# Pioneering an effect-based early warning system for hazardous chemicals in the environment

Georgios Niarchos, Nikiforos Alygizakis, Mario Carere, Valeria Dulio, Magnus Engwall, Tuulia Hyötyläinen, Roland Kallenborn, Spyros Karakitsios, Achilleas Karakoltzidis, Anna Kärrman, et al.

► To cite this version:

Georgios Niarchos, Nikiforos Alygizakis, Mario Carere, Valeria Dulio, Magnus Engwall, et al. Pioneering an effect-based early warning system for hazardous chemicals in the environment. Trends in Analytical Chemistry, 2024, 180, pp.117901. 10.1016/j.trac.2024.117901. hal-04673701

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# Pioneering an effect-based early warning system for hazardous chemicals in the environment

Georgios Niarchos<sup>a, *</sup>, Nikiforos Alygizakis<sup>b</sup>, Mario Carere<sup>c</sup>, Valeria Dulio<sup>d</sup>, Magnus Engwall<sup>e</sup>, Tuulia Hyötyläinen<sup>e</sup>, Roland Kallenborn<sup>f</sup>, Spyros Karakitsios<sup>g</sup>, Achilleas Karakoltzidis<sup>g</sup>, Anna Kärrman<sup>e</sup>, Marja Lamoree<sup>h</sup>, Maria Larsson<sup>e</sup>, Johan Lundqvist<sup>i</sup>, Laura Mancini<sup>c</sup>, Javad Mottaghipisheh<sup>a</sup>, Pawel Rostkowski<sup>j</sup>, Dimosthenis Sarigiannis<sup>g</sup>, Katrin Vorkamp<sup>k</sup>, Lutz Ahrens<sup>a</sup>

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## ARTICLE INFO

### Keywords:

Contaminants of emerging concern

Bioassays

Effect-based methods

Effect-directed analysis

Environmental monitoring

Toxicity

## ABSTRACT

Existing regulatory frameworks often prove inadequate in identifying contaminants of emerging concern (CECs) and determining their impacts on biological systems at an early stage. The establishment of Early Warning Systems (EWSs) for CECs is becoming increasingly relevant for policy-making, aiming to proactively detect chemical hazards and implement effective mitigation measures. Effect-based methodologies, including bioassays and effect-directed analysis (EDA), offer valuable input to EWSs with a view to pinpointing the relevant toxicity drivers and prioritizing the associated risks. This review evaluates the analytical techniques currently available to assess biological effects, and provides a structured plan for their systematic integration into an EWS for hazardous chemicals in the environment. Key scientific advancements in effect-based approaches and EDA are discussed, underscoring their potential for early detection and management of chemical hazards. Additionally, critical challenges such as data integration and regulatory alignment are addressed, emphasizing the need for continuous improvement of the EWS and the incorporation of analytical advancements to safeguard environmental and public health from emerging chemical threats.

## 1. Introduction

The global chemical industry is large and growing. In 2020, an estimated 29,000 unique chemicals were registered for commercial use in the EU alone, with global chemical production projected to double between 2017 and 2030 [1,2]. Researchers assert that this extensive production of chemicals exceeds the planetary safe operating space and

surpasses society's capacity to conduct appropriate safety assessments [3]. Additionally, many of these chemicals are explicitly engineered to target biological processes, finding applications in biocides, and pharmaceuticals. Notably, 62 % of the EU's total chemical consumption, equivalent to 345 million tonnes, was classified as posing a health hazard [1].

The production, use, and disposal of chemicals are managed by

<footnote><sup>a</sup> Dept. of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, PO Box 7050, SE-750 07 Uppsala, Sweden</footnote>

<footnote><sup>b</sup> Dept. of Chemistry, National and Kapodistrian University of Athens, Panepistimioupoli Zografou, 15771 Athens, Greece</footnote>

<footnote><sup>c</sup> Dept. of Environment and Health, Italian National Institute of Health, Viale Regina Elena, 299, 00161 Rome, Italy</footnote>

<footnote><sup>d</sup> INERIS, Parc Technologique ALATA, 60550 Verneuil-en-Halatte, France</footnote>

<footnote><sup>e</sup> School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden</footnote>

<footnote><sup>f</sup> Faculty of Chemistry, Biotechnology and Food Sciences (KBM), Norwegian University of Life Sciences, Christian Magnus Falsens vei 18, 1433 Ås, Norway</footnote>

<footnote><sup>g</sup> Dept. of Chemical Engineering, Environmental Engineering Laboratory, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece</footnote>

<footnote><sup>h</sup> Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment & Health, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands</footnote>

<footnote><sup>i</sup> Dept. of Animal Biosciences, Swedish University of Agricultural Sciences, PO Box 7023, 75007 Uppsala, Sweden</footnote>

<footnote><sup>j</sup> Dept. of Environmental Chemistry and Health Effects, NILU, 18 Institutveien, Akershus 2007 Kjeller, Norway</footnote>

<footnote><sup>k</sup> Dept. of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: georgios.niarchos@slu.se (G. Niarchos).</footnote>
national and international bodies to protect the environment and human health. The Stockholm Convention on Persistent Organic Pollutants under the United Nations Environmental Programme entered into force in 2004 [4] and regulates chemicals that are persistent, bio-accumulative, toxic, and subject to long-range transport. In Europe, the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) is the legislative framework for chemicals management that entered into force in 2007 and also includes chemicals in imported goods. However, while chemical restrictions are an important tool for risk management, they are typically reactive in nature. Although the REACH program intends to prevent the widespread use of problematic chemicals, ongoing examples of contamination and exposure cases indicate oversights or inefficiency, such as the global pollution issue of per-and polyfluoroalkyl substances (PFAS) [5] or the ongoing exposure to regulated phthalates [6]. Additionally, restrictions often fail to address all potential risks associated with chemical mixtures [7]. Finally, the restriction of chemicals may be followed by their replacement with potentially regrettable alternatives, which may introduce similar hazards or poorly understood risks [8].

Consequently, there is a need for detecting potential environmental and health problems associated with chemical production and use at an early stage. Most of the research-based initiatives focus on identifying contaminants of emerging concern (CECs), using *in-silico* and/or experimental screening methods [9,10]. Recent advancements in analytical chemistry have allowed for more precise, selective, and sensitive quantifications of chemical contamination, however, information on the toxicity of chemical mixtures and their bioavailability is often lacking during chemical screening [11]. Complementarily to the approaches focusing on chemical identities, effect-based methods (EBMs) can help to identify undesired effects of a chemical or mixture, i.e. assess the toxicity of bioactive compounds, aiming to determine the biological impact of a given sample through the utilisation of bioassays [12]. Based on observed effects, high-resolution non-target screening (NTS) chemical analyses can make attempts at identifying the cause of the effect in effect-directed analyses (EDAs) [13].

In Europe, the EU Water Framework Directive has implemented a Watch-List approach that supports the monitoring and assessment of emerging substances in the aquatic environment. Under the Common Implementation Strategy of the Directive, an activity on EBMs, including bioassays and biomarkers, has been carried out [12,14]. Recently, EBMs for the detection of estrogenicity have been proposed to be included in the new European Commission proposal directive as well as a new definition of Environmental Quality Standards based on effect-based trigger values (EBTs) [15,16]. A focus on early detection and action is also evident in the EU's one substance – one assessment strategy [17, 18].

As part of a comprehensive chemical risk management, the incorporation of Early Warning Systems (EWSs) emerges as a proactive solution. While EWSs are still at the stage of conceptual and technical development, existing methods for the early detection of potential environmental problems will be central elements of an EWS. In this review, we discuss the use of EBMs and their systematic integration into an EWS for hazardous chemicals. Specifically, this review i) addresses key scientific advancements in effect-based approaches in environmental monitoring, including important components such as sample preparation and EDA ii) lays the groundwork for EBMs for the early warning of hazardous chemicals and iii) discusses future perspectives for the application of an effect-based EWS. Emphasis is placed on environmental matrices, particularly aqueous ones, due to their common role as pollution vectors.

## 2. Purpose and concept of an early warning system (EWS)

The overall purpose of an EWS is the early detection of a potentially problematic chemical, raising an alert that can be followed up with minimum delay by more specific studies and/or regulatory actions.

While EWSs have been widely applied for the detection of natural hazards, their application in chemical hazard and risk assessment is less common. The goal of a chemicals-based EWS is to introduce capabilities for promptly identifying emerging chemical threats, ideally in any environmental or human media without significant delays.

For that purpose, an EWS will likely encompass a range of assessment tools and methods designed to promptly detect emerging chemical threats (see Table 1). Its primary objective is to recognize harmful chemical substances and situations with the potential to cause adverse effects on human health or the environment at the earliest possible stage. An EWS should be designed to communicate the information to relevant authorities, ensuring that appropriate action can be taken as fast as possible [20]. In the EU, several activities exist on a national level aiming to detect hazardous chemicals, such as the expert-based SamTox in Sweden; however, the need for a common EWS at EU-level has recently been recognised and discussions on its scope are currently ongoing [21,22].

EWSs should be able to analyse and process signals from the field as inputs, potentially in real-time, such as instances or clusters of health effects that are linked to chemical exposure and/or contamination. Given that new hazards may be infrequent or emerge after prolonged latency periods [23], a close monitoring strategy in space and time will be needed for signal detection. Furthermore, confirmation of a certain detected signal reduces the risks of false positives and might indicate a more serious issue spread over space and/or time.

Fig. 1 illustrates a potential framework of an EWS for chemicals and delineates its vital functions, based on discussions at EU-level [24]. An EWS begins with representative and correct sampling and sample preparation steps, as further discussed in Section 3, followed by the detection of signals, which could be the occurrence of a chemical, or an adverse effect. While literature data is one source of signals, with the strength of quality assurance through a peer-review process, the publication of scientific data is arguably a protracted process. Therefore, real-time monitoring and screening programs are important sources for an EWS. Following signal acquisition, these signals are assessed for their originality, verifying that they meet the purpose of being an early warning. As a strengthening measure of the signal assessment, the third stage of the proposed EWS gathers information related to the signal's risks, potentially followed by an assessment and prioritisation of these risks. As an output of the process, the signals are eventually communicated to the final user enabling mitigation measures. A feedback loop ensures that the EWS stays updated with the newly identified contaminants along with the associated actions.

