# Groundwater controls on legacy antibiotics and pesticides in an intensive agricultural headwater catchment

Rock S Bagagnan, Ronan Abhervé, Anniet M Laverman, Camille Vautier

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Rock S Bagagnan, Ronan Abhervé, Anniet M Laverman, Camille Vautier. Groundwater controls on legacy antibiotics and pesticides in an intensive agricultural headwater catchment. Journal of Hydrology, 2026, 669 (part B), pp.135118. 10.1016/j.jhydrol.2026.135118. insu-05504639v3

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Research papers

# Groundwater controls on legacy antibiotics and pesticides in an intensive agricultural headwater catchment

Rock S. Bagagnan<sup>a,∗</sup> <sup>†</sup>, Ronan Abhervé<sup>b,‡</sup>, Anniet M. Laverman<sup>c,§</sup>, Camille Vautier<sup>a,||</sup>

ARTICLE INFO

This manuscript was handled by Renato Mor-
bidelli, Editor-in-Chief

Keywords:

Veterinary antibiotics

Pesticides

Contaminant legacy

Hydrogeological modeling

CFCs

Transit times

Headwater catchment

A B S T R A C T

Pesticides and veterinary antibiotics, widely used in agricultural systems for crop production and livestock farming, have significant impacts on groundwater quality and ecosystem health. This study investigates their occurrence and long-term persistence in a small temperate agricultural watershed (Kervidy-Naizin, Brittany, France). Field measurements revealed that antibiotics appeared sporadically in surface water only, in contrast to pesticides (including atrazine and metolachlor), which were detected broadly in both surface water and groundwater. Additionally, chlorofluorocarbons (CFCs) were employed as age tracers to constrain a 3D physically-based hydrogeological model of the crystalline bedrock aquifer, with particle tracking used to simulate groundwater transit time distributions (TTDs). In steady state, the groundwater flow model accurately represents both the local water table observed in piezometers and the spatial extent of the average hydrographic network. The TTDs were directly linked to the pesticide measurements, providing a consistent explanation for the long-term persistence of these contaminants. The results confirmed that atrazine and its metabolites were detected in groundwater with a mean age of 10 years, decades after its ban, while metolachlor residues were found in even older groundwater (>35 years), as inferred from CFC-constrained TTDs. This demonstrates the critical role of groundwater, governed by aquifer hydraulic properties, in storing and releasing legacy pollutants long after their application ceases. Our findings demonstrate the value of integrated field-modeling approaches for elucidating legacy pollutant pathways. They emphasize the need for expanded groundwater monitoring programs that account for hydrological time lags to effectively assess long-term pollution risks and inform mitigation strategies in agro-intensive regions.

# 1. Introduction

Antibiotics and pesticides have transformed modern society by respectively revolutionizing human and veterinary medicine (Bentley & Bennett, 2003) and increasing agricultural productivity (Tudi et al., 2021). Due to their extensive use, these compounds are frequently detected in the environment (Maghsodian et al., 2022; Sharma et al., 2019). Rural watersheds, especially those with intensive agriculture and livestock, are particularly vulnerable to contamination by both antibiotics and pesticides. The application of large quantities of pesticides and manure, poor waste management and extreme weather events drive high pollutant loads in surface water, soils, and groundwater (Fouché et al., 2019; Tudi et al., 2021). The co-occurrence of antibiotics and pesticides in these environments poses ecological and human health

risks, due to their persistence, potential for additive toxicity, and pro-
motion of microbial resistance (Qiu et al., 2022). Understanding their
environmental behavior, particularly persistence and transport path-
ways, is crucial for predicting long-term impacts across interconnected
compartments.

The environmental persistence and transport of antibiotics and pes-
ticides are governed by complex interactions between compound
properties and environmental conditions. While degradation processes
(photolysis, hydrolysis, microbial activity) typically determine surface
persistence (Boxall et al., 2004; Fenner et al., 2013), groundwater resi-
dence times, referring to the time spent by a particle in an aquifer, often
become the dominant control on long-term contamination, which is a
critical but poorly quantified factor (Puckett & Hughes, 2005). Com-
pound mobility depends on water solubility, soil organic content, and

<footnote><sup>a</sup> Univ Rennes, CNRS, Geosciences - Rennes, UMR 6118, 35000 Rennes, France</footnote>

<footnote><sup>b</sup> INRAE, UMR SAS 1069, Institut Agro Rennes-Angers, 35000 Rennes, France</footnote>

<footnote><sup>c</sup> Univ Rennes, CNRS, Ecobio - UMR 6553, 35000 Rennes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: rock-stephane.bagagnan@ens-rennes.fr (R.S. Bagagnan).</footnote>
adsorption dynamics (Tolls, 2001), with heavy rainfall events often
triggering rapid transport through surface runoff and subsurface flow
(Kolpin et al., 2002).

