# High-frequency fluorescence monitoring in karst aquifers: A multivariate approach for early detection of anthropogenic contamination of water resources

Lucile Justy, Vincent Bailly-Comte, Pascal Brunet, Christelle Batiot-Guilhe

## ► To cite this version:

Lucile Justy, Vincent Bailly-Comte, Pascal Brunet, Christelle Batiot-Guilhe. High-frequency fluorescence monitoring in karst aquifers: A multivariate approach for early detection of anthropogenic contamination of water resources. Science of the Total Environment, 2026, 1019, pp.181561. 10.1016/j.scitotenv.2026.181561. hal-05528562

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# High-frequency fluorescence monitoring in karst aquifers: A multivariate approach for early detection of anthropogenic contamination of water resources

Lucile Justy<sup>a,b,c,*</sup>, Vincent Bailly-Comte<sup>b,c</sup>, Pascal Brunet<sup>a</sup>, Christelle Batiot-Guilhe<sup>a</sup>

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## HIGHLIGHTS

* Background noise correction methodology for fluorescence calibration

* Practical method for TLF calibration in tryptophan equivalents

* High-frequency monitoring for faecal contamination detection

* Contamination detection relied on essential in situ-lab comparative study

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Keywords:**

Karst

Fluorescence

Turbidity correction

Faecal contamination

Drinking water

## ABSTRACT

Karst aquifers play an essential role in drinking water supply, but their vulnerability to anthropogenic pollution, particularly during rainfall events, requires adapted monitoring methods. In this study, we developed an approach combining high-frequency in situ measurements and laboratory analyses to characterise the intensity and occurrence of organic matter fluorescence peaks, in particular Tryptophan-Like Fluorescence (TLF) peaks, associated with faecal contamination. We adapted a background noise correction method based on multiple regression applied to multispectral signals from field fluorometers. A specific calibration in TLF equivalents of the signals from an optical sensor measuring fluorescence at 285 nm is used to quantify contamination peaks. This method was tested on a dataset including 95 samples analysed in the laboratory and high-frequency data spanning 2015–2025 at 15-minute intervals from the Lez karst spring (France), showing its effectiveness in correcting optical interferences and detecting low pollution episodes. The analysis of time series underlined the importance of high-frequency monitoring to capture rapid water flow dynamics, while confirming the indispensable complementarity of laboratory analyses to better discriminate the fluorophores controlling the intensity of high-frequency in situ signals. The results highlight critical contamination periods, particularly in autumn and spring, and confirms the suitability of TLF for Early Warning System (EWS) applications. This approach broadens the methods used for water quality management in karst environments and proposes a methodological framework transferable to other sites.

<footnote><sup>a</sup> HydroSciences Montpellier (HSM), Univ. Montpellier, CNRS, IRD, IMT Mines Alès, Montpellier, France</footnote>

<footnote><sup>b</sup> Bureau de Recherches Géologiques et Minières (BRGM), Univ. Montpellier, Montpellier, France</footnote>

<footnote><sup>c</sup> G-EAU, UMR 183, Univ. Montpellier, AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote>* Corresponding author at: HydroSciences Montpellier (HSM), Univ. Montpellier, CNRS, IRD, IMT Mines Alès, Montpellier, France.
E-mail address: lucile.justy@umontpellier.fr (L. Justy).</footnote>
# 1. Introduction

The availability and quality of groundwater resources are major issues for Drinking Water Supply (DWS). In this context, organic matter represents an important indicator of water quality, particularly when a hydrosystem is tapped for population supply, as it allows the characterization of potential bacteriological contamination (Sorensen et al., 2015; Khamis et al., 2015; Frank et al., 2018; De La Torre et al., 2023; Fernández-Ortega et al., 2024). For this reason, it is both interesting and important to monitor the evolution of the quantity and origin of organic matter in water, combined with other chemical/microbiological indicators that provide extensive information on water quality.

Many studies use organic matter to (i) characterise the origin and nature of Natural Organic Matter (NOM) in aquatic systems influenced by environmental conditions (Hudson et al., 2007; Quiers et al., 2014; Frank et al., 2018), (ii) assess its potential as a tracer of groundwater flow and to characterise infiltration processes (Mudarra et al., 2011; De La Torre et al., 2023), or (iii) characterise the links between fluorescence and faecal contamination (Quiers et al., 2014; Sorensen et al., 2018a; Sorensen et al., 2018b; Frank et al., 2018).

There are different types of organic matter. It can have a pedological origin, including various compounds such as Humic-Like (Batiot et al., 2015), often associated with autochthonous fluorescent dissolved organic matter (FDOM) linked to natural biological activity, or it can be associated with allochthonous OM resulting from the remineralization of soil, plant, or water column organic matter. This is referred to as Humic-Like Fluorescence (HLF). There is also Tryptophan-Like Fluorescence (TLF), which derives from organic matter originating from amino acids (Tryptophan, Tyrosine). It also originates from biological activity, mainly associated with anthropogenic pollution (Coble et al., 2014), such as faecal contamination related to treated wastewater discharges from WasteWater Treatment Plants (WWTP), or untreated waters, into rivers (Carstea et al., 2016; Khamis et al., 2016; Fernández-Ortega et al., 2024). It has also been demonstrated that TLF is a good indicator of faecal contamination, since studies have shown that E. coli cells can produce TLF (Dartnell et al., 2013; Fox et al., 2017). These two types of organic matter are composed of fluorophores that confer them specific fluorescence properties. Measuring the fluorescence of organic matter provides quantitative and qualitative information on water quality and on flow dynamics in karst hydrosystems and allows the detection of pollution.

Karst aquifers play a major role in DWS, but their functioning, characterised by numerous exchanges between the surface and subsurface, the lack of natural filtration, and short residence times of their waters (Sarker et al., 2025), makes them particularly vulnerable to contamination and rapid fluctuations in bacteriological quality (Pronk et al., 2007). These hydrosystems are highly vulnerable to anthropogenic pressures and react very quickly (Labat et al., 2022). Strong hydrodynamic and biogeochemical reactivity, with significant turbidity peaks of different origins can be a typical response of karst systems. On the one hand, allochthonous turbidity resulting from sediments that infiltrated into the system during soil leaching, and on the other hand, autochthonous turbidity, initially present in the aquifer and linked either to residues of karstification or to a stock of sediments remobilized during rainfall events (Massei, 2001).