EBMs hold significant promise for both signal detection and strengthening, by identifying the combined effects of all known and unknown chemicals in a sample, going beyond the limitations of chemical analysis alone [25]. Recent advancements in EBMs have focused on the use of bioanalytical tools that assess the response of biological entities, either *in vivo* using whole organisms, or *in vitro* with cellular bioassays, targeting ecologically relevant endpoints in well-plate formats. EDA takes the capabilities of bioanalytical tools one step further, by combining bioassays with fractionation and chemical analysis, allowing for the detection of a wide range of potentially toxic compounds in a non-discriminative manner [26]. As of today, EBMs and EDA have mainly found applications in research, with limited adoption by the water sector and regulatory bodies. As summarized in Table 2, EBMs and/or EDA have been applied to water, soil and sediment, with a few publications discussing the integration of EBMs in contaminant water monitoring. Integrating such techniques into monitoring strategies, particularly for water quality, offers information for use in an effective EWS (Table 2). So far, contaminant-related EWSs have mainly been described for specific cases or pollutants such as heavy metals or endocrine disrupting compounds whereas state-of-the-art analytical techniques such as EBMs, EDA and NTS will enable a more comprehensive approach to early warnings of potential new pollutants.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Key stages of the proposed early warning system (EWS) for the mitigation of hazardous chemicals.</figcaption></figure>

<table><caption>**Table 1**
Key terms used in this review.</caption><thead><tr><td>Term</td><td>Common abbreviation</td><td>Definition</td></tr></thead><tbody><tr><td>Adverse outcome pathway</td><td>AOP</td><td>A sequence of events linking perturbation at the molecular level to an adverse outcome at an individual or population level</td></tr><tr><td>Contaminants of emerging concern</td><td>CEC</td><td>Chemicals that are potentially hazardous but typically not yet restricted or regulated</td></tr><tr><td>Early warning system</td><td>EWS</td><td>An integrated system of tools for identification of a problematic chemical, that enables timely action to reduce associated risks</td></tr><tr><td>Effect-based methods</td><td>EBMs</td><td>High-throughput in vitro bioassays (primarily mammalian cell models) and well plate-based in vivo assays (small organisms) for effective environmental quality assessment [19]</td></tr><tr><td>Effect-based trigger value</td><td>EBT</td><td>Thresholds of chemical concentrations, above which adverse responses are expected in organisms</td></tr><tr><td>Effect-directed analysis</td><td>EDA</td><td>A systematic technique to identify the causative agents behind biological responses by fractionating complex chemical mixtures</td></tr><tr><td>Mode of action</td><td>MoA</td><td>Biological responses that a chemical can induce when interacting with living organisms</td></tr><tr><td>Non-target screening</td><td>NTS</td><td>Analytical technique used to detect and identify a wide range of chemical compounds in a sample using high-resolution mass spectrometry [9]. Suspect screening is a subset of NTS that links molecular structures with chemical databases to identify possible matches. In this review, NTS encompasses both suspect and non-target screening</td></tr><tr><td>Relative potency value</td><td>REP</td><td>A measure of the potency of a substance to produce a biological response compared to a reference sample</td></tr></tbody></table>

### 3. Sample acquisition and preparation for an effect-based EWS

A robust EWS for hazardous chemicals starts with the accurate and reliable detection of relevant signals (Fig. 1), which strongly depends on

<table><caption>**Table 2**
Examples of effect-based approaches and EWS-related initiatives for various environmental matrices.</caption><thead><tr><td>Matrix</td><td>Effect-based approaches</td><td>Initiatives related to early warning systems (EWSs)</td></tr></thead><tbody><tr><td>Surface water</td><td>A European-wide demonstration program was carried out for effect-based monitoring of micropollutants in surface waters [27]<br/>US Great Lake monitoring [28]<br/>US surface water screening [29]<br/>A recent review report on EDA in surface waters [30]</td><td>EWS for hydrological hazards in European surface waters [31]<br/>Harmful algae hazardous effects monitoring [32]<br/>EWS for estrogenic effects in surface water [15]</td></tr><tr><td>Drinking water</td><td>EDA has been utilised to investigate tap water along the Yangtze River [33]</td><td>Application of EWS for heavy metals [34]<br/>EWS applied for heavy metal pollution accident in drinking water source [35]</td></tr><tr><td>Soil</td><td>Bioassays conducted for soils affected by mining [36]</td><td>Early warning technologies for crop pest monitoring [37]<br/>EWS for cadmium in rice production [38]</td></tr><tr><td>Sediment</td><td>Seasonal assessment of EDA in riverbank samples [39]<br/>Aryl hydrocarbon receptor (AhR) agonists in sediments [40]<br/>Application of EDA to determine estrogens in electronic waste area [41]</td><td>EWS for trace metals in karst aquifers [42]<br/>Biological EWS based on bivalve monitoring [43]</td></tr></tbody></table>

representative samples that have not been compromised during the sampling and preparation steps, for example through contamination or compound losses. The sampling acquisition and preparation steps are recognised as one of the limiting factors for the detection of low contaminant levels [44]. The importance of these steps is particularly emphasized in EBMs, where chemical enrichment becomes crucial, aiming to increase concentrations to detectable levels while minimising potential interference from the matrices under consideration [45]. In drinking water samples, an enrichment up to 100 times might be needed for detectable effects [46,47]. Another important point is to ensure sample representativeness and integrity. While this is important for any sampling procedure, unspecific responses in the context of an effect-based EWS might increase challenges to identify and exclude sampling artefacts. Quality assurance/control measures are essential to integrate into sampling strategies, including field blanks (in addition to
laboratory blanks) that reflect the sampling procedure.

## 3.1. Sampling techniques

With regard to water samples, sampling methods can be categorised broadly into grab (discrete) and passive sampling. Grab sampling techniques are common for water samples, but might encounter limitations in sample representativeness due to fluctuations in water concentrations [48]. Furthermore, large volumes might be needed to concentrate toxic chemicals to effect-relevant levels. Passive sampling techniques can overcome some of these limitations due to their capacity to concentrate large volumes over extended periods, resulting in lower detection limits [49]. These devices consist of a sorptive medium that is deployed in the monitored matrix (e.g., water, sediment), allowing the analytes of interest to diffuse into the sorbent. Analyte concentrations are subsequently calculated on a time-weighted average basis. Thus, passive sampling methods are generally not designed to capture sudden peaks in contaminant levels, which might be a disadvantage for their use for early warnings. However, individual samples will have to be collected with a high temporal resolution to ensure that contamination peaks are captured, which is a significant consideration in the context of EWSs. Grab samples can also be planned after events that could trigger contaminant peaks, such as precipitation-induced contamination or chemical spills.

In their integration over time, passive samplers first take up chemicals in a linear way, then entering a curvilinear phase and eventually reaching equilibrium between sampler and sampling medium [50]. These uptake phases are sampler- and compound-specific and depend on environmental conditions, such as temperature. Thus, the calibration of a passive sampler is not straightforward and requires equilibrium partitioning coefficients and/or uptake rates [51]. While no absolute concentrations are necessary for subsequent use of extracts in EBMs, it should still be noted that a passive sampler does not collect a substance beyond its equilibrium with the surrounding environment.

As detailed by Booj et al. [52], passive sampling allows measurements of the concentrations of freely dissolved compounds, whereas grab sampling yields total concentrations (if particles are included) or total dissolved concentrations (freely dissolved and colloidally bound). The freely dissolved part of a chemical is directly linked to bioavailability, i.e., the uptake by and reaction with biological systems, as tested in EBMs. Establishing a link between the bioavailable fraction of a chemical and its effect in a bioassay is a strong advantage of passive sampling over other sampling strategies. Based on these principles, several studies have connected passive sampling with effect-based monitoring [53–55].

Sorptive capacities of passive samplers vary according to the physical-chemical properties of the compounds, resulting in different materials used for e.g., hydrophobic and hydrophilic compounds as well as molecules with specific physical-chemical properties such as PFAS [51,56,57]. For the use in EBMs for EWSs, it is important to note that a passive sampler phase typically only covers a certain part of the chemical domain, and several passive sampler materials should be used in combination. Furthermore, extracting sorbed compounds from a passive sampler may lead to the co-extraction of sorbent material, which has been shown for silicone extractions [58] Potentially toxic matrix components need to be removed to avoid effects in the bioassay. Material blanks are essential, to be able to distinguish a positive signal in the sample from an effect potentially caused by the passive sampling material and blank contamination.

For solid samples such as soil and sediment, composite samples improve spatial or temporal coverage of an area without increasing sample number [59], but face the same issue of integrating over potentially varying concentrations. For outdoor and indoor air sampling, both active and passive air sampling methods are common [60]. Active air samplers can provide separate gas- and particle-phase samples from a clearly defined sampling volume, whereas passive air samplers

generate time-integrated data. While they have advantages due to their simplicity and low cost, they face the same limitations as described above. In particular, the frequently used polyurethane foam might contribute a background to the extract that could interfere with EBMs [61]. However, air samples collected with polyurethane passive samplers have been successfully used in toxicity testing [62], although not with a specific early warning purpose. Overall, a combination of methods and materials may be necessary for accurate contaminant signal detection without overlooking short-term changes. In cases where diurnal variations must be monitored, composite samples may also be necessary.

## 3.2. Sample preparation

Following sampling, the subsequent steps typically involve stable sample transport and storage, homogenisation of the samples, extraction of the compounds, clean-up, and concentration [63]. The preparation of samples intended for bioassay analysis comes with certain particularities different from those for chemical analysis. Addition of surrogate standards should be avoided as they can cause false positives in bioassays. To avoid degradation of contaminants of interest, sample preservation might be necessary, which can be achieved by storage at low temperatures (<-20°C), or pH adjustment to 3 or lower during sample storage.

The appropriate method for sample preparation primarily depends on the type of matrix, the properties of the compound (e.g., polarity, volatility, solubility) and the expected concentration levels [59,64]. Common methods for preparation of aqueous samples include solid phase extraction (SPE), which utilises a sorbent phase to selectively retain contaminants, and liquid-liquid extraction, which partitions contaminants using two immiscible liquids. There is a pressing need for the development and optimisation of automated SPE devices designed for the extraction of large-volume water samples [59,65], which is especially relevant in the context of early warning. Recent advancements allow for the extraction of large volume water samples on-site, utilizing mobile SPE stations [66], which can minimise the issues associated with sample transport [67–69]. Other common techniques involve solid phase microextraction (SPME), which uses a fibre coated with sorbent films, and thin-film SPME, utilised for organic compounds with low volatility [65].

There is a trade-off between the concentration of samples and matrix interference. Such issues can be addressed by applying thorough clean-up steps aiming to eliminate interfering substances [70]. However, it is important to recognize that the clean-up process itself will change the composition of the samples, with risks of false negatives. Every step of sample pre-treatment also increases the risks of contamination and thus false positives should be monitored closely through procedural blanks [71].

