Suspect Screening Analysis of Chemicals in Consumer Products

Crystallization and Solvent Evaporation Ionization Mass Spectrometry (CSEI-MS) for Rapid Detection of Drugs in Complex Matrices

Illegal addition of drugs is common but seriously threatens public health safety. Conventional mass spectrometry methods are difficult to realize direct analysis of drugs existing in some complex matrices such as seawater or soil due to the ion suppression effect and contamination to MS parts caused by nonvolatile salts. In this work, a novel crystallization and solvent evaporation ionization mass spectrometry (CSEI-MS) method was constructed and developed to achieve rapid desalting detection. CSEI only consists of a heated plate and a nebulizer and exhibits excellent desalting performance, enabling direct analysis of six drugs dissolved in eight kinds of salt solutions (up to 200 mmol/L) and three complex salty matrices. Under optimized conditions, CSEI-MS presents high sensitivity, accuracy, linearity, and intraday and interday precision. Finally, this method is applied to the quantitative analysis of drugs in seawater, hand cream, and soil. Furthermore, the highly sensitive detection of CSEI-MS is demonstrated to remain even if the detection processes are conducted within 5 s via common commercial tools.

Assessing developmental neurotoxicity of emerging environmental chemicals using multiple in vitro models: A comparative analysis

Newly synthesized chemicals are being introduced into the environment without undergoing proper toxicological evaluation, particularly in terms of their effects on the vulnerable neurodevelopment. Thus, it is important to carefully assess the developmental neurotoxicity of these novel environmental contaminants using methods that are closely relevant to human physiology. This study comparatively evaluated the potential developmental neurotoxicity of 19 prevalent environmental chemicals including neonicotinoids (NEOs), organophosphate esters (OPEs), and synthetic phenolic antioxidants (SPAs) at environment-relevant doses (100 nM and 1 μM), using three commonly employed in vitro neurotoxicity models: human neural stem cells (NSCs), as well as the SK-N-SH and PC12 cell lines. Our results showed that NSCs were more sensitive than SK-N-SH and PC12 cell lines. Among all the chemicals tested, the two NEOs imidaclothiz (IMZ) and cycloxaprid (CYC), as well as the OPE tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), generated the most noticeable perturbation by impairing NSC maintenance and neuronal differentiation, as well as promoting the epithelial-mesenchymal transition process, likely via activating NF-κB signaling. Our data indicate that novel NEOs and OPEs, particularly IMZ, CYC, and TDCIPP, may not be safe alternatives as they can affect NSC maintenance and differentiation, potentially leading to neural tube defects and neuronal differentiation dysplasia in fetuses.

A proof-of-concept study on the versatility of liquid chromatography coupled to high-resolution mass spectrometry to screen for various contaminants and highlight markers of floral and geographical origin for different honeys

The high-resolution mass spectrometry is a powerful analytical tool for improving food safety and authenticity, but still underused in official control laboratories. The present work is a proof-of-concept study overviewing how liquid-chromatography coupled to high-resolution mass spectrometry could be used simultaneously for large-scale screening of contaminants and differentiation of honey samples. Within this study, the samples were extracted using all-in-one QuEChERS-based protocol that allowed for analysis of various anthropogenic contaminants and endogenous compounds. First, targeted-analysis of 52 honey samples led to unequivocal identification of 23 chemicals, including neonicotinoids, triazole fungicides and synergist. Then, suspect-screening using MSDial software allowed for tentative identification of 30 chemicals including plasticizers, flame-retardants and additives. Suspect-screening also made it possible to highlight tentative markers of chestnut honey (deoxyvasicinone, 2-quinolone, indoleacrylic acid and kynurenic acid) and citrus honey (caffeine, 2-oxindole and indole-3-carbinol). Lastly, non-targeted analysis enabled to separate honeys by their type, floral and geographical origins.

Composition and release rates of chemicals in inkjet fabrics determined by non-targeted screening and targeted analysis

Unveiling composition and release rates of chemicals in chemical-intensive products (CIPs) such as inkjet fabrics that are applied extensively in advertising and publicizing industries, is of importance to sound management of chemicals. This study tentatively identified 212 compounds from 69 inkjet fabric samples using gas chromatograph coupled with quadrupole time-of-flight mass spectrometry (GC-QTOF-MS). Contents of six phthalate esters (PAEs) were quantified to range from 3.0 × 102 mg/kg to 3.1 × 105 mg/kg with GC-MS. Bis(2-ethylhexyl) phthalate was predominantly detected to average 96 g/kg. The inkjet fabrics collected from southern China contained fewer non-intentionally added substances (NIASs) than from northern China. Annual mass release rates (RM) of the 6 PAEs from inkjet fabrics to air were estimated to range from 1.4 × 10-2 kg/year to 2.8 × 104 kg/year in China in 2020, and the mean indoor RM was comparable with the outdoor one. Equilibrium partition coefficients of the compounds between the product and air, ambient temperature, and concentrations of chemicals in the product, are key factors leading to RM with the high variance. The findings indicate that contents of the NIASs in the CIPs should be minimized, and the refining concept should be adopted in design of the CIPs, so as to control the release of chemicals from the CIPs.