Considering their rapid response and the vulnerability of karst systems, it is necessary to be able to detect such rapid variations in indicators characterizing good or poor water quality (Vucinic et al., 2022; Vucinic et al., 2023). High-frequency fluorescence measurement is particularly suited to this type of hydrogeological context to monitor, in particular, the evolution of organic matter fluorescence with field fluorometers and to detect potential contamination. Moreover, the correlation of these high-frequency monitoring data, coupled with water sampling and laboratory analyses of chemistry, bacteriology, as well as hydrodynamic parameters, provides information on the functioning, reactivity and potential pollution that may affect karst hydrosystems

(Nowicki et al., 2019; Ward et al., 2021; Fernández-Ortega et al., 2024).

The importance of TLF monitoring as an indicator of contamination has been highlighted in numerous studies (Sorensen et al., 2015; Nowicki et al., 2019; Ward et al., 2021; Gunter et al., 2023; Mladenov et al., 2024) but the interpretation of fluorescence data for the purpose of quantifying organic compounds at high-frequency in these types of systems remains complex. Since the fluorescence signals of Humic-Like and Tryptophan-Like compounds are low, they are disturbed by turbidity variations, and the low selectivity of field fluorometers does not allow a proper isolation of each fluorophore. Strong turbidity peaks, especially during flood periods, are the source of signal interference that reduce the signal-to-noise ratio of optical measurements (Khamis et al., 2015; Gunter et al., 2023). Methods may be necessary to correct interferences that influence measurement (Schnegg and Dörfliger, 2002; Bailly-Comte et al., 2018). Initially, these methods for processing fluorescence signals were developed for applications related to the treatment or correction of artificial tracer data. Only a limited number of studies provide a complete calibration-correction methodology for TLF optics that produces robust high-frequency time series, and this is a point we sought to address in this article.

Following these corrections, TLF provides a suitable trigger for an EWS. It's a rapid, reagent-free proxy, and reacts to the arrival of anthropogenic organic matter faster than conventional microbiological analyses. An EWS based on TLF is a socio-technical device that combines continuous sensors, decision rules and laboratory checks to detect early risks of anthropogenic/faecal contamination and trigger proportionate actions (confirmation, communication, management) (Sorensen et al., 2015; Sorensen et al., 2018a; Gunter et al., 2023). This EWS architecture is particularly well suited to karst hydrosystems, where high reactivity requires high-frequency monitoring and robust triggers (Vucinic et al., 2022; Vucinic et al., 2023; Ravbar et al., 2023).

Here, we apply and adapt calibration/correction methods to enable natural tracing of flows using high-frequency monitoring of fluorescent NOM. First, (i) we present a correction method for the TLF signal that removes turbidity and inter-optic interferences; (ii) then, we compare TLF with complementary indicators to discriminate between water-flux types and (iii) characterise its seasonal variability; (iv) finally, as a preliminary step, we assess TLF as an EWS trigger. Altogether, we propose an operational protocol that isolates TLF peaks and their relative intensity, thereby identifying periods of anthropogenic contamination validated by chemical and bacteriological analyses. These developments are intended to support the design of an EWS to improve water-quality management in karst resources.

# 2. Application to the Lez karst spring

This approach was applied to the Lez karst spring (Fig. 1), part of the Multi-Scale Observatory of Flood and Underground Hydrodynamics in Karst Environment (MEDYCYSS Observatory: sokarst.org/sites /medycyss/). This experimental site is integrated into the French national network of Karst observatories (SNO Karst), attached to the Observatoire de Recherche Montpelliérain de l'Environnement (OSU-OREME) and to the Critical Zone Observatories Research Infrastructure (OZCAR). The Lez carbonate karst aquifer system consists mainly of limestone karst, with limestone and marly limestone formations (Jurassic and Cretaceous formations) (Fig. 1). It is partially overlain by impermeable formations (Clauzon et al., 2020). Its main outlet is the Lez spring, a perennial Vauclusian spring that emerges from a pool and gives rise to the river of the same name, which flows into the Mediterranean Sea (Batiot-Guilhe et al., 2014). It is mainly used for the drinking water supply of the Montpellier conurbation (Maréchal et al., 2014). It supplies 92% of the population, i.e., about 417,000 inhabitants. The underground pumping plant of the Lez spring is located at a depth of 48 m and captures water directly from the terminal conduit (Régie des Eaux de Montpellier 3M, 2023). The Lez spring is pumped at a flow of about 1 m³/s while maintaining a minimum flow of at least 0.3 m³/s in the Lez
River to ensure ecological continuity (Cousquer and Jourde, 2022). The hydrogeological basin is estimated at 250 km² (Clauzon et al., 2020). Annual recharge is largely attributed to intense Mediterranean events in autumn. Annual precipitation occurs mainly between September and December, and to a lesser extent from March to May (Dausse et al., 2019). Mean annual precipitation varies between 600 and 1500 mm (Cousquer and Jourde, 2022). In order to describe the hydro-climatic context, we carried out an analysis on the period 1990–2023 as a reference. The SAFRAN satellite data on annual precipitation totals and the average discharge data at the Lez spring show that the period 2019–2023 was characterised by low rainfall (–20 to –40% for annual totals), associated with lower discharges at the spring (–40% to –60% in 2023 for annual averages).

The waters flowing at the Lez spring result from a mixing of different types of water: a deep, highly mineralized end-member; rapid infiltration waters that are moderately mineralized and can be impacted by anthropogenic contamination; and waters circulating within the main aquifer (Caetano Bicalho et al., 2012). In a preliminary work on the Lez spring, Fernández-Ortega et al., 2024 began to identify water quality proxies through high-frequency monitoring, which we use in this study (EC: Electrical Conductivity, Cl: Chloride, DO: Dissolved Oxygen, turbidity, TLF, NO₃⁻, TOC: Total Organic Carbon). Indeed, depending on hydrological conditions, the evolution of these parameters provides considerable information, particularly on water type and the associated chemical and bacteriological quality. A decrease in electrical conductivity (EC), together with increases in NO₃⁻, TOC and total coliform concentrations, and in some cases a decrease in dissolved oxygen related to enhanced microbial respiration, indicate a strong contribution of recent infiltration waters associated with soil leaching processes (Mudry, 1990; Batiot et al., 2003; Caetano Bicalho et al., 2012). Flows from rapid infiltration do not pose a risk of contamination but may mix with treated or untreated wastewater from treatment plants (faecal

indicators such as *E. coli*) infiltrating the karst system (Batiot-Guilhe et al., 2013). This wastewater-related contamination can also be supported by a positive gadolinium anomaly as well as a high boron content (Rabiet et al., 2009). At the onset of floods following low-flow conditions, high electrical conductivity reflects a piston-flow flushing of highly mineralized storage waters within karst conduits (Mudry, 1990). This initial phase is typically characterised by elevated concentrations of major ions (Cl, Na, Mg, Li), slightly lower dissolved oxygen values associated with confined conditions, and the absence of anthropogenic contamination (no total coliforms or *E. coli*) (Caetano Bicalho et al., 2012). Then, it is necessary to distinguish between total coliforms and *Escherichia coli*, as they do not reflect the same processes. Total coliforms primarily originate from naturally occurring soil microbial populations and may be transported during infiltration and leaching processes. Consequently, their detection does not necessarily imply anthropogenic pollution or sanitary risk. In contrast, *Escherichia coli* is widely recognized as a reliable indicator of faecal contamination and is generally linked to anthropogenic inputs, especially wastewater effluents.

