Passive sampling of chemical targets in
Wastewater-Based Epidemiology: Promise, pitfalls and
pathway forward

Camille Richer, Fabrice Alliot, Thomas Thiebault

► To cite this version:

Camille Richer, Fabrice Alliot, Thomas Thiebault. Passive sampling of chemical targets in Wastewater-Based Epidemiology: Promise, pitfalls and pathway forward. Trends in Analytical Chemistry, 2026, 196, pp.118690. 10.1016/j.trac.2026.118690. hal-05470955

HAL Id: hal-05470955

https://hal.science/hal-05470955v1

Submitted on 22 Jan 2026

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Passive sampling of chemical targets in wastewater-based epidemiology: Promise, pitfalls and pathway forward

Camille Richer, Fabrice Alliot, Thomas Thiebault*

EPHE, PSL University, UMR 7619, METIS, Sorbonne University, CNRS, 4 Place Jussieu, Paris, F-75005, France

## ARTICLE INFO

### Keywords:

- Wastewater based epidemiology

- Passive sampling

- Calibration

- Polar organic pollutant

- Adsorption

- Monitoring

## ABSTRACT

Wastewater-based epidemiology (WBE) is an increasingly important approach for assessing community exposure to pharmaceuticals, illicit drugs, and biomarkers, yet its reliance on autosamplers limits spatial and temporal coverage due to high cost, maintenance, and infrastructure demands. Passive sampling (PS) offers a power-free, time-integrated alternative for monitoring polar organic contaminants in complex wastewater matrices. This review examines the performance of adsorption- and diffusion-based PS devices, emphasizing how calibration, biofouling, and sorbent-analyte interactions govern quantitative reliability. In situ calibration remains essential for robust uptake estimates, while ion-exchange and mixed-mode sorbents provide enhanced selectivity and broader analyte coverage. Beyond targeted screening, PS enables semi-quantitative, non-targeted analyses through high-resolution mass spectrometry, expanding its analytical scope. Rather than replacing autosamplers, PS should be viewed as a complementary technology capable of improving spatial and temporal resolution in WBE. Ongoing innovations in design, calibration, and data interpretation will shape its integration into future wastewater surveillance frameworks.

## 1. Introduction

Wastewater-based epidemiology (WBE) is an analytical framework that assesses human health, behavior, and exposure patterns through the detection of biomarkers and chemical tracers in raw wastewater [1]. Popularized during the SARS-CoV-2 pandemic, WBE has, in fact, a longer history of application in estimating population-level exposure to environmental pollutants, pharmaceuticals and particularly illicit drugs for which robust usage data is difficult to obtain [2]. By monitoring compounds before treatment, WBE enables the identification of emerging public health threats and the tracing of pollution sources while capturing a broader spectrum of chemical signals that may otherwise degrade or transform during treatment [3].

The current gold standard for assessing community practices within a catchment area relies on volume- or flow-proportional composite sampling using autosamplers [4]. Typically installed at the terminal points of sewer networks near wastewater treatment plants (WWTPs), these automated systems facilitate regulatory monitoring and evaluation of treatment efficiency. However, composite sampling is resource-intensive, it requires continuous power supply, regular

maintenance, manual oversight and is prone to mechanical failures [5]. Moreover, it may fail to capture short-term concentration fluctuations, such as pollution peaks or dilution events associated with rainfall [6]. As WBE increasingly integrates socio-economic and behavioral data to interpret temporal and spatial trends [7], the ability to sample determined areas upstream WWTP, with higher spatial granularity becomes essential. Yet, in-sewer sampling remains logistically demanding, expensive, and technically complex, thereby constraining the spatial resolution of WBE datasets.

Passive sampling (PS) offers a compelling, low-cost, and power-free alternative that has been widely employed in environmental monitoring since the early 2000s [8]. Compared with composite sampling, PS reduces operational demands and has the potential to lower detection limits (i.e., depending on the design and duration of deployment) by continuously accumulating analytes over deployment periods [9]. Relying on diffusion or adsorption mechanisms, passive samplers provide time-weighted average (TWA) concentrations that reflect both chronic exposure and episodic contamination events, offering a more integrated picture of pollution dynamics. The technique's versatility and capacity to target a broad spectrum of compounds make it particularly

This article is part of a special issue entitled: Contaminants Yolanda published in Trends in Analytical Chemistry.

* Corresponding author.

E-mail address: thomas.thiebault@ephe.psl.eu (T. Thiebault).
suitable for community-level wastewater surveillance [10].

Nonetheless, applying PS in raw wastewater presents unique challenges due to its complex and heterogeneous matrix. Elevated levels of suspended solids, organic matter, and microbial activity can induce biofouling (i.e., the formation of biofilms on sampler surfaces) that impairs diffusion, alters adsorption kinetics, and ultimately reduces sampling efficiency [11]. In severe cases, biofilm buildup can clog sampler membranes or restrict flow, leading to biases in analyte uptake and uncertainty in quantitative interpretation [12]. Given that wastewater composition directly affects contaminant quantification prior to treatment, a central question arises: can WBE effectively employ PS for reliable quantitative assessment?

This review explores current PS approaches for the monitoring of rather polar and hydrophilic organic contaminants, such as illicit drugs and pharmaceuticals, in raw wastewater. It evaluates device performance, deployment strategies, and methodological limitations, while highlighting the persistent challenges that hinder their broader implementation, particularly within the WBE framework. As interest in decentralized and near real-time public health surveillance continues to grow, PS stands out as a promising pathway toward high-resolution, cost-effective insights from wastewater systems, provided that its technical constraints can be systematically addressed.

## 2. Materials and methods

The articles included in this literature review were identified between January and March 2025 through systematic searches conducted on Google Scholar, Web of Science, Scopus, and ProQuest. The following search terms were employed: "wastewater", "passive sampling", "raw wastewater", "wastewater influent", "polar", "hydrophilic", "wastewater-based epidemiology", and "sewage". To ensure comprehensive coverage, the snowball sampling technique was additionally applied, allowing the identification of further relevant studies through the

reference lists of initially selected papers. The review encompassed research articles, peer-reviewed journals, books, theses, working papers, and technical reports.

Studies were excluded if they did not involve raw wastewater sampling or if they focused on organic molecules other than those used in wastewater-based epidemiology for estimating population-level consumption [13,14]. However, within a continuum of fairly varied polarity and hydrophobicity, these molecules (i.e., pharmaceuticals, illicit drugs, etc.) are mainly moderately polar to polar and predominantly ionized. The extracted data were compiled into a Microsoft Excel database. To date, only a limited number of 23 studies have investigated the use of PS for the monitoring of public health chemical tracers in raw wastewater, forming the corpus of this review (Table 1). These passive samplers, based on diffusion or adsorption mechanisms, demonstrate varying efficiencies that can be assessed according to sampler design, accumulation process and medium, target compounds, and calibration approaches.

## 3. Results

### 3.1. Common features

The fundamental principle behind PS involves the diffusion of dissolved organic molecules from the water into a receiving phase, typically a sorptive material, driven by the concentration gradient. Organic molecules naturally move from areas of higher concentration (i.e., the water) to areas of lower concentration (i.e., the sampler medium) until equilibrium is reached or the sampler is recovered. The rate and extent of uptake depend on the physicochemical properties (Fig. 1B) of the target compounds (e.g., hydrophobicity, molecular weight, ionization state), the characteristics of the sampler material (e.g., polymer type, surface area), and environmental conditions such as temperature, flow rate, and potential biofouling [36]. By using a non-invasive method to

