Sorbonne Université

Aya Par

Bahi

Dr Dirigée Par

Julien Tournebize Codirigée

Dr Sabine Sauvage

Mme Passeport

Mme Gouy

M Imfeld Gwenaël

Mme Sauvage

Aya Bahi
email: aya.bahi@inrae.fr

Sylvain Payraudeau

Gwenaël Imfeld

José- Miguel Sánchez-Pérez

Betty Chaumet

Julien Tournebize
email: julien.tournebize@inrae.fr

Modeling the fate of pesticides in artificial ponds

Keywords: Artificial pond, pesticide, dissipation, process, model 4 PESTIPOND project Artificial pond, pesticide, dissipation, process, model, review

Modeling the fate of pesticides in artificial ponds

Pesticides used to improve the quality of crops are a diffuse source of pollution affecting water quality and aquatic habitats. Studies have consistently shown that natural (e.g., lakes, ponds, and bogs) and constructed wetlands (e.g., ponds, irrigation reservoirs, and drainage ditches) intercepting drained agricultural waters can dissipate pesticide contamination. These wetlands are classified according to their privileged functions and their potential to buffer the intercepted water. The objective of this thesis is to explore pesticide fate in artificial wetlands and particularly ponds (or puddles), which we will refer to as artificial ponds. When pesticides are allowed to remain in AWWs, they undergo a series of physico-chemical processes that reduce their concentration. Subsequently, when the ponds are filled, the water discharged to surfaceand ground-water sources are less contaminated with pesticides. Therefore, as a complement to pesticide application regulations, APs are proving to be an effective technique for minimizing pesticide transfer to water resources downstream of agricultural plots.

Although many studies on the dissipation potential of ponds focus on nitrates and suspended sediments, very little is known about the behavior of pesticides. The first objective of the thesis is to study and evaluate the interactions and contribution of multiple physicochemical processes to pesticide dissipation in ponds, as well as their mathematical formulae and main controlling factors.

In the second phase, the results of this process survey were used to develop a conceptual model of pesticide fate at the scale of the APs. The model integrates the main processes of transport, transfer, and transformation within the principal compartments of the APs (water and sediments) to predict pesticide dynamics. A sensitivity study was performed to identify the processes most involved in dissipation, i.e., sorption, transformation by microorganisms, and photolysis.

Next, the model was applied to a typical drained agricultural pond (Rampillon, France), where pesticides are mostly transported in a dissolved form in the catchment. The outcome of this application allowed us to visualize the temporal evolution of pesticide distribution in the AP and the contribution of each process to its dissipation. The model outputs are in agreement with the observation made at the study site and the results of other studies reported in the literature. The model was applied to another site (Auradé, France) representing erosive environments to characterize the behavior of pesticides in particulate form.

The modeling results and the conclusions drawn from the sensitivity analysis provide a comprehensive insight into the functioning of the ponds. These results pave the way for engineers to establish design criteria to optimize the environmental efficiency of APs.

Résumé Modélisation du devenir des pesticides dans les zones humides artificielles

Les pesticides utilisés pour améliorer la qualité des produits agricoles sont une source de pollution diffuse qui affecte la qualité des ressources en eaux et des habitats aquatiques. Nombreuses études ont montré que les zones humides naturelles (ex. lacs, mares, et tourbières) ou construites (ex. étangs, réservoir d'irrigation, et fossé de drainage) qui interceptent les eaux agricoles drainées, peuvent dissiper la contamination par les pesticides. Ces zones humides sont différenciées selon leurs fonctions privilégiées et leur capacité à tamponner les eaux interceptées. Cette thèse a pour objectif d'étudier le devenir des pesticides dans les zones humides artificielles (ZHAs) (artificial ponds) de type étang ou mares. Quand les pesticides séjournent dans les ZHAs, ils subissent un ensemble de processus physicochimiques qui réduisent leur concentration. Ainsi l'eau qui est déchargée des ZHAs est moins chargée en pesticides avant de rejoindre les rivières et les nappes souterraines. Par conséquent, en complément aux actions relatives aux usages de pesticides, les ZHAs s'avèrent une technique efficace pour réduire le transfert de pesticides vers les ressources d'eau en aval des parcelles agricoles.

Bien que de nombreuses études sur le potentiel de dissipation des ZHAs portent sur les nitrates et les sédiments en suspension, on sait très peu de choses sur le comportement des pesticides. Un premier objectif de la thèse est d'étudier et évaluer les interactions et la contribution de multiples processus physico-chimiques à la dissipation de pesticides dans les ZHAs ainsi que leurs formulations mathématiques et les principaux facteurs de contrôle.

Dans un second temps, les résultats de cette étude bibliographique ont été utilisés pour développer un modèle conceptuel du devenir des pesticides à l'échelle des ZHAs. Le modèle intègre les différents processus de transport, transfert, et transformation au sein des principaux compartiments des ZHAs (eau et sédiments) pour prédire la dynamique des pesticides. Une étude de sensibilité a permis de ressortir les processus modélisés les plus influents dans la dissipation, i.e., la sorption, la transformation par microorganismes, et la photolyse.

Ensuite, le modèle a été appliqué sur une ZHA type des milieux agricoles drainés (Rampillon, France) où les pesticides sont majoritairement transportés sous forme dissoute dans le bassin versant. Les résultats de cette application ont permis de visualiser l'évolution temporelle de la répartition des pesticides dans la ZHA et la contribution de chaque processus à leur dissipation. Les résultats obtenus sont en accord avec les observations faites sur le même site et les résultats d'autres études reportées dans la littérature. Le modèle a été appliqué sur un autre site (Auradé, France) représentatif des milieux érosifs pour caractériser le comportement des pesticides sous forme particulaire.

Les résultats de la modélisation et les conclusions retenues de l'analyse de sensibilité permettent d'avoir une vision détaillée du fonctionnement des ZHAs. Ces résultats préparent la voie aux ingénieurs pour définir des critères de dimensionnement permettant d'optimiser l'efficacité environnementale des ZHAs.

Mots clés :

Zones humide artificielle ; pesticide ; dissipation ; processus ; modèle Tout d'abord, je suis très reconnaissante aux membres du jury qui ont consacré leur temps et leurs efforts à l'évaluation de ce travail : Pr. Elodie Passeport, Dr. Véronique Gouy, Dr. Gwenaël Imfeld, Pr. Jean-Marie Mouchel.

Mes plus grands remerciements sont adressés à mon directeur de thèse, Dr. Julien Tournebize, tout d'abord pour l'opportunité qu'il m'a donnée à travailler sur un sujet qui m'intéressait vivement. Deuxièmement, pour son implication continue tout au long de la thèse et son suivi régulier. De plus, il a toujours été à l'écoute, compréhensif et m'a encouragé à donner le meilleur de moi-même. De même, je tiens à exprimer ma gratitude à ma codirectrice de thèse, Dr Sabine Sauvage. Bien que nos échanges se soient déroulés le plus souvent à distance, tu as toujours été très disponible pour répondre à mes interrogations en modélisation et à me rassurer durant les phases de doute. Je n'aurais pas pu imaginer cette thèse accomplie sans mes deux directeurs de thèse.

Mes remerciements sont aussi destinés à mes collègues de l'INRAE. Je tiens à remercier tout particulièrement Cédric Chaumont pour m'avoir facilité l'accès et le traitement des données de mesures réalisées sur la zone ZH de Rampillon (Chapeau bas pour tes superpouvoirs excel). J'en profite pour remercier les Toulousains Betty Chaumet et Anne Probst pour leur contribution à ce travail au travail sur la ZH d'Auradé et de m'avoir facilité le traitement des données. Merci à Bruno Lemaire pour sa contribution sur le transport des particules dans l'eau. Merci à Hocine Henine pour avoir répondu à mes questions sur le bilan hydrique. Merci à Alban de Lavenne pour la disponibilité dont tu as fait preuve pour répondre à mes interrogations sur la calibration du modèle. Merci Fatima et Arnaud pour les agréables moments partagés lors de la pause-café. A mon voisin de bureau (à gauche), Jérémie Lebrun, alias Hyoga (ou encore le maître des gammares), merci pour tous ! Je repense à tes coups de mur et de porte qui me faisaient sursauter à chaque fois et à tous les lundis Kebab qui nous réunissaient autour d'un salade, tomates, oignons...il n'y a rien de mieux. Le trio légendaire, vous avez cru que je vous ai oublié ? Est-ce possible d'oublier cette belle aventure qu'on a vécu ces 3 dernières années ? Nos débats infinis avec Alexis (Dr Jeantet*) sur les sujets philo-socialo-gastro-écolo-théo-…. Et que Samy (Dr Chelil*) a enduré avec nous et qui s'est toujours retrouvé entre nos petites farces (d'ailleurs qui n'était pas toujours innocent :)). Samy et Alexis merci pour votre soutien et réponses à mes questions à chaque fois je toque votre porte, on n'a partagé les moments de gloire et de désespoir ensemble et j'en suis très reconnaissante. Le prochainement docteur inchaalah Alex et aussi la personne qui ne s'enlace jamais de mes histoires infinies (enfin à ce je sache) et ne se moque jamais de mes questions futiles, j'aimerai te remercier pour ta bienveillance et ton énergie qui égayaient mes journées surtout durant les derniers mois où j'en avais vraiment besoin ! Ce fut un plaisir de travailler au sein d'une si belle équipe et un shot out to Julien pour avoir pu maintenir une telle harmonie ! Finalement, j'aimerai remercier Mama, Baba, et Yassine pour TOUS et ma tante Wafaa pour ses pensées et à toute ma famille. Onni et cochti, je suis certaine que je n'aurai pas pu finir cette thèse sans votre soutien moral inconditionnel...un Merci ne suffira certainement pas. Marwati, bibich, Betty, Wiam, Mouad, Yassine et Achraf merci pour m'avoir accompagné durant cette période, votre présence m'est chère.

Merci à toute personne qui m'a aidé de loin ou de près, par un mot ou un geste ou une pensée, votre soutien restera gravé <3

Outline

Table of figures -General introduction:

Figure 1 Illustration of the different ecosystem components that are contaminated by pesticides (Leenhardt et al., 2022a) All of the corresponding footnotes can be found in (Imfeld et al., 2021) Orange and blue bars represent the inlet and outlet fluxes (µg) of bentazon, respectively. Red rectangles refer to periods with abnormalities, i.e., when the outflux is higher than the influx, or there is none of them. Green rectangles refer to the grouped periods obtained, i.e., when the outflux is higher than the influx. . view (satellite image from Google map, https://www.google.fr/maps/ accessed on 18 June 2021, Images ©2020 CNES/Airbus, Landsat, Maxar Technologies, Mapping data ©2020, France) with the sampling points used for pesticide monitoring (Chaumet et al., 2021).. On (D), the yellow and blue points refer respectively to the downstream and upstream discharge

Figure IV. 2 (A)

The cumulative mass of TSS (MTSS (ton)) in the inlet (green) and outlet (red) of the AP. Qin (L.s -1 ) and Qout (L.s -1 ) are the inlet (blue) and outlet (yellow) daily water flow rates of the AP, respectively (in log scale). (B) The daily observed TSS fluxes (g.L -1 ) in the inlet (green) and outlet (red) of the AP against the water flow rates (data from (Chaumet et al., 2021) I.A. 3 Non-linear Freundlich isotherm adsorption Kfads (mg 1-n kg -1 L n ) and desorption Kfdes (mg 1-n kg -1 L n ) coefficients. Linear isotherm coefficient Kd (L.kg -1 ) for metazachlor, isoproturon, epoxiconazole, napropamide, boscalid, prochloraz, tebuconazole, glyphosate, pyrimethanil, tetraconazole, fludioxonil, and spiroxamine. The sorption coefficients were measured in laboratory conditions with batch experiments on different pond substrates: Sediments, vegetation, and total suspended solids (TSS) (Maillard et al., 2014;Passeport et al., 2011b;Vallée, 2015) (d -1 ) of five selected pesticides (imazosulfuron, lindane, mefenacet, molinate and picloram) in different ponds, along with their physico-chemical properties S (mg.L -1 ) and Koc(L.kg -1 ) (Inao et al., 1999;Krishna et al., 2008;Mccall et al., 1985;Takagi et al., 2012;Yoshida et al., 2000). Pesticide properties were extracted from pesticide databases (ChemSpider; Kim et al., 2021;Lewis et al., 2016) I.A. 8 Biotransformation half-lives in the water column DT50bio,w (d) and sediment surface DT50bio,s (d) of nine pesticides in different wetlands, along with their mobility Koc(L.kg - 1 ) (Beringer et al., 2021;Bondarenko et al., 2004;Comoretto et al., 2008;Droz et al., 2021;Nakano et al., 2004;Passeport et al., 2011a;Passeport et al., 2013;Sahin et al., 2020). These I.A. 9 Photodegradation half-lives in the water column DT50photo (d) of pesticides with different mobility Koc (L.kg -1 ). The DT50 values were estimated in (Inao et al., 1999;Katagi et al., 2016;Rose et al., 2008;Sakellarides et al., 2003) Koc and hydrophilicities log Kow, measured in laboratory studies on sediment substrates with different organic carbon content OC (%) (Vallee et al., 2016). Along with, biotransformation half-lives at the water-sediment interface in wetlands, extracted from the PPDB. .................. 88

-Chapter II-I:

Table II-I. 1 List of the 61 detected pesticides during the monitoring periods (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019).120

Table II- Table II-I.A. 2 PPDB Half-lives and physico-chemical properties of the evaluated pesticides (Lewis et al., 2016). Half-lives at the water-sediment-water interface refer to biotransformation in both compartments, respectively. . Table II-II. 3 List of parameters selected for SA with the Sobol method and their minimummaximum values used for the uniform distribution. The minimum values of biotransformation and photolysis kinetic coefficients, kbio,w, kbio,s, and kp respectively, correspond to the shortest half-life (DT50=1 day) for each process. The maximum values of kbio,w, kbio,s, and kp correspond to the longest half-life (DT50=500 days). The minimum and maximum half-lives were taken from the pesticide properties database (PPDB) (Lewis et al., 2016). The adsorption-desorption parameter values kads and kdes were retrieved from (Comoretto et al., 2008;Nakano et al., 2004;Watanabe et al., 2006;Yoshida et al., 2000) Table II-II.A. 1 Values of the fixed parameters used for the SA. The surface area was measured in the field, the temperature modulation factor θ was extracted from the literature (Gargallo et al., 2017;Hantush et al., 2013;Sharifi et al., 2013), and the bulk density ρb of sediments was Table III. 1 Summary of pesticides monitoring data. The selected periods correspond to monitoring data without artifacts. In addition, the pesticide re-mobilization periods were not considered (i.e., pesticide outlet concentration Cout > inlet concentration Cin). Days refers to the total duration of each period. Qinmax (µg.L -1 ) is the maximum water flow rate intercepted by the Rampillon AP. Qinmean (µg.L -1 ) is the mean water flow rate intercepted by the Rampillon AP during the corresponding period. Cmax (µg.L -1 ) is the maximum detected concentration of each pesticide, Cmean (µg.L -1 ) is the mean detected concentration during the corresponding period, and the DR is the detection frequency of pesticides. T (°C) and HRT (d) are the average temperature and hydraulic residence time of each period, respectively. TUR (FTU) is the mean turbidity, NO3-(mg.L -1 ) is the mean nitrate concentration, TOC (mg.L -1 ) is the mean total organic carbon concentration, and DOC (mg.L -1 ) is the mean total dissolved organic carbon concentration. Each pesticide is associated with its type: (H) Herbicides, (F) Fungicides. ... 197

Table III. 2

The physicochemical properties and the application season of the 7 studied pesticides. Koc (mg.L -1 ) is the organic carbon-water partition coefficient, representing the mobility of the molecule. Log Kow (-) is the octanol-water partition coefficient, representing the hydrophobicity of the molecule. S (mg.L -1 ) is the water solubility of the molecule, and LOD (µg.L -1 ) is the detection limit of the molecule in water. The properties values were extracted from literature [START_REF] Barchanska | New procedures for simultaneous determination of mesotrione and atrazine in water and soil. Comparison of the degradation processes of mesotrione and atrazine[END_REF][START_REF] Catalá-Icardo | Development of a Photoinduced Chemiluminescent Method for the Determination of the Herbicide Quinmerac in Water[END_REF][START_REF] Epa | Quality Assurance Guidance Document-Model Quality Assurance Project Plan for the PM Ambient Air[END_REF]Lewis et al., 2016;PubChem, 2021) (d -1 ) and kdes (d -1 ) are the adsorption and desorption kinetic coefficients, respectively. These values are the result of the annual calibration of the parameters. The set of adsorption and desorption values for the selected pesticides were calibrated because no available data was found of the literature. The calibrated values fit in the range of other studies (Comoretto et al., 2008;Nakano et al., 2004;Watanabe et al., 2006;Yoshida et al., 2000). 207

Table III (Knäbel et al., 2012). ........ 260 Table IV. 2 Summary of the hydro-chemical parameters monitored at the inlet and outlet of the Bassioué AP. DOC (mg.L -1 ) is the concentration of the dissolved organic carbon in the AP water column, and T (° C) is the water temperature (Chaumet et al., 2022). ........................ 263 Table IV. 3 The dissolved concentrations (pesticide mass per water volume) and particulate concentrations (pesticide mass per mass of TSS) in the AP inlet and outlet: minimum, maximum, mean, and standard deviation (Chaumet et al., 2022).The pesticide physicochemical properties were extracted from the literature (Kim et al., 2021;Lewis et al., 2016). Koc: The

General Introduction

General introduction

General introduction 1 Pesticides

Definition

A pesticide is any substance or mixture of substances that prevents, destroys, repels, or reduces any pest (EFSA). A pest is an animal or plant that can damage the health of an environment and its population. Pests include insects, rodents, nematodes, fungi, weeds, viruses, and bacteria.

Other microorganisms can be classified as pests, except those on or within living humans and animals. The Environmental Protection Agency (EPA) administrator is in charge of determining which organisms qualify as pests (Stone). Accordingly, the EPA and European Food Safety Authority (EFSA) established the following classification of pesticides:

• Algaecides kill and/or slow the growth of algae.

• Antimicrobials control germs and microbes such as bacteria and viruses.

• Disinfectants control germs and microbes such as bacteria and viruses.

• Fungicides control fungal problems like molds, mildew, and rust.

• Herbicides kill or inhibit the growth of unwanted plants, also known as weeds.

• Insecticides control insects.

• Insect Growth Regulators disrupt the growth and reproduction of insects.

• Rodenticides kills rodents like mice, rats, and gophers.

• Wood Preservatives make the wood resistant to insects, fungi, and other pests. Pimentel (2009) reported that 70 000 different pest species damage crops worldwide. On average, more than 45% of annual crops for food production is lost to pest infestation (Abhilash et al., 2009;FAO, 2022). In France, yield losses due to pests and diseases can reach 96% for some cultures (e.g., potatoes) [START_REF] Cerda | Assessment of yield and economic losses caused by pests and diseases in a range of management strategies and production situations in coffee agroecosystems[END_REF]. In view of the growing food demand related to demographic expansion, the use of pesticides to control pests is becoming more and more evident. Cassou et al. (2018) claimed that 85% of pesticides are used in agriculture.

Pesticides in agriculture

Note that non-chemical pest control measures should be first considered, e.g., crop rotation, tillage, varying timing of planting or harvesting, planting trap crops, and adjusting row width.

Another non-chemical measure is using bio-pesticides (plant extract) to control and repel some insect pests (Eze et al., 2012). Chemical controls (i.e., pesticides) should be only initiated if nonchemical control measures fail or if the situation dictates that chemical controls are the only option. Actually, chemical pesticides are widespread compared to non-chemical pest control due to their relatively low cost, ease of application, efficacy, availability, and stability.

Chemical pesticides are typically fast-acting, thereby limiting crop damage (CANNA).

At the global level, total pesticide use in agriculture remained stable in 2020, with an application of 1.8 kg.ha -1 per cropland are worldwide (FAO, 2022), equivalent to 3 billion tons of pesticides applied each year (Pimentel, 2009).

Hazards of pesticides

Pesticides are particularly pernicious for the ecosystem when compared to other chemicals because they are specially manufactured to eliminate pests (Ippolito et al., 2015). As such pesticides are a major risk for terrestrial and aquatic biodiversity (Messelink et al., 2021;Mineau et al., 2013) and the ecosystem's functioning (Brühl et al., 2021). There is now compelling evidence that certain pesticides exhibit a serious hazard to humans and other life forms, as well as undesired side effects on the environment (Briggs, 2018;Edwards, 2013;Nagy et al., 2020).

Pesticides can be toxic, persistent, and accumulate easily in the environment (Fenik et al., 2011;Liao et al., 2020;Vryzas, 2018) and can contaminate air, soil, water, and biota (Figure 1, (Leenhardt et al., 2022a)). In addition to killing insects or weeds, pesticides can be poisonous to a multitude of other organisms, notably birds, fish, beneficial insects, and non-target plants.

Insecticides are usually the most acutely toxic category of pesticides. Herbicides can also harm non-target organisms (Aktar et al., 2009). In the context of this work, we instead target the risk of pesticide transfer to freshwater as a vital drinking water source and a support of aquatic ecosystems.

Figure 1

Illustration of the different ecosystem components that are contaminated by pesticides (Leenhardt et al., 2022a).

Pesticides in France

In 2020, the European Commission presented its Farm to Fork and Biodiversity strategies.

Among the strategies' objectives are to reduce by 50% the use and risk of chemical pesticides by 2030 and to reduce by 50% the use of more hazardous pesticides by 2030 (Tostado, 2022).

On a local scale, France is the largest pesticide seller in the European market partly due to its large area of arable lands (Figure 2, (Tostado, 2022)).

Figure 2

The European Union sales of pesticides in 2020 in tons. The sales are displayed by type of pesticides (insecticides, fungicides, and herbicides) (Tostado, 2022).

In a press statement issued on November 15, 2022 (DRIAAF), the French ministries in charge of agriculture and ecology announced the provisional results for pesticide sales in 2021. After almost a decade of increase, the downward trend witnessed at the national level since 2019 is confirmed, with a total sales of pesticides over the period 2019-2021 at the lowest level since 2009. The provisional data on aggregated sales for the French territory for 2021 was derived from declarations made in early 2022 and evidenced that:

• Sales in 2021 amounted to 43 013 tons (excluding products suitable for organic farming and biocontrol products), remained stable compared to 2020 (+0.7%), and are 19% below the 2012-2017 average.

• The recourse to the riskiest substances is also decreasing strongly: 87% for CMR1 (carcinogenic for humans) and 36% for CMR2 (probably human carcinogen). CMR notation originates from the regulation on the prevention of chemical risks. It is used to designate certain industrial products that cause carcinogenic effects (Kähkönen et al., 2008). Despite a drop in pesticide sales, it is still lower than the Ecophyto plan hoped (Guichard et al., 2017). Therefore, in conjunction with reducing pesticide use and better management practices, technologies could be considered to minimize pesticide transfer from agricultural plots toward water resources. Note that water contamination by pesticides also depends on the transferability of pesticides from the agricultural plot to the freshwater bodies.

Pesticide inputs into water bodies

Pesticides can reach surface water through runoff, spray drift, and erosion from treated agricultural plots. This work particularly focuses on pesticide transfer to waterbodies by runoff and subsurface drainage. Pesticides lost to runoff and erosion leave the field either dissolved in runoff water or adsorbed onto eroded soil particles. However, dissolved losses are assumed to be more important than particulate losses for most pesticides. In fact, the amount of eroded soil lost from a field is likely smaller than the water amount (Boardman et al., 2019;Reichenberger et al., 2007a). Figure 3 illustrates the global risk of freshwater pollution from agricultural insecticide application through runoff (Ippolito et al., 2015).

Another pathway for pesticide transfer into water resources is through agricultural drainage.

The purpose of installing artificial subsurface drains is to prevent saturation of the topsoil (Tournebize et al., 2020). The topsoil's saturation can impair the soil's crop development and workability. Research evidence showed that preferential flow phenomena are key to the rapid transfer of pesticides to drainage systems (Reichenberger et al., 2007a). Transport of pesticides by preferential flow to drains can lead to high transient concentrations in agricultural ditches and rivers. Scientists confirm that agricultural drainage water is a major non-point source of water pollution (Braschi et al., 2021;Villamizar et al., 2020). Accordingly, it is essential to intercept, retain, and treat drainage water before its discharge into water bodies to limit the spread of pesticide-contamination in the environment (Willkommen et al., 2022).

Figure 3

Map of the global risk of freshwater contamination by insecticides (Ippolito et al., 2015).

Pesticide properties

The transfer of pesticides from agricultural fields to surface water can also be highly dependent on their molecular properties (Brühl et al., 2021;Chen et al., 2015a). Therefore, before going any further, it is important to define the key properties of pesticides behind their environmental fate. The main characteristics of pesticides are classified as follows (CRA):

• Koc (L.kg -1 ): The organic carbon-water partition coefficient measures the mobility of a pesticide in a water-solid unit. A very high Koc value means it is strongly adsorbed onto solids and organic matter and is hardly re-mobilized. A very low value means it is highly mobile. Koc is a very important input parameter for estimating environmental distribution and environmental exposure level of chemical substances (EPA).

• Log Kow (-): The log of the octanol-water partition coefficient is a well-known and commonly used property to measure the hydrophobicity of pesticides. Log Kow is a very important parameter for predicting the distribution of a substance in various environmental compartments (water, soil, air, and biota). Pesticides with high log Kow values tend to adsorb (catch on) more readily to organic matter and sediments because of their low affinity for water (ECA). Log Kow is mostly used to evaluate a substance ability to accumulate into biota.

• S (mg.L -1 ): Water solubility measures the amount of chemical substance that can dissolve in water at a specific temperature. Water-soluble chemicals are less likely to adsorb on sediments and rather tend to degrade in water (EPA).

Artificial ponds

The literature has extensively reported measures to reduce pesticide transfer from agricultural plots to downstream water resources. The measures can be "on-site" at the field or farm scales, such as inter-rows or edge-of-field buffers or application rate reduction. Other measures are "off-site', e.g., constructed wetlands, riparian buffers, or grassed waterways (Kadlec et al., 2008;Reichenberger et al., 2007a).

In the context of agricultural drainage, free-water surface artificial (constructed) wetlands are the predominant technology deployed (Braschi et al., 2021;Reichenberger et al., 2007a). In this work, we will focus on artificial ponds (APs), which are a type of artificial wetlands. In the following, we refer to free-water surface artificial wetlands by Willkommen et al. (2022) acknowledged that pollution reduction in APs is most efficient for peaky chemo-graphs (Greiwe et al., 2021), which is a typical trait of agricultural drainage pipes, since these APs act by attenuating peak concentrations. In practice, drainage pipes are fastreacting, erratic in flow, and hold highly variable concentrations of pesticides (Sandin et al., 2018;Ulrich et al., 2012;Willkommen et al., 2019). In view of these elements, we opted to evaluate APs for their ability to reduce pesticide transfer into water hence their designation as "treatment wetlands".

Definition

APs in agricultural areas are small constructed basins filled with water. APs are often kept wet and not exceeding 2 m in depth [START_REF] Zhang | Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)[END_REF]. They are traditionally built on uplands and away from floodplains or floodways to prevent damaging natural wetlands and other aquatic resources.

As other artificial wetlands, APs are often constructed by excavating, filling, grading, diking, and installing water control structures to establish desired hydraulic flow patterns. If the site is highly permeable, an impermeable liner of compacted clay is usually installed, and the original soil is placed over the liner. Wetland vegetation is then planted or allowed to grow naturally (EPA). Therefore, the main compartments of an AP are a water column, a bottom sediment layer, and living organisms.

APs that have been effective at reducing contamination of agricultural waters are constructed downstream of agricultural plots (to intercept polluted flow) and upstream of water resources, e.g., rivers and sinkholes (to minimize pesticide input into water bodies) (Figure 4). Figure 4 Illustration of different pathways of agrochemical contamination (e.g., pesticides) toward surface-and underground-water resources and the localization of wetlands that are able to dissipate such contamination (adapted from AQUI'Brie (illustration)).

Pesticide mitigation in artificial ponds

APs are originally constructed for water storage and flood attenuation. Some APs are designed to provide habitat and food resources for wetland-dependent organisms (e.g., birds, amphibians) (Main et al., 2016) and further ecological services (Dumax et al., 2021;Kataki et al., 2021;Stefanakis, 2020). The AP function of particular interest to this study is the capacity to reduce the amount of pesticide held in agricultural waters. Several field-and laboratory-scale surveys and bibliographic reviews have attested to ponds' efficiency in reducing pesticide transfer to water resources (Dal Ferro et al., 2018;Imfeld et al., 2021;Passeport et al., 2013;Tournebize et al., 2017;Vallée, 2015).

In fact, APs intercept pesticides sourced from cultivated agricultural plots upstream through runoff or subsurface drainage. After flooding events, the resident water is released. Subsequently, pesticides in the intercepted water undergo a series of physico-chemical processes dissipating pesticide concentration between the AP inlet and outlet. In aggregate, pesticide dissipation results from trapping and/or transforming the intercepted pesticides due to a synergy of the physico-chemical processes we thoroughly investigate in Chapter I. APs can be equipped with dikes to decelerate water velocity and to extend the residence of pesticides, which ensures a longer time for dissipation (Figure 5).

Figure 5 Illustration of a typical artificial pond intercepting agricultural waters, and the mechanisms behind pesticide-contamination dissipation between the inlet and outlet (adapted from AQUI'Brie (illustration)).

Modeling pesticide fate in artificial ponds

To reduce water pollution efficiently, it is crucial to understand what happens to a pesticide between the farm plots from which it originates and the freshwater source to which it travels.

In practice, laboratory and/or on-site experimental studies allow quantifying pesticide amounts and detecting their presence in the different ecosystem components. There is clear evidence from experiments of the contamination of soils, sediments, and surface-and ground-water bodies of agroecosystems by variable pesticide residues (Estévez et al., 2012;Vryzas, 2018). However, experimental studies are restrained to the present, while numerical models have the advantage of quantification of the contribution of each process behind dissipation and prediction.

For instance, catchment-scale models help predict residue levels of pesticides and their transfer to water bodies with high spatial and temporal resolutions (Carousel et al., 2005;Li et al., 2021;[START_REF] Luo | Multimedia transport and risk assessment of organophosphate pesticides and a case study in the northern San Joaquin Valley of California[END_REF]. In general, global-scale models help simulate human and ecological health impacts from chemical emissions (Li et al., 2021;Wang et al., 2020). However, for a more elaborated understanding of pesticide fate and especially their dissipation in APs, it is wise to assess their behavior on a smaller scale.

In aggregate, pesticides are usually modeled at the catchment scale to predict their transfer from plots to freshwaters. In contrast, pesticide modeling at a smaller scale (i.e., AP-scale) is used to better comprehend their behavior in the environment.

Modeling pesticide fate at the catchment scale

One key aspect when assessing the environmental fate and effects of pesticides is the selection of the spatiotemporal scale. Recently, larger-scale modeling applications have been largely reported, presumably due to easier access to spatial databases (Ippolito et al., 2019). Several papers reviewed the existing catchment-scale models (Centanni et al., 2022;Lei et al., 2021;Payraudeau et al., 2012;Quilbé et al., 2006).

Most simulations at the catchment scale are performed with a high time resolution (daily and hourly) and generally focus on a limited number of pesticides (1-5). Such models require a thorough integration of the hydrology (e.g., inflow and discharge). In fact, the peak concentrations are ultimately driven by input pesticide loads and water volume underlying dilution and flows (Ippolito et al., 2019). A large segment of these models uses the Soil and Water Assessment Tool (SWAT) model [START_REF] Bannwarth | Pesticide transport simulation in a tropical catchment by SWAT[END_REF]Fohrer et al., 2014;Ippolito et al., 2015). SWAT (Neitsch et al., 2011) is a promising model for continuous simulations in predominantly agricultural watersheds. Other models have been used in Europe, such as SEPTWA and DRIPS. An insightful review of models predicting pesticide fluxes from land to surface water at the catchment scale can be found in (Holvoet et al., 2007). Note that, catchment scale models have limited incorporation of the landscape elements, including APs. Therefore, we focus on the APs scale in a vision of integration into a landscape model.

Edge-of-field scale: Artificial ponds

To better comprehend the measures contributing to reducing pesticide transfer into surface waters, it is required to pinpoint and investigate the possible dissipation mechanisms of pesticides, which are more manageable on a smaller scale than the catchment. Accordingly, in this study, we are interested in modeling pesticide fate and its dissipative processes at the artificial pond scale. Ippolito et al. (2019) highlighted that the existing AP-scale models integrate simplified hydrology compared to catchment-scale models and evaluate a pesticide at a time. Besides, AP-scale models usually perform at a larger time-scale than catchment modes. Such small-scale models are more geared to water quality studies. Hence, it is more challenging to acquire low frequency measurements, as it is costly and labor intensive. The well-known numerical tools to predict pesticide fate at the AP scale are the risk assessment model of the FOCUS workgroup (FOrum for the Coordination of pesticide fate models and their Use (FOCUS)), such as TOXSWA (Adriaanse, 1996). TOXSWA requires, as inputs, the outputs of other FOCUS models, i.e., PRZM (Carousel et al., 2005) and MACRO (Jarvis, 1994), providing runoff and drainage loads from the field. Such models are currently used in Europe for pesticide approval (Hernandez-Jerez et al., 2021).

Risk assessment models use synthetic environmental scenarios to predict reasonable worst-case concentrations following pesticide application. Subsequently, no site-specific data are needed.

Although this modeling approach minimizes the input data requirements, it makes the spatial relevance of the prediction uncertain. Indeed, pesticides are still transferred to the environment after their application, and their concentration in water resources is higher than acceptable regulatory concentrations (RAC) (Gassmann et al., 2015;Halbach et al., 2021;Tang et al., 2012), which questions the transposability of the results of risk assessment tests on the artificial pond sites. Therefore, our objective is to model pesticide behavior at the AP scale using realcases scenarios and on-site monitoring data for validation.

Aimed at agricultural, natural, and artificial wetlands, PESTIPOND brings the innovative concept of studying pesticides' storage and transformation processes in the different compartments of wetlands (e.g., water, sediments, and vegetation). These processes are included in a spatialized and integrative multi-criteria modeling framework for predicting the risk of pesticide transfer to surface waters at the watershed scale. The PESTIPOND concept is summarized in Figure 6 (Imfeld et al., 2021). (Imfeld et al., 2021).

Concisely, the PESTIPOND project is structured around the 3 following tasks:

• Task 1: Characterizing the biogeochemistry of pesticide dynamics on site and in response to hydro-climatic conditions within different agricultural contexts. The objective of the task is to create a typology of ponds based on their capacity to intercept and retain pesticides in order to use a multi-criteria analysis method to assist decisionmaking.

• Task 2: Identification and assessment of pesticide dissipation pathways in ponds via on-site and laboratory experiments.

• Task 3: Modeling the evaluation of pond impact on pesticide transfer to surface waters: Simulation of pond-scale pesticide dissipation and cumulative effects of ponds to generate watershed-scale pond management scenarios.

Figure 7 (Imfeld et al., 2021) abstracts the modeling framework based on the 5 steps illustrated in Figure 6.

Figure 7 Modeling framework integrating the role of ponds to control pesticide transfer at the headwater catchment and the large river basin scales. The framework involves a modeling chain from pesticide dissipation within pond compartments up to pesticide transfer in river basins. All of the corresponding footnotes can be found in (Imfeld et al., 2021). This thesis work is particularly inscribed at the interface between the second and third tasks of the PESTIPOND project and is focused on the pond scale within the modeling framework displayed in Figure 7.

Thesis outline

Bringing the above-mentioned elements together, the dissertation is organized as follows:

Chapter I [review paper (published)] is a state-of-art summarizing knowledge about pesticide fate in wetlands, particularly artificial ponds. It identifies the key processes behind pesticide dissipation along with their mathematical formulations and controlling factors.

Chapter II addresses the modeling of dissolved pesticides at the AP scale with two different approaches. Accordingly, Chapter II is split into two parts:

Chapter II-I evaluates pesticide modeling with a simple approach, i.e., the black model by application of the Rampillon AP.

Definition of wetlands and artificial ponds

In agriculture, various pests (e.g., weeds, insects, rodents, fungi, molds) can significantly deteriorate the quantity and quality of agricultural products. To protect agricultural production, chemicals called "pesticides" are commonly used. Pesticides comprise many compounds, including insecticides, fungicides, herbicides, rodenticides, molluscicides, nematicides, plant growth regulators, and others (Aktar et al., 2009). Due to their toxicity, pesticides represent a non-point source of pollution for the ecosystem and affect the quality of drinking water supplies and aquatic environments. Material solutions can be implemented as a complementary tool to safeguard the quality of water resources and to mitigate pesticide input into water bodies, such as edge-of-field and riparian buffer strips, vegetated ditches, wetlands, and artificial ponds (Vymazal et al., 2015). In this paper, we focus on constructed wetlands, especially artificial ponds, since they have the advantages of needing minimal operations, i.e., being inexpensive to operate and construct, and providing wildlife habitat (Sudarsan et al., 2021). A concise definition of wetlands and ponds and a description of their different types can be found in (Kadlec et al., 2008).

Artificial ponds are free-water surface constructed wetlands, often kept wet and not exceeding 2 m in depth [START_REF] Zhang | Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)[END_REF], and they serve different purposes. Artificial ponds can be used as detention ponds to store water for irrigation purposes (Chaumet et al., 2022;Tournebize et al., 2015) or stormwater ponds to prevent flooding (Maillard et al., 2012). Artificial ponds are also used as complementary tools to treat contaminated agricultural water (Tournebize et al., 2017). These artificial ponds intercept agricultural water through runoff or subsurface drainage from cultivated agricultural plots. Artificial ponds are constructed on an impermeable layer of sediments. In reality, impermeability is ensured by compacting the bottom soil layer because using concrete remains an expensive process. Therefore, the bottom sediment layer is not perfectly impermeable. When the bottom layer of artificial ponds is permeable and allows for surface-groundwater exchanges, pond efficiency can increase. In a recent study, Willkommen et al. (2022) showed that infiltration of pond water enhances the leaching of mobile transformation products of pesticides. Therefore, the permeable bottom layer of artificial ponds can increase the retention of mobile pesticides, especially after dry periods.

The main artificial pond compartments are a water column, a sediment layer, living organisms (i.e., vegetation and microorganisms), and suspended particles. Since the late 1980s, artificial ponds have contributed to the treatment of various wastewaters, including agricultural water (Chouinard et al., 2014;Grinham et al., 2018;Wang et al., 2018;Zamora et al., 2019;Zhuang et al., 2019). It was widely reported that artificial ponds are able to reduce the transfer of chemicals (pesticides, organics, and inorganics pollutants), physical components (solid suspended particulates), and biological elements (bacteria) to natural water resources and preserve their quality (Kasak et al., 2018;Zheng et al., 2018). For example, artificial ponds had an important removal efficiency for organic matter, solids, and nitrogen of up to 90% (de Oliveira et al., 2020;Tournebize et al., 2015). For pesticides, the removal efficiency of artificial ponds can reach 97% for the organochlorine chemical group (Matamoros et al., 2006;Rose et al., 2006), 96% for the strobilurin/strobin group, and 84% for pyrethroids (Vymazal et al., 2015).

A particular type of artificial pond is the ephemeral pond, defined as depression wetlands that temporarily hold water, usually in the spring and early summer or after heavy rains (Machtinger, 2007). The hydraulic residential time in ephemeral ponds ranges from days to weeks (Friesen-Pankratz, 2004). Therefore, they have a lower retention percentage of pesticides due to their ephemeral and seasonal nature, thereby limiting the establishment of many perennial plants. As we detail in the next section, plants are important agents in pesticide retention through sorption and settling. However, ephemeral ponds prevent the accommodation of aquatic predators, creating a unique refuge for sensitive and endemic species, which favors biodiversity and increases the microbial activity behind pesticide transformation [START_REF] Degenhardt | Dissipation of glyphosate and aminomethylphosphonic acid in water and sediment of two Canadian prairie wetlands[END_REF]Kuchta et al., 2009;Sinnathamby et al., 2020). Besides, the ephemeral property of these ponds favors the alternation between dry and wet seasons, enhancing the oxic-anoxic cycle, which stimulates the biotransformation of certain pesticides (Elsayed, 2015;Maillard et al., 2011).

Pesticide volatilization and photolysis are also enhanced in ephemeral ponds because of their shallow nature compared to deeper and more stable water bodies where the effects of solar radiation and temperature are only significant at the pond water surface (Bennett et al., 2005;Graymore et al., 2001).

In this context, significant research effort has been dedicated to understanding the fate of pesticides in artificial ponds and their link to the pond and pesticide properties (Bloomfield et al., 2006). In artificial ponds, pesticides are dissolved in water, attached to suspended particles, and sorbed on sediments and vegetation. The dissipation potential of artificial ponds results from a combination of transport, transfer, and transformation processes of pesticides. Transport processes carry pesticides following a water flow. Transfer processes allow the pesticide molecule to move from one phase to another (i.e., dissolved phase to particulate phase or dissolved phase to gas phase), and transformation degrades and modifies the original chemical structure of pesticides.

Most studies do not distinguish between the contribution of individual removal processes as they are related to each other (Gregoire et al., 2009;Liu et al., 2019;Sahin et al., 2020) and thus their impact on pesticide behavior could not be estimated. Additionally, previous reviews and studies (Stehle et al., 2011;Vymazal et al., 2015) focused mainly on the efficiency of artificial ponds to remove pesticides and on the ecological risk assessment of pesticides (Haith, 2010).

However, they did not detail the processes contributing to pesticide dissipation, while the relationship between pond efficiency and pesticide properties remains elusive. Hence, this paper investigates the relationship between dissipation processes and the main characteristics of pesticides in artificial ponds.

Processes governing the fate of pesticides in artificial ponds

The dissipation of pesticides in artificial ponds results from the synergy between bio-physicochemical processes detailed thereafter. Several studies have proved the possibility of quantifying this dissipation and predicting the fate of pesticides through a black-box model (Braschi et al., 2022). However, a black-box approach does not specify the origin of pesticide dissipation and does not detail the contribution of each process since it only considers a single dissipation rate, kdiss (d -1 ) (Yuan et al., 2020).

In this review, we describe and present different estimation methods of processes behind pesticide dissipation in artificial ponds. The form under which pesticides are introduced to the artificial pond depends on how agricultural water is drained into the artificial pond. If the watershed is drained with significant subsurface-flow, pesticides are more likely to be dissolved rather than be present in particulate form (Braschi et al., 2022;Le Cor et al., 2021). By contrast, pesticides can be present in dissolved and particulate phases if runoff occurs. Gramlich et al. (2018) assumed that subsurface drainage reduces erosion risk, while runoff may increase it.

Dissolved pesticides (DP) in water bodies are widely studied in the literature, whereas few studies focus on particulate pesticides (PP) (Climent et al., 2019). PP are transported within the artificial pond along with total suspended solids (TSS) following the water flow. According to the hydraulic regime of the artificial pond, PP can settle on the sediment bed or be suspended back to the water. By contrast, DP can be bound to the solid surfaces available in the artificial pond through sorption and become PP. Sorbed DP can be released back into water through desorption. DP can also be transported to the atmosphere when volatilized or taken up by the plants growing in the artificial pond. On the other hand, pesticides can be subject to transformation processes, whose prevailing drivers are microorganisms, solar radiation, and hydrolysis.

Processes involved in pesticide dissipation in artificial ponds depend on many factors such as the characteristics of the pond soil (i.e., organic matter (OM), organic carbon (OC), and clay content), water physico-chemical properties (pH, temperature (T), and redox conditions), vegetation, and the hydraulic conditions of the artificial pond (Papaevangelou et al., 2017;Vymazal et al., 2015). The mitigation of pesticides in artificial ponds also depends on the pesticide's physico-chemical properties. The main physico-chemical properties of a pesticide include mobility, hydrophobicity, solubility in water (S), and the pesticide half-life (DT50), i.e., the time required for the dissipation of 50% of the substance concerned (Gregoire et al., 2009).

The properties of pesticides and the processes behind their dissipation in artificial ponds are detailed in subsequent sections.

Hydraulics of artificial ponds

Hydraulic performance is a key factor controlling the pesticide dissipation function of an artificial pond. The hydraulic performance of artificial ponds includes lowering peak flow and limiting sediment export from agricultural parcels to natural water bodies (Passeport et al., 2010;Tournebize et al., 2013). Hydraulic parameters of artificial ponds include (i) the hydro period (Bojcevska et al., 2007;Convertino et al., 2013;Liu et al., 2019;Prochaska et al., 2007) and (ii) the hydraulic residence time (HRT) of the water in the artificial pond (Holland et al., 2004;Kjellin et al., 2007).

The hydraulic performance of artificial ponds depends on design parameters such as depth, the layout of the inlet and outlet, and plant spacing. In this subsection, we describe the impact of design parameters on the hydraulic performance of artificial ponds.

Multi-tracer experiments showed that shallow water depth was an essential factor enhancing sorption and that pesticide load reduction was more significant in shallow artificial ponds than in deep ones (Lange et al., 2011). A low water depth enables a uniform fluid distribution compared with greater depths. The uniformity of the fluid flow decreases the hydraulic dead zones and increases the effective volume, which is the artificial pond volume used for pesticide dissipation (Rayen et al., 2019).

Globally, an artificial pond volume can be divided into three hydraulic zones (Martinez et al., 2003b;Werner et al., 2000):

1. The main channel with the preferential flow.

2. The mixing zone, which is a temporary storage zone showing exchanges with the main channel.

3. The dead zone, which is more isolated from the main channel than the mixing zone.

Tracing studies showed that water is transported mainly by convection in the main channel, while it is transported by dispersion in the isolated zones (mixed and dead zones). The HRT in isolated zones is longer than in the main channel. Thus, isolated zones could enhance dissipation processes such as sorption, settling, and degradation (Gaullier et al., 2018). The main channel is where short circuits in the water flow can occur, causing the transport of water from the inlet to the outlet by preferential flow, which decreases the HRT and effective volume of the pond (Vieira et al., 2018). Another controlling parameter of the hydraulic performance of artificial ponds is the inlet/outlet design. Shilton et al. (2003) reported that positioning the outlet in an isolated zone from the main channel avoids short-circuiting issues and maintains the maximum hydraulic performance of artificial ponds. In addition, vegetation density reduces the water velocity and increases the HRT (Gu et al., 2017). Increasing the HRT increases the contact time between pesticides and pond substrates, giving pesticides more time to be sorbed, settled, or degraded.

A good hydraulic performance also guarantees an optimal interception of agricultural water by catching the highest concentrations of pesticides (during the post-application period) in minimum water volumes through off-/on-stream artificial ponds (Passeport et al., 2013;Passeport et al., 2010).

Objectives

Usually, the risk of pesticide transfer is evaluated using the FOCUS models. However, pesticides are still transferred to the environment after their application, and their concentration in water resources is higher than the regulatory acceptable concentrations (RAC) (Gassmann et al., 2015;Halbach et al., 2021;Tang et al., 2012), which questions the transposability of the results of risk assessment tests on the artificial pond sites. Therefore, knowledge of the behavior of pesticides in receiving ecosystems is also needed.

This review relies on a bibliographic analysis to highlight the knowledge needed to understand pesticide behavior following application on agricultural plots and interception by artificial ponds. In particular, our review evaluates a broad panel of pesticides with contrasting physicochemical properties and presents the mathematical formulations of multiple processes. We review studies of pesticide behavior in artificial ponds from the laboratory to the field scale.

Such knowledge is helpful for hierarchizing dissipation processes, estimating artificial pond efficiency, developing predictive tools for the fate of pesticides in artificial ponds (Hantush et al., 2013;Kalin et al., 2017;Walker Jr et al., 2011), and guiding stakeholders in optimizing the setting up of artificial pond in agricultural areas.