Pressures on the water resource and anthropogenic impacts can make the Lez spring more vulnerable, particularly given that it is the catchment point for the drinking water supply of the Montpellier metropolitan area. Batiot-Guilhe et al. (2013) reported on the quality of the Lez spring. The agricultural impact is very low, as there is no live-stock farming in the study area. The source of the Lez does not show any chronic contamination with pesticides. The chemical quality is generally good, except during flood periods, when certain parameters may exceed drinking water standards. Bacteriological quality revealed domestic contamination linked to wastewater (WWTP, on-site sanitation systems) (Batiot-Guilhe et al., 2013; Batiot-Guilhe et al., 2014), which can endanger user health, especially when it is used for DWS (Katz et al., 2011). In the case of this study, a sample was taken from effluent water of a wastewater treatment plant, enabling us to confirm the very high

<figure><img src="image_5.png" /><figcaption>Fig. 1. Simplified geological context of the study site.</figcaption></figure>
<table><caption>**Table 1**
Different high-frequency in situ monitoring and sample collection with laboratory analyses at the Lez spring.</caption><thead><tr><th>Monitoring</th><th>Type of monitoring</th><th>Parameters</th><th>Frequency</th></tr></thead><tbody><tr><td>Sampling hydrogeochemical monitoring</td><td>Manual sampling and laboratory analyses</td><td>Bacteriology<br>TOC<br>Major elements<br>Trace elements</td><td>Bi-monthly to multi-daily since 2006</td></tr><tr><td>GGUN FL918 fluorometer (Albilia)</td><td>High-frequency in situ</td><td>HLF<br>TLF<br>Uranine<br>Turbidity</td><td>15 min since 2015</td></tr><tr><td>YSI 6920 probe</td><td>High-frequency in situ</td><td>Electrical conductivity (EC)<br>Temperature<br>pH<br>Dissolved oxygen<br>Chloride</td><td>1 h since 2015</td></tr></tbody></table>

bacteriological status and contamination indicators.

Numerous karstic sinkholes are present in the catchment area of the Lez karst spring, located mainly in fault zones that bring aquifer formations into contact with the locally overlying impermeable formations. These karstic sinkholes increase the vulnerability of the hydrosystem by facilitating the infiltration of temporary streams into which treated or untreated wastewater from WWTP is discharged. This contamination is directly introduced into the karst system and then transferred to the discharge point via a preferential drainage axis (Caetano Bicalho et al., 2012). The effluent from the treatment plant, which has been analysed in the laboratory, flows directly into a karstic sinkhole. Artificial tracing has been carried out during periods of high and low water and confirms its connection with the source of the Lez (Boura, 2025).

# 3. Methodological development to identify contamination episodes with TLF monitoring

## 3.1. Types of monitoring implemented at the Lez spring

We present, in Table 1, the characteristics of our study site monitoring network. There are different probes for high-frequency in situ measurements, as well as laboratory analyses for regular hydrogeochemical monitoring through manual sampling. This allows the correlation of these different types of monitoring. We focus on the physicochemical parameters to characterise the contribution of the different identified end-members and the potential pollution that may impact the hydrosystem.

The HydroSciences Montpellier laboratory has conducted manual water sampling and laboratory analyses since March 2006, continuing through 2025. Sampling was performed bimonthly, as well as during flood events and low-flow periods. Total coliforms and *Escherichia coli* were quantified using the Colilert-18 method (Colisure-Quanti-Tray/2000, IDEXX™ Laboratories Inc., Westbrook, USA) on samples collected in sterile bottles. Sampling for TOC was carried out in pre-cleaned and combusted (6 h at 550 °C) amber glass bottles and then analysed using a Shimadzu TOC-V CSH model (Quiers et al., 2014). Major elements are analysed by ionic chromatography (DIONEX ICS-1000) after filtration (0.22 µm) and an acidification to 1‰ (Suprapur concentrated HNO₃) for cations. AETE-ISO platform (OSU OREME, University of Montpellier) analysed trace elements concentration carried out by ICP-MS (Inductively Coupled Plasma Mass Spectrometry, iCAP-TQ, Thermo Scientific), after filtration (0.22 µm) and acidification to 1‰ (Suprapur concentrated HNO₃). Every two weeks, data from each probe is downloaded on site. It is checked, corrected for artefacts, and archived in databases. In all the years of monitoring, certain periods have gaps (staff shortages, COVID period, equipment failure).

## 3.2. Principle of natural and artificial fluorescence measurement using a multi-optical probe

Many studies have indicated the value of in situ high-frequency measurements for continuously monitoring the evolution of NOM (natural fluorescence) or fluorescent tracers (artificial tracings) (Khamis et al., 2016; Bailly-Comte et al., 2018; Ravbar et al., 2023; Fernández-Ortega et al., 2024). Field fluorometers of the GGUN type (Albillia Särl, Auvernier, Switzerland) enable long-term, high-frequency monitoring of fluorescence evolution using three optics, with a fourth optic dedicated to turbidity monitoring by nephelometry at 650 nm (Schnegg and Dörfliger, 2002).

The optics of this type of field fluorometers are not initially dedicated to the continuous monitoring of NOM fluorescence (Batiot et al., 2015) but rather to artificial tracers such as uranine, rhodamines, and Optical Brighteners (OB). However, the OB optic has excitation and emission wavelength ranges close to HLF type OM. For TLF type compounds, a customised low-wavelength optic system (285 nm) was installed on the fluorometer (Bailly-Comte et al., 2018). Field fluorometer measurements provide continuous records of HLF and TLF type NOM, as well as turbidity.

The multi-optical system of the field fluorometer can induce interferences during measurements by the other optics, particularly due to the presence of suspended matter (SM), which can produce signal interference (Coble et al., 2014). During flood periods, karst waters can be particularly turbid, which increases the diffusion/scattering of light in the optical measurement chamber of the device and can disturb the measurements taken by the other optics. Therefore, this can lead to an overestimation of certain measured peaks. The influence of turbidity must be considered when monitoring fluorophores, especially in the case of natural fluorophores which produce lower fluorescence intensities.

Schnegg and Dörfliger (2002) presented a turbidity correction method based on the signal from synthetic turbidity standard solutions (formazine standards). This measurement allowed us to quantify the turbidity-induced signal interference that disturbs the other optics. The method first log-transforms the variables, then fits an (n-1) degree polynomial to the net signal (i.e. blank-corrected measurement).

