# Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning

Zhe-Xuan Zhang, Arnaud Huguet, Zoé Hayet, Edith Parlanti

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Zhe-Xuan Zhang, Arnaud Huguet, Zoé Hayet, Edith Parlanti. Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning. Water Research, 2025, 284, pp.123900. 10.1016/j.watres.2025.123900. hal-05108424

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# Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning

Zhe-Xuan Zhang<sup>a,b</sup> <sup>†</sup>, Arnaud Huguet<sup>b</sup> <sup>†</sup>, Zoé Hayet<sup>a</sup>, Edith Parlanti<sup>a,*</sup> <sup>‡</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

Keywords:

DOM

Fluorescence

Land-ocean continuum

Machine learning

Land use

## ABSTRACT

Differentiating the composition of Dissolved Organic Matter (DOM) in estuaries is a major environmental concern, as the DOM characteristics are closely linked to biogeochemical and ecological considerations (e.g. water properties and trophic cycling). However, tracing the spatiotemporal variations of estuarine DOM is challenging due to multiple sources and complex transformation processes. Here, we investigate the dynamics of estuarine DOM by analyzing the optical properties of DOM through UV-Visible absorbance and fluorescence spectroscopy, while also capturing the variability of DOM using machine learning algorithms and explainable artificial intelligence. To this aim, we collected sub-surface water samples (n = 249) from a human-impacted estuary with intense industrialization and urbanization in France (Seine Estuary) across distinct land use characteristics in contrasting hydrological conditions. We then applied unsupervised and supervised machine learning techniques to analyze the optical properties of DOM, which were determined by UV-Visible absorbance and Excitation-Emission Matrix (EEM) fluorescence spectroscopy combined with parallel factor analysis (PAR-AFAC). Our results show that unsupervised machine learning (K-means clustering) captures the spatial variations of DOM, identifying three distinct estuarine zones based on pronounced spatial variations of several DOM optical parameters. Supervised machine learning (Light Gradient Boosted Machine, LightGBM) further validates the rationality of the defined zonation. Subsequently, explainable artificial intelligence based on SHapley Additive exPlanations (SHAP) analysis shows that DOM in each zone has specific characteristics. Our model indicates that DOM in the Seine Estuary is primarily influenced by high molecular weight materials and autochthonous contributions in the upper estuary (Zone I). The dominant contribution to DOM in the mid-estuary (Zone II) comes from autochthonous and aromatic material as well as transformation and (photo)degradation products. Lower estuary (Zone III) is mainly characterized by aromatic DOM (subject to photodegradation), low molecular weight compounds, autochthonous DOM, as well as transformation and (photo)degradation products. Overall, this study presents a workflow for differentiating the composition of DOM, tracing the variability and dynamics of DOM along the land-to-sea continuum, and elucidating the involved processes. The approach developed in the Seine Estuary has significant implications for environmental management and can be adapted to other land-sea continuums.

## 1. Introduction

Estuaries refer to transition zones between freshwater and marine systems and play an important role in carbon cycling (Canuel and Hardison, 2016). The flux of Dissolved Organic Carbon (DOC; for ab-breviations used, refer to Table S1) exported from global rivers to the ocean is estimated to ~130 TgC.yr⁻¹ for the past two decades (Fabre et al., 2020). Dissolved Organic Matter (DOM) constitutes the largest reactive reservoir of reduced carbon on Earth (Bianchi, 2011). Within

estuaries, DOM originates from distinct sources, including allochtho-
nous DOM from soils and plant litters, autochthonous DOM produced by
biomass (e.g. bacteria and phytoplankton) in the water column, and
anthropogenic DOM from agricultural runoff, industrial, and urban ef-
luents (Li et al., 2023). Investigating DOM dynamics in estuaries is a
major environmental concern, which helps to better understand the
overall functioning of estuarine ecosystems and their role in broader
biogeochemical cycles (Vidal et al., 2023). Despite its importance,
constraining the sources and investigating the fate of estuarine DOM is

<footnote><sup>a</sup> Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, F-33600 Pessac, France</footnote>

<footnote><sup>b</sup> Sorbonne Université, CNRS, EPHE, PSL, UMR METIS, Paris 75005, France</footnote>

<footnote>* Corresponding author.
E-mail address: edith.parlanti@u-bordeaux.fr (E. Parlanti).</footnote>
still challenging, due to the multiple sources of DOM, complex in-estuary processing (e.g. microbial and photochemical alterations), and varying factors that can influence DOM composition (e.g. hydroclimate conditions and land use characteristics) (Asmala et al., 2013; Vidal et al., 2023). Hence, extensive field monitoring observations are needed to capture the highly dynamic nature of the estuarine DOM (Cao and Tzortziou, 2024). Optical techniques such as UV-Visible absorbance and Excitation-Emission Matrix (EEM) fluorescence spectroscopy are fast, sensitive, and inexpensive methods. Despite limitations with optically active compounds, they are powerful tools for studying global DOM dynamics in large-scale monitoring (Derrien et al., 2019). The EEM fluorescence spectra can then be decomposed using parallel factor analysis (PARAFAC) to provide semi-quantitative insights into the various sources of DOM (Murphy et al., 2013; Stahlschmidt et al., 2016; Stedmon et al., 2003). The UV-Visible absorbance and EEMs coupled with PARAFAC (EEM-PARAFAC) have been widely applied to characterize colored dissolved organic matter (CDOM) and fluorescent dissolved organic matter (FDOM) in diverse aquatic environments (Derrien et al., 2019). In estuarine systems, where freshwater and salt-water mix, the dynamics of CDOM and FDOM become particularly complex. Low-cost techniques that allow for broader spatial coverage and seasonal variations are necessary, prompting numerous studies that have employed such tools to investigate variations in CDOM and FDOM optical properties along estuaries, as well as the biogeochemical processes to which CDOM and FDOM is subjected (Vidal et al., 2023; Guo et al., 2014). For example, previous studies have investigated the sources, composition, and seasonal variations of CDOM and FDOM in a large urbanized estuary (Pearl River estuary, China), revealing that CDOM and FDOM spectroscopic properties are influenced by both natural and anthropogenic processes (Li et al., 2019). In another anthropogenically impacted estuary, the Yangtze Estuary in China, optical properties of DOM have been investigated, revealing that the intensities of aromatic fluorescent components show no clear trend in either the surface or bottom waters (Wang et al., 2014). In contrast, aromatic substances in the bottom water of the same estuary exhibited significant spatial variability (Zhou et al., 2022). This discrepancy highlights the need for a comprehensive monitoring framework to capture the variability of DOM and explore the biogeochemical processes involved, which is crucial for improving water quality management in estuarine systems.

