Search

People are exposed to myriad of airborne pollutants in their homes. Owing to diverse potential sources of air pollution and human activity patterns, accurate assessment of residential exposures is complex. In this study, we explored the relationship between personal and stationary air pollutant measurements in residences of 37 participants working from home during the heating season. Stationary environmental monitors (SEMs) were located in the bedroom, living room or home office and personal exposure monitors (PEMs) were worn by the participants. SEMs and PEMs included both real-time sensors and passive samplers. During three consecutive weekdays, continuous data were obtained for particle number concentration (size range 0.3-10 mu m), carbon dioxide (CO2), and total volatile organic compounds (TVOC), while passive samplers collected integrated measures of 36 volatile organic compounds (VOCs) and semi volatile organic compounds (SVOCs). The personal cloud effect was detected in >80% of the participants for CO2 and >50% participants for PM10. Multiple linear regression analysis showed that a single CO2 monitor placed in the bedroom efficiently represented personal exposure to CO2 (R2 = 0.90) and moderately so for PM10 (R2 = 0.55). Adding a second or third sensor in a residence did not lead to improved exposure estimates for CO2, with only 6-9% improvement for particles. Selecting data from SEMs when participants were in the same room improved personal exposure estimates by 33% for CO2 and 5% for particles. Out of 36 detected VOCs and SVOCs, 13 had at least 50% higher concentrations in personal versus stationary samples. Findings from this study aid improved understanding of the complex dynamics of gaseous and particle pollutants and their sources in residences, and could support the development of refined procedures for residential air quality monitoring and inhalation exposure assessment.

Introduction: Natural gas compressor stations are located throughout the country and are used to maintain gas flow and ensure continuous distribution through the pipeline network. Compressor stations emit many air contaminants including volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs). While the serious health effects associated with the inhalation of elevated pollutant levels are clear, the relationship between proximity to natural gas compressor stations and residential health effects is not well understood. Community members living near a natural gas compressor station in Eastern Ohio expressed concerns regarding their air quality; therefore, the objective of this study was to assess exposure to airborne organics in residential air near the compressor station. Methods: Our team conducted a 24-hour air sampling campaign to assess outdoor and indoor air contaminant levels at 4 homes near the Williams Salem Compressor Station in Jefferson County, Ohio. Air quality was assessed using two techniques: 1) summa canisters to quantify VOC concentrations and 2) passive air samplers to evaluate a broader panel of VOCs and SVOCs. Results: Among the three homes situated < 2 km from the compressor station, indoor benzene levels were 2-17 times greater than the Ohio Environmental Protection Agency (EPA) indoor standard due to vapor intrusion. Multiple other VOCs, including ethylbenzene, 1,2,4-trimethylbenzene, 1,2 dichloroethane, 1,3 butadiene, chloroform, and naphthalene also exceeded state standards for indoor concentrations. Several SVOCs were also detected inside and outside participants’ homes, including benzene and naphthalene derivatives. Conclusion: Our results validate the community members’ concerns and necessitate a more comprehensive epidemiological investigation into the exposures associated with natural gas compressor stations and methods to mitigate elevated exposures.Alarming levels of VOCS were detected inside of homes. Further research is needed to determine the source of VOC exposure and potential health effects.

Fine particulate air pollution (PM2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m3) are complementary metrics that estimate the ability of PM2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R2 = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM2.5 mass, reinforcing that OB estimates can complement the existing PM2.5 data in future national-level epidemiological studies.

