higher proportion in the control leaves, while BA, SA and GA7 were found in higher amount in polluted leaves fromVillette et al., 2019a.

Figure 41 :

 41 Figure 41: Xenobiotics and lipids distribution in the sludge layers.

  proportion of finer grain size in the top layer (D10 180 µm versus 550 µm) as the granulometry represented in Figure42indicated.

Figure 42 :

 42 Figure 42: Granulometry curve from the two CW's sludge layers. A) top layer granulometry curve. B) Bottom layer granulometry curve adapted from Maurer et al., 2020.

Figure 43 :

 43 Figure 43: Profile of lipids found in the CW's sludge layers.

Figure 44 :Figure 45 :

 4445 Figure 44: Poplars where leaves were collected to MSI analysis. A control plant was selected in the area considered in the nonpolluted area and a poplar growing in wetland bank considered as polluted area.

Figure 46 :

 46 Figure 46: Leaves collected from plant growing in the tertiary treatment wetland. A) poplar leaves B) Willow leaves.

Figure 47 :

 47 Figure 47: Sample strategy to collect the three reeds replicates in the first stage of the VFCW located in Falkwiller. These replicates were all collected in 50 cm radius of the water inlet.

Figure 48 :

 48 Figure 48: The three reeds replicates collected in the study site.

Figure 50 :

 50 Figure 50: Plant sample preparation for cryosectionning. A) Stem cut used for the analysis. B) Flash freezing of the sample. C) Sample embedding and placed in the cryomycrotome for the cryosectionning.

  Figure 51. Then the sections were deposited on indium-tin oxide (ITO) coated slides (Bruker, Germany) as shown in Figure 52.

Figure 51 :

 51 Figure 51: Samples cuts using the cryomycrotome and its parameters. A) Parameters optimized for the plants samples cutting. B) Sample embedding and placed in the cryomycrotome for the cryosectionning.

Figure 52 :

 52 Figure 52: Cut of samples before matrix application. A) Willow leaves cuts. B) Reed stem cuts.

Figure 53 :

 53 Figure 53: Sludge samples placed in bucket with similar size used for MSI experiments.

  preparation conditions as shown in Figure 53. Then the samples cutting could generate slices with similar areas and could be compared with each other. Slices have been carried using the cryomycrotome as shown in Figure 54.

Figure 54 :

 54 Figure 54: Sludge samples cut using the cryomycrotome.

Figure 55 :

 55 Figure 55: Sludge cut after the optimization stage.

Figure 56 :

 56 Figure 56: ITO slide containing sludge samples before the MSI analysis.

7T

  Fourier transform ion cyclotron mass spectrometer (FT-ICR-MS) SolariX XR (Bruker, Germany) equipped with a Paracell at resolving power R = 120,000 at m/z = 400. The FT-ICR-MS used for the analysis could be found in Figure 57. The detailed scheme of the FT-ICR could be found in the supplementary datas (9).

Figure 57 :

 57 Figure 57: FT-ICR-MS (SolariX XR (Bruker, Germany)).

Figure 58 :

 58 Figure 58: Reed cuts analyzed to investigate the fluorescein and sulforhodamine distribution in the whole plant.

  studied. This distribution seems to indicate that specific distribution should be found in the poplar leaves. The examples of six plants metabolites found in Figure59and three micropollutants found in Figure60, confirmed by commercial standards (retention time, exact mass and MS/MS profile if available) in the poplar leaves could illustrate this distribution. Plant metabolites distribution seems to be heterogeneous with no general trend could be observed. Some compounds are preferably detected in the outer tissues (Salicin or neurine), pheophorbide a, was mainly detected in inner tissues and piperidine could represent compounds evenly distributed in the leaves. The plants metabolites cover different functions (development,

Figure 59 :

 59 Figure 59: Distribution of plant metabolites annotated in poplar leaves (polluted the longest leaf and non-polluted the shortest).Different distribution could be observed in the leaves. Salicin (D) and neurine (B) seems to be preferably found in the outer tissue while pheophorbide a (F) are mainly found in the inner tissue. Piperidine (A), adenine (C), lyso PC 16:0 (E) are found in the different tissues. The boxplot (G) suggests that these compounds are found in higher proportion in the polluted leaves(Villette et al., 2019a).

Figure 60 :

 60 Figure 60: Micropollutants annotated in poplar leaves. Some differential metabolite features were annotated as micropollutants used as pesticides (A), personal care product derivatives (B) or drugs (C). The images (left) show a preferential localization in the peripheral tissues of the polluted leaves. Boxplots confirm that these metabolite features are differential between control and polluted leaves from Villette al., 2019a.

Figure 61 :

 61 Figure 61: Micropollutants distribution in willow tissue. Quinestrol was mainly found in internal tissues while iodosulfuron is sequestered in the vascular tissues from Villette et al., 2019a.

Figure 62 :

 62 Figure 62: Metabolism of telmisartan in willow leaves. First (I) and second (II) generation metabolites identified in the willow leaves. All these compounds were found in the leaves using MSI, except C33H30N4O3 (Villette et al., 2019b).

Figure 63 :

 63 Figure 63: MSI images of telmisartan and some of its first-and second-generation metabolites in S. alba leaves from Villette et al., 2019b.

  have shown that the parent compound of ibuprofen represents around 1% of ibuprofen form detected in Arabidopsis thaliana. The telmisartan results combined with those from Marsik et al., highlight the key role of plants in micropollutants degradation and the importance of examining parent compounds but also the different potential forms of metabolites.

Figure 64 :

 64 Figure 64: Carbamazepine and metoprolol distribution in the whole reed. The two compounds were analyzed in the different organs (roots, stem, leaves). The box represents the relative drugs intensity (according to the equation 15) found in each cut (a box is equivalent to a plants segment (3 cm) used for the MSI analysis) and representative MSI image were added to illustrate the distribution inside the different tissue.

Figure 65 :

 65 Figure 65: Carbamazepine and metoprolol metabolites distribution in the whole reed. The metabolites were analyzed in the different organs (roots, stem, leaves). The box represents the relative intensity found in each cut and representative.

Figure 66 .

 66 Figure 66. Their distribution is even more heterogeneous regarding the different compounds.

Figure 66 :

 66 Figure 66: Pesticides distribution in the whole reed. The pesticides (parathion-ethyl, mecarbam, propachlor, flamprop-methyl, dodemorph, fuberidazole) were analyzed in the different organs (roots, stem, leaves). The box represents the relative intensity found in each cut and representative MSI image were added to illustrate.

Figure 67 :

 67 Figure 67: Pesticides metabolites distribution in the whole reed. The metabolites were analyzed in the different organs (roots, stem, leaves). The box represents the relative intensity found in each cut and representative.

Figure 68 :

 68 Figure 68: Sulforhodamine and fluorescein detected in the different organs of the reed.

Figure 69 :

 69 Figure 69: Sildenafil-d3 spiked in the sludge matrix using MSI.

Figure 70 :

 70 Figure 70: Lipids identified by MSI in sludge samples A) ceramide (iso:d17:1 (4E)/25:0:20H) B) ceramide (iso:d17:1 (4E)/30:0:20H).

Figure 71 :

 71 Figure 71: Alternative process to annotate compounds in sludge samples analyzed by MSI. A) Raw data represented as a chromatogram in DataAnalysis. B) Mass spectra generated from raw data. C) Mass list representing the different m/z found in the samples.

Figure 72 :

 72 Figure 72: Study site location in Lutter village (from Google maps).

Figure 73 :

 73 Figure 73: Tertiary treatment wetland (pond) in Lutter (Grand Est, France).

Figure 74 :

 74 Figure 74: Material used to collect the geometry data. A) Bounds used to bound the system B) Inflatable boat used to measure the water depth.

  Figure 75.

Figure 75 :

 75 Figure 75: Virtual grid applied on the study site to collect the water depth.

  Figure 76B.

Figure 76 :

 76 Figure 76: Data used to build the numerical geometry. A) Coordinates imported from the data collected in the study site. B) General shape of the numerical model.

Figure 77 :

 77 Figure 77: Scheme of the check valve inclination issue.

  Figure 78.

Figure 78 :

 78 Figure 78: Scheme of the check valve translation used to determine the hydraulics perimeter. Blue ring represents the pipes and the red ring represent the check valve.