<table><caption>**Table 1**
Applications of passive sampling for polar organic chemicals quantification in raw wastewater, n.d. for not determined.</caption><thead><tr><th>Reference</th><th>Location</th><th>Targeted compounds</th><th>Calibration</th><th>Device</th><th>Accumulation phase</th><th>Analysis mode</th><th>Sampler surface area (cm²)</th></tr></thead><tbody><tr><td>[15]</td><td>Australia</td><td>Illicit drugs, pharmaceuticals and personal care products</td><td>In situ</td><td>Microporous Polyethylene Tube (MPT)</td><td>Strata X<br>Strata X + agarose gel<br>Sepra ZT<br>Sepra ZT</td><td>targeted</td><td>10<br>10<br>10 (assumed)<br>10</td></tr><tr><td>[16]</td><td></td><td>Illicit drugs and pharmaceuticals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[10]</td><td></td><td>Licit and illicit drugs</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[17]</td><td></td><td>Licit, illicit drugs and biomarkers</td><td></td><td>Gradient Time Integrative Passive Sampler (G-TIPS)</td><td>Water</td><td></td><td>3.14</td></tr><tr><td>[18]</td><td>Brazil</td><td>Licit and illicit drugs</td><td>Laboratory</td><td>Polar Organic Chemical Integrative Sampler (POCIS)</td><td>Hydrophilic-Lipophilic Balanced (HLB)</td><td></td><td>41<br>41</td></tr><tr><td>[19]</td><td></td><td>Licit, illicit drugs and biomarkers</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[20]</td><td>Norway</td><td>Illicit drugs and pharmaceuticals</td><td>In situ</td><td></td><td></td><td></td><td>48</td></tr><tr><td>[21]</td><td>Canada</td><td>Licit and illicit drugs</td><td>Laboratory</td><td></td><td></td><td></td><td>n.d.</td></tr><tr><td>[22]</td><td>France</td><td>Pharmaceuticals</td><td></td><td></td><td></td><td></td><td>n.d.</td></tr><tr><td>[23]</td><td>Norway</td><td>Illicit drugs</td><td>In situ</td><td></td><td></td><td></td><td>n.d.</td></tr><tr><td>[24]</td><td>Canada</td><td>Pharmaceuticals, personal care products and human biomarkers</td><td>Laboratory</td><td></td><td></td><td></td><td>n.d.</td></tr><tr><td>[25]</td><td></td><td>Pesticides and pharmaceuticals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[26]</td><td>South-Africa</td><td>Pharmaceuticals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[27]</td><td>The</td><td>Pharmaceuticals</td><td>Theoretical Rs</td><td>Speedisk</td><td>Divinylbenzene-HLB</td><td>targeted &amp; suspected</td><td>n.d.</td></tr><tr><td>[28]</td><td>Netherlands</td><td>Pharmaceuticals</td><td></td><td></td><td></td><td></td><td>n.d.</td></tr><tr><td>[29]</td><td>Canada</td><td>Pesticides and pharmaceuticals</td><td>Laboratory</td><td>Diffusive Gradient in Thin films (DGT)</td><td>HLB</td><td>targeted &amp; suspected</td><td>3.1<br>3.1</td></tr><tr><td>[23]</td><td></td><td>Pesticides and pharmaceuticals</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[30]</td><td>China, Vietnam</td><td>Illicit drugs &amp; antibiotics</td><td></td><td></td><td>HLB, XAD 18, XDA-1</td><td>targeted</td><td>3.14</td></tr><tr><td>[31]</td><td>China, UK</td><td>Endocrine Disrupting Chemicals</td><td></td><td></td><td>HLB, XAD18, SXLA</td><td></td><td>3.14</td></tr><tr><td>[32]</td><td>China</td><td>Illicit drugs</td><td></td><td></td><td>XAD18 binding gel</td><td></td><td>3.1</td></tr><tr><td>[33,34]</td><td>China, UK</td><td>Antibiotics</td><td></td><td></td><td>XAD18 binding gel</td><td></td><td>3.1</td></tr><tr><td>[5]</td><td>Switzerland</td><td>Pharmaceuticals &amp; pesticides</td><td></td><td>Chemcatcher</td><td>Styrene divinylbenzene-Reversed phase sulfonates</td><td></td><td>12.6</td></tr><tr><td>[35]</td><td>Czech Republic</td><td>Pharmaceuticals, personal care products, pesticides, anticorrosive</td><td>In situ</td><td>Hydrogel-based Passive Sampler (HPS)</td><td>HLB</td><td></td><td>22.7</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Passive sampling uptake over time (A) and passive sampling accumulation kinetic scheme depending on the affinity of target for the receiving phase and the immersion time (B).</figcaption></figure>

estimate the freely dissolved (bioavailable) fraction of contaminants, PS provide ecologically relevant data for risk assessment by mimicking the accumulation behavior of organic molecules.

One of the key advantages of PS is its ability to detect low concentrations of contaminants over extended periods, which is particularly useful in environments where contaminant levels fluctuate or are below detection limits of conventional methods (Fig. 1A). Additionally, passive samplers can be pre-calibrated or co-deployed with performance reference compounds (PRC) to correct for environmental variables and improve quantitative accuracy [37]. By leveraging natural diffusion processes and sorptive media, it enables the collection of TWA concentrations that are more reflective of actual environmental conditions, making it a valuable tool in water quality monitoring, regulatory compliance, and environmental research [38]. Yet, PRCs were also found not reliable particularly for sorption-based passive samplers such as POCIS in quiescent flow conditions [39,40].

In the focus of this review, raw wastewater presents a complex and dynamic matrix for the application of PS. It typically contains a wide range of organic contaminants, including pharmaceuticals, personal care products and illicit drugs at significant concentrations (i.e., at least higher than in environmental compartments). PS is well-suited to capture such variability, as it provides TWA concentrations over deployment periods ranging from days to weeks. This average concentration can only be estimated during the so-called kinetic or linear accumulation regime, whose quantitative nature is expressed by equation (1). This implies that the immersion time must also be calibrated in order to avoid waiting for non-quantitative accumulation regimes.

$$Cs = \frac{Cw \times Rs \times t}{Ms} \qquad \text{Equation 1}$$

with Cs the concentration of the target in the sampler in ng.g⁻¹, Cw the concentration of the target in the water in ng.L⁻¹, Rs the sampling rate in L.d⁻¹, t the immersion time in d and Ms the accumulation phase mass in g.

However, the heterogeneous nature of wastewater poses specific challenges. High levels of suspended solids, variable flow rates, and the presence of biofilms or surfactants can affect the uptake kinetics and equilibrium of passive samplers [11,20]. Additionally, the presence of particulate-bound contaminants, sometimes significant (>20 % of the total load) for compounds of interest such as fluoxetine or THC-COOH [41,42], may limit the ability of samplers to capture the total contaminant load or in a quantitative way, as most devices primarily target freely dissolved fractions. Here are gathered the research dealing with passive samplers use in raw wastewater, sorting by type of accumulation mechanisms i.e., adsorption, gel diffusion and passive diffusion.

## 3.2. Adsorption-based PS

The classical design of passive samplers consists of an accumulation medium and a membrane separating it from surrounding water. Among the most widely used devices in the environment monitoring is the Polar Organic Chemical Integrative Sampler (POCIS), which has become a standard tool for the analytical assessment of organic compound environmental occurrences [43]. To date, only nine studies have investigated the use of POCIS in raw wastewater for targeting polar organic contaminants and assessing their quantitative potential (Table 1). The standard POCIS configuration includes two microporous polyethersulfone (PES) membranes enclosing a solid-phase sorbent (Fig. 2), typically around 200 mg. These devices provide a large sampling surface area, between 40 and 50 cm², which enhances the accumulation capacity. While various sorbents have been studied for solid phase extraction (SPE) on wastewater [44,45] and as receiving phases for environmental water sampling [46,47], there has been little to no experimentation with alternative materials inside POCIS beyond the commonly used hydrophilic-lipophilic balanced (HLB) phase, which is a N-vinylpyrrolidone-divinylbenzene copolymer.

Due to its ability to target a wide range of contaminants, Chemcatcher, like POCIS, is a widely used passive sampler for monitoring both organic and inorganic contaminants. Although Chemcatcher has been extensively used to detect polar organic contaminants in wastewater effluents and rivers [48–50], its application in raw wastewater is limited (Table 1). Structurally, Chemcatcher and POCIS are fundamentally similar, slightly differing in the type of sorbent used. For example, Mutzner et al. used the Chemcatcher to monitor combined sewer overflow events, and reported high sampling rates (Rs), likely due to increased water flow that may have caused overestimation of contaminant concentrations [51].

Recent innovations have explored new devices for quantitatively monitoring pharmaceuticals and personal care products and drugs in wastewater. Microporous Polyethylene Tubes (MPT) have been experienced in Australian wastewater [15]. Initially developed for use in freshwater lakes [52], MPTs are composed of a 12 mm outer diameter tube with 35 % porosity and 2.5 µm pore size, filled with 400 mg of sorbent (Fig. 2). The tube consists of a semi-permeable membrane that allows diffusion and is sealed with caps at both ends. MPTs filled with Sepra ZT (same polymer than HLB), were used to target illicit drugs [10, 16]. In contrast another study tested MPTs filled with another sorbent, Strata X, another non ionized derivative of the HLB polymer, in powder or gel (with agarose) form [15]. The dispersion of the sorbent in agarose had no significant impact on Rs (i.e., sampling rate), except for compounds such as iopromide and citalopram, suggesting that agarose may influence adsorption depending on molecular size or charge. Both studies calibrated samplers in situ using raw wastewater.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Detailed composition of PS devices used in the context of WBE, depending on the accumulation mechanism (i.e., Adsorption, Adsorption + Diffusion, and Diffusion) and their structure.</figcaption></figure>

Rs in raw wastewater are also lower than in effluents, as demonstrated for PFAS using MPTs due to clogging in the former, and higher Rs values when using weak anion exchange (WAX) receiving phase (particularly Sepra ZT WAX compared to Evolute WAX) were also reported [53]. The MPT design offers a large surface area while minimizing biofouling due to its efficient structure. Since uptake is governed by diffusion, the effect of flow rate is reduced. However, field data on this device is limited with only four known applications in raw wastewater (Table 1). Additional studies have investigated MPTs in environmental waters [54–56], demonstrated MPT efficiency in WWTP effluents, even at low concentrations [57].

As already mentioned, these accumulation devices can be affected by environmental conditions such as water flow, temperature, pH, organic matter and biofouling [58,59]. Water flow, in particular, impacts POCIS performance [60]. Under low-flow conditions, contaminant uptake may be reduced due to the development of a diffusive boundary layer effect. In contrast, high or turbulent flows result in thinner diffusive boundary layers and faster permeation. In low flow environments, estimations are less reliable unless results are corrected for flow. A key limitation of POCIS is the difficulty in obtaining quantitative results considering the lack of in situ calibration methods [40]. This issue can be addressed by using performance reference compounds (PRC), as shown by Sultana et al., who used deuterated analogues of β-blockers [24], with sometimes contrasted performances [39]. These compounds, pre-loaded in PS's sorbent, structurally similar to target analytes, and absent from the sampling environment, mimic environmental interactions and allow estimation of Rs based on their dissipation. This calls into question the potential of PRCs for use in a broad target surveillance list, as each PRC can be used for different molecules [61]. Due to these limitations of PRCs, other methods of estimating flow have been proposed, using gypsum or alabaster devices whose loss of mass is representative of the water flow velocity [62,63]. However, these methods have been rarely used in wastewater.