Description and mathematical formulation of dissipation processes of pesticides in artificial ponds

Modeling is an efficient, comprehensive tool for assessing and setting up facilities to treat contaminated environments (Warren et al., 2002). There are mainly two types of models that describe pesticide fate in the environment: (i) physical 3D and 2D models that simulate the hydraulic performance of wetlands by considering water velocity profiles and the vegetation impact of the flow (Carleton et al., 2010;Luo et al., 2016;Martinez et al., 2003a;Tsavdaris et al., 2013a) by testing different internal configurations of artificial ponds (i.e., baffles). (ii)

Conceptual models that simulate the biogeochemistry and the dissipation performance of the wetland for a large-scale application (Gobas et al., 2018;Willkommen et al., 2018).

Our analysis of the literature showed that the fate of pesticides in artificial ponds had been widely discussed in the field. But most of these studies are generally limited to one pesticide or the evaluation of a specific process and are rarely supported by mathematical formulae.

However, we assume that mathematical formulations enable the integration of processes into a computational model to quantify and predict the fate of contaminants in artificial ponds. The originality of this review lies in the description of the main processes influencing the behavior of multiple pesticides in artificial ponds, namely, transport, sorption, and transformation processes. The evaluation and formulation of these processes are necessary in developing predictive models for pesticides dissipation in artificial ponds (Papaevangelou et al., 2017;Saaristo et al., 2018). In this section, we investigate the behavior of multiple pesticides with contrasting physico-chemical proprieties and describe different methods currently employed to evaluate and formulate the processes controlling dissipation in artificial ponds.

Transport processes

In conceptual models, artificial ponds are often assimilated into perfectly mixed reactors. This hypothesis introduces incertitude to the model because of its limits. In a perfectly mixed reactor, the flow patterns are considered to be similar with no short circuits and dead zones and longitudinal dispersion is neglected. Therefore, it assumes that the HRT is uniform in the artificial pond. Studies showed that the hypothesis of a perfectly mixed reactor or a plug flow overestimated the efficiency of artificial ponds in pesticide treatment by 5% (Sirivedhin et al., 2006). However, advection is still considered in conceptual models by taking into account a nominal residence time.

Usually, after several hours, the pond water becomes perfectly mixed (Alvord et al., 1996;[START_REF] Pugliese | Internal hydraulics and wind effect in a surface flow constructed wetland receiving agricultural drainage water[END_REF]. Since conceptual models simulate pesticide fate at a daily time step, the hypothesis of a perfectly mixed reaction can be used for artificial ponds. Moreover, experiments by Guo et al. (2015) showed that hydraulic patterns do not deviate much from ideal flows in shallow wetlands. Since artificial ponds do not exceed 2 m in depth, they are considered shallow and can be assumed to be perfectly mixed reactors. Besides, Uddameri (2010) proved that the assumption of a completely mixed condition is probably not correct for a wetland of >8 ha in size, which is not the case for artificial ponds (Karki, 1970). Dispersion is often considered in heterogeneous wetlands, while artificial ponds are more homogeneous. Models such as TOXSWA (from the FOCUS models) consider advection-dispersion processes, assimilate the water column to multiple water nodes, and create a multilayer sediment profile. Therefore, such models are based on more complicated equations and require a significant computation effort and several parameters to calibrate. By contrast, many conceptual models showed satisfactory results using the ideal flow hypothesis (Gobas et al., 2018;Inao et al., 1999;Krone-Davis et al., 2013;Watanabe et al., 2006). Considering the aforementioned elements, we only focus on transport processes in artificial ponds considered as perfectly mixed reactors. Hence, we did not consider dispersion-diffusion.

Pesticides reaching artificial ponds can be dissolved in water or combined with dissolved organic carbon (DOC). They can also be bound to the organic fraction of suspended matter (i.e., particulate organic carbon (POC)), representing pesticides in a particulate form. PP are carried along with suspended matter throughout the artificial pond as a result of water movements. PP can be deposited on the sediment bed due to settling and released back to water through resuspension. Settling and resuspension are transport processes that strongly depend on the particle size and the strength of the water flow in artificial ponds. Pesticides can also be the subject of advection in the water column. According to the Hjulström curve, the water velocity in an artificial pond can determine which transport process prevails (Hjulstrom, 1935).

Settling

Settling is a physical transport process through which suspended particles in water settle on the sediment bed of artificial ponds. Settling depends on the particle size of pesticides, water velocity and turbulence, temperature, and water column depth (Kadlec et al., 2008).

Settling has been shown to be efficient in the water-quality improvement function of artificial ponds, especially in lower flows, since suspended particles, on which pesticides may be attached, can leave the water column while settling on the sediment bed (O'Geen et al., 2010).

Settling is enhanced when vegetation covers a significant surface of the artificial pond;

emergent macrophytes slow down water flow and amplify settling (Vymazal, 2013). In addition, stems and leaves in the water column obstruct water flow and dissipate the energy required to support particle suspension, thereby enhancing settling (Schmid et al., 2005). Rose et al. (2008) also reported that the settling rate in a vegetated artificial pond (0.042 h -1 ) was double the rate estimated in a non-vegetated artificial pond promoting the effect of vegetation on settling. A small water depth was also reported to promote settling process; it fosters plant growth and shortens the settling distance for particles (Lange et al., 2011). This is valid for water depths between 20 cm and 50 cm (Braskerud, 2002). Pesticides associated with fine suspended particles may hardly be retained in ponds since fine particles (clay and silts) are rapidly transported across preferential pathways where the HRT is low (see sect. 1.3) (Maynard, 2009). The settling of PP is more likely to occur in hydraulically isolated zones with lower water flow and higher HRT [START_REF] Gaullier | Influence of hydrodynamics on the water pathway and spatial distribution of pesticide and metabolite concentrations in constructed wetlands[END_REF]Passeport et al., 2010). Ponds with the highest settling rate are the ones receiving agricultural surface runoff (Fiener et al., 2003;Zhang et al., 2011), where the intercepted agricultural water is heavily loaded with coarser (>200 µm) particles from the fields (Li et al., 2017). This is due to the higher TSS load from erosion that is less available in artificial ponds collecting subsurface drainage water (Koskiaho, 2003a;Maillard et al., 2011). It was also suggested that coarse particles settle before the water is distributed across pond compartments, unlike fine particles requiring a longer time to settle. As a result, fine particles become more concentrated downstream of the pond (Gan et al., 2005) because gravity induces the settling of heavy particles (>36 µm) (i.e., sand) more than lighter particles (<2 µm) (i.e., clay and OM) (Fiener et al., 2003). Several studies suggested that a large proportion of highly hydrophobic organic substances in artificial ponds were retained by settling pesticide-laden solids (sorbed on suspended solids) (Luo et al., 2009a;Matamoros et al., 2006;Rose et al., 2008). Therefore, settling may not be sufficient to trap pesticides with a stronger affinity to fine suspended particles in ponds. Consequently, a higher HRT and vegetation are required to increase the settling of fine PP (Budd et al., 2009(Budd et al., , 2011;;Fiener et al., 2003).

Settling was commonly integrated with an accumulation rate expressed empirically in pesticide dissipation models. The empirical formulations of settling rates can be found in Table I Re. The second formulation (ii) suggested by Kadlec et al. (2008) calculates the settling rate in both laminar and turbulent flows based on a drag coefficient CD that depends on Re. Formula (ii) assumes that settling results from the density difference between suspended particles and water, and thus it expresses the rate of a single and isolated spherical particle. The limitations of the first (i) and second methods (ii) are the required parameters, since in artificial ponds, neither the density nor the particle size is known. In addition, particles are not spheres or discs and can agglomerate to a larger size. Formula (iii) estimates the settling rate of suspended particles in wetlands (Hawley et al., 2014). This formulation required more parameters such as the water kinematic viscosity since it is adapted for non-spherical particles too; however, it was often applied to large particles.

Overall, formula (i) could be sufficient and easy to use for a simple simulation of PP settling in laminar flows. If the flow is turbulent with a known Re, formula (ii) would be more suitable for its accuracy. For formula (ii), particle sizes are often approximated according to their nature (i.e., clay, silt, sand, and OM). Otherwise, the settling rate could be calibrated if the required data are available. Nevertheless, the majority of settling studies are performed on nutrients, and little is known about pesticides (Hantush et al., 2013;Kalin et al., 2017;Walker Jr et al., 2011).

Regarding behavior, settling seems to be a key process of pesticide accumulation in artificial ponds alimented by surface runoff in erosive areas, especially for hydrophobic pesticides and coarse PP.

Resuspension

Resuspension is the opposite process of settling, through which PP are transported from the sediment bed to water. Resuspension depends mainly on the water flow and the water depth in artificial ponds.

Resuspension is likely to occur when water turbulence is significant (Siobhan Fennessy et al., 1994), and high water flow can re-mobilize settled PP back to the water (Collins, 2004;Jamieson et al., 2005;Vallet, 2011;Wilkes et al., 2009). It was proved that resuspension was greatly affected by the type and density of vegetation in artificial ponds (Liu et al., 2019;Schulz et al., 2001). Braskerud (2001) presumed that an increase of 30% in a vegetative cover, initially less than 20%, could reduce resuspension rates from 40% up to 100%. Rose et al. (2008) showed that the resuspension rate was reduced by half by switching to a vegetated artificial pond. Some studies suggested that deeper free-water surface wetlands enhanced settling and burial rather than resuspension (Brueske et al., 1994;Budd et al., 2011). Resuspension is likely to occur in the main channel where the water velocity is high (on page 50) [START_REF] Gaullier | Influence of hydrodynamics on the water pathway and spatial distribution of pesticide and metabolite concentrations in constructed wetlands[END_REF] and is mainly driven by wave action generated by the wind (Hawley et al., 2014). Other agitation factors may be responsible for particle resuspension, such as wildlife movements in the artificial pond (i.e., bioturbation), although quantitative studies on this topic remain scarce.

Resuspension was significantly less considered in the literature than settling. However, resuspension occurred with a slow rate of remobilization (vr ~10 -6 mm/s) (Hawley et al., 2014;Sharifi et al., 2013), while Rijn (1984) suggested that the resuspension rate vr reached one quarter of the settling rate vs.

Globally, the finding of significantly lower concentrations of PP measured at the outlet of wetlands than at the inlet suggests that the contribution of resuspension to pesticide behavior might be negligible (Budd et al., 2009;Cryder et al., 2021;Supowit et al., 2016) and, moreover, resuspension is more dominant in systems with stronger water flows (i.e., rivers) rather than artificial ponds. In addition, high water flow events are usually short and have a long return period, which decreases the occurrence of resuspension.

Advection

Among the processes behind pesticide transport in artificial ponds is advection. Advection carries pesticides in the same direction as the water flow and facilitates the homogenization of DP concentrations in the artificial pond. (Gaullier et al., 2019) showed that water transport is governed mainly by advection at a high flow rate. In a pesticide transport equation, advection is expressed by the term -ν Chapter I: The fate of pesticides in artificial ponds: State-of-the-art 57

Transfer processes

Once pesticides reach the artificial pond, they are distributed among water, sediments, TSS, and vegetation and are then transferred among artificial pond compartments. DP can be exchanged between the water column and the solid compartments due to "sorption" and transferred to the atmosphere during "volatilization". Also, DP can become PP when sorbed to TSS.

Sorption

Sorption was commonly reported as an important process in the fate of pesticides in artificial ponds (Cryder et al., 2021;[START_REF] Fernández-Pascual | Hydrological tracers for assessing transport and dissipation processes of pesticides in a model constructed wetland system[END_REF]Hand et al., 2001). In the following two subsections, we first present the theory of sorption and analyze study cases of pesticide sorption in artificial ponds. This review will focus on sorption directly related to the fate of pesticides in artificial ponds; more details on sorption can be found in the literature (Huang et al., 2003;McBride, 1994a;Pignatello, 1989;Sposito, 2008)

Sorption theory

Physically, sorption refers to all exchange processes between a dissolved substance and solid matter in a solution. Among these exchanges, adsorption and desorption are the most extensively studied for pesticide fate in the environment. Adsorption is a physical transfer process of retaining dissolved substances in water on solid surfaces. The dissolved substances are the "sorbate," and the solid surface to which they are bound is called "sorbent" or "substrate." Desorption is when the substrate releases the sorbate and returns to water (Sposito, 2008). The term "sorption" will be used hereafter to refer to adsorption plus desorption.

Most studies of sorption are conducted in the laboratory and adapted from OECD 106 guidelines for individual molecules, in which sorption experiments are performed using a batch equilibration technique (OCDE, 2000). The retention and release of the sorbate on a substrate at various concentrations and a constant temperature are described by a curve called "adsorption isotherm" and "desorption isotherm," respectively. The isotherms represent the relationship Cse=f(Cwe) during adsorption and desorption equilibrium, where Cse (µg.g -1 ) and Cwe (µg.L -1 ) are the sorbate concentration in the substrate and the sorbate concentration in the solution at the equilibrium state, respectively.

Based on the thermodynamic equilibria theory that assumes complete reversibility of the chemical reactions, the adsorption and desorption isotherms should be the same (Strawn et al., 1999). However, sorption mechanisms are driven by different kinetically controlled reactions or physical phenomena with a large variability in reaction times ranging from seconds to years, emphasizing the kinetic character of sorption (Beulke et al., 2004;Caceres-Jensen et al., 2019;Cara et al., 2021;Kaur et al., 2018;Mamy et al., 2007;Sparks, 2000;Villaverde et al., 2009;Wu et al., 2017).

A difference between adsorption and desorption kinetic rates to reach the equilibrium is represented by a mismatch between the adsorption and desorption isotherms curves. This mismatch is called "pseudo-hysteresis" or "kinetic hysteresis" (Strawn et al., 1999), also referred to as an "apparent irreversibility" [START_REF] Bladel | APPARENT IRREVERSIBILITY OF ION-EXCHANGE REACTIONS IN CLAY SUSPENSIONS[END_REF]McBride, 1994b). It is important to clarify that the term "irreversibility" does not mean that the sorbate will not desorb after its adsorption. Still, it will take a long time to reach the equilibrium state (Limousin et al., 2007).

In other words, hysteresis means that desorption requires a more significant amount of energy than needed by adsorption (Bowman et al., 1985;Koskinen et al., 1979;Wu et al., 1988), which could be caused by experimental artifacts or transformation of the sorbent (Pignatello et al., 1996).

The evaluation of sorption requires the determination of its equilibrium and kinetic properties (Douven et al., 2015). Sorption kinetics are studied with the most widely applied chemisorption models -pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich models (Azizian, 2004;Ho, 2006;Ho et al., 1998Ho et al., , 1999;;Yuh-Shan, 2004) -to describe the temporal evolution of the sorbed amount of sorbate. At the same time, sorption equilibrium is commonly modeled by the Freundlich and Langmuir isotherm models (Azizian et al., 2018;Foo et al., 2010;Mckay, 1995). As the equilibrium of kinetic sorption is reached, isotherm models are applied. The corresponding equations of kinetic and isotherm models can be found in Table I.A. 2.

Sorption of pesticides in artificial ponds

In the case of artificial ponds, the sorbates are dissolved pesticides (DP) and substrates are total suspended solids (TSS) with organic and inorganic compounds, sediments, and vegetation (Gregoire et al., 2009;Sahin et al., 2020), which represent support for the development of OM and biofilm. The Freundlich and the linear model are commonly used isotherm models to describe the equilibrium of pesticide sorption on artificial pond substrates (Liu et al., 2019;Takagi et al., 2012;Watanabe et al., 2006;Zhang et al., 2018). Pesticide adsorption is widely described by the adsorption coefficient normalized to substrate organic carbon content Kfoc and Koc for the linear isotherm:

(I-1)

𝐾 𝑓𝑜𝑐 = 𝐾 𝑓𝑎𝑑𝑠 𝑂𝐶 (I-2) 𝐾 𝑜𝑐 = 𝐾 𝑑 𝑂𝐶
where OC (%) is the organic carbon content of the pond substrates, Kfads is the Freundlich adsorption coefficient, and Kd is the distribution coefficient (Table A.2). Vallée (2015) and Passeport et al. (2011b) studied the sorption of 12 pesticides with contrasting physico-chemical properties on pond substrates (i.e., sediments, vegetation, and TSS). Sorption coefficients can be found in Table I.A. 3. Based on the Kfads values, epoxiconazole was the most highly adsorbed pesticide, followed by prochloraz. Epoxiconazole and prochloraz have a Kfoc range of 702-1962 mg 1-n f.L n f.kg -1 and 1222-8654 mg 1-n f.L n f.kg -1 , respectively (Lewis et al., 2016), which, according to the Soil Survey and Land Research Centre (SSLRC) soil persistence classification system, means that they are lowly mobile molecules (PSD, 2005). By contrast, the lowest sorption was seen for isoproturon, with the lowest Kfoc values of 55-60 mg 1-n f.L n f.kg -1 (Vallée, 2015), followed by MCPA. The low adsorption of MCPA could be due to its anionic form, inducing an electrostatic repulsion of negatively charged particles (i.e., clay and OM), as supported by Thorstensen et al. (2001). Likewise, Poissant et al. (2008) and Wu et al. (2004) reported that pesticides with higher Koc values (Koc > 500 L.kg -1 ) are most likely to be sorbed to pond solid particles, in agreement with previous findings (Papp, 2009;Saripalli, 1997). The low solubility of epoxiconazole and prochloraz also favored their retention on pond substrates.

Several studies in the literature reported that low solubility enhances pesticide adsorption (Blachnio et al., 2020;Brusseau et al., 2019;Kah et al., 2006). Sorption also depends on the hydrophobicity of the pesticide expressed by the octanol-water partition coefficient Kow. Hijosa-Valsero et al. (2016) analyzed 59 compounds in water and sediment samples. They found that herbicides in the sediments had an average S = 194 mg.L -1 and a log Kow = 3.7, while herbicides in the water phase were highly soluble with S = 1395 mg.L -1 and an average log Kow = 2.24. These results support that lowly soluble and hydrophobic pesticides (log Kow > 3) are likely to be sorbed on pond sediments, which agrees with O'Geen et al. ( 2010), who observed a 50% reduction in pesticide concentration with a log Kow > 4.2. Likewise, Tang et al. (2017) found that adsorption rates of hydrophobic pesticides were significantly higher in artificial pond vegetation (5.52-8.84 g m-2 d-1) than were hydrophilic pesticides (log Kow <3). These results

suggest that sorption may be the primary driving process in removing hydrophobic pesticides from the artificial pond water.

On the other hand, vegetation, biofilm, and the OM and clay content of the substrate play a significant role in pesticide sorption in artificial ponds. Barchanska et al. (2019) observed that the rate of artificial pond remediation of pesticides due to adsorption on vegetation reached 30%. An extensive vegetation cover can enhance adsorption by increasing the HRT Gaullier et al. (2020b). A higher HRT increases the contact time between pesticides and pond substrates, favoring adsorption and generally decreasing desorption, as observed by (Mamy et al., 2007;[START_REF] Olvera-Velona | Sorption and desorption of organophosphate pesticides, parathion and cadusafos, on tropical agricultural soils[END_REF]. Passeport et al. (2011b) and Vallée (2015) observed higher Kfads on vegetation than in sediments (Table A.3), showing greater retention on OC-rich substrates. Moore et al. (2007) and Sahin et al. (2020) also proved that diazinon preferentially sorbed on vegetation surfaces rather than on sediments. In a similar manner, Rogers et al. (2009) showed that adsorption of the lowly mobile chlorpyrifos (Koc =5509 L.kg -1 (Lewis et al., 2016)) was 10 times higher on vegetation than on sediments, and several other studies demonstrated that adsorption is more dominant in OC-rich substrates, including vegetation (Guo et al., 2000;Mahabali et al., 2014;Moore et al., 2007;Mugni et al., 2011). Biofilm was also proven to enhance adsorption for certain pesticides (Agudelo et al., 2012;Beecraft et al., 2021).

Desorption was also evaluated and was reported to be less dominant in sediments with high clay and OC content, especially for lowly soluble pesticides (Gramlich et al., 2018;[START_REF] Olvera-Velona | Sorption and desorption of organophosphate pesticides, parathion and cadusafos, on tropical agricultural soils[END_REF]Passeport et al., 2011b;Song et al., 2021;Vallée, 2015). In comparison, greater desorption was observed on some highly soluble pesticides [START_REF] Olvera-Velona | Sorption and desorption of organophosphate pesticides, parathion and cadusafos, on tropical agricultural soils[END_REF].

Hence, hysteresis was generally observed on OC and clay-rich substrates from which hydrophobic and lowly soluble pesticides are hardly desorbed (Hand et al., 2001;Li et al., 2003;Vagi et al., 2010).

In addition, kinetic sorption rates showed that desorption was slower than adsorption for six evaluated pesticides, except for picloram. The reviewed kinetic rates of adsorption and desorption can be found in Table I.A. 4. The exception of picloram can be explained by the fact that its sorption was evaluated in laboratory conditions, because in the experimental sample, there was no consideration of other processes that may compete with desorption (i.e., transformation). The interaction between desorption and transformation was also observed by Sørensen et al. (2006), who noted that slower desorption of MCPA coincided with higher microbial transformation. In fact, unlike laboratory-batch sorption observations, sorption and transformation processes co-occur under field conditions, favoring the occurrence of hysteresis in the presence of other competitive processes such as transformation and diffusion into sediment layer micropores (Mamy et al., 2007;Paszko et al., 2016;Takagi et al., 2012;Uchimiya et al., 2012). By contrast, advection/dispersion enhances sorption by facilitating the access of DP to the pond substrates (Gaullier et al., 2018).

Overall, we assume that both adsorption and desorption depend on the clay and OC content of pond substrates and on the properties of pesticides. Adsorption is more dominant for hydrophobic, lowly mobile, and lowly soluble pesticides, and desorption hysteresis is more accentuated in clayey and OC-rich pond substrates.

Volatilization

Volatilization is a process through which pesticides are transferred outside the pond, from the water to the atmosphere. This non-degradative removal process depends greatly on the pesticide molecule's vapor pressure.

There are two regimes controlling volatilization: an atmospherically controlled regime and a regime controlled by water properties. Most pesticides (80-90%) belong to the atmospherically controlled regime, where the gas exchange between the water and the atmosphere is only sensitive to the parameterization of atmospheric turbulence, and the majority of pesticides belong to it; therefore, we focus on the atmospherically controlled regime. In the atmospherically controlled regime, at a very low dimensionless Henry constant (<10 -7 ) (equivalent to a vapor pressure (vp) <10 -5 Pa), the volatilization rate is so slow that the volatilization half-life is tens to hundreds of years. In that case, volatilization becomes insignificant compared to other processes (Jacobs et al., 2012). In this regard, Höllrigl-Rosta (2017) classified volatilization according to the vapor pressure of the pesticide molecule (Table A.5).

In modeling, volatilization is often described by a first-order kinetic equation (Barchanska et al., 2019;Comoretto et al., 2008;Gobas et al., 2018;Inao et al., 1999;Singh et al., 1991;Watanabe et al., 2006) :

(I-3) 𝑑𝑀𝑤 𝑑𝑡 = -𝑘 𝑣 * 𝑀𝑤
where Mw is the mass of the DP and kv (d -1 ) is the pesticide rate constant of volatilization to the atmosphere. Volatilization can also be represented by its corresponding half-life DT50v (d):

(I-4)

𝐷𝑇 50𝑣 = ln (2) 𝑘 𝑣
Since this transfer process depends on other factors, the volatilization rate may be expressed empirically as a function both of pesticide properties and of climate conditions. Table I.A. 6

presents a simplified form of the rate of volatilization kv used in descriptive models of the fate of pesticides. In other studies, there are many complicated formulations (Bunyakan et al., 2006;Comoretto et al., 2008) in which kv depends on diffusion coefficients, air-water/water-air mass transfer coefficient, and friction. However, they are usually left for volatilization-specific studies and do not model the fate of pesticides in artificial ponds.

A bibliographic review of calculated volatilization half-lives DT50v (d) of certain pesticides can be found in Table I.A. 7. For most pesticides in artificial ponds, the vapor pressure is lower than 10 -5 Pa, which makes it possible to neglect the contribution of volatilization to the artificial pond dissipation function according to the classification in Table I.A. 5. However, it was demonstrated that some pesticides were efficiently removed from water by volatilization (Wang et al., 2014;Wang et al., 2019). In an aquatic microcosm under semi-field conditions, Laabs et al. (2007) showed that volatilization was a primary reason for high pesticide losses from the water shortly after application, especially for non-polar pesticides (i.e., endosulfan, chlorpyrifos, and trifluralin). Similarly, Comoretto et al. (2008) reported that oxadiazon had a DT50v of 23 days in a rice field, making it more vulnerable to volatilization when compared with stable MPCA and pretilachlor. kv values were either calculated using the empirical formulas in Table I.A. 6 or estimated in laboratory experiments. Empirical formulations of kv show that volatilization rates are mainly dependent on water depth, a water-air mass transfer coefficient that is a function of the wind speed, the Henry constant (or vapor pressure and solubility), and the molecular weight of the pesticide. Pesticides with a vp lower than 10 -5 require years to be volatilized according to the volatilization half-lives in Table A.7, making them virtually non-volatile. By contrast, other pesticides such as alphamethrin, molinate, endosulfan, and dieldrin need a few days to volatilize, while oxadiazon requires more days to volatilize. Through careful consideration of the vp of these substances, we suppose that these assumptions agree with the classification in Table I.A. 5. These observations showed that most pesticides are non-volatile due to their low vapor pressure. However, there are still some pesticides for which volatilization takes part during their dissipation in artificial ponds.

Transformation processes

In addition to the transport and transfer processes on page 52on page 57), pesticides face other processes that transform their original chemical structure. These processes are also designated as "degradative" processes when attempting to degrade pesticides to non-organic products (Cessna et al., 2015;Imfeld et al., 2021). The importance of transformation processes in the dissipation efficiency of artificial pond was widely proved. Pesticides can be transformed into less toxic products or, in the ideal case, wholly degraded to simple mineral products under the action of the chemical, photochemical, and biological processes (Tournebize et al., 2017). This section describes the main biotic and abiotic transformation processes to which pesticides are exposed in the different compartments of the artificial pond.

Biotransformation

Biotransformation is a process by which organic substances are transformed by microorganisms (bacteria, archaea, and fungi) into chemical substances called "metabolites, " resulting in some cases in their complete mineralization (Garcia-Rodríguez et al., 2014). If the microbial transformation is complete, it becomes a transformation, and pesticides are mineralized to CO2 and CH4. Otherwise, co-metabolism occurs, during which microorganisms degrade OM substrates, the primary source of nutrients of the bacteria, primarily to produce enzymes that will break down sequentially the pesticide molecule, representing a secondary source of nutriments of the bacteria (Arcangeli et al., 1997) and resulting in a metabolite.

Recently, in the context of an artificial pond system, some metabolites were found to be less toxic than their parent pesticides (Kang et al., 2020). However, it remains unknown whether other metabolites are less or more toxic than the mother compounds. Nonetheless, biotransformation has been extensively shown to be a significant removal process for microorganic pollutants (Hijosa-Valsero et al., 2010;Langergraber, 2008;Llorens et al., 2011;Lyu et al., 2018;Samso et al., 2013;Yang et al., 2017;Zhang et al., 2017).

Biotransformation occurs in both aerobic and anaerobic conditions. Aerobic biotransformation occurs in oxic conditions, mainly near the plant roots or the water surface where the amount of dissolved oxygen is high enough. Biotransformation is anaerobic in deep water and sediment depths with generally dissolved oxygen concentrations below 0.5 mg.L -1 (U.S.G.S, 2020). The rhizosphere can also be considered as an aerobic zone since oxygen can be transported from the plant body to the roots (Maltais-Landry et al., 2009), but researchers showed that the amount of oxygen released by the plant was too low to be considered in models of artificial pond fate (Bezbaruah et al., 2005;Llorens et al., 2011). Artificial ponds are mostly aerobic in the water column surface, with anaerobic conditions occurring at the bottom sediment layer and deep water (Vymazal et al., 2015). The sediment layer was reported to be characterized by a sharp decrease in oxygen (Martínez-Lavanchy et al., 2015;Stottmeister et al., 2003;Thomas et al., 2011;Zhang et al., 2010). Karpuzcu et al. (2013), Paszko et al. (2016), and (Mulligan et al., 2016); Paszko et al. (2016) demonstrated that anaerobic conditions would result in lower biotransformation rates than under aerobic conditions. This is agreement with a recent study of commonly used herbicides in which Droz et al. (2021) indicated that acetochlor and Smetolachlor transformation prevailed in the water phase and was insignificant in the sediment.

Microbial transformation also depends on temperature and radiation (Burrows et al., 2002), pH, OM content, and microbial population (Vink et al., 1997). Sahin et al. (2020) reported that humic substances increase biotransformation rates since they can carry microorganisms to the pesticide. This effect has been widely investigated (Cai et al., 2017;Ortega-Calvo et al., 1998;Smith et al., 2009;Tejeda-Agredano et al., 2014), mainly for hydrophobic pesticides. For instance, atrazine was highly biodegraded when sorbed onto clay-humic acid complexes (Besse-Hoggan et al., 2009). Likewise, Rong et al. (2019) found that the interfacial reactions on clay surfaces increased the biotransformation rates of methyl parathion. The exposure history to pesticides of artificial ponds also influences biotransformation. Johnson et al. (2004) demonstrated that sites with a higher pesticide exposure history had higher biotransformation rates due to microbial acclimation and microbial community adaptation.

The water level is another controlling factor of biotransformation in the sediment compartment of artificial ponds. Ma et al. (2018) speculated that a decline in the water level enhances microbial growth and activity and would modify the structure and function of microbial communities. The study further emphasized that a water volume drop-off would enhance aerobic conditions, induce plant succession to the artificial pond center, and stimulate OM accumulation and mineralization.

The aging of sediments also intensifies the sequestration of PP after their settling, thus decreasing their bioavailability and hindering microbial transformation (Ahmad et al., 2004). Budd et al. (2011) proved that once pyrethroids were settled, they were stable in dry and aerobic sediments between irrigation seasons; they were moderately persistent under flooded and anaerobic conditions, with a DT50 between 106 and 353 days. This is in line with the observation of Cryder et al. (2021), who showed that adsorption and subsequent settling were the dominant processes governing the fate of pyrethroids in ponds instead of biotransformation.

Vegetation has been widely recognized as an important stimulator of biotransformation (Moore et al., 2007;Rai, 2009;Stang et al., 2016;Sur et al., 2003;Wang et al., 2017). For instance, the removal rate of chlorpyrifos reached 0.1 mg.m -1 .day -1 due to biotransformation in vegetated mesocosms (Tang et al., 2019). Submerged stems and leaves of macrophytes are an essential habitat for biofilms (Brix, 2003;Zhang et al., 2021a). These biofilms, along with those on all other immersed solid surfaces in the artificial pond system (including dead macrophytes), are stimulators of microbial processing in artificial ponds (Brix, 2003;Dash et al., 2020;Lv et al., 2017). Several kinds of phototrophic bacteria, algae, and biofilms in the water-sediment interface and on the surface of macrophytes were proved to be important agents of pesticide transformation in artificial ponds (Katagi et al., 2016). However, biotransformation in the biofilm can be limited sometimes owing to the slow kinetics of mass transfer or enzymatic reactions [START_REF] Chen | Transformation of chloroform in model treatment wetlands: from mass balance to microbial analysis[END_REF].

In modeling, biotransformation is often described by a first-order kinetic model (Beringer et al., 2021;Green et al., 1990;Mamy et al., 2007;Pignatello, 1999;Scow et al., 1996;Torabi et al., 2020), where kbio (d -1 ) is the biotransformation kinetic constant in the compartment in question. Biotransformation can be represented either by the kinetic constant kbio (d -1 ) or by the corresponding half-life DT50bio (d) (see Eq.I.3 and Eq.I.4).

Several studies managed to estimate the biotransformation rate constants in artificial ponds in laboratory conditions, through incubation manipulations. Incubation experiments aim to calculate pesticide concentrations in the different compartments of a contaminated sample (water and sediments). Then, the concentrations are fitted to a first-order kinetic decay model to estimate the rate constants kbio and half-lives DT50bio. Table I.A. 8 presents a literature review of estimated biotransformation half-lives DT50bio.

The values of the biotransformation half-lives (Table A .8) show that the hydrophobic pesticides pretilachlor and fenthion were rapidly biodegraded in sediments; we suggest that this may be related to the fact that they were mainly sorbed on substrates easily accessible to microorganisms. Pretilachlor was more rapidly degraded in water (DT50bio,w = 9.8 d). Despite being hydrophobic and lowly mobile (Koc = 5509 L.kg -1 ), chlorpyrifos was more persistent in sediments than in water columns, and was favorable to aerobic conditions (O'Geen et al., 2010).

Comparing the behavior of pretilachlor, fenthion, and chlorpyrifos shows that hydrophobicity and low mobility may not necessarily favor biotransformation in sediments rather than in water.

On the other hand, biotransformation in sediments seemed relatively slower for more mobile pesticides (i.e., 2, 4-D, clothianidin, acetochlor, metazachlor, and S-metolachlor), except for the MCPA. We suggest that MCPA was not expected to sorb on TSS and sediments due to its low Koc. Its transformation might be more favorable under aerobic conditions (on the water surface) (PubChem, 2021), explaining its short half-life in the water phase. S-metolachlor was moderately persistent in both water and sediments; this may be explained by its complex chemical structure that blocks access to enzymes and prevents its transformation under aerobic and anaerobic conditions (Droz et al., 2021). Finally, DT50bio values showed that biotransformation was faster in oxic areas, except for acetochlor (DT50bio,w = 47 d) and clothianidin (DT50bio,w = 27.2 d) that were rapidly transformed under anaerobic conditions. This notwithstanding, it is not possible to infer any direct link between the OC content and the biotransformation rate since sites were in the same range of OC.

Compared to the values of the Pesticide Properties Database (PPDB) (Lewis et al., 2016), biotransformation half-lives were faster when estimated in artificial ponds; currently, we cannot draw any conclusions about this difference since the conditions to estimate DT50bio in PPDB have not been detailed.

Overall, biotransformation is commonly expressed by a first-kinetic order model and enhanced by increased vegetation in the artificial pond providing OM that supports microbes and increases the HRT. Therefore, we suggest that introducing a modulation coefficient according to the vegetation cover and OC content in the water and sediments of a specific site will increase kbio representability. At this time, no rule has been set up for pesticide biotransformation in artificial ponds; a direct link with the pesticide properties has also not been highlighted because biotransformation does not act alone but simultaneously with other processes. In future, a more in-depth examination of the biotransformation rates of different pesticides accounting for their interaction with other processes (i.e., adsorption and settling, adsorption/desorption, and dispersion/diffusion) is needed. There is a lack of information on biotransformation in vegetation/biofilm compartments. Therefore, further studies should be carried out to estimate corresponding transformation rates and account for a possible lag phase reflecting microbial acclimation, as shown by Passeport et al. (2011a). In pesticide databases (i.e., PPDB) there is no distinction between the different compartments (water, sediments, and vegetation) for biotransformation half-lives, making it challenging to compare the rates measured as part of artificial pond surveys with those in pesticide databases.

Globally, biotransformation remains a highly changing phenomenon that depends on both the pesticide properties and the site conditions in which microorganisms can act. Defining a specific biotransformation half-life for artificial ponds would be more informative than the PPDB DT50bio values for estimating the contribution of biotransformation to overall dissipation in artificial ponds.

Photodegradation

Photodegradation or photolysis transforms pesticides exposed to sunlight, especially at wavelengths below 400 nm. The products formed by sunlight-induced transformations are known as "photoproducts" (Gerecke et al., 2001). Photodegradation is one of the most significant abiotic degradation processes in artificial ponds (Lin et al., 2005;Papaevangelou et al., 2017;Stangroom et al., 2000).

Photolysis can be direct or indirect. During direct photolysis, pesticides absorb light energy and become excited; they can be transformed depending on the availability of the activation energy.

In indirect photolysis, the pesticide reacts with other species produced photochemically (i.e., CO3• -, hydroxyl radical 𝑂𝐻•, singlet oxygen 𝑂 1 2 , and triplet-excited state dissolved OM (DOM)), causing degradation or conversion into other products (Katagi et al., 2016;Miller et al., 2005;Pohlman et al., 1983;Zeng et al., 2013;Zepp et al., 1987). 𝑂𝐻• can be produced by nitrate photolysis, even at a concentration as low as 0.02 mg.L -1 (Vione et al., 2014). DOM generates DOM*, the precursor of singlet molecular oxygen (𝑂 1 2) in surface waters. DOM* and 𝑂 1 2 can react with pesticides (Drouin et al., 2021a). The most reactive species is 𝑂𝐻•, attacking C-H bonds and mainly controls pesticide photodegradation.

Because of the inability of solar radiation to penetrate deeper into the water, direct photolysis is efficient only in the first centimeters of the artificial pond (Frank et al., 2002). The effectiveness of photodegradation strongly depends on the shading capacity of the vegetation canopy and turbidity within the water column, which may attenuate the penetration of solar radiation (MacIntyre et al., 2006;Rose et al., 2008). This transformation process was also reported to be positively correlated with the artificial pond water pH (Le Person et al., 2016).

In natural waters, DOM and inorganic compounds are essential absorbents of sunlight and influence the photochemistry of pesticides in artificial ponds (Burrows et al., 2002). In addition, particulate matter such as sediment particles and microorganisms suspended in the water column may scatter incident light and prevent the light from penetrating the artificial pond. This phenomenon is called "the quenching effect" of OM (Larson et al., 1994;Pattanayak et al., 2018;Ruiz-Medina et al., 2018). Photochemical degradation would be more efficient in an open-water artificial pond with low turbidity (O'Geen et al., 2010;Vymazal et al., 2015). In DOC-rich ponds (DOC > 20 mg.L -1 ), photolysis is unlikely to be operative in pesticide dissipation since DOC content massively attenuates UV light transmission to DP (Waiser et al., 2004). Lagunas-Allué et al. ( 2010) observed a decrease in boscalid of less than 8% caused by direct photolysis, while the main removal processes were adsorption on OM and plant uptake.

This observation can be explained by the high OM content that prevents light penetration to pesticides and limits direct photolysis.

On the other hand, several herbicides were efficiently removed by indirect photolysis, such as atrazine, diuron, and chlorpyrifos, in a study conducted by Zeng et al. (2013). Mathon et al.

(2019) evaluated the photodegradation of 23 pesticides using in situ photo-reactors. They found that 15 pesticides were photodegraded in the summer versus 12 in winter, which showed that photolysis was not directly linked to the season. However, when Mathon et al. (2019) compared each of the 23 pesticides in terms of photolysis direct and direct+indirect experimental halflives, they found that indirect photolysis enhances degradation compared to direct photolysis.

Additionally, the contribution of direct and indirect photolysis was studied for 60 pesticides.

Direct photolysis predominated for dinitroaniline herbicides, although more than 50% of the total photodegradation rate could be attributed to indirect photolysis for the other pesticides.

This observation supports the relevance of indirect photolysis in artificial ponds when light attenuation is minimal (Katagi et al., 2016;Zeng et al., 2013).

As with most transformation processes, photodegradation was commonly computed with a first-order kinetic model (Green et al., 1990;Lam et al., 2003;Rose et al., 2008;Watanabe et al., 2006;Zeng et al., 2013), where kphoto (d -1 ) is the photodegradation kinetic constant, assumed to be the sum of direct and indirect photolysis kinetic rates (Mathon et al., 2019), and DT50photo

(d -1 ) is the photodegradation half-life. Photolysis half-lives that fitted a first-order kinetic model in pesticide fate models are presented in Table I.A. 9.

Photodegradation half-lives show that the slowest pesticides to be photodegraded were mobile and moderately mobile pesticides, according to their Koc values. By contrast, lowly mobile pesticides were rapidly photodegraded (Table A.9). Since lowly pesticides are likely to be sorbed (on page 57), we propose that they might be indirectly photodegraded once sorbed on sediments, suspended solids, or plants through chemical reactions with free radicals in the water-substrate interface.

Overall, since the penetration of solar radiation to deeper water is limited, direct photolysis is considered only in the first centimeters of the artificial pond, depending on the DOC content.

Contrarily, indirect photolysis involving excited radicals may be more relevant through photochemical degradation and is well described by a first-order kinetic rate. We suggest that introducing a modulating factor considering vegetation cover, which limits light penetration, will increase the accuracy of photolysis modeling.

Hydrolysis

Another abiotic transformation process is the hydrolysis of DP, which depends on the pH and temperature of artificial ponds. Hydrolysis was commonly considered a minor dissipation process when compared with adsorption and microbial and photochemical degradation.

However, in some cases, hydrolysis played a significant role in the dissipation function of artificial ponds, especially under alkaline conditions (Bondarenko et al., 2004;Liu et al., 2001;Papaevangelou et al., 2017;Yu et al., 2019).

For instance, chlorpyrifos proved to be sensitive to hydrolysis (Liu et al., 2001;Wu et al., 2002). Experimental studies showed that higher pH (> 8) increased the hydrolysis rate of chlorpyrifos (Agudelo et al., 2010;Baskaran et al., 2003;Sardar et al., 2005), corresponding to a half-life of 50 days (Liu et al., 2019;Mackay et al., 2006). Similarly, carbaryl insecticide was mainly degraded by photolysis and hydrolysis in a pond (Wolfe et al., 1978). Hydrolysis mainly contributed to endosulfan dissipation (Walse et al., 2002) and dichlorvos had a hydrolysis halflife of 3 days (Tomlin, 2009). By contrast, hydrolysis contributed to less than 2% of boscalid degradation (Lagunas-Allué et al., 2010). Similarly, the hydrolysis half-life of dieldrin exceeded 3 months (Singh et al., 1991) and reached 115 and 138 days for diazinon and fenthion, respectively [START_REF] Ibanez | Use of liquid chromatography quadrupole time-of-flight mass spectrometry in the elucidation of transformation products and metabolites of pesticides. Diazinon as a case study[END_REF]Mackay et al., 2006;Sakellarides et al., 2003). S-Metolachlor and butachlor were also found to be persistent to hydrolysis with a half-life of 200-1155 days at different pH (Masbou et al., 2018;Zheng et al., 2001). Sometimes, changes in diurnal pH due to photosynthesis by aquatic algae and macrophytes may result in photo-synthetically driven alkaline hydrolysis of some pesticides (Katagi, 2006(Katagi, , 2013)).

Likewise, hydrolysis can be estimated with a first-order kinetic model (Rose et al., 2008;Wang et al., 2019), where kh(d -1 ) is the hydrolysis kinetic constant and DT50h (d -1 ) is the hydrolysis half-life. It was reported that the main controlling factor of hydrolysis is pH (Deer et al., 2001;Farran et al., 1988;Sereshti et al., 2021;Stangroom et al., 2000).

Overall, most pesticides that threaten water quality were considered insensitive to hydrolysis.

This may be due to their chemical structure, such as the lack of methyl ester linkage for sulfosulfuron herbicide (Sabadie, 2002;Saha et al., 2002) and phosphodiester linkages for organophosphate pesticides (Sahin et al., 2020). In addition, artificial pond water might generally be beyond the range of alkaline or acidic conditions, making hydrolysis a very slow process (Rose et al., 2008). Therefore, we assume that hydrolysis could be neglected in the modeling of pesticide dissipation in artificial ponds.

Discussion

After reviewing various research works on pesticide dissipation in artificial ponds, it appears that some authors studied different processes, while others focused only on one specific process.

Similarly, some surveys investigated the behavior of multiple pesticides while others evaluated only one or multiple pesticides from the same chemical group (on page 52). Papers evaluating multiple processes often studied one process. Therefore, for a better understanding of artificial pond processing, this review combines the investigation of the behavior of multiple pesticides with contrasting physico-chemical properties and evaluates the transport, transfer, and transformation processes occurring in the artificial pond compartments (water, sediments, and vegetation). In the following subsections, we discuss the main assumptions of this critical review regarding the controlling factors behind processes, the estimation of parameters required to develop perdictive models, and the hierarchization of processes.

Controlling factors of the processes

As described in section 2, the processes surveyed here depend on several factors:

• The environmental conditions: hydraulic parameters, temperature, sediments and TSS composition (OC, clay content), vegetation cover, solar radiation, pH, and redox conditions.

• The pesticide physico-chemical properties: mobility Koc, hydrophobicity Kow, and vapor pressure vp.

The hydraulic regime is responsible for the residence time. A long HRT gives time for the pesticide to settle, sorb, desorb, or be transformed (Liu et al., 2019;Sahin et al., 2020). Another hydraulic parameter is the water column depth; a shallow water depth enhances photolysis and biotransformation by increasing the oxygen concentration in artificial ponds.

Sediments and TSS composition interfere in the sorption process. As presented in section 2, OC content adsorption and clay content increase the trapping of pesticides in sediments by favoring desorption hysteresis. Thus, OC and clay content increase the removal of pesticides from the artificial pond water. There was no direct link between biotransformation half-lives DT50bio in substrates and their OC content (Table A.11). The DT50bio,s values show that the set of the pesticides studied was not resistant to biotransformation in the sediment regardless of the OC content.

However, biotransformation half-lives in the water, DT50bio,w, and sediments, DT50bio,s, varied according to the pesticide properties. Nevertheless, no direct link was observed between halflife (DT50bio) and pesticide hydrophobicity (Koc) (Table A .8). This observation supports our hypothesis of process synergy, and that dissipation is explained by the interaction between processes (i.e., sorption and transformation) and not by a single process.

The pesticide properties displaying the greatest effect are Koc and Kow, since they control the distribution, mobility, and availability of pesticides in the different artificial pond compartments. We observed that Koc and Kow were determining factors for sorption, biotransformation, and photodegradation.

We also found that biotransformation half-lives were shorter in artificial ponds than half-lives in pesticide databases (i.e., PPDB). It should also be noted that biotransformation half-lives are shorter in artificial ponds sediments than in agricultural soils (Bolan et al., 1996;Passeport et al., 2011a), which supports the efficiency and usefulness of artificial ponds.

Besides biotransformation, the Koc and log Kow also affect the sorption process. Once lowly mobile and hydrophobic pesticides are sorbed onto a substrate (Koc > 1000 L.kg -1 and log Kow >3), they become harder to release back into the water. In comparison, more mobile pesticides (Koc < 1000 L.kg -1 ) are continuously transported in the artificial pond. However, it is unknown whether sorbed pesticides will be trapped in the sediment, suspended solids, or vegetation, or be desorbed afterward.

Numerous studies proved that vegetation is a determining agent in the fate of pesticides as it intervenes implicitly in the removal function of artificial ponds. It provides habitat to microorganisms responsible for pesticide transformation, decreases water velocity to increase settling, increases the OC content and composition diversity to enhance adsorption, and brings oxygen to sediments to promote aerobic transformation. In rare cases, vegetation was also responsible for trapping pesticides due to plant uptake. Nevertheless, it remains complex to estimate and quantify the effect of plant uptake on the fate of pesticides (Jorda et al., 2021).

Another phytoremediation function of vegetation is through entophytic bacteria living in the vascular tissue of plants that can potentially degrade pesticides. However, the fraction of pesticides collated in plants was often insignificant (< 10%) (Butkovskyi et al., 2021;Singh et al., 2021).

Temperature and pH increase the microbial activity behind biotransformation and stimulate photodegradation and hydrolysis.

We suggest that the interactions between processes affect their contribution to pesticide fate.

The strong relationship between a process and pesticide properties may explain the choice of studying a specific process and pesticide. For instance, sorption is more involved in hydrophobic pesticide dissipation. At the same time, volatilization contributes more to the behavior of pesticides with a significant vapor pressure (Table A.7). On the other hand, PPDB half-life values can be used to model the behavior of some pesticides if they are close to the half-lives estimated in laboratory conditions or yield satisfactory simulation results close to the observations.

Parameterization of process formulations

After the analysis of different formulations, we suggest that processes can be quantified and hierarchized according to:

• Kinetic rates k (d -1 ) for the transport and transfer processes: adsorption-desorption; settling-resuspension; and volatilization.

• Half-lives DT50 (d) for the transformation processes: biotransformation, photolysis, and hydrolysis.