For antibiotics, persistence in soils typically ranges from weeks to
years (Pan & Chu, 2016; Tamtam et al., 2011). Pesticides, persist in
aquifers for decades, with their legacy controlled by groundwater flow
dynamics deduced from observations (Puckett & Hughes, 2005). These
differences highlight how land use history, chemical properties of con-
taminants and hydrological setting ultimately determine contamination
timelines and resurgence potential in vulnerable ecosystems. Antibiotics
and pesticides have been detected in groundwater, including decades-
old water, in European agricultural regions (Kivits et al., 2018; Lopez
et al., 2015). These examples demonstrate the utility of reconstructing
contamination histories and identifying legacy pollutants; yet to the best
of our knowledge, no studies have coupled such analyses with spatially
resolved groundwater flow models to quantify transport dynamics.

Groundwater apparent age and transit time distributions (TTDs) are valuable tools to understand the persistence and transport pathways of water and associated contaminants in aquifers. Water age refers to the time water particles have spent within a catchment at any time (Botter et al., 2022). TTDs describe the range of travel times taken by particles from recharge to discharge areas, like seepage zones or wells (Benettin et al., 2022; Rinaldo et al., 2011). They give insights into groundwater vulnerability and the potential for dissolved contaminants to interact with reactive surfaces or undergo biogeochemical transformations. For example, younger water with short transit times can quickly transport pollutants to streams or wells, whereas older water with longer transit times can favor degradation or sequestration in aquifers (Kolbe et al., 2016; Severe et al., 2023). Integrating water age and TTDs into contaminant studies improves understanding of long-term behavior, identifies persistence hotspots, and evaluates management strategies¹

effectiveness (Guillaumot et al., 2021; Vautier et al., 2021). While TTDs have been successfully applied to understand nitrate legacies (Aquilina et al., 2012; Kolbe et al., 2016), previous studies combining them with the occurrence of emerging contaminants like antibiotics and pesticides in groundwater remain scarce, particularly in headwater catchments where shallow aquifers are directly connected to surface waters. Physically based models, validated with water age tracers like CFCs, could link the presence of emerging contaminants to specific historical practices and aquifer characteristics, offering insights into delayed resurgence risks and long-term predictions (Hrachowitz et al., 2016).

A few studies in the same watershed have reported antibiotics in surface and tap waters, suggesting potential long-term groundwater contamination (Charuaud et al., 2019; Jaffrézic et al., 2017). In this context, the current study assesses the legacy of antibiotics, pesticides, and their metabolites by linking their occurrence with CFC-constrained transit time distributions in a crystalline headwater aquifer with well-documented agricultural applications. To the best of our knowledge, linking transit time distribution and the legacy of the studied pollutants has not been previously applied to agricultural headwater catchments. We examine how pesticides and antibiotics distributions relate to groundwater age using dissolved atmospheric tracers (CFCs) from advanced analytical measurements, combined with simulated TTDs from a spatially resolved and calibrated hydrogeological model. This framework provides new insights into contaminant legacies, environmental risk assessment and future regulatory considerations.

## 2. Material and methods

### 2.1. Study area

The study was conducted in the Kervidy-Naizin watershed, a 5 km²

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Location of the Kervidy-Naizin catchment at the (a) national scale and (b) regional scale (Morbihan, Brittany, France) with the (c) topographic elevation of the catchment and the (d) dominant land use type on the catchment; artificialized lands in land use correspond solely to farm buildings and scattered rural habitations. P1, P2, P3 and P4 correspond respectively to field names PG4, PG5, PK3, PK4 (Molénat & Gascuel-Odoux, 2002). Geographic coordinates of S1: 265549; 6,783,308 (Lambert 93 EPSG 2154).</figcaption></figure>
headwater agricultural catchment without any wastewater treatment plant, in Brittany, France (Fig. 1). The watershed is part of a national research infrastructure, the French network of Critical Zone Observatories (OZCAR, www.ozcar-ri.org/fr) (Gaillardet et al., 2018), and of the AgrHyS Environmental Research Observatory (Fovet et al., 2018).

The catchment has an elevation varying from 93 to 140 m above sea level (m.a.s.L) with gentle slopes (less than 5%) and is characterized by loamy to loamy-sandy soils, from 0.3 to 1 m in depth. The Brioverian schist bedrock is overlain by an unconsolidated weathered layer ranging from 1 to 30 m in thickness (Molenat et al., 2008). Land use has historically been characterized by intensive agriculture and livestock breeding, with manure spread on fields as fertilization. Land use data show the predominance of agricultural fields (88%) for maize (29%), cereals such as rapeseed crops or wheat (26%), grasslands (17%), and other crops. The artificialized land area comprises farm buildings and scattered rural habitations equipped with individual septic systems. Their contribution to antibiotics release cannot be completely ruled out, but is expected to be very limited due to the low population density.

Meteorological data from the Naizin station (Lambert 93 EPSG 2154: 266650; 6783184) show that the region experiences a temperate and humid climate, with an average annual rainfall of 853 mm (± 210 mm). Annual runoff at the catchment outlet is approximately 340 mm (± 169 mm), while the mean Penman potential evapotranspiration over the period 2000–2016 is 697 mm (± 57 mm). The annual hydrological cycle can be divided into four distinct phases from rewetting in late summer to complete dryness in autumn (Humbert et al., 2015).