Such a monitoring framework may generate large datasets that potentially offer novel insights into estuarine DOM dynamics and functioning. However, large datasets can also present challenges in identifying data patterns and interpreting biogeochemical processes. For example, in the Neuse River Estuary (USA), while seasonal variations in CDOM and FDOM spectroscopic parameters are clearly tracked, capturing spatial variability—particularly zonation and dominant characteristics in specific regions—remains challenging (Hounshell et al., 2022). A significant research gap exists in the lack of a (semi-) quantitative approach to differentiate between the various types of CDOM and FDOM and assess the main characteristics within specific estuarine areas. To address these challenges, advanced statistical techniques, such as Machine Learning (ML) algorithms, can be employed to uncover hidden insights within the data, capturing the multidimensional nature of spatiotemporal data and identifying subtle trends and patterns that traditional methods, such as principal component analysis and simple visualization approaches, may overlook (Peters et al., 2014; Tahmasebi et al., 2020).

Generally, ML can be classified into unsupervised and supervised learning. Unsupervised learning algorithms are used to uncover the inherent traits and hidden patterns within unlabeled datasets (data points without corresponding labels) (Huang et al., 2021). On the other hand, supervised learning algorithms are typically used to handle labeled datasets (where each data point has a corresponding label) and make predictions through regression (predicting a continuous numeric value, such as estimating temperatures) or classification (assigning input data to predefined categories, such as identifying whether an email is

spam). Furthermore, explainable artificial intelligence is an emerging approach to provide a reasonable interpretation of the black box machine learning model by evaluating the importance of each input variable (Zhao et al., 2023). While ML has recently gained significant popularity, the applicability of ML in estuarine DOM research still needs to be explored. This might be due to the challenges and complexities associated with studying estuarine OM dynamics, which are influenced by both natural processes (Butturini et al., 2016) and anthropogenic activities (Chen et al., 2021). Additionally, obtaining high-resolution and high-quality data on estuarine OM could be time-consuming and challenging. ML algorithms often require substantial amounts of training data to build robust models, and the scarcity of data can hinder the application of ML approaches in DOM research. To the best of our knowledge, the use of machine learning and explainable artificial intelligence for classification tasks, notably differentiating DOM composition and identifying main DOM characteristics along the land sea continuum, including estuaries, is currently lacking and needs to be explored.

Previous studies showed that DOM characteristics are highly dynamic under varying hydroclimate conditions (i.e. temperature and water discharge) and land use characteristics (i.e. urban and agricultural land use), making it challenging to identify spatio-temporal variations of DOM and to evaluate the estuarine functioning (e.g. the role that estuaries play in regulating DOM dynamics) and sustainable management of estuaries (e.g. identifying regions where DOM with anthropogenic sources especially occurs) (Butturini et al., 2016; Chen et al., 2021). Advanced statistical approaches, including unsupervised, supervised machine learning, and explainable artificial intelligence may help to differentiate DOM composition and to identify main DOM characteristics in specific estuarine regions. The hypothesis was tested by investigating the DOM optical properties (i.e. UV-Visible absorbance and EEMs) of sub-surface water samples (n = 249) collected in low-flow and high-flow periods along the Seine Estuary (NW France), which spans urbanized, industrialized, and agricultural regions. This estuary is highly stressed by both natural fluctuations and anthropogenic pressures, making the estuary representative of human-impacted and contaminated hydrosystems (Dauvin, 2008). The aim of the present study is to (i) investigate the dynamics and complexities of DOM along the land-sea continuum, and (ii) evaluate the potential of machine learning and explainable artificial intelligence to identify the main DOM optical characteristics in specific estuarine zones and scenarios while providing biogeochemical interpretations. The Seine Estuary is used here as an example, but our presented workflow can be easily extended to other aquatic settings.

## 2. Materials and methods

### 2.1. Study area and sampling

The Seine Estuary acts as the outlet for the Seine drainage basin, which stands out for its high population density (200 inhabitants per square kilometer on average) and its substantial industrial and agricultural activities, accounting for 25 % of French agriculture and 25 to 30 % of French industrial activity (Meybeck et al., 2007; Romero et al., 2019). Eight land use types were identified in the Seine Estuary, categorized as agricultural, forested, shrubland, water body, bareland, grassland, urban (industrial zones included), and wetland (Fig. 1a, c). Detailed information about the sampling sites is provided in Text S1.

During 19 monitoring campaigns from June 2019 to November 2022, sub-surface water (ca. 1 m depth; 20 mL) samples (n = 249) were collected at 15 locations in contrasted seasons across the Seine Estuary with distinct land cover regimes (Fig. 1 and Table S2). For these sites, water samples were immediately filtered through pre-combusted (450 °C) 0.7 µm glass fiber filters (GF/F Whatman) on board, transferred to pre-cleaned fluorinated high-density polyethylene (FLPE) bottles (Nalgene), and stored in darkness at 4 °C until analysis. The
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (a) Map of the study area (Seine Estuary) showing the land use classification (agricultural, urban, forested, grass land, water body, shrubland, wetland, and bareland), with orange bullets representing sampling sites. The land use information was retrieved from the global surface coverage product GLOBELAND30 (http://www.globallandcover.com/). Seawater and inland water body are combined into a single category of “water body”. Industrial regions are included in “urban”. (b) Water discharge (mean monthly) of the Seine River from 2019 to 2022 measured at the Paris Austerlitz station (data retrieved from https://www.hydro.eaufrance.fr/). Sampling period of this study is shown by bullets with different color. The red bullets represent samples were collected in the low-flow (<250 m³/s) period and the blue bullets denote samples were collected in the high-flow (>250 m³/s) period. (c) Variation of the land use relative abundances along the Seine Estuary.</figcaption></figure>

absence of any contamination or residual fluorescent signal is guaranteed by means of fluorescence-based cleanliness tests on the material used and by filtration blanks. Samples collected during the five high-flow campaigns are represented by the “high-flow period” (in blue, Fig. 1b), while samples collected during all other periods are represented by the “low-flow period” (in red, Fig. 1b).