### 3.3. Diffusion and accumulation

A variant of the membrane–phase configuration incorporates a diffusion mechanism through a gel layer (Fig. 2), allowing compounds to migrate according to a concentration gradient. In WBE, the Diffusive Gradient in Thin Films (DGT), widely used for trace metal monitoring [64], and o-DGT (extended to organics), are being explored as alternatives to conventional sampling methods for monitoring pollutants,

pharmaceuticals, and illicit drugs, in WWTPs and sewer systems [65]. The DGT device (Fig. 2) typically comprises a diffusive layer controlling uptake (commonly agarose gel), a binding layer (e.g., HLB or XAD18), and a membrane filter. Analytes diffuse through the gel and are accumulated on the sorbent over time, yielding a TWA concentration of the sampled medium. Sampler performance is described by the diffusion coefficient (D), determined in the laboratory and used to estimate Rs. Initial experiments using o-DGT with an XAD18–agarose binding gel demonstrated the ability to quantify antibiotics in wastewater over a 7-day period [33], confirming the technique's capacity to detect low concentrations over extended deployments. Subsequent studies employed the same device to estimate polar organic contaminant concentrations [29], with an HLB binding gel and omitting the PES membrane. This modification reduced contaminant-membrane interactions but increased susceptibility to biofouling and interference from dissolved matter, indicating the potential benefit of an alternative protective layer. Despite these issues, TWA concentrations calculated from o-DGT measurements agreed well with POCIS results [25].

Similar applications have investigated antibiotics [34], endocrine-disrupting compounds [31], and illicit drugs [32] using DGT with non-ionic receiving phases (i.e., XAD18 or HLB), as well as o-DGT for illicit drug monitoring using HLB, XAD18, and XDA-1 [28]. Collectively, these studies highlight DGT as an effective alternative to other PS methods, providing more stable accumulation rates, particularly for substances with high partition coefficients (log Kow), compared to POCIS. However, DGT remains sensitive to biofouling, which can impair performance. Fialová et al. further applied the diffusion-gradient principle to polar organic compounds using a variant called the Hydrogel-based Passive Sampler (HPS) [35]. This device, derived from earlier work [66], consists of two sorbent gels containing the sampling phase and external diffusive gels (Fig. 2). In situ calibration in treated wastewater allowed estimation of average concentrations based on previously determined Rs values. Functionally, HPS combines features of POCIS, with two membranes and sorbent phases secured by stainless-steel rings, with the DGT-type diffusion process through a gel, operating similarly to a double-faced DGT [67]. The hydrogel layer increases the exposure window and minimizes flow-rate dependence of Rs. However, deployments exceeding 14 days are not recommended due to potential device degradation over time [35,66].
### 3.4. Passive diffusion

Verhagen et al. tested another simple device in wastewater, based on concentration gradients and chemical diffusion into the sampler, for four highly hydrophilic chemicals: acesulfame, ethyl sulfate, methamphetamine, and benzoylecgonine [17]. The Gradient Time Integrative Passive Sampler (G-TIPS, Fig. 2) is a sealed chamber filled with water as receiving phase. Diffusion occurs over time through the pores of the polyethylene membrane, at the bottom of the sampler driven by the concentration gradient. Exposed for a 31-day period in raw wastewater, simultaneously with composite sampling, G-TIPS showed linear uptake throughout the deployment. Rs ranged from 2.2 mL d⁻¹ (methamphetamine) to 4.0 mL d⁻¹ (ethyl sulfate), calculated by measuring the analytes' concentration both in the sampler and the wastewater. Unlike POCIS, G-TIPS features a thick membrane (5 mm) that overcomes the uptake limitation due to the water boundary layer. These samplers are then less affected by flow issues. Concentration gradient decreases with time until reaching an equilibrium, indicating how long the sampler can be deployed. Calculated half time to equilibrium ranged from 52 days for ethyl sulfate to 93 days for methamphetamine, enabling extended sampling periods. Moreover, the use of water receiving phase does not need sample extraction for further analyses. However, since the studied chemicals are highly hydrophilic and do not interact with the membrane, the application of G-TIPS for less polar compounds could be limited. Therefore, this device has an interesting potential for assessing hydrophilic contaminants through time-integrative sampling especially for compounds that do not necessarily require pre-concentration, which is frequently the case in raw wastewater.

## 4. Discussion

### 4.1. Accumulation process

Considering the fact that passive samplers' usage for environmental monitoring is relatively recent, few studies are available to investigate different accumulation processes and phases. The performances of the samplers are expected to rely on chemical properties of the target compounds and chosen sorbents. Hydrophilic and hydrophobic compounds should not accumulate with the same rate considering the device used. In line with approaches used in SPE, two main types of accumulation mechanisms are targeted depending on the phases, resins, or gels employed: electrostatic interactions or physisorption, and ion exchange [68]. While HLB-type phases only allow for physisorption (e.g., hydrogen bonds, π-π interactions), efficient for moderately polar to apolar compounds, only ion-exchange phases (cationic or anionic, strong or weak) enable the combination of both mechanisms [69]. However, these two mechanisms do not exhibit uniform accumulation capacities across all target compounds.

Whereas hydrophilicity and polarity are major factors controlling accumulation on HLB phases, ionization state constitutes an additional determinant for accumulation on ion-exchange phases. This feature allows for the targeting of more polar and almost systematically ionized molecules, if reversely charged phases are used or combined. Studies conducted on PFAS, whose targets are often anionic, with WAX phases, demonstrate that this prioritization works [53], but no studies to date have used exchange phases in other devices, either alone or in combination, on more traditional WBE targets. Moreover, prior to reaching the phase, compounds must diffuse through a membrane or gel layer. While such diffusion components offer the advantage of kinetic control, potentially enabling quantitative interpretation by accounting for a diffusion-driven mechanism, their influence on accumulation kinetics cannot be overlooked, particularly in the case of ionized and/or moderately apolar compounds [70].

Several studies have demonstrated that PES membranes, traditionally employed in POCIS and Chemcatcher devices, are capable of adsorbing or substantially retarding the accumulation of charged and

moderately apolar compounds (log Kow >2.5). Consequently, PES membranes have been proposed as PS materials in their own right for moderately polar analytes [71]. This sorptive behavior, however, undermines the quantitative reliability of such devices [72], and restricts their applicability during short deployment periods by lowering Rs values and generating a lag phase [73]. However, for raw wastewater, sensitivity is not necessarily the desired characteristic due to the higher concentrations of contaminants observed compared to other contexts. And this limit for short exposure times could make it possible to maintain the integrative nature of the devices over several days of deployment [19].

Furthermore, it will be necessary to balance polyvalent approaches, which are designed to accumulate compounds with average properties or to facilitate non-target screening, with more specific methods targeting highly polar compounds. Achieving this balance will require device adaptations or the strategic combination of distinct sampler properties. This is particularly relevant for the use of ion exchange resins, for which different charge states can be combined in the same sampler, enabling the capture of highly hydrophilic molecules with low affinity for HLB phases.

### 4.2. Experimental conditions

Before deployment, a calibration step is necessary to determine the sampling rate (Rs) of the device, which enables the quantitative assessment of in situ concentrations. This calibration involves comparing the amount of compound accumulated by the passive sampler with a known aqueous concentration, allowing experimental determination of Rs, the volume of water from which the compound is extracted per unit time. By accounting for compound-specific kinetics and matrix effects, Rs provides valuable insight into sampler performance. Depending on the study design, calibration can be carried out either under laboratory conditions or directly in situ. Godlew ska et al. identify three main calibration approaches: static (a closed system spiked once at the beginning), renewable (a closed system spiked at regular intervals), and flow-through (an open system) [74].

In situ calibration is often regarded as the ideal approach, as it most accurately reproduces real environmental conditions. However, PS is highly sensitive to environmental parameters [36,75]. Factors such as flow rate, temperature, pH, and suspended solids in water can significantly influence the results. Consequently, the choice of calibration method for determining Rs varies among studies. In most cases (Table 1), calibration is performed under controlled laboratory conditions (n = 11 vs n = 7, Table S1), which are easier to manage experimentally. Some devices, such as DGT samplers, do not require additional calibration for in situ deployment, as only the D needs to be determined [30]. Sampling rates are then derived from this coefficient.

As illustrated in Fig. 3, for carbamazepine, lower Rs values are observed for in situ calibration. Laboratory-derived Rs values (for both

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Carbamazepine, amphetamine and methamphetamine sampling rate (Rs) values from literature normalized to sampling area of each device, for in situ and laboratory calibration with wastewater. Receiving phases were not-ionized and derivatives of HLB. All data is available in Table S1.</figcaption></figure>
wastewater and surface water applications) show considerable discrepancies, including those reported by Yargeau et al., who determined Rs by measuring the decrease in analyte concentration in a calibration tank [21], and by Guo et al., who estimated it based on the diffusion coefficient D [32].

Overall, despite the limited data available, in situ Rs values tend to be more stable, whereas laboratory calibrations generally yield higher values under optimized conditions, potentially leading to an overestimation of field performance. This pattern is consistent across the three compounds shown in Fig. 3 and for other molecules (Table S1). In situ sampling rates are therefore considered more reliable for wastewater monitoring applications.

Conversely, Sultana et al. reported higher Rs values for samplers deployed in both raw and treated wastewater than under laboratory conditions, likely due to increased turbulence within the wastewater treatment plant [24].

