# Concealed by darkness: Combination of NMR and HRMS reveal the molecular nature of dissolved organic matter in fractured-rock groundwater and connected surface waters

M.C. Bridoux, G. Gaiffe, P. Pacholski, S. Cangemi, G. Vinci, R. Spaccini, S. Schramm

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## ► To cite this version:

M.C. Bridoux, G. Gaiffe, P. Pacholski, S. Cangemi, G. Vinci, et al.. Concealed by darkness: Combination of NMR and HRMS reveal the molecular nature of dissolved organic matter in fractured-rock groundwater and connected surface waters. Water Research, 2023, 243, pp.120392. 10.1016/j.watres.2023.120392. hal-05129737

HAL Id: hal-05129737

https://hal.univ-lorraine.fr/hal-05129737v1

Submitted on 1 Oct 2025

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1 Concealed by darkness: combination of NMR and HRMS reveal
2 the molecular nature of dissolved organic matter in fractured-
3 rock groundwater and connected surface waters

4 Bridoux, M.C.¹*; Gaiffe, G.¹; Pacholski, P.¹,²; Cangemi, S.³; Vinci³, G.; Spaccini, R.³; Schramm
5 S.²

6 ¹CEA, DAM, DIF, F-91297 Arpajon, France

7 ² Laboratoire de Chimie et de Physique-Approches Multi-échelles des Milieux Complexes
8 (LCP-A2MC), Université de Lorraine, Metz, France

9 ³ Centro Interdipartimentale di Ricerca sulla Risonanza Magnetica Nucleare per l'Ambiente,
10 l'Agroalimentare e Nuovi Materiali (CERMANU), Università di Napoli Federico II, Via
11 Università, 100, Portici (NA), 80055, Italy

12 * : Corresponding author : maxime.bridoux@cea.fr

13 Abstract

14 Detailed molecular composition of solid phase extracted dissolved organic matter (SPE<sub>DOM</sub>)
15 collected from fractured-rock groundwater was compared to connected surface river water at two
16 different watersheds in the unconfined chalk aquifer of Champagne in France using full scan
17 ultrahigh resolution electrospray and photoionization Fourier transform ion cyclotron mass
18 spectrometry (FT-ICR MS), Orbitrap tandem MS (MS/MS) and ¹H magnetic resonance
19 spectroscopy (NMR). ¹H NMR spectroscopy indicated that groundwater SPE<sub>DOM</sub> carried a higher
20 contribution of aliphatic compounds while surface river waters SPE<sub>DOM</sub> were enriched in
21 carboxyl-rich alicyclic molecules (CRAM), acetate derivatives and oxygenated units.
22 Furthermore, we show here that use of photoionization (APPI(+)) in aquifer studies is key,
23 ionizing about eight times more compounds than ESI in surface river water samples, specifically
24 targeting the dissolved organic nitrogen pool, accounting for more than 50% of the total
25 molecular space, as well as a non-polar, more aromatic fractions; with little overlap with
26 compounds detected by ESI(-) FT-ICR MS. On the other hand, groundwater SPE<sub>DOM</sub> samples
did not show similar selectivity as less molecular diversity was observed in APPI compared to ESI. Mass-difference transformation networks (MDiNs) applied to ESI(-) and APPI(+) FT-ICR MS datasets provided an overview of the biogeochemical relationships within the aquifer, revealing chemical diversity and microbial/abiotic reactions. Finally, the combination of ESI(-) FT-ICR MS and detailed Orbitrap MS/MS analysis revealed a pool of polar, anthropogenic sulphur-containing surfactants in the groundwaters, likely originating from agricultural runoff. Overall, our study shows that in this aquifer, groundwater SPE<sub>DOM</sub> contains a significantly reduced pool of organic compounds compared to surface river waters, possibly related to a combination of lack of sunlight and adsorption of high O/C formulas to mineral surfaces.

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KEYWORDS

Dissolved organic matter, nuclear resonance spectroscopy, aquifer, ultrahigh resolution mass spectrometry, collision induced dissociation, electrospray, photoionization, linear alkyl sulfates, mass difference transformation networks

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# 1. Introduction

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Groundwater contained in aquifers are the largest reservoirs of liquid freshwater on earth and play an essential role in global water and carbon cycles (Famiglietti, 2014; Gleeson et al., 2016; Abbott et al., 2019). Groundwater contains an important pool of terrestrial organic carbon in the form a very complex mixture of dissolved organic compounds containing mainly carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulphur (S) commonly referred to as dissolved organic matter (DOM) that represent a critical component of groundwater quality. Because DOM affects many biogeochemical processes in the environment (i.e., photochemical reactions, metal complexation, microbial growth, and nutrient and contaminant transport), determining the composition of DOM in groundwater is essential to better constrain its role in the global carbon cycle (Maria et al., 2019; McDonough et al., 2022) and the transport of both organic and inorganic contaminants (Fleck et al., 2004; Bauer and Blodau, 2006; Shen et al., 2015; Lawson et al., 2016; He et al., 2020).

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The conventional view of how dissolved organic matter (DOM) behaves in groundwater suggests that it becomes less able to support biological activity over time (Peter et al., 2012). However, recent research has found that a significant amount of organic carbon (13 Tg annually) released into surface waters through groundwater discharge appears to be quite biodegradable, challenging this traditional paradigm (McDonough et al., 2022). These discoveries emphasize the lack of knowledge regarding the composition and biogeochemistry of DOM in groundwater, creating a biased understanding. Indeed, aquifers are generally understudied as most DOM cycling studies have focused on surficial aquatic environments (Mc Donough et al., 2022; Zark and Dittmar, 2018; Kellerman et al., 2018). A better understanding of the different sources of
DOM in groundwaters, as well as the biogeochemical mechanisms impacting its quality, may
allow a better quantification of its role in the terrestrial carbon budget.

The unconfined Chalk aquifer is a crucial aquifer in Northern Europe, given its extensive
usage for human purposes, with a significant portion reserved for drinking water. In France, the
Chalk aquifer extends over approximately 20% of the metropolitan territory and supplies roughly
12 billion cubic meters of water annually, representing 70% of the drinking water utilized in the
Northern region of the country. However, recent research suggests that human activities are
associated with the decline in the quality of groundwater in the European Chalk aquifer (Chen et
al., 2019; Hakoun et al., 2017).