Linking Mechanistic Effects of Pharmaceuticals and Personal Care Products to Ecologically Relevant Outcomes: A Decade of Progress

There are insufficient toxicity data to assess the ecological risks of many pharmaceuticals and personal care products (PPCPs). While data limitations are not uncommon for contaminants of environmental concern, PPCPs are somewhat unique in that an a priori understanding of their biological activities in conjunction with measurements of molecular, biochemical, or histological responses could provide a foundation for understanding mode(s) of action and predicting potential adverse apical effects. Over the past decade significant progress has been made in the development of new approach methodologies (NAMs) to efficiently quantify these types of endpoints using computational models and pathway-based in vitro and in vivo assays. The availability of open-access knowledgebases to curate biological response (including NAM) data and sophisticated bioinformatics tools to help interpret the information also has significantly increased. Finally, advances in the development and implementation of the adverse outcome pathway framework provide the critical conceptual underpinnings needed to translate NAM data into predictions of the ecologically relevant outcomes required by risk assessors and managers. The evolution and convergence of these various data streams, tools, and concepts provides the basis for a fundamental change in how ecological risks of PPCPs can be pragmatically assessed. Environ Toxicol Chem 2024;43:537-548. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Heterogeneous Photodegradation Behavior of Liquid Crystal Monomers in Dust: Quantitative Structure-Activity Relationship and Product Identification

The heterogeneous photodegradation behavior of liquid crystal monomers (LCMs) in standard dust (standard reference material, SRM 2583) and environmental dust was investigated. The measured photodegradation ratios for 23 LCMs in SRM and environmental dust in 12 h were 11.1 ± 1.8 to 23.2 ± 1.1% and 8.7 ± 0.5 to 24.0 ± 2.8%, respectively. The degradation behavior of different LCM compounds varied depending on their structural properties. A quantitative structure-activity relationship model for predicting the degradation ratio of LCMs in SRM dust was established, which revealed that the molecular descriptors related to molecular polarizability, electronegativity, and molecular mass were closely associated with LCMs' photodegradation. The photodegradation products of the LCM compound 4'-propoxy-4-biphenylcarbonitrile (PBIPHCN) in dust, including •OH oxidation, C-O bond cleavage, and ring-opening products, were identified by nontarget analysis, and the corresponding degradation pathways were suggested. Some of the identified products, such as 4'-hydroxyethoxy-4-biphenylcarbonitrile, showed predicted toxicity (with an oral rat lethal dose of 50%) comparable to that of PBIPHCN. The half-lives of the studied LCMs in SRM dust were estimated at 32.2-82.5 h by fitting an exponential decay curve to the observed photodegradation data. The photodegradation mechanisms of LCMs in dust were revealed for the first time, enhancing the understanding of LCMs' environmental behavior and risks.

Plastic protective nets: A significant but neglected "reservoir" for priority chemicals as revealed by composition analysis

As chemical-intensive products, plastics are potential sources of emerging contaminants and pose risks to the ecosystem. However, knowledge on the inventory and emissions of chemicals in plastics remains scarce, prohibiting the lifecycle assessment of their environmental exposure. Herein, full compositions of plastic protective nets (PPNs, one globally used plastics) were analyzed via nontarget screening with mass spectrometry, optical emission spectrometry, infrared spectroscopy and thermogravimetric analysis. Nontarget screening identified 861 non-polymeric organic chemicals, which were classified by network-like similarity analysis into 9 communities, dominated by phthalates (PAEs), aliphatic/oxalic esters and branched alkanes. Notably, around 80.8% (696) of the chemicals were first observed in plastics, suggesting aplenty plastic additives have previously been overlooked. Quantification results indicated PPNs contained higher levels of priority chemicals, including detrimental lead (1.17 × 104 ng/g), benzotriazoles ultraviolet stabilizers (6.66 × 103 ng/g) and PAEs (1.87 × 104 ng/g) than other plastics commonly reported. Emission projections revealed that dibutyl phthalate in PPNs had an annual release (1.83 × 103 kg) comparable to that from greenhouse films in China. These findings suggest PPNs are a significant but neglected "reservoir" for priority chemicals, which could inform future research on resolving plastic compositions, so as to promote sound chemical management.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Screening for drinking water contaminants of concern using an automated exposure-focused workflow

BACKGROUND

The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential.

OBJECTIVE

Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project.

METHODS

The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH.

RESULTS

Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization.

SIGNIFICANCE

This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.