This theoretical signal interference can be calculated for each optic from the monitoring carried out for the turbidity. It can furthermore be linearly weighted by a "Turbidity Sensitivity" coefficient (noted TS) (Schnegg and Dörfliger, 2002) since standard solutions may differ from the suspended matter present in natural waters (Bailly-Comte et al., 2018). If TS = 1, the correction is applied; if TS = 0, the correction is not applied. The user can adjust the turbidity correction to other coefficients, depending on the desired correction intensity. This makes this
correction method arbitrary.

### 3.3. New calibration approach for tryptophan and turbidity solutions for field fluorometers

A calibration process was carried out on a GGUN FL30 fluorometer, commercialized by Albillia Sàrl (Auvernier, Switzerland). This fluorometer is equipped with four optics: HLF (OB) (λex = 375 nm, λem = 475 nm), Uranine (λex = 450 nm, λem = 650 nm), Turbidity (λex = 650 nm, λem = 625 nm), and TLF (λex = 285 nm, λem < 400 nm). The latter optic detects fluorescence peaks associated with sources of anthropogenic contamination such as those coming from WWTP discharges (Khamis et al., 2016; Bailly-Comte et al., 2018; Fernández-Ortega et al., 2024).

This section presents the calibration of a TLF optical system on a GGUN fluorometer using tryptophan solutions. The manufacturer has already used a calibration approach for turbidity optics with standard solutions, but this has never been presented on TLF optics. In this study, we chose to work on the TLF indicator to identify contamination episodes, but to do so, we need to be sure of what we are measuring and the range of values over which the optics can track the TLF signal (and use standard that are more concentrated than in the natural environment). As we are also interested in turbidity optics and its signal, its calibration is also presented in this section.

A practical protocol is proposed for optical calibration that can be carried out in the field with simple preparation and using stable, easily available, low-cost solutions. The innovative aspect of this point is the choice of the solvent used to dilute the standards; this solvent also serves as the blank for instrument calibration. Distilled and purified laboratory water (Milli-Q water) is not representative for local calibration, and water from the study site will already contain elements that can interfere with calibration (turbidity, natural fluorescence). Water with the characteristics of our study site is required (chemical composition and hydrochemical facies), have a similar pH, be easily available in shops, chemically and microbiologically stable, and exert no influence on the measurement (i.e., negligible turbidity). After evaluation, Evian natural mineral water was selected because it met all these criteria. However, any calcium-bicarbonate natural mineral water that is moderately mineralized, slightly basic pH, and free of gas, turbidity, and natural fluorescence may be used for both dilutions and the blank measurement during optical calibration.

Calibration files were produced using TLF standards (1, 10, 50, 100, 200, 500 ppb) prepared from a Tryptophan stock (TraceCERT®, Sigma-Aldrich Production GmbH, Switzerland) and turbidity standards (1, 5, 10, 25, 50, 100, 200, 400 NTU) prepared from a 4000 NTU formazin stock (HACH™, Loveland, USA). The turbidity solutions were measured using a turbidimeter (WTW Turb® 430 IR, Xylem Analytics, Weilheim, Germany) to verify the validity of their concentrations. To calibrate the on-site fluorometer, the standards were measured for in situ calibration. This offers the possibility to build a calibration file linking the concentrations of the standard solutions in ppb or NTU to a signal in mV.

### 3.4. A new method to obtain high-frequency in situ monitoring of low TLF compounds

The objective of this section is to present the application of the method allowing the acquisition of corrected TLF time series. In this way, the fluorescence time series capture only variability in the Tryptophan-Like fluorophores measured by the TLF optic. This provides information on hydrosystem contamination as an indicator of anthropogenic pollution.

This provides information on hydrosystem contamination as an indicator of anthropogenic pollution. The L3 optic measures (L3raw) a TLF signal but also interferences related to the presence of turbidity and fluorophores (themselves measured by the other optics L1, L2, L4). Thus, it is established that the “background noise” (L3calc) corresponds to these

interferences. The objective is to quantify this background noise to subtract it from the L3raw signal.

Schnegg and Dörfliger (2002) has confirmed the hypothesis of linearity of interference for artificial tracers. Bailly-Comte et al. (2018) based their work on the hypothesis of linearity, and their method works well for correcting artefacts from natural fluorophores. Then, the method presented in this article is based on this latter mathematical approach and models the in-situ signal as natural fluctuations explainable by linear combinations of the other fluorometer optics. It enables a corrected signal to be obtained using the following steps:

(1) The weights (p1, p2, p4, constant) are determined by multiple linear regression on the entire dataset of the time series to recalculate the signal of interest, which here corresponds to the component of the L3 signal that can be explained by the other signals. It is assumed that this calculated signal corresponds to the background noise signal L3calc, i.e., the non-specific signal of the L3 optic that interferes with the measurement of TLF. The mathematical model for calculating L3calc is written as follows:

$$L3_{calc} = \text{constant} + \sum_{i=1,2,4} (p_i * Li)$$

(2) The raw measurement of the L3 signal (L3raw) is composed of the background noise signal (L3calc) and the L3TLF signal specific to L3, which characterises the intensity of the signal from TLF compounds that we seek to quantify:

$$L3_{raw} = L3_{TLF} + L3_{calc}$$

(3) We can deduce:

$$L3_{TLF} = L3_{raw} - L3_{calc}$$

### 3.5. Detection of TLF peaks in the Lez spring database (2015–2025)

An automated peak-detection algorithm was applied to analyse TLF peaks across the entire in situ high-frequency fluorescence time series (2015 to present), identify trends, and statistically characterise their distribution without manual or visual bias.

A script in Python 3.12 (Python Software Foundation, 2025) was used to analyse the 10 years of data, at a 15-minute time step, and was mainly based on the functions findPeaks and peakProminence from the Scipy Signal library (Jones et al., 2001). For this purpose, it was necessary to set the parameters for peak recognition, including the relative peak intensity (prominence) and the peak duration (wlen). A 24-hour moving average was applied to the TLF time series to facilitate peak detection. An output file was generated for all peaks with a prominence of at least 2 eq. ppb of TLF (below this threshold, peaks blend into measurement noise) and whose duration does not exceed 5 days, to focus on the detection of contamination peaks resulting from rapid water infiltration linked to the rainfall event.