Nuclear magnetic resonance (NMR) spectroscopy is an established analytical technique that
measures the chemical shift associated with the atomic environment of specific nuclei, such as
proton (¹H) and carbon (¹³C). It has been used to study natural organic matter and other organic
compounds from soils (Vinci et al., 2021), surface waters (Li et al., 2023) and contaminated
aquifers (Thorn et al., 1998; Podgorski et al., 2021). Although NMR provides structural
information on complex mixtures, its ability to characterize complex DOM samples is limited by
its relatively low sensitivity and low spectral resolution, which depends on the strength of the
magnetic field and radio frequency pulse used and may not resolve small structural differences in
complex mixtures. To address this issue, recent methods have combined NMR and ultrahigh
resolution mass spectrometry (UHRMS) through cheminformatics or data mining techniques
(Bingol et al., 2015; Tayyari et al., 2013; Roth et al., 2019). Fourier transform mass spectrometry
(FT-ICR MS, Orbitrap MS) offers high mass accuracy (typically >1 ppm for ions < 400 m/z),
allowing for the precise identification of complex mixtures in the natural environment at the
molecular scale through non-targeted analysis (Bianco et al., 2018; Maria et al., 2019; Hawkes et
al., 2020; Jylhä-Ollila et al., 2021; Martin et al., 2021; Vinci et al., 2022). Because of the vast
variety of compounds and functional groups present in NOM, no single ionization source can
fully capture its molecular complexity. Electrospray is for example incapable of ionizing apolar
compounds that result from the abiotic degradation of lignin (Waggoner et al., 2015) or
significant fractions of heterogenous humic acid structures (Vinci et al., 2022). To detect a
broader range of organic species, including nonpolar compounds that are not detectable by ESI,
atmospheric pressure photoionization (APPI) is a more uniform ionization source with a low
susceptibility to ion suppression phenomenon and a wider linear dynamic range. Although
combining ESI and APPI is rarely used for water sample analysis, a few studies have employed
this approach (Hockaday et al., 2009; Barrow et al., 2015; Dvorski et al., 2016; Renard et al.,
2022). ESI(-) is typically the primary method used to analyze dissolved compounds, which may
result in a significant number of compounds being excluded from analysis. This is particularly
true for groundwater samples, which typically contain reduced molecular formulae
(McDonought et al., 2022) that may not be fully characterized using ESI(-) alone.

In this study, we used NMR spectroscopy together with full scan ultrahigh resolution FT-ICR
MS and mass difference transformation networks (MDiNs) to examine the molecular
composition of DOM in fractured-rock groundwater and surface river water in two different
watersheds in the unconfined chalk aquifer of Champagne, France. We utilized both ESI (-) and
APPI(+) ionization techniques in aquifers to target both polar and non-polar compounds in
groundwater and surface river DOM, and integrated the results using mass difference
transformation networks (MDiNs), providing critical new information about its chemical
diversity and the set of microbial and abiotic reactions or pathways that produced it.
## 2. Material and Methods

### 2.1. Sample preparation

A detailed description of the site and the sampling methodology can be found in the Supplemental Information (SI(1); Fig. S1; Fig. S2). Briefly, samples were filtered under vacuum, acidified using HCl and processed through solid phase extraction (SPE) following the procedure described in the Supplement Information. SPE eluates were then dried under nitrogen and stored at -25 °C until analysis.

### 2.2. Nuclear magnetic resonance spectroscopy (¹H NMR) analysis

The SPE-DOM samples dried under nitrogen were dissolved in CD₃OD (16 mg.mL⁻¹) and immediately loaded into NMR microtubes (1.7 mm) for analyses within 15 min of preparation. Detailed instrumental settings instrumental settings are described in SI(2).

### 2.3. Molecular characterization using FT-ICR MS analysis

Briefly, dry extracts were dissolved with 2 mL of MeOH, sonicated for 20 min, and finally diluted 10 times to obtain a final solution in 50/50 MeOH/H₂O for ESI. Mass spectra of SPE<sub>DOM</sub> samples were obtained using 7T FT-ICR MS (Solarix 2XR, Bruker Daltonics, GmbH, Bremen, Germany), equipped with electronics and a para-cell that enabled the quadupolar detection. Samples were directly injected with flow rate of 3 µL/min for ESI(-) and 30 µL/min for APPI(+). Detailed experimental information, instrumental settings, data processing and mass difference transformation networks are in SI(3).

### 2.4. Orbitrap LTQ-XL analysis

Collision induced dissociation (CID) tandem MS (MS²) experiments were performed using an Orbitrap LTQ-XL (Thermo Scientific, San Jose, CA, USA) on selected masses of sample SPE<sub>DOM</sub> GW<sub>South</sub> using negative electrospray. Detailed instrumental settings are listed in SI(4).
## 3. Results and discussion

Abbreviations will be used in the manuscript to refer to different spectra and derived proxies. These abbreviations include sample type (GW for groundwater, SW for surface river water), borehole identification code (north for northern watershed, south for southern watershed) or river water name, ionization source for FT-ICR MS characterization of SPE<sub>DOM</sub> samples (ESI for electrospray, APPI for photoionization), and ionization mode (+ for positive, - for negative). For example, SPE<sub>DOM</sub> GW<sub>South</sub> ESI(-) represents a groundwater sample from the southern watershed borehole, subjected to SPE<sub>DOM</sub> extraction and analyzed using negative mode electrospray ionization FT-ICR MS.

### 3.1.¹H nuclear magnetic resonance (NMR) spectroscopy

¹H NMR spectra of SPE<sub>DOM</sub> groundwater (GW<sub>North</sub>) and surface water (SW<sub>Suippe</sub> and SW<sub>Vesle</sub>) showed broad and unresolved proton peaks due to signal overlap at similar resonance frequencies (Fig. 1). Overlapping features are commonly observed in NMR analyses of complex mixtures like dissolved organic matter (Hertkorn et al., 2013, 2016; Li et al., 2023). To determine the relative abundances of key ¹H substructures, the normalized spectra were divided into five spectral regions: aliphatic units (CCCH), acetate-analogue and carboxyl-rich alicyclic molecules (XCCH), carbohydrate-like and methoxy units (OCH), olefinic (C=CH, O2CH), and aromatic compounds (Har) (Fig. 1).