Dried blood spot (DBS) metabolomics has numerous applications in newborn health screening, exposomics, and biomonitoring of environmental chemicals in pregnant women and the elderly. However, accurate metabolite quantification is hindered by several challenges: notably the "hematocrit effect" and unknown blood-spotting volumes. Different techniques have been employed to overcome these issues but there is no consensus on the optimal normalization method for DBS metabolomics, and in some cases no normalization is used. We compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium (K+)) to unnormalized data, and assessed sex-related differences in the DBS metabolome in 21 adults (group 1, n = 10 males, n = 11 females). The performance of each normalization method was evaluated using multiple criteria: (a) reduction of intragroup variation (pooled median absolute deviation, pooled estimate of variance, pooled coefficient of variation, NMDS and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, p-value distribution), and (c) influence on classification accuracy (partial least squares discriminant analysis, sparse partial least squares discriminant analysis error rates, receiver operating curve, random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on the three criteria and 13 different parameters; the performance of Hb was further demonstrated in an independent group of DBS from 18 neonates (group 2, n = 9 males, n = 9 females). Furthermore, we showed that SG and Hb are correlated in adults (r

Vegetables oils, rich in polyunsaturated fatty acids, are vulnerable to oxidation during manufacturing, processing, and food preparation. Currently, individual oxidation products are not well characterized, and hence, the health impacts of these unique lipid species remain unknown. Here, we introduce an extensive oxidized lipidomics in silico tandem mass spectrometry library and integrate these libraries within a user-friendly software covering a comprehensive redox lipidomics workflow. We apply this workflow to olive, soy, and walnut cooking oil; comparing unheated oil, oil after deep frying potatoes, and oil after oven frying potatoes. We annotated over a thousand oxidized triglycerides across 273 features (many coeluted). This software was validated against traditional chemical assays of oxidation, known oxidized lipids in castor oil, synthesized standards, and an alternate software LPPtiger. Development of these new software programs for redox lipidomics opens the door to characterize health implications of individual oxidation products.

Because of the pervasiveness, persistence, and toxicity of per- and polyfluoroalkyl substances (PFAS), there is growing concern over PFAS contamination, exposures, and health effects. The diversity of potential PFAS is astounding, with nearly 10,000 PFAS catalogued in databases to date (and growing). The ability to detect the thousands of known PFAS, and discover previously uncatalogued PFAS, is necessary to understand the scope of PFAS contamination and to identify appropriate remediation and regulatory solutions. Current non-targeted methods for PFAS analysis require manual curation and are time-consuming, prone to error, and not comprehensive. FluoroMatch Flow 2.0 is the first software to cover all steps of data processing for PFAS discovery in liquid chromatography–high-resolution tandem mass spectrometry samples. These steps include feature detection, feature blank filtering, exact mass matching to catalogued PFAS, mass defect filtering, homologous series detection, retention time pattern analysis, class-based MS/MS screening, fragment screening, and predicted MS/MS from SMILES structures. In addition, a comprehensive confidence level criterion is implemented to help users understand annotation certainty and integrate various layers of evidence to reduce overreporting. Applying the software to aqueous film forming foam analysis, we discovered over one thousand likely PFAS including previously unreported species. Furthermore, we were able to filter out 96% of features which were likely not PFAS. FluoroMatch Flow 2 increased coverage of likely PFAS by over tenfold compared to the previous release. This software will enable researchers to better characterize PFAS in the environment and in biological systems.

The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., “exposomic sensors”). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research. Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration. Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.

The purpose of this review is to summarize the application of untargeted metabolomics to identify the perturbation of metabolites or metabolic pathways associated with air pollutant exposures. Twenty-three studies were included in this review, in adults, children, or pregnant women. The most commonly measured air pollutant is particulate matter smaller than 2.5 μm. Size-fractioned particles, particle chemical species, gas pollutants, or organic compounds were also investigated. The reviewed studies used a wide range of air pollution measurement techniques and metabolomics analyses. Identified metabolites were primarily related to oxidative stress and inflammatory responses, and a few were related to the alterations of steroid metabolic pathways. The observed metabolic perturbations can differ by disease status, sex, and age. Air pollution-related metabolic changes were also associated with health outcomes in some studies. Our review shows that air pollutant exposures are associated with metabolic pathways primarily related to oxidative stress, inflammation, as assessed through untargeted metabolomics in 23 studies. More metabolomic studies with larger sample sizes are needed to identify air pollution components most responsible for adverse health effects, elaborate on mechanisms for subpopulation susceptibility, and link air pollution exposure to specific adverse health effects.

