Exposomics as a discovery engine: a systematic scoping review of emerging environmental contaminants and novel biological effects

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By combining high-resolution mass spectrometry, non-targeted analysis, multi-omics integration, wearable sensors, and computational tools, exposomics can capture the complexity of real-world chemical mixtures and uncover exposures missed by conventional monitoring. We conducted a scoping review following PRISMA-ScR guidelines to map how exposomic approaches have been applied to the detection and characterisation of under-regulated or previously unknown contaminants. Searches of four bibliographic databases and targeted grey literature (2015–2025) yielded 67 eligible studies, of which 42 were charted quantitatively. The evidence was heavily concentrated in high-income countries and focused on pesticides, PFAS, and heavy metals, with metabolic, developmental, and epigenetic outcomes most frequently reported. Across this landscape, 17 priority compounds emerged where exposomics revealed either new detections or novel biological effects, including halobenzoquinones, GenX, bisphenol S, microplastics, tungsten, and 3-hydroxyoctanedioic acid. These case exemplars illustrate how exposomics can expand hazard characterisation and provide early warning of risks that are invisible to targeted surveillance. At the same time, critical gaps persist, particularly in geographic coverage, longitudinal cohorts, data infrastructures, and mixture analysis tools. Scaling exposomics through harmonised biomonitoring systems and embedding it within One Health frameworks will be essential to accelerate discovery and to translate emerging evidence into more proactive and equitable chemical risk governance. Biological sciences/Computational biology and bioinformatics Earth and environmental sciences/Environmental sciences Exposome Emerging Contaminants Regulatory Frameworks Environmental Health Mixture Toxicity Health Risk Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Recent years have seen an unprecedented expansion in our understanding of both biogenic and anthropogenic environmental contaminants [ 1 ]. What was once a relatively discrete list of well-characterised pollutants has transformed into a complex and evolving chemical landscape [ 1 – 2 ]. Industrial innovation, agricultural intensification, and the proliferation of consumer products have released tens of thousands of synthetic compounds into the environment, many of which remain unregulated or poorly understood in terms of biological effects. Collectively known as contaminants of emerging concern, these substances act not only as individual toxicants but also as components of complex mixtures, producing subtle and sometimes unpredictable biological outcomes [ 3 ]. Traditional toxicology and monitoring frameworks, focused on single chemicals under controlled conditions, are insufficient for capturing the cumulative, interactive nature of real-world exposures [ 4 – 5 ]. Evidence now shows that chronic, low-dose exposures to diverse chemical mixtures can lead to synergistic or antagonistic effects, highlighting the need for new paradigms in exposure science [ 6 – 7 ]. The exposome has emerged as a transformative framework to meet this challenge. Rather than treating exposures in isolation, the exposome integrates external contaminants with internal biological responses across metabolic, epigenetic, immune, and microbiome domains [ 8 – 10 ]. Innovations in high-resolution mass spectrometry (HRMS), particularly non-target analysis (NTA), now allow thousands of chemical features to be profiled in a single sample, including unknown or unregulated compounds [ 11 – 12 ]. When combined with transcriptomics, proteomics and metabolomics, exposomics can reveal both the presence of novel contaminants and the pathways they perturb [ 13 ]. Complementary advances in wearable monitoring [ 14 – 15 ] and computational analytics, including machine learning and exposome-wide association studies, further enhance the resolution of exposure assessment [ 9 ]. Integration with microbiome profiling has added an additional layer, recognising that host-associated microbial communities both modulate and respond to chemical exposures [ 16 – 17 ]. Despite rapid advances, the extent to which exposomics has been applied to identify new or under-regulated contaminants remains uncertain. Landmark discoveries, including halobenzoquinones [ 18 ], GenX [ 19 ], 6PPD-quinone [ 20 ], and microplastics in human tissues [ 21 ], demonstrate its promise, but the evidence remains scattered across chemical classes, exposure matrices, and study designs. To address this, we conducted a scoping review (PRISMA-ScR, with PRISMA-S documentation) of studies published between 2015 and 2025 that used exposome-oriented methodologies for the discovery of hidden or emerging contaminants. The review aims to map the extent, range, and nature of the evidence base: identifying which contaminants have been detected, across which matrices and geographies, with which analytical workflows, and where exposomic approaches have revealed novel biological effects of established compounds. In doing so, the synthesis highlights areas of convergence and scarcity, as well as the methodological choices most consistently enabling discovery. Rather than evaluating causal effects, it provides a structured foundation for setting future research priorities and developing surveillance strategies in chemical risk governance. Methods Protocol and reporting A protocol for this scoping review was prospectively archived on Zenodo (DOI: 10.5281/zenodo.16790030 ) prior to database searches. As PROSPERO does not register scoping reviews, the Zenodo record provides a permanent, citable, and publicly accessible registration. The review was conducted in accordance with the PRISMA-ScR checklist and search reporting followed PRISMA-S recommendations. The search strategy was independently peer-reviewed using the PRESS framework. Full search strings and documentation of amendments are available in Supplementary File S1 (DOI: 10.5281/zenodo.16790030 ). Objectives and PECO framework The objective of this scoping review was to map the extent, range, and nature of research applying exposomic approaches to the detection and characterisation of emerging environmental contaminants in human and environmental matrices, including novel biological effects reported for known compounds. A PECO framework guided eligibility: Population/Environment human participants of any age, as well as environmental compartments (e.g., air, water, soil, biota) sampled for biomonitoring purposes. Exposure : non-regulated or recently regulated chemical classes, including but not limited to Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS), current-use pesticides, novel flame retardants, micro- and nanoplastics, endocrine-disrupting chemicals (EDCs), volatile organic compounds (VOCs), pharmaceuticals, and personal care products (PPCPs). Comparator : not applicable, as this review maps evidence rather than comparing interventions. Outcomes : detection, characterisation, or identification of chemicals using exposomic tools; where reported, associated biological signals or mechanistic endpoints were noted descriptively. Information sources and search strategy To ensure broad and systematic capture of both peer-reviewed and grey literature, we designed a search strategy that combined multiple databases and platforms, leveraging controlled vocabulary and free-text keywords tailored to the exposome and contaminant science fields. The search was performed in four primary bibliographic databases: Scopus, Web of Science Core Collection, PubMed, and Europe PMC. These platforms were selected for their extensive coverage of biomedical, environmental, and interdisciplinary research, as well as their inclusion of preprints and non-traditional publication types. For each database, we developed a harmonised search string to maximise consistency and reproducibility. The search logic was intentionally broad, combining the exposome concept block (e.g., “exposome,” “exposome approach,” “environmental exposure”) with contaminant-related terms, both generic (e.g., “emerging,” “novel,” “unregulated,” “contaminant*”) and specific classes (e.g., “PFAS,” “microplastics,” “flame retardants,” “pesticides,” “heavy metals,” “pharmaceuticals,” “VOCs”). To capture unpublished and non-peer-reviewed evidence, grey literature searches were conducted on agency and organizational websites, including the United States Environmental Protection Agency (EPA), European Chemicals Agency (ECHA), World Health Organization (WHO), Organisation for Economic Co-operation and Development (OECD), and environmental NGOs such as the Environmental Working Group (EWG) and ChemSec. Advanced Google site queries and internal search engines were used to retrieve reports, technical documents, and datasets from these sources, applying the same core search concepts as in database queries. All searches were limited to articles published in English between January 2015 and May 2025. The search strategy was iteratively developed in consultation with domain experts and pilot-tested to ensure both sensitivity and specificity. The first search was conducted on 17 February 2025, and results were exported for deduplication and screening. A final update search was performed on 15 May 2025 to identify newly published articles. The number of records retrieved from each source is detailed in Supplementary File S2. All records, including those from grey literature, were screened using the same inclusion and exclusion criteria (Section 2.4). Screening and study selection All records underwent a multi-stage screening process to ensure comprehensive and reproducible selection of studies relevant to the review’s objectives. Screening followed the eligibility framework described above and was conducted in accordance with the archived protocol. After deduplication, records were screened by title and abstract by two independent reviewers using pre-specified inclusion and exclusion criteria (Section 2.2). Eligible studies were those that: applied an exposome-oriented methodology or conceptual framework; focused on the detection, characterisation, or health assessment of non-regulated or emerging environmental contaminants; employed advanced analytical or computational exposomic techniques; provided original data or reproducible workflows; addressed human or environmental populations/matrices; were published in English between 2015 and 2025. Studies were excluded if they focused solely on legacy or regulated pollutants without introducing new contaminants or methodological innovations, lacked exposome-based approaches, or did not provide empirical data or reproducible workflows. Reviews, commentaries, editorials, and conference abstracts without methodological detail were also excluded. Reasons for full-text exclusion are detailed in Supplementary File S2. Disagreements between reviewers were resolved by discussion, with arbitration by a third reviewer when required. Data charting Data were extracted using a piloted REDCap template, with double extraction on 20% of records (inter-extractor concordance: 96%). Variables included study identifiers, year, country, design, matrix, contaminant class, analytical platform, processing software, and (where reported) identification confidence (Schymanski levels), regulatory status, and associated biological signals. Study population was coded as human, animal, or in vitro . The harmonised dataset is archived with DOI: 10.5281/zenodo.16790030 (Supplementary File S3). Identification confidence for all compounds was assessed using the Schymanski framework [ 22 ], adapted with strict thresholds for mass accuracy, spectral quality, and orthogonal confirmation. Levels were assigned as follows: Level 1 (confirmed structure) The compound was run as an authentic reference standard under the same method, with matching retention time or retention index and matching MS/MS (or EI) spectrum. Targeted LC/GC-MS/MS panels with certified standards, including isotope-dilution approaches, were classified as Level 1 by default. Level 2a (probable, library + orthogonal) High-quality library match (HRMS MS/MS or NIST EI; score ≥ 0.70 or ≥ 800 respectively) combined with at least one orthogonal line of evidence, such as retention index, same-method retention time, collision cross section, or MS³ fragmentation. Level 2b (probable, diagnostic evidence) Diagnostic fragments, adduct patterns, or isotope distributions strongly supported a single structure, but no in-house standard was available and orthogonal confirmation was absent. Level 3 (tentative candidate/class) Accurate mass and fragments supported a candidate structure or chemical class, but isomers were unresolved, and no orthogonal data or authentic standard was available. Level 4 (formula only) Formula assignment was supported by accurate mass and isotope distribution, but insufficient evidence existed for structural assignment. Level 5 (feature only) Accurate mass peak detected without reliable formula or structure. Orthogonal