water sample that was acidified and analyzed in Non-Purgeable Organic Carbon (NPOC) mode using Total Organic Carbon Analyzer (TOC-L, Shimadzu, Tokyo, Japan). 3 replicate analyses were made for each sample. The average value is reported here, with the relative standard deviation below 1 %.

## 2.2. DOC concentration measurement

The DOC concentrations were determined by using an aliquot of

## 2.3. Spectroscopic analyses

The spectroscopic analyses (absorbance and fluorescence) were carried out in a Hellma Suprasil® quartz cell with a path length of 1 cm.
The UV-Visible absorbance spectrum of water samples (n = 249) was analyzed using a Jasco® V-760 spectrophotometer. The excitation-emission matrix (EEM) fluorescence spectra (n = 249) were obtained between the wavelengths 240–800 nm at excitation (2 s integration time, 5 nm intervals) and 245–830 nm at emission (high CCD detector gain, 1 pixel (ca. 0.58 nm intervals)), using an Aqualog spectrofluorometer (Horiba Scientific, France) equipped with a xenon lamp (150 W), a double monochromator at excitation, and a CCD detector. Detailed spectroscopic measurement information, as well as absorbance and fluorescence indices—including spectral Slope Ratio (SR) (Helms et al., 2008), the specific ultraviolet absorbance at 254 nm (SUVA₂₅₄) (Weishaar et al., 2003), γ/α (Huguet et al., 2009; Parlanti et al., 2000), FI (McKnight et al., 2001), BIX (Huguet et al., 2009) and HIX (Zsolnay et al., 1999) —is provided in Text S2, Table S3 and S4.

## 2.4. Parallel factor analysis (PARAFAC)

The EEM fluorescence spectra can be decomposed by a multi-way PARAllel FACtor analysis (PARAFAC) into independent underlying fluorescent components (Stedmon et al., 2003). This statistical method helps to identify the fluorophores contributing to the overall spectral dataset and to estimate their relative contribution to the total DOM fluorescence. The PARAFAC model was carried out using the DOMFluor toolbox (version 1.7) in Matlab R2021b and run for 4 to 8 components with non-negativity constraints (Stedmon and Bro, 2008). A six-component model was validated after split-half validation analysis and examination of the residuals (Murphy et al., 2013; Stedmon and Bro, 2008). The spectral characteristics of the components determined by PARAFAC were further compared to those identified in the literature in other environments and by an online spectral library (OpenFluor) (Murphy et al., 2014). The similarity between the six components determined in this study and those in the database was measured using Tucker's congruence coefficient, with criteria set at 95 %.

## 2.5. Unsupervised machine learning

K-means clustering, an unsupervised machine learning technique, was used to find clusters grouping samples with similar DOM optical parameters in an unlabeled dataset (n = 249). K-means clustering was performed using the KMeans function from the cluster module of the scikit-learn library (https://github.com/scikitlearn/) (Pedregosa et al., 2011) in Python 3.9.16. The optimal number of clusters (K) was chosen using the elbow method.

## 2.6. Supervised machine learning

A supervised machine learning algorithm (Light Gradient Boosting Machine, LightGBM) (Ke et al., 2017) was trained for classifying the estuarine zones based on DOM optical properties in a labelled dataset (i. e. zonation of the estuary), implemented with the LightGBM package (https://lightgbm.readthedocs.io) in Python (3.9.16). This algorithm is a tree-based gradient boosting framework, which works by combining weak decision trees to create a strong model (Ke et al., 2017). Specifically, the LightGBM algorithm starts by constructing a single decision tree based on the input data that predicts the target variable. Further decision trees are added to the model iteratively, with each tree aiming to correct the errors of the previous tree. The LightGBM algorithm has been widely used across many domains because of the high accuracy, high training speed, and low memory consumption (Alova et al., 2021; Ke et al., 2017).

We split our dataset into 75 % training dataset and 25 % test set randomly. The training set is used for fitting the machine learning model, whereas the test set (independent set of new data that has never been used in training) is used to evaluate the model performance. We further used a standard framework (Synthetic Minority Oversampling Technique, SMOTE) to solve the class imbalance problem, which occurs

when one class (zone) contains significantly fewer samples than the other classes (Chawla et al., 2002). This technique is used to oversample an imbalanced training set, implemented with the imlearn library (Lemaître et al., 2017) (https://github.com/scikit-learn-contrib/imbalanced-learn) in Python (version 3.9.16). Evaluation of the supervised machine learning model is shown in Text S3.

## 2.7. Explainable artificial intelligence

The explainable artificial intelligence framework (SHapley Additive exPlanations, SHAP) is a game theoretical approach, which is used for interpreting black-box models such as gradient-boosting machines (Lundberg et al., 2020). The SHAP method was used in this study to evaluate the weight of distinct features (individual DOM optical properties) in the trained machine learning model, with high SHAP values indicating a stronger positive influence of that feature on the specific prediction, implemented with the SHAP package (https://github.com/slundberg/shap) in Python (3.9.16). The main DOM characteristics in each class (zonation of the estuary) were further identified.

## 2.8. Other statistical analyses

Other statistical analyses, including Principal Component Analysis (PCA), Redundancy analysis (RDA), and a locally estimated scatterplot smoothing (LOESS) method were performed using R (version 4.2.1) (Text S4).