Method validation for an effect-based EWS should encompass analytical and toxicity recovery [45], as outlined in Fig. 2, and thus address the risk of false negatives. During this process, it is advisable to initially identify a set of candidate analytes as positive controls. These should not only represent various compound groups pertinent to the matrix but also exhibit known activity in one or more of the selected bioassays. The solution should be spiked to the matrix or reference material before extraction, followed by the extraction and clean-up chosen for this effect-based approach. Ideally, the compounds of this solution should be fully recovered in terms of their concentration (target analysis), presence (non-target screening) and toxicity (bioassay testing) (Fig. 2). If fractionation is intended, the sum of the fractions should match the initially added compounds, amounts and toxicity, respectively. The procedure should ensure that biological effects are not caused by the solvent, i.e., negative controls need to be included as well. The fractionation and chemical analyses relate to EDA that will be further addressed in Section 5.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Proposed elements of a method validation procedure for sample preparation in the context of effect-based methods (EBMs) and effect-directed analysis (EDA).</figcaption></figure>

## 4. Effect-based monitoring for EWSs

Bioassays are designed to measure the potency or biological activity of a substance or a mixture of substances by evaluating its effects on an organism, tissue, or cells, thus facilitating toxicity profiling. Quantal bioassays assess binary responses, such as cell death or survival, while graded bioassays measure a spectrum of responses, such as growth inhibition. Moreover, bioassays differ in their mode of action (MoA), including enzyme, receptor and cytotoxicity assays. While a large variety of bioassays exist for various MoAs, some examples that can be relevant in the context of an EWS are presented in Section 4.1 and Table 3. Concepts developed for toxicity identification evaluation in the USA are often directed at whole organism endpoints in the toxicity tests [72].

Bioassays can also vary based on the level of biological organisation, including *in vitro* bioassays, which are conducted in controlled settings using isolated biological components like cells and enzymes, and *in vivo* bioassays, which involve whole living organs or organisms [73]. While *in vivo* bioassays were historically viewed as more biologically relevant, there has been a paradigm shift towards *in vitro* testing in recent years, due to stricter regulations, as well as recent technological advancements bolstering their reliability [12]. Additionally, methods are being developed to extrapolate *in vivo* ecotoxicological data through *in vitro* testing [74]. One such method is the use of adverse outcome pathways, which provide a framework for understanding the linkage between initial molecular events and adverse outcomes on ecosystems [75-77]. Lastly, *in vitro* assays can exhibit higher throughput, rapid response times, and cost-effectiveness [78].

A key technique in EBMs is the reporter gene assay, where genetically modified cells express a detectable reporter protein (e.g., luciferase) regulated by a specific biological process [79]. For instance, if a reporter gene assay targets androgen receptor activity, a responsive DNA sequence is introduced upstream of the gene for a signalling protein. This DNA, typically in plasmid form, is added to cultured cells exposed to environmental samples or compounds of interest. If the sample contains compounds activating the receptor, a complex forms, binding to the DNA sequence and inducing signalling protein expression. Measurement of the signalling protein production, using luminescence or fluorescence, provides a proportional quantitative indication of the overall activity toward the analysed endpoint,

reflecting the impact of all compounds present.

In the context of EWSs, (ultra)high-throughput screening of bioassays is crucial, because timely detection is required and due to the necessity to scrutinize large numbers of samples and complex chemical mixtures. High throughput has traditionally been achieved through optical detection techniques, while more recent efforts have focused on automation [80]. There can be a trade-off between throughput and biological relevance of the bioassays, however, recent advancements have helped overcome this.

Various metrics for use in EWSs and prioritisation can be derived from EBMs, such as the effect concentration, which represents the concentration necessary to induce a specific biological effect. From effect concentrations, bioanalytical equivalent concentrations (BEQs) can be derived and compared with concentrations of identified compounds [81]. Bioassays are also crucial for the determination of EBTs, which can help identify acceptable bioassay responses, enabling their use in regulation.

### 4.1. Bioassays based on MoAs

#### 4.1.1. Endocrine disruption

Endocrine disrupting chemicals include substances that are capable of interfering with hormonal systems, leading to various adverse health effects, such as alterations in sperm quality/quantity, immune system function, growth, cardiovascular problems, increased cancer risk, and more [82]. Endocrine disruptors can induce adverse effects at very low concentrations; therefore, chemical analysis often fails to account for potential risks when concentration levels are below chemical detection limits. However, many effect-based methods for endocrine disruptive effects have much higher sensitivity and can thereby quantify e.g. estrogenic effects at environmentally relevant concentrations, far below the chemical detection limit. While our understanding of the specific mechanisms disrupting these biochemical processes is limited, it is recognised that endocrine disrupting chemicals can operate through various MoAs and interact with different receptors [83]. These include stimulation or inhibition of:

i) Endogenous hormone biosynthesis or degradation

ii) Endogenous receptor expression or degradation

iii) Hormone-receptor binding

iv) Hormone-signalling pathways

v) Binding to circulating hormone-binding proteins

There is a large variety of assays that can be used to monitor endocrine disruption and capture the complexity of their MoAs. Table 3 provides an overview of key biological targets for bioassay testing, focusing specifically on endocrine disruptors within an EWS. A non-exhaustive list of chemicals that can reportedly interact with these receptors is presented. The table shows that a wide variety of chemicals can be detected with these bioassays, including bisphenol A and bisphenol derivatives, POPs, personal care products, pesticides, polycyclic aromatic hydrocarbons (PAHs) etc. Some assays are rather specific, e.g. PCBs binding to the constitutive androstane receptor (CAR), while others respond to multiple chemicals. Bioassay testing has traditionally focused on estrogens, androgens, and thyroid agonists and antagonists, however, in recent years, peroxisome proliferator-activated receptor gamma (PPARγ) and retinoid X receptors have gained increasing attention, because of their crucial roles in regulating metabolism and homeostasis.

#### 4.1.2. Mutagenicity/Genotoxicity

Chemicals exhibiting mutagenic and/or genotoxic MoAs are often deemed more severe than any other potential adverse effects, due to the potential risk to human health. When evaluating the capacity of chemical substances to interact with genetic material, it is crucial to establish the various outcomes this contact can induce. Exposure to substances
<table><caption>**Table 3**
Key biological targets for bioassay testing of endocrine disruptors in an early warning system (EWS).</caption><thead><tr><th>Receptor</th><th>Function</th><th>Endogenous ligands</th><th>Target genes and functions</th><th>Chemicals that may induce a positive response</th><th>Detection method(s)</th><th>References</th></tr></thead><tbody><tr><td>Estrogen receptor (ER)</td><td>Female sexual development</td><td>Estradiol, estriol, and estretrol</td><td>Oxytocin, Vitellogenin</td><td>Bisphenols (e.g. bisphenol A (BPA), tetrachlorobisphenol A (TCBPA), tetrabromobisphenol A (TBBPA)), parabens, phthalates, benzophenone derivative, dioxins, polyaromatic hydrocarbons (PAHs), per- and polyfluoroalkyl substances (PFAS)</td><td>ER assays, lyticase yeast estrogenic screen, ERα-CALUX, MELN cells, planar yeast estrogenic screen, VM7Luc4E2</td><td>[84–88]</td></tr><tr><td>Progesterone receptor (PR)</td><td>Female sexual development</td><td>Progesterone</td><td>Maintaining the menstrual cycle and uterus lining during pregnancy, bone formation as well as being a neurosteroid in the brain</td><td>Musk compounds, BPA, herbicides, insecticides, fungicides</td><td>Autobioluminescent yeast bioassays (e.g., BLYrPRS)</td><td>[89]</td></tr><tr><td>Androgen receptor (AR)</td><td>Male sexual development, anabolic steroid</td><td>Testosterone, dihydrotestosterone (DHT)</td><td>Anabolic metabolism, cell proliferation, cell migration, production of seminal fluid</td><td>Dioxins, polychlorinated biphenyls (PCBs), phthalates, BPA</td><td>Yeast androgen bioassays, cell proliferation assays, receptor binding, reporter gene</td><td>[90–92]</td></tr><tr><td>Aryl hydrocarbon receptor (AhR)</td><td>Xenobiotic metabolism, cellular differentiation, stem cell maintenance, immune responses, neurogenesis, circadian rhythm</td><td>5α-THB and 5β-THB</td><td>Cytochrome P450s (CYPs)</td><td>PCBs, dioxins, BPA, PAHs, pesticides</td><td>DR-CALUX, H4IIE-luc cells</td><td>[93–97]</td></tr><tr><td>Thyroid hormone (TR)</td><td>Metabolism, heart rate</td><td>Thyroid hormones (THs)</td><td>CYPs as well as various transcription factors and regulatory proteins</td><td>PCBs, BPA, dioxins, furans, polybrominated diphenyl ethers (PBDEs) and other flame retardants, phthalates pesticides, perchlorates, phytoestrogens, PFAS</td><td>TTR-TRβ, CALUX</td><td>[98]</td></tr><tr><td>Glucocorticoid receptor (GR)</td><td>Metabolism, water and electrolyte balance, immune response, normal bone development, maintenance of the cardiovascular system, stress response, reproduction</td><td>Cortisol</td><td>Anti-inflammatory, metabolic, stress response, developmental (e.g. Klf9, Zbtb16)</td><td>Bisphenols, organotins, metabolites of certain organochlorine pesticides, phthalates</td><td>Reporter gene, receptor binding, flow cytometry, imaging assays</td><td>[99,100]</td></tr><tr><td>Peroxisome proliferator-activated receptors (PPARs) with Retinoid X receptors (RXRs)</td><td>Lipid, fatty acid and cholesterol metabolism</td><td>Lipids (PPARs) Retinoic acid (RXR)</td><td>Cytochrome P450s (CYPs), glutathione S-transferase (GST)</td><td>BPA, organotins, phthalates</td><td>DPI-ELISA (DNA-Protein-Interaction Enzyme-Linked Immunosorbent Assay), Luciferase reporter gene, receptor binding</td><td>[101,102, 102]</td></tr><tr><td>Pregnane X receptor (PXR)</td><td>Xenobiotic and pharmaceutical metabolism</td><td>5 β-cholestane-3α, 7α, 12α-triol steroids</td><td>Cytochrome P450s (CYPs)</td><td>Pharmaceuticals (e.g. antibiotics, sedatives, antineoplastics), BPA, PCBs</td><td>Reporter gene (e.g., luciferase, β-galactosidase, receptor binding</td><td>[103]</td></tr><tr><td>Constitutive androstane receptor (CAR)</td><td>Xenobiotic detoxification</td><td>Androstane and derivatives</td><td>CYP2B, CYP3A, CYP2Cs, OATP2, MRP2, UGT1A1</td><td>PCBs</td><td>Reporter gene, receptor binding, transactivation assays</td><td>[104,105]</td></tr></tbody></table>

that damage DNA can lead to genetic instabilities and/or changes in gene expression patterns, which in turn can result in various disorders, such as cancer [106]. Mutagenicity refers to persistent and heritable alterations in the quantity and composition of genetic material. Genotoxicity can encompass mutagenicity; however, not all genotoxic substances are mutagenic, as exposure to them may not induce changes in the genetic material.

