# Do suspended particles matter for wastewater-based epidemiology?

Gauthier Bernier-Turpin, Régis Moilleron, Chloé Cenik, Fabrice Alliot,
Sabrina Guérin-Rechdaoui, Thomas Thiebault

► To cite this version:

Gauthier Bernier-Turpin, Régis Moilleron, Chloé Cenik, Fabrice Alliot, Sabrina Guérin-Rechdaoui, et al. Do suspended particles matter for wastewater-based epidemiology?. Water Research, In press, 280, pp.123543. 10.1016/j.watres.2025.123543. hal-05002533v2

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# Do suspended particles matter for wastewater-based epidemiology?

Gauthier Bernier-Turpin<sup>a, b, *</sup> , Régis Moilleron<sup>a</sup> , Chloé Cenik<sup>b</sup> , Fabrice Alliot<sup>b, †</sup> , Sabrina Guérin-Rechdaoui<sup>c</sup> , Thomas Thiebault<sup>b</sup>

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

ARTICLE INFO

Keywords:

Wastewater-based epidemiology
Suspended particulate matter
Sorption
Solid/liquid distribution
Combined sewer system
Wet weather flow

A B S T R A C T

As wastewater-based epidemiology (WBE) continues to evolve and expand the range of targeted compounds, some limitations remain underexplored, particularly the sorption of targeted markers onto suspended particulate matter (SPM) in raw wastewater. This issue is crucial as it could lead to underestimations in retrospective calculations. While previous studies have addressed this topic, they have primarily focused on a limited range of analytes (e.g., illicit drugs and selected pharmaceuticals) and relied on small sample sizes, highlighting the need for further research. This study aims to bridge these gaps through a six-month monitoring campaign analyzing a broad range of WBE markers (pharmaceuticals, illicit drugs, and lifestyle biomarkers) in both the dissolved and the particulate phases. A dedicated analytical method based on pressurized liquid extraction and liquid chromatography coupled with tandem mass spectrometry allowed the assessment of daily loads and distribution behavior of these compounds, providing new insights into their relevance for WBE estimates. The results demonstrated that most compounds exhibited low sorption to SPM, confirming their reliability for WBE monitoring based solely on dissolved-phase measurements. Notably, this study provides the first clear assessment of the minimal sorption of tobacco and coffee biomarkers. However, significant sorption was observed for 11 molecules, including fluoxetine, THC-COOH, and methadone, revealing a "hidden load" that could bias estimates without proper correction. Log D proved to be a useful and better than log Kow predictor of sorption potential for ionized compounds but failed to accurately predict sorption for neutral species. Additionally, wastewater dilution due to urban runoff appeared to influence compound partitioning, potentially increasing their affinity for the solid phase. Nonetheless, potential changes in the organic composition of SPM do not appear to be the driving factor, as the studied material retained stable total organic carbon (TOC) levels, most likely due to in-sewer remobilization of organic deposits which share a similar signature with domestic wastewater effluent. Further investigations are needed to identify the key parameters influencing compound affinity for SPM and their potential implications for WBE applications.

1. Introduction

In recent years, wastewater-based epidemiology (WBE) has show-
cased its potential to be an effective tool for monitoring various health-
related issues at different scales (González-Mariño et al., 2020). Whether
it concerns illicit drug consumption (Zuccato et al., 2008; Karolak et al.,
2010), smoking or drinking habits (Senta et al., 2015), or the prevalence
of certain pathologies (Boogaerts et al., 2021), WBE takes advantage of
the fact that consumed substances are mainly excreted through urine
and feces either in their unchanged or metabolized forms. Therefore,
wastewater can be considered as a largely pooled sample that provides

an anonymized picture of consumption patterns based on the analysis of
selected excretion markers (hereafter referred to as WBE biomarkers).
By providing near real-time quantitative data on a large scale with
minimal logistical requirements, WBE is increasingly seen as a valuable
method for gathering insights on specific health topics and has show-
cased the potential to complement, or even replace, more
labor-intensive data collection approaches, such as population surveys
or sales/prescription numbers (Escolà Casas et al., 2021). However,
WBE is still in development, and the back-calculation of consumed
amounts from wastewater concentration measurements involves multi-
ple layers of uncertainty at each stage of the process (Castiglioni et al.,

<footnote><sup>a</sup> Leesu, Univ Paris Est Creteil, ENPC, Institut Polytechnique de Paris, Creteil, F-94010, France</footnote>

<footnote><sup>b</sup> METIS, Sorbonne Université, CNRS, EPHE, PSL University, UMR 7619, F-75005 Paris, France</footnote>

<footnote><sup>c</sup> SIAAP, Innovation Department, FR-92700 Colombes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gauthier.bernier-turpin@u-pec.fr (G. Bernier-Turpin).</footnote>
2013). In order to minimize the uncertainties associated with WBE data, best practice protocols have emerged to ensure robust and consistent data acquisition and therefore ensure reliable interpretations that could facilitate cross-comparisons between studies.

In order to be deemed as a suitable WBE marker, a compound must fulfill some requirements (Chen et al., 2014; Gracia-Lor et al., 2017): (1) to be present at rather high concentrations in wastewater to be easily quantifiable; (2) to be excreted under a urinary metabolized form assessing specific human contribution; (3) to be stable in wastewater to limit degradation during sewer transportation and (4) to show a minimal affinity for suspended particulate matter (SPM). Therefore if it matches all the criteria mentioned above, the back-calculation between measured concentrations (excreted) and consumed amounts is mainly driven by the excretion factor between the parent compound and its metabolite. Usually based on available pharmacokinetic data, those factors are highly dependent on several external parameters (such as the volunteer panel), leading to significant heterogeneity in the available data and therefore accounting as a major source of uncertainty during the overall estimation process (Gracia-Lor et al., 2016). While refining those excretion factors seems a necessary but challenging step, there has been a rise of concern about the degradation/transformation of WBE biomarkers in sewer conditions. Although the hydraulic retention time should be as short as possible, wastewater remains a highly reactive matrix where both chemical and biological degradation are prone to occur. Some studies have conducted pilot experiments simulating sewer conditions to assess the stability of some chemicals during sewer transport, sampling and storage, therefore questioning their suitability as WBE markers (McCall et al., 2016; Li et al., 2021).