confirmation was defined as any independent line of evidence beyond library or MS/MS match, including retention index (≤ 1% deviation), retention time against an in-house standard (≤ 0.2 min deviation), collision cross section (≤ 2% deviation), or acquisition of MS³ diagnostic fragments. For LC-HRMS, a mass accuracy tolerance of ≤ 5 ppm and at least three meaningful fragment ions were required. Elemental analyses (e.g. ICP-MS, LA-ICP-MS) and polymer identifications (e.g. µ-FTIR, Py-GC/MS) were excluded from the Schymanski framework and recorded as “N/A” since the criteria apply only to small molecules. Data synthesis We summarised the extent and distribution of evidence across contaminant classes, matrices, and geographies. Results are presented as descriptive tables and figures (e.g., evidence distributions). No effect size pooling was attempted. Critical appraisal Although not required for scoping reviews, we undertook a structured appraisal of study validity to contextualize the strength of the evidence base. We applied RoB2 for randomized trials and ROBINS-E (or analogous domains) for observational studies, reporting ratings (low/some concerns/high) by domain. These appraisals were not used for exclusion or synthesis but to characterize the strengths and limitations of the evidence landscape. Full results are available in Supplementary File S3 (DOI: 10.5281/zenodo.16790030 ). Results Study selection The database and grey literature searches conducted between January 2015 and May 2025 identified 28,946 records: 10,811 from Scopus, 7,672 from Europe PMC, 5,574 from Web of Science, 3,889 from PubMed, and approximately 1,000 from targeted grey literature and preprint sources. After automated and manual deduplication, 24,746 unique citations were screened at title and abstract level. At this initial stage, more than 24,259 records were excluded as clearly out of scope, most commonly because they lacked primary data, were commentary/editorial pieces, or addressed exposures unrelated to chemical contaminants. A total of 487 articles were assessed in full text against the predefined eligibility criteria, of which 420 were excluded. The most frequent reasons were: application of conventional monitoring only, with no exposome-oriented methodology; exclusive focus on legacy pollutants already subject to long-standing regulation; exclusive focus on legacy pollutants already subject to long-standing regulation; insufficient methodological detail to allow reproducibility; purely theoretical or modelling studies without empirical data; non-English publications; studies falling outside the 2015–2025 timeframe. The final evidence set comprised 67 eligible studies, of which 60 originated from bibliographic databases and 7 from grey literature sources such as agency reports and preprints. The overall selection process is depicted in the PRISMA-ScR flow diagram (Fig. 2 ). A complete list of all 67 included studies, along with risk-of-bias appraisals, is provided in Supplementary File S3. This process highlights that while the exposomics literature is expanding rapidly, only a small fraction of published work has applied exposome-oriented tools directly to the discovery of emerging or under-regulated contaminants. Descriptive overview of included evidence Of the 67 included studies, 42 were primary research investigations that were charted quantitatively, while 25 comprised reviews, regulatory documents, and methodological papers that provided context but were not mapped. The 42 primary studies spanned fifteen countries, with the United States and China together contributing more than half of the total. This reflects both their early investment in large scale exposomics infrastructure and the availability of national biomonitoring programs such as NHANES and KoNEHS. Additional studies originated from the United Kingdom, France, Switzerland, Germany, Denmark, the Netherlands, Italy, Sweden, South Korea, India, Bangladesh, Senegal, and Pakistan. Despite this international spread, representation from low- and middle-income countries was sparse, underscoring the persistent imbalance in global coverage despite their high burden of environmental exposures. The distribution of contaminant classes across countries (Fig. 3 ) further illustrates that not only are studies concentrated in a handful of regions, but their thematic scope is uneven, with PFAS and pesticides dominating in North America and East Asia, while European studies contribute disproportionately to research on microplastics and PPCPs. The methodological approaches represented in the evidence base illustrate the breadth of contemporary exposome science. Observational human studies formed the largest group, ranging from prospective and retrospective cohorts to population based cross sectional surveys and targeted biomonitoring projects. Many of these drew on multiple biological matrices, such as urine, serum, placenta, semen, breast milk, and even deciduous teeth, which allowed for temporally resolved or life stage specific profiling. Some leveraged nested designs such as mother child cohorts and twin studies to capture inter-individual variability in exposures. These approaches were complemented by mechanistic and experimental investigations, which accounted for approximately one quarter of the included studies. Animal models and in vitro systems were often combined with multi omics readouts, including transcriptomics, metabolomics, and epigenomics, providing functional anchors for signals observed in human biospecimens. Emerging computational and high throughput tools featured prominently in several investigations. In silico annotation pipelines, machine learning algorithms, and effect directed analyses were applied to accelerate candidate identification and prioritisation. The growing use of machine learning in particular enabled researchers to integrate multidimensional datasets, uncover latent co-exposure patterns, and identify complex mixtures linked to disease risk. Wearable devices and personal passive samplers also appeared in a subset of studies, illustrating the methodological shift toward real time, individual level exposure monitoring. These innovations complement the laboratory based HRMS approaches and help capture exposures that may be episodic, highly localised, or missed by traditional biomonitoring strategies. Taken together, the included studies show both the diversity and rapid evolution of exposomic methodologies, while also revealing clear imbalances in geographic distribution and thematic scope. Figure 4 illustrates how outcome domains are distributed across contaminant classes, with metabolic, developmental, and epigenetic outcomes dominating, and reproductive, immune, and neurologic signals less frequently reported. An “Other” outcome domain captured endpoints that did not map cleanly to these prespecified categories—e.g., high-throughput/in-vitro assay readouts (cell viability, reporter activity), broad mechanistic or multi-omics screens, general toxicity or oxidative-stress markers, and organ-function panels or endocrine markers not specific to the six domains. This broad methodological foundation provides the context for understanding which contaminants have actually been detected and characterised through these approaches and sets the stage for the analysis of discovery yield presented in the following section. Discovery yield The 42 charted primary studies revealed a broad spectrum of contaminants detectable by exposomic tools. The chemical landscape encompassed pesticides, heavy metals, PFAS, PPCPs, flame retardants, combustion by-products, water disinfection by-products, and polymers such as microplastics. Some studies were designed to explore mixtures across classes, while others uncovered contaminants that could not be assigned to our predefined categories. In these cases, we retained “mixtures” as a distinct class, denoting intentional multi-chemical designs, and defined an “other” category for heterogeneous compounds such as benzotriazoles, phenolic antioxidants, or broad descriptors like endocrine-disrupting chemicals. Matrices analysed were equally diverse, spanning urine, serum, blood, semen, placenta, breast milk, and deciduous teeth in humans, alongside surface water, wastewater effluent, air, dust, sediments, and animal or in vitro models. This breadth underscores how exposomics bridges external contamination with internal exposure profiles and provides mechanistic context for emerging hazards. From this landscape, 17 compounds emerged as priority discoveries where exposomic methodologies either revealed previously unmonitored contaminants in humans and the environment or generated new biological signals for well-known chemicals (Table 1 ). Metals such as tungsten and lead, although outside the small-molecule HRMS framework, illustrate how exposomic biomarkers extend beyond organic compounds [ 9 – 10 , 23 – 25 ]. Tungsten was identified by ICP-MS in occupational biomonitoring and linked to kidney dysfunction [ 23 ], while lead was reconstructed in a time-resolved manner using dentine micro-slices from deciduous teeth, revealing elevated exposures in children later diagnosed with autism [ 24 ]. These studies [ 23 – 25 ] highlight how innovative matrices can provide temporal resolution and disease-relevant signals for long-recognised contaminants. Among organic contaminants, PFAS offered one of the clearest demonstrations of exposomics as a discovery engine [ 26 – 30 ]. GenX (HFPO-DA), a fluoropolymer substitute, was detected in serum and liver samples by HRMS with isotopically labelled standards, achieving Schymanski Level 1 with full orthogonal confirmation [ 28 ]. Its association with developmental and hepatic toxicity illustrates how exposomics can track substitute PFAS that enter circulation after regulatory shifts, anticipating the risk before widespread surveillance [ 26 – 30 ]. Disinfection by-products showed a similar trajectory. Halobenzoquinones were uncovered in drinking water and in vitro models, with some congeners confirmed against authentic standards at Level 1 and others tentatively annotated at Level 2 [ 18 ]. They proved strongly cytotoxic yet remain unregulated. Pesticides provided diverse lessons. Glyphosate, although extensively debated, has rarely been examined in untargeted exposomic frameworks. It was profiled in urine with metabolomics and microbiome assays, revealing disrupted metabolism, gut dysbiosis, and hepatic perturbation [ 13 , 31 – 32 ]. o-Hydroxybiphenyl appeared in a pre-IBD cohort, detected prior to diagnosis and associated with early gut inflammation [ 33 ]. 4-Nitrophenol, a pesticide degradation product and paraben metabolite, was observed in semen profiling and tentatively annotated at Level 2–3, with abnormal morphology as a biological signal [ 34 ]. Consumer product chemicals were another recurrent source of new signals. Exposomic semen profiling uncovered several consumer product chemicals, including dibutyl phthalate, 2-aminophenol, and 3-hydroxyoctanedioic acid, all tentatively identified at Level 2–3 but consistently linked with impaired sperm function, including reduced motility and abnormal morphology [ 34 ]. Oxybenzone, a UV filter common in sunscreens, was confirmed at Level 1 in maternal and infant biomonitoring and linked to adverse pregnancy outcomes including Hirschsprung’s disease, low birth weight, and preterm delivery [ 35 ]. These discoveries underscore the capacity of exposomics to flag risks from replacement chemicals and everyday consumer additives that remain in widespread use despite partial restrictions. Other industrial and combustion-related contaminants added further weight. Triphenyl phosphate, an organophosphate flame retardant, was reported in NHANES exposome-wide lipidomics with partial confirmation and associated with altered lipid metabolism and adiposity [ 36 ]. Fluorene, a combustion by-product, was detected in adolescents by LC-HRMS urinary biomonitoring and linked with liver enzyme elevations and fatty liver risk [ 37 ]. Microplastics represent a frontier distinct from the small-molecule HRMS framework. Identified using spectroscopic and pyrolytic approaches, with support from HRMS in some cases, microplastics were reported in human placenta and blood [ 38 – 39 ]. Confirmation relied on polymer reference spectra and morphological consistency, and early biological signals included obesity, insulin resistance, and metabolic dysfunction. Finally, exposomics expanded discovery into VOC metabolites through large-scale biomonitoring. An exposome-wide analysis of NHANES women aged 18 to 45 identified five urinary mercapturic acids: AAMA (acrylamide), AMCC/MCAMA (N,N-dimethylformamide and methyl isocyanate), CYMA (acrylonitrile), HPMMA or 2-HPMA (propylene oxide), and 34MHA (xylenes). Quantified against authentic standards, these metabolites were confirmed at Level 1 and associated with increased infertility risk, with associations persisting in mixture models [ 40 ]. Although their parent compounds are already regulated, exposomic profiling revealed their continued ubiquity in the general population and their relevance as reproductive toxicants, highlighting how exposomics