Drinking water source and exposure to regulated water contaminants in the California Teachers Study cohort.

BACKGROUND: Pollutants including metals/metalloids, nitrate, disinfection byproducts, and volatile organic compounds contaminate federally regulated community water systems (CWS) and unregulated domestic wells across the United States. Exposures and associated health effects, particularly at levels below regulatory limits, are understudied. OBJECTIVE: We described drinking water sources and exposures for the California Teachers Study (CTS), a prospective cohort of female California teachers and administrators. METHODS: Participants' geocoded addresses at enrollment (1995-1996) were linked to CWS service area boundaries and monitoring data (N = 115,206, 92%); we computed average (1990-2015) concentrations of arsenic, uranium, nitrate, gross alpha (GA), five haloacetic acids (HAA5), total trihalomethanes (TTHM), trichloroethylene (TCE), and tetrachloroethylene (PCE). We used generalized linear regression to estimate geometric mean ratios of CWS exposures across demographic subgroups and neighborhood characteristics. Self-reported drinking water source and consumption at follow-up (2017-2019) were also described. RESULTS: Medians (interquartile ranges) of average concentrations of all contaminants were below regulatory limits: arsenic: 1.03 (0.54,1.71) µg/L, uranium: 3.48 (1.01,6.18) µg/L, GA: 2.21 (1.32,3.67) pCi/L, nitrate: 0.54 (0.20,1.97) mg/L, HAA5: 8.67 (2.98,14.70) µg/L, and TTHM: 12.86 (4.58,21.95) µg/L. Among those who lived within a CWS boundary and self-reported drinking water information (2017-2019), approximately 74% self-reported their water source as municipal, 15% bottled, 2% private well, 4% other, and 5% did not know/missing. Spatially linked water source was largely consistent with self-reported source at follow-up (2017-2019). Relative to non-Hispanic white participants, average arsenic, uranium, GA, and nitrate concentrations were higher for Black, Hispanic and Native American participants. Relative to participants living in census block groups in the lowest socioeconomic status (SES) quartile, participants in higher SES quartiles had lower arsenic/uranium/GA/nitrate, and higher HAA5/TTHM. Non-metropolitan participants had higher arsenic/uranium/nitrate, and metropolitan participants had higher HAA5/TTHM. IMPACT: Though average water contaminant levels were mostly below regulatory limits in this large cohort of California women, we observed heterogeneity in exposures across sociodemographic subgroups and neighborhood characteristics. These data will be used to support future assessments of drinking water exposures and disease risk.

Quantitative microbial risk assessment (QMRA) tool for modelling pathogen infection risk to wastewater treatment plant workers.

Wastewater treatment plants (WWTPs) provide vital services to the public by removing contaminants from wastewater prior to environmental discharge or reuse for beneficial purposes. WWTP workers occupationally exposed to wastewater can be at risk of respiratory or gastrointestinal diseases. The study objectives were to: (1) quantify pathogens and pathogen indicators in wastewater aerosols near different WWTP processes/unit operations, (2) develop a QMRA model for multi-pathogen and multi-exposure pathway risks, and (3) create a web-based application to perform and communicate risk calculations for wastewater workers. Case studies for seven different WWTP job tasks were performed investigating infection risk across nine different enteric and respiratory pathogens. It was observed that the ingestion risk among job tasks was highest for "walking the WWTP," which involved exposure from splashing, bioaerosols, and hand-to-mouth contact from touching contaminated surfaces. There was also a notable difference in exposure risk during peak (5:00am-9:00am) and non-peak hours (9:00am- 5:00am), with risks during the peak flow hours of the early morning assumed to be 5 times greater than non-peak hours. N95 respirator usage reduced median respiratory risks by 77 %. The developed tool performs multiple QMRA calculations to estimate WWTP workers' infection risks from accidental ingestion or inhalation of wastewater from multiple pathogens and exposure scenarios, which can inform risk management strategies to protect occupational health. However, more data are needed to reduce uncertainty in model estimates, including comparative data for pathogen concentrations in wastewater during peak and non-peak hours. QMRA tools will increase accessibility of risk models for utilization in decision-making.

A Novel Approach to Integrate Human Biomonitoring Data with Model Predicted Dietary Exposures: A Crop Protection Chemical Case Study Using Lambda-Cyhalothrin.