Another key factor influencing WBE accuracy is the partitioning of biomarkers between dissolved and particulate phases. While WBE biomarkers are typically considered to be predominantly present in the dissolved phase due to their polar nature, studies have shown that even polar compounds, such as pharmaceuticals, can exhibit sorption onto SPM, particularly due to their widespread use and environmental persistence (Boulard et al., 2020). Previous research (Baker and Kasprzyk-Hordern, 2011; Baker et al., 2012) provided some of the earliest evidence that the partitioning of WBE biomarkers onto the solid phase could reach non-negligible levels. These studies highlighted the need to refine WBE estimates by incorporating sorption effects into back-calculation models, as later proposed by Baker et al. (2014), to prevent potential underestimations. Despite these initial findings emphasizing the importance of understanding WBE biomarker interactions with SPM, relatively few studies have further explored this issue. Those that have (e.g., Senta et al., 2013; Alvarez-Ruiz et al., 2015) have typically focused on specific compounds (e.g., illicit drugs and pharmaceuticals) and have been conducted over relatively short time frames. To address these gaps, this study monitored a wide range of WBE markers (including commonly targeted illicit drugs and pharmaceuticals as well as more recently proposed artificial sweeteners and dietary intake-related compounds) simultaneously in both dissolved and particulate phases over an extended period (~ six months).

Using this significant dataset, the study aims to:

(i) Evaluate the occurrence of a broad range of WBE biomarkers in SPM collected from wastewater samples

(ii) Compare these data with concentrations measured in the dissolved phase to estimate the distribution of targeted compounds between both phases

(iii) Investigate the chemical properties that may influence solid/liquid partitioning

Additionally, given the length of the observation period and the characteristics of the sampling site (i.e., a combined sewer system) an additional point arises:

(i) Assess how rain events, which modify wastewater composition, impact the affinity of the targeted compounds for the solid phase.

By addressing these aspects, this study seeks to provide a comprehensive assessment of the variability in sorption processes affecting WBE markers. It builds upon previous findings while expanding the scope of biomarker diversity and temporal resolution and potentially identifying new driving factors for sorption onto SPM.

## 2. Materials and methods

### 2.1. Chemicals reagents

Analytical reference standards and isotopically labelled internal standards (ILIS) were purchased depending on their availability from Cayman Chemicals, Cluzeau, LGC, Santa Cruz Biotechnology, or Sigma-Aldrich with high purity grade (at least > 93 %). Additional information concerning the targeted compounds are available in the supplementary information (Table S1).

Isotopically labelled internal standards (ILIS) were 4-methyl-benzo-triazole-d₄ (4-MBZ-d₄), acesulfame-d₄ (ACS-d₄), acetaminophen-d₄ (ACT-d₄), amisulpride-d₅ (AMI-d₅), amphetamine-d₈ (AMP-d₈), benzoylecgonine-d₃ (BZE-d₃), caffeine-¹³C₃ (CAF-¹³C₃), carbamazepin-¹³C₆ (CBZ-¹³C₆), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid-d₃ (CMPF-d₃), cotinine-d₃ (COT-d₃), diclofenac-d₄ (DCF-d₄), fexofenadine-¹³C₆ (FEX¹³C₆), fluoxetine-d₆ (FLX-d₆), ibuprofen-d₃ (IBF-d₃), irbesartan-d₆ (IRB-d₆), ketamine-d₄ (KET-d₄), metformin-d₆ (MFN-d₆), oxazepam-d₅ (OXA-d₅), oxolinic acid-d₅ (OA-d₅), propranolol-d₇ (PRO-d₇), sulfamethoxazole-¹³C₆ (SMX-¹³C₆), 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol-d₃ (THC-COOH-d₃), tramadol-¹³C-d₃ (TRA¹³C-d₃), trimethoprim-d₃ (TMP-d₃). All ILIS were diluted together in methanol to form a working mixture at 1 mg.L⁻¹.

LC-grade methanol (MeOH) and acetonitrile (ACN) were purchased from VWR (Fontenay-sous-Bois, France), analytical-grade formic acid (99 %), and EDTA disodium salt (Na₂EDTA) (99 %) were purchased from Sigma-Aldrich (St Quentin Fallavier, France), while 18.2 MΩ quality pure water (PW) was supplied with a Pure Lab Chorus water purification system (Veolia Water).

### 2.2. Sample collection

Raw wastewater (RW) samples were 24-hour flow-proportional composite. These samples were collected at the Seine Centre wastewater treatment plant (WWTP) (Colombes, France). Sampling was conducted twice a week on Wednesday and Sunday respectively to track variation between weekdays and weekends throughout the monitoring period that ranged from 07/26/2023 to 01/21/2024 (48 samples in total). The monitored WWTP collects a major part of the Parisian conurbation through a combined sewer system, serving around 2760,000 inhabitants according to WWTP authority. After being collected at the WWTP using a 1 L glass bottle, samples were stored frozen at -18 °C until being brought back to the laboratory for treatment and analysis. Hydrochemical conditions during samples collection are shown in Table S2.

### 2.3. Sample preparation and analysis

Prior to analysis, samples were thawed and filtered through 90 mm GF/F glass fiber filters (Whatman, Fontenay-sous-Bois, France) using a 0.7 µm cut size. The dissolved phase was investigated separately using a dedicated solid-phase extraction protocol for analytes enrichment and matrix clean-up described elsewhere (Bernier-Turpin et al., 2024). The quantifications are provided in Table S3.

The subsequent amount of SPM retained on the filter was then dried overnight in an oven at 50 °C and weighed (minus the original weight of the dry filter). Afterward, filters containing SPM were spiked with 50 ng
of each ILIS and placed in 34 mL extraction cells with 1 g of EDTA and then filled with dispersant (diatomaceous earth) before being kept at 4 °C overnight. Extraction was performed using pressurized liquid extraction (PLE-ASE-350, Thermo-Scientific) using methanol (MeOH) at 70 °C and 1000 psi. After the PLE step, extracts were reduced to dryness using an EZ-2 rotary evaporator (Genevac Ltd, Ipswich, UK). Samples were then reconstituted in a 15 mL amber glass vial with 2 × 1 mL of a PW/MeOH (80:20) mixture. The obtained extracts were then evaporated under a gentle nitrogen stream until dryness before being transferred to PTFE centrifugal tubes (PALL Corporation, Saint-Germain-en-Laye, France) with 2 × 150 µL of the same EMQ/MeOH mixture for 0.22 µm filtration. After 10 min of centrifugation at 10,000 rpm, the extracts were transferred to a 2 mL amber glass vial and stored at −18 °C until injection. Solid phase extraction (SPE) was considered as an additional sample purification but ultimately not adopted in this study. This was motivated by higher standard deviations obtained after SPE replications with commonly used multi-residue protocol (Bernier-Turpin et al., 2024).