reframes “known” chemicals within mixture-aware contexts. A full list of these 17 compounds, their associated biological effects, and regulatory context is outlined in Table 1 . Taken together, these 17 priority contaminants illustrate the breadth of discovery enabled by exposomics. The majority were confirmed at Schymanski Level 1 using authentic reference standards with orthogonal validation, while a smaller subset, including o-hydroxybiphenyl and 3-hydroxyoctanedioic acid, were assigned to Level 2b as probable structures without in-house standards or orthogonal confirmation. Halobenzoquinones represented a mixed group with compounds spanning Levels 1 to 2, and three categories (metals and polymers) fell outside the small-molecule HRMS framework but nonetheless yielded biologically meaningful signals. Table 1 Priority contaminants identified through exposomic approaches (with confidence levels and regulatory context) Compound Class / Source Matrix / Population Method (platform) Schymanski level Orthogonal confirmation Biological signal (domain) Regulatory context Reference Tungsten (W) Metal / industrial emissions Urine (occupational biomonitoring) ICP-MS N/A N/A Renal dysfunction, CKD acceleration Not regulated under EU/WHO drinking water standards 23 Lead (Pb) Metal / legacy pollution Deciduous teeth (children) LA-ICP-MS (time-resolved) N/A N/A Neurodevelopmental disruption, autism signals Strictly regulated worldwide (water, paint, consumer products) 24 GenX (HFPO-DA) PFAS substitute Serum & liver (rats) UPLC–HRMS (ESI−) Level 1 Reference standard (isotopically labelled IS; RT + MS/MS match) Developmental toxicity, hepatomegaly SVHC in EU; US advisory levels 25 Halobenzoquinones (HBQs) Water disinfection by-products Drinking water; in vitro models Untargeted HRMS Level 1 (with in-house stds); Level 2a (library + orthogonal); Level 2b (library/diagnostic only) Partial (standards for subset; MS/MS spectra) Cytotoxicity, DNA damage Currently unregulated 18 Oxybenzone (BP-3) UV filter (PPCP) Urine, blood (pregnant women, infants) Targeted biomonitoring Level 1 Reference standard RT + MS/MS Adverse pregnancy outcomes (birth weight, preterm, Hirschsprung’s disease signals) Regulated in parts of EU/US; widely used globally 35 Glyphosate Herbicide Urine (rats, dams/pups, adults) LC-HRMS + metabolomics Level 1 Isotope-dilution standard (RT + MS/MS) Disrupted metabolism, microbiome, liver perturbation Approved globally with scrutiny 32 Microplastics Environmental polymer pollutants Blood, placenta (humans) µ-FTIR, Py-GC/MS N/A Polymer reference spectra, morphology Metabolic dysfunction (obesity, insulin resistance) Under UNEA treaty negotiation; limited bans 38–39 Bisphenol S (BPS) BPA substitute Urine (NHANES adults & youth); in vivo LC-HRMS biomonitoring Level 1 Authentic standard (RT + MS/MS) Hormone disruption, obesity/adiposity Limited regulation (food-contact/baby products) 41 Fluorene (2-hydroxyfluorene) Combustion by-product Urine (adolescents) LC-HRMS biomonitoring Level 1 Calibration standard (RT + MS/MS) Elevated liver enzymes, fatty liver risk Monitored; no enforceable limits 37 o-Hydroxybiphenyl Pesticide/fungicide Serum (pre-IBD cohort) Untargeted GC-HRMS Level 2b Library/diagnostic only (no RI/standard) Gut inflammation, pre-diagnostic IBD signal Approved agriculturally; restricted in cosmetics 33 Triphenyl phosphate (TPHP) Flame retardant (OPFR) Serum (NHANES cohort) Exposome-wide lipidomics Level 1 Authentic standard (RT + MS/MS) Altered lipid metabolism, adiposity Restricted in children’s products; otherwise used 36 Imidacloprid Neonicotinoid insecticide Serum (NHANES cohort) EWAS (serum metabolomics) Level 1 Authentic standard (isotope-dilution; RT + MS/MS) Altered liver enzyme activity Regulated under pesticide laws 42 Dibutyl phthalate (DBP) Plasticiser (PPCP) Semen (adult males) Untargeted LC-HRMS Level 1 In-study reference standard (RT + MS/MS) Reduced sperm motility, semen quality Restricted in EU/US consumer products 34 2-Aminophenol Hair dye component Semen (adult males) Untargeted LC-HRMS Level 1 In-study reference standard (RT + MS/MS) Reduced sperm motility Covered by REACH; not specifically listed 34 4-Nitrophenol Pesticide metabolite / paraben breakdown Semen (adult males) Untargeted LC-HRMS Level 1 In-study reference standard (RT + MS/MS) Reduced sperm morphology, quality Not approved for plant protection; no MRLs 34 3-Hydroxyoctanedioic acid Cosmetic additive Semen (adult males) Untargeted LC-HRMS Level 2b Literature MS/MS match only (no in-house standard/orthogonal) Reduced sperm motility, abnormal morphology Unregulated 34 Mercapturic acids (AAMA, AMCC/MCAMA, CYMA, HPMMA/2-HPMA, 34MHA) VOC metabolites (acrylamide, DMF/methyl isocyanate, acrylonitrile, propylene oxide, xylenes) Urine (NHANES women, 18–45y) UPLC–ESI-MS/MS (isotope-dilution) Level 1 Authentic standards (RT + MS/MS; isotope labelled IS) Reproductive toxicity: increased infertility risk Regulated parent compounds, but metabolites not explicitly listed 40 Identification confidence levels were assigned according to Schymanski et al. (2014), with Level 1 requiring an authentic reference standard under the same method, Levels 2a–2b requiring high-quality spectral/library evidence with or without orthogonal confirmation, and Level 3 denoting tentative candidates; Levels 4–5 were not encountered. Elements and polymers are shown as N/A.” ICP-MS, inductively coupled plasma mass spectrometry; LA-ICP-MS, laser ablation inductively coupled plasma mass spectrometry; UPLC, ultra-performance liquid chromatography; HRMS, high-resolution mass spectrometry; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; EI, electron ionization; µ-FTIR, micro-Fourier transform infrared spectroscopy; Py-GC/MS, pyrolysis gas chromatography mass spectrometry; PPCP, pharmaceutical and personal care product; PFAS, per- and polyfluoroalkyl substances; BPA, bisphenol A; BPS, bisphenol S; BPF, bisphenol F; OPFR, organophosphate flame retardant; EWAS, exposome-wide association study; RT, retention time; RI, retention index; CCS, collision cross section; IS, internal standard; SVHC, substance of very high concern (EU REACH); NHANES, National Health and Nutrition Examination Survey (US); HBM4EU, Human Biomonitoring for Europe; UNEP, United Nations Environment Programme; UNEA, United Nations Environment Assembly; CKD, chronic kidney disease; IBD, inflammatory bowel disease; VOC, volatile organic compound; DMF, N,N-dimethylformamide; MRL, maximum residue limit; REACH, Registration, Evaluation, Authorisation and Restriction of Chemicals (EU regulation). Discussion This review provides the first consolidated account of how exposomic methodologies have been applied to uncover new contaminants and to reveal biological signals for chemicals that had long escaped detailed scrutiny. Although the field of exposomics has expanded rapidly in the past decade, only a relatively small proportion of studies have applied its tools directly to discovery. Yet those that did generated a body of evidence that is both diverse and transformative, extending far beyond the scope of conventional surveillance. Exposomics emerges from this mapping as both a broad detection platform and a practical early-warning system, capable of surfacing exposures invisible to targeted monitoring and of linking them to subtle molecular or physiological effects that anticipate later disease risk. The strength of the evidence base lies in its methodological breadth. Exposomic studies span a wide range of matrices, from traditional biospecimens such as urine and serum to more novel sources including placenta, semen, and deciduous teeth. This diversity enables insights into different life stages, exposure routes, and temporal windows. HRMS, particularly when paired with NTA, allows thousands of chemical features to be captured in a single run, while integration with metabolomics, epigenomics, and transcriptomics provides functional context and strengthens interpretation. Computational pipelines, machine learning, and effect-directed assays further extend discovery by prioritising unknowns and uncovering mixture patterns. Innovative sampling strategies, from dentine biomarkers that reconstruct exposures across childhood to wearable devices that capture short-lived or occupational peaks, illustrate the adaptability of exposomics and its ability to extend monitoring beyond conventional frameworks. At the same time, significant limitations constrain the maturity of the field. Most discoveries achieved Level 1 confidence with authentic reference standards and orthogonal validation, yet a minority remained at Level 2b, reflecting the continued challenges of standard availability and incomplete orthogonal confirmation. Many studies did not deposit spectra or provide transparent accounts of quality control, hindering reproducibility and cross-study comparison. The evidence base is also geographically uneven, dominated by investigations in North America, Europe, and East Asia, while low- and middle-income countries, despite bearing disproportionate exposure burdens, remain under-represented. Most human data derive from cross-sectional or convenience cohorts, limiting causal inference, and there is still no consensus on how best to integrate exposomic findings into regulatory decision-making. These weaknesses highlight the need for standardisation, broader global participation, and greater transparency if exposomics is to fulfil its potential. Yet despite these constraints, several investigations stand out as case exemplars that demonstrate how exposomics can generate meaningful discoveries and lessons even within a fragmented evidence base. Case exemplars as lessons Glyphosate shows how exposomics can move beyond simple quantification to provide mechanistic insight, linking exposures with metabolic disturbances and microbiome disruption [ 13 , 31 – 32 ]. GenX demonstrates how non-targeted detection followed by high-confidence confirmation can surface a replacement PFAS and propel it rapidly into regulatory consideration [ 25 – 27 ]. Exposomic semen profiling illustrates how overlooked consumer chemicals, from plasticisers to cosmetic additives, can be linked with reproductive toxicity, reframing the evidence base on male infertility [ 34 ]. The detection of microplastics in human blood and placenta highlights how exposomics is expanding into new domains, capturing exposures at the particle level once assumed to be unmeasurable in biomonitoring [ 38 – 39 ]. The discovery of 6PPD-quinone through effect-directed analysis resolved a long-standing ecological puzzle of salmon die-offs [ 20 ], while time-resolved lead biomarkers in baby teeth demonstrated how exposures can be reconstructed across developmental windows and tied to neurodevelopmental outcomes [ 24 ]. Together these cases show that the value of exposomics lies not only in detecting compounds but in redefining how exposures are conceptualised, in identifying mechanistic pathways, and in generating signals that reshape both science and regulation. Implications for research, surveillance, and policy The case exemplars described above show that exposomics does more than catalogue exposures: it actively generates discoveries that reshape how we think about chemical risk. These discoveries carry implications not only for future research but also for how surveillance systems operate and how regulators define their priorities. For research, the evidence base highlights the need for greater standardisation of methods and reporting. Routine application of the Schymanski scale, transparent documentation of blanks and quality control, and open deposition of spectra will be essential to ensure reproducibility and comparability. Equally important is the expansion of exposome-wide longitudinal cohorts that integrate omics, microbiome profiling, and novel biomarkers. Such cohorts can provide temporally resolved insights into exposure–disease pathways and extend coverage beyond the heavily studied populations of North America, Europe, and China. Addressing gaps in low- and middle-income countries is particularly urgent, given their disproportionate exposure burdens and limited monitoring infrastructure. For surveillance, exposomics offers a proactive complement to existing systems. By detecting exposures before they appear on regulatory lists, exposomics can inform prioritisation for national and regional biomonitoring initiatives such as NHANES, HBM4EU, and UNEP’s Global Monitoring Plan. Incorporating exposomic findings into watch lists and early warning systems would help shift monitoring from a reactive model, focused on legacy pollutants, toward anticipatory detection of emerging hazards. For policy, the lessons of exposomics align with a transition already underway toward class-based and mixture-aware regulation. Non-targeted discovery has shown that structurally related chemicals often share toxicodynamic properties, supporting restrictions that extend across entire classes, as seen with PFAS. Mechanistic insights from omics data and early biomarker signals strengthen the case for precautionary approaches to low-dose, chronic exposures and highlight the vulnerability of sensitive