Given the complexity and specificity of wastewater matrices, the use of laboratory-based calibrations for quantitative sampling can be questioned. Laboratory-derived Rs values often fail to reflect actual field conditions [24], introducing uncertainties in concentration estimates and limiting analyses to a semi-quantitative level. Moreover, overestimated Rs values during calibration can lead to underestimated field concentrations. To mitigate this, several studies have performed in situ calibrations to better replicate real conditions [20,23].

This type of calibration is more time consuming, labour-intensive and specific to the selected condition and site. POCIS concentration is yet considered semi-quantitative due to the lack of understanding of compounds sorption [20]. In situ calibration was also chosen by McKay et al. with MPT [15], by Verhagen et al. using MPT and G-TIPS [16,17] and by Fialová et al. with HPS [35]. Studies using POCIS, MPT and G-TIPS performed calibration on raw wastewater whereas Fialová et al. worked on WWTP effluent to calibrate their device [35]. It is therefore possible, based on the type of calibration use, to favor in situ calibration for WBE purposes while quantitative evaluation is mandatory in such context (Table 2). As this calibration is more sensitive to the wastewater matrix itself and its intrinsic characteristics than to spatial variability between distinct wastewater sampling sites [16], the influence of wastewater as a matrix appears to outweigh site-to-site differences. Consequently, in the context of parallel monitoring of similar environments (i.e., comparable nominal flow and expected concentration ranges), it does not seem necessary to recommend site-specific calibration at each individual location.

Another issue when working with wastewater samples is the immersion time of the device. When POCIS in surface water are typically immersed for 14–21 days, depending on the objectives of the study (e.g., targeted compounds, field concentration ranges), this period must be shortened in sewers due to, among other parameters, the high levels of suspended solids and elevated contaminant concentrations. Clogging and biofilm formation may also occur variously depending on the device configuration. Therefore, immersion time must be carefully optimized to ensure this benefit while preserving reliable quantitative assessment. Current work on raw wastewater sampling generally report immersion durations ranging from as short as 4 days [22,33] to 21 days [29] with

<caption>**Table 2**</caption>

Comparison of in situ and laboratory calibration approaches used for passive samplers.

<table><thead><tr><td>In situ calibration</td><td>Laboratory calibration</td></tr></thead><tbody><tr><td>Reflecting field conditions</td><td>Precise measurements under controlled conditions</td></tr><tr><td>Accounting variables as flow rate, temperature</td><td>Unaccounted environmental factors</td></tr><tr><td>Direct application of sampling rates</td><td>Useful to understand fundamental processes and optimize PS</td></tr><tr><td>Performance variability with exposure conditions</td><td>Standardized conditions facilitate devices configuration comparison</td></tr></tbody></table>

POCIS and DGT and even longer periods for the calibration purposes, up to 29 days with MPT [15] or 31 days for G-TIPS [17]. However, Chen et al. recommended an immersion time of less than 20 days for o-DGT samplers to achieve integration of wastewater concentrations [33], as did Challis et al., who reported no significant alteration during the 3-week deployment on uptake efficiency [29] with the same devices, except for the absence of PES membranes in the latter. On MPTs, McKay et al. observed increase biofouling with time although it did not alter the linear uptake of target contaminants [15] whereas Fialová et al. with HPS monitored linear uptake until 8 days, suggesting a more limited effective window [35]. This more limited application window stems from values that are ten times higher on average on HPS, demonstrating the very high heterogeneity of accumulation depending on the design of the passive samplers. As such, the polyethylene membrane appears to limit the kinetics of compound accumulation, similar to what was observed for PES membranes [73]. In POCIS case, where uptake depends on diffusion through a membrane (usually PES), biofouling tends to have a lower impact than on DGT due to the membrane polarization [35, 76]. The use of various treatments for DGT gels (i.e., metals, antibiotics, and biocides) has been proposed to limit biological fouling, with satisfactory results. However, for long exposures, other factors of uncertainty come into play, such as their heterogeneous degradation rates [77]. Given these uncertainties and the greater susceptibility to biofilm development in raw sewage, regardless of the device used (e.g., higher concentrations of suspended solids), it is recommended that the duration of use be reduced to a few days compared to surface water [78]. Therefore, setting the appropriate exposure duration should be adjusted to the targeted compounds and their accumulation kinetics to ensure linear uptake without external interference. It implies to have extensive knowledge of the interactions between the selected receiving phase and the compound of interest, which is often not readily available in the literature.

In addition to biofouling, several environmental parameters also influence experimental outcomes of passive samplers, including temperature, hydrodynamics, pH, or ionic strength.

Among these various parameters, the effects of pH and temperature have mostly been investigated in tap water because it enables convenient control of conditions in laboratory experiments. For a selection of polar compounds, several studies reported Rs values at 5 °C and 25 °C [79] or within temperature ranges more representative of sewer conditions, such as 15–25 °C [15,22] to 21 °C [60], and 18–31 °C [59]. In all these studies, higher temperatures increased the average Rs values by similar factors, ranging from 1.4 [60] to 2 [79]. Increased temperature stimulates membrane permeability (where relevant) and molecular diffusion, which may compensate for reduced retention of analytes within PS devices due to temperature-dependent partitioning effects [80] is therefore compensated for by the increase in membrane permeability (when applicable) and by faster molecular diffusion. Overall, temperature appears to have only a limited effect on passive sampling performance, although questions remain regarding the influence of microbiological activity, which is enhanced at higher temperatures.

The influence of pH on sampling rates depends on the charge of the sorbent phase and on the pKa of the target analytes. Li et al. showed that a pH change from 7 to 9 had little effect on Rs, except for basic compounds with pKa values close to this range, for which Rs increased up to twofold at pH 9. For a POCIS equipped with an HLB phase (i.e., non-ionized), the impact remained moderate for most other compounds [58], with a general increase in Rs values for non-ionized forms. Similarly, low pH values, such as pH 3, strongly favored the uptake of acidic molecules, with increases of up to a factor of six for the non-ionized form. In general, although sewer networks may exhibit inter-daily or seasonal variations in pH and temperature, particularly in small catchment areas upstream of wastewater treatment plants, it is usually sufficient to document these parameters during sampler deployment and retrieval or on a parallel composite sample rather than monitor them
continuously, as large fluctuations are not expected [60].

### 4.3. Sample preparation and extraction

The first step in ensuring reliable quantification concerns the stability of targets during deployment and transport. However, no studies specifically address the stability of chemicals in sewage once trapped in samplers, or during transport beyond basic recommendations (e.g., cooling, sealing). Beyond these recommendations, the stability of analytes over time has been found to be stable for more polar compounds for up to 6 years in POCIS and o-DGT samplers when stored in a freezer [81].

Recovery of samples is commonly performed using methanol alone [18,22,24,29], effective for extracting a broad range of organic contaminants, or in some cases combined with other solvents such as acetonitrile to further optimize recovery of analyte across varied polarities [26]. Alternative extraction use alkaline solvent (e.g. ammonia in methanol or acetonitrile; [35]), acidified solvent (e.g., formic acid [10]) or both [20,23] depending on the target compounds. Dichloromethane, which is poorly miscible with water and weakly polar, is also employed in several studies [27,28]. Elution volumes range between 5 and 100 mL, depending on the targets and sorbent used, and SPE can be directly coupled with LC-MS², improving throughput and reproducibility [82].

Although extraction procedures are not standardized, an interlaboratory test showed that the error factor in quantifying a list of molecular targets, mainly pharmaceutical products, was around 50% [38]. Another interlaboratory test carried out on composite samples of raw wastewater showed, with a smaller number of laboratories involved, matrix coefficients of variation of the same order of magnitude (~30%). Therefore, the analytical error is similar for these two matrix sampling methods [83] and does not appear to be a discriminating factor.

Analytical techniques are improving, moving beyond targeted analysis to broader screening approaches. Despite the fact that non-target screening is increasingly used and studied for detection of organic contaminants in wastewater samples [84,85], allowing identification of known and unknown compounds missed by targeted methods, this approach is just emerging in wastewater PS. It provides though a broader chemical fingerprint of wastewater and helps the detection of new pharmaceuticals, chemicals and transformation products. Non-target screening is currently more used as a qualitative analysis tool rather than quantitative analysis, semi-quantitative at best. To date, only Challis et al. have used HRMS for suspected screening of polar organic contaminants on raw wastewater samples with o-DGT to provide semi-quantitative data [25]. Suspect screening in contrast of non-target screening leads to a manageable list of targets, with known structures in opposition to the overload of data obtained with a non-target analysis, while also being limited because the data is qualitative and provisional, without confirmation by dedicated reference standards [86]. LC-MS/MS target screening is the standard for wastewater passive samplers' analysis, providing reliable data.

Complementary sampling methods are often employed to support or validate the results due to the uncertainty in quantitative assessment of PS. Grab or composite samples allow comparison with real field concentrations to what the sampler estimates, highlighting possible over- or underestimation. Usually, samples are taken over time, from 5 min to 2 h (flow or time proportional), and gathered into one. Even though passive samplers detect more contaminants than composite sampling as reported by Clokey et al. on wastewater effluent with MPTs, this additional sampling still provides a valuable basis for comparing average concentrations [52]. In general, the concentration ranges are closer between passive sampling and composite sampling than with grab sampling [16,34], which is less representative.