Comparing the ¹H spectral profiles, noticeable differences were observed between SPE<sub>DOM</sub> extracted from groundwater and surface water samples at the watershed level (Fig. 1). The majority of the spectral intensity in all samples, representing over 92% of the total spectrum in SPE<sub>DOM</sub> GW<sub>North</sub> and approximately 88.5% in both SPE<sub>DOM</sub> SW<sub>Suippe</sub> and SW<sub>Vesle</sub>, was attributed to aliphatic chemical environments consisting of OCH, XCCH, and CCCH units (signals
between 0 - 4.6 ppm) (Fig. 1 and Table 1). The main variations in these spectra were related to the abundance of aliphatic proton units (CCCH, δH ~ 0-1.9 ppm), which were particularly prominent in GWNorth, accounting for approximately 55.8% (Table 1). Specifically, intense signals at around 0.9 ppm, associated with terminal methyl groups of purely aliphatic chains, as well as signals with maximum intensity at approximately 1.3 ppm (methylene and other aliphatics), accompanied by weaker shoulder peaks ranging from 1.4 to 1.7 ppm, were present in all samples but showed decreasing relative abundances in the order of GWNorth > SWSuippe > SWVesle (Fig. 1 and Table 1).

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**Figure. 1:** Comparison of the ¹H NMR spectral profiles (δH =0-10ppm) of groundwater (GWSouth- green line) and surface water (SWSuippe - red line and SWVesle - blue line) SPEDOM samples, dissolved in CD₃OD and acquired with solvent suppression. The two gray bands indicate the exclusion regions not considered for the calculation of the relative abundances of the spectral regions consisting of: aliphatics (CCCH), acetate-analogue and carboxyl-rich alicyclic molecules (XCCH), carbohydrate-like and methoxy units (OCH), oleofinic (C=CH, O₂CH) and aromatic compounds (Hₐr). The inset show the vertical expansion of Hₐr region, subdivided into four sections consisting of: a) polycyclic aromatic compounds, polycarboxylates and six-
membered nitrogen heterocycles, b) electron withdrawing group, c) electroneutral group, d) electron donor group.

In contrast, the proportions of acetate derivatives (δH ~1.9-2.1 ppm) and remotely oxygenated aliphatic units (XCCH, δH ~1.9-3.1 ppm) containing carboxyl-rich alicyclic molecules (CRAM) and protons bonded to adjacent carbon atoms linked to nitrogen, sulfur, or halogen atoms (XCH, δH ~ 2.2-3.1 ppm), as well as the relative abundances of directly oxygenated units (OCH, δH ~ 3.1-4.65 ppm), followed a reverse order (SW<sub>Vesle</sub> > SW<sub>Suippe</sub> > GW<sub>North</sub>).

<table><caption>**Table 1:** ¹H NMR section integral, calculated as percentage on non-exchangeable protons, of the key substructure of three SPE-DOM samples.</caption><thead><tr><th>δ(¹H) [ppm]</th><th>10 - 6.5</th><th>6.5 - 5.2</th><th>4.6 - 3.1</th><th>3.1 - 1.9</th><th>1.9 - 0</th><th>10 - 5.2</th><th></th><th></th></tr><tr><th>Key substructures</th><th>H<sub>ar</sub></th><th>C=CH, O<sub>2</sub>CH</th><th>OCH</th><th>XCCH</th><th>CCCH</th><th>HC<sub>sp2</sub></th><th>H<sub>oleofinic</sub>/ H<sub>ar</sub></th><th>CCCH/H<sub>ar</sub></th></tr></thead><tbody><tr><th>GW<sub>North</sub></th><td>5.0%</td><td>2.7%</td><td>13.8%</td><td>22.7%</td><td>55.8%</td><td>7.71%</td><td>0.52</td><td>11.05</td></tr><tr><th>SW<sub>Suippe</sub></th><td>7.6%</td><td>3.8%</td><td>20.0%</td><td>28.6%</td><td>40.0%</td><td>11.41%</td><td>0.49</td><td>5.20</td></tr><tr><th>SW<sub>Vesle</sub></th><td>7.9%</td><td>3.9%</td><td>20.6%</td><td>28.7%</td><td>38.9%</td><td>11.8%</td><td>0.48</td><td>4.90</td></tr></tbody></table>

The OCH units showed distinct signals, including a prominent peak at approximately δH ~3.6 ppm attributed to methoxy (OCH<sub>3</sub>)derivatives. The remaining signals between δH ~3.1 and 4.65 ppm were likely from carbohydrates, esters, ethers, or alcohols. The relative abundance of unsaturated protons (olefinic and aromatic compounds) varied between groundwater and riverine SPEDOM samples (Fig. 1 and Table 1), with slightly higher values for SW<sub>Vesle</sub> and SW<sub>Suippe</sub> compared to GW<sub>North</sub>. Aromatic compounds contributed more to the spectra than olefinic compounds in all samples. The ratio of aliphatic to unsaturated proton signals (CCCH/H<sub>ar</sub>) was approximately two times higher in the groundwater sample GW<sub>North</sub> compared to the two riverine SPE<sub>DOM</sub> samples (Table 1). However, when considering only the chemical space represented by
protons bonded to sp2 hybridized carbon, all samples exhibited similar equilibrium in the $H_{olefinic}/H_{ar}$ ratios, with slight variations between $GW_{North}$ and $SW_{Suippe}$ and $SW_{Vesle}$ (Table 1). The $H_{ar}$ spectral regions were divided into subsections: polycyclic aromatics, polycarboxylates, and six-membered nitrogen heterocycles (a), electron withdrawing groups (b), electroneutral groups (c), and electron donor groups (d) (Fig. 1) (Hertkorn et al., 2016; Perdue et al., 2007). $SPE_{DOM}$, $GW_{North}$ had higher relative abundances of EWG and ENG substituents, while $SPE_{DOM}$, $SW_{Vesle}$ and $SW_{Suippe}$ showed higher abundances of EDG and polycyclic aromatics, polycarboxylates, and six-membered nitrogen heterocycles (Table 2).

Table 2: ¹H NMR section integral of the four subsections (Figure 1 a-d) extrapolated from the $H_{ar}$ abundance regions of three SPE-DOM samples.