Limited toxicity data on electronic cigarette (ECIG) impede evidence-based policy recommendations. We compared two popular mixed fruit flavored ECIG-liquids with and without nicotine aerosolized at 40 W (E-smoke) with respect to particle number concentrations, chemical composition, and response on physiologically relevant human bronchial and alveolar lung mucosa models cultured at air–liquid interface. E-smoke was characterized by significantly increased particle number concentrations with increased wattage (25, 40, and 55 W) and nicotine presence. The chemical composition of E-smoke differed across the two tested flavors in terms of cytotoxic compounds including p-benzoquinone, nicotyrine, and flavoring agents (for example vanillin, ethyl vanillin). Significant differences in the expression of markers for pro-inflammation, oxidative stress, tissue injury/repair, alarm anti-protease, anti-microbial defense, epithelial barrier function, and epigenetic modification were observed between the flavors, nicotine content, and/ or lung models (bronchial or alveolar). Our findings indicate that ECIG toxicity is influenced by combination of multiple factors including flavor, nicotine content, vaping regime, and the region of respiratory tree (bronchial or alveolar). Toxic chemicals and flavoring agents detected in high concentrations in the E-smoke of each flavor warrant independent evaluation for their specific role in imparting toxicity. Therefore, multi-disciplinary approaches are warranted for comprehensive safety profiling of ECIG.

Children in low- and middle-income countries are often exposed to higher levels of chemicals and are more vulnerable to the health effects of air pollution. Little is known about the diversity, toxicity, and dynamics of airborne chemical exposures at the molecular level. We developed a workflow employing state-of-the-art wearable passive sampling technology coupled with high-resolution mass spectrometry to comprehensively measure 147 children's personal exposures to airborne chemicals in Limpopo, South Africa, as part of the Venda Health Examination of Mothers, Babies, and Their Environment (VHEMBE). 637 environmental exposures were detected, many of which have never been measured in this population; of these 50 airborne chemical exposures of concern were detected, including pesticides, plasticizers, organophosphates, dyes, combustion products, and perfumes. Biocides detected in wristbands included

Exhaled respiratory droplets and aerosols can carry infectious viruses and are an important mode of transmission for COVID-19. Recent studies have been successful in detecting airborne SARS-CoV-2 RNA in indoor settings using active sampling methods. The cost, size, and maintenance of these samplers, however, limit their long-term monitoring ability in high-risk transmission areas. As an alternative, passive samplers can be small, lightweight, and inexpensive and do not require electrical power or maintenance for continual operation. Integration of passive samplers into wearable designs can be used to better understand personal exposure to the respiratory virus. This study evaluated the use of a polydimethylsiloxane (PDMS)-based passive sampler to assess personal exposure to aerosol and droplet SARS-CoV-2. The rate of uptake of virus-laden aerosol on PDMS was determined in lab-based rotating drum experiments to estimate time-weighted averaged airborne viral concentrations from passive sampler viral loading. The passive sampler was then embedded in a wearable clip design and distributed to community members across Connecticut to surveil personal SARS-CoV-2 exposure. The virus was detected on clips worn by five of the 62 participants (8%) with personal exposure ranging from 4 to 112 copies of SARS-CoV-2 RNA/m3, predominantly in indoor restaurant settings. Our findings demonstrate that PDMS-based passive samplers may serve as a useful exposure assessment tool for airborne viral exposure in real-world high-risk settings and provide avenues for early detection of potential cases and guidance on site-specific infection control protocols that preempt community transmission.