The different methods used to evaluate the previously described processes and their corresponding parameters are summarized in Table I. 1. Most studies were conducted at a laboratory scale (batch, microcosms, and mesocosms).

However, laboratory experiments are less advantageous than the rarely conducted in situ manipulations (Figure I.B. 1). Being more representative, in situ manipulations involve a better description of the processes and the fate of pesticides while considering the interactions and links between the different processes (i.e., between sorption and transformation, sorption and dispersion/diffusion), which is not easy to create in laboratory conditions. In fact, in laboratory experiments, only one process is emphasized and examined independently of other processes that usually co-occur in field conditions. This often results in the overestimation of dissipation in laboratory-scale studies.

Additionally, not all estimated half-lives are compatible with those available in the PPDB, especially biotransformation (Table A .8). This incompatibility may be explained by the different sites in which parameters were estimated, whereas for the accessible PPDB data there is no site specification. This observation questions the validity of parameters in the free PPDB data and their applicability in a process-based model that aims to describe accurately what is happening in the field. Using PPDB values in models increases the incertitude; therefore, we suggest defining a DT50 specific to ponds for each process to be used in models. The fact that DT50 values in the PPDB are higher than those calculated in situ shows a greater dissipation in artificial ponds. Because certain photodegradation half-lives in the literature present better compatibility with the PPDB values (Table A.8), they could be a good model fit.

Table 2 summarizes the processes studied for each pesticide analyzed in this review and the equation used to describe it. We noted a lack of studies quantifying and modeling dissipation processes in artificial ponds, because studies are often limited to observing the process without a modeling approach. A considerable number of pesticides (55) were studied to evaluate their behavior according to previously described processes. However, many of these pesticides have only been evaluated once. Hence, more studies are needed on these pesticides to compare their behavior and draw more robust conclusions.

Some pesticides (i.e., boscalid, chlorpyrifos, and pretilachlor) were evaluated according to multiple processes, but not necessarily with a modeling perspective (Agudelo et al., 2010;Laabs et al., 2007;Lagunas-Allué et al., 2010;Rogers et al., 2009;Tang et al., 2019;Karpuzcu et al., 2013;Mackay et al., 2006;Yu et al., 2019). Only, 57 of the 252 reviewed articles involve modeling.

Little is known about the estimation of biotransformation half-life in artificial pond compartments, except that macrophytes and algae enhance both adsorption and biotransformation of DP (Katagi et al., 2016). Similarly, anaerobic biotransformation may occur in the sediment layer, yet it is still under-researched (Maillard et al., 2014;Vandermaesen et al., 2016). Globally, it remains challenging to determine a DT50bio in each compartment with laboratory experiments due to the different behavior that a pesticide may have in each of them.

It is also challenging because laboratory experiments can hardly mimic in a reproducible manner the process interactions occurring in the field. For sorption, vegetation remains the least exploited compartment, since there were few surveys to estimate the sorption kinetics in vegetation, its biotransformation rates, or its uptake rates.

We propose that the previously mentioned controlling factors (temperature, OM, and vegetation cover) and their interactions be considered for better accuracy by integrating site-specific modulating factors in the development of pesticide fate modeling. Modulating factors also highlight the temporal variation of process efficiency, such as biotransformation prevailing during the plant growth period (Vandermaesen et al., 2016). Thus, the question that then arises is whether it is necessary to include this temporal variation in a process-based model to improve the accuracy or if, by doing so, the complexity increases.

Hierarchization of dissipation processes in artificial ponds

This review provides a primary idea on the hierarchization of processes according to their contribution to the removal function of artificial ponds.

Since adsorption can only trap pesticides in artificial ponds, degradation (i.e., complete biotransformation, photolysis, and hydrolysis) contributes more to the removal function of artificial ponds as it can permanently eliminate toxic substances from the environment.

Although little is known about whether sorbed pesticides will be degraded or released back into the environment over time, we assume that sorption is still a key process, as it determines the distribution and availability of pesticides in artificial pond compartments.

Among the transformation processes, the most rapid was biotransformation compared to photodegradation for some pesticides. When comparing the different half-lives, volatilization was found to be the slowest process, which is in line with the fact that most pesticides are nonvolatile due to their low vapor pressure (<10 -5 Pa). Lastly, hydrolysis appears to be negligible for most pesticides.

Previous reviews, such as those by Stehle et al. (2011) and Vymazal et al. (2015), did not find a direct link between pesticide dissipation or accumulation in artificial ponds and pesticide properties (i.e., Koc vs sorption). We suggest that this is because the fate of pesticides in artificial ponds does not result from a single process but a combination of different transport, transfer, and transformation processes involving multiple artificial pond compartments. This combination depends on different conditions (i.e., hydrology, climate, physico-chemical factors, and vegetation cover). We assume that the most dependent processes are sorption and biotransformation. For some moderately mobile and hydrophilic pesticides, biotransformation can be the main dissipation process in an artificial pond. After 42 days, up to 86% of diuron (Koc = 600 L.kg -1 ), atrazine (Koc = 100 L.kg -1 ), and terbuthylazine (Koc = 318 L.kg -1 ) were degraded (Anderson et al., 2002;Gregoire et al., 2009;Hijosa-Valsero et al., 2016). Also, the removal of the moderately mobile triazophos (Koc = 308 L.kg -1 ) was mostly achieved by biotransformation rather than adsorption (Cheng et al., 2007;Imfeld et al., 2009;Meng et al., 2014;Rani et al., 2001). Conversely, for the highly hydrophobic butachlor (log Kow = 4.5), adsorption was the main process contributing to its dissipation in microcosm experiments.

Many researchers ascertained that strong adsorption to sediments and limited desorption (also known as "aging") might decrease the bioavailability of pesticides and thus limit their biotransformation (Ahmad et al., 2004;Budd et al., 2011;Chaumet et al., 2021;Lee et al., 2004;Mulligan et al., 2016). All of these results emphasize the dependency between sorption and biotransformation.

When examining citation frequency, some processes (i.e., sorption, biotransformation) were more cited than others (i.e., settling, resuspension, and volatilization). If a process is not frequently cited, this could be explained by the level of difficulty in its evaluation in both laboratory and field conditions.

The previous conclusions were based on observing removal rates of different processes and pesticides. The limitations of this approach are that the relevance of some statements is questioned since they were made on the basis of a single case study. The robustness of the conclusions made in this review will be stronger if the same processes are tested on multiple pesticides and sites with contrasting properties in order to highlight the link between the impact of processes and the properties of the pesticides and artificial ponds.

Identifying the processes with a higher impact can be used to hierarchize processes based on their contribution to the dissipation function of artificial ponds. We believe that a hierarchization of relevant processes may improve the estimation of the overall efficiency of artificial ponds in reducing pesticide transfer into the environment.

In addition, the black-box model does not consider the processes mentioned above and describes pesticide behavior with an overall dissipation rate, kdiss. Although easy to apply, the black-box approach does not provide enough information on the behavior of pesticides and on We assume that a model with multiple process rates shows the complexity of modeling the fate of pesticides in artificial ponds; however, it can describe and predict pesticide behavior more realistically. We believe this work will contribute to a better understanding of artificial pond processing and optimize pesticide fate models. (Passeport et al., 2011;Passeport et al., 2013;Vallée, 2015) Kresoxim-methyl Eq.I.11 (Gobas et al., 2018) Lindane Eq.I.8 (Krishna et al., 2008) MCPA NA Eq.I.13 (Comoretto et al., 2008;Sørensen et al., 2006;Thorstensen et al., 2001) Mefenacet Eq.I.8 Eq.I.11 (Bolan et al., 1996;Watanabe et al., 2006;Yoshida et al., 2000) Mefenpyr-diethyl NA (Passeport et al., 2013)

Conclusion

The scientific community has widely proved the role and efficiency of artificial ponds in reducing pesticide transfer into the environment. However, few papers have identified the set of processes behind the behavior of pesticides in artificial ponds and opened the black box. The relationship between the dissipation potential of processes and the properties of artificial ponds and pesticides has received little attention to date.

On the basis of a review of the literature, we assumed that pesticide behavior is due to multiple processes acting to degrade or trap pesticides in artificial ponds. We noticed that the fate of pesticides results from the interaction of different processes. These processes depend on several hydraulic, climatic, and physico-chemical factors as well as vegetation cover. Since previous studies have generally focused on a single pesticide or dissipation process, this review combines research work on pesticides with contrasting properties. This extends to pesticide behavior, reflecting several processes involved in the dissipation of pesticides in a large variety of artificial ponds. We also describe and compare the methods used to quantify processes behind pesticide dissipation or accumulation in artificial ponds.

Based on the bibliometric analysis, we explain the citation frequency of a process by the difficulties of evaluating its contribution in the field or the laboratory. Moreover, investigating pesticide behavior in an interplay between transfer, transport, and transformation processes can be used to hierarchize processes based on their contribution to the dissipation function of Appendix I.A. • vs (m.s -1 ): Settling rate.

• rp (m): Average radius of the particle.

• ρp (kg.m -3 ) : Particles density.

• ρw (kg.m -3 ) : Water density.

• g (m.day -2 ) : Gravity.

• μ (kg.m -1 .day -1 ) : Water viscosity.

Reynolds number < 500 (Fiener et al., 2003;Marois et al., 2016;Reddy et al., 2008)

( 4 3 * 𝑔 * 𝑑 𝐶 𝐷 * (𝜌 𝑠 -𝜌 𝑤 ) 𝜌 𝑤 ) 1 2 (𝑖𝑖)
• vs (m.s -1 ): Settling rate.

• d (m): Particles diameter.

• ρs (kg.m -3 ) : Solid density.

• ρw (kg.m -3 ) : Water density.

• g (m.j -2 ) : Gravity.

• CD = f (Rep) , Rep : Reynolds number of the particle.

Spherical particles in water at 20°C (Kadlec et al., 2008) 𝜇 * 𝑑 -1 * ((25 + 1,2 * 𝑑 * 2 ) 0,5 -5)) 1.5 (𝑖𝑖𝑖)

𝑑 * = (𝑑 * (𝑔 * 𝜇 -2 * (𝑠 -1)) 1/3
• vs (m.s -1 ): Settling rate.

• d (m): Particles diameter.

• g (m.j -2 ) : Gravity.

• μ (kg.m -1 .day -1 ) : Water viscosity. • s : Relative density= f(ρs, ρw) Natural sand particles (Hawley et al., 2014) Chapter I: The fate of pesticides in artificial ponds: State-of-the-art Table I.A. 2 Integrated sorption kinetic and isotherm models. Kinetic models describe the temporal evolution of the sorbed amount of a dissolved substance. Kinetic rates are estimated from fitting laboratory measurements to the kinetic models. Isotherm models describe adsorption and desorption equilibrium. Likewise, isotherm constants are calculated from fitting laboratory measurements to isotherm curves.

Kinetic models Equations

Parameters Source Pseudo first order (PFO):

𝐶 𝑠 = 𝐶 𝑠𝑒 -𝑒 -𝑘 1 * 𝑡
• Cs (µg.g -1 ): Sorbate concentration in the substrate at instant t. • Cse (µg.g -1 ): Sorbate concentration in the substrate at the equilibrium. • k1 (min -1 ): Kinetic constant. (Lin et al., 2009) Pseudo second order (PSO):

𝐶 𝑠 = 𝑘 2 * 𝐶 𝑠𝑒 2 * 𝑡 1 + 𝑘 2 * 𝐶 𝑠𝑒 * 𝑡
• Cs (µg.g -1 ): Sorbate concentration in the substrate at instant t. • Cse (µg.g -1 ): Sorbate concentration in the substrate at the equilibrium. • k2 (g.mg -1 .min -1 ): Kinetic constant rate. (Boparai et al., 2011) Elovich:

𝐶 𝑠 = 1 𝛽 * ln (1 + 𝛼 * 𝛽 * 𝑡)
• Cs (µg.g -1 ): Sorbate concentration in the substrate at instant t. • α (g.mg -1 .min -1 ) , β (g.mg -1 ): Kinetic constants. (Chien et al., 1980;Ho et al., 2002) Isotherm models Equations Parameters Source Freundlich:

𝐶 𝑠𝑒 = 𝐾 𝑓 * 𝐶 𝑤𝑒 𝑛 𝑓
• Cse (µg.g -1 ): Sorbate concentration in the substrate at the equilibrium. • Cwe (µg.g -1 ): Sorbate concentration in the water at the equilibrium. • Kf (mg 1-n f.L n f.kg -1 ): Freundlich constant expressing the moleculesubstrate affinity. During adsorption Kf is called Kfads and Kfdes during desorption.

• nf (unitless): Freundlich constant.

When nf = 1, the kinetic model becomes linear and Kf becomes the classical distribution coefficient Kd. (Mckay, 1995) Langmuir:

𝐶 𝑠𝑒 = 𝐾 𝐿 * 𝐶 𝑤𝑒 1 + 𝑛 𝐿 * 𝐶 𝑤𝑒
• Cse (µg.g -1 ): Sorbate concentration in the substrate at the equilibrium. • Cwe (µg.g -1 ): Sorbate concentration in the water at the equilibrium. • KL (L.g -1 ), nL (unitless): Langmuir constants. (Mckay, 1995) Table I.A. 3 Non-linear Freundlich isotherm adsorption Kfads (mg 1-n kg -1 L n ) and desorption Kfdes (mg 1-n kg -1 L n ) coefficients. Linear isotherm coefficient Kd (L.kg -1 ) for metazachlor, isoproturon, epoxiconazole, napropamide, boscalid, prochloraz, tebuconazole, glyphosate, pyrimethanil, tetraconazole, fludioxonil, and spiroxamine. The sorption coefficients were measured in laboratory conditions with batch experiments on different pond substrates: Sediments, vegetation, and total suspended solids (TSS) (Maillard et al., 2014;Passeport et al., 2011b;Vallée, 2015). (d -1 ) of five selected pesticides (imazosulfuron, lindane, mefenacet, molinate and picloram) in different ponds, along with their physico-chemical properties S (mg.L -1 ) and Koc(L.kg -1 ) (Inao et al., 1999;Krishna et al., 2008;Mccall et al., 1985;Takagi et al., 2012;Yoshida et al., 2000). Pesticide properties were extracted from pesticide databases (ChemSpider; Kim et al., 2021;Lewis et al., 2016). Non-volatile from soil surfaces, no volatilization from greenhouses 10 -5 < ___ < 10 -4

Semi volatile 10 -4 < ___ < 5.10 -3

Semi volatile ≥ 10 -3 Volatile Table I.A. 6 Empirical formulations of volatilization rate kv (d -1 ) along with their corresponding parameters, based on a 1 st order kinetic volatilization model. • u (m.s -1 ): Wind speed.

Volatilization rate kv

• H (Pa m 3 g -1 ): Henry constant.

• T(°C) : Temperature. (Rose et al., 2008) Table I.A. 7 Review of the volatilization half-lives DT50v (d) and the corresponding volatilization rates kv(d -1 ) in ponds, of different pesticides along with their corresponding vapor pressure vp (Pa) and their volatilization classification according to Table I ,s (d) of nine pesticides in different wetlands, along with their mobility Koc(L.kg - 1 ) (Beringer et al., 2021;Bondarenko et al., 2004;Comoretto et al., 2008;Droz et al., 2021;Nakano et al., 2004;Passeport et al., 2011a;Passeport et al., 2013;Sahin et al., 2020). These half-lives were confronted to the PPDB values. I.A. 9 Photodegradation half-lives in the water column DT50photo (d) of pesticides with different mobility Koc (L.kg -1 ). The DT50 values were estimated in (Inao et al., 1999;Katagi et al., 2016;Rose et al., 2008;Sakellarides et al., 2003). • Imax (MJ m -2 h -1 ) : Maximum radiation.

Pesticide

Modulation factors of kphoto Parameters Source

• TSS (mg.g -1 ): Amount of TSS. (Rose et al., 2008) 𝑅 𝑈𝑉𝐵-𝑏

• RUVB-b: Daily UV-B radiation that penetrates the pond. (Watanabe et al., 2006) 10 The Koc values were extracted from the PPDB. First, we looked for general studies about the fate of pesticides in wetlands to record every potential process behind their dissipation. Afterward, we searched for each process whose effect was investigated in ponds on single or multiple pesticides. We selected theoretical papers for the descriptive part of processes and modeling papers to extract the equations for the formulation part in section 3. In each paper, we identified the pesticides targeted, processes investigated, site, and the scale of their evaluation (i.e., laboratory, in-situ) by focusing more on recent work. Given this, three categories of processes in ponds can be distinguished:

1. Transport is responsible for the fate of pesticides within a compartment of the pond.

2. Transfer controls redistribution of pesticides from a compartment to another.

3. Transformation degrades and modifies the original chemical structure of pesticides. Finally, resuspension and volatilization had a minor presence in search results. As for processes formulations, it was necessary to look for modeling papers that either presented a process equation within the framework of a fate model or studied just that specific process. Among the 273 reviewed papers, only 20% were about descriptive models of pesticides in ponds, and over 80% evaluated more than one pesticide. Although, most of the studies investigated a single process for a group of 2-6 pesticides. Most studies aiming to quantify a process rate were conducted at a laboratory scale in controlled conditions. They were few studies conducted in mesocosms 12 and even less in the field.

Chapter II: Modeling of dissolved pesticide fate

Chapter II-I: Modeling the fate of dissolved pesticides with a black box model 1 Introduction

As demonstrated in Chapter I, many experimental studies have been conducted on the effect of artificial ponds (APs) on pesticide dissipation at the AP scale. However, little work has been reported on the numerical modeling of pesticide fate in APs.

A model is an abstraction of a complex phenomenon. It does not contain all the features of the real system but has all its characteristics and the essential elements to solve or describe the problem (Soetaert et al., 2009). Modeling is an efficient and comprehensive tool for assessing and setting up facilities to treat contaminated environments (Warren et al., 2002). Particularly, modeling has become essential to determine the predicted environmental concentration (PEC)

of pesticides to assessing the potential environmental risk within the regulatory and registration process (Watanabe et al., 2006). Predictive models are recommended for more rational and scientifically supported decision-making. Developed models for APs range from simple simulation models (i.e., empirical, numerical, and statistical models) to more complex processbased models (Kumar et al., 2011). The black-box model is the simplest form of a predictive model since it requires a single parameter estimation, which motivates the evaluation of such models to predict pesticide behavior in APs.

Black box models include regression models (Pálfy et al., 2017), Monod models [START_REF] Langergraber | Modeling Variably Saturated Water Flow and Multicomponent Reactive Transport in Constructed Wetlands[END_REF], and first-order models (Stein et al., 2006). In the case of regression models, researchers usually perform an empirical regression to establish a significant correlation between the observed inlet and outlet pesticide concentrations at the pond scale. Even though regression models provide an overall insight into the dissipation of pesticides within APs, the model results are typically valid only for the range of data used for the simulations. The Monod models are zero-order for low concentrations and first-order for high concentrations, which are only used to quantify the biological degradation of a compound (Kumar et al., 2011;Rousseau et al., 2004). Therefore, the Monod model can underestimate pesticide dissipation, especially for pesticides dissipated by other processes, such as adsorption and photolysis. Lastly, the firstorder models are based on a first-order kinetic equation predicting an exponential behavior of the evaluated compound between the inlet and outlet of the AP. Among black-box models, firstorder models are the most commonly employed to design APs, as most processes behind pollutants behavior, such as volatilization, degradation, and sorption, are expressed by firstorder kinetic equations. Therefore, in the sequel, we focus on first-order models, which we designate "black box models".

Several authors have applied black-box models to design and predict the behavior of major pollutants in APs. For instance, these models predicted the fate of organic matter (OM), total suspended solids (TSS), metals, and nutrients (nitrates and phosphorus in APs [START_REF] Defo | Modelling approaches for simulating wetland pollutant dynamics[END_REF]Goulet et al., 2001;Jamieson et al., 2005;Kadlec, 2000;Knight et al., 2000;Mitchell et al., 2001;Stein et al., 2007;Stone et al., 2004;[START_REF] Sun | Enhanced removal of organic matter and ammoniacalnitrogen in a column experiment of tidal flow constructed wetland system[END_REF]. Conversely, few applications of the black-box model on pesticides were reported in the literature. Previously developed models concern subsurface-flow constructed wetlands (Braschi et al., 2021;Rousseau et al., 2004) rather than free-water surface wetlands involving APs. To our knowledge, only Krone-Davis et al. ( 2013) reported the application of the black box in APs on pesticides through the tank-in-series (TIS) model (Levenspiel, 1998). Another application is documented in [START_REF] Tanner | Influence of hydrological regime on wetland attenuation of diffuse agricultural nitrate losses[END_REF] but on nitrates. However, Krone-Davis et al. ( 2013) evaluated the TIS model on just three pesticides (diazinon, methomyl, and acephate), all highly soluble in the water, hydrophilic, and highly to moderately mobile. Besides, the TIS model does not integrate the temperature effect, which has been amply substantiated as a major driver of pesticide fate (on page 70).

Therefore, in the present part of Chapter II (Chapter II-I), we assess the application of the black box model on 61 different pesticides detected in the Rampillon AP while integrating temperature. The objective of the below-presented work is to evaluate the ability of a simple modeling approach (i.e., black box) to predict pesticide fate at the pond scale.

Material and methods

Study site

The studied site is the Rampillon AP (5270m 2 ), located in a 355-ha watershed upstream of the

Period and pesticide selection

In order to compare the input and output measured pesticide fluxes, specific monitoring periods Note that for some of the 60 retained periods, certain pesticides had an outlet flux higher than the inlet, indicating potential remobilization of the pesticide or monitoring artifact. However, this work aims to evaluate pesticide concentration reduction from the inlet to the outlet with a modeling approach. Therefore, a period grouping has been performed using R coding (Team, 2020) to obtain only dissipative periods. Grouping consists in summing the pesticide influx and outflux of the periods with anomalies to obtain an inflow greater than the outflow in the new period. Orange and blue bars represent the inlet and outlet fluxes (µg) of bentazon, respectively. Red rectangles refer to periods with abnormalities, i.e., when the outflux is higher than the influx, or there is none of them. Green rectangles refer to the grouped periods obtained, i.e., when the outflux is higher than the influx.

Model equations

The objective of the black box model is to quantify pesticide dissipation between the inlet and outlet of an AP by a single coefficient kdiss (d -1 ). The kdiss includes all the dissipation processes of the pesticide: retention by adsorption, transformation by microorganisms, photolysis, hydrolysis, and volatilization.

The black box model is built upon a first-order kinetic equation (Eq.II-I.1).

(II-I.1)

𝑑𝐶(𝑡) 𝑑𝑡 = -𝑘 𝑑𝑖𝑠𝑠 (𝑇) * 𝐶(𝑡)
Where C(t) (µg.L -1 ) is the pesticide concentration in the water at time t and T(°C) is the water temperature. The mass of pesticides in the AP water M(t) (µg) can be deduced from the concentration C as below:

(II-I.2)

𝑀(𝑡) = 𝐶(𝑡) * 𝑉 𝑤 (𝑡)
Where Vw(t) (L) is the water volume at time t.

The main hypothesis of black box models are listed hereafter:

(i)

The AP is considered a perfectly mixed reactor where each quantity is spatially uniform, such as pesticide concentrations and water velocity.

(ii)

The volume of water within the AP is constant during the sampling period.

Accordingly, each period is assigned a nominal hydraulic residence time (HRT). The HRT designates the total time the water (including pesticides) spends in the AP between its interception and discharge and is expressed by Eq.II.3:

(II-I.3)

𝐻𝑅𝑇 = 𝑉 𝑄 𝑜𝑢𝑡
Where V (m 3 ) is the total volume of the AP and Qout (m 3 .d -1 ) is the outflow rate.

Based on Eq.II.2 and the black box hypothesis ((i), (ii)), Eq.II.1 becomes the following: As demonstrated in Chapter I, the temperature has been extensively recognized in the literature as a major controlling factor of pesticide dissipation in water. This is so because temperature stimulates microbial activity behind biodegradation and pairs with significant solar radiation behind the photolysis of pesticides. Accordingly, the Arrhenius law (Eq.II.5) was employed to obtain the coefficient kdiss (d -1 ) at the standard temperature of 20°C. The aim of the temperature correction is the valid comparison with laboratory results, which are usually conducted at a 20°C temperature. [START_REF] Authority | Opinion on a request from EFSA related to the default Q10 value used to describe the temperature effect on transformation rates of pesticides in soil-Scientific Opinion of the Panel on Plant Protection Products and their Residues (PPR Panel)[END_REF]Matthies et al., 2017).

(II-I.5)

𝑘 𝑑𝑖𝑠𝑠 (𝑇) = 𝑘 𝑑𝑖𝑠𝑠 (20°) * 2.8 𝑇-20 10
Where kdiss (20°C) is the dissipation at a reference temperature (20°C), i.e., the temperature in which laboratory experiments are performed.

Therefore, the black box model equation takes the final form:

(II-I.6)

𝑀 𝑜𝑢𝑡 = 𝑀 𝑖𝑛 * 𝑒𝑥𝑝 (-𝑘 𝑑𝑖𝑠𝑠 (20°) * 2.8 𝑇-20 10 * 𝐻𝑅𝑇 )
Note that the black box model equation can also be expressed by the pesticide's overall halflife DT50 (d) (resulting from all dissipation processes), which is the time needed to dissipate 50% of the pesticide mass and is deduced by Eq.II.7.

(II-I.7)

𝐷𝑇 50 = ln (2) 𝑘 𝑑𝑖𝑠𝑠

Model calibration and performance

For each pesticide, the dissipation coefficient kdiss was calibrated to fit the observed fluxes Mout,obs using a linear regression function in R. Where ∑ 𝑀 𝑜𝑢𝑡,𝑠𝑖𝑚 (µg) is the total discharged mass of the pesticide during the selected periods, obtained by simulations. ∑ 𝑀 𝑜𝑢𝑡,𝑠𝑖𝑚 i(µg) s the total discharged mass of the pesticides observed in the Rampillon site during the selected periods.

The model performance can also be assessed according to the discrepancy between the simulated and observed overall dissipation, i.e., the AP efficiency. The AP efficiency can be calculated by Eq.II-I.9.

(II-I.9)

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%) = ∑ 𝑀 𝑖𝑛 -∑ 𝑀 𝑜𝑢𝑡 ∑ 𝑀 𝑖𝑛 * 100
Where ∑ 𝑀 𝑖𝑛 (µg) and ∑ 𝑀 𝑜𝑢𝑡 (µg) are the total inlet and outlet pesticide fluxes, respectively.

Results

Some of the calibrated values of half-lives DT50 (Eq.II-I.7) alongside the RE (%) of simulations are listed in By excluding the negative values, the range of calibrated DT0 becomes ~ 4-14 days. Except for the maximal half-life (13.75 d), the rest of the calibrated DT50 does not vary significantly with pesticides by a mean difference of two days and a half. On the right side of Table II-I. 2 are listed the PPDB half-lives for each process behind pesticide dissipation. Due to the large number of pesticides, it was elected to display only the PPDB half-lives of the first pesticides in each group ( (1) , (2) , and (3) ). The PPDB values evidence that the set of pesticides (Table II-I.A.2, Appendix II-I) resists hydrolysis slightly more than photolysis. However, some pesticides

(1) are rapidly photodegraded, such as bentazon, metamitron, chlorotoluron, and imazamox). In contrast, the rest of the photodegradable pesticides need, on average, 80 days to be halfdissipated. In addition, some pesticides are also resistant to biodegradation at the watersediment interface (DT50bio,s >500 d), such as crypoconazole, bentazon, and ethofumesate. The PPDB half-lives indicate that most pesticide biotransformation at the water-sediment interface (DT50bio,s) is slower by 146 days than in the water (DT50bio,w). In aggregate, the calibrated DT50 is much shorter than the PPDB half-lives, indicating a faster dissipation obtained by modeling than by laboratory experiments. To date, no relationship has been identified between the DT50 of each process and the total dissipation. Therefore, we limited the comparison to the minimum simulated DT50 and the average minimum DT50 reported by the PPDB. Accordingly, the calibration yields a total dissipation of about 30 days faster in the AP than in laboratory conditions (PPDB).

To compare the simulation results with the observations, we opted to plot the discrepancy between the modeled and measured AP efficiencies of two contrasting pesticides, i.e., A single period manifested a perfect fit between simulations and observations for both pesticides. Most periods showed a discrepancy higher than 10% from the observations for all pesticides (including the non-graphically presented), except for bentazon, chloridazon, and imdacloprid. The simulations were 30% and 19%, far from the observations for diflufenican and s-metolachlor, respectively. For some pesticides, the RE reaches 88% and the minimum RE according to the AP efficiency was of epoxiconazole (8%).

Chapter II-I: Modeling the fate of dissolved pesticides with a black box model

Discussion

The black box is the simplest modeling approach to predict pesticide dissipation at the pond scale. It requires a single parameter, i.e., the dissipation coefficient kdiss, and a limited number of input data, namely, inlet and outlet pesticide fluxes, water temperature, and the nominal HRT in the AP. The simplicity of this modeling approach prompted the choice to test it on the Rampillon AP. Therefore, the black box model was applied to simulate the overall dissipation coefficient kdiss (or half-life DT50) for 61 detected pesticides and 60 monitoring periods.

The calibration was performed using linear regression, which has the least computation cost and is commonly employed when there is only one parameter to calibrate. However, this method has limitations, such as returning negative values that are non-physical parameters in our case, as we are dealing with half-lives. The second outcome is that most calibrated pesticides manifested a much faster dissipation than the one measured in laboratory conditions (PPDB). This result suggests that dissipation in field conditions is more important than in controlled conditions, as, in the AP, the dissipation is driven by a synergy of processes enhancing pesticide mitigation, contrary to experiments where a single process is surveyed.

The literature review of Chapter I underlines the higher impact of interacting processes on pesticide dissipation compared to when they act separately.

The black box model performance was assessed by measuring the discrepancy between observations and simulations using the relative error RE (%) criteria. The comparison was performed on the AP efficiencies to reduce the inlet flux of pesticides and on the cumulative discharged fluxes. Both comparisons demonstrated variability of the performance quality with pesticides and periods. The variability is expected since pesticides have a different fate in the AP, according to their properties and application periods. Similarly, seasonal variation is also expected due to the significant link between the hydrological conditions and pesticide dissipation in APs (Chapter I, section Hydraulics of artificial ponds).

Accordingly, some periods had a RE <20%, reflecting a good model performance with respect to the simulation of the pond efficiency, whereas more pesticides were detected during periods having a RE > 20%. When evaluating the model performance according to the cumulative flux leaving the AP, most pesticides had a RE>25% for all periods included. This assertion supports the ability of the black box model to simulate pesticide dissipation with an acceptable performance, which can be improved, as we present in the second part of this chapter. However, it is noteworthy that pesticide manifesting a good model performance had a more homogeneous dynamic at the AP outlet, i.e., with a discharged cumulative flux increasing in a more linear pattern. Conversely, pesticides with a RE > 25% had a dynamic marked by sudden flux increases. The sudden flux rise could be explained by the pesticide application lining with heavy rainfall events. This result underscores the limitation of the simplified black box approach to replicate such events.

Furthermore, no significant relationship was found between the calibrated half-lives and the key physico-chemical properties of pesticides. This finding emphasizes the limitation of the black-box approach, which can only quantify the total dissipation without revealing the involved processes behind it. Identifying the processes behind pesticide dissipation provides a better understanding of pond functioning. It also enables the determination of the major controlling factors of the dissipative processes. We believe that the identified controlling factors can be manipulated to maximize the pond's efficiency to reduce pesticide transfer to water resources. Notwithstanding, the black box approach enables the identification of an overall halflife specific to APs, unlike PPDB half-lives whose source of the water and sediment samples is unknown. Besides, the black box model permits establishing an efficiency abacus based on the average HRT within the AP.

Conclusion

This 6 Appendix II-I

Chapter II-II: Modeling the fate of dissolved pesticides with a processbased model 1 Introduction

Pesticides are chemical compounds commonly used to protect agricultural production, with

Europe being among the biggest pesticide consumers in the world (Wanner et al., 2021). Due to their toxicity, pesticides are a nonpoint source of pollution to the ecosystem that affects the quality of drinking water supplies and aquatic environments. The persistence of some pesticides and their transformation products (TP) increases the risk of contamination of the environment (Sarkar et al., 2020;[START_REF] Van Meter | Chapter 11 -Human Impacts on Stream Hydrology and Water Quality[END_REF]. However, little research has been conducted on the fate of TP. Therefore, the focus of the findings presented here is on the parent molecules of pesticides.

Pesticides are often transferred into the environment as mixtures rather than as single compounds as a result of important rainfall events that increase the transferred toxicity [START_REF] Van Meter | Chapter 11 -Human Impacts on Stream Hydrology and Water Quality[END_REF]. Therefore, regulating the use of pesticides to reduce their transfer into the environment has become more challenging (Fenner et al., 2013;Imfeld et al., 2021). In practice, runoff and subsurface drainage can carry pesticides from agricultural plots to surface water resources. The agricultural water fluxes can also directly infiltrate into underground water tables that can subsequently interact with surface water. The runoff and drainage fluxes are highly loaded with pesticides, are faster, and are easier to manage compared to vertical groundwater fluxes. Therefore, some artificial ponds (AP) are designed to collect a fraction of the runoff and subsurface drainage of agricultural water contaminated by pesticides. Along with regulating pesticide applications, AP assist in safeguarding water resource quality by mitigating the pesticide concentration in the intercepted water before it is released into the environment.

Although mainly constructed for water storage and irrigation purposes, AP provide an area in which pesticides can undergo physical, chemical, and biological processes that reduce agricultural water contamination. For instance, the efficiency of AP in dissipating pesticide loads can reach 97% for the organochlorine chemical group (Matamoros et al., 2006;Rose et al., 2006), 96% for the strobilurin/strobin group, and 84% for pyrethroids (Vymazal et al., 2015). Conversely, AP manifested a lower load reduction of <20% for other pesticides, as evidenced by Stehle et al. (2011) and Passeport et al. (2013). The contribution of AP to improving surface water quality has spurred efforts to further understand and to describe the fate of pesticides in AP. Understanding and exploring the processes behind pesticide dissipation could optimize the environmental efficiency of AP, and modeling is a promising tool for this purpose.

This paper focuses on modeling the fate of pesticides in free-water surface edge-of-field AP in agricultural areas. AP are small constructed wetlands, often kept wet and not exceeding 2 m in depth (Zhang et al., 2021a). A comprehensive definition of AP can be found in (Kadlec et al., 2008). Edge-of-field AP intercept pesticides originating from cultivated agricultural plots upstream, and after flooding events the resident water is released into the environment. In the following, AP will refer to free-water surface edge-of-field AP. Many experimental and theoretical studies have been conducted on pesticide fate and mass budget in AP (Katagi et al., 2016;Maillard et al., 2014;Moore et al., 2013). However, little focus has been placed on mathematical modeling and quantification of the processes underlying the fate of pesticides in AP. Modeling is an efficient and comprehensive tool for assessing and setting up facilities to treat contaminated environments (Warren et al., 2002) and for evaluating the potential environmental risks within the regulatory and registration process (Watanabe et al., 2006). The use of predictive models is recommended for more rational and scientifically supported decision-making. The performance of AP was initially modeled with first-order decay models, also called "black box models" (Kadlec et al., 2008;Stone et al., 2004). Earlier studies have proved the possibility of quantifying and predicting the dissipation of pesticides in AP through a black box approach (Braschi et al., 2022). Although simple to develop, a black box model does not explicitly describe the origin of pesticide dissipation. It also does not detail the contribution of each process, since the model only considers the input and output data and a single generic decay coefficient k (d -1 ) representing all of the physicochemical processes (Yuan et al., 2020).

AP management requires a more elaborate pattern to provide detailed spatial and temporal information in order to optimize the mitigation of pesticide transfer and to predict pesticide dynamics under various management conditions. Accordingly, diverse process-based models (mechanistic or conceptual models) were developed. Process-based models use mathematical formulations and mass balance equations to quantify and evaluate each process underlying pesticide fate in AP. These models can help analyze the different dissipation processes of pesticides in AP and their interactions and thereby make it possible to assess the best agricultural management practices for an optimal reduction of pesticide transfer.

Over the past two decades, most process-based models have been applied to simulate urban wastewater treatment in subsurface-flow constructed wetlands (SSFCW) and rivers (Langergraber, 2008), while less has been documented for free-water surface constructed wetlands (FWSCW). Hence, it is important to develop pesticide fate models for FWSCW, especially since the models developed for rivers and SSFCW cannot be applied to FWSCW because of the differences in the hydraulic regimes. Most of the FWSCW models were dedicated to simulating nutrient behavior (Kalin et al., 2013;Son et al., 2010;Sonavane et al., 2009), and fewer models were assigned to pesticides. Among pesticide fate models are the risk assessment models such as PRZM (Carsel, 1998) and MACRO (Larsbo et al., 2003;Larsbo et al., 2005), which simulate pesticide fate in the root zone and macro-porous field soils, respectively. Such models can provide knowledge on pesticide behavior upstream of AP and quantify the necessary inputs for modeling at the pond scale. TOXSWA (Adriaanse, 1996) from the FOCUS group (Tooby, 1999) models pesticide fate in ditches.

However, few studies have been devoted to the AP scale. Existing models such as AGRO-2014

and TOXSWA are computationally costly because they require a significant number of inputs and depend on the outputs of other models (i.e., PRIZM). In addition, AGRO-2014 is limited to hydrophobic pesticide and does not simulate adsorption and desorption kinetics. Besides, it is designed for larger wetlands (> 10 000m 2 ). TOXSWA does not consider transformation processes in the water and adsorption-desorption kinetics, and it does not integrate the effect of temperature. The pond-lake module of the surface water assessment tool (SWAT) (Neitsch et al., 2011) can predict pesticide behavior in AP but does not consider temperature, desorption, or the kinetic aspect of adsorption-desorption. Several studies demonstrated that adsorptiondesorption is a key process in pesticide fate and its kinetic aspect is a controlling factor of pesticide dynamics and distribution at the water-sediment interface (Bahi et al., 2023;Cryder et al., 2021;Papaevangelou et al., 2017). Similarly, temperature was widely asserted to be a major element in pesticide fate (Kaur et al., 2018;Motoki et al., 2020;Mulligan et al., 2016).

However, these two key factors were not considered in the aforementioned models.

The dissipation potential of AP results from a combination of the processes of pesticide transport, transfer, and transformation. Transport processes carry pesticides following a water flow. Transfer processes allow the pesticide molecule to move from one phase to another (i.e., dissolved phase to particulate phase or dissolved phase to gas phase), and transformation modifies the original chemical structure of pesticides. A detailed review and comparison of the processes behind pesticide fate in AP and their main controlling factors can be found in Bahi et al. (2023).

Transport processes (i.e., settling and resuspension) mainly concern particulate pesticides originating from runoff in highly erosive areas. In pesticide fate models, settling is commonly integrated with an accumulation rate expressed empirically (i.e., Stokes' law) (Fiener et al., 2003;Hawley et al., 2014;Kadlec et al., 2008). Resuspension is significantly less studied in the literature compared with settling. When considered, resuspension was quantified with a slow velocity (~10 -6 cm.s -1 ) in models of pesticide fate (Hawley et al., 2014;Sharifi et al., 2013).

Transfer processes (i.e., adsorption and desorption) are responsible for the distribution of pesticides between the AP compartments, i.e., water, sediment, vegetation, and total suspended solids (TSS). Adsorption contributes to the trapping and accumulation of pesticides, making AP buffer zones of contamination in agricultural areas. Some models, such as AGRO-2014 and the pond-lake module of SWAT, only consider sorption equilibrium. However, sorption mechanisms are driven by different kinetically controlled reactions or physical phenomena.

Therefore, sorption has a large range of reaction time going from seconds to years, emphasizing the kinetic character of sorption (Beulke et al., 2004;Caceres-Jensen et al., 2019;Cara et al., 2021;Kaur et al., 2018;Mamy et al., 2007;Sparks, 2000;Villaverde et al., 2009;Wu et al., 2017). In pesticide models, adsorption-desorption kinetics are studied with the most widely applied chemisorption approaches, i.e., pseudo-first-order (PFO), pseudo-second-order (PSO),

and Elovich models (Azizian, 2004;Ho, 2006;Ho et al., 1998Ho et al., , 1999;;Yuh-Shan, 2004), to describe the temporal evolution of the adsorption and desorption fluxes. Another transfer process of pesticides is volatilization, which is commonly described with a water-air transfer coefficient (Bunyakan et al., 2006;Comoretto et al., 2008;Hantush et al., 2013;Jacobs et al., 2012).

Transformation processes (i.e., biotransformation, photolysis, and hydrolysis) result in transformation products (TP) of pesticides, also called "metabolites." The metabolites can either be transformed into other TP or be completely degraded into simple mineral products afterward. To date, the fate of TP is still poorly documented. Biotransformation is often described by a first-order kinetic model (Beringer et al., 2021;Green et al., 1990;Mamy et al., 2007;Pignatello, 1999;Scow et al., 1996;Torabi et al., 2020). Photolysis is described by either a simple first-order kinetic equation or more complex formulations integrating the direct (solar radiation) and indirect effects of photodegradation (photochemical reactions with radicals) (Mathon et al., 2019;Zeng et al., 2013). By contrast, hydrolysis is simply expressed by a firstorder kinetic model (Rose et al., 2008;Wang et al., 2019). Overall, in pesticide fate models, the kinetic rates of processes are estimated empirically, measured in situ, or calibrated to observations.

Compared with the black box models, PESTIPOND integrates the key processes behind pesticide fate in AP. Besides, PESTIPOND is built on the simplest form of process formulations, i.e., the first-order kinetic equations for dissipation processes and constant rates for transfer processes, and it requires a reasonable number of inputs. Unlike previous models

Material and methods

Conceptual model

As introduced earlier, in a typical AP, pesticides can be subjected to transport (i.e., settling and resuspension), transfer (i.e., sorption and volatilization), and transformation processes (i.e., photolysis, hydrolysis, and biotransformation). Since this work focuses on APs in drained catchments and thus on dissolved pesticides, the processes of settling and resuspension are not considered in PESTIPOND. After being introduced into the AP, pesticides are split between the water column and the sediment layer as a result of adsorption and desorption processes. Pesticides can undergo volatilization and degradation due to solar radiation (photolysis) and water (hydrolysis). If not completely degraded to simple non-organic products, they can be transformed by microorganisms into metabolites due to enzyme activities (biotransformation).

The major underlying assumptions of the PESTIPOND model are as follows:

i. Concentrations of pesticides are spatially uniform in water and sediment because the AP compartments are considered to be completely mixed reactors. Hence, once pesticides enter a specific compartment, they are instantly mixed with the entire content and distributed uniformly.

ii. Vegetation is not considered by the PESTIPOND model. Prior investigations have evidenced that the fraction of pesticides collated in plants was often insignificant in AP (< 10%) (Butkovskyi et al., 2021;Pérez et al., 2022;Singh et al., 2021;Wang et al., 2017). Not taking the vegetation into account means that its effect on the hydraulic flow is neglected (brakes, dead zones, etc.). Nonetheless, the choice of this hypothesis was motivated by the environmental focus of the model rather than the hydraulic focus.

iii. No advection or diffusion processes are considered by the model for the following reasons: (i) AP are often constructed on a compacted substrate where the infiltration (leaching) is not significant and the water velocity at the water-sediment interface is too low, resulting in a weak hydraulic gradient that limits advection. (ii) Several studies showed that the molecular diffusion of diverse pesticides is too low in water (~10 -9 m 2 .s - 1 at 25°C) (Chevillard et al., 2014;Fernández-Pascual et al., 2020a;Sarraute et al., 2019).

iv. PESTIPOND simulates the fate of pesticides in the first centimeter of sediment designated as the active sediment layer governing pesticide transformation and transport under flooded conditions. Previous in situ measurements suggested that beyond 1 cm of depth, pesticide residues are not significant (Inao et al., 1999;Mahugija et al., 2018;Nyantakyi et al., 2022;Takagi et al., 2012). The pore water of the active sediment layer is included in the water column compartment.

v. Transformation and volatilization processes are corrected according to the temperature change during the simulation period.

vi.

Since the fate of metabolites is still poorly documented, PESTIPOND does not consider TP.

vii. PESTIPOND does not consider the particulate phase of pesticides because the model was validated for a typical AP located in a lowly erosive area with the majority of pesticides applied in a dissolved form.

Chapter II-II: Modeling the fate of dissolved pesticides with a process-based model

Mathematical model

In this subsection, we detail the equations and the mathematical formulations of the model Erreur ! Source du renvoi introuvable. depicts the key processes of dissolved pesticides in a typical flooded AP in a lowly erosive catchment (e.g., Rampillon, which is described afterward). These processes are accounted for in the mass balance ordinary differential equations of the AP water and sediment compartments. The pesticide fate model is coupled to a hydrological model to integrate the water dynamics.

Hydrological model

The hydrological model of PESTIPOND is expressed with the following water balance equation:

(II-II.1)

𝑑𝑉 𝑊 𝑑𝑡 = 𝑄 𝑖𝑛 -𝑄 𝑜𝑢𝑡 -𝑄 𝐼 + (𝑃 -𝐸) * 𝐴
where Vw [L 3 ] is the water volume of AP surface water; A is the AP surface area [L 2 ]; Qin is the volumetric inflow rate [L 3 .T -1 ]; Qout is the AP discharge (outflow) rate [L 3 .T -1 ]; QI is the infiltration rate in the APas mentioned before, the AP substrate is impermeable and thus no infiltration will be considered (QI = 0); P [L.T -1 ] is the precipitation rate; and E [L.T -1 ] is the evapotranspiration rate. For the sole purpose of simplification, we suppose that the AP is a parallelepiped tank so that the water column depth hw [L] is expressed as Vw/A. The hydrological model computes the daily water volume Vw in the AP, which is necessary for estimating the daily mass of pesticides.

Note that the water density ρw (g.cm -3 ) is considered constant according to temperature over the study period.

Pesticide transfer model

The present section presents the mathematical expressions of the key processes behind pesticide dissipation, the parameterization, and the mass balance equations of the pesticide transfer model.

Transfer processes

Transfer processes enable pesticides to go from one compartment to another, and are mainly:

(i) sorption, which is responsible for transferring pesticides from the water column to sediment substrates (adsorption) and releasing pesticides from the sediment back to the water (desorption); (ii) volatilization, which transfers pesticides from the water column to the atmosphere.

Adsorption/Desorption:

Most of the sorption studies are conducted in the laboratory and adapted from OECD 106 guidelines for individual molecules, in which sorption experiments are performed using a batch equilibration technique (OCDE, 2000). However, these experiments evaluate only sorption equilibrium and not sorption kinetics. Based on the thermodynamic equilibria theory that assumes complete reversibility of the chemical reactions, the adsorption and desorption isotherms should be the same (Strawn et al., 1999). However, sorption mechanisms are driven by different kinetically controlled reactions or physical phenomena with a large variability in reaction times ranging from seconds to years This difference emphasizes the kinetic character of sorption (Beulke et al., 2004;Caceres-Jensen et al., 2019;Cara et al., 2021;Kaur et al., 2018;Mamy et al., 2007;Sparks, 2000;Villaverde et al., 2009;Wu et al., 2017). Sorption kinetics are studied with the most widely applied chemisorption models, i.e., first-order, pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich models (Azizian, 2004;Ho, 2006;Ho et al., 1998Ho et al., , 1999;;Yuh-Shan, 2004), to quantify adsorption and desorption fluxes.

PESTIPOND combines formulations from the literature (Marois et al., 2016;Nakano et al., 2004;Pugliese et al., 2020a;Rafique et al., 2011) to express adsorption and desorption mass fluxes as follows:

(II-II.2)

𝑀 𝑎𝑑𝑠 = 𝑘 𝑎𝑑𝑠 * 𝑀 𝑤 (𝑡) (II-II.3) 𝑀 𝑑𝑒𝑠 = 𝑘 𝑑𝑒𝑠 * 𝑀 𝑠 (𝑡)
where

Volatilization:

In environmental fate models, volatilization is widely expressed with a water-air transfer coefficient (Bunyakan et al., 2006;Comoretto et al., 2008;Hantush et al., 2013;Jacobs et al., 2012). Therefore, PESTIPOND adopts a similar formulation:

(II-II.4)

𝑀 𝑣𝑜𝑙 = -𝑘 𝑤-𝐴 * ℎ 𝑤 (𝑡) * 𝑀 𝑤 (𝑡)
where Mvol [M.T -1 ] is the volatilization mass flow rate of the pesticide, hw [L] is the depth of the water column of the AP, and kw-A [L.T -1 ] is the mass transfer coefficient from water to air.