<table><thead><tr><th rowspan="2">Section<br>δ(¹H) [ppm]</th><th colspan="4">H<sub>ar</sub></th></tr><tr><th>a<br>10 - 8</th><th>b<br>8 - 7.3</th><th>c<br>7.3 - 7</th><th>d<br>7 - 6.5</th></tr></thead><tbody><tr><td><b>GW<sub>North</sub></b></td><td>25.16%</td><td>37.89%</td><td>19.68%</td><td>17.28%</td></tr><tr><td><b>SW<sub>Suippe</sub></b></td><td>28.24%</td><td>34.51%</td><td>15.65%</td><td>21.60%</td></tr><tr><td><b>SW<sub>Vesle</sub></b></td><td>27.89%</td><td>33.85%</td><td>15.96%</td><td>22.29%</td></tr></tbody></table>

## 3.2. Ultrahigh resolution mass spectrometry (FT-ICR MS analysis)

### 3.2.1. ESI and APPI ionization selectivity for groundwater and surface water $SPE_{DOM}$

Groundwater and riverine $SPE_{DOM}$ were analyzed using ESI(-) and APPI(+) FT-ICR MS. The results showed significant chemodiversity, with up to 15 *m/z* peaks assigned in ESI(-) analysis and up to 34 molecular formulas assigned in APPI(+) analysis within a narrow mass range Fig. S3 and S4 (corresponding molecular formulae are given in Table S1-S4), highlighting a large number of diverse molecular species. ESI(-) FT-ICR MS of $SPE_{DOM}$ $GW_{South}$ (Fig. 2a) showed abundant peaks in the *m/z* 200-750 range, with a bimodal distribution centered around *m/z* 300
and m/z 600 similar to a recent study on bank infiltrated groundwater in Finland (Jylhä-Ollila, 2021). After deisotoping, blank subtraction, and formula assignment, the number of detected monoisotopic peaks (S/N ≥ 3) ranged from 2508 to 4485 in the ESI(-) analysis of SPE<sub>DOM</sub> GW<sub>North</sub> and GW<sub>South</sub>.

A large proportion of SPE<sub>DOM</sub> molecular formulae in both GW and SW samples primarily consisted of oxygen (CHO) (36-72% for GW; 52-54% for SW) when analyzed using electrospray ionization negative mode (ESI(-)) (Table 3). However, a notable percentage also contained nitrogen (CHON) (6-15% for GW; 32% for SW). The molecular makeup of dissolved organic matter detected by ESI(-) in fractured-rock groundwater from the unconfined Chalk aquifer matches that observed in Longnecker and Kujawinski's (2011) study of groundwater from Waquoit Bay National Estuarine Research Reserve, USA. Similarly, when analyzed using photoionization in positive mode (APPI(+)), a large fraction of SPE<sub>DOM</sub> molecular formulae in GW and SW samples consisted solely of oxygen (85-94% for GW; 43% for SW), while a considerable proportion also contained nitrogen (6-11% for GW; 53-54% for SW). Additionally, sulfur (CHOS) represented the third most abundant component in SPE<sub>DOM</sub>, comprising 8-28% in GW (11-12% for SW) with ESI(-) and 1-4% in GW (3-4% for SW) with APPI(+). The unexpected difference in the number of identified compounds between the neighboring groundwater samples, GW<sub>South</sub> and GW<sub>North</sub> (Fig. S1), can be attributed to the distinct characteristics of their respective watersheds, including variations in land cover and water flow patterns. Previous research has demonstrated that the composition of dissolved organic matter (DOM) in rivers and lakes is influenced by factors such as catchment properties and land cover (Mattsson et al., 2009; Jaffé et al., 2012; Lambert et al., 2015; Wagner et al., 2015; Riedel et al., 2016).
<table><caption>**Table 3:** Number of compound in each subgroup and relative percentage, arithmetic and weighted mean elemental ratio and standard deviation for each subgroup in sample GW<sub>North</sub>, GW<sub>South</sub>, SW<sub>Suippe</sub> and SW<sub>Vesle</sub> for the ESI(-) and APPI(+) ionization sources. X corresponds to chloride or bromide adducts observed in ESI(-)</caption><thead><tr><th rowspan="2">Source<br>Sample</th><th colspan="4">ESI(-)</th><th colspan="4">APPI(+)</th></tr><tr><th>GW<sub>Nort</sub></th><th>GW<sub>Sout</sub></th><th>SW<sub>Suipp</sub></th><th>SW<sub>Vesl</sub></th><th>GW<sub>Nort</sub></th><th>GW<sub>Sout</sub></th><th>SW<sub>Suipp</sub></th><th>SW<sub>Vesl</sub></th></tr></thead><tbody><tr><td>O/C<sub>w</sub></td><td>0.39</td><td>0.30</td><td>0.51</td><td>0.50</td><td>0.17</td><td>0.17</td><td>0.36</td><td>0.36</td></tr><tr><td>O/C<sub>avg</sub></td><td>0.37</td><td>0.32</td><td>0.50</td><td>0.50</td><td>0.25</td><td>0.27</td><td>0.35</td><td>0.35</td></tr><tr><td>St. Dev</td><td>0.12</td><td>0.17</td><td>0.28</td><td>0.24</td><td>0.11</td><td>0.24</td><td>0.17</td><td>0.17</td></tr><tr><td>H/C<sub>w</sub></td><td>1.64</td><td>1.49</td><td>1.13</td><td>1.13</td><td>1.63</td><td>1.63</td><td>1.03</td><td>1.02</td></tr><tr><td>H/C<sub>avg</sub></td><td>1.43</td><td>1.41</td><td>1.11</td><td>1.08</td><td>1.34</td><td>1.31</td><td>1.01</td><td>1.00</td></tr><tr><td>St. Dev</td><td>0.31</td><td>0.31</td><td>0.38</td><td>0.35</td><td>0.31</td><td>0.41</td><td>0.27</td><td>0.27</td></tr><tr><td>DBE<sub>w</sub></td><td>5.67</td><td>7.23</td><td>11.07</td><td>11.22</td><td>3.97</td><td>4.29</td><td>11.36</td><td>11.56</td></tr><tr><td>DBE<sub>avg</sub></td><td>8.61</td><td>8.56</td><td>11.42</td><td>11.68</td><td>7.39</td><td>8.32</td><td>12.83</td><td>12.90</td></tr><tr><td>St. Dev</td><td>4.66</td><td>4.72</td><td>5.55</td><td>5.33</td><td>3.72</td><td>6.78</td><td>4.00</td><td>4.10</td></tr><tr><td>AI ≤ 0</td><td>0.20</td><td>0.19</td><td>0.07</td><td>0.06</td><td>0.06</td><td>0.09</td><td>0.01</td><td>0.02</td></tr><tr><td>0 < AI < 0,5</td><td>0.78</td><td>0.79</td><td>0.87</td><td>0.87</td><td>0.90</td><td>0.82</td><td>0.82</td><td>0.81</td></tr><tr><td>0,5 ≤ AI <</td><td>0.01</td><td>0.02</td><td>0.04</td><td>0.05</td><td>0.04</td><td>0.05</td><td>0.16</td><td>0.17</td></tr><tr><td>AI ≥ 0.67</td><td>0.01</td><td>0.00</td><td>0.02</td><td>0.02</td><td>0.00</td><td>0.04</td><td>0.01</td><td>0.01</td></tr><tr><td>compound nbr</td><td>2508</td><td>4485</td><td>1555</td><td>1599</td><td>553</td><td>2138</td><td>10161</td><td>10875</td></tr><tr><td>CH</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0,2%</td><td>0,1%</td></tr><tr><td>CHO</td><td>72%</td><td>36%</td><td>52%</td><td>54%</td><td>94%</td><td>85%</td><td>43%</td><td>43%</td></tr><tr><td>CHOS</td><td>8%</td><td>28%</td><td>12%</td><td>11%</td><td>1%</td><td>4%</td><td>3%</td><td>4%</td></tr><tr><td>CHON</td><td>6%</td><td>15%</td><td>32%</td><td>32%</td><td>6%</td><td>11%</td><td>54%</td><td>53%</td></tr><tr><td>CHONS</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>CHOSX</td><td>3%</td><td>3%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>CHONX</td><td>1%</td><td>5%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>CHOX</td><td>10%</td><td>12%</td><td>3%</td><td>2%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr></tbody></table>
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<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 2:** FT-ICR MS spectra of a) SPE<sub>DOM</sub> GW<sub>South</sub> ESI(-) (up) and SPE<sub>DOM</sub> GW<sub>South</sub> APPI (+) (down), b) SPE<sub>DOM</sub> SW<sub>Vesle</sub> ESI(-) (up) and SPE<sub>DOM</sub> SW<sub>Vesle</sub> APPI (+) (down) highlighting the respective contribution of compounds classes ; c) Ven diagram displaying the repartition of total (ESI and APPI) compounds unique to each sample and those shared among the different GW and SW samples ; d) compounds classes identified using ESI(-) and APPI(+) for each sample.</figcaption></figure>
The ESI(-) spectrum of SPE<sub>DOM</sub>SW<sub>Vesle</sub> showed a distinct unimodal distribution centered around 400 m/z, while the APPI(+) spectrum of SPE<sub>DOM</sub>GW<sub>South</sub> had a unimodal distribution centered around 290 m/z. The number of detected monoisotopic peaks (S/N ≥ 3) was consistent between the river samples, with 1599 in SW<sub>Vesle</sub> and 1555 in SW<sub>Suippe</sub>. In the APPI(+) analysis of SPE<sub>DOM</sub>GW<sub>North</sub> and GW<sub>South</sub> (Fig. 2a), the number of detected monoisotopic peaks ranged from 553 to 2138 (Table 3), with approximately one-third being assigned to radical cations (M⁺) alongside the [M+H]⁺ ions.