Non-target screening of volatile organic compounds in spray-type consumer products and their potential health risks

Widespread use of spray-type consumer products can raise significant concerns regarding their effects on indoor air quality and human health. In this study, we conducted non-target screening using gas chromatography-mass spectrometry (GC-MS) to analyze VOCs in 48 different spray-type consumer products. Using this approach, we tentatively identified a total of 254 VOCs from the spray-type products. Notably, more VOCs were detected in propellant-type products which are mostly solvent-based than in trigger-type ones which are mostly water-based. The VOCs identified encompass various chemical classes including alkanes, cycloalkanes, monoterpenoids, carboxylic acid derivatives, and carbonyl compounds, some of which arouse concerns due to their potential health effects. Alkanes and cycloalkanes are frequently detected in propellant-type products, whereas perfumed monoterpenoids are ubiquitous across all product categories. Among the identified VOCs, 12 compounds were classified into high-risk groups according to detection frequency and signal-to-noise (S/N) ratio, and their concentrations were confirmed using reference standards. Among the identified VOCs, D-limonene was the most frequently detected compound (freq. 21/48), with the highest concentration of 1.80 mg/g. The risk assessment was performed to evaluate the potential health risks associated with exposure to these VOCs. The non-carcinogenic and carcinogenic risks associated with the assessed VOC compounds were relatively low. However, it is important not to overlook the risk faced by occupational exposure to these VOCs, and the risk from simultaneous exposure to various VOCs contained in the products. This study serves as a valuable resource for the identification of unknown compounds in the consumer products, facilitating the evaluation of potential health risks to consumers.

Quantitative Structure Use Relationships: Highlights from a technical summit meeting

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

Nontargeted Analysis in Tobacco Research: Challenges and Opportunities

Tobacco products are evolving at a pace that has outstripped tobacco control, leading to a high prevalence of tobacco use in the population. Researchers have been tirelessly developing suitable techniques to assess these products' emissions, toxicity, and public health impact. The nonclinical testing of tobacco products to assess the chemical profile of emissions is needed for evidence-based regulations. This testing has largely relied on targeted analytical methods that focus on constituent lists that may fall short in determining the toxicity of newly designed tobacco products. Nontargeted analysis (NTA), or the process of identifying and quantifying compounds within a complex matrix without prior knowledge of its chemical composition, is a promising technique for tobacco regulation, but it is not without challenges. The lack of standardized methods for sample generation, sample preparation, chromatographic separation, compound identification, and data analysis and reporting must be addressed so that the quality and reproducibility of the data generated by NTA can be benchmarked. This review discusses the challenges and highlights the opportunities of NTA in studying tobacco product constituents and emissions.

Prioritizing molecular formulae identified by non-target analysis through high-throughput modelling: application to identify compounds with high human accumulation potential from house dust

Because it is typically not possible to pursue compound identification efforts for all chemical features detected during non-target analysis (NTA), the need for prioritization arises. Here we propose a strategy that ranks chemical features detected in environmental samples based on a model-derived metric that quantifies a feature's attribute that makes it desirable to elucidate its structure, e.g., a high potential for bioaccumulation in humans or wildlife. The procedure involves the identification of isomers that could plausibly represent the molecular formulae assigned to NTA-detected chemical features. For each isomer, the prioritization metric is calculated using properties predicted with high-throughput methods. After the molecular formulae are ranked based on the average values of the prioritization metric calculated for all isomers assigned to a formula, the highest ranked molecular formulae are prioritized for structure elucidation. We applied this workflow to features identified in house dust, using the ratio of chemical intake through dust ingestion to chemical concentration in blood (dose-to-concentration ratio, DCR) as the prioritization metric. Collections of isomers for the molecular formulae were assembled from the PubChem database and DCR was estimated using partitioning and biotransformation properties predicted for each isomer using quantitative structure property relationships. The ten top-ranked molecular formulae with notably lower average DCR-values represented mostly compounds already known to be indoor pollutants of concern, such as two polybrominated diphenyl ethers, bis(2-ethylhexyl) tetrabromophthalate, tetrabromobisphenol A, tris(1,3-dichloroisopropyl)phosphate and the azo dye disperse blue 373.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

How Many Chemicals in Commerce Have Been Analyzed in Environmental Media? A 50 Year Bibliometric Analysis

Over the past 50 years, there has been a tremendous expansion in the measurement of chemical contaminants in environmental media. But how many chemicals have actually been determined, and do they represent a significant fraction of substances in commerce or of chemicals of concern? To address these questions, we conducted a bibliometric survey to identify what individual chemicals have been determined in environmental media and their trends over the past 50 years. The CAplus database of CAS, a Division of the American Chemical Society, was searched for indexing roles "analytical study" and "pollutant" yielding a final list of 19,776 CAS Registry Numbers (CASRNs). That list was then used to link the CASRNs to biological studies, yielding a data set of 9.251 × 10⁶ total counts of the CASRNs over a 55 year period. About 14,150 CASRNs were substances on various priority lists or their close analogs and transformation products. The top 100 most reported CASRNs accounted for 34% of the data set, confirming previous studies showing a significant bias toward repeated measurements of the same substances due to regulatory needs and the challenges of determining new, previously unmeasured, compounds. Substances listed in the industrial chemical inventories of Europe, China, and the United States accounted for only about 5% of measured substances. However, pharmaceuticals and current use pesticides were widely measured accounting for 50-60% of total CASRN counts for the period 2000-2015.