Figure 79 :

 79 Figure 79: OpenFoam structure ("OpenFoam User Guide" 2007).

  [23] described below should be found between 30 and 500 as recommended by[START_REF] Versteeg | An introduction to computational fluid dynamics: the finite volume method[END_REF]

Figure 80 :

 80 Figure 80: Tracer experiment using sulforhodamine in the artificial pond.

  already suggested the use of residence time to validate a waste stabilization pond (WSP) water flow mode. The comparison was investigated using the NAE (normalized absolute error). The NAE equation [27] could be found below:

  used to simulate the inlet flow rate and validate the model. To represent the water flow distribution, the two extreme water flow found in the wetland during our measurement campaign were selected. Besides the residence time distribution measured in the experimental field were used to validate the model. The results found in Figure 81 represent the two extreme flow rates detected.

Figure 81 :

 81 Figure 81: Velocity field in the tertiary treatment wetland obtained by CFD modeling. A) lowest inlet flowrate (4.1 m 3 /h) simulation. B) Highest inlet flowrate simulation (21.1 m 3 /h).In general terms, the velocity observed in the wetland and shown in Figure81suggest a low velocity field. Shallow water and rough sides could slow the velocity and generate this low velocity. The impact of water depth on hydraulic efficiency using numerical model has already been studied in the literature.Coggins et al., have suggested the loss of hydraulic efficiency related to the filling of a waste stabilization pond.[START_REF] Coggins | Sludge accumulation and distribution impact the hydraulic performance in waste stabilisation ponds[END_REF].

  Figure 82.

Figure 82 :

 82 Figure 82: Experimental and modeled residence time distribution comparison to validate the model.

  Figure 83.

Figure 83 :

 83 Figure 83: Sludge sampling area defined according to the velocity field simulated. Four areas were selected to represent fast and low flow rates but also the areas near the boundary conditions.

  Figure 84.

Figure 84 :

 84 Figure 84: Micropollutants number found in the different water flow areas depending on the seasons. The Figure 84 shows micropollutants found in triplicates in the sludge areas over the different seasons. The detailed results from micropollutants in each area could be found in the supplementary data (13). The results indicate that most of the samples contain between 25

Figure 85 :

 85 Figure 85: Distribution of micropollutants found specifically or in different areas.

Figure 86 :

 86 Figure 86: Concentration of compounds found at least in 2 areas, celiprolol was removed from the figure due to scale issues. The results described in Figure 86 could highlight that micropollutants which have been detected in several areas were found with similar concentration. The example of irbesartan mentioned in the Figure 87, and which has been detected in each area illustrates this homogeneous distribution in the different areas. Nevertheless, these results do not agree with those found in the literature.

Figure 87 :

 87 Figure 87: Irbesartan concentration found in the different water flow areas.

Figure 88 :

 88 Figure 88: Raw formula and putative identification found in the different water flow areas.

  structure des boues pouvant expliquer la distribution des micropolluants. Concernant ces résultats, une large diffusion de micropolluants a été retrouvée sur l'ensemble de la profondeur du profil suggérant une contamination sur l'ensemble de la profondeur. Néanmoins, l'analyse des composés retrouvés spécifiquement dans un horizon de boues, montre que les micropolluants sont majoritairement présents dans la couche supérieure et peu d'entre eux sont détectés spécifiquement dans la couche inférieure. Le contact direct avec les eaux usées non traitées, la composition en carbone, mais également la porosité et la taille des grains favorisent la sorption des polluants dans ces couches. De plus, l'analyse des lipides menée dans ce profil montre que l'activité microbienne se situe principalement au sein de l'horizon supérieur. Ainsi une dégradation de ces composés pourrait se dérouler dans cet horizon et expliquer le faible nombre de composés retrouvés spécifiquement dans l'horizon inférieur. Ces résultats ont pu montrer la large dispersion des micropolluants dans l'ensemble de l'écosystème et même au sein des organismes vivants. Ces derniers doivent mettre en place des mécanismes pour faire face aux micropolluants et notamment pour les plantes se développant dans ces systèmes. Or pour comprendre les mécanismes mis en oeuvre au sein d'une plante une localisation précise de ces micropolluants peut être nécessaire. Ainsi la seconde partie de ces travaux s'est concentrée sur la distribution spatiale de composés au sein de différents compartiments obtenue à l'aide de l'imagerie par spectrométrie de masse (IMS).L'analyse de la distribution spatiale des micropolluants au sein des feuilles du peuplier souligne une localisation spécifique de ces derniers. Au vu des résultats observés, il semble que le peuplier séquestre les micropolluants dans les tissus extérieurs des feuilles. Ce mécanisme qui semble être actif a probablement été mis en place par la plante pour confiner les micropolluants dans un espace moins toxique pour cette dernière et favoriser leur dégradation. Cette présence de micropolluants au sein de ces feuilles s'accompagne par une réponse de stress de la plante.Mais le stockage au sein d'espace spécifique n'est pas le seul processus mis en place par une plante. Une dégradation des micropolluants par voie métabolique peut également se dérouler au sein de ces dernières. L'exemple du telmisartan au sein des feuilles du saule peut parfaitement l'illustrer. En effet, les métabolites du telmisartan sont retrouvés avec des intensités plus fortes que ce dernier au sein des feuilles. Or ces métabolites ne sont pas retrouvés dans la boue ou l'eau environnante. Ces résultats suggèrent donc la dégradation de ces polluants par le biais du processus nommé « green liver ». Mais ces analyses effectuées sur les feuilles ne permettent pas d'obtenir une vision globale des processus se déroulant dans une plante. C'est pourquoi l'analyse spatiale a ensuite été effectuée sur une plante entière le roseau. En effet, le roseau, qui est la plante modèle utilisée dans les filières extensives de traitement des eaux usées avec les FPR, subit de manière chronique une exposition aux micropolluants. Les phénomènes de translocations peuvent ainsi être visualisés. Les résultats obtenus montrent qu'une large partie des micropolluants sont retrouvés dans les parties supérieures de la tige ou dans les feuilles. Ces résultats montrent bien un mouvement des micropolluants vers les parties aériennes suivant le flux de transpiration. Les différences de localisation ne s'expliquent pas par la classe de polluants ni entièrement par les propriétés physico-chimiques. Les différences de localisation peuvent montrer une différence temporelle de transfert au sein de la plante. La plante étant soumise de manière chronique aux micropolluants et le processus de transfert étant stoppé lors du sacrifice de la plante, différentes phases de transfert peuvent être observées.Néanmoins ces résultats montrent l'importance du flux d'eau dans la distribution des micropolluants. Or ces derniers retrouvés dans les boues ou les plantes proviennent des eaux usées. Les filières de traitement extensives ne présentent pas un flux d'eau homogène au sein de la filière de traitement. C'est pourquoi afin de comprendre la distribution des micropolluants dans de tels systèmes, l'analyse de l'écoulement dans des filières extensives semble primordiale. La modélisation numérique réalisée sur la ZRV a pu mettre en évidence une hétérogénéité du champ de vitesse. Elle a donc été utilisée pour définir des zones de prélèvements de boues afin de faire état de la diversité d'écoulement dans la ZRV. Cependant l'analyse des micropolluants retrouvés dans les différentes zones ne permet pas de conclure de l'influence de l'écoulement sur la distribution des micropolluants. Plusieurs hypothèses peuvent être apportées pour exprimer le manque de corrélation, comme les faibles vitesses observées dans l'ouvrage, l'absence de prise en compte du transport solide ou encore de la métabolisation des micropolluants.