The water-air transfer coefficient is expressed empirically as follows:

(II-II.5)

𝑘 𝑤-𝐴 = 𝐻 𝑅𝑇 1 𝑘 𝐶𝑂2 √ 𝑀𝑊 𝐶𝑂2 𝑀𝑊 + 1 𝑘 𝐻2𝑂 √ 𝑀𝑊 𝐻2𝑂 𝑀𝑊
where where T (°C) is the daily temperature of the air, kw-A is the reference volatilization coefficient, and θ is the temperature modulation coefficient.

Transformation processes

Transformation processes (i.e., biotransformation, photolysis, and hydrolysis) result in TP of pesticides, which can either be transformed into other TP or be completely degraded into simple mineral products. The main actors in transformation processes are (a) the microorganisms behind biotransformation in the water column and sediment layer, (b) solar radiation for photolysis, and (c) chemical reactions with water resulting in the hydrolysis of pesticides.

Several model developers used the first-order kinetic expression for transformation processes since it fits the observations well.

A similar approach was implemented in PESTIPOND using the transformation kinetic coefficients for biotransformation in water and sediment (kbio,w, kbio,s), photolysis (kp), and hydrolysis (kh). The transformation mass flux Mtransf [M.T -1 ] is expressed by the following equation, where "transf" refers to the aforementioned transformation processes:

(II-II.7)

𝑀 𝑡𝑟𝑎𝑛𝑠𝑓 = -𝑘 𝑡𝑟𝑎𝑛𝑠𝑓 * 𝑀 𝑤 (𝑡)
where ktransf [T -1 ] stands for the kinetic coefficient of each transformation process (i.e., kbio,w, kbio,s, kp, and kh), and Mw [M] is the pesticide mass in the water column. For biotransformation in the active sediment layer, Mw [M] is replaced by the pesticide mass in the sediments Ms [M].

Similar to volatilization, temperature is a stimulator of microbial activity and photochemical reactions (Jacobs et al., 2012;Ouyang et al., 2020;Vandermaesen et al., 2016). Therefore, transformation coefficients are corrected according to the Arrhenius law in Eq.II-II.6 and can be expressed with the corresponding half-life constant DT50 [T]:

(II-II.8)

𝐷𝑇 50,𝑡𝑟𝑎𝑛𝑠𝑓 (𝑇) = 𝑙𝑛(2) 𝑘 𝑡𝑟𝑎𝑛𝑠𝑓 (20°𝐶) * 𝜃 𝑇-20 .

Mass balance equations

The mass balance equations coupled to the hydrological model make it possible to quantify the contribution of the various aforementioned processes to pesticide fate in AP.

Numerical solving

The mass balance equations (Eq.II-II.9) and (Eq.II-II.10) form a system of ordinary differential equations (ODE) of the first order. The objective of the solving is to calculate the pesticide mass in the water Mw and the active sediment layer Ms at every time step t. However, the complexity of this ODE system makes the analytical solving difficult because the forcing functions (Table II-II.A. 2) are not constant in time. Therefore, the ODE system was solved numerically with an implicit Adams-Moulton scheme. The Adams methods are based on approximating the time derivatives (dM/dt) with a polynomial within the interval (t, t+dt). There are two types of Adams methods: the explicit and the implicit. The explicit type is called the "Adams-

Bashforth" (AB) method and the implicit type is called the "Adams-Moulton" (AM) method (Diethelm et al., 2002). The implicit Adams method was selected for this problem as it is adapted to stiff models [START_REF] Karimov | Adaptive explicit-implicit switching solver for stiff ODEs[END_REF]Li et al., 2008). Stiffness refers to the strong change in temporal series, such as flooding events in water flow rate functions and concentration peaks following pesticide application. In our case, the numerical solving was done via the ODE function of the SOLVING R-package. (Soetaert et al., 2010) and consists of a backward differentiation approximation of the system derivatives.

Since numerical solving approximates the analytical solution, a numerical error is introduced.

The ODE function allows one to limit the tolerated error during the solving. For our case, the maximum tolerated absolute and relative errors were fixed to 10 -8 for the selected solving scheme.

Besides the numerical scheme, the solving process requires initial conditions, which are the masses of the pesticide at the initial time step t0. For this model, initial conditions were fixed for each pesticide in both AP compartments. According to the assumption of the completely mixed reactor, the initial pesticide concentration in water Cw (t0) [M.L The initial concentration of pesticides in sediments is set to 0 Cs (t0) [M.L -3 ] since no prior observation data in sediment were available.

The time step is a key factor in numerical solving accuracy. Since the numerical solving is based on an integration between two time points t and t+dt, the shorter the time step dt, the better the solution approximation (Postawa et al., 2020). The selected time step should not be large so as to avoid divergence of the model. For instance, since the model does not integrate spillage, the model simulations will diverge if the input is an intercepted water volume during an extended period that exceeds pond capacity. Conversely, if the time step is short, it will be challenging to validate the simulations since the observation data are rarely available at a small frequency. In practice, some components, such as nitrates, are easier to acquire in short time steps, e.g., hourly-step data (Mander et al., 2021;[START_REF] Tanner | Influence of hydrological regime on wetland attenuation of diffuse agricultural nitrate losses[END_REF]. On the other hand pesticide monitoring is usually available for larger time steps. In fate models, the outputs are commonly displayed with a daily time step, and a smaller time step is used in numerical solving for approximation accuracy (Gobas et al., 2018;Hantush et al., 2013;Watanabe et al., 2006).

The selected time step for the model numerical solving is 0.001 day, and thus the simulated mass at the end of each day is conserved to form daily simulations to be compared with daily observations.

Model testing

In order to check the numerical error, to visualize the behavior, and to perform a sensitivity analysis of the model, we established a test-case scenario for simulation.

The selected site is the same as the one used for model validation in Paper II. The 5268.9-m 2

Rampillon AP (48°32′19.5″N; 3°03′46.7″E, 70 km southeast of Paris, Rampillon, France) is located on the Brie plateau, and is subjected to intensive agriculture. This artificial wetland was designed to collect drainage and runoff waters from an agricultural catchment covering 355 ha before they fall into sinkholes directly connected to the Champigny aquifer. Hence, the AP plays a buffer role against pesticides. The performance of the Rampillon AP is documented in detail by (Mander et al., 2021;Tournebize et al., 2012)

Input data for the hydrological model

For this test-case scenario, the water balance daily inputs (Qin, Qout, P, and E) and temperature

Input data for the pesticide model

The forcing functions of the PESTIPOND model are Qin(t), Qout(t), hw(t), T(t), and Cin(t) (Table II-II.A.2). Since the numerical solving is computed with a 0.001-day time step, all of the forcing functions should be implemented with the same time step. However, water flow rates and temperature are monitored daily, and the water depth hw(t) is also computed. Therefore, interpolation was used to obtain the latter forcing functions at a 0.001-day time-step. The interpolation was performed with the approxfun R-function using the "constant" option to maintain a constant value of Qin(t), Qout(t), hw(t), and T(t) during the day.

The inlet concentration of pesticide Cin(t) is monitored bimonthly through a flow-weight sampling strategy. The concentrations collected each fortnight are the average intercepted concentrations during the previous 2 weeks. For the time being, no direct link between pesticide concentration and flow rates in water bodies has been scientifically evidenced [START_REF] Gaullier | Influence of hydrodynamics on the water pathway and spatial distribution of pesticide and metabolite concentrations in constructed wetlands[END_REF]. Nevertheless, some studies have revealed a positive correlation between pesticide inlet flux and the intercepted water volume (Dabrowski et al., 2002;Matsui et al., 2002;Rabiet et al., 2010;[START_REF] Stearman | Pesticide Removal from Container Nursery Runoff in Constructed Wetland Cells[END_REF]. In light of these findings, we estimated the daily concentrations of pesticides using a flow-weighted linear interpolation expressed by Eq.II-II.12. A similar method was used by Budd et al. (2009).

(II-II.12)

𝐶 𝑖𝑛 (𝑡) = 𝐶 𝑖𝑛 Q𝑖𝑛 ̂ * 𝑄 𝑖𝑛 (𝑡)
where Cin(t) (µg.L -1 ) is the estimated inlet concentration of a pesticide at time t; Ĉin (µg.L -1 ) is the bimonthly sampled concentration, which represents the mean concentration of the intercepted concentration between time t and time t-Δt. The average time (Δt) between two successive samplings is 15 days, 𝑄 𝑖𝑛 ̂ (L.s -1 ) is the mean water flow rate during the same period of the concentration sampling, and Qin(t) (L.s -1 ) is the observed water flow rate at the inlet of the AP at time t.

Sensitivity analysis

The objective of the sensitivity analysis (SA) is to estimate the effect of the parameter values on the model outcome. SA is carried out by changing the parameter values and inspecting the response of the model output. Therefore, SA allows the model to be simplified by fixing parameters whose effect on model outcome is not significant. The goal of SA is also to verify the consistency of the model behavior (Devenish et al., 2012) or to assess the robustness of the simulation results by uncertain inputs or model assumptions (Paton et al., 2013).

Roughly speaking, SA can be divided into two types. (i) Global SA, where parameter values are drawn within a relatively large range and the effect on a model output variable(s) is recorded. Global SA is used for diverse purposes, including support of model calibration, verification, and model simplification (Pianosi et al., 2016). (ii) Local SA, also referred to as "one-at-a-time" (OAT) analysis, where the effect of a parameter is evaluated in an infinitesimally small region while other parameters are kept fixed. In practice, multiple parameters affect model outputs, hence the importance of evaluating the effect of the interaction Table II-II. 3 List of parameters selected for SA with the Sobol method and their minimummaximum values used for the uniform distribution. The minimum values of biotransformation and photolysis kinetic coefficients, kbio,w, kbio,s, and kp respectively, correspond to the shortest half-life (DT50=1 day) for each process. The maximum values of kbio,w, kbio,s, and kp correspond to the longest half-life (DT50=500 days). The minimum and maximum half-lives were taken from the pesticide properties database (PPDB) (Lewis et al., 2016). The adsorption-desorption parameter values kads and kdes were retrieved from (Comoretto et al., 2008;Nakano et al., 2004;Watanabe et al., 2006;Yoshida et al., 2000). Then, a Monte Carlo (MC) sampling was performed on the parameter values to use the MC estimators of SI and TSI. The MC method involves random sampling from the model input distributions. Subsequently, the model is run for every sample, and each simulation outcome is stored for analysis. Note that MC sampling can be costly, and thus it is recommended to choose a sampling size (n) small enough to ensure accuracy without increasing computational cost [START_REF] Tosin | A Tutorial on Sobol' Global Sensitivity Analysis Applied to Biological Models[END_REF]. In our case, n=1000; therefore, the total number of simulations for p = 7 parameters is Ns = (p+2)*n = (7+2)*n = 9000. For this test-case study, SA was performed on three representative study cases:

Parameter

i. On three pesticides with contrasting properties (Komarek et al., 2010;Lewis et al., 2016), i.e., chlorotoluron (Koc=384 L.kg -1 , log Kow=2.5), lenacil (Koc=165 L.kg -1 , log Kow=1.69), and diflufenican (Koc=5504 L.kg -1 , log Kow=4.2). This SA was carried out for the same period used in the test case (Sect. 4) to evaluate the link between the effects of processes and pesticide properties (i.e., mobility (Koc) and hydrophobicity (Kow)).

ii. (ii) On a metolachlor (Koc=120 L.kg -1 , logKow=2.9) during two periods to assess the impact of the monitoring period on hydrophilic (logKow<3) and highly mobile (Koc<500 L.kg -1 ) pesticides.

iii. (iii) On diflufenican during two periods to assess the impact of the monitoring period on hydrophobic (logKow>3) and lowly mobile pesticides (Koc>500 L.kg -1 ).

The Sobol indices SI and TSI were computed using the Jansen (Jansen, 1999) and Martinez (Martinez, 2011) methods because they are numerically stable and available in the R-packages for ODE (Weber et al., 2018).

Results and discussion

This section provides the simulation results of the test case followed by the results of the SA.

Test-case simulations

In order to assess the model behavior, we used the previously detailed input data (Chapter II-II, Section 2.5.1 and 2.5.2) and the parameters listed in Table II- ). This overestimation results from the model hypothesis assuming a linear correlation between the water flow and pesticide concentrations (Eq.II-II.12). Since the application period overlapped with a flood, the model overestimated the export of lenacil shortly after application. Nonetheless, PESTIPOND predicts an overall behavior in agreement with lenacil observations.

The rest of the simulation outputs are listed in Table II-II.A. 3. The mass balance error for the test-case simulation equals 0.005%. An amount of 9.05 g has left the AP during the monitoring period, representing almost 89% of the total intercepted mass (10.09 g). Regarding the different processes, a total mass of 1.48 g was adsorbed on sediment against 0.55 g that was desorbed, representing the largest percentages of the mass intercepted after the outflux. A total mass of 1.42 g and 0.57 g was degraded in the active sediment layer and the water column, respectively.

Accordingly, sorption (adsorption-desorption) is the process that contributes the most to pesticide fate, followed by transformation in the water-sediment interface and the water column. Figure II-II. 5 illustrates a greater degradation of lenacil during high temperature, underlining the link between temperature and pesticide dissipation (Bahi et al., 2023). Due to its high hydrophilicity (log Kow=1.69) and mobility (Koc=159 L.kg -1 ), lenacil is not expected to be highly adsorbed, and similar behavior was predicted by the PESTIPOND model.

Sensitivity analysis

As mentioned before, the output variance chosen for this SA is associated with the cumulative total mass (MT_cum). Therefore, the Based on these results, for the other SA study cases (Sect. 3), volatilization and hydrolysis rates were fixed (kv=kh=0.001) to save computation time. Only the SA results obtained with the Martinez method is discussed hereafter because compared to other methods, it can handle simulations leading to a "non-value" (NaN), which frequently occurs in environmental models (Gatel, 2018). shows that desorption (kdes) has the highest first-order and total effect on the three evaluated pesticides, explaining 40% of their behavior in AP. Transformation at the watersediment interface exhibits equal first-order and total effects for the three pesticides (SI=STI=20%). Biotransformation at the water-sediment interface (kbio_s) has the smallest effect on the highly mobile lenacil (SI=TSI=10%). Adsorption has the highest first-order (SI=20%) and total (TSI=35%) effects on lenacil compared to chlorotoluron (SI=TSI=15%) and diflufenican (SI=10%, TSI=20%). For both lenacil and diflufenican, the total effect of sorption processes is higher than their first-order effect, showing that biotransformation at the water-sediment interface has less effect on hydrophilic pesticides. This observation reveals that the low transformation of hydrophilic pesticides is caused by their low bioavailability on the water-sediment interface due to low sorption, supporting the assumptions of Ahmad et al. (2004), Chaumet et al. (2021), andMulligan et al. (2016). By contrast, highly mobile and hydrophobic pesticide behavior is more sensitive to desorption than lowly mobile and hydrophobic pesticides. We assume that this difference originates from the mobility of pesticide transfer and release from sediments contrarily to hydrophobic pesticides that are more likely to be trapped and irreversibly adsorbed onto sediments, as observed in previous sorption studies (Castan et al., 2021;Tang et al., 2019;Vagi et al., 2022). Lenacil is also the most sensitive to adsorption, which can be explained by the fact that hydrophilic pesticides need a more significant kinetic energy to be adsorbed when compared with less hydrophilic pesticides.

In order to evaluate the effect of the period on the processes, we performed SA on a hydrophilic (SI=35%, TSI=40%). By contrast, processes at the water-sediment interface exert an effect until they interact with other processes. It is assumed that the interaction involves sorption and biotransformation at the water-sediment interface since they are the processes exhibiting a significant increase in the total effect. Besides, sorption controls the bioavailability of pesticides for transformation in the active sediment layer. Practically, in order to specify which processes interact with each other, second-order Sobol indices should be calculated, which is computationally costly. Therefore, we support our assumption with the results of previous studies (Beringer et al., 2021;Cáceres-Jensen et al., 2021;Wu et al., 2017) highlighting the link between sorption and pesticide biotransformation at the water-sediment interface.

On the other hand, during the period 2019-2020, sorption had a greater effect on metolachlor behavior compared with transformation processes during 2016-2017. When comparing the hydraulics of the two periods, it was noticed that during 2019-2020 the drainage volume

(1,024,631 m 3 ) was three times higher than during 2016-2017 (315,134 m 3 ). Therefore, the difference in the hydraulic conditions of the AP could explain the sensitivity variation between the two periods; especially because of its high mobility and hydrophilicity, metolachlor is mainly present in the water column, and its fate strongly depends on the pond hydraulics. In addition, biotransformation at the water-sediment interface had a slight effect on metolachlor behavior independently of the period, which was also the case for lenacil (Fig. ). This observation supports our previous assumption of hydrophilic pesticides being less bioavailable for transformation in sediment because of their low sorption. temperature has the highest effect and explained 80% of the model output. This observation is expected since the temperature is a major controlling factor of pesticide fate in AP and influences the rates of all processes (Burrows et al., 2002;Kadlec et al., 2008;Kaur et al., 2018;Vymazal et al., 2015). Note that all of the aforementioned SAs were carried out on other variances of model outputs, i.e., Mdiss, Ms, and Mout, as well as other statistical criteria besides the cumulative mass, i.e., 90 and 10 percentiles, mean, maximum and minimum values. The different SA simulations display the same results.

Overall, SA highlighted that, except for hydrolysis and volatilization, the set of processes contributes to pesticide behavior in AP independently of their properties or application period.

The SA underlined that sorption, biotransformation, and photolysis govern pesticide behavior in AP. This assumption agrees with the results of an SA conducted with similar pesticide fate models. For instance, Boulange et al. (2012) showed that the PCPF-1 model is primarily sensitive to adsorption parameters followed by desorption and then by transformation parameters (i.e., biotransformation and photolysis). Similarly, Desmarteau et al. (2014) demonstrated that the fate of hydrophobic pesticides (Koc > 500 L.kg -1 ) simulated by AGRO-2014 is highly sensitive to adsorption and biotransformation in the active sediment layer. Inao et al. (1999) also evidenced the significant influence of adsorption-desorption and transformation on pesticide fate in AP (e.g., paddy fields).

Hereafter we list the main assumptions of our SA: Primarily, processes occurring at the water column (i.e., biotransformation in the water column and photolysis) have the most significant influence on the behavior of highly mobile and hydrophilic pesticides. By contrast, biotransformation at the water-sediment interface has a greater effect on hydrophobic and lowly mobile pesticides. Secondly, the adsorption-desorption effects increase when interacting with other processes. These two assumptions support the possible link between adsorptiondesorption and transformation. Besides, the sensitivity of hydrophilic pesticide behavior is more likely to vary between periods that have different hydraulic properties. This variation reflects the possible dependency of the fate of hydrophilic pesticides on hydraulic conditions contrarily to hydrophobic and lowly mobile pesticides. Finally, this SA underlines the major effect of temperature on pesticide behavior in AP.

These SA assumptions are useful for selecting the parameters that should be calibrated and for fixing those that have less effect in order to achieve a better model performance. Besides, SA results give insight into the processes that contribute less and can be neglected to reduce the number of input parameters and simplify the model.

Conclusion

Artificial ponds (AP) are commonly used to store water for irrigation purposes or stormwater to prevent flooding and are used as complementary tools to treat agricultural water contaminated by pesticides. These AP provide an environment for pesticides to undergo a synergy of physicochemical processes, resulting in the dissipation and/or buffering of water contamination before being transferred into the environment. Modeling is a promising tool for a better understanding of AP performance and improving their efficiency to preserve water quality. However, there are few models describing pesticide fate in edge-of-field AP while considering the key processes behind pesticide dissipation. Therefore, this part of a two-paper A global sensitivity analysis was carried out to highlight the input parameters that most influence the model outputs. The sensitivity analysis was performed through a Monte Carlo sampling of the parameters and was quantified by the Sobol method using the Martinez estimator. It was found that the processes governing pesticide behavior in AP are sorption (adsorption-desorption), biotransformation, and photolysis. Biotransformation at the watersediment interface has a greater effect on hydrophobic and lowly mobile pesticides, while biotransformation in water has a more significant effect on hydrophilic and highly mobile pesticides. The sensitivity of hydrophilic pesticides to processes could vary with time according to the hydraulic conditions of the AP. In addition, the model could be simplified by not considering volatilization and hydrolysis. However, the model limitations should be noted.

First, the assumption of a completely mixed reactor is not always the case in AP, specifically AP representing heterogeneities (e.g., with higher vegetative cover, dead zones, dikes). Besides, considering the acclimation and dynamics of microbial communities will undoubtedly improve the model performance. Nevertheless, the model is robust and simulates pesticide behavior similar to the behavior expected according to the pond and pesticide properties. The PESTIPOND model is a potential tool for designing and managing AP used as complementary tools to dissipate pesticide contamination.

Table II-II.A. 1

Values of the fixed parameters used for the SA. The surface area was measured in the field, the temperature modulation factor θ was extracted from the literature (Gargallo et al., 2017;Hantush et al., 2013;Sharifi et al., 2013), and the bulk density ρb of sediments was measured in the field. Due to their broad-spectrum toxicity, pesticides are a non-point source of pollution for the ecosystem since they are transferred from agricultural plots to natural water resources through surface runoff, leaching and subsurface drainage. Hence, the need to reduce pesticide inputs into water resources, as they are substantial drinking water supplies and aquatic habitats [START_REF] Leenhardt | Impacts des produits phytopharmaceutiques sur la biodiversité et les services écosystémiques-Résumé de l'Expertise scientifique collective-Mai[END_REF]. Pesticides have been detected in groundwater (Baran et al., 2008;Hunter, 2012), rivers (Montiel-León et al., 2019;Xu et al., 2020), and lakes (Bhardwaj et al., 2019;Kandie et al., 2020), as well as smaller wetlands (Lorenz et al., 2017;Ulrich et al., 2018), making pesticides a major cause for water quality impairment. As complements to pesticide use regulation and management practices, material solutions, such as constructed buffer zones, can be implemented to safeguard the quality of water resources and mitigate pesticide input into water bodies. The efficiency of constructed and natural buffer zones such as edge-of-field wetlands, riparian buffer strips, and vegetated ditches (Vymazal et al., 2015) to reduce pesticide transfer into the environment is widely documented in the literature (Bahi et al., 2023;Imfeld et al., 2021;Marin et al., 2022).

Parameter

This study focuses on constructed wetlands, especially edge-of-field artificial ponds (AP) since they have the advantages of needing minimal operations and providing wildlife habitat (Sudarsan et al., 2021). AP are also known for their cost-effectiveness and low energy consumption compared to other surface water treatment methods (e.g., coagulation, membrane filtration, ion exchange, photocatalytic degradation, and adsorption on black carbon and activated carbon) (Aungpradit et al., 2007;Fitch, 2014;Kearns et al., 2014;Trepel, 2010). In practice, edge-of-field APs can act as buffer zones since they are constructed downstream agricultural plots and upstream natural water resources. APs can intercept a part of agricultural water, and after important flow events, the water leaving APs can be less pesticide-loaded, and contamination transfer into the environment can be dissipated. Over the past years and in light of the worsening water shortage, the evaluation of APs environmental role has gained significant attention. The efficiency of APs in reducing pesticide transfer into the environment was widely reiterated in literature (Li et al., 2014;Tournebize et al., 2017;Vymazal et al., 2015;Zhang et al., 2014). AP provide an area for a series of physicochemical processes to dissipate pesticide water contamination.

Modeling is a practical tool to assess the performance of AP and explore the physicochemical processes behind pesticide dissipation in AP. Modeling can be used to improve the efficiency of AP to safeguard water quality. Models of varying levels of complexity have been developed

and applied to field data to gain insight into the performance of APs. However, many of these models were dedicated to simulating nutrient behavior (Kalin et al., 2013;Son et al., 2010;Sonavane et al., 2009), and fewer models were assigned to pesticides. Among pesticide fate models are the risk assessment models such as PRZM (Carsel, 1998) and MACRO (Larsbo et al., 2003;Larsbo et al., 2005), which simulate pesticide fate in the root zone and macro-porous field soils, respectively, and TOXSWA (Adriaanse, 1996) from the FOCUS group (Tooby, 1999), to model pesticide fate in ditches. These models provide knowledge about pesticide behavior upstream AP. Nevertheless, little consideration has been devoted to studying pesticides at the pond scale. Existing models such as AGRO-2014 and TOXSWA are computationally costly because they require a significant number of inputs and parameters and depend on other models' outputs (i.e., PRZM). Besides, AGRO-2014 only accounts for hydrophobic pesticides. The lake-pond module of the Soil and Water Assessment Tool (SWAT) can also simulate the fate of pesticides in AP. However, SWAT does not integrate the effect of temperature and desorption and does not consider the kinetic effect of adsorption-desorption,

given that these factors are widely reported as key drivers of pesticides fate (Burrows et al., 2002;Cryder et al., 2021;Kadlec et al., 2008;Kaur et al., 2018;Papaevangelou et al., 2017;Vymazal et al., 2015).

On this basis, a descriptive model of pesticide fate in AP "PESTIPOND" was developed.

PESTIPIPOND is built upon simple mathematical formulation with a limited number of inputs and parameters. Contrarily to black-box models considering a single decay rate of pesticides, PESTIPOND is a process-based model integrating the key processes behind pesticide fate in AP as discussed in (Bahi et al., 2023). PESTIPOND simulates the fate of different pesticides (hydrophobic and hydrophilic) intercepted through agricultural water and distributed in the water and sediment compartments of the AP. The key processes considered by the model are adsorption-desorption, biotransformation in water and sediments, photolysis, hydrolysis, and volatilization. PESTIPOND is designed to be implemented in a landscape model in fine (e.g., SWAT model) to predict the transfer of pesticides at the watershed scale.

The model program was coded using the R language. The input data consists of 10 parameters and 6 forcing variables listed in Table III.A. 2. The model was previously tested on a test-case scenario and successfully simulated the mass in water and sediments of dissolved pesticides contained in agricultural drainage water, using arbitrary parameter values and observed data.

The development, testing, and sensitivity analysis (SA) of PESTIPOND can be found in (Bahi et al., 2023, submitted). A module of PESTIPOND is dedicated to the reactive transport of adsorbed pesticides, but due to a lack of observed data, it still needs to be completely validated.

The main hypotheses of the model are recalled hereafter:

(1) Concentrations of pesticides are spatially uniform in water and sediments because the AP compartments are considered completely mixed reactors. Hence, once pesticides enter a specific compartment, they are instantly mixed with the entire content and distributed uniformly. In practice, after several hours, the pond water becomes perfectly mixed (Alvord et al., 1996;[START_REF] Pugliese | Internal hydraulics and wind effect in a surface flow constructed wetland receiving agricultural drainage water[END_REF]. Besides, the pond heterogeneities responsible for the non-uniformity of concentrations are usually considered in hydraulics-based models (Henine et al., 2022) rather than chemical-based models, e.g., for pesticides (Bahi et al., submitted,(Watanabe et al., 2000b) and nitrates (Hantush et al., 2013;Krone-Davis et al., 2013).

(2) Vegetation is not considered by the PESTIPOND model. Prior investigations have evidenced that the fraction of pesticides collated in plants were often insignificant in APs (< 10%) (Butkovskyi et al., 2021;Pérez et al., 2022;Singh et al., 2021;Wang et al., 2017). Not considering the vegetation implies neglecting its effect on the hydraulic flow (brakes, dead zones). Nonetheless, this choice of hypothesis was motivated by the environmental focus of the model rather than the hydraulic one, hence also the hypothesis (1).

(3) No advection or diffusion processes are considered by the model for the following reasons; (a) APs are often constructed on a compacted substrate where the infiltration (leaching) is not significant, and the water velocity at the water-sediment interface is too low resulting in a weak hydraulic gradient that limits advection in the sediment layer and the water-sediment interface. Besides, the water velocity in the water column of the evaluated APs is too low to induce advection of pesticides. (b) Several studies showed that the molecular diffusion of diverse pesticides is too low in the water (~10 -9 m 2 .s -1 at 25°C) (Chevillard et al., 2014;Fernández-Pascual et al., 2020a;Sarraute et al., 2019).

(4) PESTIPOND simulates the fate of pesticides in the first cm of sediments designated as the active sediment layer governing pesticide transformation and transport under flooded conditions. Previous in-situ measurements proffered that pesticide residues are not significant beyond 1cm of depth (Inao et al., 1999;Mahugija et al., 2018;Nyantakyi et al., 2022;Takagi et al., 2012). The pore water of the active sediment layer is included in the water column compartment.

(5) Transformation and volatilization processes are corrected according to the temperature change during the simulation period. The Rampillon AP was implemented in 2010 to address local environmental and health issues (Lebrun et al., 2019;Tournebize et al., 2017;Tournebize et al., 2012). It was designed to collect runoff and drainage water sourcing from agricultural plots before being fed into the Champigny water table, which constitutes a major drinking water resource. Almost 60% of the Champigny water originates from direct infiltration of agricultural runoff through sinkholes. The

Champigny water table provides drinking water for almost 1.5 million citizens, hence the priority to safeguard its quality by reducing pesticide transfer.

A typical waterlogging French soil characterizes the Ancoeur catchment. Therefore, more than 80% of the catchment and the whole Rampillon area have been subsurface drained since 1980 to prevent frequent winter soil saturation (Tournebize et al., 2012). The drains are perforated pipes buried to a depth of 90m and spaced 10m apart. The 355-ha watershed receives an annual mean rainfall of 689mm, and the annual mean drained flow is 228mm. Farmers mainly grow winter wheat (49%), sugar beet (18%), corn (14%), beans (9%), and rape (4%). The Rampillon AP comprises sub-basins separated by bunds to enhance pesticide dissipation and accumulation by increasing the water residence time (HRT) (Tournebize et al., 2012). The first sedimentation basin is 100cm deep and 300m 3 (Figure III. 1; i.e., cell 1). The 4000-m 2 intermediate zone is a shallow sub-basin of a maximum of 50cm deep, including cells 2, 3, and 4 (i.e., 1680, 1450, and 870m 2 , respectively). About 20, 60, and 50% of the inlet and outlet of cells 2, 3, and 4, respectively, were covered by vegetation in 2015: reed (Phragmites australis), bulrush (Juncus spp.), and sedge (Carex spp.). A final 1000-m 3 basin, 80 cm deep (i.e., cell 5), was implemented before the outlet. The total volume of the Rampillon pond is 2500m 3 . Sediments of the AP are composed of coarse silt for 32.8%, clay for 27.8%, fine sand for 7.7% (3.5%), coarse sand for 2.7% (2%) through a transect from inlet and outlet, and 2-2.8% for organic matter . The loamy texture of sediment is similar to the surrounding soil texture. In 10 years, 10cm of sediments accumulated in the Rampillon AP. On average, the AP intercepted 38% of the collected drainage water from 2014 to 2021, with 293 645m 3 transited annually.

Monitoring data

The Rampillon AP was initially implemented to buffer nitrate, metals and pesticides originating from intercepted agricultural water. Therefore, since 2012, the Rampillon AP is instrumented : 2014-2015, 2016-2017, 2017-2018, 2018-2019, and 2019-2020 excluding monitoring periods with artifacts and pesticide re-mobilization that is detailed afterward. The selection of the pesticide molecules for the model validation is motivated by the diversity of their chemical properties and their significant detection rate. The monitoring data of the 5268.9-m 2 AP during the selected periods are summarized in Table III.

1Erreur ! Source du renvoi introuvable.. Rainfall and PET were extracted from the SAFRAN database (Vidal et al., 2010).

Table III. 1 Summary of pesticides monitoring data. The selected periods correspond to monitoring data without artifacts. In addition, the pesticide remobilization periods were not considered (i.e., pesticide outlet concentration Cout > inlet concentration Cin). Days refers to the total duration of each period. Qinmax (µg.L -1 ) is the maximum water flow rate intercepted by the Rampillon AP. Qinmean (µg.L -1 ) is the mean water flow rate intercepted by the Rampillon AP during the corresponding period. Cmax (µg.L -1 ) is the maximum detected concentration of each pesticide, Cmean (µg.L -1 ) is the mean detected concentration during the corresponding period, and the DR is the detection frequency of pesticides. T (°C) and HRT (d) are the average temperature and hydraulic residence time of each period, respectively. TUR (FTU) is the mean turbidity, NO3-(mg.L -1 ) is the mean nitrate concentration, TOC (mg.L -1 ) is the mean total organic carbon concentration, and DOC (mg.L -1 ) is the mean total dissolved organic carbon concentration. Each pesticide is associated with its type: (H) Herbicides, (F) Fungicides. Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 199

Input data

Since 2011, outlet and inlet pesticide concentrations have been monitored in the Rampillon AP.

The AP efficiency to mitigate pesticides is calculated from the mass fluxes using Eq.III.1. Note that the mass flux corresponds to the total mass of pesticide detected in the water with no distinction between the dissolved and particulate fraction.

(III-1)

𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%) = (1 - 𝑜𝑢𝑡𝑙𝑒𝑡 𝑚𝑎𝑠𝑠 𝑓𝑙𝑢𝑥 𝑖𝑛𝑙𝑒𝑡 𝑚𝑎𝑠𝑠 𝑓𝑙𝑢𝑥 ) * 100
Where the outlet and inlet mass fluxes (µg) are deduced from the concentrations (µg.L -1 ).

Since the rate of pesticide mitigation varies with the type of pesticide, it is suitable to represent the AP performance by a mean efficiency. Compared to other periods, a significant efficiency was observed during 2016-2017 for bentazon, boscalid, and s-metolachlor. The higher dissipation could result from the high HRT (14 days), and temperature noticed back then (11°C). Similarly, mesotrione and quinmerac were significantly dissipated during 2018-2019, which had an average HRT of 9 days. In fact, a longer HRT provides

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 201 time for accumulation and transformation processes behind pesticide dissipation, and a higher temperature stimulates the microbial activity behind pesticide biotransformation and is associated with significant solar radiation responsible for photodegradation. This observation ties in with other studies demonstrating that temperature and HRT are major drivers of pesticide behavior in AP (Bahi et al., 2023;Imfeld et al., 2021;Materu et al., 2021;Pavlidis et al., 2022;Vallée, 2015).

Interestingly, mesotrione had a mean dissipation of 50% during a lower mean temperature (9°C)

and HRT (7 days). It could be explained by the high biodegradability of the molecule evidenced by short half-lives in both sediment and water, 5.3 and 5.2, respectively (Lewis et al., 2016). These observations underline the potential relationship between pesticide properties, hydro-climatic conditions, and APs performance. Therefore, this relationship will be evaluated using the PESTIPOND model (section 4 on page 221).

Table III. 2

The physicochemical properties and the application season of the 7 studied pesticides.

Koc (mg.L -1 ) is the organic carbon-water partition coefficient, representing the mobility of the molecule. Log Kow (-) is the octanol-water partition coefficient, representing the hydrophobicity of the molecule. S (mg.L -1 ) is the water solubility of the molecule, and LOD (µg.L -1 ) is the detection limit of the molecule in water. The properties values were extracted from literature [START_REF] Barchanska | New procedures for simultaneous determination of mesotrione and atrazine in water and soil. Comparison of the degradation processes of mesotrione and atrazine[END_REF][START_REF] Catalá-Icardo | Development of a Photoinduced Chemiluminescent Method for the Determination of the Herbicide Quinmerac in Water[END_REF][START_REF] Epa | Quality Assurance Guidance Document-Model Quality Assurance Project Plan for the PM Ambient Air[END_REF]Lewis et al., 2016;PubChem, 2021) and the application season was deduced from a follow-up of cultural practices in Rampillon. In terms of the detection frequency of each pesticide, bentazon was the most frequently detected pesticide, which may be due to its wide range of application periods, i.e., March, April, May, and June. Similarly, diflufenican was highly detected in the Rampillon AP because it was applied during three seasons, i.e., autumn (October and November), winter (January and February), and

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 202 spring (March). A kindred explanation can be accorded to quinmerac applied during autumn (September and October), spring (March, April, and May), and summer (June). On the other hand, Boscalid was only detected during 2014-2015 and 2016-2017. In fact, boscalid was always applied in April and once in May; therefore, if the application did not co-occur with an important spring rainfall event, the pesticide in question would be unlikely to be detected in drainage water. Besides, due to its Koc (772 L.kg -1 ) boscalid has a higher affinity to sediments, reducing its availability in the water reaching the pond. Likewise, mesotrione although being applied more frequently, it was only detected twice (2017)(2018)(2018)(2019). Mesotrione was applied during spring and summer (March, April, and June), with a low incidence of flooding events explaining the molecule's infrequent detection.

The AP efficiencies were calculated based solely on the pond inlet and outlet amount of pesticides.

It is, therefore, unknown which processes are driving the dissipation of pesticides. Therefore, the purpose of the PESTIPOND model is to simulate the behavior of pesticides and upfront the contribution of each process to the pesticide fate in APs. Hereafter are detailed the model inputs, i.e., forcing variables and parameters.

Forcing variables

The list of the forcing functions required by the model is provided in Table III.A. 3. It is important to recall that inlet and outlet concentrations are observed bi-monthly at the study site. The concentrations collected each fortnight are the average intercepted concentrations during the past two weeks. Therefore, to have a close insight into pesticide behavior and match the model daily time-step, the bi-monthly observed concentrations were transformed to daily concentrations using a water flow rate weighted interpolation. The transformation method is detailed in (Bahi et al., 2023, submitted). The daily inflow and outflow rates (Qin, Qout) were calculated from the hourly water flow rates measured on-site. The water flow rates are used to compute the inlet and outlet daily mass fluxes from the corresponding concentrations.

Min (µg.d -1 ) and Mout (µg.d -1 ) are the daily mass fluxes of the pesticide at the AP's inlet and outlet.

Cin (µg.l -1 ) and Cout (µg.l -1 ) are the daily concentrations at the inlet and outlet of the AP, respectively. The water volume Vw was computed by the hydrological model detailed in (Bahi et al., 2023, submitted). The hydrological model of PESTIPOND requires daily local rainfall and

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 203 evaporation data, which were provided by SAFRAN 14 (Vidal et al., 2010), along with the daily temperature T (°C). The water depth hw (m) was deduced by Eq.III.2.

(III.2)

ℎ 𝑤 (𝑡) = 𝑉 𝑤 (𝑡) 𝐴
Vw (m 3 ) is the water volume in the AP, and A (m 2 ) is the measured surface area of the AP.

Parameters

For the model validation, parameters were either extracted from the pesticide properties database (PPDB) (Lewis et al., 2016) or calibrated, and the AP properties were measured on-site. The list of the model input parameters is available in Table III.A. 1. The input parameters measured onsite are mainly the AP properties (Surface A=5270 m 2 and the bulk density ρb=0.9 g.cm -3 ). The temperature correction coefficient θ (Eq.II-II.6) is extracted from the literature (Sharifi et al., 2013).

The parameters related to the processes were extracted from the PPDB and calibrated if not available or if calibration would ameliorate the model performance. The calibration was performed manually and numerically using the hydroGOF R-package (Zambrano-Bigiarini, 2020). The Rcalibration function seeks the set of parameters leading to the best possible performance of the model according to an evaluation criterion (e.g., NSE and KGE (Eq.III.3 and Eq.III.4)).

The model parameters are classified in terms of processes: adsorption-desorption (kads (d -1 ) and kdes (d -1 ) for sediment layer) and transformation processes (DT50,w, DT50,s and DT50,p for sediment and water compartments). The sensitivity analysis results (Bahi et al., 2023, submitted) evidenced that the PESTIPOND model is insensitive to volatilization and hydrolysis independently of the pesticide molecular properties. Therefore, based on literature values (Jacobs et al., 2012;Rose et al., 2006), volatilization and hydrolysis rate coefficients were given a fixed value for the rest of the study (kv =kh=10 -6 d -1 ), leaving only 5 parameter values to determine (DT50,w, DT50,s, DT50,p, kads, and kdes). The parameter values used to assess the PESTIPOND model are to be found in the result section (section 3).

al., 2015). Moreover, the normalized root mean square error (NRMSE) (-) was computed to describe the discrepancy between observations and simulations of pesticide concentrations. Since molecules have different concentration ranges, it is it is statistically more appropriate to compare the NRMSE than the regular RMSE (µg.L -1 ). Therefore, NRMSE was calculated by normalizing the RMSE according to the difference between the maximum and minimum concentrations for each pesticides (Kenney et al., 1962;Sinsomboonthong, 2022) Table III. 5 lists the NSE (Eq.III.3), KGE (Eq.III.4), and NRMSE (Eq.III.5) values for the statistical comparisons for all pesticides and periods.

From the graphical and numerical outcomes, it can be noted that the annual calibration results in proper model performance. The KGE and NSE values are >0.5 and the RMSE does not exceed 0.07. Considering how wide the goodness-of-fit-range of variations are (NSE (-∞, 1], and KGE (-∞, 1]), the model performance can be considered as "good" due to the annual calibration (Lee et al., 2021;Moriasi et al., 2007;Moriasi et al., 2015). III. 4 illustrate that the adsorption-desorption parameter (kads, kdes) vary more pronouncedly with periods than transformation ones (DT50,w, DT50,s, and DT50,p). In order to evaluate this variability, we ran the model using a generic set of parameters (Table III. 5), which is the mean value of the annual-calibrated parameters (Table III. 3, Table III. 4), and estimated the performance criteria (Table III. 6). Figure III. 5 illustrates the graphical comparison between the simulations using the inter-annual set of parameters and the non-transformed observations. Table III. 6 outlines the high KGE (>0.5) and NSE (>0.6) values of the model outputs while using the inter-annual set of parameters for all pesticides. Boscalid made the exception with a KGE of 0.44. Given that the annual calibration was designed to find a proper set of parameters for each period (Table III. 3 and Table III. 4), the performance was expected to decrease when running the model with a single set of parameters independently of the period (Table III. 6). Nonetheless, the criterion values reflect in aggregate a good model performance, i.e., NSE >0.35 and KGE >0.5, except for boscalid, which indicates a not satisfactory performance according to the commonly used thresholds (Knoben et al., 2019;Moriasi et al., 2015;Towner et al., 2019). 2). On average, the relative error of boscalid discharge simulations is 36% while it is <10% for other pesticides.

Altogether, the graphical and statistical comparison of the observations and the simulations using the inter-annual set of parameters reflect a good model performance for all pesticides except for boscalid. After the quantitative evaluation of the model, the next section will describe the mass budget of pesticides within the AP and the contribution of each process to pesticide dissipation.

Pesticide mass budget

One of the PESTIPOND model's major aims is to quantify each process's contribution to the fate of pesticides. Accordingly, after running the model with the mean set of parameters, the mass budget of pesticides was assessed to illustrate the mass distribution in the pond and the contribution of each process to pesticide dissipation. Table III.A. 2 summarizes the mass budget for all the pesticides and periods of the survey. Note that the PESTIPOND model checks whether the mass balances tally during the calculations. The mean mass balance error of the set of pesticides and periods is <1 % showing that the model conserves the mass properly.

Figure III

. 6 exhibits the mass partition in the AP at the end of each period. For succinctness, only mean values will be discussed in the following. Overall, most intercepted pesticides are discharged from the pond with a mean out flux of 72%, followed by the mass remaining in the active sediment layer with a mean proportion of 12%, which leaves almost 2% pesticides in the water column.

Therefore, the mean dissipated mass between the inlet and outlet of the AP accounts for 14% of the total intercepted mass. Figure III. 7 reveals that, on average, boscalid, mesotrione, and quinmerac had the highest adsorption, which covers 35%, 36%, and 35% of the total intercepted mass, respectively (Table III.A. 3). Diflufenican, chlorotoluron, and bentazon come after with adsorption of 28%, 21%, and 14%, respectively. S-metolachlor had the lower adsorption (6%) but a significant transformation in water (24%), which is mostly due to biotransformation (23%), leaving only 1% to the other transformation processes (photolysis, hydrolysis, and volatilization). A significant transformation in the water column was also noted for bentazon (14%), which is mainly partitioned between photolysis (9%) and biotransformation (5%). On the other hand, biotransformation at the watersediment interface had an important contribution to pesticide dissipation. For instance, 18% of the mesotrione intercepted mass was biotransformed on average in the active sediment layer.

Moreover, during 2014-2015, boscalid, chlorotoluron, and diflufenican were more transformed in the sediments than in water. The transformation in water and desorption rates for the same three pesticides are negligible (<1%). Conversely, the quinmerac desorption covers 20% of the inlet mass, followed by mesotrione (10%) and bentazon (7%). The photolysis contribution to dissipation was negligible for all pesticides except for the bentazon (8%).

Temperature was the highest during 2016-2017, which overlapped with the highest, desorption and transformation of bentazon at the water-sediment interface. The same period exhibited the highest HRT.

Model extrapolation: Efficiency abacus

For further exploitation of the model outputs, PESTIPOND was run with the inter-annual parameter set described in section 3.3 and the same inputs presented in Section 2.3 but using different AP sizes in ascending order, equivalent to higher nominal HRT. For each simulation assigned to a specific AP area A (and HRT), the mean pesticide dissipation efficiency was computed (Eq.III.1). For the different simulations, only the AP area was modified, and the rest of input data were kept the same (e.g., the water flow rates, water depth, and temperature).

Note that a mean efficiency, including all pesticides, is computed for each period, and then a mean value for all periods is deduced. For the actual Rampillon area (5270m 2 ), which covers 0.15% of the drained catchment area (355ha), the mean AP efficiency is 40% for all pesticides combined. By increasing the surface area by 10 000 m 2 , the HRT is tripled, and the efficiency rises sharply to 63%. From an area that covers 0.7% of the watershed, the efficiency increases less steeply and attains about 82% (Figure III. 8). However, the variation degree decreased with increasing pond areas, as well as the efficiency itself, as observed in Figure III. 8. For the actual AP size (i.e., 0.15% of the catchment area), the efficiency doubled from 2014-2015 (30%) to 2016-2017 (62%). Afterward, the dissipation potential of the pond decreased sharply to 36% during 2017-2018 and remained around 38% for the following years. Similar behavior will be noticed in the pond's efficiency if the surface increases to 0.5%-0.7% of the catchment area, with higher minimum and (55%-66%) and maximum values (80%-87%), respectively. For a ratio of 0.15%-0.7%, the mean pond dissipation is 17% lower than the mean maximum and 10% higher than the minimum. Conversely, once the AP area covers >1% of the catchment, the discrepancy between the mean efficiencies and the minimum and maximum values decreases to ~8% and ~6%, respectively (Figure III. 8).

In addition, similar extrapolation was performed on different temperatures representing a geographical temperature gradient (Figure III. 9). Figure III. 9 exhibits a linear increase of pesticide dissipation according to ascending temperature. On average, the current temperature of the surveyed periods is 9°C, which results in a 40% efficiency of the Rampillon AP. When the actual temperature increases by 5°C, the model predicts an increase of 4% in pesticide dissipation.

A similar temperature rise is expected if the actual temperature increases by 10°C and 15°C. In aggregate, a temperature rise of 5°C will boost the mean efficiency by 4%. Therefore, overheating the current temperature by 10°C will improve the mean dissipation of pesticides by almost 13 %.

A temperature rise of 5°C will result in a maximal mean efficiency of 67%, compared to the actual maximal dissipation rate (61%). A lower variation is noticed in the minimum pond efficiency with ascending temperatures. On average, the actual minimal dissipation rate of the AP is 30%, which is also expected to rise by 4% due to a temperature increment of 5°C. On the other hand, reducing the temperature by -5°C can decrease the actual efficiency by 2%.