Separation and quantification of the targeted analytes were performed using the same LC-MS² condition as for the dissolved phase. Briefly, it consists of a 10 µL injection in an Agilent ULTIVO triple quadrupole mass spectrometer using an X Select T3 (Waters® 2.1 mm × 100 mm, 3.5 µm) column equipped with a guard column (C18, 2.1 mm, Phenomenex) for chromatographic separation with 0.1 % (v/v) formic acid in ultrapure water and 0.1 % (v/v) formic acid in acetonitrile (ACN) as mobile phase. MS-data were collected and processed using Mass Hunter software (Agilent Technologies).

Instrumental LC-MS/MS conditions including retention time, fragmentor voltage, and collision energy for precursor and product ions were optimized through direct injection of standard solutions using MassHunter Optimizer Software. All instrumental parameters are available as supplementary data (Table S4).

## 2.4. Method validation and quality control (QC)

The analytical method was validated using a larger volume of RW (approximately 2 L) to collect sufficient particulate matter. The resulting filters were divided into four samples of equivalent SPM weight: one serving as a "blank" sample, spiked only with 50 ng of ILIS, and three replicates spiked with both 50 ng of ILIS and 100 ng of standards. The recovery ratio for each targeted analyte, relative to its surrogate ILIS, was determined by comparing the three spiked replicates to the "blank" sample, following the previously described extraction protocol.

Precision was defined as the relative standard deviation (RSD) between each spiked replicate. Limits of Detection (LOD) and Limits of Quantification (LOQ) were calculated based on the signal-to-noise (S/N) ratios of the spiked samples, with S/N ratios of 3 and 10 used to determine the LOD and LOQ, respectively. Both LOD and LOQ values were normalized to ng·g⁻¹ based on the weight of the tested sample. Quantification was performed using an 11-point calibration curve made of standard solution diluted in methanol (from 1 to 1000 µg·L⁻¹).

Regarding quality control (QC), each set of analyses was performed as follows: after the injection of the calibration curve standards, a methanol sample was injected, followed by a blank sample consisting of pure water (PW) spiked with the same amount of ILIS as the environmental samples. This blank sample was re-injected every 12 injections. Finally, a random calibration standard was re-injected at the end of the sequence to assess potential signal loss.

## 2.5. Calculations

The partitioning behavior of the targeted WBE biomarkers was assessed by examining their distribution between the aqueous and particulate phases within each sample.

To determine the contribution of the particulate fraction (P) to the total analyte concentration, the following equation was applied:

$$ \%P = \frac{\left( C_{SPM} \times \frac{m_{SPM}}{V} \right)}{\left( C_{SPM} \times \frac{m_{SPM}}{V} \right) + C_{RW}} $$

Where C_SPM is the concentration of the targeted analyte in SPM (ng·g⁻¹), m_SPM is the mass of SPM in the sample (g), V is the volume of water filtered (L) and C_RW is the analyte concentration in the dissolved phase of the RW (ng·L⁻¹).

The resulting %P values provide an insight into the significance of the particulate phase in contributing to the total flux of the analyte in the sewer.

This distribution can also be quantitatively described using the solid/ aqueous partitioning coefficient (K_d), which characterizes the relative concentration of the analyte between these phases at equilibrium. The partitioning coefficient K_d (L·kg⁻¹) is calculated using the following formula:

$$ K_d (L·kg⁻¹) = \frac{C_{SPM}}{C_{RW}} $$

Where C_SPM represents the concentration that is measured in the particular phase (in ng·kg⁻¹) and C_RW the one in the dissolved phase (ng·L⁻¹).

K_d values for each analyte were only calculated for samples where data from both phases were available.

Targeted analyte loads (in g·day⁻¹) were calculated based on measured concentration, daily flow intake and SPM concentration such as follows:

$$ P\ Load\sideset{\mathrm{P}}{^\mathrm{d}}\mathrm{Load}\ (\mathrm{g}\cdot\mathrm{day}^{-1}) = \mathrm{Flow\ rate}\ \times C_{\mathrm{SPM}}\times\frac{m_{\mathrm{SPM}}}{V} $$

$$ D\ Load\sideset{\mathrm{D}}{^\mathrm{d}}\mathrm{Load}\ (\mathrm{g}\cdot\mathrm{day}^{-1}) = \mathrm{Flow\ rate}\ \times C_{\mathrm{RW}} $$

Where P and D Loads (g·day⁻¹) are the loads of targeted analyte on particulate and dissolved phase respectively, flow rate in L·day⁻¹, C_SPM the concentration of the targeted analyte on SPM (g·g⁻¹), m_SPM the mass of SPM measured (g), V the volume of raw wastewater filtered for analysis (L), and C_RW is the analyte concentration in the dissolved phase of RW (g·L⁻¹).

Concerning the discussion between WWF and DWF, samples were categorized as part of a rainfall event if the accumulated precipitation between the sampling day and the day before (n and n-1, respectively) was over 5 mm.

The statistical significance of the results displayed in the following section was assessed after verifying the normality of the dataset using the Shapiro-Wilk test. Depending on the normality outcome, either a Student's t-test or a Mann-Whitney test was applied, with a p-value threshold set at 0.05 (95 % confidence interval).

# 3. Results

## 3.1. Method validation

Most compounds showed acceptable relative recoveries (70–120 %), except for THC—COOH (<20 %), which lacked an ideal matching ILIS due to the poor response from its own surrogate ILIS (THC—COOH d3) and the impossibility to switch for an ILIS with similar properties. High precision (<20 %) was achieved for most targeted compounds, although some exhibited significant variability, as expected given the high matrix complexity (Table 1).

All LOQ values were within the 0.1–50 ng·g⁻¹ range, with most compounds quantifiable below the 10 ng·g⁻¹ threshold. Higher LOQ values for certain compounds (e.g., IBF, MOR, and THC—COOH) were attributed to limited ionization efficiency in the ESI source.
<caption>**Table 1**</caption>

Validation parameters of the PLE-LC-MS/MS analytical method including method recovery relative to the employed ILIS (RR), Residual standard deviation (RSD) and both limits of detection (LOD) and quantification (LOQ) (in ng.g⁻¹).