# 3. Results and discussion

## 3.1. Complexity of DOM characterization across the land-sea continuum

The final six-component model determined by PARAFAC analysis (C1-C6; Fig. 2 and Fig. S1) accounted for > 99.6 % of the measured spectral variation for the 249 sub-surface water samples from the Seine Estuary collected between June 2019 and November 2022. The spectral characteristics and interpretations of these components are presented in Text S2 and Table 1.

The relative fluorescence intensities of these components (percentage of fluorescence intensity of each component relative to the total fluorescence intensity for each sample) show significant temporal variations in the Seine Estuary (Fig. 3). Specifically, average relative proportions of C1, C2 and C4 are much higher in the high-flow periods (above 250 m³/s) than in the low-flow periods (below 250 m³/s; *p* < 0.05; Fig. S2). Such variability linked to hydrological conditions is also shown by the intensities of these components, absorbance (SR and SUVA₂₅₄) and fluorescence (HIX, BIX and FI) indices (Fig. 3, S2 and Fig. S3).

The distinct hydrological conditions may lead to higher proportions of aromatic compounds with high molecular weight at high flows in the Seine Estuary. Indeed, we observe that all aromatic/terrestrial indicators (intensities as well as relative fluorescence intensities of C1, C2, C4; HIX) significantly increase with increasing water discharge in the Seine Estuary (*p* < 0.05, Spearman's correlation; Fig. S4, S5). Such significant relationship indicates that higher proportions of terrestrial/aromatic DOM are flushed into the water column due to enhanced precipitation during high-flow events, as previously observed in other land-sea continuums such as the Neuse River Estuary (Hounshell et al., 2022) and the Changjiang River Estuary (Zhang et al., 2022). Moreover, the intensities and relative fluorescence intensities of photogenerated aromatic component (C6) are significantly higher at high flows compared to the low-flow period (*p* < 0.05, Wilcoxon test; Fig. S2f and Fig. S3f). This photodegradation product has high aromaticity and is suggested to be produced from terrestrial material (Ishii and Boyer, 2012). Therefore, the significantly higher proportion of C6 during high flows could be attributed to the greater amount of terrestrial DOM that is flushed into the Seine Estuary, providing more material for
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Contour plots of the six components determined by PARAFAC for sub-surface water samples (n = 249) collected in the Seine Estuary during 19 monitoring campaigns from June 2019 to November 2022. Potential origins and characteristics of these components are shown in Table 1. Ex represents excitation wavelength, and EM represents emission wavelength.</figcaption></figure>

<table><caption>**Table 1**

Spectral characteristics of the six PARAFAC components.</caption><thead><tr><th>Component</th><th>Ex (max)/ Em (max)</th><th>Potential origins and characteristics</th><th>References</th><th>Number of OpenFluor matches</th></tr></thead><tbody><tr><td>C1</td><td>240 (325)/ 429</td><td>Terrestrial substances<sup>1</sup>; Common in freshwater<sup>1</sup>; Biologically degraded and produced<sup>2</sup>; Subject to photodegradation<sup>2</sup>; Exported from agricultural catchments<sup>3</sup></td><td><sup>1</sup>Stedmon and Markager, 2005;<sup>2</sup> Zhuang et al., 2022;<sup>3</sup> Graeber et al., 2012)</td><td>82</td></tr><tr><td>C2</td><td>245/ 510</td><td>Terrestrial substances<sup>1</sup>; Common in freshwater<sup>1</sup>; High-molecular-weight and aromatic compounds<sup>1</sup>; Subject to photodegradation<sup>2</sup>; Terrestrial components in coastal environments<sup>4</sup></td><td><sup>4</sup>Kowalczuk et al., 2009)</td><td>64</td></tr><tr><td>C3</td><td>240 (300)/ 374</td><td>Biological/Microbial origin<sup>3,5</sup></td><td><sup>5</sup>Parlanti et al., 2000)</td><td>28</td></tr><tr><td>C4</td><td>265 (375)/ 445</td><td>Bacterial origin<sup>6</sup>; Terrestrial origin in agricultural aeras<sup>7</sup></td><td><sup>6</sup>Fox et al., 2017;<sup>7</sup>Amaral et al., 2020)</td><td>13</td></tr><tr><td>C5</td><td>280/ 335</td><td>Protein-like substances<sup>1,5,8</sup></td><td><sup>8</sup>Catalán et al., 2021)</td><td>83</td></tr><tr><td>C6</td><td>240/ 431</td><td>Transformation and degradation product<sup>9</sup>; Photodegradation product<sup>10</sup></td><td><sup>9</sup>Osburn et al., 2017;<sup>10</sup> Ishii and Boyer, 2012)</td><td>43</td></tr></tbody></table>

OpenFluor comparison conducted on 18/07/2023.

photochemical alteration. This is particularly true for the flood period of July 2021, with an increase in terrestrial DOM inputs concomitantly with higher levels of solar irradiation in summer (Fig. 3). In addition, highest intensities and relative proportions of C6 (photoproducts) are observed during periods of low flow in summer (June 2019; Fig. S6f, S7f). This further indicates that seasonal variations may also regulate

DOM dynamics, in addition to hydrological conditions.

To explore this further, redundancy analysis (RDA) was performed to investigate the relationship between environmental parameters and optical parameters. When combining samples under both high-flow and low-flow conditions, the RDA triplot shows that the first axis (24.24 % of the variability) is primarily linked to temperature and water discharge, highlighting the impact of seasonal and hydrological conditions on the DOM spectral characteristics in the Seine Estuary (Fig. 4). Hierarchical partitioning further elucidates that temperature and water discharge are the most important parameters controlling the DOM characteristics, accounting for 13.8 % and 13.15 % of the variance, respectively, when combining high-flow and low-flow conditions (Fig. 4 and Table S5). During high flow conditions, temperature (19.68 % of the variance) and water flow rate (16.75 % of the variance) are the two most significant parameters, particularly influencing the relative fluorescence intensity of component C5 (proteins, biological activity), whereas temperature (18.42 % of the variance) is the most important variable during low flow conditions and seems to favor the fluorescence percentage of component C6 (degradation product) (Fig. S8, S9 and Table S6, S7). In contrast to previous studies in other river watersheds which showed that land use can significantly control the DOM dynamics (Bhattacharya and Osburn, 2020), there is a minor impact of land use type in the Seine Estuary when separating high-flow and low-flow conditions or combining both conditions (Fig. 4 and Fig. S8, S9). The influence of land use characteristics on the DOM optical composition in the Seine Estuary may be masked by the dominating natural driver (i.e. hydroclimate conditions).