Figure III. 9

The mean efficiency of the Rampillon AP to dissipate pesticides from the inlet to the outlet according to increasing temperatures. T refers to the actual daily temperature in the decay coefficient. Besides, PESTIPOND uses the simplest form of mathematical formulations when compared to more complex and computationally costly environmental fate models such as TOXSWA and AGRO-2014 (Adriaanse, 1996;Gobas et al., 2018). Contrarily to other pesticide fate models (i.e., the pond/lake module of SWAT (Neitsch et al., 2011)), PESTIPOND integrates the effect of temperature and HRT as they are widely recognized as governing factors of pesticide behavior in AP. Each temperature-dependent process coefficient is adjusted to the actual site temperature. Besides, the processes are integrated into the model by kinetics; thus, the more extended the HRT, the longer the process will have time to dissipate the molecule. In contrast to SWAT, PESTIPOND integrates desorption, a considerable process for mobile and hydrophilic pesticides, as underlined by the SA and mass budget results discussed afterward.

For the model performance assessment, we adopted the following strategy. The performance of PESTIPOND was first (i) evaluated using an annual calibration (i.e., a set of parameters proper to each period (Table III. 3 and Table III. 4) and then (ii) a generic set of parameters for all periods (i.e., the mean value of the annual-calibrated parameters (Table III. 5). Since the daily observations of pesticide concentrations are not available, transformed observation data was created from bi-monthly monitoring to illustrate pesticide dynamics closely. Therefore, the model performance was assessed for both transformed and non-transformed observations for each step (i) and (ii).

Firstly, the results of steps (i) and (ii) of the performance assessment (section 5. III. 6) agreements between simulations and observations except for boscalid according to non-transformed observations.

Given the half-lives of boscalid reported in the literature (PPDB), it is biodegradable in water.

Whereas, boscalid was stable based on the observations in the Rampillon AP (Fig A.5), which means that the conditions in which the PPDB half-lives were estimated may be different from those of an AP.

Lower KGE and NSE values were noted for step (ii) compared to (i).The drop in the performance (ii) was expected since the annual calibration uses an adapted set of parameters for each period (Table III. 3 and Table III. 4), whereas a single set of parameters was used for all periods combined in the performance assessment (i) (Table III. 6).

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 223 Secondly, a lower KGE was noticed for all pesticides compared to the NSE values. The difference between the KGE and NSE values may originate from the definition of the KGE (Eq.III.3) based on the mean difference between simulations and observations, which puts more weight on extreme values. Alternatively, the NSE (Eq.III.4) estimates the discrepancy between observations and simulations evenly during the whole period. Given that a spike following their application in the agricultural plots characterizes all the pesticide chronicles, it is anticipated that the KGE will have lower values than the NSE. Notwithstanding, it is recommended to evaluate model performance with more than one criterion (NSE), thus using the KGE and NRMSE. Based on commonly used thresholds (Knoben et al., 2019;Moriasi et al., 2015;Towner et al., 2019), the KGE and NSE values indicate a "good" model performance, except boscalid, for which the model performance is considered as "not satisfactory" according to both transformed and non-transformed observations. The low model performance on boscalid is also portrayed by a significant discrepancy between the observed and simulated exported mass and outlet concentration Figure higher than other pesticides (<10%). Moreover, the average NRMSE translates to a slight discrepancy between the observed concentrations and simulations.

Note that using the transformed observations (daily) evaluates the model's ability to simulate pesticide dynamics. The non-transformed observations (bi-monthly) assess the model's capacity to predict the exported fluxes and concentrations of pesticides from the AP. Therefore, based on the graphical and statistical comparisons between the model outputs and both the transformed and non-transformed observations while using a single set of parameters (inter-annual), we assume that the PESTIPOND model is robust and able to predict the dynamics and exported fluxes and concentrations of pesticides, except for boscalid, at the AP scale.

To further explore the model outcomes and confirm the SA assumptions, we closely analyzed the mass budget of pesticides in the AP. We quantified the contribution of each process to pesticide fate. The quantification of the mass partition of pesticides revealed that most of the intercepted mass is discharged from the pond for a mean residence time of 19 days and 9 °C temperature. The remaining mass is dissipated or stored in sediments and in the water column. The pesticides that were mostly stored in the sediment layer are boscalid (Koc (L.kg -1 ) = 772, log Kow=3), chlorotoluron (Koc (L.kg -1 ) = 400, log Kow=2.5), and diflufenican (Koc (L.kg -1 ) = 550, log Kow=4.2).

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site

224

According to their Koc and Kow, these three molecules are hydrophobic (log Kow ≥ 3) and lowly mobile (Koc >500) (Lewis et al., 2016). Therefore, they are likely to be adsorbed on sediments, which is in agreement with the mass budget results (Figure III. 6). Alternatively, the pesticides manifesting a higher presence in water are mesotrione (Koc (L.kg -1 ) = 122. log Kow=0.11), smetolachlor (Koc (L.kg -1 ) = 120, log Kow=2.9), bentazon (Koc (L.kg -1 ) = 55. log Kow=2.34), and quinmerac (Koc (L.kg -1 ) = 86. log Kow=2.7), which are hydrophilic and highly mobile (Lewis et al., 2016). The results purport that PESTIPOND simulates a pesticide behavior in agreement with the one expected based on their properties.

The PESTIPOND model was initially built to hierarchize the processes behind pesticide dissipation. This hierarchization is useful for identifying the key elements to be managed in order to optimize the environmental efficiency of ponds. Thus, the mass attributed to each process was quantified and confronted with temperature and HRT. For all pesticides, adsorption is the most significant process in pesticide behavior, except for s-metolachlor (Figure III. 7), which was more distinguished by the transformation in the water column. By relating this result to the hydrophilic and mobile properties of the pesticide, it is expected that s-metolachlor undergoes limited adsorption, which increases its bioavailability for transformation in the water column than the sediment layer. A similar observation was made for s-metolachlor by (Droz et al., 2021) based on laboratory experiments. By contrast, more significant adsorption was detected for the hydrophobic boscalid, chlorotoluron, and diflufenican (Figure III. 7), followed by a transformation in the sediment layer. In fact, the significant adsorption of hydrophobic pesticides was heavily evidenced in the literature (Hand et al., 2001;Tang et al., 2017;Vagi et al., 2022) based on their high affinity to the organic carbon of sediments and hydrophobicity translated by a high Koc and log Kow, respectively. Boscalid, chlorotoluron, and diflufenican are more likely to be adsorbed on sediments and thus are more bioavailable for biotransformation at the water-sediment layer, contrarily to more hydrophilic and mobile pesticides. For instance, bentazon, s-metolachlor, and quinmerac being hydrophilic and highly mobile, are more likely to be transformed in water (Table III

.A. 2).

This result underlines the link between adsorption and pesticide bioavailability for biotransformation as suggested by previous experimental studies (Ahmad et al., 2004;Budd et al., 2011;Chaumet et al., 2021;Lee et al., 2004;Mulligan et al., 2016). The low log Kow (<3) of bentazon, mesotrione, and quinmerac indicate that they are likely to be re-mobilized from the sediment, which was reflected by the mass budget detecting a desorption flux for all monitoring

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 225 periods. For hydrophilic and highly mobile pesticides, desorption covered a non-negligible part of the intercepted mass.

On average, for all pesticides and periods combined in the case of Rampillon, adsorption covers 22% of the intercepted mass, followed by 10% for biotransformation in water and desorption with 6%, leaving 5% for biotransformation and the water-sediment interface. Photolysis covers a negligible part of the total transformation in water (<1%), except for bentazon (≈8%). However, when looking at pesticides separately, hydrophobic and lowly mobile pesticides had higher biotransformation at the water-sediment interface than in the water. The mass budget results

support the significance of adsorption, desorption, and biotransformation in the water for hydrophilic and mobile pesticides. Alternatively, biotransformation at the water-sediment interface is more pronounced, and desorption is limited for hydrophobic and lowly mobile pesticides.

SA results contended that adsorption is the most influencing process of pesticide behavior, fortifying the mass budget results showing that an important fraction of the intercepted mass was adsorbed for most pesticides. The mass budget also exhibited a higher transformation in water and desorption effect on hydrophilic and mobile pesticides, explaining why these types of molecules were more sensitive to processes occurring in the water column. Conversely, hydrophobic and lowly mobile pesticides were distinguished by higher adsorption and transformation in the active sediment layer, which is in line with the SA outcomes displaying a higher sensitivity to processes occurring at the water-sediment interface for this kind of pesticides. Combining the model results and SA outcomes, we assume that adsorption-desorption and biotransformation are major processes behind pesticide fate. Hydrophobic and lowly mobile pesticides are more likely to be biotransformed in the active sediment layer than in water. At last, volatilization and hydrolysis have a negligible contribution to pesticide dissipation.

In addition, the PESTIPOND model enables the assessment of the link between pesticide dissipation and pond properties (i.e., temperature and HRT). These results support the link between temperature, HRT, and pesticide behavior. Higher temperatures enhance the microbial activity behind the biotransformation and are accompanied by important solar radiations, which favors photolysis (Kaur et al., 2022;Law et al., 2014;Motoki et al., 2020;Rani et al., 2015). Therefore, significant pesticide transformation was noticed during periods of high temperature. In addition, higher HRT provides a longer time for pesticides to be adsorbed and transformed. Similarly, for some pesticides higher desorption was noted during low-HRT periods while it was the opposite for other pesticides. This result suggests that no direct link between desorption and HRT was noticed.

The link between temperature, HRT, and pesticide dissipation raises concerns about the impact of pesticides' application period. For instance, except for chlorotoluron, the set of pesticides is applied in spring (Table III. 2), which tends to have significant rainfall events. Therefore, during spring, pesticides are more likely to be intercepted by the AP due to runoff following rainfall events and thus be dissipated by the synergy of the above-described processes. Moreover, spring-applied pesticides are more susceptible to transformation as the temperatures rise. Although this is also the case for summer-applied pesticides, it is unlikely that these chemicals will get to the pond due to limited rainfall events. Alternatively, pesticides applied during winter and autumn, such as chlorotoluron, even though intercepted by the AP, their transformation is less expected due to the weak microbial activity associated with cold temperatures. This assumption could explain the mass budget result illustrating a lower transformation of chlorotoluron, which is applied during autumn and winter in Rampillon. By contrast, spring and summer-applied pesticides (i.e., bentazon, mesotrione, and s-metolachlor) were more favorable to transformation in the water column (Table III Besides low temperatures, winter and autumn-applied pesticides face strong flows that reduce their residence time in the pond to undergo the different dissipation processes. Therefore, to remediate this issue, the surface area of the AP can be enlarged to increase the HRT and, thus the residence time of pesticides. Accordingly, an estimation of the AP efficiencies according to ascending surface areas (Figure III. 8) was performed. The results showed that once the AP covers >1% of the drained catchment area, the dissipation of pesticides reaches 84%, which is almost twice and a half of the actual efficiency of the Rampillon AP. Tournebize et al. (2012) reported that, based on a literature review of AP performances, scientists suggested allocating 1% of the catchment area to the pond. However, farmers rejected this proposal for different reasons (land occupation, cost, operational labor cost, and maintenance). Consequently, the farmers suggested a 0.15% area for the AP to meet their requirements or acceptability, which was expected to be a less efficient remediation solution for pesticide transfer. The PESTIPOND simulations, predicting a significantly higher efficiency of the AP if it covers 1% of the catchment, supported this expectation. In addition, the extrapolation results evidenced that the HRT has a significantly higher impact on the AP efficiency when compared to the temperature rise. This assertion was expected since the HRT drives the efficiency of all processes, namely adsorption, desorption, and transformation, while temperature only influences transformation processes. This assumption is supported by the mass budget results, indicating a higher transformation of pesticides during high-HRT periods (2016-2017) even though the temperature is low. In addition, adsorption is a major dissipation process occurring mainly at the water-sediment interface. Hence, increasing the HRT by increasing the AP area is equivalent to increasing the water-sediment interface where pesticide retention occurs. This explains the higher efficiency in larger ponds and the major role of the waterinterface sediment in pesticide dissipation.

From another viewpoint, adsorption can be a concern over the long term as it accumulates pesticides in the sediment. However, recent in-situ measurements of pesticide concentrations in the Rampillon AP sediments showed that after ten years, only a few amounts of pesticides were accumulated (<7ng.g -1 ). Besides, another in-situ experiment was performed in mesocosms, evidenced that bentazon was the only pesticide sensitive to light, which supports the model result indicating a significant photolysis of the molecule in question.

The parameter set used for this model validation was compared to literature values. Due to the non-availability of adsorption-desorption parameters, kads and kdes were calibrated. The obtained values were in the order of magnitude of similar studies' calibrated parameters (Comoretto et al., 2008;Nakano et al., 2004;Watanabe et al., 2006;Yoshida et al., 2000). In addition, a strong correlation (R 2 =0.9) between kads and Koc was noticed, except for s-metolachlor (Figure III.A. 3).

A first correlation equation was defined for mobile pesticides (Koc < 120 L.kg -1 ) and a second one for lowly mobile pesticides (Koc > 300 L.kg -1 ). For mobile pesticides, the desorption parameter can be deduced from the adsorption kinetic and set to zero for lowly mobile molecules.

Transformation parameters (DT50.w. DT50.s. and DT50.p) were extracted from the PPDB (Lewis et al., 2016), and some were calibrated to improve the model performance (Table III. 6). The calibrated parameters were DT50.w. and DT50.s for 4 pesticides out of 7. Globally, the calibrated half-lives were shorter than the PPDB values. In water, the dissipation was, on average, 75, 7, and 80 days faster for bentazon, s-metolachlor, and quinmerac, respectively, than in the laboratory (i.e.,

where the PPDB values are estimated). In the water-sediment interface, the dissipation was, on average, 600 and 8 days faster for bentazon and chlorotoluron, respectively. This result indicates a faster dissipation under field conditions than in laboratory experiments. The same assumption was made by Bahi et al. (2023), suggesting that pesticides face a single process in laboratory experiments (PPDB). Contrastingly, pesticides undergo a synergy of on-site processes that enhance their dissipation owing to a shorter half-life. Boscalid was the only pesticide having a calibrated DT50.w (500 days), a hundred times longer than the PPDB value (5 days), which may explain the low model performance according to this molecule. However, other sources substantiate the belief that boscalid is stable in water and sediments and is rather adsorbed on sediments (Keith et al., 1992;Mergia et al., 2022), assisting the mass budget results (Figure III.

7).

The strong foundation on which this model is built is represented by the numerous results of pesticides with contrasting properties and application periods. However, the limitations of PESTIPOND should be recognized. First, the assumption of a completely mixed reactor is not always the case in artificial ponds, specifically those representing heterogeneities (i.e., significant vegetation cover, dikes, and dead zones). Therefore, the PESTIPOND model could be complemented if coupled with hydraulic-based models, such as 3D or 2D models. 3D computational fluid dynamics models incorporate relevant pond compartments (plant/water and sediment/plant interfaces). However, these compartments may require excessive computation time [START_REF] Tsavdaris | An experimental evaluation of sustainable drainage systems[END_REF]. Therefore, 2D models are a better alternative, as they are less computationally costly and include explicitly the vegetation patches to estimate the water pathways and their transit times in ponds (Imfeld et al., 2021;Silva et al., 2016). Secondly,

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 229 considering microbial communities' acclimation and dynamics will undoubtedly improve the model's performance. The model prediction could also be expounded by considering the fate of transformation products. Besides, if the model integrates the dynamics of organic carbon content, the adsorption-desorption effect would be better expounded.

The model was validated for bi-monthly observations of pesticides. Yet, the accuracy of PESTIPOND validation can be ameliorated if daily observations of pesticides were available.

Notwithstanding, the model is robust and simulates a pesticide behavior close to observations and the one expected based on molecular properties. In addition, In addition, PESTIPOND is a readily configurable model since the transformation parameters can be inspired from literature (PPDB)

and adsorption-desorption parameters deduced from the pesticide property Koc. Furthermore, the originality of PESTIPOND lies in the ability to predict pesticide partition in AP and quantify the contribution of each physicochemical process to their overall behavior while integrating temperature and HRT effects.

Appendix III Model inputs

Table III.A. 1 List of the PESTIPOND model variables and parameters. The details on how the input data is obtained are available in (Bahi et al. 2023. submitted).

Values

Introduction

In the previous chapters, we elaborated the behavior of dissolved pesticides (DP) and assessed the performance of PESTIPOND to simulate their fate in artificial ponds (APs). In this chapter, we will address the behavior of particulate pesticides (PP) and the efficiency of AP in reducing PP transfer into the environment. We will also display simulation results of PESTIPOND on a typical AP representative of erosive environments to evaluate the model on PP.

Sediment remobilization associated with runoff and erosion from agriculture plots has overt onsite and off-site effects (Deasy et al., 2009). On-site, the detachment and transport of sediments are responsible for large soil losses. The soil loss have practical and economic impacts on land managers and on surface water quality [START_REF] Oeurng | Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model[END_REF]. Off-site sediment depositions into streams and other water-receiving bodies may result in clogging, increased turbidity, and dead zones, which reduce the hydraulic efficiency of water bodies and disturb aquatic ecosystems (Owens et al., 2005). For instance, arable lands cover 28% of the surface area in the EU, with an average solids transport of 2.46 t.ha -1 per year (Panagos et al., 2015) and can exceed 50 t.m -2 in some specific cases. Sediment remobilization is accentuated when farming on sloping land.

Sloping farmlands typically have an impermeable layer below their topsoil (e.g. fragipan and bedrock), favoring surface transport of solids through overland flows (Lechenet et al., 2017;Sattler et al., 2007). Overland flow is a water flow process by which pesticides can be transported in dissolved and suspended forms (i.e., PP), along the surface of sloping land. In addition to PP, remobilized sediments can also carry nutrients, such as phosphorus. Phosphorus is a major cause of surface water eutrophication that deteriorates the quality of healthy aquatic ecosystems (Álvarez et al., 2017;Schoumans et al., 2014), and pesticides are toxic substances impairing the quality of drinkable water sources. Therefore, it is important to understand the transport of water-suspended particles to set up mitigation techniques for solids transport in sloping areas.

In aggregate, little vegetation cover, soil connectivity, and high soil tillage intensity lead to high sediment transports in agricultural catchments (de Castro Lima et al., 2020;Londero et al., 2018).

Rain intensity and seasons also control solids transports. For instance, a Wenng et al. (2021) survey evidenced that autumn and spring were the dominant seasons for discharge and solids exports from agricultural soils due to rainfall and snowmelt periods. Solids export during spring and autumn

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 258 result from high field activities, such as plowing during rainfall. High turbidity during winter is due to nonpermanent snow cover and rain. Besides, the solids transport intensity depends on rainfall erosivity, slope, and soil erodibility, as well as land use and land management (Deuschle et al., 2019;Didoné et al., 2021). Recent studies evidenced that some cultures, such as potatoes, enhance solids transport contrarily to cereals and apples (Commelin et al., 2022;Didoné et al., 2021).

As stated before, sediment remobilization can be associated with PP export from agricultural plots and their input into water bodies, such as rivers and lakes. Many authors have posited that rainfall/storm events result in significant overland transport leading to pesticide concentrations in rivers higher than target water quality limits (Oliver et al., 2012). Typically, hydrophobic, weakly soluble, and lowly mobile pesticides can clutch with water-suspended particulate matter and sediments (Cruzeiro et al., 2015;Tang et al., 2008), which makes them more likely to be transported in a particulate phase (Boithias et al., 2011;Taghavi et al., 2011;Taghavi et al., 2010).

For instance, Wauchope (1978) found only those pesticides with solubility <1 mg.L -1 to be transported primarily by sediments. Moreover, a survey conducted on 9 pesticides with a wide range of solubility values (1.4-3000 mg.L-1) at a catchment scale, showed that the highest average proportion (%) transported off-site in the dissolved fraction was associated with pesticides with higher solubility (Oliver et al., 2012). Therefore, lowly soluble pesticides are more likely to be transferred in the particulate form to surface waters, especially when their application calendar goes in line with important flooding and erosive events. Overall, PP concentration in runoff is significantly affected by the frequency of precipitations in the catchment and timing of the application period of pesticides (Lewan et al., 2009;Vryzas, 2018;Vryzas et al., 2011).

Numerous management strategies were suggested in the literature to reduce PP transfer from agricultural plots to water resources. For instance, flocculants (e.g., PAM), buffer strips, and sedimentation ponds aim primarily at chemicals moving associated with colloidal, such as PP (Reichenberger et al., 2007b;Schulz, 2004). In this study, we focus on the fate of PP in artificial ponds (AP). It is important to recall that AP are edge-of-field constructed wetlands designed to intercept a part of the agricultural runoff. The agricultural runoff in sloping areas is loaded with total suspended solids (TSS), including PP (adsorbed on TSS) (Luo et al., 2009a;Matamoros et al., 2006;Rose et al., 2008). Therefore, the efficiency of AP lies in providing a longer time for PP

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 259 to settle, adsorb, or be degraded. Several researchers stated that the main process of PP removal in AP is the settling (i.e., sedimentation) of TSS on which pesticides are adsorbed [START_REF] Koskiaho | Flow velocity retardation and sediment retention in two constructed wetland-ponds[END_REF]Maillard et al., 2011;O'Geen et al., 2010). Settling results in sediment accumulation, whose rates are highly variable when comparing different wetlands. According to O'Geen et al. (2010), Johnston (1991) reviewed settling in several freshwater wetlands and found that mass sediment accumulation rates ranged from 39 to 5200 g.m -2 .yr -1 , and in some cases, no settling was detected.

Out of the 39 evaluated wetlands, the mean accumulation rates of sediments reached 1680 g.m - 2 .yr -1 due to the settling of TSS. One of the major controlling factors of settling is particle size.

Previous research stated that settling is most efficient in AP intercepting agricultural water that is heavily loaded with coarser (>250 µm) particles from the fields (Fiener et al., 2003;Zhang et al., 2011). Besides, Chaumet et al. (2021) showed that clay content enhances the adsorption of DP, which increases the concentration of PP. However, PP carried by fine particles (i.e., clay) are more likely to remain suspended in the water rather than settle. Therefore, settling in AP is less efficient on TSS with a clayey and fine silt texture.

While there are many studies that account for the presence of pesticides in the dissolved phase in AP, most of those related to the presence of PP phase are limited (Climent et al., 2019;Fairbairn et al., 2015;Vryzas, 2018). Besides, the few existing surveys of PP are conducted on particulate transport upstream AP (i.e., agricultural field scale), in rivers (Commelin et al., 2022) or at the watershed scale [START_REF] El Azzi | Trace Element and Pesticide Dynamics During a Flood Event in the Save Agricultural Watershed: Soil-River Transfer Pathways and Controlling Factors[END_REF] rather than in AP. In practice, most research papers analyze pond water in its entirety or the dissolved fraction and do not examine the contribution of PP (Didoné et al., 2021;Harman-Fetcho et al., 1999). Even though this is a common practice, it may underestimate the concentrations of pesticides that have strong affinities for suspended particles.

The literature displays a poor number of pesticide fate model that considers the particulate phase of pesticides and their associated dissipation processes in AP. Most of the existing models cover the behavior of trace elements (i.e., As, Cd, Cu, Pb, Zn, Se) (Sheppard et al., 2009), organic carbon (Sharifi et al., 2013), and nutrients (i.e., phosphorus and particulate organic nitrogen) (Hantush et al., 2013). To our knowledge, the only pesticide models accounting for the particulate phase are AGRO-2014 (Gobas et al., 2018) and the pesticide module of SWAT (Chapra, 2008) Accordingly, in this chapter, we test a version of the PESTIPOND model on the Bassioué AP (Auradé, France) that accounts for the particulate phase in APs while considering the effects of temperature, particle size, and kinetic sorption. We also suggest a solids transport model simulating sediment concentration within the AP due to settling and resuspension processes.

Material and methods

Study site

The Bassioué AP is located in the Montoussé catchment (320 ha) at Auradé (Gers, France) in the south-west of France (Figure IV. 1 (Chaumet et al., 2021)). The AP is located in the upper part of the 320-ha catchment (104038.100 E, 4333004.700 N) and the drainage sub-catchment covers an area of 28 ha (Figure IV. 1 (C)). This AP was constructed in 1970 and dragged in 2015 for the last time (Wu et al., 2021). The catchment mainly lies on silty-clayey soils and undergoes

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 261 significant erosion due to steep slopes, tillage, and storm events in spring. Wheat, sunflower, and colza crops in a two-year rotation cover 90% of the catchment surface area, with pesticides mostly spread during spring (April-May) (Taghavi et al., 2010). CNES/Airbus, Landsat, Maxar Technologies, Mapping data ©2020, France) with the sampling points used for pesticide monitoring (Chaumet et al., 2021).. On (D), the yellow and blue points refer respectively to the downstream and upstream discharge measurement locations and water sampling points.

As described in (Chaumet et al., 2022;Chaumet et al., 2021) Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 262

Monitoring data

For brevity purposes, only monitoring data of variables used in the PESTIPOND model will be detailed in this part (i.e., water flow rate, TSS, and pesticide fluxes). For further information about the monitoring conducted at the Auradé catchment, the reader can refer to (Chaumet et al., 2022;Chaumet et al., 2021;Ponnou-Delaffon et al., 2020;Wu et al., 2021). In this subsection, we will present monitoring process and its main results to discuss the input variables of PESTIPOND before introducing the conceptual model.

A full-year hydro chemical survey was conducted from January 10, 2020, to January 9, 2021, to perform the mass balance for water and pesticides at yearly and seasonal scales. The Bassioué pond was instrumented upstream and downstream with hydrological V-notch weirs (1 m long, 1.20 m wide, and with a V of 50 cm with a 20° angle) custom-made in aluminum with a thickness of 6 mm to fit as closely as possible to the typology of the watercourse and the climatic conditions of the region (Figure IV. 1 (E)). Multi-parameter probes (AquaTROLL, Reignac Sur Indre) installed at these locations allowed high-frequency sampling at a 15-minute time step of dissolved oxygen, pH, temperature, nitrates, conductivity, and water level. Automatic samplers (Isco, Mellac, France)

with 24 glass bottles of 300 mL were installed at the same places on the banks. Their connection to overflow sensors (Ijinius, Mellac, France) triggered the sampling every two hours when a given water level was reached. All these devices were autonomous, thanks to solar panels. On weekly grab, samples collected in 1-L polyethylene bottles, dissolved organic carbon (DOC), major anions and cations, and suspended matter (TSS) were measured. For pesticide quantification, samples were collected in 1-L glass bottles and selected for analyses according to the hydrological survey pattern and the pesticide-spreading period.

Hydro-chemical data

The water depth measured every 15 min by the probe was converted to discharge using a rating curve. The rating curve was established based on weekly water depth measurements converted to discharge. Then, a mean value for the discharge was calculated daily (Chaumet et al., 2022). The hydro-chemical monitoring data are summarized in Table IV. 2. The inlet (upstream) and outlet (downstream) of the AP manifested a similar hydrological behavior as reflected by the mean water flow rate (Table IV. 2) and the total drained water, i.e., 33 483 m 3 upstream and 33 188 m 3 downstream the AP.

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 263 Table IV. 2 Summary of the hydro-chemical parameters monitored at the inlet and outlet of the Bassioué AP. DOC (mg.L -1 ) is the concentration of the dissolved organic carbon in the AP water column, and T (° C) is the water temperature (Chaumet et al., 2022). (Chaumet et al., 2022) shows that the upstream and downstream of the AP have similar hydrological behaviors. The water flow rate increases sharply with rainfall events. During dry events, a slight gap between the inlet and outlet flow rates. The total cumulative volume during the monitoring period is 24 661m 3 .km -2 and 95 864 m 3 .km -2 in the inlet and outlet of the AP, respectively.

Q (L.
Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 264

TSS fluxes

The TSS mass was calculated from the amount of volume filtrated and after weighting and drying the filter for 48 h in an oven at 40°C. Figure IV. 2 summarizes the TSS monitoring data in the Bassioué AP (Chaumet et al., 2022).

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 265 As shown by Chaumet et al. (2022), the cumulative exported mass of TSS is higher in the outlet than in the inlet most of the year, contrary to the discharge (Figure IV. 2). Until the end of 2020, the input of TSS is lower than its outlet flux. This observation has suggested that the AP is characterized by a significant resuspension of sediments most of the year, with a shorter period of sediment accumulation due to the settling of TSS from 05/12/2020 to 04/01/2021. Note that Chaumet et al. (2022) mentioned that resuspension is also likely to happen during the flood event of December, which is expected due to the high flow intensity than can re-mobilize sediments.

Pesticide fluxes

For pesticide analysis, water samples were filtrated on a glass-fiber filter (GF/F, pore size: 0.2 μm, Whatman) using a full glass filtration system (Buchner). The filtrate (19 mL aliquot) and the filter were collected to quantify the pesticides in the dissolved and the particulate phases, respectively (Chaumet et al., 2022). After drying at room temperature for one week, the homogenized bulk soil samples were gently crushed and sieved to collect only the fine fraction (<63 μm), which is the Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 266 most representative and reactive fraction for pesticide quantification. Daily pesticide fluxes i were calculated from the mean value between two sampling i-1 and i+1 (triggered by flooding events).

Only the previous sampling is considered (i-1) when heavy flooding events occur to avoid overestimating pesticide flux (Chaumet et al., 2022).

Six different pesticides were monitored in the AP inlet and outlet. Table IV. 3 summarizes the dissolved and particulate concentrations of the pesticides.

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 267 Table IV. 3 The dissolved concentrations (pesticide mass per water volume) and particulate concentrations (pesticide mass per mass of TSS) in the AP inlet and outlet: minimum, maximum, mean, and standard deviation (Chaumet et al., 2022).The pesticide physicochemical properties were extracted from the literature (Kim et al., 2021;Lewis et al., 2016). Koc: The organic carbonwater partition coefficient. Kow: The octanol-water partition coefficient and S is the pesticide solubility in the water For all pesticides, the maximum dissolved concentration was higher in the inlet than in the outlet, except for epoxiconazole and boscalid where the concentration increased by 26% and 38% at the outlet, respectively (Chaumet et al., 2021). This observation can be explained by the desorption of epoxiconazole and boscalid. Pesticide particulate concentrations were 2 to 5 times (and even up to 10 times for boscalid) higher in the inlet than in the AP outlet, reflecting a potential settling and/or desorption of PP in the AP, which diminish the particulate concentration and increase the dissolved fraction in the water. Beside processes, the hydraulic conditions within the AP, such as flow

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 268 patterns and water velocity during flood events could also explain pesticide concentration discrepancy between the inlet and outlet.

Except for metolachlor, all pesticides have a higher particulate concentration than dissolved one in the inlet. This difference supports our choice to integrate the particulate phase in the PESTIPOND model.

Note that pesticide concentrations were measured instantaneously or continuously every 15 min by the probe. Afterward, daily concentrations were calculated by Chaumet et al. (2022) to match our model time step. Further details on the monitoring and estimation of discharge, TSS, and pesticide fluxes in the inlet and outlet of the AP, can be found in (Chaumet et al., 2021).

Conceptual model

As introduced before (see chapter 2), in a typical AP, the key process behind pesticides fate are transport (i.e., settling and resuspension), transfer (i.e., adsorption, desorption, and volatilization), and transformation processes (i.e., photolysis, hydrolysis, and biotransformation).

The same dissipation processes of the validated version of PESTIPOND were considered in this version. The added processes are settling and resuspension to account for the fate of solid particles in AP located in highly erosive catchments.

This PESTIPOND version lies in the same assumption of the original model recalled hereafter.

Hypothesis (vii.) and (viii.) were added.

i. Concentrations of pesticides are spatially uniform in water and sediments because the AP compartments are considered completely mixed reactors. Hence, once pesticides enter a specific compartment, they are instantly mixed with the entire content and distributed uniformly. In practice, after several hours, the pond water becomes perfectly mixed (Alvord et al., 1996;[START_REF] Pugliese | Internal hydraulics and wind effect in a surface flow constructed wetland receiving agricultural drainage water[END_REF]. Besides, the pond heterogeneities responsible for the non-uniformity of concentrations are usually considered in hydraulics-based models (Henine et al., 2022;Imfeld et al., 2021) rather than chemical-based models, e.g., for pesticides (Bahi et al., submitted,(Watanabe et al., 2000b) and nitrates (Hantush et al., 2013;Krone-Davis et al., 2013).

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 269 ii.

Vegetation is not considered by the PESTIPOND model. Prior investigations have evidenced that the fraction of pesticides collated in plants were often insignificant in AP (< 10%) (Butkovskyi et al., 2021;Pérez et al., 2022;Singh et al., 2021;Wang et al., 2017).

Not considering the vegetation implies neglecting its effect on the hydraulic flow (brakes, dead zones). Nonetheless, this choice of hypothesis was motivated by the environmental focus of the model rather than the hydraulic one, hence also the hypothesis (i).

iii.

No advection or diffusion processes are considered by the model for the following reasons;

(a) AP are often constructed on a compacted substrate where the infiltration (leaching) is not significant, and the water velocity at the water-sediment interface is too low resulting in a weak hydraulic gradient that limits advection. (b) Several studies showed that the molecular diffusion of diverse pesticides is too low in the water (~10 -9 m 2 .s -1 at 25°C) (Chevillard et al., 2014;Fernández-Pascual et al., 2020a;Sarraute et al., 2019).

iv. PESTIPOND simulates the fate of pesticides in the first cm of sediments designated as the active sediment layer governing pesticide transformation and transport under flooded conditions. Previous on-site measurements proffered that pesticide residues are not significant beyond 1cm of depth (Inao et al., 1999;Mahugija et al., 2018;Nyantakyi et al., 2022;Takagi et al., 2012). The pore water of the active sediment layer is included in the water column compartment.

v.

Transformation and volatilization processes are corrected according to the temperature change during the simulation period.

vi.

Since the fate of metabolites is still poorly documented, PESTIPOND does not consider transformation products.

vii.

The transversal section of the water column is assumed rectangular.

viii.

Only the fine fraction of solids is subject to resuspension since coarse particles are hardly remobilized from the sediment layer. of the AP particles showing that the fine particles (i.e., clays and fine silts) cover 85% of the total TSS, leaving only 15% of coarse particles (i.e., coarse silts, sands, and gravel) suspended in water in the case of Bassioué AP. Therefore, in addition to the dissolved phase, two additional subcompartments (for fine and coarse particles) of the water column were considered for this PESTIPOND version. The depth of the active sediment layer is 20cm since monitoring evidenced that pesticides were mostly detected in the first 20cm of the sediment (Chaumet et al., 2021).

Similarly, the sediment layer is composed of 85% of fine particles and 15% of coarse particles in our study case.

Mathematical model

In this subsection, we will detail the equations and the mathematical formulations of the PESTIPOND model integrating the particulate phase. with the R coding language (R Core Team, 2020), and the ODE systems were solved using the "deSolve" Rpackage (Soetaert et al., 2010).

Hydrological model

The hydrological model of PESTIPOND is expressed with the following water balance equation: in the AP, as mentioned before, the AP substrate is impermeable so no infiltration will be considered (QI = 0). P [L.T -1 ] is the precipitation rate. E [L.T -1 ] is the evapotranspiration rate. For the sole purpose of simplification, the AP is a parallelepiped tank so that the water column depth hw [L] is expressed as Vw/A.

Eq. IV.1 was solved using the explicit Euler method (Griffiths et al., 2010). The hydrological model provides the daily water volume (Vw) and depth (hw) in the AP, which are necessary to estimate the daily concentrations of pesticides in the water column (Eq. IV.10) and to establish the pesticide mass balance equations (Eq. IV.9, Eq. IV.11, and Eq. IV.13).

Solids transport model

For this version of the PESTIPOND model, a solids transport model was developed to account for the TSS and PP dynamics due to settling and resuspension. (Zug et al., 1998) was originally used for sewage transport, which is typically loaded with solid particles suspended in the water.

The Velikanov energy equation calculates the capacity to transport TSS in a flow with known hydraulic characteristics (i.e., water flow rates and depth). The concentration of the transportable TSS is not unique and is located in a range limited by two curves, respectively corresponding, to the maximum and minimum TSS concentration able to be transported (Zug et al., 1998). This range is reflected by the following two equations:

(IV.2) 𝐶𝑇 𝑚𝑖𝑛 (𝑡) = 𝜂 1 𝑠 * 𝜌 𝑒 𝑠 -1 * 𝑈 𝑚 (𝑡) 𝜔 𝑠 * 𝐼 𝑝 (IV.3) 𝐶𝑇 𝑚𝑎𝑥 (𝑡) = 𝜂 2 𝑠 * 𝜌 𝑒 𝑠 -1 * 𝑈 𝑚 (𝑡) 𝜔 𝑠 * 𝐼 𝑝
Where Similarly, for each day t, Msed (t) is calculated according to Velikanov's theorem. Afterward, we deduced the daily outlet TSS concentration TSSout,sim(t) based on the assumption (i) and compared it to the observed concentrations TSSout,obs(t) to assess the performance of the solids transport model (section 3).

Note that the simulated mass of TSS, i.e., MTSS(t) by the solids transport model (Eq. IV.5) will serve to calculate the pesticide concentration in TSS from the simulated mass of PP in the water (Eq. IV.11).

Pesticide's model

Process formulations

In this section, we will present the mathematical expressions of the key processes behind pesticide dissipation, the parameterization, and the mass balance equations of the pesticide fate model.

The same expressions of transfer (i.e., adsorption, desorption, and volatilization) and transformation (i.e., biotransformation, photolysis, and hydrolysis) processes of Chapter 2 (section 2.3) were used in this PESTIPOND's version. Since settling and resuspension were considered in this version, they will be the only processes presented in this part. A detailed description of settling and resuspension can be found in (Bahi et al., 2023). In the few models where settling and resuspension were considered, they were expressed by constant rates (Gobas et al., 2018;Hantush et al., 2013;Wang et al., 2019). Similarly, in this PESTIPOND's version, settling and resuspension

Ms (t) [M]

is the daily mass of PP in the active sediment layer.

Mass balance equations

The mass balance equations coupled to the hydrological and the solids transport models enable quantifying the contribution of the previously described processes on pesticide fate in AP. -3 ] is deduced as follows:

(IV.12)

𝐶 𝑝 (𝑡) = 𝑀 𝑝 (𝑡) 𝑉 𝑤 (𝑡)
Where Mp [M] is the mass of PP simulated by Eq. IV.11 and Vw [L 3 ] is the daily water volume calculated by the hydrological model (Eq. IV.1).

Similarly, the concentration of PP in the dry mass of TSS Cp[M.M -1 ] can be deduced as follows:

(IV.13) 𝐶 𝑝 (𝑡) = 𝑀 𝑝 (𝑡) 𝑀𝑇𝑆𝑆(𝑡)

Where Mp [M] is the mass of PP simulated by Eq. IV.11 and MTSS [M] is the daily TSS mass calculated by the solids transport model (Eq. IV.5).

Chapter Where Ms [M] is the mass of pesticides in the sediments simulated by Eq. IV.14 and Msed [M] is the daily mass of sediments calculated by the solids transport model (Eq. IV.5).

The set of parameters and variables involved in the above-presented equations are available in Table IV.A. 1.

Model testing

In order to check the numerical error and visualize the behavior of the PESTIPOND model while integrating the particulate phase of pesticides, we simulate a test-case scenario. Pendimethalin was selected as a generic pesticide for this purpose. Pendimethalin is an herbicide used to control most annual grasses and common weeds in cereals, fruit, and vegetables. The pendimethalin choice was motivated by its application in the Auradé catchment and its physicochemical properties. In fact, the high hydrophobicity and low mobility of pendimethalin favor its adsorption on suspended particles (Lewis et al., 2016;PubChem, 2021). Therefore, pendimethalin is likely to be transported in a particulate phase. Pendimethalin is often applied during spring (March and April) and sometimes during autumn (September). This subsection will present the input data required to test

Input data for the hydrological model

The daily inlet and outlet flow rates (Qin, Qout) were extracted from on-site monitoring data for the hydrological model [START_REF] Lefe | Laboratory of Functional Ecology and Environment[END_REF]. Rainfall and ETP were taken from the SAFRAN database (Vidal et al., 2010). Since the model do not consider spillage, minimum and maximum water levels of 30 cm and 50 cm were imposed to the hydrological model to avoid numerical divergence. The water depth limits were fixed based on on-site observations (Chaumet et al., 2021). The numerical error of the hydrological model based on the water balance is <3 %. The daily water depth hw was deducted from the water volume Vw calculated by the hydrological model (Eq. IV.1).

Input data for the pesticide model

The forcing functions of the particle version of the PESTIPOND model are the same used for the dissolved phase (i.e., Qin(t), Qout(t), hw(t), T(t), and Cin,diss(t)) (Table IV R-package (R Core Team, 2020) to match the numerical solving time step (0.001 days).

As above-mentioned, pendimethalin was selected for this test case. Therefore, daily inlet and outlet observations of pendimethalin concentrations in dissolved and particulate phases were provided by the LEFE laboratory (LEFE, 2022). weighted by the corresponding specific discharge Qi and Qi+1 (L.s -1 .km -2 ) (Chaumet et al., 2022).

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 281 Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 282

Results and discussion

This section will first present and discuss the simulation results of the hydrological and solids transport models, then, we will assess the pesticide model outputs.

Outputs of the hydrological and solids transport models

The outputs of the hydrological model are displayed in Figure IV. 6. Since the water depth was limited by a minimum (30 cm) and a maximum (50 cm) value to avoid model divergence. Figure IV. 6 shows that the water volume is comprised between 291.3m 3 and 493.23m 3 . The 971-m 2 AP is filled after each of the four rainfall events (i.e., 03/03/2020, 14/05/2020, 12/12/2020, and 28/12/2020). The nominal HRT of the Bassioué AP varies from half a day to a month. It is noteworthy to recall the upstream basin of the AP is larger than the downstream one as described in (Chaumet et al., 2022). Therefore, heavy flood events are sometimes associated with lateral water inputs that are not considered in this work, as they are not measured. Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 285

The mean relative error of the solids transport model is 16%. The KGE and NSE are 0.71 and 0.60, respectively, reflecting a good model performance to simulate TSS dynamics within the AP due to settling and resuspension processes (Moriasi et al., 2015).

Globally, these results indicate a better simulation during periods of intensive resuspension than settling, which could be due to the difference in solids transport properties between sewage systems (for which Velikanov's model was originally developed) and APs. Moreover, the discrepancy between the simulation results and observations could be explained by the hydraulic conditions not integrated into the model, and the lateral water fluxes that are not considered in the hydrological model.

Outputs of the pesticide model

Model behavior in the conservative case

At first, we ran the PESTIPOND model to visualize the model behavior in a conservative case.

Therefore, the adsorption, desorption, biotransformation, photolysis, hydrolysis, settling, and and June, the model simulated a discharge of particulate pendimethalin, while none was observed.

Then another overestimation of the outlet concentration was noticed from July to September. The model better simulated particulate pendimethalin concentration during the rest of the period.

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 288 Therefore, the results of the conservative case, demonstrate that PESTIPOND conserves the mass of the dissolved phase and not of the particulate phase. Note that similar behavior was noted for the rest of the pesticides monitored at the Bassioué AP.

Since, these results correspond to a conservative case where no dissipation processes are considered, we assume that the weak model performance according to the particulate phase originates from the integration of the hydraulic conditions in the model. In fact, the hydraulic conditions are abstracted to a nominal HRT based on the perfectly mixed reaction assumption above-described. Another explanation is the unconsidered lateral inputs of water and the associated pesticide fluxes.

Model behavior with dissipation processes

In order to simulate pendimethalin masses that fit the observations, we used the parameter values listed in Table IV results we assume that the process-based models integrating the particulate phase manifest a better performance for DP than PP. We suggest that the weak prediction of the particle fate in APs is due to the simplified formulation of solids transport processes, i.e., settling and resuspension.

Velikanov's model implied a good performance while simulating TSS dynamics; thus, its application on pesticides could enhance the model prediction of PP behavior in AP. Otherwise, settling and resuspension could be integrated with more elaborated equations. For instance, these equations could include the wind effect, the velocity gradient according to the main channel, mixing and dead zones, and frictional tensors (Cózar et al., 2005;Kraft et al., 2013;Krause et al., 2017;Martinez et al., 2003a The model testing paves the way to identify the limits of PESTIPOND and similar process-based models according to the prediction of particulate pesticides. Therefore, we draw the following suggestions to improve the model's performance.

(i) The error of the hydrological model could be explained by the imposed minimum and maximum of the water depth or the no-integration of other water inputs, such as lateral flows. In addition, daily observations of water depth or volume are not enough to illustrate the hydrological response of the AP. Therefore, hourly observations are expected to better represent the hydrological input variables in PESTIPOND.

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 294 (ii) In practice, the settling rate is governed by particle size and density, water velocity, turbulence, salinity, wind speed, temperature, and water column depth. Hence, using a settling rate only dependent on the water depth could explain the weak performance of PESTIPOND according to particulate pesticides. Accordingly, further investigations on settling and resuspension formulations are recommended for better model predictions.

(iii) The hypothesis of a perfectly mixed reactor could also explain the weak model performance on particulate pesticide. In fact, solids transport within the pond can be highly affected by the non-uniformity of water patterns. Consequently, coupling the PESTIPOND model with a hydraulic-based model [START_REF] Drouin | Pollutant Dissipation at the Sediment-Water Interface: A Robust Discrete Continuum Numerical Model and Recirculating Laboratory Experiments[END_REF]Henine et al., 2022;Imfeld et al., 2021) is believed to improve the model performance according to particles.

Overall, modeling the fate of solid particles is undoubtedly one of the most challenging issues in hydraulics and environmental modeling. This explains the poor coverage of PP in the literature.

The prediction of PP fate is more complicated than DP because complex hydrological and hydraulic interactions govern the off-site transport of solid pesticides, and using oversimplified model concepts as surrogates may not be sufficient.

Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site

295

Appendix IV This chapter recalls the central research question that motivated this Ph.D. work and restates the thesis's primary objective. Specific research questions and results of the present work will be discussed afterward in respective sections. The key perspectives of this research exercise will be outlined in the closing.

Context and objective

The demographic growth the world is witnessing will assuredly require more food supplies and, therefore, more agriculture. It is foreseen that global warming will affect agricultural practices by elongating the growing seasons and entailing more pests to be controlled. Confronted with these scenarios, pesticide use is likely to increase. Pesticides are toxic chemicals that quickly accumulate in the environment, with some highly stable with a long half-life span. Non-point source pollution via runoff and drainage of pesticide-loaded agricultural waters account for the primary share of surface water pollution. Such pollution can also reach groundwater through infiltration or exchanges between surface and ground waters [START_REF] Elsaesser | Assessing the influence of vegetation on reduction of pesticide concentration in experimental surface flow constructed wetlands: Application of the toxic units approach[END_REF][START_REF] Stillway | Effectiveness of Constructed Water Quality Treatment Systems for Mitigating Pesticide Runoff and Aquatic Organism Toxicity[END_REF]. The excessive use, toxicity, and environmental persistence of pesticides can seriously impinge on the quality of drinking water supplies, aquatic biodiversity, and public health (Liao et al., 2020).

The Water Framework Directive [START_REF] Ec | Water framework directive[END_REF] has outlined a clear demand to reduce pesticide loads into water bodies. The United Nations (UN) Environment Program established a UN Decade of Ecosystem Restoration (2021-2030) to support nature-based solutions for sustainable management of land, water, and living resources [START_REF] Waltham | UN decade on ecosystem restoration 2021-2030-what chance for success in restoring coastal ecosystems[END_REF]Willkommen et al., 2022).

The nature-based solutions can be natural (e.g., swamps, prairie potholes, and marshes) or constructed (e.g., artificial ponds, rice fields, lakes, and lagoons) [START_REF] Nottingham | A literature review of wetland treatment systems used to treat runoff mixtures containing antibiotics and pesticides from urban and agricultural landscapes[END_REF]. In this work, we focus on edge-of-field artificial ponds (APs), which have drawn scientific interest in the last decades owing to their manifold advantages, i.e., cost-effectiveness and low energy consumption.