Table of content

 of

	MSI: Mass Spectrometry Imaging
	PAH: Polycyclic Aromatic Hydrocarbons
	RANS: Reynolds Average Navier Stokes
	SA: Sinapic Acid
	SIMS: Secondary Ion Mass Spectrometry
	SPE: Solid Phase Extraction
	TKN: Total Kjeldahl Nitrogen
	TOF: Time Of Flight
	TP: Total Phosphorus

TSS: Total Suspended Solids VFCW: Vertical Flow Constructed Wetland WFD: Water Framework Directive WHO: World Health Organization WWTP: Waste Water Treatment Plant

Table 1 :

 1 Design parameters for the Falkwiller CW based on the hydraulics and pollutant flows. Where BOD5: Biochemical Oxygen Demand 5 days, COD: Chemical Oxygen Demand, TSS: Total suspended solids, TKN: Total Kjedhal Nitrogen, TP: Total phosphorus. ............................................................... 73

Table 2 :

 2 Design characteristics of Falkwiller tertiary treatment wetlands. .......................................... 74

Table 3 :

 3 Results from micropollutants identified in the two wastewater volume conditions (10 and 50 mL). .................................................................................................................................................. 83

Table 4 :

 4 Results from micropollutants identified in the two sludge weight conditions (10 and 20 g). 83

Table 5 :

 5 Results

from micropollutants identified in the three micropollutants extraction method applied on sludge samples (liquid extraction, SPE, and Bligh & Dyer). ........................................................... 87

Table 6: Results from micropollutants identified in the two micropollutants extraction method applied on water samples (liquid extraction, SPE). ........................................................................................... 87 Table 7: Characteristics of the extraction method: LOD, LOQ, and matrix effect adapted from Villette et al.

,2019a. ........................................................................................................................................... 89 Table 8: Repeatability of the extraction method using deuterated compounds in water and sludge samples. ................................................................................................................................................. 90 Table 9: Different databases used for the compounds identification.................................................... 96 Table 10: Annotations found in the sludge layers. ............................................................................. 104 Table 11: Translocation factor calculated for carbamazepine and metoprolol considering the different reed sections. ....................................................................................................................................... 138 Table 12: Design characteristics of Lutter wetlands........................................................................... 154 Table 13: Micropollutants number found each season in the different water flow areas. .................. 169 Table 14: Irbesartan and its metabolites found in the different areas. ................................................ 173

.2.3. Drugs and personal care products: micropollutants linked to domestic activities

	-organochlorines,
	-organophosphates,
	-carbamates,
	-phoenix,
	-pyrethroid.
	1.2Drugs could also present a wide compound diversity, used for human and animals' health. The
	French Public Health Code defines drugs as all compounds with preventive or curative

Table 2 :

 2 Design characteristics of Falkwiller tertiary treatment wetlands.

Table 3 :

 3 Results from micropollutants identified in the two wastewater volume conditions (10 and 50 mL).

		50 Ml	10 mL
	Micropollutants found in water sample	48	12

Table 4 :

 4 Results from micropollutants identified in the two sludge weight conditions (10 and 20 g).

Table 5 :

 5 Results from micropollutants identified in the three micropollutants extraction method applied on sludge samples (liquid extraction, SPE, and Bligh & Dyer).

		Liquid extraction without SPE	Liquid extraction with SPE	Bligh & Dyer
	Micropollutants found in sludge sample	82	33	51

Table 6 :

 6 Results from micropollutants identified in the two micropollutants extraction method applied on water samples (liquid extraction, SPE).

		Liquid extraction without SPE	Liquid extraction with SPE
	Micropollutants found in water sample	48	31

Table 7 :

 7 Characteristics of the extraction method: LOD, LOQ, and matrix effect adapted fromVillette et al., 2019a.

		LOD	LOQ	Water matrix	Sediment
		(µg/mL)	(µg/mL)	effect	matrix effect
	Acetaminopher-d4	10	10	97%	98%
	Gemfibrozil-d6	1	1	98%	98%
	N-Desmethyl sildenafil-d8	1	10	94%	96%
	Sulfamethoxazole-d4	10	10	97%	98%
	Diclofenac-d4	1	10	97%	98%
	Bezafibrate-d4	1	10	98%	97%

Table 8 :

 8 Repeatability of the extraction method using deuterated compounds in water and sludge samples using average area, standard deviation and coefficient of variation (CV).

			Water			Sediment
		Average area	Standard deviation	CV	Average area	Standard deviation	CV
	Acetaminophen-d4	1.45E+07 1.32E+06	9%	7.70E+06	2.27E+06	29%
	Sulfamethoxazole-d4 1.69E+08 4.37E+06	3%	9.26E+07	6.57E+06	7%
	N-desmethyl
		3.01E+05 9.37E+04	31%	1.59E+04	1.07E+03	7%
	sildenafil-d8
	Gemfibrozil-d6	6.19E+08 8.34E+07	13%	4.20E+08	4.40E+06	1%
	Diclofenac-d4	4.39E+08 6.79E+06	2%	6.51E+08	3.03E+07	5%
	Bezafibrate-d4	3.97E+08 3.25E+07	8%	5.87E+08	5.96E+07	10%

Table 9 :

 9 Different

		databases	used	for	the	compounds	identification.	a	(http://foodb.ca/),	b
	(http://kanaya.naist.jp/KNApSAcK/),	c	(https://www.plantcyc.org/),	d	(http://phenol-explorer.eu/),	e
	(http://www.swisslipids.org/), f (https://www.norman-network.net/), g (http://www.t3db.ca/), h (http://www.swgdrug.org/), i
	(http://www.eurl-pesticides.eu), j (http://www.ymdb.ca/), k (http://www.ecmdb.ca/). The other databases were commercially
	available.
	Database type
	Global database	NIST14 242,477 compounds	Wiley 10 620,000 compounds	FoodDB a 15,959 compounds
	Plant database	KNApSAck b 40,053 compounds	PlantCyc c 2,837 compounds	Phenol Explorer d 488 compounds
	Lipids database	Swiss Lipids e 570,677 compounds
									Maurer,
	Toxic database	Norman f 40,053	TargetScreener T3DB g 2,246 3,003	Pleger, Weber 8,650
		compounds	compounds	compounds	compounds
	Drugs database	SWGDRUGS h 2,672
		Compounds
	Pesticides database	EURL-FV i 761
		compounds
	Bacteria/Yeast	YMDB j		ECMD k
	database

Table 11 :

 11 Translocation factor calculated for carbamazepine and metoprolol considering the different reed sections.

	Translocation factor	Leaves vs roots	Bottom stem vs Roots	Top stem vs roots	Leaves vs Bottom stem	Top stem vs bottom stem	Leaves Vs top stem
	Carbamazepine	468	3.4	0.7	139	0.21	654
	Metoprolol	317	9.3	114	34	12	2.8

Table 12 :

 12 Design characteristics of Lutter wetlands.

Table 13 :

 13 Micropollutants number found each season in the different water flow areas.

	Area 1	Area 2	Area 3	Area 4
	Celiprolol	Diflufenicane	Acebutolol	Amiodaron
	Irbesartan	Etofenprox	Celiprolol	Climbazole
	Oxadiazon	Irbesartan	Etofenprox	Diflufenicane
	Tramadol	Oxadiazon	Irbesartan	Etofenprox
		Permethrim	Isoconazole	Irbesartan
		Propafenone	Venlafaxine	Oxadiazon
		Tramadol		Venlafaxine

Table 14

 14 could also illustrate this comment. The irbesartan decrease is related to an increase of metabolites detection, suggesting a transformation of this drug.

Table 14 :

 14 Irbesartan and its metabolites found in the different areas.

	Compounds	Area 1	Area 2	Area 3	Area 4
	Irbesartan (µg/kg)	118.81 ±23.84	95.21 ±43.05	125.12 ±66.22	76.82 ±11.69
	C25H26N6O2 (area AU)		1466.33 ±480.72
	C25H28N6O2		22862.17
	(area AU)		±7059.86
	C26H30N6O2 (area AU)		5038.83 ±1993.28
	C25H30N6O3				5816.33
	(area AU)				±2482.14