<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Pharmaceuticals</th><th>ILIS</th><th>RR (%)</th><th>RSD (%)</th><th>LOD (ng. g⁻¹)</th><th>LOQ (ng. g⁻¹)</th></tr></thead><tbody><tr><td>Clarythromycin (CLY)</td><td>OA-d5</td><td>27.7</td><td>21.0 %</td><td>7.3</td><td>24.4</td></tr><tr><td>Ofloxacin (OFX)</td><td>OA-d5</td><td>101.7</td><td>42.0 %</td><td>1.4</td><td>4.8</td></tr><tr><td>Sulfamethoxazole (SMX)</td><td>SMX-<sup>13</sup>C<sub>6</sub></td><td>91.8</td><td>2.4 %</td><td>1.1</td><td>3.5</td></tr><tr><td>Trimethoprim (TMP)</td><td>TMP-d3</td><td>88.0</td><td>8.8 %</td><td>0.4</td><td>1.2</td></tr><tr><td>Acetaminophen (ACT)</td><td>ACT-d4</td><td>83.3</td><td>21.6 %</td><td>0.5</td><td>1.5</td></tr><tr><td>Diclofenac (DCF)</td><td>DCF-d4</td><td>89.9</td><td>13.8 %</td><td>1.3</td><td>4.3</td></tr><tr><td>Ibuprofen (IBF)</td><td>IBF-d3</td><td>123.7</td><td>0.5 %</td><td>2.9</td><td>9.8</td></tr><tr><td>Ketoprofen (KTP)</td><td>CBZ-<sup>13</sup>C<sub>6</sub></td><td>105.3</td><td>13.1 %</td><td>1.7</td><td>5.7</td></tr><tr><td>Atenolol (ATL)</td><td>AMI-d5</td><td>79.3</td><td>14.8 %</td><td>&lt; 0.1</td><td>0.1</td></tr><tr><td>Metoprolol (MPL)</td><td>TMP-d3</td><td>43.6</td><td>3.6 %</td><td>0.2</td><td>0.5</td></tr><tr><td>Propranolol (PRO)</td><td>PRO-d7</td><td>77.3</td><td>5.5 %</td><td>6.1</td><td>20.2</td></tr><tr><td>Salbutamol (SBL)</td><td>AMI-d5</td><td>103.6</td><td>18.0 %</td><td>0.1</td><td>0.4</td></tr><tr><td>Carbamazepine (CBZ)</td><td>CBZ-<sup>13</sup>C<sub>6</sub></td><td>110.1</td><td>3.1 %</td><td>0.4</td><td>1.4</td></tr><tr><td>Gabapentin (GBN)</td><td>CBZ-<sup>13</sup>C<sub>6</sub></td><td>145.7</td><td>24.0 %</td><td>0.8</td><td>2.7</td></tr><tr><td>Amisulpride (AMI)</td><td>AMI-d5</td><td>109.9</td><td>7.9 %</td><td>0.2</td><td>0.7</td></tr><tr><td>Citalopram (CTP)</td><td>FEX<sup>13</sup>C<sub>6</sub></td><td>53.5</td><td>15.0 %</td><td>1.3</td><td>4.3</td></tr><tr><td>Fluoxetine (FLX)</td><td>FLX-d6</td><td>105.1</td><td>18.5 %</td><td>2.5</td><td>8.3</td></tr><tr><td>Metformin (MFN)</td><td>MFN-d6</td><td>85.3</td><td>32.0 %</td><td>1.0</td><td>3.2</td></tr><tr><td>Cetirizine (CTZ)</td><td>FEX<sup>13</sup>C<sub>6</sub></td><td>160.4</td><td>15.1 %</td><td>1.1</td><td>3.8</td></tr><tr><td>Fexofenadine (FEX)</td><td>FEX<sup>13</sup>C<sub>6</sub></td><td>147.5</td><td>24.0 %</td><td>0.2</td><td>0.7</td></tr><tr><td>Candesartan (CAN)</td><td>TMP-d3</td><td>91.9</td><td>25.6 %</td><td>0.4</td><td>1.4</td></tr><tr><td>Irbesartan (IRB)</td><td>IRB-d6</td><td>105.8</td><td>8.7 %</td><td>&lt; 0.1</td><td>&lt; 0.1</td></tr><tr><td><strong>Illicit drugs and substances of abuse</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Anhydroeogonine Methyl Ester (AEME)</td><td>BZE-d3</td><td>76.9</td><td>27.4 %</td><td>1.3</td><td>4.4</td></tr><tr><td>Amphetamine (AMP)</td><td>AMP-d8</td><td>91.0</td><td>1.7 %</td><td>0.2</td><td>0.8</td></tr><tr><td>Benzoyleggonine (BZE)</td><td>BZE-d3</td><td>105.6</td><td>3.8 %</td><td>0.1</td><td>0.2</td></tr><tr><td>Cocaine (COC)</td><td>BZE-d3</td><td>46.9</td><td>17.4 %</td><td>0.1</td><td>0.2</td></tr><tr><td>Methamphetamine (MAMP)</td><td>AMP-d8</td><td>122.5</td><td>3.8 %</td><td>0.3</td><td>1.0</td></tr><tr><td>Ecstasy (MDMA)</td><td>TRA<sup>13</sup>C-d3</td><td>71.9</td><td>4.2 %</td><td>&lt; 0.1</td><td>0.1</td></tr><tr><td>Ketamine (KET)</td><td>KET-d4</td><td>86.1</td><td>20.7 %</td><td>1.6</td><td>5.5</td></tr><tr><td>6-mono-acetyl-morphine (6-MAM)</td><td>BZE-d3</td><td>84.8</td><td>28.2 %</td><td>1.9</td><td>6.4</td></tr><tr><td>Codeine (COD)</td><td>BZE-d3</td><td>120.6</td><td>25.3 %</td><td>1.2</td><td>3.9</td></tr><tr><td>EDDP</td><td>PRO-d7</td><td>29.3</td><td>18.9 %</td><td>&lt; 0.1</td><td>0.1</td></tr><tr><td>Methadone (METD)</td><td>PRO-d7</td><td>79.9</td><td>9.4 %</td><td>0.4</td><td>1.2</td></tr><tr><td>Morphine (MPH)</td><td>TRA<sup>13</sup>C-d3</td><td>59.8</td><td>8.3 %</td><td>7.2</td><td>24.1</td></tr><tr><td>Naloxone (NLX)</td><td>BZE-d3</td><td>96.6</td><td>31.7 %</td><td>0.8</td><td>2.7</td></tr><tr><td>Tramadol (TRA)</td><td>TRA<sup>13</sup>C-d3</td><td>103.2</td><td>2.7 %</td><td>0.1</td><td>0.5</td></tr><tr><td>Diazepam (DZM)</td><td>OXA-d5</td><td>83.4</td><td>7.7 %</td><td>0.9</td><td>2.9</td></tr><tr><td>Oxazepam (OXA)</td><td>OXA-d5</td><td>81.7</td><td>8.1 %</td><td>4.6</td><td>15.2</td></tr><tr><td>THC—COOH</td><td>IBF-d3</td><td>15.3</td><td>33.2 %</td><td>4.0</td><td>13.3</td></tr><tr><td><strong>Lifestyle markers</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Paraxanthine (PXM)</td><td>CAF-<sup>13</sup>C<sub>3</sub></td><td>98.5</td><td>20.2 %</td><td>1.0</td><td>3.5</td></tr></tbody></table>