## 2.2. Sampling strategy and analytical methods

Samples were collected in streams (3 sites: S1 to S3), shallow piezometers (~12 m deep with screen depth of 11.5–12.5 m, 4 sites: P1 to P4) and a deep piezometer (~30 m deep with screen depth of 29.5–30 m, P0) once in October 2024, February 2025 and May 2025 (Fig. 1). Sampling dates were chosen to ensure the absence of rainfall in the days preceding sampling and to capture contrasting hydrological conditions representative of the catchment's annual hydrological cycle: post-summer low flows and rewetting (October), winter high-flow and recharge conditions (February), and spring-summer transitional conditions (May). In-stream sample locations were chosen at the outlet of the watershed (S1), and upstream the confluence of the two tributaries (S2 and S3) to assess the overall contribution of each sub-basin. Groundwater sample locations were selected according to piezometer depth and land-use to compare contamination patterns between shallow groundwater (~12 m) interacting directly with agricultural soils and deep groundwater (~30 m), for a spatially integrated view of surface–groundwater connectivity and contamination. Observed water table depths ranged 2–3 m during sampling.

For pesticide and pharmaceutical analyses, single samples were collected at the same time and locations in 500 mL High-Density Polyethylene plastic bottles (Agboola & Benson, 2021; Miranda et al., 2022) and kept in a dark cooler (~4°C) to minimize photo- and biodegradation during transport to the laboratory. Nineteen antibiotics and 30 pesticides were targeted and analyzed using a multi-contaminant solid-phase extractions combined with liquid chromatography coupled with tandem mass spectrometry (SPE-LC-MS/MS) method described and validated in previous work (Le Gaudu et al., 2022; Tuc Dinh et al., 2011). Targeted antibiotics were selected based on sales data reports (Anses, https://www.anses.fr/fr/content/suivi-des-ventes-dantimicrobiens-veterinaires) and surveys in previous studies on the study catchment focusing on veterinary practices and animal species (Charuaud et al., 2019). Pesticides were also selected based on sales, land use and cultivated crops (Le Gaudu et al., 2022). A list of the targeted compounds with LODs and LOQs is provided in table S1. Interpretations were based on LOQs (antibiotics: 2.6–50 ng/L; pesticides: 0.6–15 ng/) which are adequate for environmental relevant concentrations. Concentrations below the LOQ (whether or not they exceeded the LOD) were not

interpreted due to the uncertainty at such low ng/L levels.

In addition, raw CFC-11, CFC-12 and CFC-113 concentrations in pptv were measured in the deep piezometer (~30 m) and in the 4 shallow piezometers (~12 m) once in April and October 2024 to estimate groundwater age and validate model simulations. Samples were collected in metallic bottles with a tubing system avoiding any contact with the atmosphere. CFCs analyses were made by gas chromatography coupled with an electron capture detector (GC-ECD) using the method described in Ayraud et al (Ayraud et al., 2008). The principle behind the use of CFCs as tracers is that they dissolve in recharge water at concentrations that reflect their atmospheric mixing ratios at the time of infiltration. Once in the aquifer, they behave as conservative tracers and by comparing measured concentrations to historical atmospheric curves, it is possible to estimate the apparent age of groundwater and constraining recharge periods, thereby providing a tool for model-based simulations of transit-time distributions.

## 2.3. Hydrogeological modeling

Using the HydroModPy tool (Gauvain et al., 2024), a groundwater model was developed with MODFLOW (Harbaugh, 2005) to simulate flow and head in steady-state. All simulations are done in steady state since it allows to assess the general behavior of the headwater catchment (Goode, 1996; Loáiciga, 2004). Drain package (DRN) of MODFLOW (Harbaugh, 2005) set at the topographic surface allows to handle seepage occurring when the water table intersects the surface. The recharge provided by SURFEX version 8.1 (Le Moigne et al., 2020) (https://www.umr-cnrm.fr/surfex/) forced by the SAFRAN meteorological reanalysis (Vidal et al., 2010), was directly applied at the top of the water table. From a digital elevation model (DEM) of 25 m resolution (BD ALTI at France scale, https://geoservices.ign.fr/bdalti) and a flat bottom of the aquifer at 0 m.a.s.L, the 3D model was discretized into 50 vertical constant thickness layers. The model domain with a no-flow boundary condition was 20% greater than the actual catchment area to avoid lateral flow errors and minimize boundary effects. Hydraulic conductivity (K) decreased exponentially with depth from $K_{max} = 5 \cdot 10^{-5}$ m/s (Molenat et al., 2008). A spatial gradient linked to topographic elevation was introduced so that decay was stronger upstream than downstream, reflecting the ~30 m thicker weathered zone in the lowlands (Molenat et al., 2008; Molénat et al., 2013; Molénat & Gascuel-Odoux, 2002). The exponential decay rate $\alpha$ according to depth was estimated using the spatial distribution of the stream network. Following the approach described in Abhervé et al (Abhervé et al., 2023, 2025), we optimized with a dichotomy method the $\alpha$ value from 1/5 to 1/100 m$^{-1}$, by minimizing the mismatch distance between the simulated stream network and the observed one (Fig. 1) (map from the French national database BD TOPO, https://geoservices.ign.fr/bdtopo). The model is further validated with observed averaged groundwater hydraulic heads in 10 piezometers measured daily from 1998 to 2024.