### 4.4. Potential and promising aspects for WBE applications

To employ a raw wastewater sampling device for the quantitative assessment of population-level usage of specific compounds, two fundamental conditions must be met: (1) ensuring that the composition of the collected sample is representative of the sampling period, and (2) establishing that this composition can be quantitatively linked to a flow rate within the sewer system. While the first condition appears achievable, following recent advancements in PS technologies, pending consideration of several critical parameters (e.g., immersion duration, membrane or diffusion gel typology, and type of accumulation phases), and a major challenge remains for the latter to the capacity for quantitative interpretation. Although passive samplers have demonstrated their potential for estimating average concentrations over immersion periods, generally shorter in raw wastewater than in surface waters, conversion to load-based assessments still requires the use of a flow estimation device, similar to those employed with automatic samplers [4]. While it is possible to obtain information upstream near most WWTPs, for which flow data is collected and may be used for quantitative back calculation, it is more difficult to monitor upstream sewerage networks or combined sewer overflows, for which flow monitoring is not systematic. This may reduce the attractiveness of passive samplers compared to automatic samplers for such areas. Although passive samplers do not require batteries or external power sources to operate, the need for an additional height/velocity sensor complicates the overall deployment system. Nevertheless, a promising avenue could lie in working with ratios between target analytes and compounds naturally excreted in quantifiable amounts, i.e., population biomarkers. As illustrated by the use of PMMoV and CrAssphage in virology [87], compounds such as cotinine or 5-HIAA appear [88] to be promising candidates for evaluating target analytes via relative enrichments. However, this approach requires further validation, as these intrinsic population markers often exhibit physicochemical properties distinct from those of the analytes of interest.

For sewers in which flow estimation is available (through height/velocity sensor or alternative technique), another consideration is the sensitivity of passive samplers to flow rate variations, which can be substantial, particularly at the upstream ends of sewer networks. While passive samplers have frequently demonstrated their value in hydrologically stable environments such as rivers, doubts remain regarding their ability to yield integrative data under highly variable flow conditions. Nonetheless, some studies, particularly those conducted at combined sewer overflow sites, have shown that passive samplers are effective even in such dynamic settings recording spatio-temporal patterns of organic contaminants [89].

While several studies have demonstrated the potential of passive samplers to capture the dynamics of certain organic compounds in raw wastewater, particularly illicit drugs, their ability to preconcentrate rarely detected compounds, such as urinary metabolites linked to air pollutants [90] or new psychoactive substance uses [91], remains underexploited. Similarly, the limitations of certain devices for highly polar or ionized compounds appear to be overcome by using non-adsorptive PS [17].

As previously noted, despite its broad screening capacity, HRMS is still rarely applied to passive samplers [92,93]. The pseudo-quantitative nature of HRMS presents limitations for its use in strict wastewater-based epidemiology. Nevertheless, integrating this technique with existing targeted monitoring strategies remains highly relevant for enhancing the chemical surveillance of urban effluents [94]. In this context, HRMS can contribute valuable qualitative or semi-quantitative information on the presence and diversity of substances [95], thereby complementing quantitative approaches used to estimate population-level patterns.
## 5. Conclusion

Passive sampling stands at the frontier of WBE, offering a pathway toward decentralized, continuous, and low-cost monitoring of chemical markers. Across diverse designs, such as POCIS, Chemcatcher, MPT, DGT, HPS, and G-TIPS, these devices consistently demonstrate the capacity to integrate contaminant signals over time with minimal infrastructure or energy requirements. Their simplicity and portability make them attractive tools for expanding the spatial and temporal scope of wastewater surveillance, particularly in contexts where conventional autosamplers are impractical.

However, translating this potential into quantitative reliability requires overcoming key technical and methodological barriers. Raw wastewater is a highly variable and particulate-rich matrix that promotes biofouling, membrane clogging, and non-linear uptake. Calibration, especially when performed in situ, remains the critical step for ensuring meaningful data, while the selection and saturation behavior of sorbent materials determine the limits of quantitative interpretation. Future research should prioritize mixed-mode and ion-exchange sorbents to enhance selectivity and broaden chemical coverage, coupled with shorter deployment durations adapted to wastewater complexity and marker concentrations.

Progress also depends on harmonizing protocols for calibration, deployment, extraction, and data interpretation. Standardization and interlaboratory comparison will be essential to transform PS from a promising research tool into an operational component of WBE. In the near term, passive samplers should be viewed as complementary rather than replacement technologies, valuable for capturing transient events, improving spatial resolution, and supporting semi-quantitative ratio-based approaches that integrate population biomarkers.

With ongoing innovation in sampler design, sorbent chemistry, and analytical workflows such as HRMS-enabled non-target screening, PS is poised to become a cornerstone of next-generation WBE, unlocking broader, more equitable, and more agile insights into community health from the wastewater beneath our cities.

### CRediT authorship contribution statement

**Camille Richer:** Writing – original draft, Validation, Formal analysis, Data curation, Conceptualization. **Fabrice Alliot:** Writing – review & editing, Validation, Investigation, Data curation. **Thomas Thiebault:** Writing – review & editing, Writing – original draft, Validation, Supervision, Funding acquisition, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

Sorbonne Université, through the Expres initiative, is gratefully acknowledged for providing a Ph.D. grant to C.R. Two anonymous reviewers are thanked for their contributions to improving the manuscript.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.trac.2026.118690.

### Data availability

Data will be made available on request.

### References

[1] M. Lorenzo, Y. Picó, Wastewater-based epidemiology: current status and future prospects, Curr. Opin. Environ. Sci. Health (2019), https://doi.org/10.1016/j.coesh.2019.05.007.

[2] E. Zuccato, C. Chiabrando, S. Castiglioni, R. Bagnati, R. Fanelli, Estimating community drug abuse by wastewater analysis, Environ. Health Perspect. 116 (2008) 1027–1032, https://doi.org/10.1289/ehp.11022.

[3] Y. Picó, D. Barceló, Identification of biomarkers in Wastewater-based epidemiology: main approaches and analytical methods, TrAC Trends Anal. Chem. (2021) 116465, https://doi.org/10.1016/j.trac.2021.116465.

[4] C. Ort, M.G. Lawrence, J. Rieckermann, A. Joss, Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: are your conclusions valid? A critical review, Environ. Sci. Technol. 44 (2010) 6024–6035, https://doi.org/10.1021/es100779n.

[5] L. Mutzner, E.L.M. Vermeirssen, C. Ort, Passive samplers in sewers and rivers with highly fluctuating micropollutant concentrations – better than we thought, J. Hazard. Mater. 361 (2019) 312–320, https://doi.org/10.1016/j.jhazmat.2018.07.040.

[6] V. Furrer, L. Mutzner, C. Ort, H. Singer, Micropollutant concentration fluctuations in combined sewer overflows require short sampling intervals, Water Res. X 21 (2023) 100202, https://doi.org/10.1016/j.wroa.2023.100202.

[7] P.M. Choi, B. Tscharke, S. Samanipour, W.D. Hall, C.E. Gartner, J.F. Mueller, K. V. Thomas, J.W. O'Brien, Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology, Proc. Natl. Acad. Sci. (2019) 201910242, https://doi.org/10.1073/pnas.1910242116.

[8] D.A. Alvarez, J.D. Petty, J.N. Huckins, T.L. Jones-Lepp, D.T. Getting, J.P. Goddard, S.E. Manahan, Development of a passive, in situ, integrative sampler for hydrophilic organic contaminants in aquatic environments, Environ. Toxicol. Chem. 23 (2004) 1640–1648, https://doi.org/10.1897/03-603.

[9] T. Görecki, J. Namiesnik, Passive sampling, TrAC Trends Anal. Chem. 21 (2002) 276–291, https://doi.org/10.1016/S0165-9936(02)00407-7.

[10] R. Verhagen, S.L. Kaserzon, K.V. Thomas, J.F. Mueller, B.J. Tscharke, Exploring drug consumption patterns across varying levels of remoteness in Australia, Sci. Total Environ. 903 (2023) 166163, https://doi.org/10.1016/j.scitotenv.2023.166163.

[11] A.G. Shakallis, H. Fallowfield, K.E. Ross, H. Whiley, The application of passive sampling devices in wastewater surveillance, Water 14 (2022) 3478, https://doi.org/10.3390/w14213478.

[12] P. Wang, J.K. Challis, Z.-X. He, C.S. Wong, E.Y. Zeng, Effects of biofouling on the uptake of perfluorinated alkyl acids by organic-diffusive gradients in thin films passive samplers, Environ. Sci. Process. Impacts 24 (2022) 242–251, https://doi.org/10.1039/D1EM00436K.

[13] P.M. Choi, B.J. Tscharke, E. Donner, J.W. O'Brien, S.C. Grant, S.L. Kaserzon, R. Mackie, E. O'Malley, N.D. Crosbie, K.V. Thomas, J.F. Mueller, Wastewater-based epidemiology biomarkers: past, present and future, TrAC Trends Anal. Chem. 105 (2018) 453–469, https://doi.org/10.1016/j.trac.2018.06.004.

[14] J. O'Keeffe, Wastewater-based epidemiology: current uses and future opportunities as a public health surveillance tool, Environ. Health Rev. 64 (2021) 44–52, https://doi.org/10.5864/d2021-015.

[15] S. McKay, B. Tscharke, D. Hawker, K. Thompson, J. O'Brien, J.F. Mueller, S. Kaserzon, Calibration and validation of a microporous polyethylene passive sampler for quantitative estimation of illicit drug and pharmaceutical and personal care product (PPCP) concentrations in wastewater influent, Sci. Total Environ. (2019) 135891, https://doi.org/10.1016/j.scitotenv.2019.135891.