  Tous composés introduits dans l'environnement et pouvant générer un risque ou un problème peuvent être évoqués sous la dénomination de contaminants ou polluants. En effet, le rejet de contaminants au sein de l'environnement et plus particulièrement dans les milieux aquatiques a entrainé la mise en place de système de traitement afin de préserver la ressource. Il faut cependant attendre les années 1960-1970 pour que de tels systèmes soient mis en place en France et la loi de 1992 pour fixer des objectifs de traitement. Ces filières de traitement ont tout d'abord été concentrées sur les zones urbaines générant le plus grand flux de polluants. Mais la directive cadre sur l'eau de 2000 a fixé le bon état des masses d'eau en Europe. C'est pourquoi les zones rurales nécessitent également la mise en oeuvre de filières de traitement. Cependant le contexte rural avec des moyens financiers moins importants a conduit à l'introduction de filières extensives tel que les filtres plantés de roseaux (FPR) ou des lagunes.Malgré l'absence de prise en compte de ces micropolluants dans les ouvrages de traitement, l'étude de la littérature montre que les FPR ont une certaine efficacité pour abattre une partie des micropolluants issus des eaux usées. Cependant, la plupart des études se concentrent sur une catégorie ou un nombre restreint de composés servant de modèle pour comprendre les mécanismes se déroulant dans ces ouvrages. Mais cette approche avec un choix de composés peut biaiser les conclusions. Or les avancées technologiques des dernières années, notamment en spectrométrie de masse à haute résolution, ont permis d'étudier un spectre large de composés, présents même à de très faibles concentrations. Ces avancées permettent également de ne plus se concentrer uniquement sur les molécules mères, mais également les produits de dégradation qui eux aussi peuvent présenter un risque majeur pour l'environnement.Au sein de ces filières de traitement, l'un des sous-produits présentant un enjeu majeur est la boue accumulée. En effet, leur gestion est un point crucial pour les exploitants et demande ainsi une connaissance fine de leur contenu pour choisir la filière de traitement adaptée à ce sousproduit. Mais l'analyse ne peut pas être réduite au seul compartiment boues, car celui possède des interactions avec les plantes et les eaux présentes dans ce système. Lors d'une précédente thèse(Maximilien Nuel, 2017), il a été démontré que les résidus médicamenteux peuvent être retrouvés au sein des différents compartiments de l'environnement. Étant concentré sur une seule famille de polluants, il parait essentiel pour comprendre le devenir de ces micropolluants, d'étudier leur distribution en utilisant un spectre large dans les eaux et les plantes se développant dans ces systèmes. En effet, les micropolluants accumulés au sein des boues sont un enjeu majeur pour leur gestion. Pour comprendre cette répartition, différentes méthodes peuvent être combinées. Au sein de ce mémoire, cette étude de la distribution des micropolluants a été menée selon trois axes majeurs : Celle-ci fut basée sur la classification mentionnée par Schymanski(Schymanski et al., 2014). Pour cela, une méthodologie d'identification sur l'ensemble des échantillons a également été développée. Cette identification a été permise par l'utilisation du logiciel Metaboscape 3.0. (Bruker) et l'optimisation de ces paramètres afin d'extraire le plus grand nombre de composés. À partir du jeu de données extrait, l'outil SmartFormula a été utilisé pour générer les formules brutes de composés. Puis les composés possédant une formule brute ont été annotés afin d'obtenir une identification putative. L'identification est un défi important pour la métabolomique en mode non ciblée, car il n'existe pas de bases de données uniques regroupant l'ensemble des composés d'intérêt recherchés dans cette étude. Pour cela, les bases de données d'intérêt ont été importées localement et une méthode d'identification basée sur l'outil « Analyte list » a été menée. À l'aide de la méthode développée, les différents compartiments ont donc pu être analysés. Ces premiers résultats ont permis d'obtenir une vision des compartiments en interaction. Cependant, la boue qui demeure l'un des problèmes majeurs de gestion des exploitants ne peut être restreinte à sa couche superficielle. Pour optimiser la Pour cela, les différents organes de plantes ont été analysés. En effet, l'approche a tout d'abord été menée sur des feuilles de plantes ayant grandi dans la ZRV (peuplier et saule). été exportées vers la plateforme open source Metaspace et vers le logiciel Metaboscape. De plus, les plantes pouvant être au coeur de procédés de dégradation, les métabolites (conjugués et catabolites) des micropolluants ont également été étudiés. Pour cela, ils ont été générés sur deux générations en utilisant les prédictions in silico à l'aide du logiciel Metabolites Predict 2.0 (Bruker). Mais l'analyse des seules feuilles des plantes peut introduire un biais sur les processus se déroulant au sein des plantes. Ainsi, une plante entière (un roseau) a été analysée en utilisant la méthode mentionnée précédemment. Pour cela, le roseau a été sectionné à intervalles réguliers et l'ensemble des organes ont été analysés. Cette analyse d'une plante entière permet de visualiser les localisations préférentielles, mais également d'étudier les translocations se déroulant au sein des plantes. En effet, la littérature évoque le flux de transpiration comme l'un des vecteurs du mouvement des micropolluants au sein des plantes. Ce flux d'eau semble être l'un des vecteurs des micropolluants dans les plantes. Mais leur présence au sein des plantes est liée à celle dans les eaux usées et les boues. La distribution des eaux usées pourrait avoir un impact majeur sur la répartition spatiale des micropolluants dans les ouvrages et donc dans les boues. Or les flux d'eau dans ces ouvrages (FPR et ZRVs) ne sont pas homogènes et vont donc provoquer une hétérogénéité dans le contenu des micropolluants. Une fois la simulation convergée, des zones de prélèvements ont été définies sur la base des différences de champs de vitesse et de zones d'intérêt (entrée, sortie). Ces zones de prélèvements ont été utilisées pour collecter des boues et analyser le contenu en micropolluants. Ainsi les résultats obtenus permettront de visualiser si un lien entre la vitesse d'écoulement au sein d'une ZRV et la distribution de micropolluants existe. Les résultats obtenus sur la distribution des micropolluants montrent la dispersion de ces derniers au sein de l'ensemble de l'écosystème. En effet, l'ensemble des classes des polluants recherchés a été retrouvé dans les différents compartiments (eau, boues, plantes). La comparaison du profil métabolomique suggère néanmoins une distribution hétérogène selon le compartiment considéré. Même si la répartition ne dépend pas de la classe des composés, il n'en demeure pas moins que certaines molécules ont une affinité plus forte avec les phases liquides, ou se sorbe mieux sur certaines matrices. Néanmoins, ces propriétés physicochimiques ainsi que les paramètres gouvernant le devenir de ces micropolluants ne permettent pas à eux seuls d'expliquer totalement la distribution observée. De plus, l'analyse effectuée à la fois dans le FPR et la ZRV montre que cette distribution est observée dans la filière de traitement, mais également dans la zone réceptionnant son rejet et donc dans l'environnement.

	Principaux résultats
	Démarche expérimentale
	L'approche expérimentale s'est déroulée en plusieurs phases suivant les axes mentionnés dans
	l'introduction générale. Tout d'abord ces travaux se sont concentrés sur la visualisation d'un
	spectre large de micropolluants au sein des différents compartiments de l'écosystème du FPR
	et de la zone de rejet végétalisé (ZRV). Pour étudier la distribution des micropolluants au sein
	des écosystèmes, une stratégie d'échantillonnage a été développée. Elle visait à collecter les
	échantillons dans un périmètre de 1 m². Dans ce périmètre, les compartiments en interactions
	ont été prélevés (eau, boues de la couche supérieure et plantes (roseau, peuplier)). Puis une Une fois les feuilles de ces derniers collectées, une optimisation de la coupe des échantillons a méthode d'extraction des micropolluants a été mise au point et optimisée. Pour cela, différentes été réalisée afin de conserver l'intégrité des tissus. Ces échantillons ont ensuite été analysés en
	méthodes ont été comparées (extraction liquide-liquide, SPE, Bligh & Dyer). Suite à cette phase utilisant le MALDI Imaging et l'ultra haute résolution grâce à un FT-ICR (SolariX 7T, Bruker d'optimisation, la méthode d'extraction liquide-liquide a été retenue en utilisant 10 g de boues et 50 mL d'eau. Les échantillons ont ensuite été analysés en LC-HRMS/MS en mode Daltonics). La distribution spatiale a été obtenue avec une résolution de 50 µm et les spectres

L'ensemble des filières mis en oeuvre en France a été conçu sur les problématiques du XX ème siècle et la macropollution (carbone, azote et phosphore). Ainsi les polluants émergents et donc les micropolluants n'ont pas ou très peu été pris en compte lors de la conception de ces ouvrages. Ces micropolluants sont issus de sources diverses, mais rejoignent l'environnement majoritairement par les eaux usées, faisant des stations d'épuration un point de rejet majeur vers l'environnement. Or, ils représentent un risque pour l'environnement, mais également pour la santé humaine. Au cours des dernières années, cette problématique a été prise en compte comme le montre l'exemple des Watch list dans la législation européenne.