The assessment of genotoxicity is a key component of the evaluation of surface water quality. Numerous EBMs permit the evaluation of genotoxicity. Several priority substances of the EU Water Framework Directive have mutagenicity or genotoxicity properties (e.g. PAHs, benzene). Annex VIII of the directive identifies compounds “that possess carcinogenic or mutagenic properties” as among the main pollutants. In vitro EBMs for the detection of mutagenic and clastogenic potentials are

used under REACH (Council Regulation (EC) No 440/2008).

Several EBMs can be used to assess genotoxicity in the presence or absence of an external metabolic activation, e.g. by the use of S9-mix in the Ames or micronucleus tests, Comet assay, P53 assay, SOS-umu test, SOS-chromo test and others. No single test can detect all genotoxic mechanisms. Therefore, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use recommends using a combination of mutagenicity tests, known as a battery of tests. Examples of tests that can be used for mutagenicity and genotoxicity tests in the context of EWSs, are presented in the following. The selection of tests can vary depending on the aim of the study or monitoring programme. The typical format of a standard test battery involves:
i. A bacterial reverse gene mutation test (e.g., Ames test), to identify potential genetic changes and detect genotoxic substances

ii. Assessment of genotoxicity in mammalian cells *in vivo/in vitro*

The Ames test, also known as the “reversion assay,” is a common method for assessing chemical compounds’ mutagenic potential using specific bacteria strains. In the early 1970s, this simple and fast bioassay used Salmonella typhimurium/E. coli strains with histidine operon mutations [107,108]. These strains, termed auxotropic mutants, lack the ability to synthesize histidine and thus cannot grow on histidine-deficient agar; however, exposure to mutagenic compounds or spontaneous mutations can restore histidine synthesis and colony formation. For the Ames tests several strains can be used to detect different types of mutations. The Ames test and the Ames Fluctuation Test are standardised according to International Standardisation Organisation (ISO) (ISO 16240:2005, ISO 11350:2012)

The Comet assay is a sensitive and economical technique for checking DNA strand fractures in eukaryotic cells. The process entails embedding cells within agarose gel and subsequently lysing them with detergents and salt. The comet tail, an elongated representation of the cell, is generated when fragmented DNA migrates more rapidly through an agarose matrix under the influence of an electrical current [109]. This technique is employed in human monitoring studies and ecotoxico-cological studies in various sentinel organisms [110–114].

Chromosome aberration tests can be used to identify potential risks associated with mutagenic xenobiotics [115]. These involve mammalian cells (e.g., human peripheral blood lymphocyte cultures) exposed to suspected genotoxic compounds in the presence or absence of S9 mix. Exposure to mutagenic xenobiotics can cause DNA damage, including double-strand breaks and structural chromosome aberrations.

The micronucleus assay is a method designed to investigate genotoxicity by detecting micronuclei in the cytoplasm of cells during the development phase and it is useful for the evaluation of chromosome structure. It has been included in the Organisation for Economic Co-operation and Development (OECD) guidelines for chemical testing [116]. The micronucleus assay is standardised according to ISO (ISO 21427-1:2006, parts 1 and 2).

In general, genotoxicity tests can be used to assess the combined action of potentially hazardous compounds present in surface waters in complex mixtures highlighting synergistic, additive and antagonist effects at sub-lethal concentrations [107]. EBTs could be unnecessary because the qualitative detection of mutagenicity in a waterbody is a signal (Yes/No) of status. Therefore, the establishment of a threshold is of limited practical utility after definition of a suitable enrichment procedure [14].

Overall, it is recommended to employ a test battery comprising the Ames test, Comet assay, and micronucleous assay, to comprehensively assess genotoxicity in EWSs for emerging and traditional chemicals, including mixture effects.

### 4.1.3. Neurotoxicity

Neurotoxicity refers to the ability of chemical, biological, or physical agents, to induce adverse functional or structural alterations in the nervous system. Developmental neurotoxicity is particularly concerned with the effects of toxicants on the developing nervous system of organisms. The developing brain and nervous system is more sensitive to toxic effects than the mature brain and nervous system. The number of neuroactive compounds released into the ecosystems has increased over the past few years, and there is, therefore, a growing interest in assessing the related potential risks for environmental and human health [117]. A literature study comparing 30 different MoAs estimated that neuroactive compounds formed the largest category (13 %) [118]. Moreover, the neuroactive compounds in ecosystems can be present at low levels, and can also form synergistic mixtures with unknown effects.

The selection of *in vitro* assays for neurotoxicity could be guided by adverse outcome pathways relevant for eco-neurotoxicity [14]. The

assessment of (developmental) neurotoxicity is possible on non-mammalian cells since the mechanisms underlying the development and function of the nervous system are well conserved across the phylogenetic tree. Many biochemical processes are identical in mammals and in non-mammalian species. For example, the zebrafish (*Danio rerio*) represents a suitable model for chemical testing, owing to its small size, embryonic transparency, and rapid development. Integrating data (*in vitro* and *in vivo*) from diverse sources, can provide a comprehensive understanding of neurotoxic effects of chemical pollutants, both in terms of ecological and human health [119]. Behavioural analyses, such as the study of locomotion, have been proposed as critical in detecting potential neuroactive effects [120]. Furthermore, the spontaneous tail coiling in zebrafish embryos might represent another important endpoint in neurotoxicity assessment [121]. This method allows a very quick screening since it can be performed within 24 h.

The main neurotoxicity endpoints for zebrafish that can be used in EWSs are:

i. Locomotor activity. Monitoring swimming behaviour in zebrafish is key for neurotoxicity detection, with locomotion responses comparable to mammals [122]. Selderslaghs et al. [123] elaborated a new method for locomotor analysis, with an assessment at 96, 120, 144, 168 and 192 h post fertilisation using a camera with behavioural tracking software.

ii. Acetylcholinesterase. Screening zebrafish for neurotoxicity induced by chemicals on the cholinergic system is common, and is linked with several cognitive functions and processes [124].

iii. Lateral tail movements. These are the first spontaneous behaviours observed in zebrafish embryos. The tail coiling test consists of the evaluation of spontaneous tail coiling frequency in zebrafish embryos, as a possible indicator of neurotoxic compounds [125].

### 4.2. Quality control of bioassays

Quality assurance and control considerations are crucial to reduce the risk of false positives or negatives in EBMs and thus ensure the accuracy and reliability of the results (Fig. 2). Bioassays can exhibit variability between tests, which can be mitigated by incorporating a biological reference material or a chemical standard with known activity. This can help with calibration of bioassays and improve their comparability. Evaluation of accuracy, precision, sensitivity, and robustness is essential during method validation, along with assessing potential matrix effects and technical considerations [126]. The overall quality can be assessed by a z-factor developed for bioassays, based on high and low level reference materials [127]. The OECD and the ISO provide test guidelines for commonly used bioassays (e.g., estrogenic effects, androgenic effects, genotoxicity, acute toxicity in fish cells) to ensure method consistency and reliability.

## 5. Effect-directed analysis (EDA)

### 5.1. Principle of EDA

Introduced in the early 1980s, EDA aims to identify drivers of toxicity in environmental samples, employing a fractionation strategy [70,128]. Utilizing NTS approaches, EDA is a powerful tool for characterising known toxicants and identifying unknown chemicals in complex environmental mixtures [129]. While EBMs contribute to an EWS through toxicity alerts, EDA takes this signal a step further by attempting to identify the specific toxicant, which can be essential for risk assessors and regulators. Recent progress in high-resolution fractionation methods coupled with NTS has significantly enhanced the efficiency and effectiveness of EDA, reducing the time required for fractionation to a matter of seconds [130]. This development positions EDA comparably to EBMs in terms of time requirements, as both methodologies utilize
similar *in vitro* assays.

In the EWS context, EDA plays a crucial role in identifying toxic CECs through effect-based experiments, fractionation, and analytical chemistry tools, particularly high-resolution mass spectrometry [128]. In a typical EDA workflow, environmental extracts undergo testing for toxicological effects using one bioassay or a series of bioassays. The extract displaying effects is then fractionated based on physicochemical characteristics such as polarity, vapour pressure, and molecular size. Each fraction is individually tested for its ability to reproduce the observed effect using the same bioassays. The active fractions are selected for further analysis to characterize the responsible toxic compound(s) in NTS approaches. Finally, the presence of these chemicals is validated by comparing with analytical standards. Similar to EDA approaches, toxicity identification evaluation systems have been applied, mainly outside of Europe. The concept is essentially the same, but toxicity tests include several phases and endpoints have mainly been the survival, growth, and reproduction of whole organisms [72].

Over the last forty years, a variety of EDA methods have been
developed and applied to identify the key contributors to toxicity across
a range of environmental media, including sediments, soils, sludge, air
particulate matter, effluents, surface water, and groundwater [45,59].
However, the major challenge in applying and implementing EDA lies in
analysing biota samples, with limitations including selection of bio-
assays and toxicity tests, sample preparation and clean-up steps,
selecting representative organs containing toxicants, identifying un-
known chemicals, confirming identified chemicals, and the limitations
as a routine monitoring tool [131].

5.2. Fractionation methods

Fractionating a mixture of chemicals is a key component in EDA, to reduce the complexity of the samples and assess chemical mixture effects [13,45]. Fractionation should avoid any losses of relevant compounds (causing false negatives) as well as contamination (causing false positives), while achieving a stepwise separation based on physicochemical properties of the response-inducing compounds.