life stages. These emerging themes are summarised in Fig. 5 , which illustrates how exposomic discoveries are beginning to reshape regulatory paradigms across six domains, from class-based restrictions to precautionary principles and interdisciplinary science-to-policy initiatives. Taken together, this review shows that exposomics is no longer a conceptual ambition but a practical foundation for twenty-first century environmental health. It has demonstrated the ability to uncover hidden contaminants, reframe the biological effects of familiar chemicals, and extend monitoring into new exposure domains. At the same time, it faces challenges of confidence, transparency, and equity that must be addressed if its discoveries are to be translated into robust policy action. Conclusion From 2015 to 2025, exposomics has shifted from a conceptual framework to a practical discovery engine in environmental health. By integrating high-resolution mass spectrometry, non-targeted analysis, multi-omics, and innovative biomarkers, exposomic studies have revealed contaminants previously absent from regulatory watchlists and uncovered new biological signals for legacy compounds. This review shows that exposomics can detect hidden exposures such as microplastics in human tissues, novel PFAS substitutes like GenX, and unexpected reproductive toxicants in semen, while also reframing well-known compounds such as glyphosate and bisphenol S through new mechanistic insights. Collectively, these findings demonstrate the exposome’s dual function as a platform for broad detection and as an early-warning system that can anticipate health risks before they are evident in traditional monitoring. The evidence also highlights persistent gaps. Most findings remain at tentative confidence levels, spectra deposition and QC reporting are inconsistent, and discovery is heavily concentrated in high-income countries. Addressing these challenges will require harmonised protocols, broader geographic coverage, and systematic integration of exposomics into national and global biomonitoring frameworks. Looking ahead, the value of the exposome lies not only in its ability to expand the list of known contaminants but in its potential to reshape how chemical risk is governed. Embedding exposomic indicators into early warning systems, surveillance platforms, and mixture-aware regulatory strategies can enable more proactive and equitable protection of health. Realising this promise will depend on sustained collaboration between researchers, policymakers, and industry to build open data infrastructures, advance longitudinal cohorts, and apply One Health perspectives that connect human, animal, and ecosystem health. A decade of innovation has shown what is possible; the next must deliver coordinated, data-driven action to address not only known hazards but also the hidden complexity of everyday exposure. Declarations This study did not involve a clinical trial requiring registration. Clinical trial number: not applicable. Funding and Acknowledgements This paper is based on independent research funded by the National Institute for Health Research (NIHR). Oluwatobi Kolawole was fully funded and Tim Marczylo and Atallah El Zein are part-funded by the NIHR Health Protection Research Unit (HPRU) in Health Impacts of Environmental Hazards at Imperial College London. References Wang F, Xiang L, Leung KSY, Elsner M, Zhang Y, Guo Y, et al. 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08:28:23","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143355,"visible":true,"origin":"","legend":"","description":"","filename":"2e230d0d3fe2482aa679e1f4bb75ad621structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/594775578df340784477c29b.xml"},{"id":91966334,"identity":"ac5db2df-d79a-44d3-a07c-236ce9c5d6c4","added_by":"auto","created_at":"2025-09-23 08:28:24","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152988,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/132436e838b67f7fd8b21ff8.html"},{"id":91966361,"identity":"ece32e2d-1a36-4e89-9650-12ed3c8dcc9a","added_by":"auto","created_at":"2025-09-23 08:28:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234193,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of traditional and exposomic paradigms in chemical risk assessment. Traditional approaches focus on individual chemicals and linear responses, whereas exposomic strategies emphasise cumulative exposures, class-based regulation, and advanced real-time monitoring technologies.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/9bf1df8891695856420f3fc1.png"},{"id":91966401,"identity":"49874199-bb96-4394-a04f-4582a8e7fd9e","added_by":"auto","created_at":"2025-09-23 08:28:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511380,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram summarising the search and screening process. Diagram generated in R 4.4.0 with the PRISMA2020 package (v 1.1.1); and machine-readable screening log are archived at Zenodo (DOI: 10.5281/zenodo.16790030).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/68024fa2db68d0f586aa2352.png"},{"id":91966338,"identity":"02e3e4ce-ea2a-4e3a-b2ec-dcfe39ea2b3d","added_by":"auto","created_at":"2025-09-23 08:28:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":626098,"visible":true,"origin":"","legend":"\u003cp\u003eCountry × contaminant class (with super-region inset). Tiles show the number of unique studies per country–class (unweighted); “Mixtures” = intentional multi-class exposures and “Other” = chemicals not assignable to predefined classes. The right inset summarizes share of discovery by super-region (North America, Europe, Asia, etc.) using 1/n weighting for multi-country studies so shares sum to 100%. Discoveries cluster in the USA/Europe/China, with sparse activity across Africa and South Asia.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/4c60f7ebd7f8ec5c7242a8a6.png"},{"id":91967373,"identity":"f31e018d-a00e-49d4-a965-84def478471c","added_by":"auto","created_at":"2025-09-23 08:36:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248936,"visible":true,"origin":"","legend":"\u003cp\u003eOutcome domains by contaminant class (lollipop panels). Each panel shows unique studies per outcome domain for a class; stems/dots are coloured by domain, and labels report counts. Signals concentrate in metabolic and developmental domains for PFAS, pesticides, and metals, with few immune studies; reproductive findings appear mainly for PFAS/PPCPs. Other = non-canonical chemicals (e.g., ‘EDCs’, benzotriazoles, phenolic antioxidants). Mixtures = intentional cross-class exposures.Other = HTS/in-vitro and mechanistic/unspecified endpoints that don’t align with the six prespecified domains.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/a99ee4d22bf5556fa96e8f0f.png"},{"id":91966400,"identity":"5e429a32-72eb-4deb-88b1-e3a4bfad8fb9","added_by":"auto","created_at":"2025-09-23 08:28:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":655523,"visible":true,"origin":"","legend":"\u003cp\u003eMajor exposome-informed regulatory shifts over the past decade, illustrating six emerging trends: class-based and mixture-aware controls, adoption of non-targeted detection, integration of multiomics, recognition of low-dose and life-stage vulnerabilities, precautionary action under uncertainty, and strengthened international coordination.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/9bda9b2dcf754ff586b7c2cb.png"},{"id":94461307,"identity":"ab5f975c-4bc6-47d3-96e3-9994c6414453","added_by":"auto","created_at":"2025-10-27 14:59:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3163633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/e6803292-1b44-44df-a5bd-43182d8362fc.pdf"},{"id":91967371,"identity":"2c2a3277-e843-458b-8a55-6f3d961d70d3","added_by":"auto","created_at":"2025-09-23 08:36:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":275769,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklistdirect.docx","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/0a4725214990d5a835f0c28c.docx"},{"id":91966397,"identity":"df422e57-1ee2-44cc-ba39-5f131afc4ccd","added_by":"auto","created_at":"2025-09-23 08:28:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39044,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileS3v2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7543046/v1/70be4a66c6fe6e5a092d3343.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exposomics as a discovery engine: a systematic scoping review of emerging environmental contaminants and novel biological effects","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent years have seen an unprecedented expansion in our understanding of both biogenic and anthropogenic environmental contaminants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. What was once a relatively discrete list of well-characterised pollutants has transformed into a complex and evolving chemical landscape [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Industrial innovation, agricultural intensification, and the proliferation of consumer products have released tens of thousands of synthetic compounds into the environment, many of which remain unregulated or poorly understood in terms of biological effects. Collectively known as contaminants of emerging concern, these substances act not only as individual toxicants but also as components of complex mixtures, producing subtle and sometimes unpredictable biological outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditional toxicology and monitoring frameworks, focused on single chemicals under controlled conditions, are insufficient for capturing the cumulative, interactive nature of real-world exposures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Evidence now shows that chronic, low-dose exposures to diverse chemical mixtures can lead to synergistic or antagonistic effects, highlighting the need for new paradigms in exposure science [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The exposome has emerged as a transformative framework to meet this challenge. Rather than treating exposures in isolation, the exposome integrates external contaminants with internal biological responses across metabolic, epigenetic, immune, and microbiome domains [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Innovations in high-resolution mass spectrometry (HRMS), particularly non-target analysis (NTA), now allow thousands of chemical features to be profiled in a single sample, including unknown or unregulated compounds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. When combined with transcriptomics, proteomics and metabolomics, exposomics can reveal both the presence of novel contaminants and the pathways they perturb [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Complementary advances in wearable monitoring [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and computational analytics, including machine learning and exposome-wide association studies, further enhance the resolution of exposure assessment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Integration with microbiome profiling has added an additional layer, recognising that host-associated microbial communities both modulate and respond to chemical exposures [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite rapid advances, the extent to which exposomics has been applied to identify new or under-regulated contaminants remains uncertain. Landmark discoveries, including halobenzoquinones [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], GenX [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], 6PPD-quinone [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and microplastics in human tissues [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], demonstrate its promise, but the evidence remains scattered across chemical classes, exposure matrices, and study designs. To address this, we conducted a scoping review (PRISMA-ScR, with PRISMA-S documentation) of studies published between 2015 and 2025 that used exposome-oriented methodologies for the discovery of hidden or emerging contaminants. The review aims to map the extent, range, and nature of the evidence base: identifying which contaminants have been detected, across which matrices and geographies, with which analytical workflows, and where exposomic approaches have revealed novel biological effects of established compounds. In doing so, the synthesis highlights areas of convergence and scarcity, as well as the methodological choices most consistently enabling discovery. Rather than evaluating causal effects, it provides a structured foundation for setting future research priorities and developing surveillance strategies in chemical risk governance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eProtocol and reporting\u003c/h2\u003e\u003cp\u003eA protocol for this scoping review was prospectively archived on Zenodo (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.16790030\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.16790030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) prior to database searches. As PROSPERO does not register scoping reviews, the Zenodo record provides a permanent, citable, and publicly accessible registration. The review was conducted in accordance with the PRISMA-ScR checklist and search reporting followed PRISMA-S recommendations. The search strategy was independently peer-reviewed using the PRESS framework. Full search strings and documentation of amendments are available in Supplementary File S1 (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.16790030\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.16790030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eObjectives and PECO framework\u003c/h3\u003e\n\u003cp\u003eThe objective of this scoping review was to map the extent, range, and nature of research applying exposomic approaches to the detection and characterisation of emerging environmental contaminants in human and environmental matrices, including novel biological effects reported for known compounds. A PECO framework guided eligibility:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePopulation/Environment\u003c/strong\u003e\u003cp\u003ehuman participants of any age, as well as environmental compartments (e.g., air, water, soil, biota) sampled for biomonitoring purposes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e: non-regulated or recently regulated chemical classes, including but not limited to Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS), current-use pesticides, novel flame retardants, micro- and nanoplastics, endocrine-disrupting chemicals (EDCs), volatile organic compounds (VOCs), pharmaceuticals, and personal care products (PPCPs).