Optimisation and implementation of QuEChERS-based sample preparation for identification and semi-quantification of 694 targeted contaminants in honey, jam, jelly, and syrup by UHPLC-Q/ToF high-resolution mass spectrometry

A screening and semi-quantitative method was developed for the analysis of 694 various contaminants in honey, jam, jelly and syrup samples by ultrahigh-performance liquid chromatography and quadrupole time-of-flight mass spectrometry. Sample preparation, which was optimised using split factorial design, was based on acetate-buffered version of QuEChERS, followed by a clean-up step and a concentration step to enhance sensitivity of analytes. The method was validated according to SANTE/11312/2021 guidelines. The screening detection and limits of identification were established as being less than or equal to 0.05 mg.kg^-1 for 89% and 74% of the contaminants, respectively. The validated screening method was applied to 50 concentrated sugary products. Overall, 46% of the samples were positive to pesticide residues. Most of the positive samples (78%) contained mixtures of pesticide residues. Three time-and-cost saving convenient strategies suitable for high-throughput analysis were proposed for the targeted semi-quantification of the previously contaminants identified in samples.

Photocatalytic Degradation of Methylene Blue Using N-Doped ZnO/Carbon Dot (N-ZnO/CD) Nanocomposites Derived from Organic Soybean

This study reports on successful synthesis of carbon dots (CDs), nitrogen-doped zinc oxide (N-ZnO), and N-ZnO/CD nanocomposites as photocatalysts for degradation of methylene blue. The first part was the synthesis of CDs utilizing a precursor from soybean and ethylenediamine as a dopant by a hydrothermal method. The second part was the synthesis of N-ZnO with urea as the nitrogen dopant carried out by a calcination method in a furnace at 500 °C for 2 h in an N2 atmosphere (5 °C min-1). The third part was the synthesis of N-ZnO/CD nanocomposites. The characteristics of CDs, N-ZnO, and N-ZnO/CD nanocomposites were analyzed through Fourier transform infrared (FTIR), UV-vis absorbance, photoluminescence (PL), high-resolution transmission electron microscopy (HR-TEM), X-ray diffraction (XRD), thermal gravimetry analysis (TGA), field-emission scanning electron microscopy energy-dispersive spectroscopy (FESEM EDS), X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) analysis. Based on the HR-TEM analysis, the CDs had a spherical shape with an average particle size of 4.249 nm. Meanwhile, based on the XRD and HR-TEM characterization, the N-ZnO and N-ZnO/CD nanocomposites have wurtzite hexagonal structures. The materials of N-ZnO and N-ZnO/CD show increased adsorption in the visible light region and low energy gap E g. The E g values of N-ZnO and N-ZnO/CDs were found to be 2.95 and 2.81 eV, respectively, whereas the surface area (S BET) values 3.827 m2 g-1 (N-ZnO) and 3.757 m2 g-1(N-ZnO/CDs) belonged to the microporous structure. In the last part, the photocatalysts of CDs, N-ZnO, and N-ZnO/CD nanocomposites were used for degradation of MB (10 ppm) under UV-B light irradiation pH = 7.04 (neutral) for 60 min at room temperature. The N-ZnO/CD nanocomposites showed a photodegradation efficiency of 83.4% with a kinetic rate of 0.0299 min-1 higher than N-ZnO and CDs. The XRD analysis and FESEM EDS of the N-ZnO/CDs before and after three cycles confirm the stability of the photocatalyst with an MB degradation of 58.2%. These results have clearly shown that the N-ZnO/CD nanocomposites could be used as an ideal photocatalytic material for the decolorization of organic compounds in wastewater.

A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data

A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

Target and Nontarget Screening of Organic Chemicals and Metals in Recycled Plastic Materials

Increased demand for recycling plastic has prompted concerns regarding potential introduction of hazardous chemicals into recycled goods. We present a broad screening of chemicals in 21 plastic flake and pellet samples from Canadian recycling companies. From target analysis, the organophosphorus ester flame retardants and plasticizers exhibited the highest detection frequencies (DFs) (5-100%) and concentrations (<DL-4,700 ng/g), followed by brominated/chlorinated flame retardants (<DL-2,150 ng/g, 5-76% DFs). The perfluoroalkyl acids were least detected at the lowest concentrations (<0.01-0.70 ng/g, 5-19% DFs). Using nontargeted analysis, 217 chemicals were identified as Level 1 (authentic standard) or 2 (library match), with estimated individual concentrations up to 1030 ng/g (highest: 2-hexyl hydroxy benzoate, 100% DF). Total (Σ60) element concentrations were between 0.005 and 2,980 mg/kg, with highest concentrations for calcium (2,980 mg/kg), sodium (617 mg/kg), and iron (156 mg/kg). Collectively >280 chemicals were detected in recycled plastic pellets and flakes, suggesting potential incorporation into recycled goods. Individual concentrations indicate unintentional trace contamination following European Union threshold limits for recycled granules (500 mg/kg) and waste plastic flakes (1,000 mg/kg), although do not reflect toxicological thresholds, if any. Our study highlights that while recycling addresses sustainability goals, additional screening of goods and products made from recycled plastics is needed to fully document potentially hazardous chemicals and exposure.