APs are shallow manufactured wetlands usually used for water storage and flood prevention but also proved efficient in reducing pesticide transfer into the environment [START_REF] Moore | Mitigation of metolachlorassociated agricultural runoff using constructed wetlands in Mississippi, USA. Agriculture[END_REF]Tournebize et al., 2012;Vallée, 2015). In fact, APs act as buffer zones as they are constructed downstream of drained agricultural plots and upstream of water resources. Therefore, APs intercept drainage and runoff of pesticide-loaded water and dissipate pesticides due to a series of physico-chemical processes. Consequently, the water discharged to the environment when the AP is filled is less pesticide-contaminated.

The environmental asset of APs in preserving water quality downstream of agricultural areas sparked our concern to investigate pesticide fate in APs thoroughly. Specifically, this work is part of the National Research Agency project PESTIPOND (PESTIPOND), which addresses the role of ponds in controlling pesticide transfer at the river and catchment scales. Our task is to assess the fate of pesticides at the AP scale to incorporate the cumulative effect of ponds into a landscape tool to predict pesticide transfer at the watershed scale. Accordingly, our goal is subdivided into the following main objectives:

1-The understanding of pesticide behavior by investigating the physico-chemical processes behind the fate of different pesticides in APs.

2-The development of a simple and robust process-based to predict the fate of dissolved pesticides at the pond scale (PESTIPOND).

3-The assessment of PESTIPOND's ability to integrate the particulate phase of pesticides.

2 Identification of processes behind pesticide fate in artificial ponds:

A review study

The water treatment role of APs has spurred research into the functioning of APs and the physicochemical processes behind pesticide dissipation. However, the documented studies focus on only one pesticide or a limited number of pesticides with similar characteristics. Further, these studies only assess one process at a time without addressing the interaction between the different dissipation mechanisms (Liu et al., 2019;Sahin et al., 2020). Two noteworthy reviews have addressed the efficiency of APs in reducing pesticide transfer (Stehle et al., 2011;Vymazal et al., 2015). Nevertheless, they focused mainly on the efficiency of APs without detailing the processes contributing to pesticide dissipation, while the relationship between APs' efficiency and pesticide properties remains elusive.

Given this research question, an extensive literature review was conducted on the processes behind the fate of 55 evaluated pesticides in APs. The core aims of the review are:

1-To identify and describe the key physico-chemical processes behind pesticide dissipation in APs.

2-To highlight the commonly employed methods to assess, quantify, and integrate dissipative processes into predictive modeling tools.

3-To discuss the major controlling factors of pesticide dissipation in AP.

4-To introduce assumptions of process hierarchization in terms of their contribution to pesticide dissipation.

The first outcome of this review is that APs comprise a water column, a bottom sediment layer, and living organisms. Pesticides can be either dissolved in the water (DP) or in a particulate form, i.e., combined with total suspended solids (TSS). APs are more likely to intercept particulate pesticides (PPs) in sloping and high erosive areas marked by considerable solid transport.

Secondly, the review evidenced that pesticide dissipation in APs results from the synergy between physico-chemical processes responsible for pesticide transport in the water, transfer between the compartments, and transformation.

The transport of pesticides along the hydraulic flow relates more to PPs. According to the hydraulic conditions (water velocity and the hydraulic residence time (HRT)), PPs can be deposited on the sediment layer during low flow (settling) and be re-suspended in the water during high-flow periods (resuspension). The rest of the transfer and transformation processes involve both pesticide phases (DP and PP).

Once pesticides reach the AP, they are distributed among water (including TSS), sediments, and vegetation and are then transferred among the compartments. DP in the water column can be bound to the solid compartments by adsorption and released back to water by desorption. DP can also be transferred from water to the atmosphere through volatilization. DP can become PP when adsorbed to TSS.

Lastly, DP and PP can be transformed into less toxic products or, in the ideal case, wholly degraded into simple mineral products. The main surveyed transformation processes are biotransformation under the action of microorganisms, photolysis due to solar radiation, and hydrolysis by the chemical reaction with water. The products of transformation processes are called transformation products (TPs), and in the case of biotransformation, metabolites. The fate of TPs in water is still poorly documented in the literature; therefore, TPs were not considered in this study. In concrete, the challenge in metabolite analysis is to trace back to the parent pesticide molecules.

Each referred process contributes to pesticide dissipation from the AP inlet to the outlet. Settling and adsorption accumulate PP and DP in the AP, respectively, which may subsequently be biotransformed at the water-sediment interface. Photolysis and hydrolysis degrade the pesticide molecules into less toxic elements. Biotransformation of pesticides yields metabolites whose fate is still uncertain. Nonetheless, Kang et al. (2020) and Zhang et al. (2021b) established that many pesticides produce less toxic metabolites upon biotransformation. Resuspension and desorption can re-mobilize PP and DP, respectively, from the sediment layer back to the water, which increases pesticide availability to processes occurring in the water (biotransformation, photolysis, hydrolysis, and volatilization). In fewer cases, pesticides re-mobilized by resuspension and desorption can be discharged from the pond.

Based on studies that quantified the contribution of processes to the total pesticide dissipation or compared the decay rate of each process, we established primary assumptions about pesticide hierarchization. Accordingly, adsorption and biotransformation were identified as the major processes behind pesticide dissipation and settling for PP. The fewer photosensitive pesticides were significantly dissipated by photolysis. Adversely, hydrolysis and volatilization were less involved in the dissipative role of APs. These hierarchization assumptions were solely based on the result of laboratory and in-situ experiments we reviewed.

Afterward, having identified the fundamental processes behind pesticide fate, we figured that incorporating those into a numerical model would likely improve the prediction of pesticide dissipation in APs. Therefore, it is necessary to determine how these processes are formulated mathematically. Meanwhile, the papers we reviewed were more oriented toward experimental and theoretical process research rather than modeling. In fact, only 57 papers among the 257 reviewed works reported the mathematical formulations of processes. In aggregate, most dissipation processes were commonly expressed with a first-order kinetic equation based on a single parameter (i.e., the process kinetic coefficient). Certain processes in fewer cases were expressed with more complicated equations involving a higher number of parameters, especially settling and resuspension. However, these complex formulations were mainly employed in hydraulic-based models that describe pesticide transport according to different flow pathways rather than dissipation. Besides, settling and resuspension were rather modeled in wetlands with different hydraulic conditions than APs [START_REF] Carmen | Vertical intertidal variation of organic matter stocks and patterns of sediment deposition in a mesotidal coastal wetland[END_REF][START_REF] Li | Wave-driven sediment resuspension and salt marsh frontal erosion alter the export of sediments from macro-tidal estuaries[END_REF][START_REF] Liu | Modeling hurricane-induced wetlandbay and bay-shelf sediment fluxes[END_REF].

The literature review also enabled us to pinpoint the main controlling factors driving the performance of each process. The controlling factors are split between pesticide and pond properties. The main pesticide properties are hydrophobicity (log Kow), mobility (Koc (L.kg -1 )), and solubility (S (mg. L-1 )). The APs properties that control most of the dissipation of pesticides are the temperature and the HRT that depends on the water discharge rate and the pond area. In fact, increasing the HRT increases the contact time between pesticides and pond substrates, giving pesticides more time to be adsorbed-desorbed, settled, or transformed and thus dissipated.

Meanwhile, temperature enhances the microbial activity behind biotransformation and stimulates the photodegradation and hydrolysis of pesticides.

In light of this knowledge, we considered the viability of developing a numerical model to predict pesticide fate in APs.

Modeling the fate of dissolved pesticides in artificial ponds

Modeling is an efficient and comprehensive tool for assessing and setting up facilities to treat contaminated environments (Warren et al., 2002). Numerical models are particularly useful for better insight into pollutant removal processes in APs and for improving existing design criteria of APs [START_REF] Langergraber | Simulation of the treatment performance of outdoor subsurface flow constructed wetlands in temperate climates[END_REF]Lyu et al., 2018).

Therefore, this section discusses the core results of two modeling approaches applied to the Rampillon AP: a black box model and a process-based model PESTIPOND. The Rampillon AP is illustrative of constructed ponds in drained areas where pesticides are primarily transported in a dissolved form. Afterward, we will discuss the integration of the particulate phase in the PESTIPOND model via a study case at the Auradé AP.

Modeling pesticide fate with a black box model

In literature, the performance of AP was initially modeled with first-order decay models, also called "black-box models" (Kadlec et al., 2008;Stone et al., 2004) to design and predict the behavior of major pollutants in AP, such as organic matter (OM), TSS, metals, and nutrients (nitrates and phosphorous). Conversely, few applications of the black-box model on pesticides in APs were reported. To our knowledge, only Krone-Davis et al. (2013) reported the application of addition, we noticed that pesticides manifesting a good model performance had a more homogeneous dynamic at the AP outlet, i.e., with a discharged cumulative flux increasing in a more linear pattern. Adversely, pesticides with a RE > 25% had a dynamic marked by sudden flux increases. The sudden flux rise could generate by the pesticide application upstream of the AP and lining with heavy rainfall events. This result underscores the limitation of the simplified black box approach in replicating the pronounced dynamics of certain pesticides.

Overall, the black box model enabled the simulation of an overall dissipation coefficient kdiss (d -1 ) (and equivalent half-life DT50 (d)) for 36 pesticides after excluding numerical artifacts. However, the half-life ranges were significantly shorter than the literature values. Adversely, Krone-Davis et al. ( 2013) reported calibrated half-lives in the range of the literature values for the 3 evaluated pesticides. The comparison of the two models is not obvious since the TIS was calibrated for a short period (10 days) while the black box was calibrated for 60 monitoring periods.

The evaluation of the simple modeling approach at the Rampillon site emphasized the following limitations of the black box:

-The black box model integrates the hydrology by a simple mean HRT neglecting the water volume dynamics within the AP.

-The black box approach does not consider re-mobilization processes (i.e., desorption and resuspension of PP).

-The model provides information on the overall dissipation of pesticides without underlying the dissipative processes behind nor their synergy. Hence, the model yields limited knowledge of pesticide fate, which renders the model extrapolation.

-The black box model performance is only satisfactory for certain pesticides.

-No significant relationship was found between the calibrated half-lives (for the overall dissipation) and the key physicochemical properties of pesticides (Koc, log Kow, and S).

This confirms the inability of the black box model to convey comprehensive knowledge about the link between pesticide behavior and APs' performance.

In response to these issues, we developed a more exhaustive and complete model, PESTIPOND, to predict pesticide fate in APs.

and was quantified by the Sobol method using the Martinez estimator. Accordingly, adsorptiondesorption, biotransformation, and photolysis were identified as the major processes controlling pesticide fate in APs. Conversely, hydrolysis and volatilization were the least influencing processes. These findings agree with the SA conducted on similar pesticide fate models, i.e., PCPF-1 (Boulange et al., 2012) and AGRO-2014(Desmarteau et al., 2014).

Different scenarios of pesticides with contrasting mobility and hydrophilicity were used to conduct the SA. Therefore, the SA underlined the link between pesticide properties and the model sensitivity to the dissipative process. For instance, processes occurring in the water column (i.e., biotransformation in the water and photolysis) have the most significant influence on the behavior of highly mobile (low Koc) and hydrophilic pesticides (low log Kow). By contrast, biotransformation at the water-sediment interface is more effective on hydrophobic and lowly mobile pesticides. These SA results indicate the link between pesticide adsorption capacity and its availability for transformation, as previously noticed by (Ahmad et al., 2004). Given that, we assume that adsorbed pesticides (hydrophobic and lowly mobile) are more likely to be transformed in the water-sediment interface, as supported by Beringer et al. (2021), Cáceres-Jensen et al.

(2021), and Wu et al. (2017) experimentally. Adversely, unbound pesticides (hydrophilic and highly mobile) are more likely to be transformed in the water column. A similar assumption was reported by Anderson et al. (2002), Gregoire et al. (2009), andHijosa-Valsero et al. (2016). Lastly, the model manifested a significant sensitivity to the temperature, which is expected given the strong dependency of transformation processes on temperature, as widely proven in literature (Papaevangelou et al., 2017;Vymazal et al., 2015;Yu et al., 2019).

Model validation main results of the PESTIPOND application in Rampillon

In The input data (forcing variables and the AP's size) were measured on-site, the hydrological model computed the water volume, and the model parameters (processes' kinetics) were calibrated. First, PESTIPOND's parameters were calibrated annually and then inter-annually to assess the model's robustness. Since the daily observations of pesticide concentrations are not available, transformed observation data was created from bi-monthly monitoring to illustrate pesticide dynamics closely.

Therefore, the model performance was assessed for both transformed and non-transformed observations in the annual and inter-annual calibrations.

The first outcome is that the performance assessment proffered graphical and statistical agreements between simulations and observations of the discharge fluxes for all pesticides except boscalid.

Given the half-lives of boscalid reported in the literature, it is a stable molecule. Therefore, the stability of the boscalid suggests that the PESTIPOND model does not consider the processes behind its dissipation, which explain the lower model performance for boscalid.

Primary outcome of the PESTIPOND model application in Rampillon

Processes and pesticide properties

The simulated mass budget of pesticides in the AP was closely examined, which underlines that most of the intercepted mass of pesticides is discharged from the AP for a mean HRT of 8 days and 9 °C temperature. The remaining mass is dissipated or stored in sediments and in the water column. The pesticides that were mostly stored in the sediment layer are hydrophobic (log Kow ≥

3) and lowly mobile (Koc >500). Alternatively, the pesticides manifesting a higher presence in water are hydrophilic and highly mobile. Scientists observed similar behavior in laboratory conditions (Ahmad et al., 2004;Budd et al., 2009;Chaumet et al., 2021;Lee et al., 2004). Such results purport that PESTIPOND simulates a pesticide behavior in agreement with the one expected based on their properties. Besides, no significant relationship was found between the overall dissipation coefficient of the black box model and the pesticide properties, unlike the PESTIPOND model parameters that exhibited a linear correlation with the pesticide mobility (Koc).

Overall, the set of PESTIPOND parameters matches the PPDB values. The parameters of transformation processes are identical to half-lives provided by the PPDB, and the adsorptiondesorption parameters are correlated to the Koc, also available in the PPDB. This finding supports that the PPDB values are sufficient to describe pesticide fate.

Process hierarchization

Additionally, the mass budget results confirmed the assumptions of process hierarchization established following the review exercise and the SA. For all pesticides, adsorption is the most significant process in pesticide behavior, except for s-metolachlor, which was more distinguished by the transformation in the water column. By relating this result to the hydrophilic and mobile properties of the pesticide, it is expected that s-metolachlor undergoes limited adsorption, which increases its bioavailability for transformation in the water column than the sediment layer. Based on laboratory experiments, a similar observation was made for s-metolachlor by Droz et al. (2021).

Conversely, transformation at the water-sediment interface was more pronounced for hydrophobic pesticides more favorable to adsorption, as supported by adsorption studies (Castan et al., 2021;Tang et al., 2019;Vagi et al., 2022). For hydrophilic and highly mobile pesticides, desorption covered a non-negligible part of the intercepted mass.

The mass budget results support the significance of adsorption, desorption, and biotransformation in the water for hydrophilic and mobile pesticides. Alternatively, biotransformation at the watersediment is more pronounced, and desorption is limited for hydrophobic and lowly mobile pesticides. Bentazone was the only pesticide significantly photodegraded, which was also observed in mesocosms experiments in Rampillon that are not presented in this study.

The predominance of adsorption suggests that the AP is a potential accumulation area for pesticide contamination. Nevertheless, a recent analysis of sediment samples in the Rampillon AP detected only a small pesticide amount ten years after the AP construction, i.e., a maximum concentration of 6ng per gram of sediments. Accordingly, APs can be maintained long without accumulating high pesticide amounts but more likely dissipating them.

The role of the surface area and temperature in the efficiency of APs

The results of the literature review (Chapter I), SA results (Chapter II-2), and mass budget of PESTIPOND simulations (Chapter III) highlighted the impact of the HRT and temperature on pesticide dissipation and, thus, on APs' efficiency. All of these findings evidenced that higher temperature yields higher transformation of pesticides, and higher HRT provides a longer time for them to adsorb on sediments and be transformed.

Practically, the HRT is controlled by the surface area A of the AP, and the climatic conditions of the AP location define the temperature T. In order to assess the role of HRT and temperature in APs' efficiency, we performed an extrapolation of the PESTIPOND model according to both factors.

A/ Hydraulic residence time

Firstly, we extrapolated the PESTIPOND model to higher HRT. Therefore, we simulated the efficiency of Rampillon AP to dissipate pesticides from the inlet to the outlet by keeping the same inputs used for the model validation but using increasing surfaces (equivalent to increasing HRT).

The surface represents the area occupied by the AP in the catchment. The extrapolation results indicated that once the AP covers >1% of the drained catchment area, the dissipation of pesticides reaches 85%, which is twice the actual efficiency of the Rampillon AP. Tournebize et al. (2012) reported that, based on a literature review of AP performances, scientists suggested allocating 1% of the catchment area to the pond. However, farmers rejected this proposal for different reasons (land occupation, cost, operational labor cost, and maintenance).

Consequently, the farmers suggested a 0.15% area for the AP to meet their requirements or acceptability, which was expected to be a less efficient remediation solution for pesticide transfer.

The PESTIPOND extrapolation, predicting a significantly higher efficiency of the AP if it covers 1% of the catchment, supported this expectation.

B/ Temperature

Similar extrapolation was performed on different temperatures representing a geographical and climatic gradient. We noticed a linear increase in pesticide dissipation according to ascending temperature. Therefore, a pond constructed in a warmer environment might be more efficient than in colder media. Nonetheless, the extrapolation demonstrated that a rise/drop by 5°C in temperature is expected to increase/decrease the AP efficiency by only 4%/2%.

Accordingly, the AP area is awaited to have a more pronounced effect on the performance of APs compared to temperature. This is promising since the pond size is a factor that can be controlled, unlike temperature.

The added values and limitations of the PESTIPOND model

The evaluation of the PESTIPOND model by application in Rampillon provides insight into the added values and limitations of the model, as listed in the following:

Added values:

(1) PESTIPOND integrates the key physico-chemical processes behind pesticide dissipation to predict the fate of dissolved pesticides with contrasting properties under different hydrological contexts at the pond scale accurately.

(2) PESTIPOND simulates pesticide discharge from the AP with a lower error than the blackbox model.

(3) PESTIPOND requires a limited number of inputs compared to other risk assessment models (i.e., from the FOCUS group (Tooby, 1999)):

-Input variables: temperature, flow rates, and pesticide fluxes can be measured on-site.

-Parameters: the kinetic coefficients of the processes can be extracted from literature for transformation processes, as the PPDB values matched the calibration results, which leaves only 2 parameters to calibrate (adsorption-desorption).

(4) The PESTIPOND model can be parameterized by values available in the PPDB, unlike the black box, whose only parameter must be calibrated, as it is a pond-specific dissipation coefficient (unavailable in the PPDB).

(5) PESTIPOND enables the assessment of the link between pesticide dissipation and (i) pond properties, i.e., temperature and HRT, and (ii) the pesticide properties (mobility and hydrophobicity).

(6) In parallel to predicting pesticide dynamics in the AP, PESTIPOND can hierarchize the processes and quantify their contribution to the overall dissipation.

Limitations:

(1) The assumption of a completely mixed reactor is not always the case in APs, specifically those representing heterogeneities (i.e., significant vegetation cover, dikes, and dead zones). Therefore, the PESTIPOND model could be complemented if coupled with hydraulic-based models, such as 3D (e.g., DELFT 3D [START_REF] Lemaire | Tracing and hydraulic modelling of a constructed wetland[END_REF], under review) or 2D models [START_REF] Drouin | Pollutant Dissipation at the Sediment-Water Interface: A Robust Discrete Continuum Numerical Model and Recirculating Laboratory Experiments[END_REF], which will provide detailed insight into water velocities and integrate the advection-dispersion of pesticides (Imfeld et al., 2021;Silva et al., 2016).

(2) Considering microbial communities' acclimation and dynamics will undoubtedly improve the model performance, specifically regarding biotransformation.

(3) If the PESTIPOND model integrates the dynamics of organic carbon content, the adsorption-desorption effect would be better expounded.

(4) Although the model integrates the key processes in pesticide dissipation, additional processes might influence the behavior of some pesticides but are not integrated. These processes are adsorption on vegetation and biofilm, plant uptake, and phytoremediation.

Modeling the fate of particulate pesticides in artificial ponds

In addition to their dissolved form, pesticides can have a particulate form when adsorbed to suspended matter. The appearance of pesticides in particulate form is more prominent in sloping catchments where solid transport is significant due to erosion.

Hence, after developing and validating the PESTIPOND model for dissolved pesticides, we tested its performance according to particulate pesticides (PPs). Accordingly, the PESTIPOND model was tested on the Bassioué AP. Bassioué is a generic artificial pond located in a sloping agricultural catchment (Auradé) with significant solid. PESTIPOND was tested for the highly hydrophobic, lowly mobile, and lowly soluble pendimethalin as a generic pesticide in particulate form. The dataset available for testing the model covers the year 2020.

The particle version of the PESTIPOND model requires more input variables and parameters since it considers additional processes (settling and resuspension) and compartments, i.e., the total suspended solids in the water column (TSS). In addition to the hydrological model used in the original version, the particle version of PESTIPOND includes a sub-model for solid transport. The solid transport model predicts the dynamic of solids in the AP due to settling and resuspension processes. The input data of the model are the water flows and concentrations of the dissolved and particulate phases, both upstream and downstream of the AP. Similarly to the previously described version of PESTIPOND, the process coefficient and velocities for settling and resuspension are model parameters. The input data was provided by (LEFE), the adsorption-desorption and settlingresuspension parameters were calibrated, and the remaining ones were extracted from the PPDB.

The simulation results of pendimethalin illustrate the ability of PESTIPOND to predict the global dynamics of the dissolved and particulate phases of pendimethalin. However, the model underestimates the total exported mass from the AP for both dissolved and particulate phases. The mean relative errors proffered that the model is more performant for the dissolved phase than the particulate phase of pesticides, which reflects the model's limitation in simulating the particle phase.

We assume that the PESTIPOND limitation to predict PP fate compared to DP result from the oversimplification of the hydraulic conditions within the AP, mainly due to the perfectly mixed reactor hypothesis. In fact, we assume that solids transport processes, such as settling and resuspension, are more sensitive to the non-uniformity of hydraulic pathways than other processes (e.g., adsorption and transformation), which are more involved in DP fate. As it is, the impact of hydraulics on solids transport is firmly established in the literature [START_REF] Borthakur | Perfluoroalkyl acids on suspended particles: Significant transport pathways in surface runoff, surface waters, and subsurface soils[END_REF]Gaullier et al., 2020a;Gregoire et al., 2009). Consequently, coupling the PESTIPOND model with a hydraulic-based model [START_REF] Drouin | Pollutant Dissipation at the Sediment-Water Interface: A Robust Discrete Continuum Numerical Model and Recirculating Laboratory Experiments[END_REF]Henine et al., 2022;Imfeld et al., 2021) is believed to improve the model performance according to particles.

In this context, it is noteworthy to underline the benefit of tracer studies to assess hydraulics in APs (Passeport et al., 2010). [START_REF] Ulrich | Hydrological tracers, the herbicide metazachlor and its transformation products in a retention pond during transient flow conditions[END_REF] indicated that tracer experiments efficiently document wetland hydraulics, detention, and mixing within APs. Therefore, tracers are expected to provide more accurate insight into pesticides distribution within the pond by considering the varying HRT in the different hydraulic zones as evidenced for other substances (e.g., nitrates, phosphorous, and metals) [START_REF] Pugliese | Internal hydraulics and wind effect in a surface flow constructed wetland receiving agricultural drainage water[END_REF]Wu et al., 2021;Yang et al., 2017).

A second ground of the model limitation is that the water balance needed to take into account all the input fluxes (lateral flows) by a lack of measurements. In addition, daily water volume observations do not precisely cover the AP's hydrological response. Therefore, hourly observations can improve the model's performance. In practice, high-frequency sampling allows:

(i) An assessment of much more reliable estimations of pesticide fluxes [START_REF] Bieroza | Seasonal variation in phosphorus concentrationdischarge hysteresis inferred from high-frequency in situ monitoring[END_REF]Wenng et al., 2021).

(ii) A better estimation of the contribution of each hydrograph component (e.g., rising limb, falling limb, and first flush) to pesticide transport [START_REF] Granger | High Temporal Resolution Monitoring of Multiple Pollutant Responses in Drainage from an Intensively Managed Grassland Catchment Caused by a Summer Storm[END_REF][START_REF] Ulrich | Hydrological tracers, the herbicide metazachlor and its transformation products in a retention pond during transient flow conditions[END_REF].

(iii) A thorough investigation of pesticide response patterns to rainfall events [START_REF] Bende-Michl | High-frequency nutrient monitoring to infer seasonal patterns in catchment source availability, mobilisation and delivery[END_REF][START_REF] Ulrich | Hydrological tracers, the herbicide metazachlor and its transformation products in a retention pond during transient flow conditions[END_REF].

Lastly, the settling and resuspension rates are practically governed by particle size and density, water velocity, turbulence, salinity, wind speed, temperature, and water column depth. Hence, using a uniform rate only dependent on the water depth could explain the weak performance of PESTIPOND according to PPs. Accordingly, further investigations on settling and resuspension formulations are recommended for better model predictions.

Perspectives

Building upon these elements, the perspectives of this work are the following:

1-The PESTIPOND model was validated for predicting pesticide fate at the pond scale.

Subsequently, the model can be integrated into a landscape-modeling tool (e.g., SWAT) to predict the cumulative effect of ponds at the catchment scale. This work is already underway (Joffre et al., under review) using the default pond module of SWAT. However, the pond module of SWAT does not consider the temperature effect and adsorptiondesorption kinetics. Therefore, we suggest the integration of PESTIPOND in SWAT to evaluate pesticide fate at the catchment scale.

2-The metabolites of pesticides were not considered for lack of documentation about their fate. However, since the PESTIPOND model quantifies the biotransformation rate of pesticides, a sub-compartment of metabolites could be added to the conceptual model and handled as the rest of the compartments (pesticides in water and sediments).

3-The PESTIPOND model simulations were confronted with observations of pesticides in the water column. Given that recent monitoring of pesticides in the sediment has been available, the model simulation of pesticide mass in the sediment layer could be compared to the corresponding observations. 4-As previously suggested, coupling the PESTIPOND model with a 2D/3D hydrological model is highly encouraged to assess the impact of hydraulic pathways on both dissolved and particulate pesticide fate.

5-The black box model approach manifested good performance for some pesticides; it would be relevant to seek an explanation for the model's poor performance for the other pesticides.

Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7

 234567 Figure 2The European Union sales of pesticides in 2020 in tons. The sales are displayed by type of pesticides (insecticides, fungicides, and herbicides)(Tostado, 2022). ........................ 31 Figure 3 Map of the global risk of freshwater contamination by insecticides (Ippolito et al., 2015). ........................................................................................................................................ 32 Figure 4 Illustration of different pathways of agrochemical contamination (e.g., pesticides) toward surface-and underground-water resources and the localization of wetlands that are able to dissipate such contamination (adapted from AQUI'Brie (illustration)). .............................. 35 Figure 5 Illustration of a typical artificial pond intercepting agricultural waters, and the mechanisms behind pesticide-contamination dissipation between the inlet and outlet (adapted from AQUI'Brie (illustration)). ................................................................................................ 36 Figure 6 Evaluation and prediction of pesticide dissipation in APs and the role of APs at the catchment scale. (A) Identification of the fluxes, processes, and compartments of APs. (B) Innovative tools to distinguish pesticide degradative and non-degradative processes. (C) Conceptual modeling framework in APs. (D) Modeling the catchment-pond connectivity to integrate the cumulative effect of ponds in terms of risks of pesticides transfer and fluxes at the catchment scale and elaborate management scenarios. TSS: total suspended solids; DOC: dissolved organic carbon; N: nitrogen; P: phosphorus; POC: particulate organic carbon (Imfeld et al., 2021). .............................................................................................................................. 39 Figure 7 Modeling framework integrating the role of ponds to control pesticide transfer at the headwater catchment and the large river basin scales. The framework involves a modeling chain from pesticide dissipation within pond compartments up to pesticide transfer in river basins.

Figure I . 1

 I1 Figure I. 1 Summarized elements and steps detailed in this work aiming to develop a processbased model, which, in contrast to black models, involves multiple processes in an interplay between different artificial pond compartments. ...................................................................... 77

  Figure II-I. 7 The graphs display compare the simulated and observed AP efficiencies of diflufenican and s-metolachlor. The middle black line refers to the case where simulations are equal to the observation. The red dashed lines refer to a +10% discrepancy (upside the black line) and a -10% (downside the black line). Similarly, the blue dashed lines indicates a +/-20% discrepancy. The dots represent the evaluated periods. ......................................................... 126

  measurement locations and water sampling points. ............................................................... 261

-

  Figure IV. 3 The conceptual model of PESTIPOND integrating the particulate phase. The processes in red stand for transformation processes, and the green ones represent nondegradative processes, which do not change the original pesticide chemical structure. ........ 270

  . ............................................................................................................... 83 Table I.A. 4 Adsorption and desorption kinetics (kads, kdes)

  . ........ 84 Table I.A. 5 Volatilization classes according to the vapor pressure values (Höllrigl-Rosta, 2017). ........................................................................................................................................ 84 Table I.A. 6 Empirical formulations of volatilization rate kv (d -1 ) along with their corresponding parameters, based on a 1 st order kinetic volatilization model. ................................................. 85 Table I.A. 7 Review of the volatilization half-lives DT50v (d) and the corresponding volatilization rates kv(d -1 ) in ponds, of different pesticides along with their corresponding vapor pressure vp (Pa) and their volatilization classification according to Table I.A. 5................... 85 Table

  half-lives were confronted to the PPDB values. ...................................................................... 86 Table

  . ............................................................ 87Table I.A. 10 Modulating factors applied to the photolysis rate kphoto to account for controlling factors (i.e., Temperature, depth, and radiation). ..................................................................... 87 Table I.A. 11 Biotransformation half-lives DT50bio,s (d) of five pesticides with different mobility

I. 2 -

 2 List of the calibrated DT50 (d) (deduced from the calibrated kdiss) for some pesticides alongside the literature (i.e., PPDB) half-lives. DT50bio,w (d) is the biotransformation half-life in the water column. DT50bio,s (d) is the biotransformation half-life at the watersediment interface column. DT50p (d) is the photolysis half-life in the water column. DT50h (d) is the hydrolysis half-life. RE (%) refers to the relative error of the simulations. NA values indicate non-available data. .................................................................................................... 125 Table II-I. 3 Classification of the pesticides according to the RE between simulations and observations of cumulative fluxes (µg). ................................................................................. 129 Calibration Results Of Kdiss (H -1 ), Corresponding DT50 (D), And RE(%) For The Evaluated Pesticides. ....................................................................................................... 133

  ...................................................................................... 134 -Chapter II-II: Table II-II. 1 Summary of the lenacil data during five monitoring periods. The monitoring period refers to the start and end of the monitoring. The intercepted volume by the AP (mm) is the normalized intercepted volume (m 3 ) by the watershed area (355 ha). The influx (mg) is the total intercepted mass of lenacil during each period. Cin.max (µg.L -1 ) and Cin.mean (µg.L -1 ) are the maximum and mean observed inlet concentration of lenacil, respectively. Detection rate refers to the percentage of lenacil detected from the total amount applied. ..................................... 156 Table II-II. 2 List of the input parameters used for the test-case simulation ........................ 156

  . ......................................................................... 161 -Appendix II-II:

  measured in the field. ............................................................................................................. 174 Table II-II.A. 2 List of the PESTIPOND model variables and parameters. ......................... 175 Table II-II.A. 3 Summary of the model outputs for the test-case scenario (i.e., minimum, maximum, median, mean, and the lower (first) and upper (third) quantile). MT is the total mass of lenacil in the AP. Mw is the mass of lenacil in the water column. Ms is the mass of lenacil in the active sediment layer. Min and Mout are the lenacil influx and out flux masses, respectively. Mdeg is the total degraded mass due to dissipative processes (i.e., biotransformation, photolysis, hydrolysis, and volatilization). Mdegw and Mdegs are the degraded masses in the water and the active sediment layer of the AP, respectively. Mads and Mdes are the adsorbed and desorbed masses, respectively. .............................................................................................................. 176 -Chapter III:

  and the application season was deduced from a follow-up of cultural practices in Rampillon. ........................................................................................................... 201 Table III. 3 Values of the model parameters related to adsorption and desorption for the 20 study cases. kads

. 4

 4 Values of the model parameters related to transformation processes, i.e., DT50,w (d), DT50,s (d), and DT50,p (d), are the biotransformation in water, in the active sediment layer, and the photolysis half-lives, respectively. Most of transformation parameters were extracted from the PPDB and (*) are the calibrated values. Similarly to adsorption-desorption parameters, these values are the result of the annual calibration of the parameters. ................................. 207 Table III. 5 The mean value of the annual-calibrated parameters, and the PPDB values of transformation half-lives, i.e., (DT50,w, DT50,s, and DT50,p). The physicochemical properties were extracted from the PPDB or other pesticide databases if not available. ........................ 211 Table III. 6 Statistical comparison of simulations and daily observations of pesticide fluxes. The left part of the table list the KGE, NSE, and NRMSE values using an inter-annual set of parameters, i.e., the mean of the annual-calibrated parameter values, and the right part is for the annual calibration ................................................................................................................... 212 -Appendix III: Table III.A. 1 List of the PESTIPOND model variables and parameters. The details on how the input data is obtained are available in (Bahi et al. 2023. submitted)................................ 231 Table III.A. 2 Summary of the mass budget (g) for the studied pesticides. MT is the total mass of the pesticide at the end of each period. Mw and Ms are the pesticide masses in water and sediments at the end of each period, respectively. Min is the total intercepted mass. Mout the total mass discharged from the pond and Mtr is the total transformed mass. Mtr. w and Mtr. s are total transformed mass in water and sediments, respectively. Mads and Mdes are the total adsorbed and desorbed mass, respectively. Err (%) is the mass balance error of each simulation. ................................................................................................................................................ 238 Table III.A. 3 The mean percentage (%) of each process to the total intercepted mass of pesticides. ............................................................................................................................... 240 Table III.A. 4 The mean efficiency (%) of the Rampillon AP according to different pond areas. A is the real AP area (5270m 2 ). .............................................................................................. 241 Table III.A. 5 The mean efficiency (%) of the Rampillon AP according to different temperatures. T is the real daily temperature in the AP. ........................................................ 244 -Chapter IV:

  organic carbon-water partition coefficient. Kow: The octanol-water partition coefficient and S is the pesticide solubility in the water .................................................................................... 267 Table IV. 4 List of the parameter values used in the Velikanov's equation. η1 and η2 were calibrated manually. ............................................................................................................... 274 Table IV. 5 List of the input parameters used for pendimethalin simulation. PPDB refers to the Pesticide Properties Database (Lewis et al., 2016). ............................................................... 279 Table IV. 6 List of the input parameters used for pendimethalin simulations. ..................... 288 -Appendix IV: Table IV.A. 1 List of the input variables and parameters of the particulate version of the PESTIPOND model. .............................................................................................................. 295

Figure 6

 6 Figure 6 Evaluation and prediction of pesticide dissipation in APs and the role of APs at the catchment scale. (A) Identification of the fluxes, processes, and compartments of APs. (B) Innovative tools to distinguish pesticide degradative and non-degradative processes. (C) Conceptual modeling framework in APs. (D) Modeling the catchment-pond connectivity to integrate the cumulative effect of ponds in terms of risks of pesticides transfer and fluxes at the catchment scale and elaborate management scenarios. TSS: total suspended solids; DOC: dissolved organic carbon; N: nitrogen; P: phosphorus; POC: particulate organic carbon(Imfeld et al., 2021).

  Chapter II-II [research paper (to be submitted)] introduces a process-based model called PESTIPOND developed in this work to predict pesticide dissipation at the pond scale. This part elaborates on the development and sensitivity analysis of the PESTIPOND model. Chapter III [research paper (to be submitted)] outlines the results of the application and validation of the PESTIPOND model with monitoring data from the Rampillon site.Chapter IV evaluates the integration of the particulate phase of pesticides in the PESTIPOND model with an application at the Auradé site.A general discussion and conclusion part closes the dissertation.

  T -1 ] is the water velocity and Cw [M.L -3 ] is the pesticide concentration in water. In conceptual models, advection is represented by the nominal HRT in the artificial pond.

  the contribution of each process to the dissipative function of artificial ponds. Therefore, this paper discusses the elements required to develop a simple process-based model involving the transfer, transport, and transformation processes in an interplay of artificial pond compartments to open the black box (Figure I. 1).

Figure I. 1

 1 Figure I. 1 Summarized elements and steps detailed in this work aiming to develop a processbased model, which, in contrast to black models, involves multiple processes in an interplay between different artificial pond compartments.

  Volatilization classes according to the vapor pressure values (Höllrigl-Rosta, 2017). Vapor pressure (Pa) at 20°C Volatilization class < 10 -5Non-volatile < 10 -4

  Pa.m 3 .mol -1 ): Henry coefficient at 25°C.• R (Pa.m 3 .mol -1 K -1 ): Universal gas constant. • T (K): Absolute temperature.• MW (g.mol -1 ): Molecular Weight of the chemical compound.• hw (m): Water depth.(Jacobs et al., 2012) 1 1 0,0036 + 0,01 * 𝑢 1,2 + 8.3𝑇 𝐻 * (3,6 + 5 * 𝑢 1,2 )

  * 𝑒 -25 * 𝑇𝑆𝑆 * 𝑧 • z (m): Depth. • I0 (MJ m -2 h -1 ): Actual radiation.

Figure I.B. 1

 1 Figure I.B. 1 Diagram showing the number of searched articles according to the type of processes, and the number of papers we reviewed. We reviewed papers with the most information about pesticides and processes in ponds. We did not analyze papers with little information, out of context, or redundant. The bottom of the diagram refers to the enumeration of papers according to study scale. Among the set of processes, according to transformation processes, biodegradation was the most one surveyed with a total of 267 papers, followed by 91 studies of sorption (Figure I.B. 1). Photolysis and settling were less evaluated; only 28 and 26 articles cited them, respectively.

  agricultural runoff through sinkholes (Figure II-I. 1).

Figure

  Figure II-I. 1 Aerial sight of the Rampillon watershed (355ha). (A) The watershed comprises two arterial ditches (blue lines) and a buried ditch (dashed line). The red arrow points toward sinkholes. (B) displays one of the 42 sinkholes contained in the watershed.After many deliberations between scientists, regional administrators, and farmers, it was decided to allocate 0.15% of the watershed to the construction of this wetland. The Rampillon AP covers 5270m 2 with a 2300m 3 volume and a useful 7m³ volume per drained hectare. On average, the AP has been dimensioned for a theoretical water residence time of 7 days. The drainage/runoff water from upstream agricultural plots flows into a ditch nearby the AP (Fig.II-I.2). On average, the AP intercepts 38% of the ditch water. The inlet and outlet of the AP are directly connected to the ditch. The outlet of the AP is via an overspill and is therefore not controlled. The water depth of the AP fluctuates between 1m at the inlet and outlet and ~10cm in the middle area. Vegetation and macrophytes cover about 40% of the AP surface.

Figure

  Figure II-I. 2 Aerial sight of the Rampillon artificial pond (5270m 2 ). The orange arrow points to the pond inlet, i.e., the water entrance. The cyan arrow points to the pond outlet, where the water regains the ditch. The dark blue line refers to the ditch nearby the AP.2.2 Monitoring dataThis Rampillon AP belongs to a local public structure in charge of river maintenance "Syndicat Mixte des 4 Vallées de la Brie" (SM4VB). Since its construction in 2010, the AP has been used by(INRAE) to monitor water quality and biodiversity. This section outlines the monitoring data of interest for this study, i.e., water flow rate, temperature, and pesticide concentrations.The monitoring instrumentation is displayed inFigure II-I. 3.

Figure

  Figure II-I. 3 (A) The monitoring and automatic sampling station. (B) The inlet pipe intercepting ditch water. (C) The ditch outlet localization. (D) Sampling probes at the AP outlet for discharge measurements.The water flow rate is measured every 15min at the inlet and outlet of the AP by the Doppler Effect. The outflow is similar to the inflow since the outlet is not controlled and only depends on the water level of the AP. The water flow rate of the ditch is also monitored by measuring the water level in a known section pipe. This measurement enables quantifying the nonintercepted volumes by the AP. The water temperature is measured using a pressure transducer model Madofil (IRIS) nearby the outlet.

Figure

  Figure II-I. 4 Illustrations of the variables of interest monitored during 30/10/2019-31/05/2020. The blue line refers to the outlet water flow rate Qout (L.s -1 ). The green segments refer to the bimonthly sampled flux (µg) of herbicides. The orange segments refer to the bi-monthly sampled fungicides flux (µg) of. The black dashes indicates the cumulative mass (µg) of all detected pesticides.

  were selected according to the detection of at least one pesticide and the proper functioning of the instruments. Eventually, 60 periods were selected from 2011 to 2019 with a mean duration of 15 days (Figure II-I. 5), and 61 detected pesticides after excluding transformation products (Table II-I.1).

Figure II-I. 5

 5 Figure II-I. 5 Sampling duration of the 61 selected pesticides. The orange line refers to the mean duration (15 days).

  Figure II-I. 6 illustrates an example of the period grouping for bentazon. Periods are numbered from 1 to 30, with an average duration of 15 days.

Figure II-I. 6

 6 Figure II-I. 6 Illustrations of the studied periods for bentazon before (A) and after (B) grouping.Orange and blue bars represent the inlet and outlet fluxes (µg) of bentazon, respectively. Red rectangles refer to periods with abnormalities, i.e., when the outflux is higher than the influx, or there is none of them. Green rectangles refer to the grouped periods obtained, i.e., when the outflux is higher than the influx.

(

   Chapter II-I: Modeling the fate of dissolved pesticides with a black box model 𝑘 𝑑𝑖𝑠𝑠 (𝑇) * 𝐻𝑅𝑇 Where Min (µg) and Mout (µg) are the masses of pesticides in the inlet and outlet, respectively, and HRT (d) is the mean HRT of each evaluated period.

  diflufenican and s-metolachlor (Figure II-I. 7). According to the Koc and log Kow values, diflufenican (Koc (L.kg -1 ) = 550, log Kow = 4.2) is hydrophobic and lowly mobile, while smetolachlor (Koc (L.kg -1 ) = 120, log Kow=2.9) is hydrophilic and moderately mobile (EPA; Lewis et al., 2016).

Figure II-I. 7

 7 Figure II-I. 7The graphs display compare the simulated and observed AP efficiencies of diflufenican and s-metolachlor. The middle black line refers to the case where simulations are equal to the observation. The red dashed lines refer to a +10% discrepancy (upside the black line) and a -10% (downside the black line). Similarly, the blue dashed lines indicates a +/-20% discrepancy. The dots represent the evaluated periods.

Figure II-I. 7

 7 Figure II-I. 7 indicates that during most of the evaluated periods, the simulations of diflufenican and s-metolachlor are close to the observations, except for two periods for diflufenican. A single period manifested a perfect fit between simulations and observations for

Figure

  Figure II-I. 8).

Figure

  Figure II-I. 8 (A) represents a simulation with a lower RE < 25%. Conversely,

Figure

  Figure II-I. 8 exhibit that for some pesticides, the simulated cumulative fluxes are close to observations with a RE <25%, while other pesticide simulations exhibit a lower quality with a RE>25%. Hence, Table II-I. 3 evidences that most evaluated pesticides yielded a RE<25%, reflecting a good model performance according to the total discharged pesticide flux from the AP.

Figure II-I. 9

 9 Figure II-I. 9 Scatter plot of the calibrated kdiss involving all evaluated pesticides and periods against the key physico-chemical properties pesticides, i.e., Koc (L.kg -1 ), log Kow (-), and S (mg.L -1 ).

  work assesses the application of the black box model on 61 pesticides monitored in the artificial pond of Rampillon from 2011 to 2019. The black box is a simple modeling approach aiming to predict pesticide flux at the pond outlet from the inlet flux. Contrarily to processbased models, the black box is based on a single equation involving a first-order kinetic rate that represents the overall dissipation of pesticide in APs without delving into the details of the mechanisms behind it. Moreover, the black box model is limited to an average hydraulic residence time to represent the hydraulic conditions of the AP. Nevertheless, this simple approach manages to predict the overall dissipation of different pesticides according to the selected performance criteria.The black box model performance was evaluated by the estimation of the relative error between the simulations and observations. The evaluation was performed on the AP efficiency in dissipating pesticides between the inlet and outlet and the discharged flux of pesticides. In aggregate, most surveyed pesticides have a relative error of < 25%. However, good model performance was often noticed for pesticides having a homogenous dynamic. Therefore, the limit of the black box model is to reproduce pesticide dynamics with sharp spikes due to application timing in heavy rainfall events.Although the black box model can simulate the overall dissipation of pesticides, it does not provide any insight into the different mechanisms underlying this dissipation nor the factors governing the efficiency of the ponds. As stated in Chapter I, investigating pesticide behavior in an interplay between the transfer, transport, and transformation processes and their controlling factors can enable to hierarchize the processes based on their contribution to the performance of artificial ponds. Once these findings are reached, we believe they will be useful in managing, constructing, or restoring wetlands effectively.Accordingly, in the second part of the present Chapter (Chapter II-II), we present a processbased model, "PESTIPOND". PESTIPOND is a process-based model developed to predict pesticide behavior at the pond scale while integrating the key transfer, transport, and transformation processes behind pesticide dissipation, as well as, the hydrological and temperature effects.

  (i.e., SWAT,AGRO-2014, and PCPF), PESTIPOND accounts for the effect of temperature and adsorption-desorption kinetics, since these are major drivers of pesticide behavior in AP. The model simulates the dynamics of pesticides in both water and sediment compartments of AP and predicts the exported fluxes of pesticides before being transferred to water resources. The PESTIPOND model is designed to be tested on real-case ponds in order to be validated and then integrated into landscape models (e.g., SWAT) for predicting pesticide fate at the watershed scale.The performance of AP depends on the local spatiotemporal conditions of the site (i.e., hydrology, season, hydraulic residential time (HRT), interception capacity) and the physicochemical properties of the pesticides (i.e., hydrophobicity(Koc), solubility(log Kow), and half-life (DT50))(Tournebize et al., 2017). Therefore, Paper I and II will consider all of these elements in the assessment of the PESTIPOND model. The present paper outlines the development and sensitivity analysis of the numerical physical-based model, while the model validation and calibration with in situ data are detailed in Paper II.

Figure II-II. 1

 1 Figure II-II. 1 Conceptual fate model of pesticides in artificial ponds. The processes in red represent transformation processes and those in green non-degradative processes, which do not change the pesticide chemical structure. Erreur ! Source du renvoi introuvable. depicts the conceptual model for pesticide fate in AP. T he conceptual model shows the general behavior of dissolved pesticides when intercepted by AP. The PESTIPOND model partitions the AP into two main compartments: the water column and the sediment layer.

  Mads [M.T -1 ] is the adsorbed mass flow rate of pesticides on sediments; kads [T -1 ] is the adsorption rate coefficient of the pesticide; Mw(t) [M] is the pesticide mass in the water column at time t; Mdes [M.T -1 ] is the desorbed mass flow rate of pesticides from sediments; kdes [T -1 ] is the desorption rate coefficient of the pesticide; and Ms(t) [M] is the pesticide mass in the active soil layer at time t.