In addition to regulating the terrestrial/aromatic DOM, hydroclimate conditions can also influence the production and reactivity of autochthonous DOM (Butturini et al., 2016). For example, protein-like DOM is strongly produced in the Tordera River during the drought condition (Butturini et al., 2016). Low precipitation can increase the water residence time, which allows for considerable production and degradation of autochthonous DOM (Singh et al., 2019). The river thus become a reactive system for autochthonous DOM at low flows (Butturini et al., 2016). In the same way, in the Seine Estuary, optical parameters for autochthonous contributions (intensity and relative fluorescence intensities of C3 and C5; FI, BIX, and γ/α) are significantly higher in the low-flow condition than in the high-flow one (p < 0.05, Wilcoxon test; Fig. S2 and Fig. S3) and significantly decrease with increasing water discharge (p < 0.05, Spearman's correlation; Fig. S4, S5). At the same
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Contour plots showing the spatial and temporal variations of the relative fluorescence intensity (percentage of total fluorescence) of the six PARAFAC components: (a) C1, (b) C2, (c) C3, (d) C4, (e) C5, (f) C6; the fluorescence indices (g) fluorescence index - FI, (h) humification index - HIX, (i) biological index - BIX, (j) fluorescence intensity ratio γ/α; and the absorbance indices (k) specific UV absorbance - SUVA<sub>254</sub>, (l) spectral slope ratio - SR; for the samples collected in the Seine Estuary from upstream (kilometer point (KP) 246) to downstream (KP 361) during 19 campaigns between June 2019 and November 2022 (n = 249). KP is defined as the distance in kilometers from the city of Paris (KP 0).</figcaption></figure>

time, the high temperature and slow water flow velocity promote the phytoplankton growth and associated microbial activities, thus releasing more autochthonous DOM into the water column. Indeed, temperature is related to the phytoplankton biomass (Chl *a*) and indicators for autochthonous DOM (i.e. %C3 and %C5) that score negatively on the first axis of the RDA (Fig. 4a). Such seasonal variability of autochthonous DOM is also observed in many other estuaries (Hounshell et al., 2022; Singh et al., 2019).

While the variation of DOM in the Seine Estuary is clearly captured by dividing the dataset based on the water discharge, the spatial distribution of DOM seems to be much more complex (Fig. 3). Thus, the relative fluorescence of the terrestrial/aromatic components (i.e. %C1,

%C2, and %C4) is much higher than for the rest of the components over the whole estuary during the high discharge periods occurring in winter (December to March generally), which is in good agreement with the high HIX values, with no particular trends between upstream and downstream (Fig. 3). Furthermore, two autochthonous components (%C3 and %C5) and fluorescence proxies (FI, BIX and γ/α) do not show similar spatial variations. When samples are grouped based on hydrological conditions (high-flow and low-flow conditions), there are also distinct spatial variations observed for these indicators (Fig. S6). Such a heterogeneity could be due to distinct sources and transformation processes of estuarine DOM (Asmala et al., 2018). Hence, it remains difficult to identify the spatial trends in DOM composition along the estuary by
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** (a) RDA analysis between available environmental variables (purple arrows) and DOM optical parameters (black arrows), including the relative fluorescence intensity of the six PARAFAC components: %C1 to %C6; the fluorescence indices: fluorescence index – FI, humification index – HIX, biological index – BIX, fluorescence intensity ratio γ/α; and the absorbance indices: specific UV absorbance - SUVA₂₅₄, spectral slope ratio – SR. Samples are colored according to hydrological conditions, including high-flow (>250 m³/s - blue) and low-flow (<250 m³/s - red) periods. (b) The individual importance of different environmental variables explaining the variation in DOM optical parameters were assessed by hierarchical partitioning analysis. Significance level is indicated by asterisks: \*p < 0.05; \*\*p < 0.01; \*\*\*p < 0.001; ns, not significant, p > 0.05. p-values are from permutation tests (999 randomizations). Physical parameters (turbidity, temperature, dissolved oxygen - DO), inorganic nutrients, and Chlorophyll *a* (Chl *a*) are measured by Serre-Fredj et al. (2023). Dissolved Inorganic Nitrogen (DIN) = NO₃⁻-N + NH₄⁺-N + NO₂⁻-N. Urban, Agricultural, and Water body represents relative abundances of land use types.</figcaption></figure>

simply visualizing the variations of the DOM optical properties or by
grouping samples based on hydrological conditions (Fig. 3 and Fig. S6).

## 3.2. DOM composition captured by unsupervised machine learning

To investigate the hidden patterns of the complex estuarine DOM data and explore the potential (temporal and spatial) variability, we perform an unsupervised machine learning approach with unlabeled data. Specifically, we applied the K-means clustering to identify clusters of similar samples based on DOM optical composition, revealing four distinct clusters based on DOM optical composition (Fig. 5 and Text S6). These clusters separate samples by allochthonous vs. autochthonous contributions and are influenced by spatial distribution, hydrological conditions, and seasonal variations. Notably, clusters 1 and 3 are associated with higher terrestrial input and stronger water discharge, while clusters 2 and 4 show greater autochthonous contribution and lower flow conditions, with distinct seasonal distributions (Text S6).

Further insights into these clusters can be obtained by grouping samples using these 4 clusters in the RDA triplot, as RDA can evaluate the effects of environmental variables on the DOM optical properties in the different clusters (Fig. 6). This highlights the relationship between phytoplankton biomass (Chl *a*) and autochthonous DOM parameters, particularly %C3, which scored negatively on the first axis of the RDA where Cluster 4 is located (Fig. 6), indicating that Cluster 4 captures water samples characterized by high contributions from biological and microbial origins, potentially linked to primary productivity. In addition, temperature is related with the transformation and (photo)degradation product (%C6) that scored negatively on the second axis of the RDA, where Cluster 1 is mainly located (Fig. 6). This further suggests that Cluster 1 captures the waters associated with transformation and (photo)degradation processes.