Column chromatography is the most prevalent fractionation tech-
nique, with liquid chromatography (LC) and gas chromatography (GC),
targeting semipolar to polar and semipolar to nonpolar substances,
respectively. Fractionation includes time-dependent fractions or signal-
induced fractions using simultaneous UV-detection [13,132]. Recent
advancements in fractionation allow for downscaling the process,
improving possibilities of applying EDA in routine chemical screening
[133]. Automated fractionation speeds up the process in EDA, combined
with ≥96-well plates for replicable toxicity data [128]. The multi-well
plate approach minimizes handling, solvent exchange, and evapora-
tion steps, enhancing throughput and reducing risks of losses or
contamination [134]. This method aligns with various *in vitro* and
small-scale *in vivo* bioassays, including the zebrafish embryo toxicity
assay.

Quality assurance measures to confirm that the fractionation process
does not result in the loss of toxic components involve reconstituting
fractions into a complete mixture; if there are no losses, the recon-
stituted sample should exhibit the same toxicity as the unfractionated
one. However, it is worth noting during effect-based recovery evaluation
that removing matrix or compounds may increase overall effects, as seen
in mutagenicity and *in vitro* endocrine effects where the presence of
antagonists may conceal an effect [135]. To enhance the accuracy of
toxicity recovery evaluation, it is therefore advisable to complement it
with analytical recovery assessment. This approach also helps identify
compound groups and properties responsible for inadequate toxicity
recovery.

5.3. Identification and confirmation of toxicants

Identifying toxicants in complex mixtures involves analytical

techniques such as NTS. However, identifying compounds in a toxic
fraction does not prove causation and confirmation steps are needed.
These approaches include analytical confirmation, *in vitro* or *in vivo* ef-
fect confirmation, and hazard confirmation under realistic exposure
conditions. The toxicity confirmation can follow a tiered approach; the
first step focuses on confirming tentatively identified structures of tox-
icants by comparing mass spectra with standard compounds, and the
second tier aims to confirm that the identified compounds are indeed
causing the observed toxicity, for instance through correlation analysis,
which investigates whether there is a consistent relationship between
the concentration, or spiking tests, which involves spiking samples with
additional amounts of the suspected toxicants and retesting [136].

A common challenge is the lack of analytical standards. Data sources provide varying levels of confirmation, from confirmed structures to tentative candidates with evidence for possible structures [45]. A set of suspect lists for high resolution mass spectrometry data evaluation and NTS guidance has been compiled under the umbrella of the NORMAN network [137,138]. In the context of EWSs, the need for signal validation needs to be balanced with the wish of an early alert that enables follow-up action.

5.4. Virtual EDA

Multivariate statistics, particularly partial least square analysis, can integrate chemical and toxicological data in EDA [139,140]. This method identifies co-varying chemical signals, acting as a “virtual fractionation” to pinpoint candidate chemicals explaining effects, even from unidentified sources. It enhances understanding beyond traditional methods and could be a valuable component in an EWS, but success may require many samples.

5.5. *Iceberg modelling*

Iceberg modelling describes comparisons between BEQs derived from bioassays (BEQbio) and chemical analysis (BEQchem) [141,142]. BEQchem is calculated from the measured concentration of a given compound in a sample and its relative effect potency [141], assuming additive effects of the chemicals sharing a common MoA. In this concept, BEQbio represents the total toxicity (the iceberg) whereas BEQchem only represents the visible part (the tip of the iceberg). For example, in a study on the influence of rain events on chemical pollution of rivers, 290 measured chemicals only explained 8 % of the total toxicity in the samples [143]. Thus, iceberg modelling provides a quantitative indication of how well toxicity-drivers can be identified. Samples with large differences between BEQbio and BEQchem (i.e. a small BEQchem/BEQbio ratio) could be prioritized for EDA and other follow-up action under an EWS.

6. Workflow of an effect-based EWS

A potential workflow of incorporating effect-based methods in an
EWS for chemicals is presented in Fig. 3. After sampling the relevant
matrices and conducting the appropriate sample preparation, the first
step of the EWS is based on EBMs using bioassays, such as those detailed
in Section 4. If the results are in compliance with EBTs or preliminary
limit values associated to the specific bioassays, no further action is
required. If the effect-based screening results exceed the EBTs or pre-
liminary limit values, this can constitute an initial early warning signal,
leading to further investigation for confirmation of the signal. This
confirmation process will initially focus on strengthening the signal and
will subsequently address the causality investigation, as also outlined in
Fig. 1. Strengthening of the signal entails additional investigative ac-
tions, including existing data to assess the spatial and temporal occur-
rence as well as the intensity of the signal. It is important to assess
whether this signal occurs at a local level or has broader implications,
impacting the environment on a large scale. This investigation
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The proposed workflow for an effect-based early warning system (EWS) for chemicals, after sampling and sample preparation. EBT: Effect-based trigger value. EDA: Effect-directed analysis.</figcaption></figure>

necessitates retrieving as far as possible existing information concerning the pressures and activities within the area where the signal occurred. Such data can serve as additional evidence to reinforce the signal. Additionally, the generation of new monitoring data, while potentially time-consuming, may prove essential.

If the signal lacks sufficient support due to insufficient data or if the available data exhibit poor quality, the signal should be archived and placed on a waiting list until more robust evidence becomes available (Fig. 3). If the criteria for confirmation of the signal are met, the actions for toxicant discovery should take place to identify the contaminants responsible for the observed effects and the sources. Toxicant discovery can be conducted via iceberg modelling and EDA, as described in Section 5. If iceberg modelling cannot satisfactorily explain the observed effect, or if it is not possible to perform iceberg modelling, e.g. due to lack of relative potency values, EDA can be used to confirm the causes of the bioassay effects, through identifying the toxicant structures and verifying their correlation (Fig. 3).

In some cases, it is possible to anticipate the contaminants most likely linked to the activity detected by the bioassay. When such predictions are feasible, it becomes possible to initiate management actions without need for final proof of specific toxic driver(s) from the chemical analysis. It is important in EWSs to expedite the process and enable

action despite time-consuming steps for chemical analysis and identification of compounds. Finally, in the case of a confirmed early warning signal through this process, the signal can be communicated to the relevant policy makers, enabling appropriate risk management actions (Fig. 3). These can involve regulatory measures, environmental remediation actions, enhanced monitoring efforts, and public health interventions, for example by conducting population exposure screenings.

## 7. Concluding remarks and future perspectives

The development of an EWS for hazardous chemicals in the environment represents a significant leap towards proactive environmental risk management. Although discussions regarding the design and implementation of such an EWS are still at an early stage, the vision of an effect-based EWS is increasingly attainable. This review has highlighted recent advancements in effect-based methods and EDA, facilitating their integration within an EWS framework.

The proposed workflow is an initial step toward an EWS for chemicals. Several limitations must be addressed to optimise the system's effectiveness. One primary challenge is the complexity of integrating biological and chemical data which can be labour-intensive and technically demanding. Innovative techniques such as NTS and EDA need
more harmonisation to ensure comparability and reproducibility. Implementing these techniques on a routine basis requires significant investment in technology, personnel, and training.

Timeliness is another concern, as delays in data collection, analysis, and response can undermine the primary goal of early detection. The optimisation of real-time technologies (e.g., auto-sampling or chemical sensors) and the enhancement of high-throughput EDA are expected to facilitate improvements in this direction. Ensuring the sensitivity and specificity of bioassays is crucial to avoid false positives or negatives. Multiplexed bioassays can hold significant promise in detecting multiple analytes in a single test. EBTs should also be carefully calibrated to avoid setting thresholds that are too high or too low.

Despite these challenges, the future of an effect-based EWS for hazardous chemicals holds considerable promise. Continuous advancements in bioanalytical tools and sampling techniques will likely enhance the system's capabilities. Future research is expected to improve data integration methods, enhancing the sensitivity and specificity of bioassays, and developing more robust methodologies. New approach methodologies (NAMs) are expected to expand the toolbox of EBMs, although their standardisation and validation should be improved.

The long-term sustainability of an EWS will depend on adapting to technological advancements while ensuring robustness. In EDA, ongoing research aims to enhance throughput through miniaturisation of bioassays and the application of multidimensional fractionation tools (GCxGC, LCxLC). Improved workflows for mass spectral data analysis are also expected to increase the rate of successful chemical identifications. Additionally, more comprehensive studies on the bioavailability and effects of chemical mixtures will provide deeper insights, leading to better-informed decision-making. While this review focuses on environmental samples, the potential for an effect-based EWS can be expanded to other matrices, such as products, food and human samples. However, more research is needed to validate EBMs and EDA in other matrices.

It is important to note that the success of an EWS depends on regulatory and policy alignment. Ensuring that the system is connected to existing policy frameworks and that regulatory bodies can respond promptly is essential. Constant technological investment accompanied with training and education is required, to ensure state-of-the-art technologies and improve the early detectability of problematic chemicals, for example through real-time monitoring and high-throughput analysis. Data integration should be implemented robustly, creating platforms that can handle the complex datasets generated by EBMs, NTS, and EDA. Developments in computational tools including machine learning will offer possibilities for further automation and efficiency in signal identification and the related processes (Fig. 1). Feedback from practical applications should be used in a systematic way to improve the EWS. Case studies testing the conceptual workflow of the effect-based EWSs in real-world applications will help evaluate the benefits and limitations, and apply modifications to the suggested workflow. These steps can form the basis for future guidelines for implementation of an effect-based EWS, aligned with the goal of proactive environmental and public health protection.

### CRediT authorship contribution statement

**Georgios Niarchos:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Conceptualization. **Nikiforos Alygizakis:** Writing – review & editing, Investigation, Conceptualization. **Mario Carere:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. **Valeria Dulio:** Writing – original draft, Visualization, Methodology, Conceptualization. **Magnus Engwall:** Writing – original draft, Visualization, Conceptualization. **Tuulia Hyötyläinen:** Writing – original draft, Conceptualization. **Roland Kallenborn:** Writing – original draft, Conceptualization. **Spyros Karakoltzidis:** Writing – original draft, Conceptualization.

**Anna Kärrman:** Writing – original draft, Conceptualization. **Marja Lamoree:** Writing – review & editing, Writing – original draft, Conceptualization. **Maria Larsson:** Writing – review & editing, Writing – original draft, Visualization, Conceptualization. **Johan Lundqvist:** Writing – review & editing, Writing – original draft, Conceptualization. **Laura Mancini:** Writing – original draft. **Javad Mottaghipisheh:** Writing – review & editing, Writing – original draft. **Pawel Rostkowski:** Writing – review & editing, Conceptualization. **Dimosthenis Sarigiannis:** Funding acquisition, Conceptualization. **Katrin Vorkamp:** Writing – review & editing, Writing – original draft, Conceptualization. **Lutz Ahrens:** Writing – review & editing, Methodology, Funding acquisition, 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.