A non-target screening study of high-density polyethylene pipes revealed rubber compounds as main contaminant in a drinking water distribution system

Polyethylene (PE) pipes are often the material of choice for water supply systems, thanks to their favorable properties, such as high strength-density ratio and corrosion resistance. However, previous studies have shown that organic compounds can migrate from PE pipes to the water. This study aimed to identify potential organic compounds migrating from high-density PE (HDPE) pipes used to distribute drinking water in Denmark, based on laboratory experiments and sampling in the distribution system using a two-tiered study design. In the first tier, migration of volatile and semi-volatile organic compounds (VOCs and semi-VOCs) from HDPE pipes were investigated over one, three, and nine days in laboratory experiments, performed according to modified standards for migration testing (EN 12,873-1). The analytical workflow consisted of solid-phase extraction (SPE) for 10,000 times enrichment and gas chromatography - mass spectrometry (GC-MS) analysis from the water phase after migration. A total of 133 compounds originating from the PE pipes were detected. Thirty-one compounds were detected by suspect screening (SS), while the remaining 102 compounds were detected by non-target screening (NTS) analysis. Among the detected compounds were also hindered amine stabilizers (HALS), flame retardant, and plasticizer tris(2-chloroethyl) phosphate. In the second tier, drinking water from a water distribution system in Copenhagen, Denmark, with a newly installed HDPE pipe was sampled and analyzed with GC-MS and liquid chromatography high-resolution mass spectrometry (LCHRMS). A total of 51 compounds were detected in the water, 12 of which were assigned to migration from HDPE. Surprisingly, HDPE antioxidants and their degradation products contributed only a relatively small percentage of the total measured compound intensities in the drinking water distribution system. Instead, a larger proportion of the compounds detected were assigned to rubber seals, used upstream in the water system from the abstraction site to delivery at the consumer tap. Seals are considered trifle in the larger picture of materials in contact with drinking water, however these results may cause a reconsideration of this position.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Chemicals of concern in personal care products used by women of color in three communities of California

BACKGROUND

Personal care products (PCPs) may contain chemicals associated with adverse health effects. Prior studies found differences in product use by race/ethnicity and suggest some women are disproportionately exposed to chemicals of concern (CoCs).

OBJECTIVE

We quantified chemicals linked to cancer, reproductive or developmental harm, or endocrine disruption in PCPs used by women of color.

METHODS

We documented PCPs in stores frequented by Black, Latina, and Vietnamese women in their communities in California and CoCs on ingredient labels of 546 unique hair, skin, makeup, nail, deodorant/perfume, and intimate care products. Community partners chose 31 products for a combined targeted and suspect screen (National Institute of Standards and Technology mass spectral library search) two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) analysis to detect chemicals not on ingredient labels.

RESULTS

We found that 65% of labels included CoCs, and 74% of labels had undisclosed ingredients listed as "fragrance." The most prevalent chemicals were parabens, cyclosiloxanes, and formaldehyde releasers. GCxGC-TOFMS found additional CoCs, including fragrances, solvents, preservatives, ultraviolet filters, and contaminants.

SIGNIFICANCE

These findings contribute to awareness of potentially hazardous chemicals in PCPs, can help estimate disparities in chemical exposure, and complement research on health inequities due to chemical exposures from various contributors.

IMPACT STATEMENT

This study is one of the first detailed assessments of chemicals of concern found in various types of PCPs used by several racial/ethnic groups. We found that over half of the 546 products selected by community partners as marketed to and/or used by them contained ingredients linked to cancer, reproductive or developmental harm, or endocrine disruption. Laboratory analysis identified additional chemicals in a subset of products, including unlabeled fragrance chemicals and contaminants. Elucidating exposures to chemicals in PCPs is important for risk assessment and health inequity research.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

The chemical landscape of high-throughput new approach methodologies for exposure

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

Suspect Screening of Liquid Crystal Monomers (LCMs) in Sediment Using an Established Database Covering 1173 LCMs

Recent studies have suggested that liquid crystal monomers (LCMs) are emerging contaminants in the environment, and knowledge of this class of substances is very rare. Here, we reviewed existing LCM-related documents, i.e., publications and patents, and established a database involving 1173 LCMs. These 1173 LCMs were further calculated for their physicochemical properties, i.e., persistence (P), bioaccumulation (B), long-range transport potential (LRTP), and Arctic contamination and bioaccumulation potential (ACBAP). We found that 476 out of them were P&B chemicals (99% of them were halogenated), and 320 of them could have ACBAP properties (67% of them were halogenated). This LCM database was further applied for suspect screening of LCMs in n = 33 sediment samples by use of gas chromatography coupled to quadrupole time-of-flight mass spectrometry (GC-QTOF/MS). We tentatively identified 26 LCM formulas, which could have 43 chemical structures. Two out of these 43 suspect LCM candidates, 1-butoxy-2,3-difluoro-4-(4-propylcyclohexyl) benzene (3cH4OdFP) and 1-ethoxy-2,3-difluoro-4-(4-pentyl cyclohexyl) benzene (5cH2OdFP), were fully confirmed by a comparison of unique GC and MS characteristics with their authentic standards. Overall, our present study expanded the previous LCM database from 362 to 1173, and 1173 LCMs in this database were calculated for their physicochemical properties. Meanwhile, taking n = 33 sediment samples as an exercise, we successfully developed a suspect screening strategy tailored for LCMs, and this strategy could have promising potential to be extended to other environmental matrices.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies

Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

Chemical Characterization of Recycled Consumer Products Using Suspect Screening Analysis

Recycled materials are found in many consumer products as part of a circular economy; however, the chemical content of recycled products is generally uncharacterized. A suspect screening analysis using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) was applied to 210 products (154 recycled, 56 virgin) across seven categories. Chemicals in products were tentatively identified using a standard spectral library or confirmed using chemical standards. A total of 918 probable chemical structures identified (112 of which were confirmed) in recycled materials versus 587 (110 confirmed) in virgin materials. Identified chemicals were characterized in terms of their functional use and structural class. Recycled paper products and construction materials contained greater numbers of chemicals than virgin products; 733 identified chemicals had greater occurrence in recycled compared to virgin materials. Products made from recycled materials contained greater numbers of fragrances, flame retardants, solvents, biocides, and dyes. The results were clustered to identify groups of chemicals potentially associated with unique chemical sources, and identified chemicals were prioritized for further study using high-throughput hazard and exposure information. While occurrence is not necessarily indicative of risk, these results can be used to inform the expansion of existing models or identify exposure pathways currently neglected in exposure assessments.

A Comprehensive Non-targeted Analysis Study of the Prenatal Exposome

Recent technological advances in mass spectrometry have enabled us to screen biological samples for a very broad spectrum of chemical compounds allowing us to more comprehensively characterize the human exposome in critical periods of development. The goal of this study was three-fold: (1) to analyze 590 matched maternal and cord blood samples (total 295 pairs) using non-targeted analysis (NTA); (2) to examine the differences in chemical abundance between maternal and cord blood samples; and (3) to examine the associations between exogenous chemicals and endogenous metabolites. We analyzed all samples with high-resolution mass spectrometry using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in both positive and negative electrospray ionization modes (ESI+ and ESI-) and in soft ionization (MS) and fragmentation (MS/MS) modes for prioritized features. We confirmed 19 unique compounds with analytical standards, we tentatively identified 73 compounds with MS/MS spectra matching, and we annotated 98 compounds using an annotation algorithm. We observed 103 significant associations in maternal and 128 in cord samples between compounds annotated as endogenous and compounds annotated as exogenous. An example of these relationships was an association between three poly and perfluoroalkyl substances (PFASs) and endogenous fatty acids in both the maternal and cord samples indicating potential interactions between PFASs and fatty acid regulating proteins.

Development of a strategic approach for comprehensive detection of organophosphate pesticide metabolites in urine: Extrapolation of cadusafos and prothiofos metabolomics data of mice to humans

Objectives

The comprehensive detection of environmental chemicals in biospecimens, an indispensable task in exposome research, is advancing. This study aimed to develop an exposomic approach to identify urinary metabolites of organophosphate (OP) pesticides, specifically cadusafos and prothiofos metabolites, as an example chemical group, using an original metabolome dataset generated from animal experiments.

Methods

Urine samples from 73 university students were analyzed using liquid chromatography–high‐resolution mass spectrometry. The metabolome data, including the exact masses, retention time (t_R), and tandem mass spectra obtained from the human samples, were compared with the existing reference databases and with our original metabolome dataset for cadusafos and prothiofos, which was produced from mice to whom two doses of these OPs were orally administered.

Results

Using the existing databases, one chromatographic peak was annotated as 2,4‐dichlorophenol, which could be a prothiofos metabolite. Using our original dataset, one peak was annotated as a putative cadusafos metabolite and three peaks as putative prothiofos metabolites. Of these, all three peaks suggestive of prothiofos metabolites, 2,4‐dichlorophenol, 3,4,5‐trihydroxy‐6‐(2,4‐dichlorophenoxy) oxane‐2‐carboxylic acid, and (2,4‐dichlorophenyl) hydrogen sulfate were confirmed as authentic compounds by comparing their peak data with both the original dataset and peak data of the standard reagents. The putative cadusafos metabolite was identified as a level C compound (metabolite candidate with limited plausibility).

Conclusions

Our developed method successfully identified prothiofos metabolites that are usually not a target of biomonitoring studies. Our approach is extensively applicable to various environmental contaminants beyond OP pesticides.