une étude en spectre large des micropolluants dans les différents compartiments de l'environnement (eau, boues, plantes) à l'aide de la LC-HRMS/MS, une distribution spatiale des polluants réalisée à l'aide du MALDI-Imaging, une étude de la distribution des micropolluants en couplant les flux modélisés numériquement en CFD avec une analyse en spectre large. d'ionisation positive et en utilisant la méthode TargetScreener développée par Bruker pour l'identification de micropolluants. Or l'analyse non ciblée de composés conduit à un large spectre de composés. Il a donc été nécessaire de définir une stratégie d'identification des composés. gestion de l'enlèvement des boues, il est nécessaire d'obtenir une vision sur le profil vertical de ces boues. Pour cela, un carottage au sein des ouvrages a été réalisé. L'analyse de la structure, mais également du contenu en macro et micropolluants a été réalisée afin de conclure sur la diffusion de ces micropolluants sur le profil vertical. Ces résultats peuvent fournir une première information sur une distribution spatiale des polluants dans ces systèmes. Mais dans ces systèmes alliant plantes, eaux et boues, les plantes peuvent jouer un rôle essentiel dans le devenir des micropolluants. Or une localisation précise des micropolluants au sein de ces dernières peut avoir une importance cruciale pour comprendre les bioprocédés se déroulant au sein de ces dernières. Ces travaux se sont donc ensuite concentrés sur cette analyse spatiale des micropolluants à l'aide de l'imagerie par spectrométrie de masse. ont été calibrés en utilisant les pics de la matrix HCCA. L'analyse des données s'est ensuite déroulée en plusieurs temps. Les données ont été importées dans le logiciel SCiLs Lab2016b pour visualiser la distribution des polluants. Or pour obtenir l'identification des composés les données ont Ainsi l'analyse des flux d'eau dans les ouvrages semble primordiale pour comprendre la distribution des micropolluants. Cette distribution peut être obtenue par la création d'un modèle numérique simulant les flux au sein d'un ouvrage. Pour cela, les conditions expérimentales retrouvées sur le site d'étude ont été relevées (géométrie de l'ouvrage par coordonnées GPS, mesures du débit d'entrée, prise des conditions aux limites…). Une fois le relevé de ces conditions aux limites effectuées, un modèle numérique a été créé. Ce dernier a permis de simuler les écoulements au sein de la ZRV en considérant les équations de Navier-Stokes dans un régime transitoire.

85 Thermal Conductivity mW/(m*K) 145 145

  Supplementary datas 2: Compounds used to determine the LOD and LOQ in the different matrices fromVillette et al., 2019a. Chemical properties of the pesticides mostly found in the reed.