  H [Pa.m 3 .mol -1 ] is the Henry constant coefficient at 25°C of the pesticide; R [Pa.m 3 .mol - 1 .K -1 ] is the universal gas constant; T [K] is the air temperature; kCO2 [M.T -1 ] is the mass transfer coefficient of CO2 in water; MWCO2 [mol] is the molecular weight of CO2; MWhO2 [mol] is the molecular weight of H2O; MW [mol] is the molecular weight of the pesticide; kH2O [M.T -1 ] is the mass transfer coefficient of H2O in water; and MWH2O [mol] is the molecular weight of H2O.Since pesticides volatilization increases with temperature(Davie-Martin et al., 2015;[START_REF] Taylor | Understanding Trends in Pesticide Volatilization from Agricultural Fields Using the Pesticide Loss via Volatilization Model[END_REF], the volatilization coefficient kw-A is corrected according to the Arrhenius law:(II-II.6) 𝑘 𝑤-𝐴 (𝑇) = 𝑘 𝑤-𝐴 (20°𝐶) * 𝜃𝑇-20

--

  Mass balances quantify the temporal evolution of the state variables of the model over an infinitesimal period of time (dt). The state variables of the model are the pesticide mass in the water column Mw Chapter II-II: Modeling the fate of dissolved pesticides with a process-based model [M] and the pesticide mass in the active sediment layer Ms [M]. Hereafter we present the mass balance equations of pesticides in both compartments. (𝑡) * 𝑄 𝑖𝑛 (𝑡) -𝐶 𝑜𝑢𝑡 (𝑡) * 𝑄 𝑜𝑢𝑡 (𝑡) -(𝑘 𝑏𝑖𝑜,𝑤 + 𝑘 𝑝 + 𝑘 ℎ + 1 ℎ 𝑤 (𝑡) * 𝑘 𝐿-𝐴 ) * 𝜃 𝑇(𝑡)-20 * 𝑀 𝑤 (𝑡) + 𝑘 𝑑𝑒𝑠 * 𝑀 𝑠 (𝑡) -𝑘 𝑎𝑑𝑠 * 𝑀 𝑤 (𝑡) where Mw(t) [M] is the pesticide mass in the water column at time t; Cin(t) [M.L -3 ] is the concentration of pesticides at the AP inlet; Qin(t) is the volumetric inflow rate [L 3 .T -1 ]; and Cout(t) [M.L -3 ] is the concentration of pesticides at the AP outlet. The model assumptions state that the AP is assigned to a completely mixed reactor. Thus, the concentration of pesticides in the AP water (Cw [M.L -3 ]) after being subjected to the processes is equal to the concentration at the outlet Cout(t) = Cw(t). Qout is the AP discharge (outflow) rate [L 3 .T -1 ]; kbio,w [T -1 ] is the biotransformation coefficient in water at 20°C; kp, kh, and kL-A [L.T -1 ] are photolysis, hydrolysis, and volatilization coefficients, respectively, in water at 20°C; hw [L] is the water depth at time t; kads and kdes [T -1 ] are adsorption and desorption kinetic coefficients, respectively; and dt [T] = (t+dt)t is the time step of model (iteration step). Active sediment layer: (II-II.10) 𝑑𝑀 𝑠 (𝑡) 𝑑𝑡 = -𝑘 𝑏𝑖𝑜,𝑠 * 𝜃 𝑇(𝑡)-20 * 𝑀 𝑠 (𝑡) -𝑘 𝑑𝑒𝑠 * 𝑀 𝑠 (𝑡) + 𝑘 𝑎𝑑𝑠 * 𝑀 𝑤 (𝑡) where Ms(t) [M] is the pesticide mass in the active sediment layer at time t and kbio,s [T -1 ] is the biotransformation coefficient in the active sediment layer at 20°C. The parametrization of the PESTIPOND model is illustrated in Table II-II.A. 2 (Appendix II-II).

  -3 ] is equal to the initial concentration observed at the outlet Cout (t0) [M.L -3 ]. The initial mass Mw (t0) [M] is deducted from the initial concentration: (II-II.11) 𝑀 𝑤 (𝑡 0 ) = 𝐶 𝑤 (𝑡 0 ) * 𝑉 𝑤 (𝑡 0 ).

(

  T) were extracted from the monitoring data of the Rampillon site. The daily water depth hw was deducted from the water volume Vw calculated by the hydrological model (Eq.II-II.1). Since the model does not consider spillage, an initial water level and a maximum water level corresponding to observations were imposed on the hydrological model to avoid numerical divergence. The numerical error of the hydrological model is <1 %. The hydrological input data are summarized in Figure II-II. 2. The total intercepted volume by the AP covers 62.68% of the total drained volume in the 355-ha watershed (not illustrated).

Figure II-II. 2

 2 Figure II-II. 2 Summary of the input data for the hydrological model, i.e., total rainfall (mm) and ETP (mm) during 2014-2015, and the total inlet and outlet volumes of Rampillon proportional to the catchment area (355 ha).

Figure

  Figure II-II. 3 exhibits the observations of the water flow rates and the concentration of lenacil, as well as the estimated daily concentrations at the inlet from the autumn of 2014 (14/10/2014) to the summer of 2015 (03/06/2015). Note that the outflow rate Qout (L.s -1 ) in Rampillon is relatively similar to the inflow rate. The graph outlines that the mean inlet concentration of lenacil remains low (0.008-0.013 µg.L -1 ) during the autumn and winter seasons and starts to increase with the arrival of spring (0.017-0.053 µg.L -1 ) to reach the maximum value (0.0086 µg.L -1 ) between 05/05/2015 and 19/05/2015, and decrease afterward during the summer (0.026 µg.L -1 ). The increase in the concentration originates from the pesticide application on 25/04/2015, 17/05/2015, and 08/05/2015. The AP collected a total lenacil mass of 3974 mg, throughout a total volume of 427,500 m 3 during the monitoring period 2014-2015.

Figure II-II. 3

 3 Figure II-II. 3 Daily observations of the inflow rate Qin (L.s -1 ) (blue lines), the bimonthly sampled concentrations Ĉin (µg.L -1 ) (red segments), and the estimated daily concentration of lenacil Cin(t) (µg.L -1 ) (orange lines) from October 2014 (autumn) to June 2015 (summer) at the inlet of the Rampillon AP. The black arrow marks the start of lenacil application (25/04/2015).

  II. 2 to simulate the dynamics of lenacil in the water column and the active sediment layer of the AP (Figure II-II. 4 C)). For better representativity, the output concentrations are displayed at a daily time step first (Figure II-II. 4(A)), and the simulations are then represented bimonthly to be compared with the original observations (Figure II-II. 4 (B)).

Figure

  Figure II-II. 4 (A) Daily simulated (dark points) and observed (purple line) concentrations of lenacil at the outlet, Cout,sim (µg.L -1 ) and Cout,obs, respectively. (B) Bi-monthly outlet lenacil concentration Ĉout,sim (µg.L -1 ) (purple lines) and the bi-monthly observed outlet concentrations Ĉout (µg.L -1 ) (red lines) during 2014-2015. Note that the average frequency of pesticide observations is 2 weeks. Therefore, the duration between two successive observations can be longer or shorter than 2 weeks. (C) Simulated stock (g) of lenacil in the water column Mw (green line) and in the active sediment layer Ms (dashed green line). The black arrow indicates the first application of lenacil.

  displays a low adsorption along the period and a higher desorption reflecting the remobilization of lenacil due to its high mobility and hydrophilicity. These results are in line with outcomes of theoretical and laboratory studies that demonstrate the low retention and high remobilization of hydrophilic pesticide. For instance, greater desorption was observed on highly soluble and hydrophilic pesticides[START_REF] Olvera-Velona | Sorption and desorption of organophosphate pesticides, parathion and cadusafos, on tropical agricultural soils[END_REF]. Conversely, hydrophobic and lowly soluble pesticides are hardly desorbed(Hand et al., 2001; Li et al., 2003; Liu et al., 2019; Vagi et al., 2022).

Figure II-II. 5

 5 Figure II-II. 5 Allocation of the mass fluxes of each process according to the total intercepted mass of lenacil against temperature T (°C) during the period 14/10/2014-03/06/2015. The dashed white line refers to the start of lenacil application.

  Sobol indices were estimated for MT_cum of different study cases. The first SA was performed on a random period and pesticide scenario among the available monitoring data (Figure II-II. 6). Figure II-II. 6shows that that the first-order and total Sobol indices (SI, TSI) of kv and kh are insignificant (~0) for both methods (Jansen and Martinez), reflecting that the model output is insensitive to volatilization (kv) and hydrolysis (kh) either taken alone or interacting with other parameters. In addition, the two methods used to estimate Sobol indices yield the same results, which supports the robustness of the SA.

Figure II-II. 6

 6 Figure II-II. 6 First-order Sobol indices (SI; blue points) and total Sobol indices (TSI; red triangles) of the model output during 14/10/2014-02/06/2015 for chlorotoluron. The SA was performed on the seven parameters representing the set of processes behind pesticide behavior in AP. The Sobol indices were estimated using two variance estimators, i.e., the Jansen method (left) and the Martinez method (right).

  The SA results are displayed in Fig.II-II.7, Fig.II-II.8 and Fig.II-II.9 for the previously described study cases (Sect. 2.5) (i), (ii), and (iii), respectively.

Figure II-II. 7

 7 Figure II-II. 7 Sensitivity analysis (SA) of five input parameters representing the key processes behind pesticide fate in AP (i.e., adsorption, desorption, biotransformation in water, biotransformation in active sediment layer (water-sediment interface), and photolysis). Volatilization and hydrolysis were not considered since they do not influence the model outputs (Fig.II-II.4). SA was performed on the simulated cumulative total mass (MT_cum) of chlorotoluron (A), lenacil (B), and diflufenican (C) during 14/10/2014-02/06/2015 after the MC sampling of the parameters. The blue points correspond to the first-order Sobol index (SI), and the red triangles to the total Sobol index (TSI).

Fig

  Fig. shows that desorption (kdes) has the highest first-order and total effect on the three

  and highly mobile molecule, i.e., metolachlor (Figure II-II. 8), and a hydrophobic and lowly mobile molecule, i.e., diflufenican (Figure II-II. 9) during two different periods.

Figure II-II. 8

 8 Figure II-II. 8 Sensitivity analysis performed with metolachlor (Koc=120 L.kg -1 , logKow=2.9) during two random periods among the available monitoring periods (i.e., 07/11/2016-12/09/2017 and 30/10/2019-19/05/2020).

Figure II-II. 9

 9 Figure II-II. 9 Sensitivity analysis performed with diflufenican (Koc=5504 L.kg -1 , logKow=4.2) during two random periods (i.e., 14/10/2014-03/06/2015 and 30/10/2019-19/05/2020). Fig.II-II.8 shows that during the period 2016-2017, processes occurring at the water column (i.e., biotransformation and photolysis) have the highest effect on metolachlor behavior

Fig

  Fig.II-II.9 shows that the results of SA for diflufenican remain unchanged in both periods except for the first-order adsorption effect manifesting a slight decrease during 2019-2020. This could originate from the low presence of hydrophobic pesticides in the water column that is thus less impacted by the variation in the hydraulic conditions, in contrast to hydrophilic pesticides (Fig.II-II.8). Lastly, SA was also performed on the correction factor θ to assess the effect of temperature (T) on pesticide behavior (Appendix II-II, Fig.II-II.A.2). SA results showed that

  series describes the development of a process-based model of pesticide fate in AP and its application for a numerical case study based on a Rampillon pond configuration. The PESTIPOND model integrates physicochemical processes into a mathematical model and uses the mass conservation principle to simulate the mass of pesticides in pond compartments (i.e., water and sediment). In contrast to black box models, PESTIPOND considers the key dissipation and accumulation processes in each pond compartment. The model also includes a hydrological model to account for the water volume variation. Compared with other processbased models, PESTIPOND integrates the temperature effect and is designed to predict the behavior of all pesticides (hydrophobic and hydrophilic). The mathematical model is based on a coupled system of ordinary nonlinear differential equations solved numerically by the finitedifference method. A test-case simulation enabled us to exhibit the model behavior and visualize the outputs. The low value of the calculated errors (<1%) revealed the adequacy of the numerical scheme. The PESTIPOND model is designed to simulate the mass of pesticides leaving the pond and thus released to water resources, as well as the distribution and contribution of each process to the fate of pesticides in AP.

Figure

  Figure II-II.A. 2 First-order Sobol indices (SI; blue points) and total Sobol indices (TSI; red triangles) of the model output for diflufenican during 30/10/2019-19/05/2020. The SA was performed on the seven parameters representing the set of the processes behind pesticide behavior in AP and the temperature correction factor θ. The same SA was performed on other pesticides and different periods giving similar results.

( 6 )

 6 Figure III. 1 Map visualizing the localization of the AP of Rampillon (Seine-et-Marne, France) within the Seine River basin. Circled numbers indicate the different cells separated by bunds and considered for the spatial sampling(Lebrun et al., 2019).The white arrows refer to the ditch from where the AP intercepts agricultural water.

  to continuously monitor nutrient and pesticide fluxes and major water physicochemical parameters (i.e., flowrate, temperature, and dominant ions) at the inlet and outlet of the site (Figure III. 1). The typical monitoring stations comprise a flowmeter based at the water level, a Doppler (Sigma 950, Hach), a multi-parameter spectrophotometer (Spectrolyser UV-vis, Scan) for hourly measurements of turbidity and nitrates, and an automatic sampler managed for bi-monthly flow-weight sampling strategies. IRIS (IRIS Instruments, Orleans, France) also measured water temperature and water level using a pressure transducer model Madofil close to the outlet. Rainfall data were obtained through a local pluviometer installed at the study site.The daily potential evapotranspiration (PET) data are available in the SAFRAN database(Vidal et al., 2010). PET data values are calculated following the Penman-Monteith formula. The hydrology of the 355-ha watershed is summarized in Figure III. 2 It was chosen to validate the model upon the following five periods

Figure III. 2

 2 Figure III. 2 Summary of hydrological inputs of the 355-ha watershed where the Rampillon AP is implemented. The blue bars represent the local annual rainfall, the brown bars represent

  Figure III. 3 depicts the total efficiency of the Rampillon AP in dissipating each of the seven evaluated in this work. The main physicochemical properties of the evaluated pesticides are listed in Table III. 2.

Figure III. 3

 3 Figure III. 3 Percentage of the mean efficiency of the Rampillon AP to reduce the concentration between the inlet and outlet (bars) of the studied pesticides. The red line represents the mean temperature T (°C) of each period and the blue line refers to the mean hydraulic residence time HRT (d) in the AP during flow periods. The data is displayed according to the periods used for model validation.On average, the Rampillon AP dissipates 23% of the total intercepted flux of selected pesticides.The highest dissipation rate (48%) goes to boscalid during 2016-2017, followed by quinmerac (34%) and mesotrione (36%) during 2018-2019. Mesotrione was dissipated by 33% equally during its two years of monitoring, i.e.,2017-2018 and 2018-2019. The mitigation of s-metolachlor varied sharply throughout the years, with a dissipation rate going from 30% during 2016-2017 to 5% during 2014-2015 and 2019-2020. A similar variation was noticed for quinmerac that was dissipated up to 36% during 2018-2019, while its inlet mass was barely reduced during 2014-2015.

  (Eq.III.5). The NSE and KGE values range from -∞ to 1 and require data on both simulated and observed pesticide fluxes. NSE and KGE values close to 1 imply that the model simulations fit the observations owing to good model performance. The NRMSE ranges from 0 to 1, and lower values indicate a small discrepancy between simulations and observations. 𝑡 ) -min (𝑋 𝑡 ) Where, 𝑋 𝑡 * and 𝑋 𝑡 correspond to the observations and the simulations at the time step t, respectively. 𝑋 𝑇 * and 𝑋 𝑇 are the mean values of the observed and simulated pesticide fluxes, respectively, throughout the whole period of interest T. 𝑐𝑜𝑣(𝑋 𝑡 * , 𝑋 𝑡 ) refers to the covariance between 𝑋 𝑡 * and 𝑋 𝑡 while σ indicates the standard deviation.

Figure III. 4

 4 Figure III. 4 (A) and (B) outline that the simulations of the daily concentrations and masses of bentazon and diflufenican align with the observations.Figure III. 4 (B) shows that the outlet concentrations increase after the pesticide application, i.e., during spring for bentazon and autumn for diflufenican. The model simulates the higher exportation of bentazon (40g) in the water compared to diflufenican (3.5g) during 2014-2015 (Figure III. 4 (A)). During the same year, quinmerac was also exported significantly, with a total mass of 43g, while the exportation of the other pesticides (i.e., boscalid, chlorotoluron, and s-metolachlor) did not exceed 10g (Figure III.A. 1).

Figure III. 5

 5 Figure III. 5 underlines that the simulations fit also the non-transformed observations (bimonthly) of pesticide concentrations. The simulated concentration of s-metolachlor was underestimated compared to the observations of two samplings (22/05/2017 and 19/05/2020). Similar underestimation was noted for s-metolachlor mass exportation during 2019-2020 in Figure III.A. 1, which is also translated by its lower KGE (0.53) compared to other pesticides (Table III. 6). Conversely, the model overestimated the concentration of boscalid during 2014-2015 and

Figure III. 5

 5 Figure III. 5 Graphical comparison of the bi-monthly observations (y-axis) and simulations (xaxis) of all pesticide outlet concentrations and periods combined. Each color points out a specific pesticide. The black line in the middle refers to simulations equal to observations (Y=X). The full points refer to simulations with the annual-calibrated parameters and the empty squares refer to the simulation with the inter-annual set of parameters.

Figure III. 5

 5 Figure III. 5 shows that using the inter-annual set of parameters results in a good fit between the simulations and the non-transformed observations of pesticide concentrations. The s-metolachlor simulated concentration (0.5µg.L -1 ) was underestimated compared to the observations (0.77µg.L - 1 ) during 2019-2020. The underestimation was more accentuated during 2016-2017 when the simulated concentration of s-metolachlor (0.06 µg.L -1 ) was ten times lower than the observations (0.67 µg.L -1 ). The poor performance of the model evidenced by the low KGE value of boscalid is also noticed by the overestimation of the exported mass during 2016-2017 and the pronounced underestimation during 2014-2015 (Figure III.A. 2). On average, the relative error of boscalid

Figure III. 6

 6 Figure III. 6 Graphical representation of the proportion of pesticide masses to the total input mass.

Figure III. 7

 7 Figure III. 7 Graphical representation of the percentage of each process to the total mass intercepted by the pond and, the average temperature T (°C) and HRT (d) of each period.

Figure III. 8

 8 illustrates the mean efficiency for all pesticides and periods combined, and TableIII.A. 4 lists the efficiency values for each pesticide and period evaluated. For this extrapolation study, boscalid was excluded because it yielded poor model performance (section 3.2).

Figure III. 8

 8 Figure III. 8The mean efficiency of the Rampillon AP to dissipate pesticides from the inlet to the outlet according to the different sizes of the pond. The x-axis represents the percentage (%) of the area occupied by the AP in the total catchment area (355ha), and the values between () refers to the corresponding nominal HRT in days. The y-axis corresponds to the mean efficiency of the studied periods. The upper and lower grey areas refer to the discrepancy between the mean and the maximum and minimum efficiencies, respectively.

Figure III.A. 4

 4 Figure III.A. 4 illustrates the mean efficiency of pesticides for each period according to the AP area. The figure exhibits an annual variation of the pond efficiency independently of the area.

  2) proffered graphical (Figure III.4, Figure III.5) and statistical (Table

III. 7

 7 and Figure III.A. 1). Besides, boscalid simulations induce a model relative error (36%)

  The mass budget results (Figure III. 7) highlighted a higher transformation of bentazon, boscalid, and s-metolachlor 2016-2017, characterized by the highest mean temperature (11°C) and HRT (14 days). Besides, bentazon, diflufenican, and s-metolachlor underwent higher adsorption during 2019-2020. The same pesticide had a lower transformation during 2018-2019, characterized by a lower mean Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 226 temperature (8°C). Moreover, the desorption of bentazon and mesotrione was more significant during 2018-2019, when the HRT was approximately 6 days.

  .A. 3). The model extrapolation results evidenced that a temperature rise of 10°C will increase the mean dissipation potential of the AP by 13% (Figure III. 8). In comparison, a temperature drop of 5°C decreases the efficient by only 2%. These results provides insight into the geographical variation of AP efficiencies between warm and cold areas.

Figure III.A. 1

 1 Figure III.A. 1 Graphical comparison of simulated (purple lines) and observed (dark points) cumulative masses of the pesticides monitored during 2019-2020 in the outlet and the corresponding cumulative influx mass (orange line).

Figure III.A. 2

 2 Figure III.A. 2 Graphical comparison of simulated (purple lines) and observed (dark points) cumulative masses of pesticides in the outlet and the corresponding cumulative influx mass (orange line) for all pesticides and periods combined.

Figure III.A. 3

 3 Figure III.A. 3 Linear correlation between the calibrated adsorption parameter (kads) and the mobility (Koc) of pesticides (extracted from the PPDB (Lewis et al., 2016)). The left graph illustrates the correlation for mobile pesticides (low Koc) and the right graph displays the correlation for lowly mobile pesticides (high Koc).

Figure IV. 1

 1 Figure IV. 1 Location of the field study site at the country scale (A), the Garonne basin scale (B), and the Auradé catchment scale (C). The colors at the right illustrate the mean land cover in 2008-2009, as an example, since the land is occupied on a yearly rotation. (D) is an aerial view (satellite image from Google map, https://www.google.fr/maps/ accessed on 18 June 2021, Images ©2020 CNES/Airbus, Landsat, Maxar Technologies, Mapping data ©2020, France) with the sampling points used for pesticide monitoring(Chaumet et al., 2021).. On (D), the yellow and blue points refer respectively to the downstream and upstream discharge measurement locations and water sampling points.

  , the AP is 60 m long and 21 m large (in the middle part) with a surface area of 971 m 2 (for the entire pond system). The pond depth reaches 3 m in downstream parts with a water depth ranging from 30 to 50 cm. The Bassioué AP has been filled by a huge amount of sediments eroded from the cultivated plots upstream and transported by several consecutive storm events, particularly between 2016 and 2018. In spring 2018, an erosive flood event resulted in a sediment clump (5 m long, 7 m large) in the upstream part of the AP (Fig IV.1 (D)). Since 2019, the secondary AP (downstream) has become the main vegetation compartment of the Bassioué AP (Fig IV.1 (D)).

Figure

  Figure IV. 2 (A)The cumulative mass of TSS (MTSS (ton)) in the inlet (green) and outlet (red) of the AP. Qin (L.s -1 ) and Qout (L.s -1 ) are the inlet (blue) and outlet (yellow) daily water flow rates of the AP, respectively (in log scale). (B) The daily observed TSS fluxes (g.L -1 ) in the inlet (green) and outlet (red) of the AP against the water flow rates (data from(Chaumet et al., 2021)).

Figure IV. 3

 3 Figure IV. 3 The conceptual model of PESTIPOND integrating the particulate phase. The processes in red stand for transformation processes, and the green ones represent non-degradative processes, which do not change the original pesticide chemical structure.

Figure IV. 3

 3 Figure IV. 3 depicts the main compartments of a typical AP in an erosive catchment with significant solids transport, i.e., a water column and a sediment layer. Since the intercepted water by AP in sloping areas is loaded with solid particles, we introduced a compartment of suspended particles in the water column. Chaumet et al. (2021) analyzed the texture and micro-granulometry

  Figure IV. 3 depicts various transport, Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 271 transfer, and transformation processes of DP and PP in a typical flooded AP system in erosive areas. These processes are accounted for in the mass balance equations. A mass balance equation was established for each pond compartment: The water column including the sub-compartments of the DP, fine fraction of PP, and coarse fraction of PP, and a compartment for the active sediment layer. The pesticide fate model is coupled to a hydrological model and TSS mass budget to account for the TSS, water dynamics, and the hydraulic residence time (HRT). The equations of the pesticide mass balances, the hydrological model, and the TSS mass budget were all expressed by ordinary first-order differential equation (ODE) as following. The set of equations was written

(

  -𝑄 𝑜𝑢𝑡 -𝑄 𝐼 + (𝑃 -𝐸) * 𝐴 Where Vw [L 3 ] is the water volume of AP surface water. A is the AP surface area [L 2 ]. Qin the volumetric inflow rate [L 3 .T -1 ]. Qout the AP discharge (outflow) rate [L 3 .T -1 ]. QI the infiltration rate

  CTmin [M.L -3 ] and CTmax [M.L -3 ] are the minimum and maximum TSS concentrations to be transported with the water flow, respectively. η1 and η2 (unitless) are the critical efficiency coefficients of settling and resuspension, respectively. ωs is the settling velocity of suspended particles [L.T -1 ], and Um is the mean velocity of the water [L.T -1 ]. ρe is the water density [M.L -1 ], and s (unitless) is the relative density of sediment to water. Ip (L.L -1 ) is the slope. Um [L.T -1 ] is calculated by the following equation: Where L (L) is the mean width of the AP. If TSS [M.L -3 ] is the TSS concentration, the solids transport is conditioned as follows: Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 273 a) If TSS< CTmin, resuspension of sediments occurs until TSS= CTmin. b) If CTmin <TSS< CTmax, no settling or resuspension occurs. c) If TSS> CTmax, settling occurs until TSS= CTmax. The objective of the transport model is to estimate the daily TSS mass MTSS (g) in the water column and the daily mass of sediments Msed (g) in the active sediment layer. Therefore, we first estimate the daily MTSS based on the inlet and outlet TSS fluxes using an Euler solving scheme: (IV.5) 𝑀𝑇𝑆𝑆(𝑡 + 𝛥𝑡) = 𝑀𝑇𝑆𝑆(𝑡) + 𝑇𝑆𝑆 𝑖𝑛 (𝑡) * 𝑄 𝑖𝑛 (𝑡) * 𝛥𝑡 -𝑇𝑆𝑆 𝑜𝑢𝑡 (𝑡) * 𝑄 𝑜𝑢𝑡 (𝑡) * 𝛥𝑡 Where Δt refers to the time step. In our case, Δt= 1 day. TSSin [M.L -3 ] and TSSout [M.L -3 ] are the daily inlet and outlet TSS fluxes of the AP. Then we deduce the daily TSS concentration is the water column: t) [M.L -3 ] is the daily TSS concentration in the water column, and Vw [L 3 ] is the daily water volume calculated by the hydrological model (Eq.IV.1). TSS (t) is recalculated according to Velikanov's theorem (i.e., conditions a), b), and c)). The values of the parameters used in Velikanov's equation are listed in

  Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 275 pesticide fluxes are expressed with constant rates. Therefore, the settling and resuspension mass fluxes are described by the following equations: * 85% * 𝑀 𝑠 (𝑡) Where Mset [M.T -1 ] is the daily settling flux of PP. Mp (t) [M] is the daily mass of PP in the water column. vs,f and vs,c [L.T -1 ] are the settling rates of the fine and coarse PP, respectively. Mres [M.T -1 ] is the daily re-suspended flux of PP in the active sediment layer pesticides. PP in the sediment layer are DP adsorbed of sediments. vr [L.T -1 ] is the resuspension rate of fine particles.

  Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 279 the PESTIPOND model on PP. Only pesticide monitoring from the 2019-2020 year was available, and whose database was used for this model testing. The values of the parameters used for this test are listed in

  Figure IV. 4 exhibits the daily observations of pendimethalin concentration in the dissolved and particulate forms and the water flow rate. Note that the daily concentrations are the mean concentration between two grab samples i and i+1

Figure

  Figure IV. 4 (A) Daily observations of the inlet and outlet concentrations of dissolved pendimethalin in the water column (Cin,diss(µg.L -1 ), Cout,diss(µg.L -1 )) in the AP. (B) Daily observations of the inlet and outlet concentrations of particulate pendimethalin in the water column (Cin,p(µg.L -1 ), Cout,p(µg.L -1 )). (C) Daily observations of the inlet and outlet concentrations of particulate pendimethalin in the suspended solids (Cin,p(µg.g -1 ), Cout,p(µg.g -1 )). The blue dashes refer to the daily inflow rate with a logarithmic scale.

Figure IV. 5

 5 Figure IV. 5 Simulated water volume Vw (m 3 ) and daily-observed inflow and discharge volumes Vin (m 3 ) and Vout (m 3 ), respectively. The left y-axis represents the daily rainfall (mm) and evapotranspiration PET (mm) measured at the hydrological station next to the Bassioué AP (Chaumet et al., 2022). The transport model (subsection 2.4.2) was built to compute the daily TSS mass MTSS (µg) in the water column within the pond from the inlet and outlet TSS fluxes and water flow rates. In order to check the Velikanov model performance, we compared the simulated TSS concentration in the outlet with the observations (Figure IV. 6).

Figure IV. 7

 7 Figure IV. 7 (A) shows that the solids transport model manages to reproduce the global dynamic of the observed TSS concentration. However, the model slightly underestimates the discharged TSS concentration during the spring rainfall event (March-May) and strongly during 28/12/2020.

Figure IV. 7

 7 Figure IV. 7 (B) exhibits a good fit between the observed and simulated cumulative masses of TSS except at the start and end of the period as observed for the daily fluxes (Figure IV. 7 (A)).

  Figure IV. 7 (A)Simulations of the conservative case (with no processes) (red lines) and observations (dark points) of cumulative mass of dissolved pendimethalin at the AP outlet and the observed cumulative mass the inlet (green line). Graph (B) exhibits the daily observations and simulations of the mass of dissolved pendimethalin with a logarithmic scale for legibility.

Figure

  Figure IV. 8 (A) Simulations of the conservative case (with no processes) (red lines) and observations (dark points) of the cumulative mass of particulate pendimethalin at the AP outlet and the observed cumulative mass in the inlet (green line). Graph (B) exhibits the daily observations and simulations of the mass of particulate pendimethalin with a logarithmic scale. Therefore, empty points refer to zero values of the inlet PP concentration.

Figure IV. 8

 8 Figure IV. 8 displays the similarity between simulations of the cumulative mass of dissolved pendimethalin at the outlet and the observed inlet mass without the activation of processes. Since no processes occurred, PESTIPOND simulated an outlet mass close to the inlet mass for the dissolved phase. In that case, the model conserves well the dissolved mass since the mass balance error <3%.

  . 6 and the simulation results are displayed in Figure IV. 9 and Figure IV. 10.

Figure

  Figure IV. 9 (A)Simulations with processes considered (red lines) and observations (dark points) of cumulative mass of dissolved pendimethalin at the AP outlet and the observed cumulative mass at the inlet (green line). Graph (B) exhibits the daily masses of dissolved pendimethalin with a logarithmic scale.

Figure

  Figure IV. 10 (A) Simulations with processes considered (red lines) and observations (dark points) of cumulative mass of particulate pendimethalin at the AP outlet and the observed cumulative mass at the inlet (green line). Graph (B) exhibits the daily masses of particulate pendimethalin. (A) and (B) are represented with a logarithmic scale.

Figure IV. 9

 9 Figure IV. 9 (A) shows that for the dissolved phase, the simulations could not reach the observations of the cumulative mass contrarily to the particulate phase Figure IV. 10 (A)), where the simulations overestimate the observations starting from the December rainfall event. Overall, the discrepancy between the simulations and observations of the cumulated masses proportionally to the total intercepted flux reaches 36% and 26% for the dissolved and particulate phases, respectively.

  Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 2933 ConclusionAfter being tested and validated for the dissolved phase of pesticides, the PESTIPOND model was tested on particulate pesticides. For this intention, the model was tested on the Bassioué site (Auradé, France), a typical AP of sloping farmlands where the solids transport associated with agricultural runoff is significant. The particle version of the model requires more input variables and parameters since it considers additional processes (i.e., settling and resuspension) and compartments (i.e., suspended solids in the water column). In addition to the hydrological model used in the original version, the particle version of PESTIPOND includes a sub-model for solids transport. The solids transport model predicts the dynamic of solids in the AP due to settling and resuspension processes.At first, the solids transport model was developed based on an adapted version of Velikanov's model to estimate TSS dynamics. Afterward, PESTIPOND was tested for the highly hydrophobic, lowly mobile, and lowly soluble pendimethalin as a generic pesticide in particulate form. The simulation results illustrate the ability of PESTIPOND to predict the global dynamics of the dissolved and particulate phases of pendimethalin. However, the model underestimates the total exported mass from the AP for both dissolved and particulate phases. The mean relative errors proffered that the model is more performant for the dissolved phase than the particulate phase of pesticides.

  Photolysis of DP kp T -1 Hydrolysis of DP kh T -1 Volatilization of DP kv T -1Temperature factor θ Unitless

  order to validate the PESTIPOND model, we compared the simulations of different pesticide fluxes at the AP outlet with observations of 5 monitoring periods (2014-2020) in Rampillon. The statistical comparison of simulations and observations was based on 3 well-known criteria: Nash Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and the normalized root mean square error (NRMSE).

Table IV . 1

 IV Comparison between the existing pesticide fate models in AP and the particulate version of the PESTIPOND model according to the considered processes and inputs. The AGRO-2014 inputs are provided by the FOCUS-PRZM model

Table I . 1

 I The commonly established methods in the literature to quantify and parametrize a process for a pesticide dissipation modeling approach in artificial ponds.

	Process	Method	Parameter Units
		Transport
	Settling-Resuspension Empirical formulas	vs; vr	m.day -1
		Transfer
	Adsorption-Desorption Batch experiments	kads; kdes	day -1
		+
		Kinetic model fitting
	Volatilization	Empirical formulas	kv	day -1
		Transformation
	Biotransformation	Incubation experiments	DT50bio	day -1
		+
		Kinetic model fitting
	Photolysis	Laboratory experiments DT50photo	day -1
	Hydrolysis	Empirical formulas	DT50h	day -1

Table I . 2

 I List of the surveyed processes for each pesticide cited in the reviewed papers, and the equation used to estimate the rate of processes. NA means that the process was evaluated without a modeling approach.Tang et al., 2019; Wang et al., 2019 ; Karpuzcu et al., 2013; Liu et al., 2019; Mackay et al., 2006; Yu et al., 2019)

					Processes
	Pesticides	SET	RES	SOR	VOL	BIO	PHO	HYD	Source
	Acetochlor					Eq.I.13			(Droz et al., 2021)
	Aclonifen					NA			(Passeport et al., 2013)
	Alphamethrin				Eq.I.11				(Comoretto et al., 2008)
	Atrazine			NA		NA			(Besse-Hoggan et al., 2009;(Moore et al., 2013); Zeng et al., 2013)
	Bifenthrin	NA							(Cryder et al., 2021)
	Boscalid			NA		NA	NA	Eq.I.17	(Lagunas-Allué et al., 2010; Vallée, 2015)
	Butachlor			NA		NA		NA	(Masbou et al., 2018; Torabi et al., 2020; Zheng et al., 2001)
	Carbaryl							NA	(Wolfe et al., 1978)
	Chlorothalonil					NA			(Passeport et al., 2013)
	Chlorotoluron					NA			(Passeport et al., 2013)
	Chlorpyrifos			NA	NA	Eq.I.13		NA
	Clothianidin					Eq.I.13			(Beringer et al., 2021)
	Cyfluthrin	NA							(Cryder et al., 2021)
	Cyproconazole					NA			(Passeport et al., 2013)
	Diazinon			NA		Eq.I.13		Eq.I.17
	Dichlorvos					NA		Eq.I.17	(Sahin et al., 2020; Tomlin, 2009)
	Dieldrin				Eq.I.11			Eq.I.17	(Singh et al., 1991)
	Diuron					NA			(Zeng et al., 2013)
	Endosulfan				Eq.I.11		Eq.I.15	Eq.I.17	(Rose et al., 2008; Singh et al., 1991; Walse et al., 2002)
	Epoxiconazole			NA		NA			(Passeport et al., 2011; Passeport et al., 2013)
	Ethofumesate					NA			(Passeport et al., 2013)
	Fenitrothion						Eq.I.15		(Sakellarides et al., 2003)
	Fenpropidin					Eq.I.13			(Passeport et al., 2013)
	Fenthion					NA	Eq.I.15	NA	(Sahin et al., 2020; Sakellarides et al., 2003)
	Fipronil	NA							(Cryder et al., 2021)
	Fludioxonil			NA			Eq.I.15		(Katagi et al., 2016; Maillard et al., 2014)
	Fluometuron			NA			Eq.I.15	Eq.I.17	(Hand et al., 2001; Rose et al., 2008)
	Glyphosate			NA					(Maillard et al., 2014)
	Imazosulfuron			Eq.I.8					(Takagi et al., 2012)
	Imidacloprid			NA					(Mahabali et al., 2014)
	Isoproturon			NA		NA

(Agudelo et al., 2010; Laabs et al., 2007; Sardar et al., 2005 ; Rogers et al., 2009; (Ibáñez et al., 2006; Mackay et al., 2006; Sahin et al., 2020; Sakellarides et al., 2003)

Table I .

 I 2 (continued)

					Processes
	Pesticides	SET	RES	SOR	VOL	BIO	PHO	HYD	Source
	Metazachlor			NA		Eq.I.13			(Passeport et al., 2011; Passeport et al., 2013)
	Methyl parathion		NA	NA					(Rong et al., 2019; (Schulz et al., 2003)
	Molinate			Eq.I.8	Eq.I.11		Eq.I.15		(Inao et al., 1999)
	Napropamide			NA		NA			(Passeport et al., 2013; Vallée, 2015)
	Oxadiazon				Eq.I.11				(Comoretto et al., 2008)
	Parathion-Methyl						Eq.I.15		(Sakellarides et al., 2003)
	Picloram			Eq.I.8					(McCall et al., 1985)
	Pretilachlor			Eq.I.8	Eq.I.11	Eq.I.13	Eq.I.15		(Vallée, 2015)
	Prochloraz			NA		NA			(Passeport et al., 2013)
	Prosulfocarb					NA			(Gobas et al., 2018)
	Pyraclostrobin				Eq.I.11				(Maillard et al., 2014)
	Pyrimethanil			NA					(Droz et al., 2021; Masbou et al., 2018; Passeport et al., 2013)
	S-Metolachlor					Eq.I.13	Eq.I.15	NA	(Comoretto et al., 2008; Nakano et al., 2004; Watanabe et al., 2006; Yoshida et al., 2000)
	Spiroxamine			NA					(Maillard et al., 2014)

Tebuconazole

NA NA

(Maillard et al., 2014; Passeport et al., 2011; Passeport et al., 2013; Vallée, 2015)

Terbuthylazine NA

(Hijosa-Valsero et al., 2016)

Triazophos NA

(Cheng et al., 2007)

Trifluralin NA

(Laabs et al., 2007)

Table I .

 I A. 1 Formulation of settling rates of suspended particles in wetlands and associated parameters.

	Settling rate vs (m.s -1 ) Parameters	Conditions	of	Source
					application
	2 9	*	𝑟 𝑝 2 * (𝜌 𝑝 -𝜌 𝑤 ) * 𝑔 𝜇	(𝑖)

Table I .

 I A. 4 Adsorption and desorption kinetics(kads, kdes)

.A. 5.

	Pesticide	vp (Pa) 7 Classification DT50v (d) kv (d -1 )	Source
	Alphamethrin	2.3*10 -3 Volatile	0.2	3.01
	Oxadiazon	1.49*10 -5 Semi volatile 24.8	0.03	(Comoretto et al., 2008)
	Kresoxim-methyl 2.29*10 -6 Non-volatile	96924	0.00
	Pyraclostrobin	2.60*10 -8 Non-volatile	6850343 0.00	(Gobas et al., 2018)
	Molinate	1.3	Volatile	2.6	0.27	(Inao et al., 1999)
	Endosulfan	7.59*10 -1 Volatile	3	0.23
	Dieldrin	3.20*10 -3 Volatile	4.4	0.16	(Singh et al., 1991)
	Pretilachlor	1.2*10 -4 Semi volatile 346.5	0.00
	Mefenacet	6.39*10 -7 Non-volatile	5941	0.00	(Watanabe et al., 2006)

Table I .

 I A. 8 Biotransformation half-lives in the water column DT50bio,w (d) and sediment surface DT50bio

Table

						DT50bio,w	DT50bio,s
	Pesticide	Koc(L/kg)	DT50bio,w (d)	DT50bio,s (d)	Site	(d)	(d) 8	Source
						PPDB	PPDB
	Acetochlor	156	Anaerobic: 59 Aerobic: 31	Anaerobic: 47 Aerobic: 53	Wetland OC=2.3%	40.5	19.7	(Droz et al., 2021)
	Chlorpyrifos	5509	-	Anaerobic: 125-746 Aerobic: 1.8 -4.9	Urban stream sediment OC=1.8%	-9	36.5	(Bondarenko et al., 2004)
	Clothianidin	123	-	Anaerobic: 27.2 Aerobic: 115	Wetland OC=0.2%	-	-	(Beringer et al., 2020)
	Diazinon	165-189	-	Aerobic: 53.32		4.3	10.4
					Lake sediment			(Sahin et al.,
					OC= 2.22%			2020)
	Fenthion	1500	-	Aerobic: 5.5		-	92
	MCPA	100	-	5.1*	Paddy field OC=0.9%,	-	25	(Comoretto et al., 2008)
					Clay=47%
	Metazachlor	134	216*	20.6*	Wetland OC = -	-	-	(Passeport et al., 2013)
	Pretilachlor	1159	9.8*	19.0*	Paddy field OC=2.3%	-	-	(Nakano et al., 2004)
	S-Metolachlor	300	Anaerobic: 199 Aerobic: 29	Anaerobic: 182 Aerobic: 151	Wetland OC=2.3%	88	365	(Droz et al., 2021)
	Epoxiconazole 300		170	Wetland OC=2%	1000	103.6	(Passeport et al., 2011)

Table I

 I

		Koc 10 (L/kg)	DT50photo (d) PPDB	Source
	Atrazine	100	385	2.6	(Mathon et al. 2019)
	Dimethoate	5.2-50	495	175	(Mathon et al. 2019)
	Diuron	680	93.7	43	(Mathon et al. 2019)
	Endosulfan	11500	12	-	(Rose et al., 2008)
	Fenitrothion	2000	0.5	3.5	(Sakellarides et al., 2003)
	Fenthion	1500	1.6	0.4	(Sakellarides et al., 2003)
	Fludioxonil	145 600	9	10	(Katagi et al., 2016)
	Fluometuron	202	1	stable	(Rose et al., 2008)
	Isoproturon	126	41.3	48	(Mathon et al. 2019)
	Molinate	190	54	stable	(Inao et al., 1999)
	Parathion-Methyl 1500	25	9	(Sakellarides et al., 2003)
	Simazine	130	173	1.9	(Mathon et al. 2019)

.A. 10 Modulating factors applied to the photolysis rate kphoto to account for controlling factors (i.e., Temperature, depth, and radiation).

Table I .

 I A. 11 Biotransformation half-lives DT50bio,s (d) of five pesticides with different mobility Koc and hydrophilicities log Kow, measured in laboratory studies on sediment substrates with different organic carbon content OC (%)(Vallee et al., 2016). Along with, biotransformation half-lives at the water-sediment interface in wetlands, extracted from the PPDB.

	Appendix I.B.
	Pesticides Methodology (L/kg) 11 log (Kow) OC (%) Koc The present graph displays the methodology adopted to realize this critical review of process DT50bio,s DT50bio,s (d) (d) PPDB
	Boscalid formulations and controlling factors behind pesticide dissipation in ponds. This work started 9500 2.96 1.23 39.4
	545 with a bibliometric research. We used the listed research equations in different bibliographic 1.5 29.8
	research tools*:		1.7	9.4
	Isoproturon -(constructed wetland) AND ((wastewater OR agricultural) AND treatment) 122 2.5 1.2 100 149 1.5 61
	1.7 -pesticide AND (dissipation OR mitigation) AND (wetland OR pond) 63.9
	Napropamide -(pesticide OR "Organic pollutant") AND wetland AND model 885 3.3 1.23 82.7 1.5 80.1	316
			1.7	54
	Prochloraz	378 4.6	1.2	100
			1.5	17.2	359
			1.7	10.2
	Tebuconazole	769 3.7	1.2	30.5
			1.5	30.6	365
			1.7	1.4

-(pesticide OR "Organic pollutant") AND wetland AND fate -pesticide AND process AND (pond OR wetland) -pesticide AND transfer AND (wetland OR pond) -process AND (rate OR model OR kinetic) AND pesticide AND pond *: Web of Science (1), Scopus (2), Google Scholar (3), EndNote (4), and Science direct (5).

(1):https://www.webofscience.com/wos/woscc/basic-search (2):https://www.scopus.com/search/form.uri?display=basic#basic (3): https://scholar.google.com/schhp?hl=fr (4): https://www.myendnoteweb.com/EndNoteWeb.html Italic represents the name of the processes (i.e., sorption/adsorption/desorption, biodegradation/degradation, settling/sedimentation/burial, resuspension, photolysis/photodegradation, volatilization, and hydrolysis).

Table II -

 II I. 1 List of the 61 detected pesticides during the monitoring periods (2011-2019).

	Chapter II-I: Modeling the fate of dissolved pesticides with a black box model
		Herbicides	Insecticides
	2,4 -DP	Glyphosate	Clothianidin
	2 ,4 -D	Imazamox	Imidacloprid
	Aclonifen	Isoproturon	Thiamethoxam
	Amidosulfuron	Lenacil
	Atrazine	MCPA	Fungicides
	Benoxacor	Mesosulfuron -methyl	Azoxystrobin
	Bentazon	Mesotrione	Boscalid
	Bromacil	Metamitron	Cyproconazole
	Bromoxynil	Metazachlor	Cyprodinil
	Chloridazon	Metolachlor	Epoxiconazole
	Chlorotoluron	Metribuzin	Flutriafol
	Clomazone	Napropamide	Metalaxyl
	Clopyralid	Nicosulfuron	Metconazole
	Dicamba	Pendimethalin	Oxadixyl
	Diflufenican	Propyzamide	Picoxystrobin
	Dimethachlor	Prosulfocarb	Prochloraz
	Dimethenamide	Prosulfuron	Propiconazole
	Dinoterb	Quinmerac	Silthiofam
	Ethofumesate	Terbuthylazine	Tebuconazole
	Florasulam	Tribenuron -methyl
	Flufenacet	Triflusulfuron -méthyls
	Fluroxypyr
	Flurtamone
			121

  Modeling the fate of dissolved pesticides with a black box model 124 the black box model's performance. For this study, a RE threshold of 25% was selected to assess the model performance.

	(II-I.8)
	𝑅𝐸 (%) =	∑ 𝑀 𝑜𝑢𝑡,𝑠𝑖𝑚 -∑ 𝑀 𝑜𝑢𝑡,𝑜𝑏𝑠 ∑ 𝑀 𝑜𝑢𝑡,𝑜𝑏𝑠

The model performance was assessed by estimating the relative error RE (%). The RE gauges the discrepancy between simulated Mout,sim and observed Mout,obs fluxes and is expressed by Eq.II.8. Therefore, the lower the RE, the better Chapter II-I:

Table II-I. 2, and

  were compared to literature values, i.e., extracted from the largest pesticide properties database (PPDB)(Lewis et al., 2016). The PPDB provides half-lives of processes behind the overall dissipation of pesticides (i.e., biotransformation in the water and sediments, photolysis, and hydrolysis). The corresponding half-life is measured in laboratory conditions at a 20°C temperature for each process. A further description of the different processes is available in Chapter I. The calibrated values of the rest of pesticides are listed in

	Table II-I.A. 1 (Appendix II-I).

Table II -

 II I. 2 List of the calibrated DT50 (d) (deduced from the calibrated kdiss) for some pesticides alongside the literature (i.e., PPDB) half-lives. DT50bio,w (d) is the biotransformation half-life in the water column. DT50bio,s (d) is the biotransformation half-life at the watersediment interface column. DT50p (d) is the photolysis half-life in the water column. DT50h (d) is the hydrolysis half-life. RE (%) refers to the relative error of the simulations. NA values indicate non-available data.

		Calibration		Literature (PPDB)
	Pesticides	DT50 (d)	RE(%)	DT50bio,w (d) DT50bio,s (d) DT50p (d) DT50h (d)
	Bentazon	13.75	< 25%	80	716	4	stable
	Glyphosate	3.57	> 25%	9.9	74.5	69	stable
	Cyproconazole	-20.63	< 25%	NA	1000	40	stable
	AMPA	7.6	< 25%	5.47	132	NA	NA
	Prosulfocarb Silthiofam	-0.97	< 25%	0.94	214	stable	stable
	Lenacil	5.07	< 25%	91	112	stable	stable
	Clomazone	3.9	> 25%	NA	54	stable	stable
	Terbuthylazine 2-hydroxy	6.14	> 25%	NA	NA	NA	NA

Table II -

 II

I.2

shows that the calibrated half-lives range from -20.63 to 13.75 days. Negative halflives are non-physical despite resulting in good model performance, translated by a RE <25%.