The particle tracking method of MODPATH (Pollock, 2016) was used to simulate pathlines and transit times. Porosity, defined in this study as the specific yield (Sy) under unconfined conditions, followed a similar exponential decay such as K. Based on literature and works in crystalline rocks, the exponential decay value of Sy is divided by a factor 2 (Bernabé et al., 2003; Cardenas & Jiang, 2010) in comparison of K (for example 1/20 m$^{-1}$ for K is 1/40 m$^{-1}$ for Sy). A specific storage Ss of $1.10^{-5}$ was kept homogeneous in the model. $Sy_{max}$ was calibrated using age tracer concentrations in water. Under an advective transport hypothesis with neither diffusion-dispersion nor degradation, forward and backward particle tracking were employed to simulate groundwater flow paths and transit time distributions (TTDs) discharging in surface water and piezometers. In forward tracking, particles were released from the water table (one particle per pixel) and discharged to streams. In backward tracking, 25,000 particles were injected along each piezometer and traced back to the water table. The groundwater TTDs were derived only considering the particles in the screened areas of the piezometers.
Each particle age was used to estimate its corresponding CFC tracer concentration, using the Piecewise Cubic Hermite Interpolating Polynomial method (Fritsch & Butland, 1984) to derive continuous functions for the historical atmospheric CFC concentrations (NOAA data). Then the total simulated concentration in the piezometer was calculated according to the formula:

$$C_{sim_i} = \sum_{1}^{n} w_i * Catm_{t-\tau_i} \quad (1)$$

where Csim is the simulated concentration, wᵢ is the relative weight of the i-th particle time, Catm is the atmospheric CFC concentration at time t - τᵢ, and τᵢ is the travel time. Finally, the simulated tracer concentration was compared to field measurements in these piezometers, and model performance indicators, the Root Mean Square Error (RMSE) and the Pearson correlation coefficient (r), were computed.

# 3. Results

## 3.1. Pollutants in surface and groundwater

The distribution of antibiotics and pesticides in surface water (S1, S2, S3) and groundwater (P0, P1, P2, P3, P4) revealed distinct patterns in contamination dynamics (Fig. 2). Among the 19 targeted antibiotics, only trace concentrations of amoxicillin (8 ng/L) and oxytetracycline (80 ng/L) were observed in October 2024 in surface water sampling sites S1 and S2, respectively. These antibiotics remained below their quantification limits of 6.8 ng/L and 5 ng/L in groundwater samples across all three sampling periods. The other antibiotics were below the LOQs at all sites (2.6–50 ng/L).

In contrast to antibiotics, pesticides were omnipresent in both surface and groundwater, with metabolite concentrations often exceeding those of the parent compounds. Among the 30 targeted pesticides, 14 were detected in both surface and groundwater while the others were below the quantification limits (0.6–15 ng/L). Fig. 2 shows the distribution of the 14 quantified pesticides across the eight sampling sites, including the five piezometers (P0, P1, P2, P3, P4) and the three surface water locations (S1, S2, S3), over three sampling campaigns conducted

in October 2024, February 2025, and May 2025. Across all sites, pesticide detection was more frequent and at higher concentrations in surface waters (S1 to S3) compared to groundwater. Metolachlor ESA, and metolachlor NOA were detected in surface waters, during all three campaigns, with log-transformed concentrations often exceeding 6, corresponding to values over 800 ng/L. These metabolites were also detected in some groundwater piezometers, notably in P1, P3, and P4, although at lower concentrations compared to surface water. Other metabolites like hydroxyatrazine, DEA and DIA were also observed consistently in surface waters, with log-transformed concentrations generally below 5 (~100 ng/L). These compounds were also found in piezometers, especially P1, P2, P3, and P4, with generally higher concentrations than in surface waters. Among the parent compounds, metolachlor and atrazine were recurrently detected in surface waters and occasionally in groundwater. Atrazine was detected in surface waters (S1, S2, and S3), especially during the February and May 2025 campaigns, at log concentrations below 3 (equivalent to ~20 ng/L). Other compounds such as bentazone, nicosulfuron, diflufenican, dicloroaniline, carbendazim, imidaclopride and tebuconazole were detected sporadically across both compartments.

Groundwater piezometers P2 and P0 showed very low pesticide concentrations and limited detection, with most compounds either below the limit of quantification (LOQ) or not detected, indicating minimal contamination at these locations.