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

There are thousands of different per- and polyfluoroalkyl substances (PFAS) in everyday products and in the environment. Discerning the abundance and diversity of PFAS is essential for understanding sources, fate, exposure routes, and the associated health impacts of PFAS. While comprehensive detection of PFAS requires the use of nontargeted mass spectrometry, data processing is time intensive and prone to error. While automated approaches can compile all mass spectrometric evidence (e.g., retention time, isotopic pattern, fragmentation, and accurate mass) and provide ranking or scoring metrics for annotations, confident assignment of structure often still requires extensive manual review of the data. To aid this process, we present FluoroMatch Visualizer, an open-source free software which was developed to provide interactive visualizations which include normalized mass defect plots, retention time versus accurate mass plots, MS/MS fragmentation spectra, and tables of annotations and metadata. All graphs and tables are interactive and have cross-filtering such that when a user selects a feature, all other visuals highlight the feature of interest. Several filtering options have been integrated into this novel data visualization tool, specifically with the capability to filter by PFAS chemical series, fragment(s), assignment confidence, and MS/MS file(s). FluoroMatch Visualizer is part of FluoroMatch Suite, which consists of FluoroMatch Modular, FluoroMatch Flow, and FluoroMatch Generator. FluoroMatch Visualizer enables annotations to be extensively validated, increasing annotation confidence. The resulting visualizations and datasets can be shared online in an interactive format for community-based PFAS discovery. FluoroMatch visualizer holds potential to promote harmonization of nontargeted data processing and interpretation throughout the PFAS scientific community.

Several states and transit agencies have recently introduced zero-emission buses (ZEBs) to their fleets, including battery electric buses, fuel cell battery electric buses, and fuel cell plug-in hy...

Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment and pose a potential health hazard. Suspect and nontarget screening with liquid chromatography (LC)–high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. To date, no automated open source PFAS data analysis software exists to mine these extensive data sets. We introduce FluoroMatch, which automates file conversion, chromatographic peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ∼7 000 PFAS fragmentation patterns based on rules derived from standards and literature, and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (iterative exclusion) nearly doubled the number of annotations. The software application is demonstrated by characterizing PFAS in landfill leachate as well as in leachate foam generated to concentrate the compounds for remediation purposes. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 71% of the all-ion fragmentation (CF2)n related fragments. By improving the throughput and coverage of PFAS annotation, FluoroMatch will accelerate the discovery of PFAS posing significant human risk.

The exposome is a concept that underlines the critical relationship between health and environmental exposures, including environmental toxicants. Currently, most environmental exposures that contribute to the exposome have not been characterized. Dried-blood spots (DBS) offer a cost-effective, reliable approach to characterize the blood exposome, which consists of diverse endogenous and exogenous chemicals, including persistent and bioaccumulating organic compounds. Current challenges involve prioritizing the identification by state-of-the-art mass spectrometry of likely up to tens of thousands of compounds present in blood; characterizing substances that represent a mixture of myriad constituent compounds; and detecting trace level contaminants, especially in quantity-limited matrices like DBS. This contribution reviews recent trends in DBS analysis of chemical pollutants and highlights the need for continued research in analytical chemistry to advance the field of exposomics.

The capability of ambient particles to generate in vivo reactive oxygen species (ROS), called oxidative potential (OP), is a potential metric for evaluating the health effects of particulate matter (PM) and is supported by several recent epidemiological investigations. Studies using various types of OP assays differ in their sensitivities to varying PM chemical components. In this study, we systematically compared two health-relevant acellular OP assays that track the depletion of antioxidants or reductant surrogates: (i) the synthetic respiratory-tract lining fluid (RTLF) assay that tracks the depletion of ascorbic acid (AA) and glutathione (GSH) and (ii) the dithiothreitol (DTT) assay that tracks the depletion of DTT. Yearlong daily samples were collected at an urban site in Atlanta, GA (Jefferson Street), during 2017, and both DTT and RTLF assays were performed to measure the OP of water-soluble PM2.5 components. PM2.5 mass and major chemical components, including metals, ions, and organic and elemental carbon were also analyzed. Correlation analysis found that OP as measured by the DTT and AA depletion (OPDTT and OPAA, respectively) were correlated with both organics and some water-soluble metal species, whereas that from the GSH depletion (OPGSH) was exclusively sensitive to water-soluble Cu. These OP assays were moderately correlated with each other due to the common contribution from metal ions. OPDTT and OPAA were moderately correlated with PM2.5 mass with Pearson's r=0.55 and 0.56, respectively, whereas OPGSH exhibited a lower correlation (r=0.24). There was little seasonal variation in the OP levels for all assays due to the weak seasonality of OP-associated species. Multivariate linear regression models were developed to predict OP measures from the particle composition data. Variability in OPDTT and OPAA were not only attributed to the concentrations of metal ions (mainly Fe and Cu) and organic compounds but also to antagonistic metal–organic and metal–metal interactions. OPGSH was sensitive to the change in water-soluble Cu and brown carbon (BrC), a proxy for ambient humic-like substances.