<table><thead><tr><th>Pharmaceuticals</th><th>ILIS</th><th>RR (%)</th><th>RSD (%)</th><th>LOD (ng. g⁻¹)</th><th>LOQ (ng. g⁻¹)</th></tr></thead><tbody><tr><td>Caffeine (CAF)</td><td>CAF-<sup>13</sup>C<sub>3</sub></td><td>162.2</td><td>15.8 %</td><td>0.2</td><td>0.5</td></tr><tr><td>Cotinine (COT)</td><td>COT-d3</td><td>66.9</td><td>20.8 %</td><td>3.6</td><td>12.1</td></tr><tr><td>Nicotine (NIC)</td><td>COT-d3</td><td>111.9</td><td>25.6 %</td><td>3.6</td><td>12.0</td></tr><tr><td>Acesulfame (ACS)</td><td>ACS-d4</td><td>106.8</td><td>11.1 %</td><td>0.2</td><td>0.8</td></tr><tr><td>Saccharin (SAC)</td><td>ACS-d4</td><td>92.2</td><td>6.7 %</td><td>3.4</td><td>11.2</td></tr><tr><td>Sucralose (SUC)</td><td>ACS-d5</td><td>44.3</td><td>27.1 %</td><td>1.8</td><td>6.1</td></tr><tr><td>CMPF</td><td>CMPF-d3</td><td>158.6</td><td>17.7 %</td><td>10.3</td><td>34.3</td></tr><tr><td>Daidzein (DZN)</td><td>ACS-d4</td><td>45.2</td><td>19.0 %</td><td>0.9</td><td>3.1</td></tr><tr><td>Enterodiol (EOL)</td><td>IBF-d3</td><td>57.2</td><td>28.1 %</td><td>0.6</td><td>2.0</td></tr><tr><td>Enterolactone (EON)</td><td>IBF-d3</td><td>29.2</td><td>35.4 %</td><td>8.4</td><td>27.8</td></tr><tr><td>8-isoprostaglandin F2α</td><td>ACS-d4</td><td>68.5</td><td>29.0 %</td><td>0.7</td><td>2.2</td></tr><tr><td>Dinor-11β-Prostaglandin F2α</td><td>ACS-d4</td><td>86.3</td><td>15.3 %</td><td>3.6</td><td>11.9</td></tr><tr><td>Prostaglandin E2 (PE2)</td><td>IBF-d3</td><td>91.0</td><td>24.5 %</td><td>13.1</td><td>43.7</td></tr><tr><td>5-methyl-1H-benzotriazole (MBZ)</td><td>4-MBZ-d4</td><td>56.3</td><td>6.1 %</td><td>&lt;0.1</td><td>0.1</td></tr></tbody></table>

## 3.2. Occurrence of targeted analytes in SPM

Among the 54 targeted compounds, 12 of them (6-MAM, DZM, MTP, MOR, NLX, SBL, CMPF, DZN, 8-isoprostaglandin F2α, CMPF, Dinor-11β-Prostaglandin F2α, and PE2) were never detected according to the criteria employed (< LOD) and have therefore been discarded from the following discussion.

All measured concentrations along with the number of detections (if different from 48) are displayed in Fig. 1.

### A) Pharmaceuticals

Most of the targeted pharmaceuticals (14 out of 20) were systematically detected on SPM samples at median concentrations ranging from 8 ng.g⁻¹ (ATL) up to 1892 ng.g⁻¹ with the antidiabetic MFN. Non-steroidal anti-inflammatory drugs (NSAIDs) were the prominent class of compounds with high concentrations observed for DCF (131 ng.g⁻¹), ACT (462 ng.g⁻¹), and to a lesser extent IBF (106 ng.g⁻¹) and KTP (31 ng.g⁻¹). It should be noticed that DCF concentrations exhibited significant variability across samples (38–16,388 ng.g⁻¹). At the same time, IBF fell below the LOQ in one sample (n = 47) while all the other NSAIDs maintained a 100 % detection frequency.

All the three monitored antidepressants (AMI, CTP, and FLX) were close to the 100 ng.g⁻¹ range, although CTP and FLX (n = 42) were not measured in every samples. Among the antihypertensives, IRB showed a notably high median concentration (548 ng.g⁻¹), far exceeding its homolog CAN (17 ng.g⁻¹). Similarly, the anticonvulsant GBN (151 ng.g⁻¹) and the antihistaminic FEX (81 ng.g⁻¹) were found at significantly higher median levels than their counterparts CBZ (13 ng.g⁻¹) and CTZ (15 ng.g⁻¹, with n = 31). Lastly, the antibiotic OFX (283 ng.g⁻¹) was the most prevalent among this class, showing higher median concentrations compared to both SMX (59 ng.g⁻¹) and TMP (32 ng.g⁻¹).

### B) Drugs and substances of abuse

Compared to the pharmaceutical compounds, concentrations of illicit drugs and their metabolites were generally lower, and the number of detections varied much more across the monitored samples. Cocaine
<figure><img src="image_3.png" /><figcaption>Fig. 1. Occurrence of targeted WBE markers (A) Pharmaceuticals, B) Illicit drugs and substances of abuse and C) Lifestyle markers in collected SPM samples. The number of samples in which each analyte was quantified is indicated when it differs from the total number of samples (n = 48). The line within the box marks the median value while boundaries indicate the 25th and 75th percentiles, error bars indicate the maximum and minimal value with ± 1.5 σ variations and circles indicate individual values outside this range.</figcaption></figure>

and its metabolites displayed moderate levels, with BZE, the primary
urinary metabolite of COC, detected in all 48 samples at a median
concentration of 46 ng.g⁻¹, while COC itself was present in 30 samples at
a lower median concentration (3 ng.g⁻¹). AEME, another compound
associated with cocaine use (crack consumption) (Steenbeek et al.,

2022), was found in 43 samples, with a median concentration of 7 ng. g⁻¹.