[16] R. Verhagen, B.J. Tscharke, J. Clokey, C. Gerber, M. Ghetia, S.L. Kaserzon, K. V. Thomas, J.F. Mueller, Multisite calibration of a microporous polyethylene tube passive sampler for quantifying drugs in wastewater, Environ. Sci. Technol. 55 (2021) 12922–12929, https://doi.org/10.1021/acs.est.1c02900.

[17] R. Verhagen, S.L. Kaserzon, B.J. Tscharke, P. Shukla, J. Clokey, B. Vrana, K. V. Thomas, J.F. Mueller, Time-Integrative passive sampling of very hydrophilic chemicals in wastewater influent, Environ. Sci. Technol. Lett. 7 (2020) 848–853, https://doi.org/10.1021/acs.estlett.0c00685.

[18] R.Z. Hahn, M.F. Bastiani, L. de L.F. Lizot, A. Schneider, I.C. da Silva Moreira, Y. F. Meireles, M.F. Viana, C.A. do Nascimento, R. Linden, Long-term monitoring of drug consumption patterns during the COVID-19 pandemic in a small-sized community in Brazil through wastewater-based epidemiology, Chemosphere 302 (2022) 134907, https://doi.org/10.1016/j.chemosphere.2022.134907.

[19] R.Z. Hahn, M.F. Bastiani, L. de Lima Feltraco Lizot, I.C. da Silva Moreira, Y. F. Meireles, A. Schneider, C.A. do Nascimento, R. Linden, Determination of a comprehensive set of drugs of abuse, metabolites and human biomarkers in wastewater using passive sampling followed by UHPLC-MS/MS analysis, Microchem. J. 172 (2022) 106960, https://doi.org/10.1016/j.microc.2021.106960.

[20] J.A. Baz-Lomba, C. Harman, M. Reid, K.V. Thomas, Passive sampling of wastewater as a tool for the long-term monitoring of community exposure: illicit and prescription drug trends as a proof of concept, Water Res. 121 (2017) 221–230, https://doi.org/10.1016/j.watres.2017.05.041.

[21] V. Yargeau, B. Taylor, H. Li, A. Rodayan, C.D. Metcalfe, Analysis of drugs of abuse in wastewater from two Canadian cities, Sci. Total Environ. 487 (2014) 722–730, https://doi.org/10.1016/j.scitotenv.2013.11.094.

[22] E. Bailly, Y. Levi, S. Karolak, Calibration and field evaluation of polar organic chemical integrative sampler (POCIS) for monitoring pharmaceuticals in hospital wastewater, Environ. Pollut. 174 (2013) 100–105, https://doi.org/10.1016/j.envpol.2012.10.025.
[23] C. Harman, M. Reid, K.V. Thomas, In situ calibration of a passive sampling device for selected illicit drugs and their metabolites in wastewater, and subsequent year-long assessment of community drug usage, Environ. Sci. Technol. 45 (2011) 5676-5682, https://doi.org/10.1021/es1021124j.

[24] T. Sultana, C. Murray, M. Ehsanul Hoque, C.D. Metcalfe, Monitoring contaminants of emerging concern from tertiary wastewater treatment plants using passive sampling modelled with performance reference compounds, Environ. Monit. Assess. 189 (2016) 1, https://doi.org/10.1007/s10661-016-5706-4.

[25] J.K. Challis, X.O. Almiraall, P.A. Helm, C.S. Wong, Performance of the organic-diffusive gradients in thin-films passive sampler for measurement of target and suspect wastewater contaminants, Environ. Pollut. 261 (2020) 114092, https://doi.org/10.1016/j.envpol.2020.114092.

[26] R. Amdany, L. Chimuka, E. Cukrowska, Determination of naproxen, ibuprofen and triclosan in wastewater using the polar organic chemical integrative sampler (POCIS): a laboratory calibration and field application, WaterSA 40 (2014) 407-414, https://doi.org/10.4314/wsa.v40i3.3.

[27] C. Zillien, T. Groenveld, O. Schut, H. Beeltje, D. Blanco-Ania, L. Posthuma, E. Roex, A. Ragas, Assessing city-wide pharmaceutical emissions to wastewater via modelling and passive sampling, Environ. Int. 185 (2024) 108524, https://doi.org/10.1016/j.envint.2024.108524.

[28] C. Zillien, C. van Loon, M. Gülpen, K. Tipatet, B. Hanssen, H. Beeltje, E. Roex, R. Oldenkamp, L. Posthuma, A.M.J. Ragas, Risk-management tool for environmental prioritization of pharmaceuticals based on emissions from hospitals, Sci. Total Environ. 694 (2019) 133733, https://doi.org/10.1016/j.scitotenv.2019.133733.

[29] J.K. Challis, M.L. Hanson, C.S. Wong, Development and calibration of an organic-diffusive gradients in thin films aquatic passive sampler for a diverse suite of polar organic contaminants, Anal. Chem. 88 (2016) 10583-10591, https://doi.org/10.1021/acs.analchem.6b02749.

[30] X. Liu, R. Zhang, H. Cheng, M.S. Khorram, S. Zhao, T.T. Tham, T.M. Tran, T. B. Minh, B. Jiang, B. Jin, G. Zhang, Field evaluation of diffusive gradients in thin-film passive samplers for wastewater-based epidemiology, Sci. Total Environ. 773 (2021) 145480, https://doi.org/10.1016/j.scitotenv.2021.145480.

[31] W. Chen, S. Pan, H. Cheng, A.J. Sweetman, H. Zhang, K.C. Jones, Diffusive gradients in thin-films (DGT) for in situ sampling of selected endocrine disrupting chemicals (EDCs) in waters, Water Res. 137 (2018) 211-219, https://doi.org/10.1016/j.watres.2018.03.029.

[32] C. Guo, T. Zhang, S. Hou, J. Lv, Y. Zhang, F. Wu, Z. Hua, W. Meng, H. Zhang, J. Xu, Investigation and application of a new passive sampling technique for in situ monitoring of illicit drugs in waste waters and Rivers, Environ. Sci. Technol. 51 (2017) 9101-9108, https://doi.org/10.1021/acs.est.7b00731.

[33] C.-E. Chen, H. Zhang, G.-G. Ying, K.C. Jones, Evidence and recommendations to support the use of a novel passive water sampler to quantify antibiotics in wastewaters, Environ. Sci. Technol. 47 (2013) 13587-13593, https://doi.org/10.1021/es402662g.

[34] C.-E. Chen, H. Zhang, G.-G. Ying, L.-J. Zhou, K.C. Jones, Passive sampling: a cost-effective method for understanding antibiotic fate, behaviour and impact, Environ. Int. 85 (2015) 284-291, https://doi.org/10.1016/j.envint.2015.10.001.

[35] P. Fialová, R. Grabic, K. Grabicová, P. Nováková, H. Švecová, S. Kaserzon, K. Thompson, B. Vrana, Performance evaluation of a diffusive hydrogel-based passive sampler for monitoring of polar organic compounds in wastewater, Sci. Total Environ. 864 (2023) 161071, https://doi.org/10.1016/j.scitotenv.2022.161071.

[36] V. Fauvelle, S.L. Kaserzon, N. Montero, S. Lissalde, I.J. Allan, G. Mills, N. Mazzella, J.F. Mueller, K. Boojij, Dealing with flow effects on the uptake of polar compounds by passive samplers, Environ. Sci. Technol. 51 (2017) 2536-2537, https://doi.org/10.1016/j.acs.est.7b00558.

[37] J.N. Huckins, J.D. Petty, J.A. Lebo, F.V. Almeida, K. Boojij, D.A. Alvarez, W. J. Cranor, R.C. Clark, B.B. Mogensen, Development of the Permeability/Performance reference compound approach for in situ calibration of semipermeable membrane devices, Environ. Sci. Technol. 36 (2002) 85-91, https://doi.org/10.1021/es010991w.

[38] K. Boojij, S. Crum, B. Vrana, R. Grabic, N.A.O. Morin, K. Parmentier, C. Kech, P. Krystek, K. Noro, B. Becker, R. Lohmann, L. Malleret, S.L. Kaserzon, C. Miège, F. Alliot, F. Pfeiffer, D. Crowley, M. Rakowska, T. Ocelka, G.B. Kim, L. Röhler, Ongoing laboratory performance Study on chemical analysis of hydrophobic and hydrophilic compounds in three aquatic passive samplers, Environ. Sci. Technol. 58 (2024) 6772-6780, https://doi.org/10.1021/acs.est.3c10272.

[39] R. Buzier, R. Guibal, S. Lissalde, G. Guibaud, Limitation of flow effect on passive sampling accuracy using POCIS with the PRC approach or o-DGT: a pilot-scale evaluation for pharmaceutical compounds, Chemosphere 222 (2019) 628-636, https://doi.org/10.1016/j.chemosphere.2019.01.181.

[40] C. Harman, I.J. Allan, P.S. Bäuerlein, The challenge of exposure correction for polar passive Samplers—The PRC and the POCIS, Environ. Sci. Technol. 45 (2011) 9120-9121, https://doi.org/10.1021/es2033789.

[41] N. Van Wichelen, D. Burgard, M.C. Campos-Manas, C. Simarro-Gimeno, F. Hernández, C. Ort, T. Boogaerts, N. Salgueiro-Gonzalez, S. Castiglioni, F. Béen, P. de Voogt, A. Covaci, A.L.N. van Nuijs, L. Bijlsma, A key factor in monitoring cannabis consumption trends through wastewater analysis: partitioning of THCCOOH between the liquid and solid phase of influent wastewater, Water Res. 267 (2024) 122462, https://doi.org/10.1016/j.watres.2024.122462.