### Data availability

No data were used for the research described in the article.

### Acknowledgments

This work was carried out in the framework of the European Partnership for the Assessment of Risks from Chemicals (PARC) and has received co-funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101057014. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. The authors also wish to thank their national funding sources for supplementary funding.

### References

[1] UNEP, Global Chemicals Outlook II: From Legacies to Innovative Solutions, United Nations Environment Programme (UNEP), Geneva, 2019.

[2] ECHA, REACH - Registration, Evaluation, Authorisation and Restriction of Chemicals Regulation — Registered Substances Factsheets, European Chemicals Agency. https://echa.europa.eu/information-on-chemicals/registered-substances (accessed 2024/08/05).

[3] L. Persson, et al., Outside the safe operating space of the planetary boundary for novel entities, Environ. Sci. Technol. 56 (3) (2022) 1510–1521.

[4] UNEP, "Stockholm Convention on persistent organic pollutants (POPs) - Text of the Convention." https://www.pops.int/TheConvention/Overview/Textofthe Convention/tabid/2232/Default.aspx (accessed 2024/08/05).

[5] D. Muir, et al., Levels and trends of poly- and perfluoroalkyl substances in the Arctic environment – an update, Emerging Contam. 5 (2019) 240–271.

[6] H. Frederiksen, et al., Exposure to 15 phthalates and two substitutes (DEHTP and DINCH) assessed in trios of infants and their parents as well as longitudinally in infants exclusively breastfed and after the introduction of a mixed diet, Environ. Int. 161 (2022) 107107.

[7] F. Borchert, Managing Chemical Risks in the EU: Data Use and New Approaches for Decision-Making, Department of Environmental Science, Stockholm University, 2024.

[8] Z. Wang, I.T. Cousins, M. Scheringer, K. Hungerbuehler, Hazard assessment of fluorinated alternatives to long-chain perfluoroalkyl acids (PFAAs) and their precursors: status quo, ongoing challenges and possible solutions, Environ. Int. 75 (2015) 172–179.

[9] B. González-Gaya, et al., Suspect and non-target screening: the last frontier in environmental analysis, Anal. Methods 13 (16) (2021) 1876–1904.

[10] D. Muir, X. Zhang, C.A. de Wit, K. Vorkamp, S. Wilson, Identifying further chemicals of emerging arctic concern based on ‘in silico’ screening of chemical inventories, Emerging Contam. 5 (2019) 201–210.

[11] H. Xiao, et al., Effect-directed analysis of aryl hydrocarbon receptor agonists in sediments from the Three Gorges Reservoir, China, Environ. Sci. Technol. 50 (20) (2016) 11319–11328.

[12] A.-S. Wernersson, et al., The European technical report on aquatic effect-based monitoring tools under the water framework directive, Environ. Sci. Eur. 27 (1) (2015) 7.
[13] W. Brack, Effect-directed analysis: a promising tool for the identification of organic toxicants in complex mixtures? Anal. Bioanal. Chem. 377 (3) (2003) 397-407.

[14] EC, Technical Proposal for Effect Based Monitoring and Assessment under the Water Framework Directive, 2021.

[15] E. Simon, et al., Biological effect and chemical monitoring of Watch List substances in European surface waters: steroidal estrogens and diclofenac – effect-based methods for monitoring frameworks, Environ. Int. 159 (2022) 107033.

[16] EU Commission, Proposal for a Directive Amending the Water Framework Directive, the Groundwater Directive and the Environmental Quality Standards Directive, vol. 540, COM, 2022, p. 2022.

[17] J. van Dijk, M. Gustavsson, S.C. Dekker, A.P. van Wezel, Towards 'one substance – one assessment': an analysis of EU chemical registration and aquatic risk assessment frameworks, J. Environ. Manag. 280 (2021) 111692.

[18] Proposal for a Directive of the European Parliament and of the council amending Directive 2011/65/EU of the European Parliament and of the Council as regards the re-attribution of scientific and technical tasks to the European Chemicals Agency. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2023%3A781%3AFAIN, 2023.

[19] P.A. Neale, et al., Application of effect-based methods to water quality monitoring: answering frequently asked questions by water quality managers, regulators, and policy makers, Environ. Sci. Technol. 57 (15) (2023) 6023-6032.

[20] European Commission, Study for the Strategy for a Non-toxic Environment of the 7th Environment Action Programme – Final Report, Publications Office, 2017.

[21] Directorate-General for Environment, Proposal for a Regulation Establishing a Common Data Platform on Chemicals, 2023.

[22] P. Marx-Stoelting, et al., A walk in the PARC: developing and implementing 21st century chemical risk assessment in Europe, Arch. Toxicol. 97 (3) (2023) 893-908.

[23] N.G.M. Palmen, Early Warning Systems to Detect New and Emerging Risks in Europe, 2016.

[24] G. Goldenman, et al., Study for the Strategy for a Non-toxic Environment of the 7th Environment Action Programme, European Commission, 2017.

[25] T. Backhaus, et al., Assessing the ecological impact of chemical pollution on aquatic ecosystems requires the systematic exploration and evaluation of four lines of evidence, Environ. Sci. Eur. 31 (2019) 1-9.

[26] R. Altenburger, et al., Future water quality monitoring: improving the balance between exposure and toxicity assessments of real-world pollutant mixtures, Environ. Sci. Eur. 31 (1) (2019) 1-17.

[27] Z. Tousova, et al., European demonstration program on the effect-based and chemical identification and monitoring of organic pollutants in European surface waters, Sci. Total Environ. 601-602 (2017) 1849-1868.

[28] D.R. Ekman, et al., Biological effects-based tools for monitoring impacted surface waters in the Great Lakes: a multiagency program in support of the Great Lakes Restoration Initiative, Environ. Pract. 15 (4) (2013) 409-426.

[29] B.R. Blackwell, et al., Potential toxicity of complex mixtures in surface waters from a nationwide survey of United States streams: identifying in vitro bioactivities and causative chemicals, Environ. Sci. Technol. 53 (2) (2019) 973-983.

[30] A. Yusuf, et al., Monitoring of emerging contaminants of concern in the aquatic environment: a review of studies showing the application of effect-based measures, Anal. Methods 13 (43) (2021) 5120-5143.

[31] L. Alfieri, P. Salamon, F. Pappenberger, F. Wetterhall, J. Thielen, Operational early warning systems for water-related hazards in Europe, Environ. Sci. Pol. 21 (2012) 35-49.

[32] J.H. Kim, et al., Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method, Water Res. 207 (2021) 117821.

[33] J. Guo, et al., Effect-directed analysis based on the reduced human transcriptome (RHT) to identify organic contaminants in source and tap waters along the yangtze river, Environ. Sci. Technol. 56 (12) (2022) 7840-7852.

[34] Z. Liu, Z. Zhang, J. Sang, Y. Yin, X. Ding, Development and application of early warning system for heavy metal pollution accident in drinking water source area, Process Saf. Environ. Protect. 183 (2024) 293-306.

[35] S. Hong, J. Lee, J. Cha, J. Gwak, J.S. Khim, Effect-directed analysis combined with nontarget screening to identify unmonitored toxic substances in the environment, Environ. Sci. Technol. 57 (48) (2023) 19148-19155.

[36] O. Andreu-Sánchez, et al., Soil and freshwater biohauls to assess ecotoxicological impact on soils affected by mining activities in the iberian pyrite belt, Toxics 10 (7) (2022) 353. https://www.mdpi.com/2305-6304/10/7/353.

[37] Q. Wu, J. Zeng, K. Wu, Research and application of crop pest monitoring and early warning technology in China, Frontiers of Agricultural Science and Engineering 9 (1) (2022) 19-36.

[38] H. Yan, et al., High-precision early warning system for rice cadmium accumulation risk assessment, Sci. Total Environ. 859 (2023) 160135.

[39] D. Oberleitner, L. Stütz, W. Schulz, A. Bergmann, C. Achten, Seasonal performance assessment of four riverbank filtration sites by combined non-target and effect-directed analysis, Chemosphere 261 (2020) 127706.

[40] J. Lee, et al., Identification of AhR agonists in sediments of the Bohai and Yellow Seas using advanced effect-directed analysis and in silico prediction, J. Hazard Mater. 435 (2022) 128908.

[41] Q. Ma, et al., Effect-directed analysis of estrogenic chemicals in sediments from an electronic-waste recycling area, Environ. Pollut. 306 (2022) 119369.

[42] N. Ravbar, J. Mulec, C. Mayaud, M. Blatnik, B. Kogovšek, M. Petrić, A comprehensive early warning system for karst water sources contamination

risk, case study of the Unica springs, SW Slovenia, Sci. Total Environ. 885 (2023) 163958.

[43] A. Pilbala, et al., Real-time biological early warning system based on freshwater mussels' valvometry data, EGUSphere 2023 (2023) 1-21.

[44] A. Charriau, S. Lissalde, G. Poulier, N. Mazzella, R. Buzier, G. Guibaud, Overview of the Chemcatcher® for the passive sampling of various pollutants in aquatic environments Part A: principles, calibration, preparation and analysis of the sampler, Talanta 148 (2016) 556-571.

[45] W. Brack, et al., Effect-directed analysis supporting monitoring of aquatic environments – an in-depth overview, Sci. Total Environ. 544 (2016) 1073-1118.

[46] B. Escher, P. Neale, F. Leusch, Sampling, sample preparation and dosing, in: Bioanalytical Tools in Water Quality Assessment, IWA Publishing, 2021.

[47] M.E. Müller, B.I. Escher, M. Schwientek, M. Werneburg, C. Zarfl, C. Zwiener, Combining in vitro reporter gene bioassays with chemical analysis to assess changes in the water quality along the Ammer River, Southwestern Germany, Environ. Sci. Eur. 30 (1) (2018) 20.

[48] D.A. Alvarez, et al., Comparison of a novel passive sampler to standard water-column sampling for organic contaminants associated with wastewater effluents entering a New Jersey stream, Chemosphere 61 (5) (2005) 610-622.

[49] F.Y. Lai, C. Rauert, L. Gobelius, L. Ahrens, A critical review on passive sampling in air and water for per- and polyfluoroalkyl substances (PFASs), TrAC, Trends Anal. Chem. 121 (2019) 115311.

[50] P. Mayer, J. Tolls, J.L. Hermens, D. Mackay, Peer reviewed: equilibrium sampling devices, Environ. Sci. Technol. (2003) 184A-191, https://doi.org/10.1021/es032433i.