Among the opiate-related compounds, COD was detected in all
samples at a 39 ng.g⁻¹, so was TRA (20 ng.g⁻¹), while the methadone
consumption metabolite EDDP appeared in 33 samples at 36 ng.g⁻¹ and

<figure><img src="image_4.png" /><figcaption>Fig. 2. Proportion of analyte loads sorbed onto SPM (in %) for (A) Pharmaceuticals, (B) Illicit drugs and substances of abuse, and (C) Lifestyle markers, based on the mean value across all monitored samples. Black lines represent the observed range across samples, with minimum and maximum values indicated by perpendicular black lines.</figcaption></figure>
METD itself was present in 32 samples at 13 ng.g⁻¹

Other substances were detected at varying frequencies, including KET in 46 samples (56 ng.g⁻¹), MDMA in all 48 samples (14 ng.g⁻¹), and MAMP in 47 samples (2 ng.g⁻¹). Notably, THC-COOH had the highest median concentration at 189 ng.g⁻¹ but was not detected in all 46 samples (n = 44), due to analytical limitations (LOQ > 10 ng.g⁻¹).

### C) Lifestyle markers

Alongside the previously discussed substances, various other compounds were detected at different concentrations. The compounds related to coffee consumption, CAF, and its metabolite PXN, were found at concentrations of 1899 ng.g⁻¹ and 211 ng.g⁻¹ respectively. Regarding tobacco use, NIC was measured at 42 ng.g⁻¹, while its metabolite, COT, was detected at 41 ng.g⁻¹. The artificial sweeteners ACS, SAC, and SUC were identified at concentrations of 369 ng.g⁻¹, 146 ng.g⁻¹, and 112 ng.g⁻¹, respectively, with ACS exhibiting the highest level among them. Additionally, markers for lignin-rich food intake, EOL and EON, were detected in 36 and 33 samples, showing concentrations of 13 ng.g⁻¹ and 160 ng.g⁻¹, respectively.

### 3.3. Estimating the proportion of loads sorbed onto SPM (%P)

The contribution of the particulate phase to total loads varied significantly among the targeted compounds (Fig. 2). Most compounds exhibited negligible sorption (i.e., <5 %), including commonly

monitored pharmaceuticals (ACT, IBF), illicit drugs/substances of abuse (BZE, MDMA), and all compounds classified as "lifestyle markers" (artificial sweeteners, dietary intake products, tobacco, coffee, etc.).

Some compounds displayed noticeable affinity for the solid phase such as KET (5.7 %) AMP (8 %, up to 25 %), AEME (12.4 %) and PRO (11.2 %). Both METD (13.6 %) and its metabolite EDDP (14.3 %) also exhibited significant levels of sorption, potentially raising concerns about the accuracy of opioid turnover monitoring. Among the three monitored antidepressants, AMI (3.5 %) showed low affinity for SPM, whereas CTP (13.6 %) and, most notably, FLX (36.6 %, up to 67.9 %) exhibited substantial sorption.

Finally both DCF (16.3 % up to 70.8 %) and THC-COOH (14.5 %, up to 61.3 %) showed high variability in their affinity with SPM, with solid-phase loads even reaching predominant value compared to the dissolved phase (Table S3).

## 4. Discussion

### 4.1. Influence of chemical properties on solid/liquid partitioning

Most compounds exhibiting substantial sorption in Fig. 2 also had high octanol-water partitioning coefficients (Kow, Table S1), suggesting that Kow could serve as an indicator of a compound's affinity for SPM, as previously observed for some organic molecules (Doucette, 2003). This relationship is often quantified using the solid-liquid partition coefficient (Kd), which assumes equilibrium between dissolved and

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Experimental values of Kd (logarithmic scale) of targeted compounds ranked by their respective log D value (increasing order from left to right). The line within the box marks the median value while boundaries indicate the 25th and 75th percentiles, error bars indicate the maximum and minimal value with ± 1.5 σ variations and circles indicate individual values outside this range. Neutral species are marked with a *.</figcaption></figure>
particulate phases. In this study, Kd values were experimentally deter-
mined for each compound based on their concentrations in both phases
and initially compared to their hydrophobic properties (i.e., log Kow).
Experimental Kd values (expressed as median values in logarithmic
form) varied widely, from 0.6 for COC and NIC to 3.5 for FLX (Fig. 3),
likely reflecting the diverse physicochemical properties of the targeted
compounds. Log Kow values ranged from -2.3 for MFN to 6.2 for
THC-COOH (Table S1). Overall, most compounds displayed relatively
low log Kd values (i.e., below 2) emphasizing their limited affinity for
SPM, as indicated by the minimal fraction of total loads carried by the
solid phase (Section 3.3).

These findings align with previous studies, which reported weak SPM
affinity for pharmaceuticals (Petrie et al., 2014), illicit drugs (Baker and
Kasprzyk-Hordern, 2011; Senta et al., 2013) and artificial sweeteners
(Subedi and Kannan, 2014a). Despite their frequent use in WBE studies,
the affinity of tobacco (COT, NIC) and coffee (CAF, PXN) biomarkers for
SPM has been poorly investigated (Darwano et al., 2014). The low
sorption levels observed here confirm their relevance for WBE estimates.
Similarly, EOL and EON, which have been recently evaluated as dietary
intake markers for seeds, whole grains, fruits, and vegetable, but mainly
regarding their potential stability in sewer conditions (Choi et al., 2020)
and their potential affinity for SPM has been left aside. Here we assess
that both EOL and EON displayed a low tendency for SPM sorption and
therefore are suitable for further interpretations on dietary intake
habits.

Higher affinity for the solid phase was primarily observed in less
polar compounds, including the antidepressant FLX (log Kd 3.5, log Kow
4.1), which exhibited both a higher affinity and log Kow value than
analogous compounds (AMI and CTP), as well as METD (log Kd 2.8, log
Kow 4.2) and its metabolite EDDP (Kd 2.9, Kow 5.5), in agreement with
previous studies (Baker and Kasprzyk-Hordern, 2011). The high vari-
ability in THC-COOH partitioning (log Kd 2.7, log Kow 6.2), has also
been documented (Van Wichelen et al., 2024), raising concerns about
the reliability of cannabis consumption monitoring, due to the lack of
alternative low-sorption markers (Pandopulos et al., 2022).