The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

Aspartame exposures in the US population: Demonstration of a novel approach for exposure estimates to food additives using NHANES data.

BACKGROUND: Exposure to food additives is widespread but up-to-date and accurate intake estimates are rarely available. The safety of the food additive aspartame is the subject of recent controversy and intake estimates for this nonnutritive sweetener are typically derived from surrogates such as diet soda consumption. OBJECTIVE: We describe an approach for developing nationally representative dietary exposure estimates for food additives that combines intake from dietary recalls and grocery purchasing information. METHODS: We combined NielsenIQ Homescan Consumer Panel purchasing data with the USDA Global Branded Food Products database and the National Health and Nutrition Examination Survey to estimate aspartame intake and prevalence of consumption for the US population. We examined points of departure for aspartame from CompTox Chemicals Dashboard to provide context for exposures and potential effects. RESULTS: Mean, 90th percentile, and 95th percentile aspartame intake estimates are below the acceptable daily intake (50 mg/kg/day) and are lower than estimates from previous decades. Groups with the highest aspartame intakes are non-Hispanic whites, 60- to 69-year-olds, and individuals on diabetic diets. Aspartame exposure is highly prevalent (62.6%) in the US including sensitive populations such as pregnant women and children. IMPACT STATEMENT: Exposure to the widely consumed food additive aspartame is not well characterized, and concerns about potential health effects remain despite assurances of safety when consumed under conditions of intended use. This work provides current intake estimates for the US population with important comparisons across demographic groups and individuals on special diets. The approach includes ingredient statement and grocery purchasing data to capture all aspartame-containing products, beyond diet soda, in intake estimates. This framework also has the potential for application to other food ingredients.

Application and demonstration of meso-activity exposure factors to advance estimates of incidental soil ingestion among agricultural workers.

BACKGROUND: Soil is an understudied and underregulated pathway of chemical exposure, particularly for agricultural workers who cultivate food in soils. Little is known about how agricultural workers spend their time and how they may contact soil while growing food. Exposure factors are behavioral and environmental variables used in exposure estimation. OBJECTIVES: Our study aimed to derive exposure factors describing how growers engage in different tasks and use those factors to advance the use of time-activity data to estimate soil ingestion exposures among agricultural workers. METHODS: We administered a meso-activity-based, season-specific soil contact activity questionnaire to 38 fruit and vegetable growers. We asked growers to estimate the frequency and duration of six meso-activities and describe how they completed them. We used questionnaire data to derive exposure factors and estimate empirical and simulated exposures to a hypothetical contaminant in soil via incidental ingestion using daily, hourly, and hourly-task-specific ingestion rates. RESULTS: We generated exposure factors characterizing the frequency and duration of six meso-activities by season, and self-reported soil contact, glove use, and handwashing practices by meso-activity and season. Seasonal average daily doses (ADDs) were similar across all three forms of ingestion rates. No consistent patterns regarding task-specific contributions to seasonal or annual ADDs were observed.

Temporal trend of microenvironmental time-activity patterns of the Seoul population from 2004 to 2022 and its potential impact on exposure assessment.