[42] G. Bernier-Turpin, R. Moilleron, C. Cenik, F. Alliot, S. Guérin-Rechdaoui, T. Thiebaut, Do suspended particles matter for wastewater-based epidemiology? Water Res. 280 (2025) 123543 https://doi.org/10.1016/j.watres.2025.123543.

[43] C. Miège, H. Budzinski, R. Jacquet, C. Soulier, T. Pelte, M. Coquery, Polar organic chemical integrative sampler (POCIS): application for monitoring organic micropollutants in wastewater effluent and surface water, J. Environ. Monit. 14 (2012) 626-635, https://doi.org/10.1039/C1EM10730E.

[44] P. Gago-Ferrero, E.L. Schymanski, A.A. Bletsou, R. Aalizadeh, J. Hollender, N. S. Thomaidis, Extended suspect and non-target strategies to characterize emerging polar organic contaminants in raw wastewater with LC-HRMS/MS, Environ. Sci. Technol. 49 (2015) 12333-12341, https://doi.org/10.1021/acs.est.5b03454.

[45] D. Salas, F. Borrull, N. Fontanals, R.M. Marcé, Combining cationic and anionic mixed-mode sorbents in a single cartridge to extract basic and acidic pharmaceuticals simultaneously from environmental waters, Anal. Bioanal. Chem. 410 (2018) 459-469, https://doi.org/10.1007/s00216-017-0736-5.

[46] V. Fauvelle, N. Mazzella, F. Delmas, K. Madarassou, M. Eon, H. Budzinski, Use of mixed-mode ion exchange sorbent for the passive sampling of organic acids by Polar Organic Chemical Integrative Sampler (POCIS), Environ. Sci. Technol. 46 (2012) 13344-13353, https://doi.org/10.1021/es3035279.

[47] A. Iparraguirre, A. Prieto, A. Vallejo, M. Moeder, O. Zuloaga, N. Etxebarria, A. Paschke, Tetraphasic polar organic chemical integrative sampler for the determination of a wide polarity range organic pollutants in water. The use of performance reference compounds and in-situ calibration, Talanta 164 (2017) 314-322, https://doi.org/10.1016/j.talanta.2016.11.034.

[48] A. Gravell, G.R. Fones, R. Greenwood, G.A. Mills, Detection of pharmaceuticals in wastewater effluents—a comparison of the performance of chemcatcher® and polar organic compound integrative sampler, Environ. Sci. Pollut. Res. 27 (2020) 27995-28005, https://doi.org/10.1007/s11356-020-09077-5.

[49] B. Petrie, A. Gravell, G.A. Mills, J. Youdan, R. Barden, B. Kasprzyk-Hordern, In situ calibration of a new chemcatcher configuration for the determination of polar organic micropollutants in wastewater effluent, Environ. Sci. Technol. 50 (2016) 9469-9478, https://doi.org/10.1021/acs.est.6b02216.

[50] R.F.A. Robinson, G.A. Mills, R. Grabic, A. Borík, G.R. Fones, Quantification and risk assessment of polar organic contaminants in two chalk streams in Hampshire, UK using the Chemcatcher passive sampler, Sci. Total Environ. 939 (2024) 173316, https://doi.org/10.1016/j.scitotenv.2024.173316.

[51] L. Mutzner, E.L.M. Vermeirssen, S. Mangold, M. Maurer, A. Scheidegger, H. Singer, K. Boojij, C. Ort, Passive samplers to quantify micropollutants in sewer overflows: accumulation behaviour and field validation for short pollution events, Water Res. 160 (2019) 350-360, https://doi.org/10.1016/j.watres.2019.04.012.

[52] V. Fauvelle, N. Montero, J.F. Mueller, A. Banks, N. Mazzella, S.L. Kaserzon, Glyphosate and AMPA passive sampling in freshwater using a microporous polyethylene diffusion sampler, Chemosphere 188 (2017) 241-248, https://doi.org/10.1016/j.chemosphere.2017.08.013.

[53] R. Mackie, D.W. Hawker, S. Ghorbani Gorji, K. Boojij, X. Qu, K. Bowles, S. Shea, C. P. Higgins, S. Kaserzon, Application of a microporous polyethylene tube passive sampler for monitoring Per- and Polyfluoroalkyl substances in wastewater effluent and effluent, ACS ES&T Water 4 (2024) 2281-2291, https://doi.org/10.1021/acsestwater.4c00125.

[54] C. Gardiner, A. Robuck, J. Becanova, M. Cantwell, S. Kaserzon, D. Katz, J. Mueller, R. Lohmann, Field validation of a novel passive sampler for dissolved PFAS in surface waters, Environ. Toxicol. Chem. 41 (2022) 2375-2385, https://doi.org/10.1002/etc.5431.

[55] S.L. Kaserzon, S. Vijayasarathy, J. Bräunig, L. Mueller, D.W. Hawker, K.V. Thomas, J.F. Mueller, Calibration and validation of a novel passive sampling device for the time integrative monitoring of per- and polyfluoroalkyl substances (PFASs) and precursors in contaminated groundwater, J. Hazard. Mater. 366 (2019) 423-431, https://doi.org/10.1016/j.jhazmat.2018.12.010.

[56] Y. Soriano, R. Alvarez-Ruiz, J.E. Clokey, S.G. Gorji, S.L. Kaserzon, Y. Picó, Determination of organic contaminants in L'Albufera Natural Park using microporous polyethylene tube passive samplers: an environmental risk assessment, Sci. Total Environ. 903 (2023) 166594, https://doi.org/10.1016/j.scitotenv.2023.166594.

[57] J.E. Clokey, D.W. Hawker, R. Verhagen, S. Ghorbani Gorji, E.R. Knight, K. V. Thomas, S.L. Kaserzon, Calibration of a microporous polyethylene tube passive sampler for polar organic compounds in wastewater effluent, Sci. Total Environ. 874 (2023) 162497, https://doi.org/10.1016/j.scitotenv.2023.162497.

[58] H. Li, P.A. Helm, G. Paterson, C.D. Metcalfe, The effects of dissolved organic matter and pH on sampling rates for polar organic chemical integrative samplers (POCIS), Chemosphere 83 (2011) 271-280, https://doi.org/10.1016/j.chemosphere.2010.12.071.

[59] Y. Yabuki, T. Nagai, K. Inao, J. Ono, N. Aiko, N. Ohtsuka, H. Tanaka, S. Tanimori, Temperature dependence on the pesticide sampling rate of polar organic chemical integrative samplers (POCIS), Biosci. Biotechnol. Biochem. 80 (2016) 2069-2075, https://doi.org/10.1080/09168451.2016.1191329.

[60] A. Togola, H. Budzinski, Development of polar organic integrative samplers for analysis of pharmaceuticals in aquatic systems, Anal. Chem. 79 (2007) 6734-6741, https://doi.org/10.1021/ac070559i.

[61] K. Boojij, S. Chen, Review of atrazine sampling by polar organic chemical integrative samplers and Chemcatcher, Environ. Toxicol. Chem. 37 (2018) 1786-1798, https://doi.org/10.1002/etc.4160.

[62] D. O'Brien, M. Bartkow, J.F. Mueller, Determination of deployment specific chemical uptake rates for SDB-RPD Empore disk using a passive flow monitor (PFM), Chemosphere 83 (2011) 1290-1295, https://doi.org/10.1016/j.chemosphere.2011.02.089.

[63] K. Boojij, Passive sampler exchange kinetics in large and small water volumes under mixed rate control by sorbent and water boundary layer, Environ. Toxicol. Chem. 40 (2021) 1241-1254, https://doi.org/10.1002/etc.4989.

[64] S. Scally, W. Davison, H. Zhang, In situ measurements of dissociation kinetics and labilities of metal complexes in solution using DGT, Environ. Sci. Technol. 37 (2003) 1379-1384, https://doi.org/10.1021/es0202060.
[65] R. Zhang, X. Liu, S. Zhao, G. Zhang, Wastewater-based epidemiology empowered by o-DGT passive sampling: quantifying pharmaceuticals consumption hotspots in rural China's untreated watersheds, Ecotoxicol. Environ. Saf. 303 (2025) 118957, https://doi.org/10.1016/j.ecoenv.2025.118957.

[66] J. Urfk, B. Vrana, An improved design of a passive sampler for polar organic compounds based on diffusion in agarose hydrogel, Environ. Sci. Pollut. Res. 26 (2019) 15273-15284, https://doi.org/10.1007/s11356-019-04843-6.

[67] A.A. Menegário, E. de Almeida, M.B.T. Zanatta, L.F.P.P. Moreira, L.H. Zóia, J.L. M. Viana, C.H. Kiang, Double-faced diffusive gradient in thin films sampler (dF-DGT): proposing an improved DGT sampler design for water deployments, Anal. Chim. Acta 1336 (2025) 343531, https://doi.org/10.1016/j.aca.2024.343531.

[68] H. Ellis, K. Proctor, M. Robertson, J. Bagnall, B. Kasprzyk-Hordern, A new wide-scope, multi-biomarker wastewater-based epidemiology analytical method to monitor the health and well-being of inhabitants at a metropolitan scale, Anal. Bioanal. Chem. 417 (2025) 5983-6005, https://doi.org/10.1007/s00216-025-06097-3.