## 4. Results

### 4.1. Calibration of a field fluorometer to measure TLF organic compounds and turbidity in situ

The TLF optic and the turbidity optic were calibrated following the method described in Section 3.3, and applies a polynomial fit of (n-1) degree for n calibration points after transforming the variables into log form. The manufacturer usually uses this approach for turbidity optics and has already presented it. However, the calibration of a TLF optic has not yet been presented. Thus, we apply this method and present the results of the TLF optic calibration only. Fig. 2 shows the calibration performed at Lez Spring using the following formula for the six
<figure><img src="image_6.png" /><figcaption>Fig. 2. 5th-degree polynomial according to the method of Schnegg and Dörfliger (2002) with Tryptophan concentration (T) and blank corrected L3 measurements (L3 in the formula).</figcaption></figure>

calibration solutions:

A calibration file specific to the TLF optic is obtained. It is now possible to measure and calculate TLF concentrations with the field fluorometer over the range from 0 to 500 eq. ppb of TLF, as for the Turbidity optic over the range from 0 to 100 NTU. This calibration file offers the possibility to standardize and facilitate the inter-comparison of results with the literature.

The relationship is nearly linear for the TLF (R² = 1.0) and is similarly nearly linear for Turbidity optics (not shown; R² = 0.99). The logarithmic transformation helps better account for potential nonlinearities, particularly for lower values or those near saturation,

though this is not observed here. Note that a perfectly linear relationship would yield a₁ = 1. We now have a calibration file specific to the TLF optics of the Lez source fluorimeter, which will be used to process the acquired data.

## 4.2. Application of the correction of the tryptophan-like optic signal

The method presented in Section 3.4 was applied to demonstrate the effect of applying this correction to a dataset over a three-month period in 2015. This period was chosen because it represents two very different responses to the correction, notably with the most intense peak in

<figure><img src="image_7.png" /><figcaption>Fig. 3. Raw Tryptophan-Like Fluorescence time series (grey curve in eq. ppb of TLF), corrected Tryptophan-Like-Fluorescence using the method presented (3.4) (purple curve in eq. ppb of TLF), Humic-Like-Fluorescence (red curve in mV), Uranine (light green curve in mV), Raw Tryptophan-Like-Fluorescence (orange curve in mV), Turbidity (green curve in NTU), measured with a field fluorometer.</figcaption></figure>
fluorescence measured at the source of the Lez (Fig. 3). In Fig. 3A, in August, peaks for raw TLF compounds (grey curve) were observed, accompanied by peaks for HLF compounds (red curve), uranine (light green curve, probably influenced by HLF), as well as strong turbidity peaks (dark green curve). When the correction method (3.4) is applied to the TLF time series (corrected TLF, purple curve), the first TLF peaks in August are strongly attenuated. It can be considered that they were influenced by interferences due to turbidity and fluorophores present in the water (detected by the other optics).

In Fig. 3B, in October, a strong TLF peak occurred again but gener-
ated little response from HLF and uranine, and no turbidity was detec-
ted. When looking at the corrected TLF time series, the peak remains
practically unchanged after the correction. It is considered that the TLF
peak originates solely from the detection of anthropogenic
contamination.

The application of this method enables us to obtain a TLF time series
corrected for the influence of turbidity, but also for all the other fluo-
rophores also measured by the other optics. We now have a validated
TLF time series, enabling robust and actionable detection of
contamination.

4.3. Cross analysis of lab analyses and in situ measurements

The detection of a TLF peak can be an indicator of anthropogenic contamination, but it is essential to confirm this contamination with other indicators measured at the study site (Lapworth and Sorensen, 2018; Gunter et al., 2023; Ravbar et al., 2023; Fernández-Ortega et al., 2024). The results presented in the next subsections (4.3.1; 4.3.2; 4.4) correspond to the temporal variations of parameters from high-frequency in situ monitoring and manual sampling with laboratory

analyses, measured at the Lez spring since 2015, under highly con-
trasting hydrological conditions. We chose to apply this co-variation
analysis on different time scales to demonstrate how our contamina-
tion detection and confirmation protocol works. Section 4.3.1 presents
an analysis at the event scale, while Section 4.3.2 presents an analysis at
the hydrological year scale. Section 4.4 presents an analysis based on 10
years of collected data. These graphs (Figs. 4 and 5) present two data
sources:

(i) High-frequency in situ monitoring: TLF compounds (eq. ppb of TLF, corrected for turbidity and cross-optics interferences), HLF (mV), turbidity (NTU), chlorides (mg/L), dissolved oxygen (mg/L), and electrical conductivity (µS/cm; T° ref. 25 °C). They also include spring discharge (m³/s; DREAL – HydroPortail, station Y320 0010) and precipitation (mm; SAFRAN on the grid cells covering the catchment).

(ii) Water sampling and laboratory analyses: total coliforms and *E. coli* (Most Probable Number per 100 mL, MPN/100 mL), TOC (mg/L), gadolinium anomaly and boron (µg/L), along with other indicators of anthropogenic contamination, particularly from wastewater infiltration into the hydrosystem.

For the interpretation of these data, we rely on a nomenclature
(Table 2) that we have developed based on the literature and the anal-
ysis of our databases to establish indicators representing the different
types of water in the Lez karst system, which may occur entirely or
through mixtures. The nomenclature also presents a fourth type of water
corresponding to raw WWTP effluent, established from analyses carried
out on samples collected at the plant outlet without dilution, used as an
indicator of pollution from discharges. The purpose of using this

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Time series of the first recharge events at the Lez spring, Montpellier (France) at the beginning of the hydrological cycle (August-October 2015). Comparison of different parameters: i) high-frequency in situ monitoring: TLF (Tryptophan-Like Fluorescence, corrected, eq. ppb of TLF), HLF (Humic-Like Fluorescence, mV), turbidity (NTU), chlorides (mg/L), dissolved oxygen (mg/L), EC (µS/cm with T° Ref at 25 °C), discharge at the spring (m³/s), precipitation (mm/day); ii) laboratory sampling and analyses: total coliforms (MPN/100 mL), *E. coli* (MPN/100 mL), TOC (mg/L), gadolinium anomaly (no unit), lithium (µg/L), and boron (µg/L). The crosses added on the TLF monitoring time series indicate the manual water sampling and analysed measured in the laboratory. A, B, C, D are focuses on events.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Temporal evolution of different physicochemical, hydrodynamic, and bacteriological parameters at the Lez spring during the 2020–2021 hydrological cycle comparing different parameters: i) high-frequency in situ monitoring: TLF (Tryptophan-Like Fluorescence, corrected and expressed, eq. ppb of TLF), HLF (Humic-Like Fluorescence, mV), turbidity (NTU), chlorides (mg/L), dissolved oxygen (mg/L), EC (µS/cm with T°Ref at 25 °C), discharge at the spring (m³/s), and precipitation (mm/day); ii) laboratory sampling and analyses: total coliforms (MPN/100 mL), *E. coli* (MPN/100 mL), TOC (mg/L), gadolinium anomaly (no unit), lithium (µg/L), and boron (µg/L). The crosses added on the TLF monitoring time series indicate the manual sampling measured in the laboratory. A, B, C, D are focuses on events.</figcaption></figure>