BACKGROUND: Time-activity pattern (TAP) is an important parameter for determining personal exposure to environmental pollutants. Changes in TAPs could have significant implications for the alterations in outcomes of exposure assessments. OBJECTIVE: This study aimed to evaluate the Seoul population's long-term change in TAPs, along with variations by sociodemographic group. METHODS: In 2004, 2009, 2014, and 2019, the Time Use Survey of Statistics Korea collected the TAP information of 4036, 2610, 3337, and 2793 Seoul residents, respectively. In 2022, the TAP information of 4401 Seoul residents was collected for Korean Air Pollutant Exposure (KAPEX) research. The microenvironmental TAP changes in the Seoul population from 2004 to 2022 were assessed based on age, gender, work status, and day type. RESULTS: From 2004 to 2022, Seoul people increasingly spent more time in indoor residences (from 14.8 ± 5.1 h to 15.8 ± 4.5 h) and less time in other indoors (from 7.2 ± 4.5 h to 5.9 ± 4.2 h). Their transit time constantly decreased from 2004 (1.4 ± 1.8 h) to 2022 (1.2 ± 1.3 h), whereas the outdoor time fluctuated throughout the years. From 2004 to 2022, the time of the day spent by Seoul people in residential indoor shifted to later in the morning (2004: 8:30 am; 2022: 9:00 am) and earlier in the evening (2004: 9:30 pm; 2022: 7:00 pm); however, the opposite was true for other indoors (2004: from 8:30 am to 9:30 pm; 2022: from 9:00 am to 7:00 pm) and transits (2004: 7:30-9:30 am and 3:00-8:00 pm; 2022: 7:30-9:00 pm and 5:00-9:00). The time of the day spent in outdoors increased from 2004 to 2019, with a distinct peak observed in 2022 (12:00 pm-2:00 pm). The microenvironmental time trends of adolescents and late-adulthoods differed from those of the other age groups, while those of males differed from females. Also, the microenvironmental time trends of the employed differed from those of the unemployed, and those during weekdays differed from those during weekends. IMPACT STATEMENT: Microenvironmental TAP should be essentially considered to estimate the actual exposure to pollutants. This study demonstrates the Seoul population's long-term changes in TAP throughout the 18 years as the significant parameter in exposure assessment. Notably, the microenvironmental TAPs of Seoul people shifted, with variations across different sociodemographic groups. Previous studies in Korea did not consider the TAP shifts in exposure assessment; this study highlights the importance of aligning TAP data with concurrent environmental pollutant data and emphasizes the need for refined data collection in future exposure assessments.

Ecotoxicological soil risk assessment under the new soil exposure framework - an impact assessment.

For the European risk assessment (RA) for soil organisms exposed to plant protection products (PPPs) endpoints from ecotoxicological laboratory studies are compared with predicted environmental concentrations in soil (PECSOIL) at first tier. A safety margin must be met; otherwise, a higher tier RA is triggered (usually soil organism field studies). A new tiered exposure modeling guidance was published by EFSA to determine PECSOIL. This work investigates its potential impact on future soil RA. PECSOIL values for >50 active substances and metabolites were calculated and compared with the respective endpoints for soil organisms to calculate the RA failure rate. Compared to the current (FOCUS) exposure modeling, PECSOIL values for all EU regulatory zones considerably increased, e.g., resulting in active substance RA failure rates of 67%, 58% and 36% for modeling Tier-1, Tier-2 and Tier-3A, respectively. The main driving factors for elevated PECSOIL were soil bulk density, crop interception and wash-off, next to obligatory modeling and scenario adjustment factors. Spatial PECSOIL scenario selection procedures result in agronomically atypical soil characteristics (e.g., soil bulk density values in Tier-3A scenarios far below typical European agricultural areas). Consequently, exposure modeling and ecotoxicological study characteristics are inconsistent, which hinders scientifically reasonable comparison of both in the RA.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling.

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.

Contribution of arsenic and uranium in private wells and community water systems to urinary biomarkers in US adults: The Strong Heart Study and the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Chronic exposure to inorganic arsenic (As) and uranium (U) in the United States (US) occurs from unregulated private wells and federally regulated community water systems (CWSs). The contribution of water to total exposure is assumed to be low when water As and U concentrations are low. OBJECTIVE: We examined the contribution of water As and U to urinary biomarkers in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially/ethnically diverse urban U.S. communities. METHODS: We assigned residential zip code-level estimates in CWSs (µg/L) and private wells (90th percentile probability of As >10 µg/L) to up to 1485 and 6722 participants with dietary information and urinary biomarkers in the SHFS (2001-2003) and MESA (2000-2002; 2010-2011), respectively. Urine As was estimated as the sum of inorganic and methylated species, and urine U was total uranium. We used linear mixed-effects models to account for participant clustering and removed the effect of dietary sources via regression adjustment. RESULTS: The median (interquartile range) urine As was 5.32 (3.29, 8.53) and 6.32 (3.34, 12.48) µg/L for SHFS and MESA, respectively, and urine U was 0.037 (0.014, 0.071) and 0.007 (0.003, 0.018) µg/L. In a meta-analysis across both studies, urine As was 11% (95% CI: 3, 20%) higher and urine U was 35% (5, 73%) higher per twofold higher CWS As and U, respectively. In the SHFS, zip-code level factors such as private well and CWS As contributed 46% of variation in urine As, while in MESA, zip-code level factors, e.g., CWS As and U, contribute 30 and 49% of variation in urine As and U, respectively. IMPACT STATEMENT: We found that water from unregulated private wells and regulated CWSs is a major contributor to urinary As and U (an estimated measure of internal dose) in both rural, American Indian populations and urban, racially/ethnically diverse populations nationwide, even at levels below the current regulatory standard. Our findings indicate that additional drinking water interventions, regulations, and policies can have a major impact on reducing total exposures to As and U, which are linked to adverse health effects even at low levels.