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eComparator\u003c/b\u003e: not applicable, as this review maps evidence rather than comparing interventions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e: detection, characterisation, or identification of chemicals using exposomic tools; where reported, associated biological signals or mechanistic endpoints were noted descriptively.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eInformation sources and search strategy\u003c/h3\u003e\n\u003cp\u003eTo ensure broad and systematic capture of both peer-reviewed and grey literature, we designed a search strategy that combined multiple databases and platforms, leveraging controlled vocabulary and free-text keywords tailored to the exposome and contaminant science fields.\u003c/p\u003e\u003cp\u003eThe search was performed in four primary bibliographic databases: Scopus, Web of Science Core Collection, PubMed, and Europe PMC. These platforms were selected for their extensive coverage of biomedical, environmental, and interdisciplinary research, as well as their inclusion of preprints and non-traditional publication types. For each database, we developed a harmonised search string to maximise consistency and reproducibility. The search logic was intentionally broad, combining the exposome concept block (e.g., \u0026ldquo;exposome,\u0026rdquo; \u0026ldquo;exposome approach,\u0026rdquo; \u0026ldquo;environmental exposure\u0026rdquo;) with contaminant-related terms, both generic (e.g., \u0026ldquo;emerging,\u0026rdquo; \u0026ldquo;novel,\u0026rdquo; \u0026ldquo;unregulated,\u0026rdquo; \u0026ldquo;contaminant*\u0026rdquo;) and specific classes (e.g., \u0026ldquo;PFAS,\u0026rdquo; \u0026ldquo;microplastics,\u0026rdquo; \u0026ldquo;flame retardants,\u0026rdquo; \u0026ldquo;pesticides,\u0026rdquo; \u0026ldquo;heavy metals,\u0026rdquo; \u0026ldquo;pharmaceuticals,\u0026rdquo; \u0026ldquo;VOCs\u0026rdquo;).\u003c/p\u003e\u003cp\u003eTo capture unpublished and non-peer-reviewed evidence, grey literature searches were conducted on agency and organizational websites, including the United States Environmental Protection Agency (EPA), European Chemicals Agency (ECHA), World Health Organization (WHO), Organisation for Economic Co-operation and Development (OECD), and environmental NGOs such as the Environmental Working Group (EWG) and ChemSec. Advanced Google site queries and internal search engines were used to retrieve reports, technical documents, and datasets from these sources, applying the same core search concepts as in database queries.\u003c/p\u003e\u003cp\u003eAll searches were limited to articles published in English between January 2015 and May 2025. The search strategy was iteratively developed in consultation with domain experts and pilot-tested to ensure both sensitivity and specificity. The first search was conducted on 17 February 2025, and results were exported for deduplication and screening. A final update search was performed on 15 May 2025 to identify newly published articles. The number of records retrieved from each source is detailed in Supplementary File S2. All records, including those from grey literature, were screened using the same inclusion and exclusion criteria (Section 2.4).\u003c/p\u003e\n\u003ch3\u003eScreening and study selection\u003c/h3\u003e\n\u003cp\u003eAll records underwent a multi-stage screening process to ensure comprehensive and reproducible selection of studies relevant to the review\u0026rsquo;s objectives. Screening followed the eligibility framework described above and was conducted in accordance with the archived protocol.\u003c/p\u003e\u003cp\u003eAfter deduplication, records were screened by title and abstract by two independent reviewers using pre-specified inclusion and exclusion criteria (Section 2.2). Eligible studies were those that:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eapplied an exposome-oriented methodology or conceptual framework;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003efocused on the detection, characterisation, or health assessment of non-regulated or emerging environmental contaminants;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eemployed advanced analytical or computational exposomic techniques;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eprovided original data or reproducible workflows;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eaddressed human or environmental populations/matrices;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ewere published in English between 2015 and 2025.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eStudies were excluded if they focused solely on legacy or regulated pollutants without introducing new contaminants or methodological innovations, lacked exposome-based approaches, or did not provide empirical data or reproducible workflows. Reviews, commentaries, editorials, and conference abstracts without methodological detail were also excluded.\u003c/p\u003e\u003cp\u003eReasons for full-text exclusion are detailed in Supplementary File S2. Disagreements between reviewers were resolved by discussion, with arbitration by a third reviewer when required.\u003c/p\u003e\n\u003ch3\u003eData charting\u003c/h3\u003e\n\u003cp\u003eData were extracted using a piloted REDCap template, with double extraction on 20% of records (inter-extractor concordance: 96%). Variables included study identifiers, year, country, design, matrix, contaminant class, analytical platform, processing software, and (where reported) identification confidence (Schymanski levels), regulatory status, and associated biological signals. Study population was coded as human, animal, or \u003cem\u003ein vitro\u003c/em\u003e. The harmonised dataset is archived with DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.16790030\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.16790030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Supplementary File S3).\u003c/p\u003e\u003cp\u003eIdentification confidence for all compounds was assessed using the Schymanski framework [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], adapted with strict thresholds for mass accuracy, spectral quality, and orthogonal confirmation. Levels were assigned as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 1 (confirmed structure)\u003c/strong\u003e\u003cp\u003eThe compound was run as an authentic reference standard under the same method, with matching retention time or retention index and matching MS/MS (or EI) spectrum. Targeted LC/GC-MS/MS panels with certified standards, including isotope-dilution approaches, were classified as Level 1 by default.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 2a (probable, library\u0026thinsp;+\u0026thinsp;orthogonal)\u003c/strong\u003e\u003cp\u003eHigh-quality library match (HRMS MS/MS or NIST EI; score\u0026thinsp;\u0026ge;\u0026thinsp;0.70 or \u0026ge;\u0026thinsp;800 respectively) combined with at least one orthogonal line of evidence, such as retention index, same-method retention time, collision cross section, or MS\u0026sup3; fragmentation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 2b (probable, diagnostic evidence)\u003c/strong\u003e\u003cp\u003eDiagnostic fragments, adduct patterns, or isotope distributions strongly supported a single structure, but no in-house standard was available and orthogonal confirmation was absent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 3 (tentative candidate/class)\u003c/strong\u003e\u003cp\u003eAccurate mass and fragments supported a candidate structure or chemical class, but isomers were unresolved, and no orthogonal data or authentic standard was available.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 4 (formula only)\u003c/strong\u003e\u003cp\u003eFormula assignment was supported by accurate mass and isotope distribution, but insufficient evidence existed for structural assignment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLevel 5 (feature only)\u003c/strong\u003e\u003cp\u003eAccurate mass peak detected without reliable formula or structure.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eOrthogonal confirmation was defined as any independent line of evidence beyond library or MS/MS match, including retention index (\u0026le;\u0026thinsp;1% deviation), retention time against an in-house standard (\u0026le;\u0026thinsp;0.2 min deviation), collision cross section (\u0026le;\u0026thinsp;2% deviation), or acquisition of MS\u0026sup3; diagnostic fragments. For LC-HRMS, a mass accuracy tolerance of \u0026le;\u0026thinsp;5 ppm and at least three meaningful fragment ions were required.\u003c/p\u003e\u003cp\u003eElemental analyses (e.g. ICP-MS, LA-ICP-MS) and polymer identifications (e.g. \u0026micro;-FTIR, Py-GC/MS) were excluded from the Schymanski framework and recorded as \u0026ldquo;N/A\u0026rdquo; since the criteria apply only to small molecules.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData synthesis\u003c/h2\u003e\u003cp\u003eWe summarised the extent and distribution of evidence across contaminant classes, matrices, and geographies. Results are presented as descriptive tables and figures (e.g., evidence distributions). No effect size pooling was attempted.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCritical appraisal\u003c/h3\u003e\n\u003cp\u003eAlthough not required for scoping reviews, we undertook a structured appraisal of study validity to contextualize the strength of the evidence base. We applied RoB2 for randomized trials and ROBINS-E (or analogous domains) for observational studies, reporting ratings (low/some concerns/high) by domain. These appraisals were not used for exclusion or synthesis but to characterize the strengths and limitations of the evidence landscape. Full results are available in Supplementary File S3 (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.16790030\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.16790030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStudy selection\u003c/h2\u003e\u003cp\u003eThe database and grey literature searches conducted between January 2015 and May 2025 identified 28,946 records: 10,811 from Scopus, 7,672 from Europe PMC, 5,574 from Web of Science, 3,889 from PubMed, and approximately 1,000 from targeted grey literature and preprint sources. After automated and manual deduplication, 24,746 unique citations were screened at title and abstract level. At this initial stage, more than 24,259 records were excluded as clearly out of scope, most commonly because they lacked primary data, were commentary/editorial pieces, or addressed exposures unrelated to chemical contaminants. A total of 487 articles were assessed in full text against the predefined eligibility criteria, of which 420 were excluded. The most frequent reasons were:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eapplication of conventional monitoring only, with no exposome-oriented methodology;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eexclusive focus on legacy pollutants already subject to long-standing regulation;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eexclusive focus on legacy pollutants already subject to long-standing regulation;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003einsufficient methodological detail to allow reproducibility;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003epurely theoretical or modelling studies without empirical data;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003enon-English publications;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003estudies falling outside the 2015\u0026ndash;2025 timeframe.