Log Kow appears to be a useful predictor of a compound's affinity for the solid phase, though, exceptions exist. For example, IBF and CAN (log Kow of 3.7 and 5, respectively) exhibited lower affinity (log Kd values of 1.2 and 1.8, respectively) than more hydrophilic compounds like AMP (log Kow 1.76, log Kd 2.6). This suggests that log Kow alone cannot fully predict SPM affinity, as previously noted (Sigmund et al., 2022). Factors such as ionization states of the target compound and the sorbent characteristics must also be considered to better understand sorption mechanisms.

Most targeted compounds contain functional groups that ionize
within the typical sewer pH range (7.5–8.5, Table S1 and S2), impacting
their reactivity and solubility beyond what log Kow alone describes, and
log D, which accounts for ionization, appears to be a more reliable
predictor of SPM affinity.

For instance, both IBF and CAN are negatively charged due to
carboxyl deprotonation, resulting in lower log D values (0.5 and 0,
respectively) compared to their log Kow values (3.7 and 5), better
reflecting their hydrophilic nature and weak SPM affinity.

However, log D alone is not systematically a perfect predictor of SPM
affinity. For example, AMP (log D – 0.6) exhibited stronger SPM affinity
than CBZ (log D 2.3), likely due to its cationic ionization state, which
enhances electrostatic interactions and cation exchange with negatively
charged organic matter in SPM (Droge and Goss, 2013). Consequently,
positively charged compounds, like AMP, may exhibit stronger sorption
to SPM, whereas neutral or negatively charged molecules typically
display weaker interactions with SPM. Notably, the AMP SPM affinity
observed in this study is slightly higher than previously reported (Baker
and Kasprzyk-Hordern, 2011), though similar values have been docu-
mented elsewhere (Subedi and Kannan, 2014b).

Sorption of neutral compounds sorption primarily depends on hy-
drophobic partitioning, hydrogen-bonding and π-π electron dono-

acceptor interaction (depending on their chemical structure for the
two latter), meaning their affinity for SPM is largely governed by their
log Kow values (which should be close to log D). Conversely, negatively
charged molecules, such as IBF and CAN, tend to be repelled by SPM due
to its predominantly negative charge (due to organic matter and mineral
particles) explaining their low affinity despite relatively high log Kow
values. Unlike neutral or anionic compounds, cationic molecules benefit
from additional electrostatic interactions and cation exchange, leading
to stronger SPM sorption.

Overall, based on Figs. 2 and 3, log D is a fairly good predictor of the
solid-phase affinity, but this holds primarily for charged species. Neutral
compounds such as CBZ, EOL, and EON exhibited lower SPM affinity
than their log D values (> 2) would suggest.

4.2. Rain events impact on wastewater composition: a driving factor for partitioning?

While hydrophilic properties and ionization states help explaining
compound sorption tendencies, they cannot fully account for the vari-
ability across samples. Other SPM characteristics such as total organic
carbon (TOC) levels and mineral composition, may also influence
sorption by providing potential interaction sites that strengthen or
weaken adsorption. Raw wastewater is typically rich in organic matter,
promoting both hydrophobic and electrostatic interactions with cationic
species (J. Li et al., 2020). However, in combined sewer system, rainfall
events may alter SPM composition, potentially disrupting solid-liquid
equilibrium.

To assess this impact, this study examined how rainfall events affect
wastewater composition and, by extension, the solid-liquid partitioning
of the targeted compounds. Rain events were defined as samples where
total precipitation within the 48 h preceding the sample collection was
≥ 5 mm. Based on this criterion 36 of the 48 samples were collected
during dry weather (DWF), while 12 were taken during rain events
(WWF).

As expected for combined sewer, rain events increased the influent
volume at the WWTP due to the mixing of domestic wastewater with
urban runoff, significantly diluting some hydrochemical parameters
measured in the WWTP, such as TKN, total P and NH₄, though carbo-
naceous parameters (TOC, BOD, SPM and COD) showed no significant
changes (Figure S1).

To determine whether this shift affected the solid-liquid equilibrium
of the targeted compounds, Kd values were compared between WWF
and DWF samples.

Overall, partitioning shifted slightly under wet conditions, with most
compounds exhibiting higher median Kd values during WWF (Fig. 4A).
Statistical analysis identified significant differences (p-value < 0.05) for
nine compounds (yellow dots, Fig. 4A), including five cationic species
(ATL, CAF, COD, COT, and PXN), and four others exhibiting diverse
ionization states: CBZ and SUC (neutral), TMP (partially neutral and
cationic) and FEX (zwitterionic) (Fig. 4B). Notably, Kd variability was
much greater in WWF samples, suggesting that changes in wastewater
composition disrupt solid-liquid partitioning.

The initial hypothesis was that urban runoff would significantly alter
the geochemistry of our WWF samples, leading to a decrease in SPM
concentration and a depletion in its organic content, in favor of a more
mineral-rich composition. However, the acquired data (Table S2) indi-
cate that neither SPM nor TOC concentrations were significantly
affected by rainfall events. Additionally, the geochemical composition of
our samples remained largely unchanged in terms of organic content, as
evidenced by the stable TOC/SPM ratio across all monitored samples.
Previous studies conducted within the same catchment have similarly
demonstrated that runoff contributions did not significantly impact the
quantity or quality of SPM. Instead, WWF caused the erosion of organic
layers accumulated during dry weather, leading to the in-sewer remo-
bilization of some material with a similar signature to those of typical
domestic effluent (Gasperi et al., 2010).
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** (A) Linear regression analysis of median Kd values measured during wet weather flow (WWF) and dry weather flow (DWF). The green line represents the regression fit, while the dark line indicates the theoretical 1:1 ratio. Yellow points highlight compounds with a statistically significant difference in median Kd between WWF and DWF. (B) Boxplot of all recorded Kd values for compounds showing a statistically significant difference in median Kd, with WWF represented in blue and DWF in brown.</figcaption></figure>

While this could explain similar partitioning between WWF and DWF, the stronger partitioning observed during WWF remains unclear. One possible explanation is that the organic layer remobilized during WWF already carries the targeted analytes, thereby enhancing the signal associated with the particle loads. However, this hypothesis cannot be confirmed within the scope of this study, and further investigation into these deposits is necessary. Another explanation is that WWF has been shown to favor TOC sorption onto SPM, enriching its organic content, leading to an increased availability of potential adsorption sites (Kafi et al., 2008). Finally, one of the most significant effects of rainfall on wastewater composition is the increased presence of metals, particularly Cu, Pb, and Zn. These metals are known to be highly sorbed onto particulate matter, with over 95 % of their mass associated with the solid phase during wet weather flow. Given that metal species can promote complexation (Z. Li et al., 2020), their increased presence on SPM during rain events may also contribute to the observed shifts in sorption equilibria in favor of wet weather conditions.