## 3.2. Model calibration

The optimized distribution of hydraulic conductivity (K) along a west-east transect (Fig. 3a) revealed the heterogeneity, captured with the exponential decay rate and the spatial gradient. The model grid showed high K zones (notably 10⁻⁵ m/s) near the western boundary, transitioning to lower-K layers 10⁻⁷ – 10⁻⁸ m/s eastward. The calibrated exponential decay parameter of 1/21.2 m⁻¹ represented the depth-dependent reduction in K. The validation of the model parameterization against measured piezometric levels (Fig. 3b) demonstrated good model performance, with a root mean squared error (RMSE) of 1.84 m,

<figure><img src="image_3.png" /><figcaption>Fig. 2. Heatmap of antibiotics and pesticides expressed in natural log of concentrations in surface water (S1, S2, S3) and groundwater (P0, ~30 m; P1-2, P3-4, ~12 m) during the three sampling campaigns (October 2024, February and May 2025). DEA: Desethylatrazine; DIA: Desisopropylatrazine; ESA: ethane sulfonic acid; NOA: N-desmethyl oxanilic acid.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Optimal hydrogeological model outputs showing (a) the K field grid in a west-east catchment transect with vertical distribution of hydraulic conductivity; (b) the model performance with the distance mismatch between the simulated and the observed hydrographic network (green to red) and (c) the correlation between simulated and observed (1–6 m) water table depth across piezometers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

bias of 0.84 m, and $R^2$ of 0.88. The 1:1 correlation line of observed and simulated water levels across piezometers validated the calibration's effectiveness, with slight discrepancies in elevated sites (PG5 and PG6) showing localized variability not entirely captured by the model. The model showed minimal error distances of less than 25 m for more than 90% of the simulated hydrographic network against the observed hydrographic network (Fig. 3c).

Porosity (or specific yield) (Sy) was calibrated by comparing TTD-derived CFCs with measured concentrations (CFC 11, CFC 12, CFC 113) in piezometers. For the different Sy values, normalized RMSE were higher for CFC 113, while those errors were almost equivalent for CFC 12, and CFC 11 that had the lowest values (Fig. 4a). The optimal $Sy_{max}$ value is 13% for CFC 11 but we considered $Sy_{max}$ = 10% since it minimizes the errors for CFC 12 and CFC 113 (Fig. 4a). Fig. 4b shows the correlation between simulated and measured concentrations across piezometers, at $Sy_{max}$ = 10% with pearson r-squared coefficients of 0.96, 0.90 and 0.82 respectively for CFC 11, CFC 12 and CFC 113.

### 3.3. Transit time distributions

The particle tracking simulations, conducted using the optimal specific yield ($Sy_{max}$ = 10%), resulted in distinct Transit Time Distributions (TTDs), probability density function of particles and pathlines in groundwater (Fig. 5f) and surface water from groundwater discharge (Fig. 6a). These TTDs reflected the spatial groundwater flow partitioning, with variations in water age with depth and location. The shallow piezometers P1 and P2 (~12 m deep), in close proximity, showed similar TTDs. A dominant probability peak is observed at ~ 8 years in P1 (Fig. 5a) and at 8 to 10 years in P2 (Fig. 5b), with the cumulative

probability increasing sharply from 0 to 100% at that peak. Similarly, the other shallow piezometers P3 and P4 (~12 m deep) exhibited narrow TTDs centered at ~ 8–9 years for P3 (Fig. 5c) and ~ 6–7 years for P4 (Fig. 5d). The cumulative probability rapidly increased to 100% for both. The deep piezometer P0 (~30 m deep) exhibited a broad TTD from 35 years onwards, with multiple peak probability density (PDF) between 35–60 years accounting for more than 60% of the water particles in the screened area (Fig. 5e) and a long tail extending beyond 60 years. This was also confirmed by the pathlines (Fig. 5f).

Fig. 6 showed the pathlines of particles ending in streams in less than 5 years (a1), 5 to 20 years (a2), 20 to 50 years (a3) and more than 50 years (a4). Streams were dominated by less than 20 years old water as confirmed by Fig. 6b showing the density function of water transit times at the sampling locations S1 (b1), S2 (b2) and S3 (b3). The distributions showed dominant peaks before 20 years, intermediate peaks between 20 and 50 years, and smaller negligible peaks beyond 50 years (< $10^{-3}$).

## 4. Discussion

### 4.1. Contaminant spatial variability and persistence along source pathways

In our study, antibiotics were only observed sporadically in surface waters, whereas pesticides were present in both surface and groundwater for the three sampling dates. The low occurrence of antibiotics in surface water, likely due to surface runoff, flushing of remaining compounds during the post-summer low flows and rewetting period, or dilution, and their absence in groundwater are consistent with previous findings. Veterinary antibiotics were also absent below a depth of 23 m

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Model Specific yield calibration showing (a) normalized root mean squared errors (RMSE) between simulated and observed CFC tracers concentration for different simulated porosity values, and (b) detailed model performance for simulated and measured CFC concentrations across piezometers for $Sy_{max}$ = 10%.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 5. Transit Time Distributions with Probability Density Functions (PDF) of particles in (a-d) the shallow piezometers, (e) the deep piezometer, and (f) the pathlines for the porosity $Sy_{max}$ = 10%.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. Particles transit time in streams showing pathlines ending in streams after different time ranges (a1 to a4); and Probability Density Functions (PDF) of particles at sampling locations S1 to S3 (b1 to b3).</figcaption></figure>