The APPI(+) mass spectrum of SPE<sub>DOM</sub>SW<sub>Suippe</sub> contained higher molecular weight compounds compared to SPE<sub>DOM</sub>GW<sub>South</sub>. Over 10,000 molecular formulas were assigned in APPI(+) analysis of both SPE<sub>DOM</sub> SW samples (Table 3) with a substantial number of compounds identified as radical cations. The Venn (Fig. S5) and Van Krevelen (Fig. S6) diagrams showed contrasting selectivity between ESI(-) and APPI(+) ionization modes for groundwater and river water samples. Photoionization is known to ionize a greater number of ions compared to ESI, including both protonated and radical ions, expanding the range of detectable compounds (Hockaday et al., 2009; Podgorski et al., 2012; Barrow et al., 2015). Additionally, APPI is effective in ionizing low-polarity compounds with low oxygen numbers and saturation degree (Renard et al., 2022). As a result, molecular formulas detected by APPI have lower O/C ratios compared to those detected by ESI in both groundwater and riverine water samples (Table 3 and Fig. S6). Various indices calculated based on direct infusion FT-ICR MS analysis (SI3) revealed differences between ESI and APPI, with higher DBE and AI<sub>mod</sub> values for groundwater samples in ESI, while surface water samples showed the opposite trend (Table 3). APPI preferentially ionized SPE<sub>DOM</sub> with greater unsaturation and higher aromaticity, consistent with observations in other natural waters and organic matter samples (Hockaday et al.,
296 2009; Renard et al., 2002; Vinci et al., 2022). On the other hand, ESI(-) detected a small amount
297 of polar amino sugars and carbohydrates which were not detected by APPI(+) (Fig. S7). These
298 findings align with previous observations that APPI(+) selectively ionizes aromatics and
299 compounds with π electron systems (Barrow et al., 2010; Podgorski et al., 2012), has high
300 sensitivity towards lignin oligomers (Kosyakov et al., 2021), and is more efficient in ionizing
301 lipids such as free fatty acids, esters, and triglycerides (Cai and Syage, 2006) compared to ESI(-).

302 To further assess the selectivity of each ionization method, the samples were divided into
303 subclasses (SPE<sub>DOM</sub>-O, SPE<sub>DOM</sub>-N, and SPE<sub>DOM</sub>-S). Radar plots in Fig. S8 represent the number
304 of molecular formulas as a function of the number of oxygen atoms for each subclass. Across all
305 subclasses, a wide range of oxygen classes (O<sub>1</sub>-O<sub>15</sub>) was observed in both ESI(-) and APPI(+)
306 ionization. The presence of various functional groups, including carboxylic, hydroxylated,
307 peroxylated, esterified, and ether groups, likely contributed to the observed range of SPE<sub>DOM</sub>-O
308 species. APPI(+) efficiently ionized nonpolar compounds with low oxygen content (O<sub>1</sub>-O<sub>2</sub>).