### 4.3. Can SPM provide significant insight on WBE estimations?

In an attempt to further explore the relationship between dissolved and particulate phases, the six months of cross-monitoring were used to duplicate temporal interpretation on both estimated loads to assess whether the trends observed in RW loads were mirrored in SPM data.

One of the key point first to highlight is the proportionality that exists between dissolved and particulate loads. In fact, compounds typically found in very high concentrations in the dissolved phase (such as MFN, ACS, ACT, CAF, etc.), and often depict as demographic markers, also display the most significant loads in the particulate phase, despite having a very low affinity for the solid phase. This is particularly interesting because many of these compounds are among the most hydrophilic ones, and therefore their presence in SPM would have not been expected, at least at such a high level. However, all of these compounds were quantified in 100 % of the samples, showing that the initial concentration in the dissolved phase is a determinant of the potential concentration in the particulate phase. While this has limited impact on WBE estimates it certainly raises more concerns about the environmental fate of these ubiquitous compounds.

One common interpretation made in WBE studies is to express the variability of collected data through the prism of temporal trends. One that has been particularly highlighted is the recreational pattern of illicit drug consumption that is embodied by higher loads on weekend days

(Karolak et al., 2010). However, whether these dynamics are reflected in SPM or not has never been discussed (to the author's knowledge).

When splitting the measured loads based on the sampling day (Fig. 5), both BZE and MDMA showed statistically significant higher loads on week-end days (8.1 and 3.8 g.day⁻¹, respectively) compared to week-day (6.7 and 2.1 g.day⁻¹, respectively). That is in line with RW observations, as both compounds are also more prevalent during the weekend. COC also displayed this pattern in RW measurements, which is also consistent with its recreational use, although this trend is not reflected in SPM loads. This can be explained by the lower amount of COC measured on SPM, which led to both unsystematic detection and lower variability among measured loads. However, COC itself is not considered as a reliable marker for assessing its own consumption, as its concentration is more likely to be influenced by different processes such as direct disposal or degradation during storage and transport, and therefore BZE has been widely adopted as the preferential marker in most of the recent studies (Castiglioni et al., 2011) since it exhibits a higher stability and is specific to human metabolism.

In contrast, several other targets (ATL, CAF, COD, PXN, SAC, SMX, and TMP) exhibited higher loads on weekdays. This weekday increase is confirmed for CAF, PXN, and TMP in RW measurements. Some WBE studies assessing coffee consumption suggest it may correlate with workdays, leading to higher weekday intake, though evidence directly linking workdays to increased coffee consumption remains open to debate (Senta et al., 2015).

Concerning the variations observed in SPM among pharmaceuticals (ATL, SMX, and TMP), no clear causality explains this weekly pattern. ATL, as a chronic medication, may reflect differences in consumption/excretion in certain populations leaving the catchment area during weekends. However, this pattern is more surprising for antibiotics. Both SMX and TMP are almost exclusively prescribed together as Cotrimoxazole, which can explain their synchronized trends. Nevertheless, antibiotic use is typically sporadic and more influenced by seasonal factors than weekly fluctuations (Coutu et al., 2013). However, this weekly pattern has already been observed in wastewater during a previous long-term study conducted in the same country, though no definitive explanation has been proposed (Thiebault et al., 2017).

As a result, the reflection of temporal trends commonly observed in the dissolved compartment within the SPM fraction may open new perspectives for the field of WBE. While dissolved phase monitoring remains more suitable for the common WBE targets as explored in this study, the growing interest in WBE to track some pathogens such as
<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison of targeted WBE biomarkers mean loads (g.day⁻¹) between sampling days (Wednesday on the left, Sunday on the right). Red bars indicate significantly higher loads on Sunday, while green bars indicate significantly higher loads on Wednesday (p-value < 0.05). Biomarkers with no statistically significant differences are shown in blue. Biomarker acronyms are marked similarly when the same statistical criteria are met in RW samples.</figcaption></figure>

bacteria, parasites or viruses (Maréchal et al., 2023; Mercier et al., 2022) could benefit from a deeper mining understanding of SPM dynamics and the associated biomarker content.

## 5. Conclusion

* An analytical method combining PLE for sample preparation with LC-MS², enabled the detection and quantification of a broad range of WBE markers in SPM from RW samples. Concentrations varied significantly, ranging from trace levels below 5 ng.g⁻¹ for cocaine to 1899 ng.g⁻¹ for caffeine;

* The majority of studied compounds exhibited low affinity for the solid phase (i.e., <5 %) suggesting that excluding SPM analysis does not compromise the reliability of WBE estimates based on the dissolved phase alone;

* Compounds with lower hydrophilicity or a cationic ionization state at typical wastewater pH showed greater sorption tendencies. Therefore, SPM analysis should be considered for accurate WBE interpretations. However, refining a universal correction factor for sorption remains challenging due to the high variability in sorbed loads across samples;

* Sampling conditions must be taken in account when performing WBE estimates on combined sewer system, as rain events are more likely to remobilize in-sewer deposits that might favors the adsorption of targeted compounds on the solid phase;

* Temporal trends commonly observed in the dissolved phase may also be reflected in the SPM compartment, suggesting the potential for integrating SPM analysis into WBE monitoring frameworks.

## CRediT authorship contribution statement

**Gauthier Bernier-Turpin:** Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. **Régis Moilleron:** Writing – review & editing, Supervision, Funding acquisition. **Chloé Cenik:** Methodology, Investigation, Data curation. **Fabrice Alliot:** Writing – review & editing, Methodology, Investigation. **Sabrina Guérin-Rechdaoui:** Writing – review & editing, Resources. **Thomas Thiebault:** Writing – review & editing, Supervision, Methodology, Funding acquisition, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gauthier Bernier-Turpin reports financial support was provided by French National Research Agency. If there are other authors, they 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

The authors gratefully acknowledge V. Bremont (SIAAP) for providing influent wastewater samples and the French National Agency for Research (ANR-21-CE03-0005) for financial support.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2025.123543.
# Data availability

Data will be made available on request.

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