Integrating different tools and technologies to advance drinking water quality exposure assessments.

Contaminants in drinking water are a major contributor to the human exposome and adverse health effects. Assessing drinking water exposure accurately in health studies is challenging, as several of the following study design domains should be addressed as adequately as possible. In this paper, we identify the domains Time, Space, Data Quality, Data Accessibility, economic considerations of Study Size, and Complex Mixtures. We present case studies for three approaches or technologies that address these domains differently in the context of exposure assessment of drinking water quality: regulated contaminants in monitoring databases, high-resolution mass spectrometry (HRMS)-based wide-scope chemical analysis, and effect-based bioassay methods. While none of these approaches address all the domains sufficiently, together they have the potential to carry out exposure assessments that would complement each other and could advance the state-of-science towards more accurate risk analysis. The aim of our study is to give researchers investigating health effects of drinking water quality the impetus to consider how their exposure assessments relate to the above-mentioned domains and whether it would be worthwhile to integrate the advanced technologies presented into planned risk analyses. We highly suggest this three-pronged approach should be further evaluated in health risk analyses, especially epidemiological studies concerning contaminants in drinking water. The state of the knowledge regarding potential benefits of these technologies, especially when applied in tandem, provides more than sufficient evidence to support future research to determine the implications of combining the approaches described in our case studies in terms of protection of public 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.

Near-field exposures and human health impacts for organic chemicals in interior paints: A high-throughput screening.

Interior paints contain organic chemicals that might be harmful to painters and building residents. This study aims to develop a high-throughput approach to screen near-field human exposures and health impacts related to organic chemicals in interior paints. We developed mass balance models for both water- and solvent-based paints, predicting emissions during wet and dry phases. We then screened exposures and risks, focusing on Sri Lanka where residential houses are frequently repainted. These models accurately predict paint drying time and indoor air concentrations of organic chemicals. Exposures of both painter and household resident were estimated for 65 organic chemicals in water-based and 26 in solvent-based paints, considering 12 solvents. Chemicals of concerns (CoCs) were identified, and maximum acceptable chemical contents (MACs) were calculated. Water-based paints generally pose lower health risks than solvent-based paints but might contain biocides of high concern. The total human health impact of one painting event on all household adults ranges from 1.5 × 10-3 to 2.1 × 10-2 DALYs for solvent-based paints, and from 4.1 × 10-4 to 9.5 × 10-3 DALYs for water-based paints. The present approach is a promising way to support the formulation of safer paint, and is integrated in the USEtox scientific consensus model for use in life cycle assessment, chemical substitution and risk screening.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics.

BACKGROUND: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions. OBJECTIVE: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics. METHODS: We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition. RESULTS: We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations. IMPACT: Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Exposure hazards of particles and volatile organic compounds emitted from material extrusion 3D printing: Consolidation of chamber study data.

Ultrafine particles and volatile organic compounds (VOCs) have been detected from material extrusion 3D printing, which is widely used in non-industrial environments. This study consolidates data of 447 particle emission and 58 VOC emission evaluations from a chamber study using a standardized testing method with various 3D printing scenarios. The interquartile ranges of the observed emission rates were 109-1011 #/h for particles and 0.2-1.0 mg/h for total VOC. Print material contributed largely to the variations of particle and total VOC emissions and determined the most abundantly emitted VOCs. Printing conditions and filament specifications, included printer brand, print temperature and speed, build plate heating setup, filament brand, color and composite, also affected emissions and resulted in large variations observed in emission profiles. Multiple regression showed that particle emissions were more impacted by various print conditions than VOC emissions. According to indoor exposure modeling, personal and residential exposure scenarios were more likely to result in high exposure levels, often exceeding recommended exposure limits. Hazardous VOCs commonly emitted from 3D printing included aromatics, aldehydes, alcohols, ketones, esters and siloxanes, among which were various carcinogens, irritants and developmental and reproductive toxins. Therefore, 3D printing emits a complex mixture of ultrafine particles and various hazardous chemicals, exposure to which may exceed recommended exposure limits and potentially induce acute, chronic, or developmental health effects for users depending on exposure scenarios.