Ecotoxicological risk assessment of contaminants of emerging concern identified by "suspect screening" from urban wastewater treatment plant effluents at a territorial scale

Urban wastewater treatment plants (WWTP) are a major vector of highly ecotoxic contaminants of emerging concern (CECs) for urban and sub-urban streams. Ecotoxicological risk assessments (ERAs) provide essential information to public environmental authorities. Nevertheless, ERAs are mainly performed at very local scale (one or few WWTPs) and on pre-selected list of CECs. To cope with these limits, the present study aims to develop a territorial-scale ERA on CECs previously identified by a "suspect screening" analytical approach (LC-QToF-MS) and quantified in the effluents of 10 WWTPs of a highly urbanized territory during three periods of the year. Among CECs, this work focused on pharmaceutical residue and pesticides. ERA was conducted following two complementary methods: (1) a single substance approach, based on the calculation for each CEC of risk quotients (RQs) by the ratio of Predicted Environmental Concentration (PEC) and Predicted No Effect Concentration (PNEC), and (2) mixture risk assessment ("cocktail effect") based on a concentration addition model (CA), summing individual RQs. Chemical results led to an ERA for 41 CEC (37 pharmaceuticals and 4 pesticides) detected in treated effluents. Single substance ERA identified 19 CECs implicated in at least one significant risk for streams, with significant risks for DEET, diclofenac, lidocaine, atenolol, terbutryn, atorvastatin, methocarbamol, and venlafaxine (RQs reaching 39.84, 62.10, 125.58, 179.11, 348.24, 509.27, 1509.71 and 3097.37, respectively). Mixture ERA allowed the identification of a risk (RQmix > 1) for 9 of the 10 WWTPs studied. It was also remarked that CECs leading individually to a negligible risk could imply a significant risk in a mixture. Finally, the territorial ERA showed a diversity of risk situations, with the highest concerns for 3 WWTPs: the 2 biggest of the territory discharging into a large French river, the Rhône, and for the smallest WWTP that releases into a small intermittent stream.

Comprehensive chemical characterization of indoor dust by target, suspect screening and nontarget analysis using LC-HRMS and GC-HRMS

Since humans spend more than 90% of their time in indoor environments, indoor exposure can be an important non-dietary pathway to hazardous organic contaminants. It is thus important to characterize the chemical composition of indoor dust to assess the total contaminant exposure and estimate human health risks. The aim of this investigation was to perform a comprehensive chemical characterization of indoor dust. First, the robustness of an adopted extraction method using ultrasonication was evaluated for 85 target compounds. Thereafter, a workflow combining target analysis, suspect screening analysis (SSA) and nontarget analysis (NTA) was applied to dust samples from different indoor environments. Chemical analysis was performed using both gas chromatography and liquid chromatography coupled with high resolution mass spectrometry. Although suppressing matrix effects were prominent, target analysis enabled the quantification of organophosphate/brominated flame retardants (OPFRs/BFRs), liquid crystal monomers (LCMs), toluene diisocyanate, bisphenols, pesticides and tributyl citrate. The SSA confirmed the presence of OPFRs but also enabled the detection of polyethylene glycols (PEGs) and phthalates/parabens. The combination of hierarchical cluster analysis and scaled mass defect plots in the NTA workflow confirmed the presence of the above mentioned compounds, as well as detect other contaminants such as tetrabromobisphenol A, triclocarban, diclofenac and 3,5,6-trichloro-2-pyridinol, which were further confirmed using pure standards.

Suspect Screening, Prioritization, and Confirmation of Environmental Chemicals in Maternal-Newborn Pairs from San Francisco

Our proof-of-concept study develops a suspect screening workflow to identify and prioritize potentially ubiquitous chemical exposures in matched maternal/cord blood samples, a critical period of development for future health risks. We applied liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF/MS) to perform suspect screening for ∼3500 industrial chemicals on pilot data from 30 paired maternal and cord serum samples (n = 60). We matched 662 suspect features in positive ionization mode and 788 in negative ionization mode (557 unique formulas overall) to compounds in our database, and selected 208 of these for fragmentation analysis based on detection frequency, correlation in feature intensity between maternal and cord samples, and peak area differences by demographic characteristics. We tentatively identified 73 suspects through fragmentation spectra matching and confirmed 17 chemical features (15 unique compounds) using analytical standards. We tentatively identified 55 compounds not previously reported in the literature, the majority which have limited to no information about their sources or uses. Examples include (i) 1-(1-acetyl-2,2,6,6-tetramethylpiperidin-4-yl)-3-dodecylpyrrolidine-2,5-dione (known high production volume chemical) (ii) methyl perfluoroundecanoate and 2-perfluorooctyl ethanoic acid (two PFAS compounds); and (iii) Sumilizer GA 80 (plasticizer). Thus, our workflow demonstrates an approach to evaluating the chemical exposome to identify and prioritize chemical exposures during a critical period of development.