<table><caption>**Table 2**

Indicators of different types of water flow at the Lez spring study site.</caption><thead><tr><th>Indicators</th><th>Type of water flow</th></tr></thead><tbody><tr><td>✓ HLF – TU – total coliforms – TOC<br/>✓ DO – Cl – EC</td><td>Rapid infiltration/soil leaching (Perotin, 2021)</td></tr><tr><td>✓ TLF – <i>E. coli</i> – Cl – B – Gd anomaly<br/>✓ DO</td><td>Anthropogenic contamination:<br/>Indicator of faecal contamination (Nowicki et al., 2019; Aram et al., 2021; Frank et al., 2022)<br/>Indicator of WWTP discharge (Rabiet et al., 2009)</td></tr><tr><td>✓ EC – Cl – B – Li</td><td>Deep end-member waters (Caetano Bicalho et al., 2012)</td></tr><tr><td>✓ EC – total coliforms – <i>E. coli</i> – Cl – B – Gd anomaly</td><td>WWTP effluent</td></tr></tbody></table>

nomenclature is to facilitate the identification of anthropogenic contamination events linked to sewage treatment plant discharges by confirming a peak in TLF with other indicators.

To facilitate the presentation and interpretation of the results (Figs. 4 and 5), summary tables (Tables 3 and 4) present the different events on which we focus, the evolution of the indicator parameters, and the inferred origin of the detected water flows. The parameters include hydrodynamic data, high-frequency fluorometric signals, high-frequency physicochemical measurements, and laboratory analyses of samples collected at the Lez spring. The indicators that appear in the table are those that have changed. If an indicator does not appear, it means that it did not react during the selected event.

It is crucial to perform a cross-analysis of the different parameters to confirm anthropogenic contamination from a detected TLF peak. For this purpose, two temporal scales were selected (Figs. 4 and 5) to account for the different hydrodynamic and hydrogeochemical response dynamics of the Lez spring, at the event scale (flood episode) or at the hydrological cycle scale.

### 4.3.1. Event analysis of floods: first recharge event in 2015

This section presents an event analysis of the first recharge event of the hydrological cycle, following the dry season in 2015 (Fig. 4). This event was chosen because, over a period of several months, it is possible to observe the influence of different types of water and detect the highest TLF peak recorded at the site. This peak coincided with sampling carried out at the time of highest intensity of TLF. This enables us to observe variations in other indicators during a peak of TLF. Moreover, over a period of a few months, the characteristic hydrological dynamics of the Lez spring could be observed, notably the identification of an input from the deep end-member, through continuous measurements and manual sampling with laboratory analyses. Events were selected and analysed in Table 3 using the nomenclature previously presented (Table 2).

Our high-frequency monitoring shows that turbidity alone is not a consistent indicator of contamination. Thus, in Fig. 4D, a TLF peak is detected without turbidity, but confirmed by *E. coli*, Boron and Gadolinium anomaly.
<caption>**Table 3**</caption>

Presentation of the different events indicating a TLF contamination episode, accompanied by the reactive parameters and the interpretation of their indication on the water flows contributing to the study site during the period from August to October 2025.

<table><thead><tr><th>Event</th><th>Parameter reactivity</th><th>Interpretation of flow origin</th></tr></thead><tbody><tr><td>(A) 13/08/2015</td><td>P = 100 mm (10 h) - Spring discharge = 0 m³/s</td><td rowspan="2">Rapid infiltration flow resulting from soil OM leaching and dilution effect</td></tr><tr><td>15/08/2015</td><td>✓ TU: 10 to 20 NTU<br/>✓ HLF: 2.1 to 5.5 mV<br/>✓ DO: 6.4 to 4.0 mg/L<br/>✓ Cl: 45.7 to 30.9 mg/L<br/>✓ EC: 756 to 706 µS/cm<br/>No manual sampling during event</td></tr><tr><td>(B) 23/08/2015</td><td>P = 77 cm (4 h) - spring discharge ≈ 2 m³/s</td><td rowspan="2">Rapid infiltration flow resulting from soil OM leaching and dilution effect</td></tr><tr><td></td><td>✓ HLF: 1.4 to 3.3 mV<br/>✓ DO: 6.1 to 5.4 mg/L<br/>✓ Cl: 52.1 to 36.4 mg/L<br/>✓ EC: 786 to 728 µS/cm<br/>✓ Total coliforms: >2419.6 MPN/100 mL<br/>✓ E. coli: 410.6 MPN/100 mL</td></tr><tr><td>(C) 15-20/09/2015</td><td>✓ TOC: 2.8 mg/L<br/>P = 0 mm - spring discharge ≈ 0.5 m³/s</td><td rowspan="2">Input from the deep end-member: highly mineralized waters (piston effect) without anthropogenic contamination mixed with rapid infiltration flow (contamination)</td></tr><tr><td></td><td>✓ Cl: 70 mg/L<br/>✓ EC: 860 µS/cm<br/>✓ Total coliforms: 214.1 to 517.2 MPN/100 mL<br/>✓ E. coli: 8.6 to 28.5 MPN/100 mL<br/>✓ B: 24 to 29.5 µg/L<br/>✓ Li: 3 to 6.8 µg/L</td></tr><tr><td>(D) 05/10/2015</td><td>P = 30 mm - spring discharge ≈ 0.5 m³/s</td><td rowspan="2">Confirmation of anthropogenic contamination by faecal contamination indicators (E. coli) and wastewater discharge indicators (Gd anomaly and B)</td></tr><tr><td></td><td>✓ TLF: 0 to 13.7 eq. ppb of TLF<br/>✓ DO: 6.0 to 4.6 mg/L<br/>✓ Cl: 39.8 to 45.9 mg/L<br/>✓ EC: 727 to 758 µS/cm<br/>✓ Total coliforms: >2419.6 MPN/100 mL<br/>✓ E. coli: >2419.6 MPN/100 mL<br/>✓ B: 22 to 29.6 µg/L<br/>✓ Gd anomaly: 2 to 12.2</td></tr></tbody></table>

### 4.3.2. Analysis at the scale of a hydrological year: hydrological cycle 2020–2021

Following the event-scale analysis (Section 4.3.1), the assessment is extended to a full hydrological year. For such longer-term analyses, a clean time series with minimal data gaps or instrumental drift and regular sampling is required to facilitate interpretation. The 2020–2021 hydrological year was selected because it provides a more complete record in terms of both high-frequency in-situ monitoring and manual water sampling (Fig. 5). This year is characterised by autumn flood events, small spring floods, and a summer low-flow period. However,

<caption>**Table 4**</caption>

Presentation of the different events indicating a TLF contamination episode, accompanied by the reactive parameters and the interpretation of their indication on the water flows contributing to the study site during the period from September 2020 to August 2021.