	Matrix Matrix Matrix	Quantity (µg) Quantity (µg) Quantity (µg) 10 1 water solubility Sulfamethoxazole-d4 Standard Standard Standard Sulfamethoxazole-d4 Pharmaceutical Ethotoin kebuzone summer Supplementary data 12: Dodemorph Area 1 Area 2 Area 3 Area 1 Area 2 Area 3 Area 1 Area 2 Area 3 5.25E+07 1.50E+07 5.53E+07 5.35E+06 6.74E+06 5.16E+06 top layer bottom layer Average area Average area Average area 4.09E+07 5.75E+06 0,000000278 0,0000231 0,00214 compartment MW (g/mol) pubmed molecular volume comptox (cm3) water solubility S/N 1 S /N2 S/N 1 S /N2 S/N 1 S /N2 1.20E+01 9.00 4.00 5.00 comptox (mol/L) Reed 204.089878 171 1.23e-2 Reed 322.131742 260 3.15e-3 spring autumn Flamprop methyl Fuberidazole Mecarbam Parathion S/N 3 Average S/N standard deviation error % S/N 3 Average S/N standard deviation error % S/N 3 Average S/N standard deviation error % 2.00E+01 1.37E+01 2.25E+07 55% 2.00 3.67 8.63E+05 15% water reed 0,147 4,88 0,000664 log Kow log Dow pKa Neutral fraction 1.05 11.29 100% 4,17 0% winter Acebutolol 4-Methyl-5-Acebutolol 4-Methyl-5-thiazoleethanol thiazoleethanol ethyl LogP: Octanol--5.57 3.21 2.42 2.66 3.69 Aliskiren Acebutolol Alimemazine Acebutolol Almitrine Almitrine Aliskiren Alimemazine Water Butoxycaine Benzethonium Benproperine Amiodarone	(%) i Ionic fraction (%) 100% Propachlor 2.28
	Sludge		10 10 0,1 Bisoprolol Acetominophen-d4 N-Desmethyl sildenafil-d8 Sulfamethoxazole-d4 Melting Point Celiprolol	2.86E+07 9.14E+07 5.29E+05 Sludge °C	2.69E+07 5.40E+07 6.15E+05 325.225308 1.66E+07 2.01E+07 5.75E+05 58.9 Celiprolol	2.40E+07 5.52E+07 5.73E+05 315	8.00 9.00 1.00 7.97e-2 Benzethonium 5.00 7.00 2.00 109	3.00 4.00 2.00 2.15 161	5.33 6.67 1.67 35.2 Benzethonium 6.50E+06 3.57E+07 4.34E+04 9,5 100% 35.4	27% 65% 8%	67.8
	Methanol Methanol	1 0,1 0,01 0,001 0,0001 1 0,1 0,01 0,01 0,001 fenethylline Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 N-Desmethyl sildenafil-d8 N-Desmethyl sildenafil-d8 N-Desmethyl sildenafil-d8 Sulfamethoxazole-d4 Sulfamethoxazole-d4 Chlorpyriphos irbesartan Boiling Point Climbazole Telmisartan Deacetyldiltiazem Tramadol Desoxycortone enantate Water Solubility Diflufenican CARBETAMIDE sludge reed 2.21E+06 2.68E+05 1.34E+04 0.00 0.00 1.28E+07 1.43E+06 8.33E+04 4.52E+04 9.99E+02 Sludge Sludge °C Desoxycortone 2.28E+06 1.35E+06 1.92E+05 1.64E+05 7.71E+03 4.26E+03 0.00 0.00 0.00 0.00 7.95E+06 3.53E+06 7.26E+05 3.60E+05 5.79E+04 1.78E+04 5.16E+04 5.31E+04 0.00 2.36E+03 341.185175 Citalopram 428.23246 337 enantate 0,276 0,00828 0,000000246 Sludge 514.236876 Diflufenican Sludge 263.188529 EDDP mol/L 3.02e-4 Etofenprox (NH4) 236.116092 Dodemorph II Fipronil-sulfide 0,001 N-Desmethyl sildenafil-d8 2.96E+03 8.12E+02 0.00 10 Diclofenac-d4 5.39E+08 3.80E+08 3.34E+08 10 0,0000399 Galaxolidone sludge reed 272.17763 Vapor Pressure MmHg 1.28e-5 EDDP Fipronilsulfone (NH4) Etofenprox (NH4) Flurtamone	1.94E+06 2.08E+05 8.47E+03 0.00 0.00 8.09E+06 8.37E+05 5.30E+04 4.99E+04 1.12E+03 0,00214 271 328 415 Desoxycortone enantate 6.00 5.00 3.00 2.00 3.00 3.00 0.00 0.00 0.00 0.00 3.00 4.00 2.00 1.00 1.00 3.00 1.00 2.00 1.00 0.00 0,0000323 2.00 1.00 2.00 0.00 0.00 2.00 2.00 1.00 1.00 1.00 4.43e-3 2.04 Celiprolol 4.06e-6 5.31 396 359 Citalopram 3.00e-6 8.42 Climbazole 3.03e-2 3.01 3.12e-4 3.67e-3 EtG (Na) 0,0000323 201 9.81e-3 1.75 Etofenprox (NH4) 1.26E+03 1.00 1.00 1.00 4.18E+08 3.50E+01 4.40E+01 4.10E+01 0,0303 1.70e-6 5.9 4.72e-6 1.96e-6 Fenticonazole Fipronilsulfone (NH4)	4.33 2.00 2.67 0.00 0.00 3.00 1.67 1.67 1.33 6.67E-01 367 Desoxycortone enantate 5.18E+05 27% 5.41E+04 26% 4.64E+03 55% 0.00 -0.00 4.64E+06 57% 5.41E+05 65% 3.30E+04 62% 4.18E+03 8% 1.18E+03 106% 0,000783 Celiprolol 10.03 100% 4,12 0% 358 Citalopram 3,65 Climbazole 9,23 99% 7.24e-4 1.49e-4 Diflufenican 11,3 100% Dodemorph II -1.00 1.53E+03 122% 4.00E+01 1.08E+08 26% 3.37e-6 1.17e-5 Etofenprox (NH4) Fipronil-sulfide	100% 296 50% 1% 3.64e-3 7.27e-4
	Water Sludge Water Methanol Water	10 1 0,1 0,01 0,001 10 1 0,1 0,01 0,001 0,0001 10 1 0.1 0.01 henry constant N-Desmethyl sildenafil-Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 Acetominophen-d4 d8 Diclofenac-d4 Diclofenac-d4 Diclofenac-d4 dodemorph Fipronilsulfone (NH4) Doxapram Flash Point Furathiocarb Histamine Irbesartan N-Desmethyl sildenafil-d8 1 N-Desmethyl sildenafil-d8 0,1 N-Desmethyl sildenafil-d8 0,01 N-Desmethyl sildenafil-d8 0,001 N-Desmethyl sildenafil-d8 10 N-Desmethyl sildenafil-d8 0.001 Diclofenac-d4 0.0001 Diclofenac-d4 10 Diclofenac-d4 1 Diclofenac-d4 0.1 Diclofenac-d4 0.01 Diclofenac-d4 0.001 Diclofenac-d4 THIOCYCLAM-HYDROGENOXALATE Surface Tension Isoconazole Isothipendyl Dinonyl phthalate 9.51E+06 1.14E+07 1.48E+06 1.93E+06 2.04E+05 1.83E+05 1.03E+04 1.01E+04 0.00 0.00 5.25E+06 5.07E+06 4.85E+05 5.72E+05 4.65E+04 3.63E+04 1.88E+03 1.11E+03 0.00 0.00 0.00 0.00 5.09E+07 4.07E+07 4.42E+06 3.28E+06 3.77E+05 3.10E+05 top layer sludge reed water 281.271865 1.07E+07 1.60E+06 1.85E+05 1.04E+04 0.00 5.65E+06 7.51E+05 5.79E+04 1.15E+03 0.00 0.00 3.52E+07 2.79E+06 2.52E+05 Irbesartan °C 114 Isoconazole JWH-018-M-7-OH-Ind sludge reed 378.230728 Ketobemidone water 1.05E+07 4.20E+06 2.13E+06 2.33E+06 9.74E+05 4.54E+05 2.91E+05 7.77E+04 2.96E+04 1.36E+04 3.92E+03 0.00 0.00 0.00 0.00 7.31E+05 3.66E+05 4.24E+05 3.24E+04 3.37E+04 1.84E+04 0.00 0.00 4.15E+03 2.64E+08 2.63E+08 2.65E+08 3.74E+07 3.32E+07 3.49E+07 3.79E+06 2.76E+06 2.91E+06 3.17E+05 3.13E+05 2.31E+05 2.88E+04 2.82E+04 2.27E+04 4 alpha-hydRoxymethyl-1.05E+07 6.00 1.67E+06 4.00 1.90E+05 1.00 1.03E+04 1.00 0.00 1.00 5.32E+06 9.00 6.03E+05 5.00 4.69E+04 5.00 1.38E+03 1.00 0.00 0.00 0.00 0.00 4.23E+07 5.00 3.50E+06 1.00 3.13E+05 1.00 bottom layer 315 3.02e-4 Flecainide 237 Harman Irbesartan 343 1.42e-4 Isoconazole 4.00 1.00 1.00 1.00 1.00 5.00 5.00 2.00 2.00 0.00 0.00 4.00 3.00 1.00 5.60E+06 2.00 2.00 1.25E+06 3.00 2.00 1.33E+05 2.00 2.00 5.85E+03 1.00 1.00 0.00 0.00 0.00 5.07E+05 3.00 1.00 2.82E+04 1.00 1.00 1.38E+03 0.00 0.00 2.64E+08 3.80E+01 4.00E+01 3.52E+07 8.00 8.00 3.15E+06 1.00 1.00 2.87E+05 1.00 2.00 2.66E+04 1.00 1.00 3,08 E-09 0,000000126 1.00 3.00 1.00 3.00 2.00 3.00 1.00 1.00 1.00 0.00 0.00 7.00 1.00 1.00 water 2.52 183 1.00 1.00 2.00 1.00 0.00 3.00 1.00 1.00 4.50E+01 7.00 1.00 1.00 1.00 5,24 E-08 dyn/cm 31.4 47.9 56.3 Memantine Lidocaine sludge reed water 271.000671 142 5.80e-2 O-Desmethylnortramadol Methadone 4beta-methyl-5alpha-cholesta-8,24-dien-3beta-ol Di-n-butyl phthalate Metformin 1-O-hexadecyl-sn-glycerol N-Isobutyl-2,4,8-decatrienamide orphenadrine ergosta-5,7,22,24(28)-tetraen-3beta-ol Tetrahydro-harmine Ceramide (t20:0) Di-n-butyl phthalate 0,0000012 3,46 E-09 0,00000156 Acebutolol sludge/water 336.204907 301 6.12 10-3 1.71 Venlafaxine sludge/water 277.204179 262 2.14e-3 3.2 Diethofencarb Water 267.147058 243 2.55e-4 3.02 metaldehyde Water 176.104859 0.85 metoprolol Water 267.183444 259 0.147 1.88 Index of Refraction -1.46 1.59 1.67 Molar JWH-018-M-7-OH-Ind O-Desmethylvenlafaxine. Methoprene Peak 1 Fragm 279 Desvenlafaxine cm 3 86.4 86.1 53.5 Ketobemidone Propafenone Nortriptyline Refractivity Polarizability Å 3 34.3 34.1 21.2 Metformin O-Desmethylvenlafaxine. Pyrethrins: Cinerin