An emerging class of unregulated disinfection byproducts, halobenzoquinones (HBQs), has gained recent interest following suggestions of enhanced toxicity compared to regulated disinfection byproducts. While the kinetics of HBQ hydrolysis in water have been well characterized, the stability of HBQs in cell culture media, a critical parameter when evaluating toxicity in vitro, has been overlooked. The objective of this study was: (1) to contrast the stability of a prevalent HBQ, 2,6-dichloro-1,4-benzoquinone (DCBQ), in cell culture media and water, and (2) to evaluate the cytotoxicity of parent and transformed DCBQ compounds as well as the ability of these compounds to generate intracellular reactive oxygen species (ROS) in normal human colon cells (CCD 841 CoN) and human liver cancer cells (HepG2). The half-life of DCBQ in cell media was found to be less than 40 min, compared to 7.2 h in water at pH 7. DCBQ induced a concentration-dependent decrease in cell viability and increase in ROS production in both cell lines. The parent DCBQ compound was found to induce significantly greater cytotoxicity compared to transformed DCBQ products. We demonstrate that the study design used by most published studies (i.e., extended exposure periods) has led to a potential underestimation of the cytotoxicity of HBQs by evaluating the toxicological profile primarily of transformed HBQs, rather than corresponding parent compounds. Future in vitro toxicological studies should account for HBQ stability in media to evaluate the acute cytotoxicity of parent HBQs.

Organic contaminants are released into the air from building materials/furnishings, personal care, and household products. Wearable passive samplers have emerged as tools to characterize personal chemical exposures. The optimal placement of these samplers on an individual to best capture airborne exposures has yet to be evaluated.To compare personal exposure to airborne contaminants detected using wearable passive air samplers placed at different positions on the body.Participants (n = 32) simultaneously wore four passive Fresh Air samplers, on their head, chest, wrist, and foot for 24 hours. Exposure to 56 airborne organic contaminants was evaluated using thermal desorption gas chromatography high resolution mass spectrometry with a targeted data analysis approach.Distinct exposure patterns were detected by samplers positioned on different parts of the body. Chest and wrist samplers were the most similar with correlations identified for 20% of chemical exposures (Spearman's Rho0.8, p 0.05). In contrast, the greatest differences were found for head and foot samplers with the weakest correlations across evaluated exposures (8% compounds, Spearman's Rho0.8, p 0.05).The placement of wearable passive air samplers influences the exposures captured and should be considered in future exposure and epidemiological studies.Traditional approaches for assessing personal exposure to airborne contaminants with active samplers presents challenges due to their cost, size, and weight. Wearable passive samplers have recently emerged as a non-invasive, lower cost tool for measuring environmental exposures. While these samplers can be worn on different parts of the body, their position can influence the type of exposure that is captured. This study comprehensively evaluates the exposure to airborne chemical contaminants measured at different passive sampler positions worn on the head, chest, wrist, and foot. Findings provide guidance on sampler placement based on chemicals and emission sources of interest.