<table><thead><tr><th>Event</th><th>Parameter reactivity</th><th>Interpretation of flow origin</th></tr></thead><tbody><tr><td>(A) 06/09/2020</td><td>P = 60 mm - spring discharge = 0 m³/s<br/>✓ TLF: 0 to 4.5 eq. ppb of TLF<br/>✓ DO: 6.8 to 6.3 mg/L<br/>No manual sampling during event</td><td rowspan="2">Input of rapid infiltration and potential anthropogenic contamination but not confirmed due to lack of sampling and laboratory analyses</td></tr><tr><td>(B) 22/09/2020</td><td>P = 0 mm - spring discharge = 1.8 m³/s<br/>✓ TU: 1 to 4.9 NTU<br/>✓ DO: 6.6 to 5 mg/L<br/>✓ Cl: 36.8 to 116 mg/L (4 am.)<br/>✓ EC: 755 to 901 µS/cm<br/>✓ TLF: 0 to 5.8 eq. ppb of TLF<br/>✓ TU: 4.9 to 6.5 NTU<br/>✓ HLF: 1.8 to 3.4 mV<br/>✓ DO: 5 to 4.5 mg/L<br/>✓ Cl: 116 to 82.5 mg/L<br/>✓ EC: 901 to 730 µS/cm<br/>✓ Total coliforms: >2419.6 MPN/100 mL<br/>✓ E. coli: 488.4 MPN/100 mL</td></tr><tr><td>(C) 22/12/2020</td><td>P = 20 mm - spring discharge = 3 m³/s<br/>✓ TLF: 0 to 4.7 eq. ppb of TLF<br/>✓ TU: 1 to 6.8 NTU<br/>✓ HLF: 1 to 4.5 mV<br/>✓ DO: 6 to 4.6 mg/L<br/>✓ Cl: 31.3 to 28.5 mg/L<br/>✓ EC: 714 to 699 µS/cm<br/>No manual sampling during event</td><td>Rapid infiltration flow resulting from soil OM leaching. No contamination confirmed due to lack of manual sampling</td></tr></tbody></table>

the year 2020–2021 cannot be considered hydrologically representative of the behaviour of the Lez spring, being part of the five particular years presented in Section 4, marked by rainfall totals below normal. Table 4 presents selected events analysed according to the same methodological criteria described in Table 2.

The interpretation of high-frequency TLF signals and bacteriological indicators (Tables 3 and 4) requires a clear distinction between contamination sources and associated hydrological processes. Total coliforms largely belong to the natural soil microbial community and can be mobilized during soil leaching and rapid infiltration events, without necessarily reflecting anthropogenic or faecal pollution. In contrast, E. coli is a more specific indicator of faecal contamination and is commonly associated with anthropogenic sources such as wastewater effluents. The TLF signal is primarily sensitive to dissolved organic matter of anthropogenic origin and therefore responds preferentially to contamination events related to wastewater inputs. As a result, the few contrasting responses between TLF, total coliforms, and E. coli do not indicate contradictory signals, but rather reflect the influence of different contamination sources and transport mechanisms within the karst system.

Additional TLF peaks can also be noted during the year, less significant, not reaching 3 eq. ppb of TLF, and showing, for the periods with
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Trend of anthropogenic Tryptophan-Like organic contamination impacting the Lez spring, showing 46 TLF peaks detected over the period 2015–2025. Fig. 6a is the intensity of TLF peaks detected by month, during 2015–2025. Fig. 6b is the number of detected peaks by month, during 2015–2025. Fig. 6c is the cumulated rainfall by month during 2015–2025.</figcaption></figure>

available data, similar but weaker responses compared to those pre-
sented above, i.e., an increase in turbidity and HLF, a decrease in DO and
conductivity due to an input of rapid infiltration water diluting the
groundwaters. The other samples taken during this 2020–2021 hydro-
logical year were carried out less regularly (COVID period) outside flood
periods and do not allow confirmation of faecal contamination.

4.4. *Analysis of contamination risk periods to establish vigilance periods*

First, we studied the hydrodynamic and hydrochemical response,
particularly to identify the strongest TLF peaks, at different scales
(several months to a hydrological year). Here, we aim to study the dis-
tribution of TLF peaks (>2 eq. ppb of TLF = background noise limit)
over the hydrological year across the entire time series between 2015
and 2025, according to the method presented in Section 3.5. Fig. 6
shows (a) in histogram form, the number of peaks detected according to
months, and (b) in boxplot form, the intensities of peaks detected ac-
cording to months. This represents the detection of 46 TLF peaks over
the ten years analysed.

On Fig. 6a, we can note two periods with the highest number of TLF
peaks: 41% of detected peaks occur in autumn (September to November)
and 32% in spring (March to May). If we correlate this information with
rainfall totals over the area, this corresponds to the periods with the
most precipitation. Fig. 6b shows that the months of October and
November remain the months with the strongest TLF contamination
peaks, which can exceed 11 eq. ppb of TLF contamination intensity
values are lower in spring (2.78 to 3.64 eq. ppb of TLF and 2.54 to 3.50
eq. ppb of TLF in March and April, with averages of 2.97 and 3.14 eq.
ppb of TLF, respectively). If we compare this to precipitation in Fig. 6c,
the greater the cumulative rainfall, the higher the contamination in-
tensity. Thus, in autumn, when rainfall is the most intense, contamina-
tion peaks are the most numerous and the strongest. In spring, rainfall is
weaker, many peaks are detected but at lower intensity. In winter

(December to February), contamination peaks are present but in smaller
numbers and weaker, depending on whether the years are more or less
rainy. In summer (June and July), peaks are rare except in August, due
to summer rains in certain years.

5. Discussion

In our study, we chose to rely on TLF signals to detect episodes of
contamination. Our approach is explicitly linked to *E. coli*, a reliable
indicator of faecal contamination (Lapworth and Sorensen, 2018; Sor-
ensen et al., 2018a; Bedell et al., 2020; Sorensen et al., 2020; Ward et al.,
2021; Gunter et al., 2023; Fernández-Ortega et al., 2024), particularly
when it comes to wastewater inputs (Bedell et al., 2022; Mladenov et al.,
2024) and other parameters.