II Desvenlafaxine Metoprolol Terbutryn Oxadiazon 10 Verapamil Ceramide (t25:0) 0,000000129 0,0000012 10 0,000000428 0,00000117 methomyl water reed 162.046299 138 0.209 1.24 N, N-dibutylformamide water reed 157.146664 182 6.61e-2 A-1.01 Density g/cm 3 0.891 1.34 1.28 Molar Volume O-Desmethylnortramadol Tiemonium Pyrethrin I cm 3 315 256 143 O-Desmethyltramadol Tramadol Terbutryn Tramadol alprenolol water sludge 249.172879 247 2.79.10-2 3.1 Pheniramine Triclocarban Tiapride	3.67 2.67 1.00 1.67 1.33 5.67 3.67 2.67 1.33 0.00 0.00 5.33 1.67 1.00 Fipronilsulfone (NH4) 9.52E+05 9% 2.34E+05 14% 1.15E+04 6% 1.26E+02 1% 0.00 -2.95E+05 6% 1.36E+05 23% 1.08E+04 23% 4.34E+02 32% 0.00 -0.00 -7.98E+06 19% 8.37E+05 24% 6.22E+04 20% reed 8,08 89% 210 174 Irbesartan Isoconazole 7,23 53% Metformin 1.67 4.35E+06 78% 2.00 9.69E+05 77% 2.00 1.39E+05 105% 1.00 7.02E+03 120% 0.00 0.00 -2.33 1.96E+05 39% 1.00 8.49E+03 30% 3.33E-01 2.40E+03 173% 4.10E+01 1.35E+06 1% 7.67 2.13E+06 6% 1.00 5.59E+05 18% 1.33 4.84E+04 17% 1.00 3.36E+03 13% 1,06 E-08 1,21 E-10 49.2 52.8 Methadone 7 40% Methoprene Peak 2 Fragm 279 SPIROXAMINE (10E)-9-Hydroxy-13-oxo-10-octadecenoic acid Episterol Cyclopropanecarboxylic 0,00000077 1.71 9,6 100% 8,91 98% 12,8 100% -3,9 0% 9,7 100% 1.52 1.56 Nandrolone phenylpropionate 79.5 71.0 Octhilinone 31.5 28.1 O-Desmethylvenlafaxine. Desvenlafaxine Oxadiazon acid, 2-[1-(3,3-dimethylcyclohexyl)ethoxy ]-2-methylpropyl ester 2,2'-((3-(Dodecyloxy) 3,48 0% -0,065 0% 1.27 1.33 Piperonylbutoxide Fragm 177 260 220 Proguanil propyl) imino)bisethanol 3.1 9,6 100% Propafenone	136 40.8 60% 2% 1.55 59.3 23.5 1.10 186
	Methanol Sludge Sludge	10 1 0,1 0,01 0,001 0,0001 1 0,1 0,01 0,001 10 1 0.1 0.01 0.001 log P O-desmethylvenlafaxine water sludge Gemfibrozil-d6 9.27E+08 Gemfibrozil-d6 7.04E+07 Gemfibrozil-d6 4.86E+06 Gemfibrozil-d6 3.64E+05 Gemfibrozil-d6 2.22E+04 Gemfibrozil-d6 7.99E+02 N-Desmethyl sildenafil-d8 9.14E+04 N-Desmethyl sildenafil-d8 4.12E+03 N-Desmethyl sildenafil-d8 0.00 N-Desmethyl sildenafil-d8 0.00 Diclofenac-d4 2.07E+08 Diclofenac-d4 2.82E+07 Diclofenac-d4 2.55E+06 Diclofenac-d4 2.55E+05 Diclofenac-d4 1.98E+04 top layer 8,1 Henry's Law atm-m 3 /mole Propranolol Rivaroxaban TRIPHENYL-PHOSPHATE water sludge LogKoa: -Sertraline Octanol-Air Sildenafil 4,11 4,84 6,6 4,46 Factor Viscosity Cp Terbutryn Tiapride Tridemorph (main component) Peak2 8.Bioaccumulation 6.70E+08 5.69E+08 7.22E+08 1.01E+02 7.90E+01 1.05E+02 9.50E+01 1.85E+08 26% 5.74E+07 4.35E+07 5.71E+07 7.00 9.00 8.00 8.00 1.34E+07 24% 3.63E+06 3.04E+06 3.84E+06 1.00 1.00 1.00 1.00 9.29E+05 24% 3.11E+05 2.43E+05 3.06E+05 1.00 1.00 1.00 1.00 6.03E+04 20% 2.58E+04 1.62E+04 2.14E+04 1.00 1.00 1.00 1.00 4.85E+03 23% 4.72E+03 2.35E+03 2.62E+03 1.00 1.00 1.00 1.00 1.97E+03 75% 3.83E+04 5.19E+04 6.05E+04 1.00 1.00 1.00 1.00 2.76E+04 46% 7.21E+02 1.27E+03 2.04E+03 1.00 1.00 1.00 1.00 1.83E+03 90% 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.41E+08 2.34E+08 2.28E+08 4.80E+01 6.40E+01 3.70E+01 4.97E+01 1.79E+07 8% 3.44E+07 2.81E+07 3.02E+07 9.00 9.00 6.00 8.00 3.59E+06 12% 2.76E+06 2.36E+06 2.56E+06 2.00 2.00 1.00 1.67 2.00E+05 8% 2.21E+05 2.42E+05 2.39E+05 1.00 1.00 1.00 1.00 1.70E+04 7% 1.70E+04 1.50E+04 1.73E+04 1.00 1.00 1.00 1.00 2.38E+03 14% bottom layer water Reed 15,1 11,1 263.188529 236 7.08e-2 3.2 2,96 0% 100% 5.03e-6 3.63e-9 6.94e-8 9.59e-10 3.18e-7 Tramadol Terbutryn 1.24e-7 Triclocarban Tiemonium 326.070796 258 2.06e-5 4.59 8.99 10.8 9.38 10.9 9.33 7.38 Tridemorph (main component) Tramadol Peak2 Venlafaxine Venlafaxine -0,1 1,78 -1,33 7,7 8,3 3,97 17,3 6,1 7,8 10.0 -134 10.7 Venlafaxine
			10		Bezafibrate-d4	5.24E+08 5,7	3.82E+08	3.25E+08	4.10E+08 7,7	4.20E+01	4.00E+01	4,6	3.70E+01	3.97E+01	1.02E+08 4,98	25%
			10 10 1 Factor Bioconcentration Gemfibrozil-d6 Sulfamethoxazole-d4 Bezafibrate-d4	4.49E+08 2.05E+08 4.20E+07 -1,37 -	4.15E+08 1.69E+08 3.54E+07	4.33E+08 1.17E+08 3.01E+07 239	4.32E+08 1.63E+08 3.58E+07 6,5	9.50E+01 1.10E+01 6.00 240	8.10E+01 1.40E+01 5.00	3,6 271	1.17E+02 1.60E+01 4.00	9.77E+01 1.37E+01 5.00 16.7	1.69E+07 4.39E+07 5.97E+06	121	4% 27% 17%	16.1
			1 0.1 Soil Adsorp. Sulfamethoxazole-d4 Bezafibrate-d4	2.42E+07 4.02E+06 3,67 L/kg	1.51E+07 2.96E+06	1.25E+07 2.63E+06 1.62e+3	1.73E+07 3.21E+06 7,7 1.68e+3 2.00 1.00	2.00 2.00 4.99e+3 1,9	2.00 1.00	2.00 1.33 255	6.14E+06 7.25E+05 8,4 1.43e+3 36% 23%	187
	Methanol Methanol	1 0,1 0.01 Coeff. (Koc) Sulfamethoxazole-d4 Gemfibrozil-d6 Bezafibrate-d4	4.65E+07 2.57E+06 3.21E+05 9,29	4.77E+07 1.74E+06 2.70E+05	4.80E+07 1.44E+06 2.19E+05	4.74E+07 1.92E+06 2.70E+05	9.00 3.00 2.00	1.40E+01 2.00 1.00	4,6	1.50E+01 1.00 1.00	1.27E+01 2.00 1.33	8.01E+05 5.85E+05 5.08E+04 4,5	2% 31% 19%
	Water		0,1 0,01 0,001 0.0001 Hydroxylation Gemfibrozil-d6 Gemfibrozil-d6 Sulfamethoxazole-d4 Bezafibrate-d4 0,01 Sulfamethoxazole-d4 0.001 Bezafibrate-d4 10 Atmos. cm 3 /molecule*sec 4.04E+06 3.15E+06 3.11E+05 2.35E+05 3.55E+03 3.14E+03 0.00 6.28E+02 1.12E+05 1.45E+05 1.28E+04 1.54E+04 3,8	2.88E+06 2.95E+05 2.30E+03 6.14E+02 1.02E+05 4.40E+03 1.65e-11	3.36E+06 2.81E+05 3.00E+03 4.14E+02 1.20E+05 1.09E+04 1.82e-11 1.00 1.00 1.00 0.00 1.00 1.00	1.00 1.00 1.00 1.00 4.00 1.00 4.31e-11 2.00 1.00 1.00 1.00 1.00 1.00 2,44	1.33 1.00 1.00 6.67E-01 2.00 1.00 1.83e-11	6.11E+05 4.04E+04 6.35E+02 3.59E+02 2.22E+04 5.76E+03 4,7 1.59e-11 18% 87% 21% 14% 19% 53%	1.45e-11
			0,001 0,0001 Rate	Gemfibrozil-d6 Sulfamethoxazole-d4	2.57E+04 0.00	1.65E+04 0.00	2.55E+04 0.00	2.26E+04 0.00	1.00 0.00	1.00 0.00			3.00 0.00	1.67 0.00	5.29E+03 0.00	--
			Biodeg. Half-	Days		18.4		3.35		3.36			25.0	4.30	3.36
			10	Life	Bezafibrate-d4	2.48E+08	2.39E+08	2.52E+08	2.46E+08	4.30E+01	4.80E+01		5.60E+01	4.90E+01	6.38E+06	3%
			10 10 1 Fish Biotrans. Gemfibrozil-d6 Sulfamethoxazole-d4 Bezafibrate-d4	2.00E+08 1.30E+08 3.50E+07 Days	1.96E+08 1.30E+08 3.22E+07	2.23E+08 1.37E+08 3.52E+07 43.2	2.06E+08 1.32E+08 3.41E+07	3.70E+01 1.80E+01 9.00 0.747	5.30E+01 1.80E+01 9.00 0.128	3.60E+01 2.20E+01 7.00	4.20E+01 1.93E+01 8.33 0.443	1.45E+07 3.81E+06 1.64E+06	0.500	7% 3% 5%	0.491
			1 1 Half-Life (Km) Gemfibrozil-d6 Sulfamethoxazole-d4	2.95E+07 1.67E+07	3.08E+07 1.67E+07	3.89E+07 1.68E+07	3.31E+07 1.68E+07	6.00 6.00	6.00 6.00			5.00 3.00	5.67 5.00	5.09E+06 5.65E+04	15% 0%
	Water		0.1	Bezafibrate-d4	3.62E+06	2.51E+06	2.92E+06	3.02E+06	1.00	2.00			1.00	1.33	5.59E+05	19%
	Sludge Water		0,1 0,1	Gemfibrozil-d6 Sulfamethoxazole-d4	2.58E+06 1.62E+06	2.39E+06 1.62E+06	2.92E+06 1.53E+06	2.63E+06 1.59E+06	2.00 1.00	1.00 1.00			1.00 1.00	1.33 1.00	2.68E+05 5.20E+04	10% 3%
			0.01	Bezafibrate-d4	2.75E+05	2.93E+05	2.15E+05	2.61E+05	1.00	1.00			2.00	1.33	4.09E+04	16%
			0,01 0,01	Gemfibrozil-d6 Sulfamethoxazole-d4	2.31E+05 1.29E+05	2.27E+05 1.29E+05	2.16E+05 1.07E+05	2.25E+05 1.22E+05	2.00 1.00	1.00 1.00			1.00 1.00	1.33 1.00	8.04E+03 1.25E+04	4% 10%
			0,001 0,001 0.001	Gemfibrozil-d6 Sulfamethoxazole-d4 Bezafibrate-d4	1.67E+04 3.70E+03 1.68E+04	1.28E+04 3.70E+03 1.91E+04	1.52E+04 2.29E+03 1.30E+04 area 4 outlet 1.49E+04 1.00 3.23E+03 2.00 1.63E+04 1.00	1.00 1.00 1.00			1.00 2.00 1.00	1.00 1.67 1.00	1.95E+03 8.16E+02 3.08E+03	13% 25% 19%
				summer		spring		autumn				winter