Sewage sludge and wastewater include urine and feces from an entire community, and it is highly likely that this mixture contains chemicals whose presence is dependent on levels of SARS‐CoV‐2 in the community. We analyzed primary sewage sludge samples collected in New Haven, Connecticut, USA, during the initial wave of the COVID‐19 pandemic using liquid chromatography coupled with high‐resolution mass spectrometry and performed an exploratory investigation of correlations between chemical features and COVID‐19 metrics including concentrations of severe acute respiratory syndrome–coronavirus 2 (SARS‐CoV‐2) RNA in the sludge and local COVID‐19 case numbers and hospital admissions. Inclusion of all chemical features in this analysis is key for discovering potential indicator compounds for COVID‐19, whose structures may not be known. We found correlations with COVID‐19 metrics for several identified chemicals as well as many unidentified features in the data, including three potential indicator molecules that are recommended for prioritization in future studies on COVID‐19 in wastewater and sludge. These features have molecular weights of 108.0935, 318.1214, and 331.1374. While it is not possible to achieve prediction of COVID‐19 epidemiological metrics from the one data set used in the present study, advances in this research area are important to share as scientists worldwide work on discovering efficient methods for tracking SARS‐CoV‐2 in wastewater and the environment. Environ Toxicol Chem 2021;00:1–9. © 2021 SETAC

Per- and polyfluoroalkyl substances (PFAS) are a family of more than 7,000 fluorinated compounds. The carbon-fluorine bond of PFAS provides desirable hydrophobic and oleophobic properties and stability that has led to widespread usage in consumer products and industrial applications. The strength of the carbon-fluorine bond also prevents appreciable degradation once released into the environment. Consequently, various household products can release volatile and nonvolatile PFAS into the indoor environment that often concentrate in dust. We discuss the diversity of PFAS in settled dust, emission sources of these chemicals, changes in PFAS profiles in dust over the past century, and the implications for human health.Sources of PFAS found in dust include building materials and furnishings and consumer products used in typical indoor spaces. Daycares and workplaces are emphasized as locations with widespread exposure due to the presence of treated carpeting and industrial-strength cleaners. Comparison and interpretation of findings across studies are complicated by the different ways in which PFAS are screened across studies. We further discuss recent developments in non-targeted software for the comprehensive annotation of PFAS in indoor dust and emphasize the need for comprehensive and harmonized analytical workflows. We highlight the detection and diversity of PFAS in settled dust collected from various indoor spaces, including locations with vulnerable subpopulations. There are opportunities for future research to leverage settled dust as a sentinel environmental matrix to evaluate the link between inhalation and ingestion routes of PFAS exposure to adverse health.

Children in low- and middle-income countries are often exposed to higher levels of, and more vulnerable to, the health effects of air pollution. Little is known about the diversity, toxicity, and dynamics of airborne chemical exposures at the molecular level. We developed a workflow employing state-of-the-art wearable passive sampling technology coupled with high-resolution mass spectrometry to comprehensively measure 147 children’s personal exposures to airborne chemicals in Limpopo, South Africa, as part of the VHEMBE study. 637 environmental exposures were detected, many of which have never been measured in this population, including 50 airborne chemical exposures of concern in the children, including biocides, plasticizers, organophosphates, dyes, combustion products, and perfumes. Biocides detected in wristbands included p,p'-DDT, p,p'-DDD, p,p'-DDE, propoxur, piperonyl butoxide, and triclosan. 27% of exposures were significantly different across seasons. Our study provides the first report covering hundreds of chemical exposures among African children, demonstrating chemical exposures warranting further study.

BACKGROUND AND AIM: Children and other vulnerable populations in the developing world are increasingly prone to experience adverse health issues from environmental exposures. Comprehensive identifi...

Ventilation is of primary concern for maintaining healthy indoor air quality and reducing the spread of airborne infectious disease, including COVID-19. In addition to building-level guidelines, increased attention is being placed on room-level ventilation. However, for many universities and schools, ventilation data on a room-by-room basis are not available for classrooms and other key spaces. We present an overview of approaches for measuring ventilation along with their advantages and disadvantages. We also present data from recent case studies for a variety of institutions across the United States, with various building ages, types, locations, and climates, highlighting their commonalities and differences, and examples of the use of this data to support decision making.