in Dutch intensive livestock farming areas (Kivits et al., 2018). In a drinking-water catchment in Lower Saxony, Germany, only trimethoprim was observed in 11 out of 15 shallow groundwater samples, whereas several veterinary antibiotics were found in surface water (Burke et al., 2016). Antibiotics including trimethoprim, oxytetracycline, and enrofloxacin were frequently detected in surface water of headwater catchments in Brittany at concentrations ranging from 3 ng/L to 2070 ng/L (Jaffrezic et al., 2017). A broader study in Brittany's intensive livestock areas found veterinary pharmaceuticals in surface water samples, with concentrations from 5 to 2946 ng/L (Charuaud et al., 2019). The absence of antibiotics in groundwater likely indicates the dominance of sorption and degradation processes. Antibiotics, such

as tetracyclines and fluoroquinolones, have very high adsorption co-efficients ($K_d$), often ranging from 500 to over 10,000 L/kg (Tolls, 2001). Oxytetracycline, one of the specially targeted compounds, binds strongly to soil organic matter and clay minerals, which significantly inhibits its leaching potential (Kemper, 2008; Pan & Chu, 2016). Biotic degradation and photodegradation, while primarily relevant in surface environments, also contribute to the overall attenuation of antibiotics before they infiltrate deeper aquifers. Degradation of antibiotics, applied via manure, within the top meter of soil, resulted in low levels (ng/L) in groundwater (Mehrtens et al., 2020). Similarly, sporadic detection of antibiotics were reported in surface samples from fields receiving manure with negligible or absent detection in the underlying sandy soils
and deeper subsurface (Watanabe et al., 2010). These studies support that antibiotics—even when present in surface waters at low levels—might be attenuated under the sampled conditions, as reflected by their absence in groundwater. This attenuation is likely driven by sorption to the soil matrix, degradation processes occurring in the soils, and dilution effects. Therefore, while our results are consistent with previous studies of veterinary antibiotics, the absence of antibiotics in both shallow and deep groundwater in our study does not imply a guaranteed long-term barrier to groundwater contamination.

The widespread detection of pesticides in both our surface and groundwater—including banned compounds like atrazine (prohibited in the EU since 2003, EU Directive 2004/248/EC)—aligns with studies in agricultural catchments worldwide, where legacy pesticides were found in groundwater years after peak usage (Bexfield et al., 2021; Kolpin et al., 2002; Tesoriero et al., 2007). These findings highlight the environmental persistence of pesticides, mobility, and potential for long-term remobilization. Unlike antibiotics like tetracyclines which are often retained in soils due to sorption (Kemper, 2008; Tolls, 2001), pesticides like herbicides with low sorption coefficients ($K_d$ of 5–30 and 0.5–2 L/kg respectively for Metolachlor and Metolachlor ESA), moderate hydrophobicity (log octanol-water partition coefficient $K_{ow}$ ~ 2–3) and limited microbial degradation (Fenner et al., 2013), easily migrate to groundwater. Their transformation products (e.g., metolachlor ESA/OXA) are typically more polar and persistent than parent compounds (Pasquini et al., 2025), explaining their dominance in older groundwater (Huntscha et al., 2008). The absence of seasonal concentration variations in groundwater suggests a steady release from soil reservoirs, consistent with the “legacy contamination” previously described in American agricultural watersheds (Tesoriero et al., 2007). The presence of metolachlor and its metabolites (ESA and NOA) in surface and groundwater raises health concerns (Bexfield et al., 2021; Pasquini et al., 2025) and underlines the importance of including pesticide metabolites in groundwater contamination assessments, even in small headwater catchments.

Higher pesticide concentrations in surface waters during winter/ spring campaigns likely reflect seasonal application patterns and increased runoff/recharge events during wet periods, as observed for other water quality parameters (Humbert et al., 2015). The detection of these compounds in ~ 12 m shallow piezometers (P1 to P4) indicates rapid transport to transmissive aquifers, while their presence in the deep piezometer P0 (~30 m) demonstrates deep groundwater contamination, a pattern also reported in intensive farming regions (Cabrera et al., 2024; Schipper et al., 2008). These results highlight the differential vulnerability of shallow and deep aquifers inferred from the model, with shallow zones (~10 m) acting as immediate receptors of recent applications, while deeper layers function as long-term reservoirs where legacy contaminants persist and re-emerge decades after their use (Tesoriero et al., 2007). This pattern illustrates the limits of generic buffer zones and emphasizes the importance of accounting for hydrogeological connectivity when assessing groundwater protection.

## 4.2. Aquifer influence on groundwater flow partitioning and transport pathlines

The calibrated exponential decay rate applied in our model (1/21 m⁻¹) is consistent with values used in earlier studies of crystalline aquifers in Brittany, where transmissivity decreases strongly over the first tens of meters in fissured/fractured/weathered schist (Molénat & Gascuel-Odoux, 2002). The optimized porosity ($Sy_{max}$ ≈ 10%) corresponds to effective porosity at the soil to the layer closest to the surface. Averaged over the model depth, the Sy is about 0.4–0.5%, that is consistent with values reported in previous studies for the similar study site (Molénat & Gascuel-Odoux, 2002). This result is also consistent with reported ranges for crystalline schists, where effective porosity typically spans 5–20% in the weathered zone and < 1% at depth (Singhal & Gupta, 2010). A simple Sy-sensitivity test shows the system is most

sensitive at low $Sy_{max}$ (10–15%), where CFC 11 and CFC 12 relative errors (table S2) change sharply, indicating that small variations in $Sy_{max}$ strongly influence simulated groundwater ages and TTDs. At higher Sy values, changes become more moderate and largely linear (especially for CFC 113, confirming that in crystalline headwater aquifers, $Sy_{max}$ and the exponential decay rate are the dominant parameters controlling age estimates.