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe final evidence set comprised 67 eligible studies, of which 60 originated from bibliographic databases and 7 from grey literature sources such as agency reports and preprints. The overall selection process is depicted in the PRISMA-ScR flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A complete list of all 67 included studies, along with risk-of-bias appraisals, is provided in Supplementary File S3. This process highlights that while the exposomics literature is expanding rapidly, only a small fraction of published work has applied exposome-oriented tools directly to the discovery of emerging or under-regulated contaminants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive overview of included evidence\u003c/h2\u003e\u003cp\u003eOf the 67 included studies, 42 were primary research investigations that were charted quantitatively, while 25 comprised reviews, regulatory documents, and methodological papers that provided context but were not mapped. The 42 primary studies spanned fifteen countries, with the United States and China together contributing more than half of the total. This reflects both their early investment in large scale exposomics infrastructure and the availability of national biomonitoring programs such as NHANES and KoNEHS. Additional studies originated from the United Kingdom, France, Switzerland, Germany, Denmark, the Netherlands, Italy, Sweden, South Korea, India, Bangladesh, Senegal, and Pakistan. Despite this international spread, representation from low- and middle-income countries was sparse, underscoring the persistent imbalance in global coverage despite their high burden of environmental exposures. The distribution of contaminant classes across countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) further illustrates that not only are studies concentrated in a handful of regions, but their thematic scope is uneven, with PFAS and pesticides dominating in North America and East Asia, while European studies contribute disproportionately to research on microplastics and PPCPs.\u003c/p\u003e\u003cp\u003eThe methodological approaches represented in the evidence base illustrate the breadth of contemporary exposome science. Observational human studies formed the largest group, ranging from prospective and retrospective cohorts to population based cross sectional surveys and targeted biomonitoring projects. Many of these drew on multiple biological matrices, such as urine, serum, placenta, semen, breast milk, and even deciduous teeth, which allowed for temporally resolved or life stage specific profiling. Some leveraged nested designs such as mother child cohorts and twin studies to capture inter-individual variability in exposures. These approaches were complemented by mechanistic and experimental investigations, which accounted for approximately one quarter of the included studies. Animal models and \u003cem\u003ein vitro\u003c/em\u003e systems were often combined with multi omics readouts, including transcriptomics, metabolomics, and epigenomics, providing functional anchors for signals observed in human biospecimens.\u003c/p\u003e\u003cp\u003eEmerging computational and high throughput tools featured prominently in several investigations. In silico annotation pipelines, machine learning algorithms, and effect directed analyses were applied to accelerate candidate identification and prioritisation. The growing use of machine learning in particular enabled researchers to integrate multidimensional datasets, uncover latent co-exposure patterns, and identify complex mixtures linked to disease risk. Wearable devices and personal passive samplers also appeared in a subset of studies, illustrating the methodological shift toward real time, individual level exposure monitoring. These innovations complement the laboratory based HRMS approaches and help capture exposures that may be episodic, highly localised, or missed by traditional biomonitoring strategies.\u003c/p\u003e\u003cp\u003eTaken together, the included studies show both the diversity and rapid evolution of exposomic methodologies, while also revealing clear imbalances in geographic distribution and thematic scope. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates how outcome domains are distributed across contaminant classes, with metabolic, developmental, and epigenetic outcomes dominating, and reproductive, immune, and neurologic signals less frequently reported. An \u0026ldquo;Other\u0026rdquo; outcome domain captured endpoints that did not map cleanly to these prespecified categories\u0026mdash;e.g., high-throughput/in-vitro assay readouts (cell viability, reporter activity), broad mechanistic or multi-omics screens, general toxicity or oxidative-stress markers, and organ-function panels or endocrine markers not specific to the six domains. This broad methodological foundation provides the context for understanding which contaminants have actually been detected and characterised through these approaches and sets the stage for the analysis of discovery yield presented in the following section.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDiscovery yield\u003c/h2\u003e\u003cp\u003eThe 42 charted primary studies revealed a broad spectrum of contaminants detectable by exposomic tools. The chemical landscape encompassed pesticides, heavy metals, PFAS, PPCPs, flame retardants, combustion by-products, water disinfection by-products, and polymers such as microplastics. Some studies were designed to explore mixtures across classes, while others uncovered contaminants that could not be assigned to our predefined categories. In these cases, we retained \u0026ldquo;mixtures\u0026rdquo; as a distinct class, denoting intentional multi-chemical designs, and defined an \u0026ldquo;other\u0026rdquo; category for heterogeneous compounds such as benzotriazoles, phenolic antioxidants, or broad descriptors like endocrine-disrupting chemicals. Matrices analysed were equally diverse, spanning urine, serum, blood, semen, placenta, breast milk, and deciduous teeth in humans, alongside surface water, wastewater effluent, air, dust, sediments, and animal or in vitro models. This breadth underscores how exposomics bridges external contamination with internal exposure profiles and provides mechanistic context for emerging hazards.\u003c/p\u003e\u003cp\u003eFrom this landscape, 17 compounds emerged as priority discoveries where exposomic methodologies either revealed previously unmonitored contaminants in humans and the environment or generated new biological signals for well-known chemicals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Metals such as tungsten and lead, although outside the small-molecule HRMS framework, illustrate how exposomic biomarkers extend beyond organic compounds [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Tungsten was identified by ICP-MS in occupational biomonitoring and linked to kidney dysfunction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while lead was reconstructed in a time-resolved manner using dentine micro-slices from deciduous teeth, revealing elevated exposures in children later diagnosed with autism [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These studies [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] highlight how innovative matrices can provide temporal resolution and disease-relevant signals for long-recognised contaminants.\u003c/p\u003e\u003cp\u003eAmong organic contaminants, PFAS offered one of the clearest demonstrations of exposomics as a discovery engine [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. GenX (HFPO-DA), a fluoropolymer substitute, was detected in serum and liver samples by HRMS with isotopically labelled standards, achieving Schymanski Level 1 with full orthogonal confirmation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Its association with developmental and hepatic toxicity illustrates how exposomics can track substitute PFAS that enter circulation after regulatory shifts, anticipating the risk before widespread surveillance [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Disinfection by-products showed a similar trajectory. Halobenzoquinones were uncovered in drinking water and \u003cem\u003ein vitro\u003c/em\u003e models, with some congeners confirmed against authentic standards at Level 1 and others tentatively annotated at Level 2 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. They proved strongly cytotoxic yet remain unregulated.\u003c/p\u003e\u003cp\u003ePesticides provided diverse lessons. Glyphosate, although extensively debated, has rarely been examined in untargeted exposomic frameworks. It was profiled in urine with metabolomics and microbiome assays, revealing disrupted metabolism, gut dysbiosis, and hepatic perturbation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. o-Hydroxybiphenyl appeared in a pre-IBD cohort, detected prior to diagnosis and associated with early gut inflammation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. 4-Nitrophenol, a pesticide degradation product and paraben metabolite, was observed in semen profiling and tentatively annotated at Level 2\u0026ndash;3, with abnormal morphology as a biological signal [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsumer product chemicals were another recurrent source of new signals. Exposomic semen profiling uncovered several consumer product chemicals, including dibutyl phthalate, 2-aminophenol, and 3-hydroxyoctanedioic acid, all tentatively identified at Level 2\u0026ndash;3 but consistently linked with impaired sperm function, including reduced motility and abnormal morphology [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Oxybenzone, a UV filter common in sunscreens, was confirmed at Level 1 in maternal and infant biomonitoring and linked to adverse pregnancy outcomes including Hirschsprung\u0026rsquo;s disease, low birth weight, and preterm delivery [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These discoveries underscore the capacity of exposomics to flag risks from replacement chemicals and everyday consumer additives that remain in widespread use despite partial restrictions.\u003c/p\u003e\u003cp\u003eOther industrial and combustion-related contaminants added further weight. Triphenyl phosphate, an organophosphate flame retardant, was reported in NHANES exposome-wide lipidomics with partial confirmation and associated with altered lipid metabolism and adiposity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Fluorene, a combustion by-product, was detected in adolescents by LC-HRMS urinary biomonitoring and linked with liver enzyme elevations and fatty liver risk [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicroplastics represent a frontier distinct from the small-molecule HRMS framework. Identified using spectroscopic and pyrolytic approaches, with support from HRMS in some cases, microplastics were reported in human placenta and blood [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Confirmation relied on polymer reference spectra and morphological consistency, and early biological signals included obesity, insulin resistance, and metabolic dysfunction.\u003c/p\u003e\u003cp\u003eFinally, exposomics expanded discovery into VOC metabolites through large-scale biomonitoring. An exposome-wide analysis of NHANES women aged 18 to 45 identified five urinary mercapturic acids: AAMA (acrylamide), AMCC/MCAMA (N,N-dimethylformamide and methyl isocyanate), CYMA (acrylonitrile), HPMMA or 2-HPMA (propylene oxide), and 34MHA (xylenes). Quantified against authentic standards, these metabolites were confirmed at Level 1 and associated with increased infertility risk, with associations persisting in mixture models [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although their parent compounds are already regulated, exposomic profiling revealed their continued ubiquity in the general population and their relevance as reproductive toxicants, highlighting how exposomics reframes \u0026ldquo;known\u0026rdquo; chemicals within mixture-aware contexts.