The unsupervised machine learning approach is thus able to classify
the DOM data into distinct clusters, each representing waters with
unique DOM characteristics as well as hydrological and seasonal

conditions. The interpretation of these clusters is summarized in Table S9.

We further explore the spatial dynamics of DOM optical properties for each cluster in the Seine Estuary, observing diverse behaviors of the optical parameters of DOM within these clusters from upstream to downstream (Fig. 7). Notably, the spatial trend of %C3 (biological/microbial component) within Cluster 4 shows an initial increase followed by a decrease (Fig. 7c). Given that Cluster 4 captures waters during periods of low flow in spring and summer (Table S9), the elevated %C3 values within this cluster could potentially be attributed to higher primary productivity in this region during such low-flow conditions. Indeed, phytoplankton blooms are observed in this region (260<KP<340), especially during low-flow periods, as indicated by Chla, δ¹⁵N values, and the production of certain bacterial lipids (Zhang et al., 2024). This zone is also characterized by significantly higher annual primary production, exceeding 50 g C m⁻² y⁻¹ (Serre-Fredj et al., 2023). Hence, the increased contributions from biological/microbial sources might be associated with phytoplankton-related processes and an elevated annual primary production. However, the other autochthonous component (%C5) within Cluster 4 shows a contrasting trend (Fig. 7e), suggesting distinct sources and/or transformation processes for these components. In addition, within Cluster 1, %C5 and especially FI show an initial increase followed by a decreasing trend (Fig. 7e and g). This indicates higher contributions from protein-like component and microbial DOM in the aforementioned zone (260<KP<340) during both summer and winter, encompassing a combination of low and high-water discharge conditions. Furthermore, in this zone (260<KP<340), a significant decrease in %C2 and %C4 within Cluster 1 is observed (Fig. 7b and d), indicating lower contributions from terrestrial substances and/or DOM with bacterial origin. Based on pronounced spatial variations in DOM optical properties that are consistent with those of bulk parameter (δ¹⁵N and Chl *a*) and bacterial lipids (Zhang et al., 2024), this specific region (260<KP<340) can separate the estuary into three parts: Zone I (KP<260 km); Zone II (260 km<KP<340 km); Zone III (KP>340 km).
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** (a) Determination of optimal number of clusters (K) in K-means clustering based on the elbow method. (b) PCA analysis of DOM optical parameters (including the relative fluorescence intensity of the six PARAFAC components: %C1 to %C6; the fluorescence indices: fluorescence index – FI, humification index – HIX, biological index – BIX, fluorescence intensity ratio γ/α; and the absorbance indices: specific UV absorbance - SUVA<sub>254</sub>, spectral slope ratio – SR). Samples (n = 249) within different clusters were highlighted with 95 % concentration ellipses. Adonis analysis (999 permutations) was performed to assess how much variation of DOM optical proxies is explained by the grouping (clusters). (c-d) Box plots showing the distribution of (c) KP (Kilometric Point; defined as the distance in kilometers from the city of Paris (KP 0)) and (d) mean monthly water discharge for the 4 clusters determined by K-means clustering. Statistical testing in (c-d) was performed with a Wilcoxon test (*p* < 0.05; **p** < 0.01; ****p < 0.0001). (e) Proportion of samples of the different seasons within each cluster. WCSS represents within-cluster sum of squared error.</figcaption></figure>

km).

Overall, this unsupervised machine learning approach more efficiently traces DOM dynamics compared to traditional methods such as simply visualizing the data or grouping based on hydrological conditions. Notably, the unsupervised machine learning approach takes both hydrological conditions and seasonality into account, grouping together similar DOM samples, tracing pronounced spatial variabilities that cannot be captured by traditional methods. Estuarine zonation is further identified based on pronounced spatial variations of several parameters (%C3, %C5, %C2, and FI) especially in Cluster 1 and Cluster 4, supported by previous investigations of bulk parameters (δ<sup>15</sup>N and Chl α) and lipid biomarkers. However, identifying the primary DOM characteristics within each zone remains a challenge as distinct DOM parameters show different variabilities in each zone (Fig. 7). In addition, the rationality of the defined zonation needs to be strengthened.

### 3.3. Rationality of the estuarine zonation evaluated by supervised machine learning

To simplify conditions and obtain a sufficient number of balanced samples for each group for further analysis, we combined samples from Cluster 1 and Cluster 3 to represent an overall high-flow scenario (n = 113). This scenario mainly includes winter and flood periods in summer, characterized by high-flow and median levels of water discharge (Fig. 5d, e and Table S9). In contrast, samples from Cluster 2 and Cluster 4 are combined to indicate a low-flow scenario (n = 136), primarily involving spring, summer, and autumn, characterized by low flow (Fig. 5d, e and Table S9). Notably, this categorization takes into account both seasonality and hydrological conditions. We further evaluate the rationality of the defined zonation in these high-flow and low-flow scenarios.

To evaluate the rationality of the defined zonation in 3.2, we performed a supervised machine learning classification model to relate specific DOM optical compositions to each zone of the estuary in high-
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** RDA analysis between available environmental variables and DOM optical parameters (including the relative fluorescence intensity of the six PARAFAC components: %C1 to %C6; the fluorescence indices: fluorescence index – FI, humification index – HIX, biological index – BIX, fluorescence intensity ratio γ/α; and the absorbance indices: specific UV absorbance - SUVA<sub>254</sub>, spectral slope ratio - SR). Samples are colored according to clusters determined by K-means clustering. Physical parameters (turbidity, temperature, dissolved oxygen - DO), inorganic nutrients, and Chlorophyll *a* (Chl *a*) are measured by Serre-Fredj et al. (2023). Dissolved Inorganic Nitrogen (DIN) = NO<sub>3</sub><sup>-</sup>-N + NH<sub>4</sub><sup>+</sup>-N + NO<sub>2</sub><sup>-</sup>-N. Urban, Agricultural, and Water body represents relative abundances of land use types.</figcaption></figure>

flow and low-flow scenarios. An ensemble machine learning classification method, named Light Gradient Boosted Machine (LightGBM) was evaluated in this study. The overall high scores for distinct parameters indicate the machine learning model could classify DOM optical properties as belonging to one of three zones with good performance (Fig. S11). We name the developed machine learning model as light Gradient Boosting Machine classification for DOM (GBM_DOM), further confirming the rationality of the defined estuarine zonation.