The model captures the shallow transmissive zone typical of crystalline aquifers, where the exponential decrease in hydraulic properties create a contrast between fast shallow pathways and long-term storage in the deeper low-K matrix (Guihéneuf et al., 2014). Contaminant variable sorption and mobility (Schaffer & Licha, 2015) further differentiates antibiotic and pesticide retention, explaining the long-term persistence at various depths (Fenner et al., 2013; Tolls, 2001). The correlation of modeled and observed watertable depths ($R^2$ = 0.88, RMSE = 1.84 m, bias = 0.84 m), and the correct spatial simulation of the stream network, achieved under steady-state calibration, shows the suitability of the exponential decay parameterization for hydraulic properties. However, discrepancies in simulated heads at upland piezometers (Fig. 3b) suggest localized heterogeneity that is not fully captured by the model. The decay structure in hydraulic properties reflects the aquifer’s geological stratification, where permeability decreases with depth due to weathering gradients (Guihéneuf et al., 2014). Moreover, the heterogeneity (Fig. 3a) directly impacts groundwater transit times, with high K layers facilitating rapid water transport (years) and low K layers acting as retention domains (decades). This aquifer architecture is the primary control on legacy pollutant persistence. High K pathways enable rapid pollutant transport, while properties decay with depth promotes longer retention in deeper strata (Guihéneuf et al., 2014). These properties explain the spatial variability in transit times—critical for long-term risk assessment. It implies the potential future contaminant resurgence, particularly for recently banned metolachlor (2024, EU Commission Implementing Regulation 2024/20), that could persist for decades, with groundwater acting as a secondary source to surface waters.

Therefore, transit time distributions (TTDs) highlight the spatial variability of groundwater ages driven by aquifer heterogeneity and hydraulic properties. Shallow piezometers (P1–P4; ~12 m) display young ages (<10 years) characteristic of rapid flushing through high-permeability layers, whereas the deep piezometer (P0; ~30 m) shows a multi-modal TTD with older water (30–60 years), reflecting slower flow in low-K strata (Hrachowitz et al., 2016; Kolbe et al., 2016). Surface waters mirror this pattern, mixing recent groundwater inputs from shallow flowpaths with older contributions from deeper layers, confirming the role of vertical connectivity and aquifer structure in shaping stream age distributions.

However, uncertainties should be considered as the parsimonious nature of the model implies simplifications and hypotheses on hydraulic properties, and soil physical processes are not explicitly represented. Transport is simulated as purely advective, without explicit representation of sorption, diffusion, or degradation processes, and the steady-state setup does not capture transient recharge or water table dynamics. These simplifications may bias the links between TTDs and contaminant concentrations by underestimating mixing, breakthrough patterns, or misrepresenting potential reaction zones. Finally, the use of CFC-derived TTDs may underestimate mixing and neglect degradation effects in anoxic zones.

## 4.3. Groundwater age and evidence of emerging contaminant legacy

Groundwater age modeling provides understanding of contaminant long-term persistence. The transit times estimates (10–35 years) in the current study align with previous studies of similar fractured crystalline bedrock aquifers of schist, also in Brittany (France). Recent ages less than 15 years were estimated in shallow groundwater, less than 20 m deep (Ayraud et al., 2008). In intermediate depths from 20 to 30 m,
values of 20 to 30 years were estimated (Ayraud et al., 2008; Marçais et al., 2018), while values exceeding 40 years were estimated in deep groundwater (>30 m) (Guillaumot et al., 2021; Kolbe et al., 2016). These age-depth relationships reflect the aquifer's permeability structure, where weathering creates high-K near-surface zones with rapid flow and low-K deeper layers with slow matrix flow (Lachassagne et al., 2021). The congruence between our CFC-derived ages and previous estimates validates the methodological approach. Minor discrepancies between CFC-derived ages and modeled TTDs may arise either from simplifications and modeling parameterization leading to CFC overestimations or field measurements uncertainties including potential CFC degradation in anoxic zones or along the measurement (Marçais et al., 2018; Szabo et al., 1996).