[51] T.P. Rusina, F. Smedes, M. Koblizkova, J. Klanova, Calibration of silicone rubber passive samplers: experimental and modeled relations between sampling rate and compound properties, Environ. Sci. Technol. 44 (1) (2010) 362-367.

[52] K. Booij, et al., Passive sampling in regulatory chemical monitoring of nonpolar organic compounds in the aquatic environment, Environ. Sci. Technol. 50 (1) (2016) 3-17.

[53] A.K. Rosenmai, et al., Effect-based assessment of recipient waters impacted by on-site, small scale, and large scale waste water treatment facilities–combining passive sampling with in vitro bioassays and chemical analysis, Sci. Rep. 8 (1) (2018) 17200.

[54] M. Sonavane, et al., An integrative approach combining passive sampling, bioassays, and effect-directed analysis to assess the impact of wastewater effluent, Environ. Toxicol. Chem. 37 (8) (2018) 2079-2088.

[55] Z. Tousová, et al., Analytical and bioanalytical assessments of organic micropollutants in the Bosna River using a combination of passive sampling, bioassays and multi-residue analysis, Sci. Total Environ. 650 (2019) 1599-1612.

[56] R. Verhagen, et al., Time-integrative passive sampling of very hydrophilic chemicals in wastewater influent, Environ. Sci. Technol. Lett. 7 (11) (2020) 848-853.

[57] S.E. Hale, B. Canivet, T. Rundberget, H.A. Langberg, I.J. Allan, Using passive samplers to track Per and Polyfluoroalkyl Substances (PFAS) emissions from the paper industry: laboratory calibration and field verification, Front. Environ. Sci. 9 (2021) 796026.

[58] B. Brockmeyer, U.R. Kraus, N. Theobald, Accelerated solvent extraction (ASE) for purification and extraction of silicone passive samplers used for the monitoring of organic pollutants, Environ. Sci. Pollut. Control Ser. 22 (2015) 19887-19895.

[59] S. Huang, et al., Recent advances in sampling and sample preparation for effect-directed environmental analysis, TrAC, Trends Anal. Chem. (2022) 116654.

[60] L. Ahrens, T. Harner, M. Shoeib, M. Koblizkova, E.J. Reiner, Characterization of two passive air samplers for per-and polyfluoroalkyl substances, Environ. Sci. Technol. 47 (24) (2013) 14024-14033.

[61] S. Papazian, C. Fornaroli, B. Bonneille, E. Pesquet, H. Xie, J.W. Martin, Silicone foam for passive sampling and nontarget analysis of air, Environ. Sci. Technol. Lett. 10 (11) (2023) 989-997.

[62] A. Érseková, K. Hilscherová, J. Klánová, J.P. Giesy, J. Novák, Effect-based assessment of passive air samples from four countries in Eastern Europe, Environ. Monit. Assess. 186 (2014) 3905-3916.

[63] L. Martín-Pozo, B. de Alarcón-Gómez, R. Rodríguez-Gómez, M.T. García-Córcoles, M. Çipa, A. Zafra-Gómez, "Analytical methods for the determination of emerging contaminants in sewage sludge samples. A review," Talanta 192 (2019) 508-533.

[64] T. Ruan, P. Li, H. Wang, T. Li, G. Jiang, Identification and prioritization of environmental organic pollutants: from an analytical and toxicological perspective, Chem. Rev. 123 (17) (2023) 10584-10640.

[65] J.B. Wilcockson, F.A. Gobas, Thin-film solid-phase extraction to measure fugacities of organic chemicals with low volatility in biological samples, Environ. Sci. Technol. 35 (7) (2001) 1425-1431.

[66] O. Jonsson, E. Paulsson, J. Kreuger, TIMFIE sampler-a new time-integrating, active, low-tech sampling device for quantitative monitoring of pesticides in whole water, Environ. Sci. Technol. 53 (1) (2018) 279-286.

[67] M.A. Stravs, C. Stamm, C. Ort, H. Singer, Transportable automated HRMS platform "MS2field" enables insights into water-quality dynamics in real time, Environ. Sci. Technol. Lett. 8 (5) (2021) 373-380.

[68] A.L. Coes, N.V. Paretti, W.T. Foreman, J.L. Iverson, D.A. Alvarez, Sampling trace organic compounds in water: a comparison of a continuous active sampler to continuous passive and discrete sampling methods, Sci. Total Environ. 473-474 (2014) 731-741.

[69] T. Schulze, et al., Assessment of a novel device for onsite integrative large-volume solid phase extraction of water samples to enable a comprehensive chemical and effect-based analysis, Sci. Total Environ. 581-582 (2017) 350-358.
[70] Z. Tian, M.H. McMinn, M. Fang, Effect-directed analysis and beyond: how to find causal environmental toxicants, Exposome 3 (1) (2023).

[71] P. Hajeb, L. Zhu, R. Bossi, K. Vorkamp, Sample preparation techniques for suspect and non-target screening of emerging contaminants, Chemosphere 287 (2022) 132306.

[72] R.M. Burgess, K.T. Ho, W. Brack, M. Lamoree, Effects-directed analysis (EDA) and toxicity identification evaluation (TIE): complementary but different approaches for diagnosing causes of environmental toxicity, Environ. Toxicol. Chem. 32 (9) (2013) 1935-1945.

[73] E. Dopp, H. Pannekens, F. Itzel, J. Tuerk, Effect-based methods in combination with state-of-the-art chemical analysis for assessment of water quality as integrated approach, Int. J. Hyg Environ. Health 222 (4) (2019) 607-614.

[74] J. Stadnicka-Michalak, K. Schirmer, In vitro-in vivo extrapolation to predict bioaccumulation and toxicity of chemicals in fish using physiologically based toxicokinetic models, in: In Situ Bioavailability and Toxicity of Organic Chemicals in Aquatic Systems, (Springer, 2019, pp. 229-258.

[75] P.D. Noyes, et al., Evaluating chemicals for thyroid disruption: opportunities and challenges with in vitro testing and adverse outcome pathway approaches, Environ. Health Perspect. 127 (9) (2019) 095001.

[76] K. Coady, et al., When are adverse outcome pathways and associated assays "fit for purpose" for regulatory decision-making and management of chemicals? Integrated Environ. Assess. Manag. 15 (4) (2019) 633-647.

[77] B. Escher, P. Neale, F. Leusch, Adverse outcome pathways of chemicals in aquatic organisms, in: Bioanalytical Tools in Water Quality Assessment, (IWA Publishing, 2021, pp. 1-13).

[78] D.L. Villeneuve, et al., High-throughput screening and environmental risk assessment: state of the science and emerging applications, Environ. Toxicol. Chem. 38 (1) (2019) 12-26.

[79] J. Delroisse, L. Duchatelet, P. Flammang, J. Mallefet, Leaving the dark side? Insights into the evolution of luciferases, Front. Mar. Sci. 8 (2021) 673620.

[80] N.M. Morato, M.T. Le, D.T. Holden, R. Graham Cooks, Automated high-throughput system combining small-scale synthesis with bioassays and reaction screening, Slas technology: translating life sciences innovation 26 (6) (2021) 555-571.

[81] S. Zhou, S. Peng, W. Brack, J.A. Doering, T.-B. Seiler, H. Hollert, Bioanalytical equivalents and relative potencies for predicting the biological effects of mixtures, Sci. Total Environ. 763 (2021) 143030.

[82] E. Diamanti-Kandarakis, et al., Endocrine-disrupting chemicals: an Endocrine Society scientific statement, Endocr. Rev. 30 (4) (2009) 293-342.

[83] T. Encarnação, A.A.C.C. Pais, M.G. Campos, H.D. Burrows, Endocrine disrupting chemicals: impact on human health, wildlife and the environment, Sci. Prog. 102 (1) (2019) 3-42.

[84] J.R. Barrett, EDCs and Estrogen Receptor Activity: a Pathway to Safer Chemical Design? (NLM-Export, 2014, pp. 207-267).

[85] N. Fuentes, P. Silveyra, Estrogen receptor signaling mechanisms, Advances in protein chemistry and structural biology 116 (2019) 135-170.

[86] T.T. Schug, A. Janesick, B. Blumberg, J.J. Heindel, Endocrine disrupting chemicals and disease susceptibility, J. Steroid Biochem. Mol. Biol. 127 (3-5) (2011) 204-215.

[87] M.M. Lam, M. Engwall, M.S. Denison, M. Larsson, Methylated polycyclic aromatic hydrocarbons and/or their metabolites are important contributors to the overall estrogenic activity of polycyclic aromatic hydrocarbon-contaminated soils, Environ. Toxicol. Chem. 37 (2) (2018) 385-397.

[88] D.L. Villeneuve, et al., Verification of in vivo estrogenic activity for four per- and polyfluoroalkyl substances (PFAS) identified as estrogen receptor agonists via new approach methodologies, Environ. Sci. Technol. 57 (9) (2023) 3794-3803.

[89] Y. Huang, et al., Rapid and reagent-free bioassay using autobioluminescent yeasts to detect agonistic and antagonistic activities of bisphenols against rat androgen receptor and progesterone receptor, J. Steroid Biochem. Mol. Biol. 222 (2022) 106151.

[90] H.-J. Jin, J. Kim, J. Yu, Androgen receptor genomic regulation, Transl. Androl. Urol. 2 (3) (2013) 158.

[91] E.R. Cooper, K.C. McGrath, A.K. Heather, In vitro androgen bioassays as a detection method for designer androgens, Sensors 13 (2) (2013) 2148-2163.

[92] P. Roy, M. Alevizaki, I. Huhtaniemi, In vitro bioassays for androgens and their diagnostic applications, Hum. Reprod. Update 14 (1) (2008) 73-82.

[93] P.A. Behnisch, K. Hosoe, S.-i. Sakai, Brominated dioxin-like compounds: in vitro assessment in comparison to classical dioxin-like compounds and other polyaromatic compounds, Environ. Int. 29 (6) (2003) 861-877.

[94] M. Larsson, D. Orbe, M. Engwall, Exposure time-dependent effects on the relative potencies and additivity of PAHs in the Ah receptor-based H4IIE-luc bioassay, Environ. Toxicol. Chem. 31 (5) (2012) 1149-1157.

[95] M. Larsson, J. Hagberg, J.P. Giesy, M. Engwall, Time-dependent relative potency factors for polycyclic aromatic hydrocarbons and their derivatives in the H4IIE-luc bioassay, Environ. Toxicol. Chem. 33 (4) (2014) 943-953.