309 **3.2.3. Compositional differences between groundwater and river water SPE<sub>DOM</sub> samples**

310 Combining molecular formulas identified by ESI(-) and APPI(+) revealed that both SW
311 SPE<sub>DOM</sub> samples exhibited higher molecular diversity, skewed towards higher *m/z* ratios (Fig. S3
312 and S4), and contained compounds with higher *m/z* values compared to GW samples.
313 Furthermore, regardless of the ionization source used, SPE<sub>DOM</sub> from groundwater samples
314 showed greater reduction compared to SPE<sub>DOM</sub> from surface river water, as indicated by all
315 indices in **Table 3**. Kendrick mass defect (KMD<sub>CH2</sub>) plots were employed to compare the
316 molecular composition of each sample (Fig. S9). Homologous species, characterized by the same
317 Kendrick mass, were analyzed to determine the degree of degradation and oxygen enrichment.
Higher KMD values indicated less degraded, more oxygenated compounds, while lower KMD values suggested more degraded, less oxygenated (but hydrogen-enriched) compounds (Fudyma et al., 2021). **Fig. S9 A-D** shows that surface river waters (Suippe and Vesle) contained complex compounds with significantly higher oxygen and less degradation than borehole groundwater samples (Southern and Northern watersheds), regardless of the ionization source (ESI or APPI). The groundwater samples exhibited more altered compounds, lower oxygenation levels, and higher hydrogen enrichment. Groundwater samples had a high proportion of lignin-like compounds (>55%) and relatively more lipid and protein-like compounds. In contrast, surface river waters contained more condensed hydrocarbon-like compounds (**Fig. S7**). A Venn diagram (**Fig. 2C**) was used to compare the composition of groundwater and river samples. The diagram revealed that a substantial number of formulas (12,428 compounds, 90% of chemical space) were common to SW<sub>Vesle</sub> and SW<sub>Suippe</sub>. Additionally, both groundwater samples shared 1,351 individual formulas (representing 21% and 45% of GW<sub>South</sub> and GW<sub>North</sub> chemical spaces, respectively). Surprisingly, only a small number of compounds (572 individual formulas) were shared among all SPE<sub>DOM</sub> samples, making up just 2.7% of the total chemical space.

A substantial disparity was observed in the ESI(-) compositional space between GW<sub>South</sub> and GW<sub>North</sub>. Specifically, GW<sub>South</sub> exhibited a higher number fraction of SPE<sub>DOM-S</sub> and SPE<sub>DOM-N</sub> compared to GW<sub>North</sub>. In contrast, SPE<sub>DOM-O</sub> was the dominant component of GW<sub>North</sub>, accounting for 72% of its composition (**Table 3** and **Fig. 2D**).

Differences between GW<sub>South</sub> and GW<sub>North</sub> were less pronounced in APPI(+) analysis, with a majority of compounds assigned to SPE<sub>DOM-O</sub> (85% for GW<sub>South</sub>, 95% for GW<sub>North</sub>). GW<sub>South</sub> ESI(-) had fewer O# species and a lower O/Cw ratio compared to GW<sub>North</sub>. GW<sub>South</sub> SPE<sub>DOM-S</sub> contained a relatively higher frequency of compounds between O<sub>3</sub> and O<sub>10</sub>, while GW<sub>North</sub> had
fewer sulfur-containing compounds. Both surface water samples displayed similar distributions in ESI(-) and APPI(+), with SPE<sub>DOM-O</sub> being the largest compound class (55% of total molecular anions) analyzed using ESI(-) (Table 3).

These findings align with observations made for Suwannee River Natural Organic Matter as described by Martin et al. (2021). In summary, both surface river waters contained very similar compositions characterized by a high molecular diversity, centered toward higher *m/z* compounds and containing higher molecular weight compounds, when compared to groundwater SPE<sub>DOM</sub> samples. In turn, groundwater SPE<sub>DOM</sub> from both watersheds were much more reduced than river water SPE<sub>DOM</sub>.

In well-oxygenated environments like surface river waters, the degradation of various types of dissolved organic matter (DOM) occurs, with certain compounds preferentially degraded due to their high hydrogen-to-carbon (H/C) ratios (i.e. aliphatics, protein-like, lipid-like, formulae containing heteroatoms) (McDonough et al., 2022). Groundwater, being generally more reduced, limits DOM oxidation due to both biotic and abiotic factors, such as microbial consumption of oxygen and lack of sunlight, respectively. Lv et al. (2016) showed that highly unsaturated or oxygen-rich molecules tend to attach to iron oxyhydroxides, while smaller molecules with lower unsaturation or fewer oxygen groups remain in solution. The decreased occurrence of high O/C compounds in groundwater can potentially be attributed to their adsorption onto mineral surfaces. This phenomenon could contribute to the prevalence of smaller and less oxidized compounds. In this context, under reduced and dark groundwater conditions, there is accumulation and potential long-term retention of both photolabile compounds with low O/C ratios and biolabile compounds with high H/C ratios as observed by McDonough et al. (2022) in the unconfined aquifer of Maules Creek and Wellington in Australia. In contrast, DOM in well
oxygenated rivers typically consists of a continual mixing of fresh DOM sources originating
from primary producers, along with DOM resulting from biodegradation and photodegradation
in predominantly oxygenated conditions, resulting in an accumulation of (McDonough et al.,
2022).

### 3.2.4. Molecular characteristic of dissolved organic nitrogen (DON) in groundwater and surface water $SPE_{DOM}$

The Van Krevelen diagrams depicting N-containing molecular formulas (Fig. 3 for APPI(+)
and Fig. S10 for ESI(-) reveal a significant presence of DON constituents and a high level of
molecular diversity, particularly in the N-containing molecular characteristics detected through
APPI(+), and illustrate the major disparities in $SPE_{DON}$ between groundwater and surface waters
within each watershed of the unconfined chalk aquifer.

This finding is consistent with previous studies that have also observed the ability of
photoionization to identify a greater number of nitrogen-containing compounds compared to ESI
(Hockaday et al., 2009; Vinci et al., 2022; Wang et al., 2022). In the unconfined chalk aquifer
groundwater and connected surface water samples, nitrogen-containing compounds were only
composed of CHON as CHONS or CHN were not detected. APPI(+) in $SPE_{DOM}$ showed a
greater diversity of nitrogen-containing compounds compared to $SPE_{DOM}$ (Fig. 3). While
$SPE_{DOM}$ had only one nitrogen atom in its molecular formulas, $SPE_{DOM}$ showed a
broader distribution of $N_1$Ox to $N_5$Ox compounds. Lignin-like compounds dominated the
nitrogen-containing molecules (Fig. S11), consistent with previous studies on agricultural runoff
(Li et al., 2018), lakes (Liu et al., 2020), and urban streams/rivers (Lusk and Toor, 2016). With
15-20% of the CHON composition, condensed hydrocarbons were the second largest
components of CHON compounds in SW, while lipids and unsaturated hydrocarbon-like compounds were relatively abundant components of CHON in GW samples. Land use in the unconfined chalk aquifer area is largely agricultural (> 80%) with wheat, barley, sugar beet and alfala as major crops (Cao, 2020). Besides, forest covers about 15% of the study area, mainly on the Berru and Champagne mounts and the riparian areas.