[69] G. Bernier-Turpin, T. Thiebault, F. Alliot, E. Mebold, S. Guérin-Rechdaoui, M. Oliveira, J. Le Roux, R. Moilleron, Target and non-target screening of biomarkers in wastewater: towards a unique analytical methodology for sample preparation, Anal. Methods 16 (2024) 6241-6256, https://doi.org/10.1039/D4AY00843J.

[70] M. Caban, H. Lis, P. Stepnowski, Limitations of integrative passive samplers as a tool for the quantification of pharmaceuticals in the environment - a critical review with the latest innovations, Crit. Rev. Anal. Chem. 0 (2021) 1-40, https://doi.org/10.1080/10408347.2021.1881755.

[71] H. MacKeown, C. Scapuzzi, B. Benedetti, M. Di Carro, E. Magi, Polyethersulfone potential in single and dual-phase passive samplers: a review, Microchem. J. 210 (2025) 112958, https://doi.org/10.1016/j.microc.2025.112958.

[72] S. Endo, Y. Matsuura, Characterizing sorption and permeation properties of membrane filters used for aquatic integrative passive samplers, Environ. Sci. Technol. 52 (2018) 2118-2125, https://doi.org/10.1021/acs.est.7b05144.

[73] E.L.M. Vermeirssen, C. Dietschweiler, B.I. Escher, J. van der Voet, J. Hollender, Transfer kinetics of polar organic compounds over polyethersulfone membranes in the passive samplers pocis and chemcatcher, Environ. Sci. Technol. 46 (2012) 6759-6766, https://doi.org/10.1021/es3007854.

[74] K. Godlew ska, P. Stepnowski, M. Paszkiewicz, Pollutant analysis using passive samplers: principles, sorbents, calibration and applications. A review, Environ. Chem. Lett. 19 (2021) 465-520, https://doi.org/10.1007/s10311-020-01079-6.

[75] E.L.M. Vermeirssen, J. Asmin, B.I. Escher, J.-H. Kwon, I. Steimen, J. Hollender, The role of hydrodynamics, matrix and sampling duration in passive sampling of polar compounds with Empore™ SDB-RPS disks, J. Environ. Monit. 10 (2008) 119-128, https://doi.org/10.1039/B710790K.

[76] R. Wang, K.C. Jones, H. Zhang, Monitoring organic pollutants in waters using the diffusive gradients in the thin films technique: investigations on the effects of biofouling and degradation, Environ. Sci. Technol. 54 (2020) 7961-7969, https://doi.org/10.1021/acs.est.0c00224.

[77] C. Pichette, H. Zhang, W. Davison, S. Sauvé, Preventing biofilm development on DGT devices using metals and antibiotics, Talanta 72 (2007) 716-722, https://doi.org/10.1016/j.talanta.2006.12.014.

[78] N. Morin, J. Camilleri, C. Cren-Olivé, M. Coquery, C. Miège, Determination of uptake kinetics and sampling rates for 56 organic micropollutants using "pharmaceutical" POCIS, Talanta 109 (2013) 61-73, https://doi.org/10.1016/j.talanta.2013.01.058.

[79] H. Li, P.A. Helm, C.D. Metcalfe, Sampling in the Great Lakes for pharmaceuticals, personal care products, and endocrine-disrupting substances using the passive polar organic chemical integrative sampler, Environ. Toxicol. Chem. 29 (2010) 751-762, https://doi.org/10.1002/etc.104.

[80] T. Thiebault, Raw and modified clays and clay minerals for the removal of pharmaceutical products from aqueous solutions: state of the art and future perspectives, Crit. Rev. Environ. Sci. Technol. 50 (2020) 1451-1514, https://doi.org/10.1080/10643389.2019.1663065.

[81] J.K. Challis, M.L. Hanson, C.S. Wong, Pharmaceuticals and pesticides archived on polar passive sampling devices can be stable for up to 6 years, Environ. Toxicol. Chem. 37 (2018) 762-767, https://doi.org/10.1002/etc.4012.

[82] M.H. Belay, U. Precht, P. Mortensen, E. Marengo, E. Robotti, A fully automated online SPE-LC-MS/MS method for the determination of 10 pharmaceuticals in wastewater samples, Toxics 10 (2022) 103, https://doi.org/10.3390/toxics10030103.

[83] A.L.N. van Nuijs, F.Y. Lai, F. Been, M.J. Andres-Costa, L. Barron, J.A. Baz-Lomba, J.-D. Berset, L. Benaglia, L. Bijlsma, D. Burgard, S. Castiglioni, C. Christophoridis, A. Covaci, P. de Voogt, E. Emke, D. Fatta-Kassinos, J. Fick, F. Hernandez, C. Gerber, I. González-Mariño, R. Grabic, T. Gunnar, K. Kannan, S. Karolak, B. Kasprzyk-Hordern, Z. Kokot, I. Krizman-Matasic, A. Li, X. Li, A.S.C. Löve, M. Lopez de Alda, A.-K. McCall, M.R. Meyer, H. Oberacher, J. O'Brien, J.B. Quintana, M. Reid, S. Schneider, S.S. Simoes, N.S. Thomaidis, K. Thomas, V. Yargeau, C. Ort, Multi-year inter-laboratory exercises for the analysis of illicit drugs and metabolites in wastewater: development of a quality control system, TrAC Trends Anal. Chem. 103 (2018) 34-43, https://doi.org/10.1016/j.trac.2018.03.009.

[84] J. Hu, Y. Lyu, H. Chen, S. Li, W. Sun, Suspect and nontarget screening reveal the underestimated risks of antibiotic transformation products in wastewater treatment plant effluents, Environ. Sci. Technol. 57 (2023) 17439-17451, https://doi.org/10.1021/acs.est.3c05008.

[85] X. Wang, N. Yu, J. Yang, L. Jin, H. Guo, W. Shi, X. Zhang, L. Yang, H. Yu, S. Wei, Suspect and non-target screening of pesticides and pharmaceuticals transformation products in wastewater using QTOF-MS, Environ. Int. 137 (2020) 105599, https://doi.org/10.1016/j.envint.2020.105599.

[86] B. Schulze, A.L. Heffernan, S. Samanipour, M.J. Gomez Ramos, C. Veal, K. V. Thomas, S.L. Kaserzon, Is nontarget analysis ready for regulatory application? Influence of peak-picking algorithms on data analysis, Anal. Chem. 95 (2023) 18361-18369, https://doi.org/10.1021/acs.analchem.3c03003.

[87] R.H. Holm, M. Nagarkar, R.A. Yeager, D. Talley, A.C. Chaney, J.P. Rai, A. Mukherjee, S.N. Rai, A. Bhatnagar, T. Smith, Surveillance of RNase P, PMoV, and CrAssphage in wastewater as indicators of human fecal concentration across urban sewer neighborhoods, Kentucky, FEMS Microbes 3 (2022) xtac003, https://doi.org/10.1093/femsmc/xtac003.

[88] C. Chen, C. Kostakis, J.P. Gerber, B.J. Tscharke, R.J. Irvine, J.M. White, Towards finding a population biomarker for wastewater epidemiology studies, Sci. Total Environ. 487 (2014) 621-628, https://doi.org/10.1016/j.scitotenv.2013.11.075.

[89] L. Mutzner, C. Bohren, S. Mangold, S. Bloem, C. Ort, Spatial differences among micropollutants in sewer overflows: a multisite analysis using passive samplers, Environ. Sci. Technol. 54 (2020) 6584-6593, https://doi.org/10.1021/acs.est.9b05148.

[90] N.I. Rousis, E. Zuccato, S. Castiglioni, Wastewater-based epidemiology to assess human exposure to pyrethroid pesticides, Environ. Int. 99 (2017) 213-220, https://doi.org/10.1016/j.envint.2016.11.020.

[91] L. Gent, R. Paul, The detection of new psychoactive substances in wastewater. A comprehensive review of analytical approaches and global trends, Sci. Total Environ. 776 (2021) 146028, https://doi.org/10.1016/j.scitotenv.2021.146028.

[92] R.B. Carneiro, M.-C. Nika, R. Gil-Solsona, K.S. Diamanti, N.S. Thomaidis, L. Corominas, P. Gago-Ferrero, A critical review of wastewater-based epidemiology as a tool to evaluate the unintentional human exposure to potentially harmful chemicals, Anal. Bioanal. Chem. (2024), https://doi.org/10.1007/s00216-024-05596-z.

[93] P. Fialová, A. Vojs Stanová, R. Grabic, B. Vrana, Combination of Passive Sampling and Non-targeted Analysis of Environmental Water Samples, in: Springer, Berlin, Heidelberg, n.d.: pp. 1-45. https://doi.org/10.1007/698_2025_1213.

[94] S. Santana-Viera, P.A. Lara-Martín, E. González-Mazo, High resolution mass spectrometry (HRMS) determination of drugs in wastewater and wastewater based epidemiology in Cadiz Bay (Spain), J. Environ. Manage. 341 (2023) 118000, https://doi.org/10.1016/j.jenvman.2023.118000.

[95] P. Cetl, M. Tysklind, E. Golosovskaja, K. Grabicová, B. Vrana, R. Grabic, Mapping the limits of passive samplers in water: chemical space coverage using nontargeted liquid Chromatography-High-Resolution mass spectrometry (LC-HRMS) analysis, ACS ES&T Water 5 (2025) 5928-5937, https://doi.org/10.1021/acsestwater.5c00565.