### 3.4. Explainable artificial intelligence and biogeochemical interpretations

We then used an explainable artificial intelligence approach, named SHAP (Shapley Additive exPlanations) analysis (Lundberg et al., 2020), to interpret the black box machine learning model (GBM_DOM) and evaluate the effect of input variables (DOM optical properties) on the model prediction (outcome in Zone I, Zone II, or Zone III). This would mean that such approach enables us to identify the dominating DOM characteristics within each zone. The weight of DOM parameters for GBM_DOM in high-flow and low-flow scenarios is sorted by SHAP values (Fig. 8), with higher SHAP values indicating stronger positive feature importance (Lundberg et al., 2020).

The SHAP summary plots go into further detail about how each DOM parameter influences the model outcome in each zone. In these plots (Fig. 8), the input variables (DOM optical properties) with higher impact on the model performance are shown at higher positions. The colored bullets reflect the SHAP value of each sample in the training set, and their hue indicates the observed data value, ranging from low (blue) to high (red). The bullets on the right side of the SHAP summary plot represent positive SHAP values (positive effect on the model output), whereas the bullets on the left side of the plot reflect negative SHAP values (negative effect on the model output). For example, %C2 ranks

highest in Zone I during the low-flow scenario, with red bullets showing negative SHAP values (Fig. 8a). This indicates that %C2 is the most important parameter influencing the model's predictions, although %C2 contributes negatively to the model outcome. In contrast, during the high-flow scenario, %C2 is at the top position in Zone III, with red bullets having high SHAP values (Fig. 8a), indicating that %C2 represents the most important parameter that influences the model's predictions, contributing positively to the model outcome. In other words, the main DOM characteristic in Zone III during the high-flow scenario is %C2. The ranking of the primary DOM characteristics (parameters with high values that have a positive effect) and their interpretations in each zone for both high-flow and low-flow scenarios is then summarized in Table S10 and Table S11, respectively.

During both high-flow and low-flow scenarios, Zone I is mainly contributed by SUVA<sub>254</sub> and is dependent on the conditions of FI, %C5, and %C3 (Fig. 8 and Table S10). Given that %C2 and %C6 represent negative effects, this indicates that aromatic compounds do not dominate this zone in either scenario. Therefore, SUVA<sub>254</sub> may represent DOM molecular weight rather than aromaticity in this context. This indicates that CDOM in the upper estuary (Zone I) is characterized by a mixture of high molecular weight compounds and autochthonous material, regardless of seasonality, as higher SUVA<sub>254</sub>, FI, %C3, and %C5 positively impact the model output for this zone (Fig. 8 and Table S10 and S11). Such information is less clearly captured through traditional approaches (i.e., simple visualization; Fig. 3) or by unsupervised machine learning (Fig. 7), which might be masked by complex hydrological conditions and/or the mixing of land use types within this highly dynamic area, along with the large number of parameters that need to be considered. By using machine learning and explainable artificial intelligence, we can effectively uncover hidden DOM signatures, identifying the spatial specificity of distinct DOM characteristics and potential controlling factors. In the Neuse River Basin, agricultural and urban land use were closely linked to higher proportions of high molecular weight and autochthonous DOM, respectively (Bhattacharya and Osburn, 2020). Hence, in the upper Seine Estuary (Zone I), simultaneous observation of high proportions of both high molecular weight and autochthonous material could be explained by significant contributions from both soil erosion and anthropogenic inputs (i.e. domestic and/or urban effluents). The high contributions from %C3 and especially %C5 may be linked to the discharge from urban wastewater treatment plants, which increases biological activity in the aquatic environment, leading to an increase in the proportion of proteinaceous and autochthonous compounds. Indeed, this zone is characterized by relatively higher agricultural and urban land use (Fig. 1; site 10–14). In addition to the influence of the Seine watershed itself, the mixed agricultural and urban land use types in this area introduces extra complexity in DOM characterization. Monitoring and characterizing the variations in absorbance (SUVA<sub>254</sub>) and fluorescence (FI, %C3, and %C5) indicators within this zone can thus provide insights into the dynamics of DOM with distinct sources.