<figure><img src="image_8.png" /><figcaption>**Figure 3:** Van Krevelen diagrams of N-containing compounds CHON (N₁Ox, N₂Ox, N₃Ox, N₄Ox and N₅Ox) detected by APPI(+) in A) SPE<sub>DOM</sub> GW<sub>South</sub>; B) SPE<sub>DOM</sub> SW<sub>Vesle</sub>; C) SPE<sub>DOM</sub> GW<sub>North</sub> and D) SPE<sub>DOM</sub> SW<sub>Suipe</sub>.</figcaption></figure>

A recent study by Yang et al. (2022) also found that lignin and condensed aromatics were highly prevalent in the DON pool of agricultural runoff and surface waters. The increased occurrence of lignin-like components in dissolved organic nitrogen (DON) is thought to come from the breakdown of lignified vascular plant tissues and organic matter derived from soil and
sediment. Also, Osborne et al. (2013) have shown in a series of bioassay experiments that microbes are able to convert bioavailable lipid- and protein-like N-containing compounds into more oxygenated and unsaturated (more refractory), N-containing lignin-like compounds. Condensed hydrocarbons in the DON pool may in turn arise from photodegradation of dissolved organic matter and microbial processing of lignins and tannins (Lie et al., 2018; Ward et al., 2013). Conversely, the presence of polar and bioavailable compounds such as lipids, proteins and amino sugars likely originate from microbes and are primarily leached from soil organic matter, organic fertilizers, and indigenous microorganisms (Feng et al., 2016).

### 3.2.5. Molecular characteristic of dissolved organic sulfur (DOS) in groundwater and surface water $SPE_{DOM}$

S-containing molecular formulas in the samples were primarily represented by compounds containing a single sulfur atom ($CHO_xS_1$). The Van Krevelen diagrams showed that groundwater $SPE_{DOM-S}$ is more aliphatic and reduced compared to surface water (Fig. S12). Both groundwater samples contained a distinct pool of aliphatic and highly saturated compounds with H/C ~ 2 and low DBE values (Fig. S12a and S12b). Intense, negatively charged ions separated by 14 mass units were detected in $SPE_{DOM}$ $GW_{South}$, corresponding to aliphatic sulfonic acids and sulfates (Gonsior et al., 2011). An O/S ratio > 4 suggests the presence of organosulfates or sulfonates with additional oxygen-containing functional groups, known for producing intense anions in ESI(-).

In the Van Krevelen diagram (Fig. S12a), a larger pool of sulfur-containing compounds with reduced characteristics was observed exclusively in groundwater $SPE_{DOM}$ samples. These compounds, falling in the region associated with aromatic sulfonic acids, have H/C ratios
between 1.3 and 1.8 and O/C ratios ranging from 0.1 to 0.3. They were tentatively identified as
linear alkyl benzene sulfonates (LAS), dialkyl tetralin sulfonates (DATS), and sulfophenyl
carboxylic acids (SPC). The most abundant compounds in the ESI GW<sub>South</sub> SPE<sub>DOM</sub>-S<sub>1</sub>O<sub>3</sub> series
were found to have high carbon numbers (C<sub>14</sub>-C<sub>18</sub>) and DBE values of 3, 4, 5, and 8 (Fig. S13a).
CID MS<sup>2</sup> experiments on selected masses (m/z 297.15285 and 325.18429) from the SPE<sub>DOM-S1O3</sub>
series confirmed the presence of linear alkyl benzene sulfonates (LASs) (Fig. S13b). The CID
MS<sup>2</sup> spectra exhibited intense sulfophenyl anions at m/z 183.01266, consistent with similar
sulfonates reported in previous studies (Lara-Martin et al., 2010; Gonsior et al., 2011).
Additionally, dialkyl tetralin sulfonates and degradation products, such as sulfophenyl carboxylic
acids, were detected in the groundwater samples, predominantly in GW<sub>South</sub>. LASs were
primarily found in SPE<sub>DOM</sub> GW<sub>South</sub>, with limited presence in GW<sub>North</sub>, while SPE<sub>DOM</sub> samples
from the Vesle and Suippe rivers contained mainly SPE<sub>DOM-O</sub> compounds.

Alkyl sulfonates (i.e. LAS) have previously been reported from estuarine and coastal zones
(Zoller, 2006), rivers (Loos et al., 2009), urban (Tubau et al., 2010) and agricultural (Jimenez-
Diaz et al., 2007) aquifers. the detection of aliphatic sulfonic acids and sulfates in the chalk
aquifer of Champagne is likely related to a local source of anthropogenic contamination. LAS
can enter natural ecosystems as pollutants through the direct application of untreated wastewater
for irrigation and the use of sewage sludge as agricultural fertilizer (Holt et al., 1989). It can also
enter the soil via the inclusion of detergents in pesticide formulations as emulsifiers, dispersing
agents, and spreading agents (Jensen, 1999).