\u003c/p\u003e\u003cp\u003eA full list of these 17 compounds, their associated biological effects, and regulatory context is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Taken together, these 17 priority contaminants illustrate the breadth of discovery enabled by exposomics. The majority were confirmed at Schymanski Level 1 using authentic reference standards with orthogonal validation, while a smaller subset, including o-hydroxybiphenyl and 3-hydroxyoctanedioic acid, were assigned to Level 2b as probable structures without in-house standards or orthogonal confirmation. Halobenzoquinones represented a mixed group with compounds spanning Levels 1 to 2, and three categories (metals and polymers) fell outside the small-molecule HRMS framework but nonetheless yielded biologically meaningful signals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePriority contaminants identified through exposomic approaches (with confidence levels and regulatory context)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass / Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMatrix / Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethod (platform)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSchymanski level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOrthogonal confirmation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBiological signal (domain)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRegulatory context\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTungsten (W)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetal / industrial emissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine (occupational biomonitoring)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICP-MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRenal dysfunction, CKD acceleration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot regulated under EU/WHO drinking water standards\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLead (Pb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetal / legacy pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeciduous teeth (children)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLA-ICP-MS (time-resolved)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNeurodevelopmental disruption, autism signals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStrictly regulated worldwide (water, paint, consumer products)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenX (HFPO-DA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePFAS substitute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum \u0026amp; liver (rats)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUPLC\u0026ndash;HRMS (ESI\u0026minus;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference standard (isotopically labelled IS; RT\u0026thinsp;+\u0026thinsp;MS/MS match)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDevelopmental toxicity, hepatomegaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSVHC in EU; US advisory levels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHalobenzoquinones (HBQs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater disinfection by-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrinking water; \u003cem\u003ein vitro\u003c/em\u003e models\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1 (with in-house stds); Level 2a (library\u0026thinsp;+\u0026thinsp;orthogonal); Level 2b (library/diagnostic only)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePartial (standards for subset; MS/MS spectra)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCytotoxicity, DNA damage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCurrently unregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxybenzone (BP-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUV filter (PPCP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine, blood (pregnant women, infants)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTargeted biomonitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference standard RT\u0026thinsp;+\u0026thinsp;MS/MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdverse pregnancy outcomes (birth weight, preterm, Hirschsprung\u0026rsquo;s disease signals)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRegulated in parts of EU/US; widely used globally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlyphosate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHerbicide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine (rats, dams/pups, adults)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC-HRMS\u0026thinsp;+\u0026thinsp;metabolomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIsotope-dilution standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDisrupted metabolism, microbiome, liver perturbation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eApproved globally with scrutiny\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicroplastics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironmental polymer pollutants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood, placenta (humans)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026micro;-FTIR, Py-GC/MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePolymer reference spectra, morphology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMetabolic dysfunction (obesity, insulin resistance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUnder UNEA treaty negotiation; limited bans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBisphenol S (BPS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBPA substitute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine (NHANES adults \u0026amp; youth); in vivo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC-HRMS biomonitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuthentic standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHormone disruption, obesity/adiposity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLimited regulation (food-contact/baby products)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluorene (2-hydroxyfluorene)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombustion by-product\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine (adolescents)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC-HRMS biomonitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCalibration standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eElevated liver enzymes, fatty liver risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMonitored; no enforceable limits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eo-Hydroxybiphenyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePesticide/fungicide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum (pre-IBD cohort)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted GC-HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLibrary/diagnostic only (no RI/standard)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGut inflammation, pre-diagnostic IBD signal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eApproved agriculturally; restricted in cosmetics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriphenyl phosphate (TPHP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlame retardant (OPFR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum (NHANES cohort)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExposome-wide lipidomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuthentic standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAltered lipid metabolism, adiposity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRestricted in children\u0026rsquo;s products; otherwise used\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImidacloprid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeonicotinoid insecticide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerum (NHANES cohort)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEWAS (serum metabolomics)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuthentic standard (isotope-dilution; RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAltered liver enzyme activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRegulated under pesticide laws\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDibutyl phthalate (DBP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlasticiser (PPCP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemen (adult males)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted LC-HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIn-study reference standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReduced sperm motility, semen quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRestricted in EU/US consumer products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Aminophenol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHair dye component\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemen (adult males)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted LC-HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIn-study reference standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReduced sperm motility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCovered by REACH; not specifically listed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4-Nitrophenol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePesticide metabolite / paraben breakdown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemen (adult males)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted LC-HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIn-study reference standard (RT\u0026thinsp;+\u0026thinsp;MS/MS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReduced sperm morphology, quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot approved for plant protection; no MRLs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-Hydroxyoctanedioic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCosmetic additive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemen (adult males)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUntargeted LC-HRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLiterature MS/MS match only (no in-house standard/orthogonal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReduced sperm motility, abnormal morphology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUnregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMercapturic acids (AAMA, AMCC/MCAMA, CYMA, HPMMA/2-HPMA, 34MHA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVOC metabolites (acrylamide, DMF/methyl isocyanate, acrylonitrile, propylene oxide, xylenes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrine (NHANES women, 18\u0026ndash;45y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUPLC\u0026ndash;ESI-MS/MS (isotope-dilution)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuthentic standards (RT\u0026thinsp;+\u0026thinsp;MS/MS; isotope labelled IS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReproductive toxicity: increased infertility risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRegulated parent compounds, but metabolites not explicitly listed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIdentification confidence levels were assigned according to Schymanski et al. (2014), with Level 1 requiring an authentic reference standard under the same method, Levels 2a\u0026ndash;2b requiring high-quality spectral/library evidence with or without orthogonal confirmation, and Level 3 denoting tentative candidates; Levels 4\u0026ndash;5 were not encountered. Elements and polymers are shown as N/A.\u0026rdquo;\u003c/p\u003e\u003cp\u003eICP-MS, inductively coupled plasma mass spectrometry; LA-ICP-MS, laser ablation inductively coupled plasma mass spectrometry; UPLC, ultra-performance liquid chromatography; HRMS, high-resolution mass spectrometry; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; EI, electron ionization; \u0026micro;-FTIR, micro-Fourier transform infrared spectroscopy; Py-GC/MS, pyrolysis gas chromatography mass spectrometry; PPCP, pharmaceutical and personal care product; PFAS, per- and polyfluoroalkyl substances; BPA, bisphenol A; BPS, bisphenol S; BPF, bisphenol F; OPFR, organophosphate flame retardant; EWAS, exposome-wide association study; RT, retention time; RI, retention index; CCS, collision cross section; IS, internal standard; SVHC, substance of very high concern (EU REACH); NHANES, National Health and Nutrition Examination Survey (US); HBM4EU, Human Biomonitoring for Europe; UNEP, United Nations Environment Programme; UNEA, United Nations Environment Assembly; CKD, chronic kidney disease; IBD, inflammatory bowel disease; VOC, volatile organic compound; DMF, N,N-dimethylformamide; MRL, maximum residue limit; REACH, Registration, Evaluation, Authorisation and Restriction of Chemicals (EU regulation).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis review provides the first consolidated account of how exposomic methodologies have been applied to uncover new contaminants and to reveal biological signals for chemicals that had long escaped detailed scrutiny. Although the field of exposomics has expanded rapidly in the past decade, only a relatively small proportion of studies have applied its tools directly to discovery. Yet those that did generated a body of evidence that is both diverse and transformative, extending far beyond the scope of conventional surveillance. Exposomics emerges from this mapping as both a broad detection platform and a practical early-warning system, capable of surfacing exposures invisible to targeted monitoring and of linking them to subtle molecular or physiological effects that anticipate later disease risk.\u003c/p\u003e\u003cp\u003eThe strength of the evidence base lies in its methodological breadth. Exposomic studies span a wide range of matrices, from traditional biospecimens such as urine and serum to more novel sources including placenta, semen, and deciduous teeth. This diversity enables insights into different life stages, exposure routes, and temporal windows. HRMS, particularly when paired with NTA, allows thousands of chemical features to be captured in a single run, while integration with metabolomics, epigenomics, and transcriptomics provides functional context and strengthens interpretation. Computational pipelines, machine learning, and effect-directed assays further extend discovery by prioritising unknowns and uncovering mixture patterns. Innovative sampling strategies, from dentine biomarkers that reconstruct exposures across childhood to wearable devices that capture short-lived or occupational peaks, illustrate the adaptability of exposomics and its ability to extend monitoring beyond conventional frameworks.