The DOM optical properties in Zone II (mid-estuary) in the high-flow scenario are mainly influenced by FI, and in the low-flow scenario by %C3 (Fig. 8 and Table S10). This suggests that DOM in this zone is generally dominated by DOM with biological/microbial origin, which is consistent with the high annual primary production (Serre-Fredj et al., 2023) and the presence of phytoplankton bloom observed in this area (Zhang et al., 2024). During the demise of phytoplankton blooms, intense viral infections release phytoplankton-derived metabolites (autochthonous DOM) into the water column, a process known as the "viral shunt", which connects primary production to DOM cycling (Wilhelm and Suttle, 1999). In addition to the viral shunt, autochthonous DOM can also be produced directly by phytoplankton and microbial degradation (Kinsey et al., 2018). Therefore, the release and subsequent transformation of phytoplankton-derived DOM could explain the dominant contributions of autochthonous DOM in Zone II, which potentially documents the footprint of the phytoplankton-related
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Spatial variations of DOM optical parameters (including the relative fluorescence intensity of the six PARAFAC components: (a) %C1, (b) %C2, (c) %C3, (d) %C4, (e) %C5, (f) %C6; the fluorescence indices (g) fluorescence index – FI, (h) humification index – HIX, (i) biological index – BIX, (j) fluorescence intensity ratio γ/α; and the absorbance indices (k) specific UV absorbance - SUVA<sub>254</sub>, (l) spectral slope ratio – SR) for each of the clusters determined by K-means clustering. The trends showing spatial variations were plotted based on locally estimated scatterplot smoothing (LOESS), with shaded area representing 95 % confidence intervals. Samples (n = 249) were grouped into 4 clusters determined by K-means clustering. Kilometric Point (KP) denotes the distance in kilometers from the city of Paris.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>(b) Cluster 2 and Cluster 4 (low-flow scenario)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** The ranking of feature importance for each zone for (a) clusters 1 and 3 (high-flow scenario) and (b) clusters 2 and 4 (low-flow scenario) based on LightGBM (Light Gradient Boosted Machine) and SHAP (SHapley Additive exPlanations) library, with each bullet indicating a training example. The colorbar denotes the value of DOM optical parameters (including the relative fluorescence intensity of the six PARAFAC components: %C1 to %C6; the fluorescence indices: fluorescence index – FI, humification index – HIX, biological index – BIX, fluorescence intensity ratio γ/α; and the absorbance indices: specific UV absorbance - SUVA₂₅₄, spectral slope ratio – SR) from low (blue) to high (red).</figcaption></figure>

Additionally, it is likely that agricultural land use contributes to the export of soil-derived DOM, leading to an increase in aromatic content of DOM in adjacent aquatic environments (Chen et al., 2021). Hence, the elevated signatures of aromatic DOM (high HIX; Fig. 8 and Table S10, S11) during the high-flow scenario can also be attributed to the high proportions of agricultural land use in this zone (Fig. 1). In addition, the abundant terrestrial/aromatic DOM in this region may further undergo photobleaching (Ishii and Boyer, 2012). Significant photochemical degradation processes are evidenced by positive contributions of photodegradation products (%C6) in Zone II (Fig. 8 and Table S10, S11). Overall, the use of the machine learning and explainable artificial intelligence suggests that transformation processes (both microbial and photochemical processing) and land use types control the DOM characteristics.

In the lower section of the Seine Estuary (Zone III), the DOM optical properties are identical to typical coastal environments, with low molecular weight CDOM (high SR) and high autochthonous contribution (high BIX) (Fig. 8 and Table S10, S11). The elevated water discharge has the potential to flush nutrient-rich waters into this zone, which could potentially trigger phytoplankton blooms in this area. Such processes could also result in the release of substantial amounts of autochthonous DOM. In addition, DOM characteristics in this zone are mainly influenced by %C2 both in high-flow and low flow scenarios (Fig. 8 and Table S10). Interestingly, this component has an opposite influence on the

model prediction compared to the other terrestrial component (%C1). %C1 shows negative contributions for this zone in both scenarios (Fig. 8). This suggests that %C1 and %C2, both previously categorized as terrestrial aromatic components, actually show different behaviors in Zone III. One possible explanation for such a distinction is that these components are influenced by varying levels of photodegradation. Previous studies have shown that only C2 can be produced through photochemical processes, whereas C1 cannot (Ishii and Boyer, 2012). The positive contribution of %C2 in this zone thus implies the possible accumulation of photochemically produced material in this area. Distinct behavior of terrestrial fluorescent components in this area highlights the complex sources and transformation processes of estuarine DOM, which could be assessed by machine learning and explainable artificial intelligence.

Overall, a generalized estuarine zonation in this study typically consists of 3 zones, including the upper (Zone I), mid (Zone II), and lower (Zone III) estuary, with each zone showing specific DOM characteristics and biogeochemical processes in high-flow and low-flow scenarios (Fig. 9).

## 4. Conclusions and environmental implications

Combining unsupervised and supervised machine learning can provide novel insights into differentiating the DOM composition related to
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Schematic diagrams showing the zonation of the Seine Estuary in terms of DOM dynamics in low-flow and high-flow scenarios. Kilometric Point (KP) denotes the distance in kilometers from the city of Paris.</figcaption></figure>

both seasonal and spatial variations in estuaries. Specifically, by applying unsupervised machine learning, groups of samples characterized by comparable DOM optical parameters, can be identified, providing an initial understanding of DOM dynamics and a zonation of the estuary according to the optical properties of DOM. Thereafter, supervised machine learning can validate and evaluate the rationality (i.e. according to accuracy in the classification results) of the generalized zonation defined from unsupervised learning. The application of explainable artificial intelligence can further help identify the dominant DOM parameters within each zone under different scenarios, thereby guiding watershed management. For example, during a high-flow scenario, urban wastewater treatment plants may release more protein-like material into the estuary. In this case, policymakers may pay special attention to Zone I by monitoring optical parameters (e.g. %C5) to help mitigate the effects of pollution. During a low-flow scenario, phytoplankton-related processes can release and transform DOM. Monitoring optical parameters (e.g. %C3) in a specific region (Zone II) may help evaluate the potential risk of eutrophication. The approach proposed in this study can be easily applied to other systems beyond estuaries, such as rivers, lakes, or coastal oceans to identify the main DOM characteristics and zonation (Text S7). The established workflow can significantly contribute to environmental management and decision-making processes, which may lead to more sustainable and effective policies.

### CRediT authorship contribution statement

**Zhe-Xuan Zhang:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation,

Conceptualization. **Arnaud Huguet:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization. **Zoé Hayet:** Writing – review & editing, Investigation, Formal analysis, Data curation. **Edith Parlanti:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization.

### 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

We would like to thank Léo Chasselin (CREC, Université de Caen-Normandie) for his assistance during the field campaigns, Mahaut Sourzac (EPOC, Université de Bordeaux) for her analytical support in DOM optical spectroscopy analysis, and Emmanuel Aubry (METIS, Sorbonne Université) for DOC analysis. This study was funded by GIP Seine-Aval (SARTRE project) and CNRS/INSU and OFB (the EC2CO RUNTIME project).

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2025.123900.
# Data availability

Data are made available in the manuscript or in the Supporting Information.

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