3.2.6. Application of mass difference transformation networks (MDiNs)
FT ICR-MS analysis with high mass accuracy (<1 ppm) allows the deduction of biogeochemical relationships and networks based on molecular formulas. Mass-difference networks (MDiNs) connect known abiotic and biotic reactions through defined mass differences (Breitling et al., 2006; Moritz et al., 2017; Ayala-Ortiz et al., 2023). By considering the *m/z* values of a sample as nodes and representing common mass differences as edges, biogeochemical transformation networks can be constructed, representing biogeochemical, biotic, or abiotic reactions (Tziotis et al., 2011; Schmitt-Kopplin et al., 2019; Traquete et al., 2022). We used the MetaboDirect pipeline to analyze the data and construct mass difference transformation networks (Ayala-Ortiz et al., 2023). **Fig. 4A** and **4B** display the networks from SPE<sub>DOM</sub>GW<sub>South</sub> and GW<sub>North</sub>, while **Fig. 4C** and **4D** represent the networks from SPE<sub>DOM</sub>SW<sub>Vesle</sub> and SPE<sub>DOM</sub>SW<sub>Suippe</sub>. Nodes in the networks are color-coded based on compound types, such as amino sugars, lignin, tannins, carbohydrates, lipids, unsaturated hydrocarbons, condensed hydrocarbons, and proteins. This graphical representation visually depicts the complexity of dissolved organic matter and highlights connections between different chemical families. Using the MetaboDirect pipeline, mass differences (Δ*m/z*) between peaks in each sample are compared to a predefined biochemical transformation key (Table S5). For example, a mass difference (Δ*m/z*) of 14.01565 Da corresponds to methylation (CH<sub>2</sub> substitution for -H), while a Δ*m/z* of 42.010565 represents acetylation (C<sub>2</sub>H<sub>2</sub>O substitution for a hydrogen atom) (Breitling et al., 2006; Tziotis et al., 2011). Mass-difference networks (MDiNs) provide an overview of the biogeochemical relationships within the FT-ICR MS DOM spectrum, revealing chemical diversity and microbial/abiotic reactions. Besides, analyzing the potential chemical transformations (both biotic and abiotic) in abundance can offer a more comprehensive
472

understanding of the variations in dissolved organic matter (DOM) dynamics. This, in turn, helps elucidate the influence of DOM degradation and bioavailability within the aquifer.

473

474

With approximately 70% of observed biogeochemical transformations, DOM biogeochemistry seems to be driven by abiotic processes in the unconfined Chalk groundwaters and surface river waters (Fig. 5; Fig. S14) with an abundance of putative "abiotic" reactions that involve additions methylation, hydrogenation/dehydrogenation, oxidation, condensed/dehydration, nitro-reduction. These general observations were confirmed by the MDiNs built using the APPI(+) FT-ICR MS dataset (Fig S14), although APPI(+)-built MDiNs revealed GW had significant higher putative abiotic reactions compared to SW. Besides, the river waters were also driven by an abundance of putative amino acid) transformation (gains of losses of individual amino-acids), in line with the higher observed abundance of N-containing compounds in SW. On the other hand, GW samples displayed less transformations linked to amino acids and GW<sub>South</sub> was almost devoid of these biogeochemical reactions (Fig. 5). Amino acids, as the fundamental constituents of proteins, are bioreactive components of DOM. Protein degradation can occur through abiotic mechanisms or enzymatic reactions, involving hydrolysis of peptide bonds and breaking down the protein structure into smaller oligopeptides, dipeptides, and amino acids (Pantoja et al., 1997). The higher proportion of putative "amino acid" transformations detected may reflect the extent of diagenetic processing of proteins in river waters. These results suggest that microbes in SW may have an increased ability to degrade or produce proteins compared to GW microbes, at least in this aquifer.

475

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478

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481

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493
<figure><img src="image_9.png" /><figcaption>**Figure 4:** Mass difference transformation network (MDiN) corresponding to samples A) ESI(-) SPE<sub>DOMGW</sub><sub>South</sub>; B) ESI(-) SPE<sub>DOMGW</sub><sub>North</sub>; C) ESI(-) SPE<sub>DOMSW</sub><sub>Vesles</sub> and D) ESI(-) SPE<sub>DOMSW</sub><sub>Suipe</sub>.</figcaption></figure>
513

514

<figure><img src="image_10.png" /><figcaption>B) “Amino acid”-like transformations</figcaption></figure>

<figure><img src="image_11.png" /></figure>

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518

519

520

521

<figure><img src="image_12.png" /><figcaption>C) “Carbohydrate”-like transformations</figcaption></figure>

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529

<figure><img src="image_13.png" /><figcaption>D) “Other” transformations</figcaption></figure>

530

531

532

**Figure 5:** Barcharts displaying the twelve most frequent A) “abiotic”, B) “amino acid”, C) “carbohydrate” and D) “other” putative chemical transformations for ESI(-) SPE<sub>DOM</sub> SW<sub>Suippe</sub> (blue); SW<sub>Vesle</sub> (orange); GW<sub>South</sub> (grey) and GW<sub>North</sub> (yellow).

533

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539
540 **4. Conclusion**

541 This study offers a spatial snapshot of the molecular composition and biogeochemical
542 transformations of SPE<sub>DOM</sub> from groundwater and surface waters collected from two distinct
543 watersheds in the Champagne region of northern France. ¹H NMR spectroscopy combined with
544 both ESI(-) and APPI(+) FT-ICR MS in combination with mass difference transformation
545 networks were applied here for the first time to our knowledge in the study of DOM in aquifers.

546 ¹H NMR spectroscopy indicated that groundwater SPE<sub>DOM</sub> carried a higher contribution of
547 aliphatic compounds while surface river waters SPE<sub>DOM</sub> were enriched in carboxyl-rich alicyclic
548 molecules, acetate derivatives and oxygenated units. This study has further shown the benefits of
549 using photoionization (APPI) in aquifer studies, given that it ionized approximately eight times
550 more compounds than ESI in surface river waters, specifically targeting the dissolved organic
551 nitrogen pool (accounting for more than 50% of the total composition) as well as a non-polar,
552 more aromatic fraction; with little overlap with compounds detected by ESI. Groundwater
553 SPE<sub>DOM</sub> samples did not show similar selectivity as lower molecular diversity was observed in
554 APPI(+) compared to ESI(-), suggesting groundwater did not contain complex dissolved organic
555 nitrogen and non-polar DOM compounds. By employing ESI(-) FT-ICR MS in conjunction with
556 comprehensive Orbitrap MS/MS analysis, we showed that the dissolved organic sulfur pool in
557 groundwaters was dominated by polar anthropogenic sulfur-containing surfactants likely derived
558 from agricultural fertilizers. Overall, our study shows that in this unconfined chalk aquifer,
559 groundwater SPE<sub>DOM</sub> contains a reduced pool of organic compounds, possibly related to a
560 combination of lack of sunlight and adsorption of high O/C formulas to mineral surfaces. Finally,
561 we show that the use of mass difference transformation network to both ESI(-) and APPI(+) FT-
562 ICR MS data is a powerful approach that provides insights into understanding the complex

563 chemical networks occurring within the unconfined chalk aquifer of Champagne.

564
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