Enhanced Nontarget Screening Algorithm Reveals Highly Abundant Chlorinated Azo Dye Compounds in House Dust

Humans spend 90% of their time indoors, but the majority of indoor pollutants remain unknown. In this study, a nontarget screening algorithm with reduced false discovery rates was developed to screen indoor pollutants using the Toxic Substances Control Act (TSCA) database. First, a putative lock mass algorithm was developed for post-acquisition calibration of Orbitrap mass spectra to sub-ppm mass accuracy. Then, a one-stop screening algorithm was developed by combining MS¹ spectra, isotopic peaks, retention time prediction, and in silico MS² spectra. A sufficient true positive rate (73%) and false discovery rate (5%) were achieved for the screening of halogenated compounds at a score cutoff of 0.28. Above this cutoff, 427 chemicals were detected from 24 house dust samples, including 39 chlorinated compounds. While some identified halogenated compounds (e.g., triclosan) are well known, 18 previously unrecognized chlorinated azo dyes were detected with high abundance as the largest class of chlorinated compounds. Two chlorinated azo dyes were confirmed with authentic standards, but the two most abundant chlorinated azo dyes were missed by the algorithm due to the limited breadth of the TSCA database. These compounds were annotated as chlorinated analogues of Disperse Blue 373 and Disperse Violet 93 using the DIPIC-Frag method. This study revealed the presence of highly abundant chlorinated azo dyes in house dusts, highlighting their potential health risks in the indoor environment.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Use of comprehensive target analysis for determination of contaminants of emerging concern in a sediment core collected from Beppu Bay, Japan

In recent years, concern about the release of anthropogenic organic micropollutants referred to as contaminants of emerging concern (CECs) has been growing. The objective of this study was to find potential CECs by means of an analytical screening method referred to as comprehensive target analysis with an automated identification and quantification system (CTA-AIQS), which uses gas and liquid chromatography combined with mass spectrometry (GC-MS and LC-QTOF-MS). We used CTA-AIQS to analyze samples from a sediment core collected in Beppu Bay, Japan. With this method, we detected 80 compounds in the samples and CTA-AIQA could work to useful tool to find CECs in environmental media. Among the detected chemicals, three PAHs (anthracene, chrysene, and fluoranthene) and tris(isopropylphenyl)phosphate (TIPPP) isomers were found to increase in concentration with decreasing sediment depth. We quantified TIPPP isomers in the samples by means of targeted analysis using LC-MS/MS for confirmation. The concentration profiles, combined with previous reports indicating persistent, bioaccumulative, and toxic properties, suggest that these chemicals can be categorized as potential CECs in marine environments.

Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework

A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.

A suspect screening analysis for contaminants of emerging concern in municipal wastewater and surface water using liquid-liquid extraction and stir bar sorptive extraction

The presence of contaminants of emerging concern (CECs) in wastewater effluent and surface waters is an important field of research for analytical scientists. This study takes a suspect screening approach to wastewater and surface water analysis using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS). Two extraction procedures, traditional liquid-liquid extraction (LLE) and stir bar sorptive extraction (SBSE), were utilized and evaluated for their application to wastewater and surface water samples. Both techniques were evaluated regarding their recovery rates, range of compound classes extracted, and on their application to discovery of CECs. For the 14 surrogate compounds analyzed, LLE was able to extract all of them in each matrix with a recovery range of 19% to 159% and a median value of 74%. For SBSE, the recovery rates ranged from 19% to 117% with the median value at 66%, but only 8 of the compounds were able to be extracted because of the polarity bias for this extraction method. A new method of SBSE calibration was also developed using direct liquid injection of the internal standards before desorption of the stir bars. Initial findings indicate increased sensitivity and a greater range of unknown analyte recovery for SBSE, especially in the more dilute effluent and surface water samples. With the methods used in this study, SBSE has a concentration factor of approximately 416, improving that of LLE, which is 267. Suspect screening analysis was utilized to tentatively identify 32 CECs in the samples, the majority of which were pharmaceuticals and personal care products. More CECs were found using SBSE than LLE, especially in the surface water samples where 13 CECs were tentatively identified in the SBSE samples compared to 6 in the LLE samples.

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and y randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (Q2) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a Q2 of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a Q2 of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem.

Recent progresses in analytical GC and LC mass spectrometric based-methods for the detection of emerging chlorinated and brominated contaminants and their transformation products in aquatic environment

This paper is an overview of screening methods recently developed for emerging halogenated contaminants and their transformation products. The target screening methods are available only for a limited number of emerging pollutants since the reference standards for these compounds are not always available, but a risk assessment of those micropollutants in environment must be performed anyhow. Therefore, the chromatographic techniques hyphenated with high resolution mass spectrometry (HRMS) trend to become indispensable methods for suspect and non-target screening of emerging halogenated contaminants. HRMS is also an effective tool for tentatively identification of the micropollutants' transformation products existing in much lower concentrations. To assess the transformation pathway of halogenated contaminants in environment, the non-target screening methods must be combined with biodegradation lab experiments and also with advanced oxidation and reduction processes that can mimic the transformation on these contaminants in environment. It is expected that in the future, the accurate-mass full-spectra of transformation products recorded by HRMS will be the basic information needed to elucidate the transformation pathways of emerging halogenated contaminants in aquatic environment.