To ensure signal interpretability, in-situ calibration at the study site
is essential. The TLF literature reports that calibrations are sometimes
suboptimal: manufacturer-integrated calibrations (poorly documented),
Tryptophan standard solutions prepared in deionised water that is not
representative of the site, and absence of a final curve (Khamis et al.,
2015; Sorensen et al., 2018a; Nowicki et al., 2019; Sorensen et al., 2020;
Ward et al., 2021; Bedell et al., 2022). Therefore, we developed a cali-
bration file adapted to the Lez fluorimeter (Fig. 2). The method devel-
oped by Schnegg and Dörfliger (2002) works well but can be replaced by
a linear relationship. However, their method better captures slight non-
linearities between the concentration of the target fluorophore and the
measured signal.

Next, it is necessary to ensure that the corrected TLF signal is free
from interference. In the literature, turbidity correction is often
obscured (correction integrated into the instrument without local
adjustment) or absent despite known interference (Sorensen et al.,
2018a; Nowicki et al., 2019; Bedell et al., 2020; Sorensen et al., 2020;
Ward et al., 2021; Bedell et al., 2022). The use of artificial ‘natural’
turbidity solutions to mimic geogenic suspended matter is documented
(Khamis et al., 2015), but highly dependent on the nature of the particles. Here, we propose an explicit correction that compensates for the effect of turbidity and interfering fluorophores. Fig. 3 illustrates the effectiveness of this correction and the production of a robust chronicle that can be used for detection. Nevertheless, multi-parameter validation remains essential (Lapworth and Sorensen, 2018; Gunter et al., 2023; Ravbar et al., 2023; Fernández-Ortega et al., 2024). The nomenclature used to identify flows at the Lez spring (Table 2) proved decisive in interpreting this complex system. The change of scale – event-based (Fig. 4) and one hydrological cycle (Fig. 5) – made it possible to confirm episodes of contamination associated with WWTP discharges (E. coli, Cl, B, Gd anomaly). On the other hand, Fig. 5 highlights a limitation related to manual water sampling and laboratory analyses, which are sometimes insufficient to validate all detections; it is crucial to combine high-frequency monitoring with targeted laboratory analyses, particularly during periods of vigilance (Fig. 6, autumn and spring).

Finally, as the study site is not subject to significant agricultural and pastoral impacts, the intensity of the TLF peaks observed remains moderate compared to other case studies presented in the literature (Nowicki et al., 2019; Ward et al., 2021; Fernández-Ortega et al., 2024; Mladenov et al., 2024). This does not constitute a limitation, as these peaks nevertheless detect episodes of contamination, mainly linked to WWTP discharges, and confirmed by sampling/laboratory analyses. In summary, TLF is proving to be an excellent indicator that can be used in an EWS to detect contamination and trigger targeted checks (e.g., E. coli). It's in situ measurement without consumables and near real-time risk communication (Sorensen et al., 2018a; Nowicki et al., 2019; Bedell et al., 2020; Ward et al., 2021; Bedell et al., 2022; Mladenov et al., 2024) argue in favour of implementing an EWS based on TLF signals and integrated multi-parameter analysis.

## 6. Conclusion

This study meets the stated objectives: (i) to correct the TLF signal by eliminating the effect of turbidity and inter-optical interference; (ii) to use multi-parameter analysis to interpret episodes and confirm contamination; (iii) to describe seasonal variations and trends at the site; (iv) evaluate TLF as a trigger for EWS. Together, these results provide a robust protocol for measuring, correcting, interpreting and using TLF as an anthropogenic contamination indicator, systematically backing it up with complementary parameters (chemistry, bacteriology, hydrodynamics). Application to the study site has refined our understanding of periods of reactivity and seasonal windows of vigilance, enabling us to adapt monitoring and water sampling strategies.

In perspective, operationally, setting up a fluorescence-based EWS requires several steps: (i) local calibration of fluorimeters, interference corrections; (ii) definition of local operational thresholds, using a multi-indicators approach; (iii) telemetry and procedures to be followed (vigilance - alert - confirmatory sampling) (Ravbar et al., 2023; Mladenov et al., 2024). Thresholds are not universal: they must be specific to the hydrogeochemical context and instrumentation, as previous studies have shown (Khamis et al., 2015; Lapworth and Sorensen, 2018; Nowicki et al., 2019; Ward et al., 2021; Gunter et al., 2023). Then, in this work, it will be necessary to distinguish between 'presence/absence' thresholds for faecal bacterial indicator and contamination alert thresholds of TLF, based on monitoring and vigilance windows. It may be worthwhile to test the method on other karst basins and types of contamination, incorporating other faecal and/or microbiological indicators as well as other geological contexts. This will enable the transferability of the method to be assessed and inter-site variability to be studied. Including the generalization of this methodology, the development of a data processing interface and the harmonization of calibration protocols will facilitate the sharing of this method, better

comparability of data, as well as interoperability of this methodology. The next stage of this work will ensure the transition from a validated method to practical implementation to anticipate and manage water quality risks.

## CRediT authorship contribution statement

**Lucile Justy:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Vincent Bailly-Comte:** Writing – review & editing, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Pascal Brunet:** Resources, Data curation. **Christelle Batiot-Guilhe:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT (GPT-5) in order to help with the English translation of this document. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

## 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.

## Acknowledgements

This work has been funded by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program, reference ANR-22-PEXO-0012, as part of the OneWater – Eau Bien Commun national research program. We also thank the KARST Observatory Network (SNO KARST) and their approach to enhance knowledge-sharing and promote cross-disciplinary research on karst systems within the framework of the OZCAR Critical Zone network Research Infrastructure. This network includes the MEDYCYSS observatory (OSU OREME) that enables the instrumentation of the Lez aquifer, helped by ABIHS chemistry and AETE teams or chemical analyses and the HSM field team for hydrological monitoring. We also thank La Régie des Eaux de Montpellier Méditerranée Métropole for their cooperation and for facilitating access to the Lez spring site. This work also benefits from methodological advancements and expertise developed through monitoring activities at the Fontaine de Nîmes Spring observatory, managed by the BRGM and belonging to SNO KARST and OZCAR observatories networks. We gratefully thank the three anonymous reviewers for their time and thoughtful suggestions, which significantly improved the clarity, rigor, and overall quality of this manuscript.

## Data availability

Data will be made available on request.

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Glossary

B: Bore

Cl: Chloride

DO: Dissolved Oxygen

DOC: Dissolved Organic Carbon

DWS: Drinking Water Supply

EC: Electrical Conductivity

EWS: Early Warning System

FDOM: Fluorescent Dissolved Organic Matter

Gd: Gadolinium

HLF: Humic-Like Fluorescence

Li: Lithium

NO₃⁻: Nitrates

NOM: Natural Organic Matter

SM: Suspended Matter

TLF: Tryptophan-Like Fluorescence

TLF eq.: Tryptophan-Like Fluorescence equivalent

TOC: Total Organic Carbon

TU: Turbidity

WWTP: Wastewater Treatment Plant