Table II -

 II I. 3 Classification of the pesticides according to the RE between simulations and observations of cumulative fluxes (µg).

	RE < 25%		RE > 25%
	Aclonifen;	Bentazon;	Boscalid;	Clomazone; Ethofumesate; Glyphosate;
	Chloridazon; Chlorotoluron; Imidacloprid;	Metamitron; Metolachlor; Prosulfuron;
	Isoproturon;	Mesosulfuron-methyl;	Terbuthylazine 2-hydroxy; Tribenuron-
	Propyzamide; Quinmerac; Atrazine 2-	methyl.
	hydroxy; Atrazine desethyl; atrazine
	desethyl deisopropyl; Thiamethoxam; 24D;
	Benoxacor; Clopyralid; Dimethachlor;
	Dimethenamide;	Triflusulfuron-methyl;
	Clothianidin; Metconazole; Atrazine;
	Azoxystrobin;		Cyproconazole;
	Epoxiconazole; Imazamox; Metazachlor;
	Napropamide;		Nicosulfuron;
	Tebuconazole; Oxadixyl; MCPA; AMPA;
	Diflufenican; Fluroxypyr; Mesotrione;
	Pendimethalin; 2,6-dichlorobenzamide.

Table II -

 II I.A. 1 Calibration Results Of Kdiss (H -1 ), Corresponding DT50 (D), And RE(%) ForThe Evaluated Pesticides.

	Chapter II-I: Modeling the fate of dissolved pesticides with a black box model
	Pesticide	Kdiss (H-1)	DT50 (Days)	RE (%)
	2,6-Dichlorobenzamide	0.0031	9.32	11
	Aclonifen	0.0211	1.37	14
	AMPA	0.0105	2.75	-71
	Atrazine	0.0188	1.54	-17
	Atrazine Desethyl	0.0023	12.56	-3
	Atrazine Desethyl Deisopropyl	0.0028	10.31	1
	Azoxystrobin	0.0031	9.32	-21
	Bentazon	0.0036	8.02	-4
	Boscalid	0.0034	8.49	1
	Chloridazon	0.0062	4.66	5
	Chlorotoluron	0.0073	3.96	3
	Clomazone	0.0114	2.53	52
	Cyproconazole	0.0042	6.88	7
	Diflufenican	0.0156	1.85	18
	Epoxiconazole	0.007	4.13	19
	Ethofumesate	0.0001	288.81	0
	Glyphosate	0.011	2.63	18
	Imazamox	0.0052	5.55	2
	Imidacloprid	0.0078	3.70	-10
	Isoproturon	0.0198	1.46	20
	Lenacil	0.0101	2.86	-20
	MCPA	0.0038	7.60	17
	Mesosulfuron-Methyl	0.0016	18.05	4	134
	Mesotrione	0.003	9.63	6
	Metamitron	0.0167	1.73	-12
	Metazachlor	0.0057	5.07	25
	Metolachlor	0.0081	3.57	4
	Nicosulfuron	0.0039	7.41	-40
	Oxadixyl	0.0141	2.05	-10
	Pendimethalin	0.0291	0.99	-16
	Prosulfuron	0.0025	11.55	12
	Quinmerac	0.0009	32.09	7
	Tebuconazole	0.0066	4.38	-54
	Terbuthylazine 2-Hydroxy	0.0097	2.98	51
	THIAMETHOXAM	0.0064	4.51	1
	TRIBENURON-Methyl	0.0603	0.48	-50

Table II -

 II I.A. 2 PPDB Half-lives and physico-chemical properties of the evaluated pesticides(Lewis et al., 2016). Half-lives at the water-sediment-water interface refer to biotransformation in both compartments, respectively.[START_REF] Sun | Enhanced removal of organic matter and ammoniacalnitrogen in a column experiment of tidal flow constructed wetland system[END_REF]. Enhanced removal of organic matter and ammoniacalnitrogen in a column experiment of tidal flow constructed wetland system. Journal of biotechnology 115,189-197. Tanner, C. C., and Kadlec, R. H. (2013). Influence of hydrological regime on wetland attenuation of diffuse agricultural nitrate losses. Ecological Engineering 56, 79-88.

	Chapter II-I: Modeling the fate of dissolved pesticides with a black box model
							DT50 (d)					Physico-chemical properties
	Code SANDRE Code SANDRE Code SANDRE	Pesticide Propiconazole Pesticide Pesticide Propyzamide Isoproturon Prosulfocarb	Photolysis stable Photolysis Photolysis 41 48 stable	kp (d -1 ) NA kp (d -1 ) kp (d -1 ) 0.017 0.014 NA	Hydrolysis 53.5 Hydrolysis Hydrolysis stable 1560 stable	Half-lives kh (d -1 ) sediment Water-Half-lives 0.013 561 kh (d -1 ) kh (d -1 ) Water-sediment NA 94 Water-sediment 0 149 NA 214	kbio,s (d -1 ) 0.001 kbio,s (d -1 ) 0.007 kbio,s (d -1 ) 0.005 NA	Water 6 Water 21 Water 40 0.94	kbio,w (d -1 ) 0.116 kbio,w (d -1 ) 0.033 kbio,w (d -1 ) 0.017 NA	Physico-chemical Koc (L.kg -1 ) log Kow properties 1200-8100 3.72 Physico-chemical properties Koc (L.kg -1 ) log Kow 200-800 3.27 Koc (L.kg -1 ) log Kow 126 2.5 1367-2339 4.48
	2,6-Dichlorobenzamide Lenacil Prosulfuron Team, R. C. (2020). R: A language and environment for statistical computing. R Foundation stable NA stable NA 10000 stable NA stable NA 112 242 0.003 stable NA 173	0 0.006 0.004	10000 91 103	0 0.008 0.007	30 75-254 4-251	0.38 1.69 1.5
	24D MCPA Quinmerac for Statistical Computing, Vienna, Austria. 38 0.018 0.05 13.863 66.1 0.01	stable stable stable	NA NA NA	18.2 17 179.4	0.038 0.041 0.004	7.7 13.5 88.7	0.09 0.051 0.008	20-136 50-62 85	-0.821 -0.81 -1.41
	Aclonifen Amidosulfuron Mesosulfuron-methyl Silthiofam Tournebize, J., Chaumont, C., and Mander, U. (2017). Implications for constructed wetlands to 197 0.004 stable NA 14.3 0.048 stable NA stable NA 50.1 0.014 46 0.015 253 0.003 48.9 0.014 16 0.043 stable NA 269 0.003 AMAPA NA NA NA NA 132 0.005 Mesotrione 89 0.008 stable NA 5.2 0.133 Tebuconazole stable NA stable NA 365 0.002 mitigate nitrate and pesticide pollution in agricultural drained watersheds. Ecological	4.2 56.9 44 5 5.47 5.3 42.6	0.165 0.012 0.016 0.139 0.127 0.131 0.016	3.9 36 11 173-327 8027 15-390 470-6000	4.369 -0.48 3.72 -1.561 -1.631 0.111 3.7
	Atrazine Metalaxyl Terbuthylazine Engineering 103, 415-425. 2.6 stable stable	0.267 NA NA	86 162 stable	0.008 0.004 NA	80 56 70	0.009 0.012 0.01	NA 56 6	NA 0.012 0.116	26-1164 30-284 151-514	2.7 1.751 6.312
	Atrazine 2-Hydroxy Atrazine Desethyl Deisopropyl Metconazole Thiamethoxam Atrazine Desethyl Metamitron Metazachlor Terbuthylazine 2-hydroxy (hydroxyterbuthylazine) Tournebize, J., Gramaglia, C., Birmant, F., Bouarfa, S., Chaumont, C., and Vincent, B. (2012). NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 83 0.008 stable NA 465 2.7 0.257 stable NA 40 0.02 34.657 480 0.001 11.1 stable NA stable NA 20.6 NA NA NA NA NA	NA NA NA 0.001 0.017 0.062 0.034 NA	NA NA NA 8 30.6 10.5 216 NA	NA NA NA 0.087 0.023 0.066 0.003 NA	57-188 24-3000 NA 797 68 128 110 219	2.09 1.511 NA 3.85 -0.13 0.85 2.49 NA
		Azoxystrobin Metolachlor Tribenuron-methyl	8.7 stable stable	0.08 NA NA	stable stable 31	NA NA 0.022	205 365 139	0.003 0.002 0.005	6.1 88 86.2	0.114 0.008 0.008	207-594 22-2320 31	2.5 3.4 0.38
		Benoxacor Bentazone Metribuzin Triflusulfuron-methyl	1 4 0.2 13.9	0.693 0.173 3.466 0.05	21 stable stable 32	0.033 NA NA 0.022	NA 716 50 24	NA 0.001 0.014 0.029	NA 80 51 30	NA 0.009 0.014 0.023	109 0-46 60 40	2.69 -0.46 1.75 0.96
		Boscalid Napropamide	30 1.5	0.023 0.462	stable stable	NA NA	NA 316		NA 0.002	NA 28	NA 0.025	9500 218-700	2.96 3.301
		Bromacil Nicosulfuron	stable 202	NA 0.003	stable stable	NA NA	NA 41.5		NA 0.017	NA 65	NA 0.011	12-126 63-567	1.88 0.61
		Chloridazon Oxadixyl	40 stable	0.017 NA	stable stable	NA NA	137 21	0.005 0.033	NA 25	NA 0.028	33-380 12	1.19 0.65
		Chlorotoluron	0.12	5.776	stable	NA	352	0.002	42	0.017	150-420	2.5
		Clomazone Pendimethalin	stable 21	NA 0.033	stable stable	NA NA	54 16	0.013 0.043	NA 4	NA 0.173	60-573 6500-43863	2.54 5.4
		Clopyralid Picoxystrobin	271 21	0.003 0.033	stable 24	NA 0.029	NA 56		NA 0.012	148 7.5	0.005 0.092	0.4-12.9 741	-2.631 3.6
		Prochloraz	1.5	0.462	stable	NA	359		0.002	2	0.347	2225	3.5
												136

CO-DESIGN OF CONSTRUCTED WETLANDS TO MITIGATE PESTICIDE POLLUTION IN A DRAINED CATCH-BASIN: A SOLUTION TO IMPROVE GROUNDWATER QUALITY. Irrigation and Drainage 61, 75-86.

Warren, C. S.,

Mackay, D., Bahadur, N. P., and Boocock, D. G. (2002)

. A suite of multisegment fugacity models describing the fate of organic contaminants in aquatic systems: application to the Rihand Reservoir, India. Water Res 36, 4341-55.

Watanabe, H., Takagi, K., and Vu, S. H. (2006)

. Simulation of mefenacet concentrations in paddy fields by an improved PCPF-1 model. Pest Manag Sci 62, 20-9.

Table II -

 II The choice of the pesticide is prompted by the ease of monitoring lenacil as it is applied in different seasons (winter and spring) and can be traced over a large part of the year. Besides, period was chosen because of the high detection rate of lenacil (TableII-II.1). II. 1 Summary of the lenacil data during five monitoring periods. The monitoring period refers to the start and end of the monitoring. The intercepted volume by the AP (mm) is the normalized intercepted volume (m 3 ) by the watershed area (355 ha). The influx (mg) is the total intercepted mass of lenacil during each period. Cin.max (µg.L -1 ) and Cin.mean (µg.L -1 ) are the maximum and mean observed inlet concentration of lenacil, respectively. Detection rate refers to the percentage of lenacil detected from the total amount applied.

	Chapter II-II: Modeling the fate of dissolved pesticides with a process-based model
	Period	Monitoring period	Intercepted volume (mm)	Flux (mg)	Cin,max (µg.L-1)	Cin,mean (µg.L-1)	Detection rate %
	2014-2015 14/10/2014 to 03/06/2015	120.42	3974	0.086	0.02	100
	2016-2017 07/11/2016 to 12/09/2017	29.16	4946	1.4	0.1	35.5
	2017-2018 12/09/2017 to 13/06/2018	188.52	3585	0.14	0.015	16.7
	2018-2019 17/10/2018 to 03/07/2019	62.97	1049	0.12	0.015	14.8
	2019-2020 30/10/2019 to 19/05/2020	80.2	8836	0.066	0.03	92.2

. It was decided to test the model on the herbicide lenacil during 2014-2015 at the Rampillon AP scale. Lenacil monitoring data are summarized in Table II-II. 1 and the parameters used for this test case are listed in Table II-II. 2

Table II -

 II II. 2 List of the input parameters used for the test-case simulation

	Parameter	Unit	Value	Source
	kbio,w	d -1	0.023	PPDB
	kbio,s	d -1	0.034	PPDB
	kp	d -1	6.93E-06	PPDB
	kv	d -1	1.08E-10	PPDB
	kh	d -1	6.93E-06	PPDB
	kads	d -1	0.08	Calibration
	kdes	d -1	0.01	Calibration

Table II -

 II II.A. 2 List of the PESTIPOND model variables and parameters.

	Value			Parameter		Symbol	Unit
				Biotransformation in the active sediment	kbio,s	T -1
				layer
				Photolysis		kp	T -1
				Hydrolysis		kh	T -1
				Henry constant		H	Pa.m 3 .mol -1
		Literature/PPDB		Gas constant		R	Pa.m 3 .mol -1 .K -1
				Mass transfer coefficient of CO2 in water kCO2	M.T -1
				Mass transfer coefficient of HO2 in water kH2O	M.T -1
				Molecular weight of CO2	MWCO2	mol
				Molecular weight of HO2	MWH2O	mol
				Molecular weight of the pesticide	MW	mol
				Temperature factor		θ	Unitless
	On-site measurements		Surface area of the AP		A	L 2
				Bulk density of the sediment layer	ρb	M.L-3
		Calibration		Adsorption kinetic coefficient	kads	T -1
				Desorption kinetic coefficient	kdes	T -1
	Literature/Calibration		Biotransformation in water	kbio,w	T -1
				Forcing variables
				Inlet concentration of the pesticide	Cin(t)	M.L -3
				Inflow rate		Qin(t)	L 3 .T -1
	On-site measurements		Water depth		hw(t)	L
				Temperature		T(t)	°C
				Rainfall		P(t)	L
				Evapotranspiration		PET(t)	L
			Unit	Outflow rate	Value	Qout(t)	L 3 .T -1
	A	Hydrological model	m -2	Water depth	5270	hw(t)	L
	θ		Unitless Water volume	1.1	Vw(t)	L 3
	ρb		g.cm-3 Pesticide mass in the water 2.3 State variables	Mw(t)	M
		Model outputs		Pesticide mass in the active sediment	Ms(t)	M
				layer

Table II -

 II II.A. 3 Summary of the model outputs for the test-case scenario (i.e., minimum, maximum, median, mean, and the lower (first) and upper (third) quantile). MT is the total mass of lenacil in the AP. Mw is the mass of lenacil in the water column. Ms is the mass of lenacil in the active sediment layer. Min and Mout are the lenacil influx and out flux masses, respectively. Mdeg is the total degraded mass due to dissipative processes (i.e., biotransformation, photolysis, hydrolysis, and volatilization). Mdegw and Mdegs are the degraded masses in the water and the active sediment layer of the AP, respectively. Mads and Mdes are the adsorbed and desorbed masses, respectively.

	Outputs (µg)	Min	Q1	Median	Mean	Q3	Max
	MT	52,370 138,987 111,127 114,416	124,149 439,472
	Mw	148.8	2699.9	6536.6	17,640.1	14,703	332,729.9
	Ms	6030	86,233	99,696	96,776	108,546 133,142
	Min	0	4068	14,953	43,315	35,506	1,805,669
	Mout	1	6266	16028	42,858	341,84	1,658,273
	Mdeg	0.44	151.67	199.91	262.76	294.18	1958.18
	Mdegw	0.0008 6.22	13	58.21	33.12	1576.96
	Mdegs	0.44	137.21	173.43	204.55	237.98	700.50
	Mads	0.01	246.85	605.26	1595.36	1511.72 28,020.79
	Mdes	1	862.12	986.87	961.34	1086.18 1331.70

Table III

 III

	.1 (continued)
	Period	Duration (days)	Qinmax (L.s -1 )	Qinmean (L.s -1 )	Study case	Pesticide	Cmax (µg.L -1 )	Cmean (µg.L -1 )	Detection frequency	T	HRT	TUR	NO3	TOC	DOC
					14	Mesotrione (H)	1.449	0.5527	22.20%
	17/10/2018 -03/07/2019	260	72.32	9.95	15	S-Metolachlor (H) 0.4	0.1039	44.40%	8.63	9	16.8	13.2	33	20.7
					16	Quinmerac (H)	2.681	0.758	25.90%
					17	Bentazon (H)	0.082	0.0497	21.40%
					18	Chlorotoluron (H) 0.113	0.0757	42.90%
	30/10/2019 -19/05/2020	203	91.42	16.23	19	Diflufenican (H)	0.039	0.017	78.60%	8.45	6	7.1	10.2	6.4	3.7
					20	S-Metolachlor (H) 0.77	0.0767	92.90%

Table III . 5

 III The mean value of the annual-calibrated parameters, and the PPDB values of transformation half-lives, i.e., (DT50,w, DT50,s, and DT50,p). The physicochemical properties were extracted from the PPDB or other pesticide databases if not available. Table III. 6 Statistical comparison of simulations and daily observations of pesticide fluxes. The left part of the table list the KGE, NSE, and NRMSE values using an inter-annual set of parameters, i.e., the mean of the annual-calibrated parameter values, and the right part is for the annual calibration

			Inter-annual calibration		Annual calibration
	Pesticides		KGE	NSE		NRMSE		KGE	NSE	NRMSE
	Bentazone		0.74	0.79		0.07		0.75	0.79	0.07
	Boscalid		0.44	0.64		0.06		0.68	0.68	0.06
	Chlorotoluron		0.74	0.87		0.01		0.83	0.82	0.05
	Diflufenican		0.76	0.78		0.07		0.74	0.73	0.05
	Mesotrione Pesticides S-Metolachlor DT50,w Bentazon 5 Quinmerac	Calibrated 0.93 DT50,s 0.63 DT50,p 0.58 0.76 100 3 0.54 0.84	0.03 kdes 0.04 0.15 0.01 kads 0.04	DT50,w 80	PPDB 0.69 DT50,s 0.58 716 0.65	0.87 DT50,p 0.75 4 0.86	0.04 PPDB/Literature Koc 0.05 log Kow 55 2.34 0.04
	Boscalid	500	500	stable 0.63	0	5	545	stable	772	3
	Chlorotoluron	44	300	30	0.42	0	44	308	30	400	2.5
	Diflufenican	200	175	133	0.51	0	200 16	175	stable	550	4.2
	Mesotrione	5.3	5.2	89	0.69 0.05	5.3	5.2	89	122	0.11
	S-metolachlor	1.5	43	146	0.07 0.01	9	43	146	120	2.9
	Quinmerac	3.84	180	66	0.43 0.02	88	179	66	86	2.7

Table III .

 III A. 3The mean percentage (%) of each process to the total intercepted mass of pesticides.

							2019-2020
		Influx/Outflux	Total mass	Transformation	Adsorption-Desorption	MBE
	Pesticides	∑ 𝑴 𝒊𝒏 ∑ 𝑴 𝒐𝒖𝒕 Mw	Ms	∑ 𝑴 𝒕𝒓.𝒘 ∑ 𝑴 𝒕𝒓.𝒔 ∑ 𝑴 𝒂𝒅𝒔	∑ 𝑴 𝒅𝒆𝒔	Err (%)
	Bentazon	3.16	2.11	0.09	0.39	0.54	0.03	0.54	0.12	0.00
	Boscalid	-	-	-	-	-		-	-	-	-
	Chlorotoluron	12.80	11.20	0.00	1.36	0.04	0.20	1.56	0.00	0.00
	Diflufenican	8.24	6.10	0.01	1.80	0.01	0.32	2.12	0.00	0.00
	Mesotrione	-	-	-	-	-		-	-	-	-
	S-metolachlor	20.74	13.32	0.88	1.45	4.96	0.14	1.80	0.21	0.00
	Quinmerac	-	-	-	-	-		-	-	-	-
	Mean	11.24	8.18	0.24	1.25	1.39	0.17	1.50	0.08	0.00
				Biotransformation	Biotransformation
		Photolysis		in water			in sediments	Adsorption Desorption
	Bentazon		8.68		5.21			2.14		14.63	6.82
	Boscalid		0.00		0.04			3.18		35.49	0.00
	Chlorotoluron		0.35		0.24			2.82		21.53	0.00
	Diflufenican		0.09		0.06			5.45		28.27	0.00
	Mesotrione		0.27		4.52			17.86		36.19	10.68
	S-METOLACHLOR	0.25		23.97			1.25		5.87	1.26
	Quinmerac		0.46		7.94			1.70		35.26	19.32

  However, AGRO-2014 requires additional variables that are estimated by the PRZM model of the FOCUS group(Knäbel et al., 2012) (i.e., TSS and PP exports at the field scale)Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 260 to simulate the behavior of TSS and PP at the AP scale. Therefore, AGRO-2014 is computationally costly and needs a significant number of inputs and parameters. Besides, the pesticide module of SWAT considers a constant partition between the dissolved and particulate phases, which is different in field conditions. In addition, SWAT does not consider the effect of temperature, particle size, and kinetic sorption. Based on these elements, an evaluation of the pond module of SWAT to simulate pesticide behavior at the catchment scale can be found in(Joffre et al, under review). Comparison between the existing pesticide fate models in AP and the particulate version of the PESTIPOND model according to the considered processes and inputs. The AGRO-2014 inputs are provided by the FOCUS-PRZM model(Knäbel et al., 2012).

	Table IV. 1 AGRO-2014	SWAT	PESTIPOND
	Settling	✓	✓	✓
	Resuspension	✓	✓	✓
	Kinetic sorption	x	x	✓
	Temperature effect	x	x	✓
	Inputs from landscape modeling tools	✓	x	x
	Inputs from in-situ measurements	x	✓	✓

(Table IV.

1Table II-I. 1).

  The solids transport model was Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 272 developed based on the adapted version of Velikanov's model on the Bassioué AP. The adapted version of Velikanov's model

Table IV . 4.

 IV List of the parameter values used in the Velikanov's equation. η1 and η2 were calibrated manually.

	Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé
	site
	Table IV. 4 Parameter	Value	Unit	Source
	η1	0.003	-	Calibrated
	η2	0.006	-	Calibrated
	s	2	-	Literature (Zug et al., 1998)
	L	21	m	Measured
	ωs	2.3	m.s -1	Literature (Zug et al., 1998)
	Ip	4.5%	m.m -1	Measured (Wu et al., 2021)
				274

  Mass balances quantify the temporal evolution of the state variables of the model over an infinitesimal period of time (dt). The state variables of this model version are the mass of DP and PP in the Where Mdiss (t) [M] is the mass of DP in the water column at time t. Cin,diss (t) [M.L -3 ] the concentration of pesticides at the AP inlet. Qin (t) the water inflow rate [L 3 .T -1 ]. Qout the AP discharge (outflow) rate [L 3 .T -1 ]. kbio,diss [T -1 ] is the biotransformation coefficient of DP at 20°C. kp, kh and kL-A [L.T -1 ] are photolysis, hydrolysis, and volatilization coefficients in water at 20°C.

	Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé
	site site
	Water column: Dissolved phase
	(IV.9) Where Mp (t) [M] is the mass of PP in the water column at time t. Cin,p (t) [M.L -3 ] the concentration
	𝑑𝑀 𝑑𝑖𝑠𝑠 𝑑𝑡 of PP in the water at the AP inlet. kbio,p [T -1 ] is the biotransformation coefficient of PP at 20°C. vs,f, = 𝐶 𝑖𝑛,𝑑𝑖𝑠𝑠 (𝑡) * 𝑄 𝑖𝑛 (𝑡) -𝑀 𝑑𝑖𝑠𝑠 𝑉 𝑤 (𝑡) * 𝑄 𝑜𝑢𝑡 (𝑡) -(𝑘 𝑏𝑖𝑜,𝑑𝑖𝑠𝑠 + 𝑘 𝑝 + 𝑘 ℎ + 1 ℎ 𝑤 (𝑡) * 𝑘 𝐿-𝐴 ) * 𝛳 𝑇(𝑡)-20 vr [T -1 ] are the settling and resuspension coefficients of fine PP, respectively. vs,c [T -1 ] is the settling
	* 𝑀 𝑑𝑖𝑠𝑠 (𝑡) -(𝑘 𝑎𝑑𝑠,𝑇𝑆𝑆 (𝑡) + 𝑘 𝑎𝑑𝑠,𝑠𝑒𝑑 (𝑡)) * 𝑀 𝑑𝑖𝑠𝑠 (𝑡) + 𝑘 𝑑𝑒𝑠,𝑇𝑆𝑆 (𝑡) * 𝑀 𝑝 (𝑡) + 𝑘 𝑑𝑒𝑠,𝑠𝑒𝑑 (𝑡) velocity of coarse PP.
	* 𝑀 𝑠 (𝑡) The concentration of PP in the water Cp[M.L
	The concentration of DP in the water Cdiss[M.L -3 ] can be deduced as follows:
	(IV.10)
	𝐶 𝑑𝑖𝑠𝑠 (𝑡) =	𝑀 𝑑𝑖𝑠𝑠 (𝑡) 𝑉 𝑤 (𝑡)
	Where Mdiss (t) [M] is the simulated mass of DP (Eq. IV.9) and Vw [L 3 ] is the daily water volume
	calculated by the hydrological model (Eq. IV.1).
	Water column: Particulate phase
	(IV.11)
	water column, Mdiss [M] and Mp [M], respectively, and the pesticide mass in the active sediment layer Ms [M]. Hereafter are presented the mass balance equations of pesticides in the AP 𝑑𝑀 𝑝 𝑑𝑡 = 𝐶 𝑖𝑛,𝑝 (𝑡) * 𝑄 𝑖𝑛 (𝑡) -𝑀 𝑝 (𝑡) 𝑉 𝑤 (𝑡) * 𝑄 𝑜𝑢𝑡 (𝑡) -𝑘 𝑏𝑖𝑜,𝑝 * 𝛳 𝑇(𝑡)-20 * 𝑀 𝑝 (𝑡) + 𝑘 𝑎𝑑𝑠,𝑇𝑆𝑆 * 𝑀 𝑑𝑖𝑠𝑠 (𝑡)
	compartments. A list of the model parameters, inputs, and state variables can be found in Table -𝑘 𝑑𝑒𝑠,𝑇𝑆𝑆 * 𝑀 𝑝 (𝑡) -1 ℎ 𝑤 (𝑡) * (𝑣 𝑠,𝑓 * 85% + 𝑣 𝑠,𝑐 * 15%) * 𝑀 𝑝 (𝑡) + 1 * 𝑣 𝑟 ℎ 𝑤 (𝑡) IV.A. 1. * 85% * 𝑀 𝑝
		276 277

hw [L] is the water depth at time t. kads,TSS, kads,sed [T -1 ] are adsorption kinetic coefficients on TSS and sediments, respectively. kdes,TSS kdes,sed [T -1 ] are desorption kinetic coefficients on TSS and sediments, respectively, and dt [T] = (t+dt)t is the time step of the model (iteration step).

  IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site

	Active sediment layer
	(IV.14)
	𝑑𝑀 𝑠 𝑑𝑡	= -𝑘 𝑏𝑖𝑜,𝑠 * 𝛳 𝑇(𝑡)-20 * 𝑀 𝑠 (𝑡) + 𝑘 𝑎𝑑𝑠,𝑠𝑒𝑑 * 𝑀 𝑑𝑖𝑠𝑠 (𝑡) -𝑘 𝑑𝑒𝑠,𝑠𝑒𝑑 * 𝑀 𝑠 (𝑡) +	1 ℎ 𝑤 (𝑡)	* (𝑣 𝑠,𝑓 * 85%
		+ 𝑣 𝑠,𝑐 * 15%) * 𝑀 𝑝 (𝑡) -	1 ℎ 𝑤 (𝑡)	* 𝑣 𝑟 * 85% * 𝑀 𝑠 (𝑡)
	Where Ms (t) [M] is the mass of PP in the active sediment layer at time t. kbio,s [T -1 ] is the
	biotransformation coefficient of PP in the active sediment layer at 20°C.
	The concentration of PP in the active sediment layer Cs[M.M -1 ] can be deduced as follows:
	(IV.15)
		𝐶 𝑠 (𝑡) =	𝑀 𝑠 (𝑡) 𝑀𝑠𝑒𝑑(𝑡)
					278

  Table IV. 5 The parameter significations are available in Table IV.A. 1. List of the input parameters used for pendimethalin simulation. PPDB refers to the Pesticide Properties Database(Lewis et al., 2016).

	Table IV. 5 Parameter	Values	Unit	Source
	kbio,w	0.035	d -1	Calibration
	kbio,p	0.033	d -1	Calibration
	kbio,s	0.033	d -1	PPDB
	kp	0.043	d -1	PPDB
	kv	6.10 -7	d -1	(PubChem, 2021)
	kh	0	d -1	PPDB
	kads,sed	0	d -1	Calibration
	kdes,sed	0	d -1	Calibration
	kads,TSS	0	d -1	Calibration
	kdes,TSS	0.1	d -1	Calibration
	vs,f	4	d -1	Calibration
	vs,c	5	d -1	Calibration
	vr	0.1	d -1	Calibration

  Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 280 forcing functions (i.e., TSSin(t), TSSout(t), TSS(t), Cin,p (t)). The set of forcing functions was taken from daily observations, except the TSS concentration TSS (t) in the water column of the AP, which was calculated by the solids transport model (see subsection 2.4.2). Similarly, to the original model version, the forcing functions were interpolated with the approxfun R-function of "stats"

.A. 1) plus the solid phase

Table IV . 6

 IV List of the input parameters used for pendimethalin simulations.

	Parameters	Value	Unit	Source
	kbio,w	0.035	d -1	Calibration
	kbio,p	0.043	d -1	Calibration
	kbio,s	0.043	d -1	PPDB
	kp	0.033	d -1	PPDB
	kv	0	d -1	PPDB
	kh	0	d -1	PPDB
	kads,sed	0	d -1	Calibration
	kdes,sed	0	d -1	Calibration
	kads,TSS	0	d -1	Calibration
	kdes,TSS	0	d -1	Calibration
	vs,f	4	d -1	Stocks' law/Calibration
	vs,c	6	d -1	Stocks' law/Calibration
	vr	0.1	d -1	Calibration

  ). Nevertheless, this would result in a more complicated and computationally costly model. Besides, for this study case a single monitoring data was available to test the particulate version of PESTIPOND. Therefore, it is advisable to test PESTIPOND on other periods with differing hydrologic behavior to further elucidate the model's limits.

Table IV .

 IV A. 1 List of the input variables and parameters of the particulate version of the PESTIPOND model.

Forcing functions/External variables

  Chapter IV: Integration of the particulate phase in PESTIPOND and application on the Auradé site 296

		Inlet concentration of DP pesticide	Cin,diss(t)	M.L -3
		Inlet concentration of PP pesticide	Cin,p (t)	M.L -3
	On-site measurements	Inflow rate	Qin(t)	L 3 .T -1
		Outflow rate	Qout(t)	L 3 .T -1
		TSS concentration in the inlet	TSSin(t)	M.L -3
		TSS concentration in the outlet	TSSout (t)	M.L -3
		Temperature	T(t)	°C
		Rainfall	P(t)	L
		Evapotranspiration	PET(t)	L
		Outflow rate	Qout(t)	L 3 .T -1
	Hydrological model	Water depth	hw(t)	L
		Water volume	Vw(t)	L 3
	Solids transport model	Mass of TSS in the water column	MTSS(t)	M
		Mass of the active sediment layer	Msed(t)	M

Table IV .

 IV A.1 (continued)

		State variables
		Mass of DP in the water column	Mdiss(t)	M
	Model outputs	Mass of PP in the water column	Mp(t)	M
		Mass of PP in the active sediment layer Ms(t)	M

Laboratoire d'Hydrologie et de Géochimie de Strasbourg, LHyGeS, UMR 7517 CNRS-UNISTRA-ENGEES, Strasbourg -https://www.lhyges.unistra.fr/

Environnement, Territoires et Infrastructures, ETBX, UR IINRAE, Bordeaux-Cestashttps://www.irstea.fr/fr/recherche/unites-derecherche/etbx

 4 Hydrosystèmes continentaux anthropisés-Ressources, Risques, Restauration, HYCAR, UR INRAE, Antonyhttps://www.irstea.fr/fr/recherche/unites-derecherche/hycar

Sorption coefficients (Kd, Kf) were extracted from the pesticide database PPDB.

Not measured.

vp (Pa) is the vapor pressure of the pesticide at 20°C. The values were extracted from pesticide properties databases PPDB, PubChem and ChemSpider.

It was not specified under which conditions (aerobic or anaerobic) biotransformation half-lives were measured.

A case study corresponds to a specific pesticide during a certain monitoring period.

The split sample test consists in splitting the observation data into two periods. The parameters are calibrated over the first period. Next, the model performance is evaluated by running the calibrated set of parameters obtained over the second period.

The biotransformation half-life in water of diflufenican was not available in the PPDB so it was extracted from EFSA(2008). Conclusion regarding the peer review of the pesticide risk assessment of the active substance diflufenican. EFSA Journal 6, 122r.

General discussion and conclusion

Remerciements

Ce travail de thèse a été réalisé au sein de l'unité de recherche Hydrosystèmes Continentaux Anthropogéniques -Ressources, Risques et Restauration (HYCAR), à l'institut de recherche INRAE d'Antony. Par ailleurs, une semaine a été passée au Laboratoire d'Ecologie Fonctionnelle et Environnement (LEFE) de l'école INP de Toulouse. Je tiens donc à remercier toutes les personnes appartenant à ces institutions. Cette thèse a été financée par l'INRAE et le projet PESTIPOND de l'Agence Nationale de la Recherche (ANR). artificial ponds. We suggest that this hierarchization may also improve the estimation of the overall efficiency of artificial ponds in pesticide dissipation. This work is a detailed analysis of the literature to improve our understanding of the potential of artificial ponds to dissipate incoming pesticide loads and thus lead to better management and, therefore, improved water quality. This work paves the way for the scientific community to develop generic process-based models that, contrary to black-box models, will predict pesticide behavior at the artificial pond scale while integrating the key dissipation processes.

Acknowledgments:

This work was financially supported by the French National Research Agency ANR [ANR-18-CE32-0007], as a part of the project PESTIPOND, and the French National Research Institute of Agriculture, Alimentation and Environment.

This work is part of the PESTIPOND project (PESTIPOND, 2019) launched by the French National Research Agency (ANR). PESTIPOND is an innovative 48-month collaborative research project associating partners from 4 research units 1 2 3 4 in France and

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Outputs with the annually calibrated parameters

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site

Outputs with the inter-annually calibrated parameters

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site

Abbreviations Abbreviation Description

Author Contributions:

Conceptualization: A.B, J.T, S.S, S.P, G.I.; Writing -Original Draft: A.B; Writing -Review & Editing: J.T, S.S, S.P, G.I, J.P, B.C; Supervision: J.T, S.S; Project administration: G.I; Funding acquisition: G.I, J.T, S.S, S.P, J.P.

Declaration of competing interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Chapter II: Modeling of dissolved pesticide fate between parameters (bivariate of multivariate SA) rather than looking at one parameter at a time (univariate SA) (Soetaert et al., 2009). Moreover, for nonlinear models such as PESTIPOND, the interactions of the parameters are ubiquitous and very important to evaluate, but local analysis cannot capture their effects on the model response (Steinmann et al., 2020;[START_REF] Tosin | A Tutorial on Sobol' Global Sensitivity Analysis Applied to Biological Models[END_REF]. These reasons, as well as the relatively small number of parameters (i.e., seven parameters), make the Sobol method a valid choice for global SA (Sobol, 1993) to determine the overall significance of the model parameters and to describe their nonlinear effect and interactions on the model outputs. The Sobol method also enables the hierarchization of processes behind pesticide fate in AP.

The Sobol method is a variance-based method that has been widely used in recent studies (Morrison et al., 2020;Steinmann et al., 2020). A concise explanation of the principles and mathematical formulations of Sobol SA can be found in (Nossent et al., 2011). The objective of the Sobol method is to estimate how much the variance of model output(s) decreases after fixing a model parameter(s). For this study, the variance evaluated is associated with the simulated cumulative total mass of pesticides in the AP. This variance is then decomposed to the amount of variance explained by a parameter Xi or its interaction(s) with other parameters.

The Sobol method quantifies sensitivity through the first-order sensitivity index (SI) and the total sensitivity index (TSI). The SI indicates the proportion of output variance removed by fixing Xi, and TSI displays the proportion of output variance associated with Xi variability and all of its interactions with other parameters in the model. (Zhang et al., 2015).

Chapter II-II: Modeling the fate of dissolved pesticides with a process-based model

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 204

Model validation strategy

The validation of the PESTIPOND model is based on the assessment of the simulations of pesticide fluxes against the available observations using the 20 study cases (Table III. 1). Other pesticide fate models (Kalin et al., 2013;Watanabe et al., 2000a) were validated using a single pesticide molecule or a single period to evaluate the model performance. Alternatively, PESTIPOND was validated upon field monitoring data of 7 pesticides with contrasting molecular properties (i.e., solubility, hydrophilicity, and mobility) (Table III. 2) during the 5 evaluated periods (Table III.

1). The split-simple test (SST 15 ) (KlemeŠ, 1986) is a common evaluation method for this type of model. However, the SST requires sizeable observation data. Even though the monitoring database of the Rampillon AP is consistent (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022), it includes periods of no application or flooding events responsible for pesticide transfer, which restrains the database size for SST use.

The validation strategy of the PESTIPOND comprises two steps (i) and (ii):

(i) Based on the observations of pesticide concentrations, the model parameters are optimized for each period to assess their stability and consistency. The variability of parameters between periods indicates the degree of the model's robustness.

(ii) In order to survey the parameters' variability and asses the model robustness, the performance of PESTIPOND is evaluated using a single set of parameters (interannual) for all periods (i.e., the mean value of the annual parameters).

Note that for (i) and (ii), the model performance is assessed using both the transformed observations (daily observations) and the non-transformed observations (bi-monthly observations). When the model is validated for the bi-monthly observations, a bi-monthly flowweighted concentration is re-calculated from the daily simulation results to match the observations' time scale.

To quantitatively assess model performance, the well-known Nash Sutcliffe efficiency (NSE) objective criterion (Nash et al., 1970) (Eq.III.3) was adopted. An additional metric was used to assist the NSE criterion, i.e., Kling-Gupta efficiency (KGE) criteria (Gupta et al., 2009) (Eq.III.4).

The two criteria are known for properly evaluating nutrients and chemical fate models (Moriasi et

Results

This section reports the results of PESTIPOND performance and explores the model outputs. The first results correspond to the annual calibration of parameters (section 3.1), followed by the outcome of the model assessment using the inter-annual set of parameters for all periods (section 3.2). The last results describe the mass budget of pesticides in the AP and the quantification of process contributions (section 3.3).

Chapter III: Application of the process-based model PESTIPOND on the Rampillon site 207

Model assessment based on annual calibration

An annual calibration of the model parameters according to pesticide fluxes is performed over the selected periods. The obtained parameter values are set out in Table III. 3 and Table III. 4.

Table III. 3 Values of the model parameters related to adsorption and desorption for the 20 study cases. kads (d -1 ) and kdes (d -1 ) are the adsorption and desorption kinetic coefficients, respectively. These values are the result of the annual calibration of the parameters. The set of adsorption and desorption values for the selected pesticides were calibrated because no available data was found of the literature. The calibrated values fit in the range of other studies (Comoretto et al., 2008;Nakano et al., 2004;Watanabe et al., 2006;Yoshida et al., 2000).

Discussion

PESTIPOND is a time-dependent model (daily-step) developed to predict the fate of pesticides at the ponds' scale before being transferred to the water resources of the agricultural catchment. The model is designed to be implemented in a landscape-modeling tool (e.g., SWAT (Neitsch et al., 2011)) to predict pesticide behavior at the catchment scale. The PESTIPOND modeling approach is built upon the mass balance of pesticides in the two main compartments of AP, i.e., the water column and the active sediment layer, also designated as the water-sediment interface, while considering the key physicochemical processes behind pesticide behavior. PESTIPOND requires hydro-climatic input data (i.e., rainfall, PET, temperature, inflow and outflow rates) and monitoring data of the intercepted pesticides (inlet concentrations).

A global sensitivity analysis was performed and documented in (Bahi et al., 2023, submitted). SA outcomes evidenced the insensitivity of the PESTIPOND model to hydrolysis and volatilization processes. By contrast, adsorption and desorption showed the most significant influence on pesticide behavior independently of their molecular properties. Similar SA assumptions were made by Boulange et al. (2012) and Desmarteau et al. (2014) for other environmental fate models. In addition, SA evidenced that the biotransformation at the water-sediment interface is more effective on hydrophobic and lowly mobile pesticides, while biotransformation in water is more effective on hydrophilic and highly mobile pesticides. Figure III.A. 4 advances the variation of the AP efficiency to dissipate pesticides with time. This variation can be translated by the seasonal change of the impact of processes on pesticide fate. This observation fortifies the SA assumption outlining that (1) the sensitivity of hydrophilic pesticides to sorption and transformation processes varies with time according to the hydraulic conditions of the AP and ( 2) that temperature has a major effect on the set of processes, particularly enhancing pesticide transformation. Afterward, the model was calibrated and validated using monitoring data of 7 pesticides with contrasted properties (i.e., hydrophobicity and mobility) during five periods. PESTIPOND performance was graphically and statistically evaluated.

The originality of the model lies in integrating and exploring the key physicochemical processes to predict the export of pesticides from AP contrarily to black-box models using a single generic

Conclusion

PESTIPOND is a process-based model developed to predict the fate of pesticides in APs. The model is designed to be integrated into landscape agro-hydrological modeling tools to extrapolate the prediction to the catchment scale.

The key assumptions to be drawn from this study are (i) that adsorption-desorption and transformation are governing processes in pesticide fate. (ii) Hydrophobic and lowly mobile pesticides are more likely to be transformed at the water-sediment interface. Although the fate of the transformation products is still unknown, the exported amount of mother pesticide molecules will be dissipated before reaching natural water resources. (iii) Hydrophilic pesticides, despite being less retained in AP, can be subjected to transformation in the water column, especially during summer and spring, when temperature arises. A higher HRT will increase the dissipation probability for both hydrophilic and hydrophobic pesticides in the water column. Longer HRT provides more time for pesticides to be adsorbed and transformed within the AP. Accordingly, the PETIPOND model predicted that the actual efficiency of the AP covering 0.15% of the drained catchment would double if the pond's surface area covered at least 1% of the catchment. By contrast, the model's predictions evidenced that a temperature rise of 10°C will increase the dissipation of pesticides by only 8%. It is noteworthy that a temperature rise entails a more significant transformation and hence more TP. However, the model does not consider these latter, which can be addressed later by adding a TP compartment to predict its fate in APs.

Given that, we assume that PESTIPOND provides key elements that are useful to design and manage ponds with optimal efficiency. Hence, these ponds can be complementary solutions to pesticide use regulation to reduce the transfer of agricultural contamination into the environment.

PESTIPOND can be implemented afterward in landscape modeling tools to extrapolate the prediction of pesticide behavior from the pond scale to the catchment scale.

Pesticide mass budget

Table III.A. 2 Summary of the mass budget (g) for the studied pesticides. MT is the total mass of the pesticide at the end of each period. Mw and Ms are the pesticide masses in water and sediments at the end of each period, respectively. ∑ M in is the total intercepted mass. ∑ M out the total mass discharged from the pond and ∑ M tr is the total transformed mass. ∑ M tr.w and ∑ M tr.s are total transformed mass in water and sediments, respectively. ∑ M ads and ∑ M des are the total adsorbed and desorbed mass, respectively. Err (%) is the mass balance error of each simulation.

2014-2015

Influx the black box in AP on pesticides through the tank-in-series (TIS) model (Levenspiel, 1998).

However, the TIS model was tested on 3 pesticides, all highly soluble in the water, hydrophilic, and highly to moderately mobile. Besides, the TIS model does not integrate the temperature effect, previously identified as a major driver of pesticide fate. Therefore, the black box model was tested on 61 pesticides monitored in the Rampillon AP during 2011-2019. At first, the kdiss was calibrated by a linear regression so that the simulated discharge of pesticides from the AP fit the observations. The calibrated values were confronted to pesticide half-lives (DT50 (d)) measured in laboratory conditions and reported in the largest Pesticide Properties Database (PPDB (Lewis et al., 2016)). On average, the calibrated overall half-lives were 21 times faster than the one measured in laboratory conditions (PPDB). This result suggests that dissipation in field conditions is more important than in controlled conditions, as, in the AP, the dissipation is driven by a synergy of processes enhancing pesticide mitigation, contrary to experiments where a single process is surveyed. Similarly, our literature investigation (Chapter I)

underlined the higher impact of interacting processes on pesticide dissipation compared to when they act separately, mainly between adsorption-desorption and biotransformation (Sørensen et al., 2006). The link between these two processes will be detailed afterward.

The model performance was assessed by measuring the discrepancy between observations and simulations of discharged pesticides using the relative error RE (%) criteria. The black box model application yielded good performance (RE <25%) on most evaluated pesticides. The RE values demonstrated variability of the performance quality with pesticides and periods. The variability was expected since pesticides have different fates in the AP, according to their properties and application periods. Similarly, we also expected the RE seasonal variation due to the significant dependency of pesticide dissipation on hydrological conditions (i.e., water flow rate and HRT). In

Modeling pesticide fate with a process-based model PESTIPOND

Several elaborate reviews addressing processes behind pollutant fate in APs (Imfeld et al., 2021;Willkommen et al., 2022) suggest that the acquired knowledge should be integrated into models to predict contaminant fate more realistically. Drawing on the knowledge gained from the review work on the different processes involved in pesticide fate and their controlling factors (Chapter I), as well as on the limitations of the black box model (Chapter II-Part 1), we developed a processbased model (PESTIPOND).

Model development

Contrarily to black-box models, PESTIPOND considers the key dissipation processes in each pond compartment: Adsorption-desorption, biotransformation in water and sediments, photolysis, hydrolysis, and volatilization. The model also includes a hydrological model to account for the water volume variation based on a water balance equation (Eq.II-I.1). Against other process-based models, i.e., the pond module of SWAT (Neitsch et al., 2011) and PCPF-1 (Watanabe et al., 2006), PESTIPOND integrates the temperature effect. Besides, contrary to AGRO-2014 (Gobas et al., 2018), PESTIPOND is designed to predict the behavior of pesticides with contrasting properties (i.e., hydrophobic and hydrophilic).

The mathematical model of PESTIPOND is based on a coupled system of ordinary non-linear differential equations (ODE) solved numerically by the finite-difference method. The ODE represent the pesticide mass balance in the main compartments of the AP, i.e., the water column and the water-sediment interface (the active sediment layer). Each of the processes included in the mass balances was expressed relying on the formulations used in environmental prediction models reviewed earlier (Chapter I), e.g., (Gobas et al., 2018;Hantush et al., 2013;Sharifi et al., 2013).

To grant the model simplicity, each process was implemented with a first-order kinetic equation inspired by Torabi et al. (2020), Wang et al. (2019), andZeng et al. (2013). The PESTIPOND model requires inputs: Forcing variables (e.g., temperature, water flow rates, pesticide inlet fluxes), parameters (i.e., the kinetic coefficient of processes), and the size of the AP (Area) to simulate the outlet flux of pesticides at the AP scale.

Sensitivity analysis

A global sensitivity analysis (SA) was carried out to highlight the input parameters that most influence the model outputs. The SA was performed by a Monte Carlo sampling of the parameters