[96] M.M. Lam, R. Bülow, M. Engwall, J.P. Giesy, M. Larsson, Methylated PACs are more potent than their parent compounds: a study of aryl hydrocarbon receptor-mediated activity, degradability, and mixture interactions in the H4IIE-luc bioassay, Environ. Toxicol. Chem. 37 (5) (2018) 1409-1419.

[97] P.-Y. Wu, et al., Novel endogenous ligands of aryl hydrocarbon receptor mediate neural development and differentiation of neuroblastoma, ACS Chem. Neurosci. 10 (9) (2019) 4031-4042.

[98] P.A. Behnisch, H. Besselink, R. Weber, W. Willand, J. Huang, A. Brouwer, Developing potency factors for thyroid hormone disruption by PFASs using TTR-β CALUX® bioassay and assessment of PFASs mixtures in technical products, Environ. Int. 157 (2021) 106791.

[99] N.C. Nicolaides, G. Chrousos, T. Kino, Glucocorticoid Receptor, (Endotext, 2020).

[100] L. Van Moortel, et al., Novel assays monitoring direct glucocorticoid receptor protein activity exhibit high predictive power for ligand activity on endogenous gene targets, Biomed. Pharmacother. 152 (2022) 113218.

[101] J. Brtko, Z. Dvorak, Natural and synthetic retinoid X receptor ligands and their role in selected nuclear receptor action, Biochimie 179 (2020) 157-168.

[102] M.V. Catani, V. Tullio, M. Maccarrone, V. Gasperi, DNA-Protein-Interaction (DPI)-ELISA assay for PPAR-γ receptor binding, in: Endocannabinoid Signaling: Methods and Protocols, (Springer, 2022, pp. 133-143.

[103] C. Lynch, S. Sakamuru, M. Xia, Screening method for the identification of compounds that activate pregnane X receptor, Current protocols 2 (12) (2022).

[104] J. Külbeck, et al., New in vitro tools to study human constitutive androstane receptor (CAR) biology: discovery and comparison of human CAR inverse agonists, Mol. Pharm. 8 (6) (2011) 2424-2433.

[105] M. Pinne, E. Ponce, J.L. Raucy, Transactivation assays that identify indirect and direct activators of human pregnane X receptor (PXR, NR112) and constitutive androstane receptor (CAR, NR113), Drug Metabol. Lett. 11 (2) (2017) 128-137.

[106] N. Ren, M. Atyah, W.-Y. Chen, C.-H. Zhou, The various aspects of genetic and epigenetic toxicology: testing methods and clinical applications, J. Transl. Med. 15 (2017) 1-13.

[107] F.C. da Silva Junior, M.B.M.C. Felipe, D.E. F.d. Castro, S.C.d.S. Araújo, H.C. N. Sisenando, S.R. Batistuzzo de Medeiros, A look beyond the priority: a systematic review of the genotoxic, mutagenic, and carcinogenic endpoints of non-priority PAHs, Environ. Pollut. 278 (2021) 116838.

[108] S. Tejs, The Ames test: a methodological short review, Environmental Biotechnology 4 (1) (2008) 7-14.

[109] E. Cordelli, M. Bignami, F. Pacchierotti, Comet assay: a versatile but complex tool in genotoxicity testing, Toxicology Research 10 (1) (2021) 68-78.

[110] I.C. Costa-Amaral, et al., Environmental assessment and evaluation of oxidative stress and genotoxicity biomarkers related to chronic occupational exposure to benzene, Int. J. Environ. Res. Publ. Health 16 (12) (2019) 2240.

[111] C. Ladeira, L. Smajdova, The use of genotoxic biomarkers in molecular epidemiology: applications in environmental, occupational and dietary studies, AIMS genetics 4 (3) (2017) 166-191.

[112] J. de Lapuente, et al., The Comet Assay and its applications in the field of ecotoxicology: a mature tool that continues to expand its perspectives, Front. Genet. 6 (2015) 180.

[113] C.L. Santos, B. Pourrut, J.M. Ferreira de Oliveira, The use of comet assay in plant toxicology: recent advances, Front. Genet. 6 (2015) 216.

[114] G. Gajski, et al., The Comet Assay in Animal Models: from Bugs to whales (Part 1 Invertebrates), vol. 779, (Mutation Research/Reviews in Mutation Research, 2019, pp. 82-113.

[115] M. Registre, R. Proudlock, The in vitro chromosome aberration test, in: Genetic Toxicology Testing, (Elsevier, 2016, pp. 207-267).

[116] OECD, Test No. 487: in Vitro Mammalian Cell Micronucleus Test, (OECD Publishing, 2016).

[117] J. Legradi, et al., An ecotoxicological view on neurotoxicity assessment, Environ. Sci. Eur. 30 (2018) 1-34.

[118] W. Busch, S. Schmidt, R. Kühne, T. Schulze, M. Krauss, R. Altenburger, Micropollutants in European rivers: a mode of action survey to support the development of effect-based tools for water monitoring, Environ. Toxicol. Chem. 35 (8) (2016) 1887-1899.

[119] I.W. Selderslaghs, J. Hooyberghs, R. Blust, H.E. Witters, Assessment of the developmental neurotoxicity of compounds by measuring locomotor activity in zebrafish embryos and larvae, Neurotoxicol. Teratol. 37 (2013) 44-56.

[120] J. Hellou, Behavioural ecotoxicology, an "early warning" signal to assess environmental quality, Environ. Sci. Pollut. Control Ser. 18 (2011) 1-11.

[121] A. Ogungbemi, D. Leuthold, S. Scholz, E. Küster, Hypo-or hyperactivity of zebrafish embryos provoked by neuroactive substances: a review on how experimental parameters impact the predictability of behavior changes, Environ. Sci. Eur. 31 (1) (2019) 1-26.

[122] J. Pinheiro-da-Silva, A.C. Luchiari, Embryonic ethanol exposure on zebrafish early development, Brain Behav 11 (6) (2021) e02062.

[123] I.W. Selderslaghs, J. Hooyberghs, W. De Coen, H.E. Witters, Locomotor activity in zebrafish embryos: a new method to assess developmental neurotoxicity, Neurotoxicol. Teratol. 32 (4) (2010) 460-471.

[124] J.A. Koenig, T.L. Dao, R.K. Kan, T.M. Shih, Zebrafish as a model for acetylcholinesterase-inhibiting organophosphorus agent exposure and oxime reactivation, Ann. N. Y. Acad. Sci. 1374 (1) (2016) 68-77.

[125] A.O. Ogungbemi, E. Teixido, R. Massei, S. Scholz, E. Küster, Optimization of the spontaneous tail coiling test for fast assessment of neurotoxic effects in the zebrafish embryo using an automated workflow in KNIME, Neurotoxicol. Teratol. 81 (2020) 106918.

[126] B. Escher, P. Neale, F. Leusch, Bioanalytical Tools in Water Quality Assessment, (IWA publishing, 2021).

[127] J.-H. Zhang, K.R. Oldenburg, Z-Factor, in: M. Schwab (Ed.), Encyclopedia of Cancer, (Springer Berlin Heidelberg, 2009, pp. 3227-3228.

[128] T.J.H. Jonkers, et al., High-Performance data processing workflow incorporating effect-directed analysis for feature prioritization in suspect and nontarget screening, Environ. Sci. Technol. 56 (3) (2022) 1639-1651.

[129] H. Dong, A.A. Cuthbertson, S.D. Richardson, Effect-directed analysis (EDA): a promising tool for nontarget identification of unknown disinfection byproducts in drinking water, Environ. Sci. Technol. 54 (3) (2020) 1290-1292.

[130] C.J. Houtman, et al., Characterisation of (anti-)progestogenic and (anti-) androgenic activities in surface and wastewater using high resolution effect-directed analysis, Environ. Int. 153 (2021) 106536.
[131] E. Simon, M.H. Lamoree, T. Hamers, J. de Boer, Challenges in effect-directed analysis with a focus on biological samples, TrAC, Trends Anal. Chem. 67 (2015) 179-191.

[132] L. Bengtström, et al., Non-targeted screening for contaminants in paper and board food-contact materials using effect-directed analysis and accurate mass spectrometry, Food Addit. Contam. 33 (6) (2016) 1080-1093.

[133] N. Zwart, et al., High-throughput effect-directed analysis using downscaled in vitro reporter gene assays to identify endocrine disruptors in surface water, Environ. Sci. Technol. 52 (7) (2018) 4367-4377.

[134] C.J. Houtman, et al., High resolution effect-directed analysis of steroid hormone (ant)agonists in surface and wastewater quality monitoring, Environ. Toxicol. Pharmacol. 80 (2020) 103460.

[135] J.M. Weiss, T. Hamers, K.V. Thomas, S. van der Linden, P.E.G. Leonards, M. H. Lamoree, Masking effect of anti-androgens on androgenic activity in European river sediment unveiled by effect-directed analysis, Anal. Bioanal. Chem. 394 (5) (2009) 1385-1397.

[136] W. Brack, et al., How to confirm identified toxicants in effect-directed analysis, Anal. Bioanal. Chem. 390 (2008) 1959-1973.

[137] H.M. Taha, et al., The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry, Environ. Sci. Eur. 34 (1) (2022) 104.

[138] J. Hollender, et al., NORMAN guidance on suspect and non-target screening in environmental monitoring, Environ. Sci. Eur. 35 (1) (2023) 75.

[139] I. Eide, G. Neverdal, B. Thorvaldsen, R. Arneberg, B. Grung, O.M. Kvalheim, Toxicological evaluation of complex mixtures: fingerprinting and multivariate analysis, Environ. Toxicol. Pharmacol. 18 (2) (2004) 127-133.

[140] C. Hug, M. Sievers, R. Ottermanns, H. Hollert, W. Brack, M. Krauss, Linking mutagenic activity to micropollutant concentrations in wastewater samples by partial least square regression and subsequent identification of variables, Chemosphere 138 (2015) 176-182.

[141] P.A. Neale, B.I. Escher, In vitro bioassays to assess drinking water quality, Current Opinion in Environmental Science & Health 7 (2019) 1-7.

[142] E.B. Reiter, B.I. Escher, E. Rojo-Nieto, H. Nolte, U. Siebert, A. Jahnke, Characterizing the marine mammal exposome by iceberg modeling, linking chemical analysis and in vitro bioassays, Environmental Science: Process. Impacts 25 (11) (2023) 1802-1816.

[143] P.A. Neale, et al., Assessing the mixture effects in in vitro bioassays of chemicals occurring in small agricultural streams during rain events, Environ. Sci. Technol. 54 (13) (2020) 8280-8290.