The groundwater age distribution provides critical context for understanding contaminant persistence patterns as summarised in table S3. Atrazine, extensively applied from 1960 s-2003 in France (Mahé et al., 2020), appears exclusively in ~12 m shallow groundwater (10 year age range) and surface waters, demonstrating its multi-decadal persistence and slow remobilization from soils in the shallow aquifer. In contrast, metolachlor – widely used from the 1970s to 2023 (Blaser, 2015) – and its metabolites are present in both younger (10 years) and older groundwater (>35 years), consistent with decades of sustained agricultural use and progressive leaching from soils. In the deep piezometer (~30 m; >35 years) detections are limited to metolachlor metabolites, which may originate from legacy inputs with long transit times, rapid infiltration with slow matrix-controlled diffusion, or river-aquifer exchanges along connected fracture zones in the schist bedrock (Becker & Shapiro, 2003; Cranswick & Cook, 2015). Nevertheless, (i) the consistency observed across the three sampling dates, (ii) the groundwater age structure derived from CFCs and modelling, (iii) the documented land-use and agricultural history, and (iv) external studies (Mahé et al., 2020; Bexfield et al., 2021) all reinforce the interpretation of groundwater as a “legacy” archive of past agricultural practices, in which the delayed emergence of contaminants complicates mitigation efforts.

## 4.4. Implications for water quality monitoring and policy

Given the transit times of 10 to 35 ± years inferred from this catchment model, legacy pollutants like metolachlor, banned in the EU since 2024 (EU Commission Implementing Regulation 2024/20), may persist for over 20 years, evading detection under current surveillance frameworks. Water quality monitoring frameworks prioritize surface water over groundwater; our results demonstrate the importance of taking into account groundwater and its decadal transport timescales revealing long-term persistence of contaminants. It highlights contamination originating from historical agricultural practices rather than current applications. To address this gap, groundwater surveillance programs should be expanded to track persistent contaminants and their metabolites, even decades after regulatory bans. The persistence of metolachlor and its metabolites (e.g., METO-ESA) in groundwater is virtually inevitable, given the pollutant's historical use and the aquifer's multi-decadal memory. This lag effect means that even if metolachlor were banned today, our site modeling results suppose that the residues would continue to emerge in groundwater discharge zones well into the 2040s, underscoring the need for proactive monitoring.

Additionally, model results reveal the dual regime of the aquifer, which underscores that contaminants reaching shallow piezometers within decades can take > 30 years to purge from deep storage. Fast pathways (P1-2, P3-4) dominated by young water but susceptible to intermittent mixing with older, contaminated groundwater, suggest that pollutants may be flushed relatively quickly. On the other hand, slow pathways (P0), characterized by multi-decadal storage, act as long-term pollutant reservoirs that could particularly sustain chronic contamination even after surface sources are mitigated. These findings emphasize that monitoring and remediation must target shallow aquifers as

immediate receptors and deep aquifers as long-term reservoirs of legacy pesticides.

## 5. Conclusion

This study highlights the contrasting behaviors of antibiotics and pesticides in an intensively farmed headwater catchment in Brittany, France. Antibiotics were only found in surface waters, likely due to strong soil sorption and rapid degradation. In contrast, pesticides and their metabolites were ubiquitous in both surface and groundwater, persisting decades after regulatory bans and acting as legacy pollutants in this context. Groundwater age tracers (CFCs) and particle-tracking models revealed a hydrogeological legacy effect, with contaminants potentially stored in aquifers for 10–35 years before resurging in discharge zones. The transit time distributions (TTD) analysis reveals a strong depth-dependent control on groundwater age, driven by the aquifer structure and its hydraulic properties. The fast and slow flow regimes help to explain the complex legacy pollutant behavior in the aquifer, with deep layers acting as long-term flow and shallow zones facilitating more rapid transport. These insights can be helpful in predicting contaminant persistence and designing effective groundwater management strategies. Although site-specific, this integrated monitoring–modeling approach could provide a useful basis for understanding legacy contaminant dynamics in similar settings. Long-term monitoring including metabolites, additional age tracers (CFCs, SF<sub>6</sub>, <sup>3</sup>H/<sup>3</sup>He) and large-scale modeling, could help refine predictions and support more informed water-resource management in agricultural landscapes.

### CRediT authorship contribution statement

**Rock S. Bagagnan:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Investigation, Formal analysis, Data curation. **Ronan Abhervé:** Writing – review & editing, Visualization, Supervision, Software, Conceptualization. **Anniet M. Laverman:** Writing – review & editing, Supervision, Conceptualization. **Camille Vautier:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

### Funding

This work was supported by funding from the Ecole Normale Supérieure de Rennes (ENS Rennes, France) and the Region Bretagne through the ARED program.

### 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.

### Acknowledgments

The authors thank Fabrice Alliot (Sorbonne University, France) for contaminant analysis, Patrice Petitjean (GeOHeLiS, Geosciences Rennes, France), Barbara Yvard (Condate-Eau, Geosciences Rennes, France) for chemical analyses, as well as Emily Jardé and Khalil Hanna for discussions on veterinary antibiotics and molecular chemistry. We are also grateful to Ophélie Fovet, Céline Bouillis, Mikaël Faucheux, and Yannick Hamon (INRAE, UMR SAS, France), for providing access and data on the study site and Darak Tanmey for assistance during fieldwork. We thank the editor and the three anonymous reviewers for constructive and valuable comments on the manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jhydrol.2026.135118.

## Data availability

Data will be made available on request.

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