Loïc MAURER Mécanismes de transfert et dynamique d'accumulation d'un spectre large de micropolluants au sein de l'écosystème des filtres plantés et de zones de rejet végétalisées associées.

O-(9Z-tetradecenoyl)-(R)carnitine

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List of supplementary files

Sludge layers physical properties

Micropollutants distribution in the sludge layers is probably linked both to compounds and to sludge properties through biotic and abiotic processes. Hence, the physical properties of the different sludge layers could not be neglected in this study as they influence the micropollutants distribution. Therefore, three parameters describing the sludge structure were measured: the granulometry, the bulk density and the porosity.

The granulometry distribution was performed according to the standard French NF EN 933-1.

After drying sludge layers at 150 °C (until the sludge mass becomes stable), the samples layers estimate this contamination, a coring should be performed and analyzed using the non-target approach. To obtain the micropollutants results coming only from wastewater, a control coring was also carried out in the bank of the CW to remove the background as mentioned in the materials and methods section. Therefore, in this section only the micropollutants specific to the CW will be discussed. This approach will provide a close up view of micropollutants only coming from the wastewater. The xenobiotics and lipids annotations number have been summarized in the Table 10. The main results underlined by these annotations performed in the coring show that the main part of the lipids and micropollutants investigated will diffuse through the entire depth (319 single names annotated), suggesting the xenobiotics transport by wastewater. On the other hand, regarding the results found in a specific layer, a higher specificity could be noticed in the top layer with 191 single names while 38 single names were identified in the bottom layer.

This sequestration in the top layer has already been mentioned in the literature, suggesting the key role of the first soil layer. [START_REF] Biel-Maeso | Removal of personal care products (PCPs) in wastewater and sludge treatment and their occurrence in receiving soils[END_REF]Gros et al., 2019). As mentioned in the previous section, the number of micropollutants hides the diversity of compounds. Indeed, even if the same micropollutants classes have been detected, different trends of micropollutants distribution could be observed as shown in Figure 41.

Samples preparation for mass spectrometry imaging acquisition

Once the different organs separated, the aerial part (stem and leaves) has been cut in regular intervals (each 3cms). Concerning the roots, the same method could not be applied due to the networked roots system. Therefore, a cut of the primary roots (3 cm), a secondary root associated, and tertiary roots were collected. All these samples were directly embedded in an M-1 solution and flash frozen in liquid nitrogen after the cutting. An example of samples flash frozen could be found in Figure 50. Then these samples were directly stored at -80 °C before the analysis.

v

Organic micropollutants in situ location and metabolization in a whole reed growing in the constructed wetland

The whole plant investigation has led to the identification of some micropollutants distributed in the different organs. The results introduced in the Figure 64 represent micropollutants identified in the first biological replicates. The distribution introduced in the following figures will concern the micropollutants found with a specific distribution. Therefore, two drugs (carbamazepine and metoprolol) and six pesticides (parathion-ethyl, mecarbam, propachlor, flamprop methyl, dodemorph and fuberidazole) were investigated respectively in Figure 64 and Figure 66.

the transpiration stream and be evenly distributed in the whole as shown in the figure 68. On the other hand, the difference observed with micropollutants distribution could be partly explained by the exposure time. Indeed, the tracer experiment was carried out in a shorter injection time (ten days). Tracer was accumulated in the roots and these results will be in accordance to those mentioned in the literature. The translocation was not yet fully set up. But an extended experiment time would lead to results similar to those mentioned in micropollutants distribution in our works. Therefore, these results also suggest the importance of exposure time to select the organs to investigate and the lack of chronic exposure during 8 months led the authors to investigate mainly the roots.

Compounds distribution in the sludge layers

The analysis of the CW sludge samples in MSI was performed using the Metaspace platform.

Indeed, this sludge should be mainly composed of lipids and the platform. Metaspace contain several lipids databases. To evaluate, the acceptable mass error, an internal standard (sildenafil-d3) was applied on the slide and in the sludge samples as shown in Figure 69. The mass deviation was fixed at 2 ppm as the sildenafil-d3 spiked and found in the sludge samples was detected with an error of 0.9 ppm.

Chapter 4:

Computational Fluid Dynamics coupled with micropollutants distribution in an artificial pond wetland

Governing equations and boundary conditions

Once the preprocessing step has been completed, the water flows simulation and the equation solving could be performed. This simulation was also carried out using OpenFoam 4.0. The method applied is conservative (inlet flux in a cell is the same as the one leaving in the adjacent cell). Therefore, this method is well adapted to solve the Navier-Stokes equation that governing the hydrodynamics in this kind of system. To obtain the velocity field inside the artificial pond, the continuity and the momentum equations [24-25], respectively and have been solved.

𝛻. 𝑢 = 0

[24]

Where u is the velocity, p the pressure, 𝜐 the kinematic viscosity and t the time.

However several hypotheses are required to solve these equations. First, even if wastewater was the fluid consider in the model without particles transport. Besides, as the fluid is wastewater it has been studied as an incompressible fluid. Furthermore, the boundary conditions indicate a turbulence flow (at least in the inlet pipes). Therefore, the equations were solved using an unsteady mode. The solver pimpleFoam was selected. Indeed, pimpleFoam is a large time step transient solver and could be well adapted to the present case. Besides, to solve the equations, a RANS method has been used and the k-Ɛ standard model was selected to as turbulence model. This model is well adapted for water flow simulations more precisely in ponds systems [START_REF] Alvarado | CFD study to determine the optimal configuration of aerators in a full-scale waste stabilization pond[END_REF][START_REF] Ouedraogo | Impact of sludge layer geometry on the hydraulic performance of a waste stabilization pond[END_REF]. Then the simulation was solved using second order discretization schemes. The Euler scheme was used for the time discretization and the Gauss Linear for the space derivative. Finally, the model has been considered as converged, when the residuals value was lower than 10 -7 and the velocity field does change over times' step, it was considered that the model had converged.

Nevertheless, this solving step should also consider the boundary conditions. The external conditions could also influence the result found in the model. Therefore, the following conditions have been considered.

Inlet flow rate: Flow rate was measured in the study site for each simulation [START_REF] Nuel | Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms[END_REF].

The average inlet flow rate was used as no significant difference could be observed in the

Supplementary datas

Supplementary datas 1: Water quality found in the Largue at Spechbach-Le -Bas.