\u003c/p\u003e\u003cp\u003eAt the same time, significant limitations constrain the maturity of the field. Most discoveries achieved Level 1 confidence with authentic reference standards and orthogonal validation, yet a minority remained at Level 2b, reflecting the continued challenges of standard availability and incomplete orthogonal confirmation. Many studies did not deposit spectra or provide transparent accounts of quality control, hindering reproducibility and cross-study comparison. The evidence base is also geographically uneven, dominated by investigations in North America, Europe, and East Asia, while low- and middle-income countries, despite bearing disproportionate exposure burdens, remain under-represented. Most human data derive from cross-sectional or convenience cohorts, limiting causal inference, and there is still no consensus on how best to integrate exposomic findings into regulatory decision-making. These weaknesses highlight the need for standardisation, broader global participation, and greater transparency if exposomics is to fulfil its potential. Yet despite these constraints, several investigations stand out as case exemplars that demonstrate how exposomics can generate meaningful discoveries and lessons even within a fragmented evidence base.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCase exemplars as lessons\u003c/h2\u003e\u003cp\u003eGlyphosate shows how exposomics can move beyond simple quantification to provide mechanistic insight, linking exposures with metabolic disturbances and microbiome disruption [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. GenX demonstrates how non-targeted detection followed by high-confidence confirmation can surface a replacement PFAS and propel it rapidly into regulatory consideration [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Exposomic semen profiling illustrates how overlooked consumer chemicals, from plasticisers to cosmetic additives, can be linked with reproductive toxicity, reframing the evidence base on male infertility [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The detection of microplastics in human blood and placenta highlights how exposomics is expanding into new domains, capturing exposures at the particle level once assumed to be unmeasurable in biomonitoring [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The discovery of 6PPD-quinone through effect-directed analysis resolved a long-standing ecological puzzle of salmon die-offs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while time-resolved lead biomarkers in baby teeth demonstrated how exposures can be reconstructed across developmental windows and tied to neurodevelopmental outcomes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Together these cases show that the value of exposomics lies not only in detecting compounds but in redefining how exposures are conceptualised, in identifying mechanistic pathways, and in generating signals that reshape both science and regulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImplications for research, surveillance, and policy\u003c/h2\u003e\u003cp\u003eThe case exemplars described above show that exposomics does more than catalogue exposures: it actively generates discoveries that reshape how we think about chemical risk. These discoveries carry implications not only for future research but also for how surveillance systems operate and how regulators define their priorities.\u003c/p\u003e\u003cp\u003eFor research, the evidence base highlights the need for greater standardisation of methods and reporting. Routine application of the Schymanski scale, transparent documentation of blanks and quality control, and open deposition of spectra will be essential to ensure reproducibility and comparability. Equally important is the expansion of exposome-wide longitudinal cohorts that integrate omics, microbiome profiling, and novel biomarkers. Such cohorts can provide temporally resolved insights into exposure\u0026ndash;disease pathways and extend coverage beyond the heavily studied populations of North America, Europe, and China. Addressing gaps in low- and middle-income countries is particularly urgent, given their disproportionate exposure burdens and limited monitoring infrastructure.\u003c/p\u003e\u003cp\u003eFor surveillance, exposomics offers a proactive complement to existing systems. By detecting exposures before they appear on regulatory lists, exposomics can inform prioritisation for national and regional biomonitoring initiatives such as NHANES, HBM4EU, and UNEP\u0026rsquo;s Global Monitoring Plan. Incorporating exposomic findings into watch lists and early warning systems would help shift monitoring from a reactive model, focused on legacy pollutants, toward anticipatory detection of emerging hazards.\u003c/p\u003e\u003cp\u003eFor policy, the lessons of exposomics align with a transition already underway toward class-based and mixture-aware regulation. Non-targeted discovery has shown that structurally related chemicals often share toxicodynamic properties, supporting restrictions that extend across entire classes, as seen with PFAS. Mechanistic insights from omics data and early biomarker signals strengthen the case for precautionary approaches to low-dose, chronic exposures and highlight the vulnerability of sensitive life stages. These emerging themes are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which illustrates how exposomic discoveries are beginning to reshape regulatory paradigms across six domains, from class-based restrictions to precautionary principles and interdisciplinary science-to-policy initiatives.\u003c/p\u003e\u003cp\u003eTaken together, this review shows that exposomics is no longer a conceptual ambition but a practical foundation for twenty-first century environmental health. It has demonstrated the ability to uncover hidden contaminants, reframe the biological effects of familiar chemicals, and extend monitoring into new exposure domains. At the same time, it faces challenges of confidence, transparency, and equity that must be addressed if its discoveries are to be translated into robust policy action.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom 2015 to 2025, exposomics has shifted from a conceptual framework to a practical discovery engine in environmental health. By integrating high-resolution mass spectrometry, non-targeted analysis, multi-omics, and innovative biomarkers, exposomic studies have revealed contaminants previously absent from regulatory watchlists and uncovered new biological signals for legacy compounds. This review shows that exposomics can detect hidden exposures such as microplastics in human tissues, novel PFAS substitutes like GenX, and unexpected reproductive toxicants in semen, while also reframing well-known compounds such as glyphosate and bisphenol S through new mechanistic insights. Collectively, these findings demonstrate the exposome\u0026rsquo;s dual function as a platform for broad detection and as an early-warning system that can anticipate health risks before they are evident in traditional monitoring.\u003c/p\u003e\u003cp\u003eThe evidence also highlights persistent gaps. Most findings remain at tentative confidence levels, spectra deposition and QC reporting are inconsistent, and discovery is heavily concentrated in high-income countries. Addressing these challenges will require harmonised protocols, broader geographic coverage, and systematic integration of exposomics into national and global biomonitoring frameworks.\u003c/p\u003e\u003cp\u003eLooking ahead, the value of the exposome lies not only in its ability to expand the list of known contaminants but in its potential to reshape how chemical risk is governed. Embedding exposomic indicators into early warning systems, surveillance platforms, and mixture-aware regulatory strategies can enable more proactive and equitable protection of health. Realising this promise will depend on sustained collaboration between researchers, policymakers, and industry to build open data infrastructures, advance longitudinal cohorts, and apply One Health perspectives that connect human, animal, and ecosystem health. A decade of innovation has shown what is possible; the next must deliver coordinated, data-driven action to address not only known hazards but also the hidden complexity of everyday exposure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study did not involve a clinical trial requiring registration. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding and Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is based on independent research funded by the National Institute for Health Research (NIHR). Oluwatobi Kolawole was fully funded and Tim Marczylo and Atallah El Zein are part-funded by the NIHR Health Protection Research Unit (HPRU) in Health Impacts of Environmental Hazards at Imperial College London.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang F, Xiang L, Leung KSY, Elsner M, Zhang Y, Guo Y, et al. Emerging contaminants: A One Health perspective. The Innovation. 2024;5(4):100612. https://doi.org/10.1016/j.xinn.2024.100612.\u003c/li\u003e\n\u003cli\u003eNaidu R, Biswas B, Willett IR, Cribb J, Singh BK, Nathanail CP, et al. Chemical pollution: A growing peril and potential catastrophic risk to humanity. 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JAMA Network Open. 2019;2(5):e193644. https://doi.org/10.1001/jamanetworkopen.2019.3644.\u003c/li\u003e\n\u003cli\u003eGodbole AM, Chen A, Vuong AM. Associations between neonicotinoids and liver function measures in US adults: National Health and Nutrition Examination Survey 2015\u0026ndash;2016. Environmental Epidemiology. 2024;8:e0310. https://doi.org/10.1097/EE9.0000000000000310.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Material","content":"\u003cp\u003eSupplementary Files S1 and S2 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Exposome, Emerging Contaminants, Regulatory Frameworks, Environmental Health, Mixture Toxicity, Health Risk Assessment","lastPublishedDoi":"10.21203/rs.3.rs-7543046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7543046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOver the past decade, exposome science has evolved from a conceptual framework into a practical discovery engine for environmental health. By combining high-resolution mass spectrometry, non-targeted analysis, multi-omics integration, wearable sensors, and computational tools, exposomics can capture the complexity of real-world chemical mixtures and uncover exposures missed by conventional monitoring. We conducted a scoping review following PRISMA-ScR guidelines to map how exposomic approaches have been applied to the detection and characterisation of under-regulated or previously unknown contaminants. Searches of four bibliographic databases and targeted grey literature (2015\u0026ndash;2025) yielded 67 eligible studies, of which 42 were charted quantitatively. The evidence was heavily concentrated in high-income countries and focused on pesticides, PFAS, and heavy metals, with metabolic, developmental, and epigenetic outcomes most frequently reported. Across this landscape, 17 priority compounds emerged where exposomics revealed either new detections or novel biological effects, including halobenzoquinones, GenX, bisphenol S, microplastics, tungsten, and 3-hydroxyoctanedioic acid. These case exemplars illustrate how exposomics can expand hazard characterisation and provide early warning of risks that are invisible to targeted surveillance. At the same time, critical gaps persist, particularly in geographic coverage, longitudinal cohorts, data infrastructures, and mixture analysis tools. Scaling exposomics through harmonised biomonitoring systems and embedding it within One Health frameworks will be essential to accelerate discovery and to translate emerging evidence into more proactive and equitable chemical risk governance.\u003c/p\u003e","manuscriptTitle":"Exposomics as a discovery engine: a systematic scoping review of emerging environmental contaminants and novel biological effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 08:28:04","doi":"10.21203/rs.3.rs-7543046/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"760f71a3-8ad7-4499-8e92-1d9899d01c5a","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55028631,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55028632,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-10-27T12:18:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 08:28:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7543046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7543046","identity":"rs-7543046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}