A novel online survey approach designed to measure consumer sunscreen application thickness-implications for estimating environmental emissions.

BACKGROUND: The effects of ultraviolet (UV) filters in the aquatic environment have been well studied, but environmental exposures remain unclear and understudied. Consumer usage directly influences the amount of sunscreen products, and subsequently UV filters, potentially released into the environment. OBJECTIVE: To conduct a literature review of previous research into sunscreen application thickness, develop a questionnaire protocol designed to semi-quantify sunscreen usage by US consumers, and conduct a large-scale survey to determine a sunscreen application thickness (to face and body) that is more refined than conservative defaults. The United States Food & Drug Administration (US FDA) recommends a sunscreen application rate of 2 mg/cm2. This value is typically used as a worst-case assumption in environmental exposure assessments of UV filters. METHODS: Designed a novel approach to estimate lotion sunscreen application thickness using an online questionnaire protocol employing visual references and self-reported height and weight of the respondents. A literature review was also conducted to collect historical sunscreen usage. RESULTS: Over 9000 people were surveyed in the US, and after the dataset was refined, their sunscreen application thickness was estimated based on calculated body surface area and reported sunscreen amounts. The mean and median values for survey respondents are 3.00 and 1.78 mg/cm2, respectively, for facial application thickness and 1.52 and 1.35 mg/cm2, respectively, for body application thickness. Earlier research from 1985-2020 reported 36 of the 38 values are below the US FDA's recommended application thickness of 2 mg/cm2 (range 0.2-5 mg/cm2). IMPACT STATEMENT: This web-based survey is the first of its kind, designed specifically to quantify sunscreen application in a large and diverse set of consumers. This method provides a greater reach to larger populations thus enabling more granular data analysis and understanding. Exposure assessments of sunscreen ingredients typically use conservative parameters. These data can refine those assessments and allow for more informed and science-based risk management decisions.

Children's lead exposure in the U.S.: Application of a national-scale, probabilistic aggregate model with a focus on residential soil and dust lead (Pb) scenarios.

Lead (Pb) exposures from soil and dust ingestion contribute to children's blood lead levels (BLLs) in the United States. The U.S. Environmental Protection Agency (EPA)'s Strategy to Reduce Lead Exposures and Disparities in U.S. Communities and the Federal Action Plan to Reduce Childhood Lead Exposure describe multi-pronged collaborative approaches. These include reducing multi-media lead exposures nationally using analytical tools such as EPA's Stochastic Human Exposure and Dose Simulation model for lead [SHEDS-Pb; formerly known as SHEDS-IEUBK (Integrated Exposure Uptake Biokinetic model)], which was initially developed and applied with a focus on children's drinking water exposures. In this study we applied SHEDS-Pb to determine what residential soil Pb and dust Pb concentrations (individually and their sum) can keep BLLs of potentially exposed young children in the general U.S. population below specified values, considering aggregate exposures from water, soil, dust, food, and air. We considered two age groups (1 to <2 years and 2 to <6 years), two BLL values (5 µg/dL and 3.5 µg/dL), and two population percentiles (95th and 97.5th). Sensitivity analyses were conducted using several alternative model inputs and data sets, yielding 15 scenarios summarized in the paper. Of those scenarios, we focused on ones with the most recent science and available data. Modeled soil Pb concentrations by age group, population percentile and reference BLL scenarios for the focus scenarios ranged from 70 ppm to 220 ppm; and modeled dust Pb concentrations ranged from 110 ppm to 240 ppm. These results are consistent with current soil and dust Pb concentrations in the U.S. general population and are lower than most of the current U.S. Federal standards. Estimated BLLs compared well with measured BLLs from CDC's NHANES 2009-2016 (0-27 % relative error for focus scenarios). This analysis can be used to inform EPA and other federal Pb efforts.

Herbicide exposure during occupational knapsack spraying in French gardeners and municipal workers.

CONTEXT: There is a lack of data on pesticide exposure levels during spraying with a knapsack, while it could have important implications for their users' health. METHODS: We assessed levels and determinants of exposure in 24 male private landscapers/gardeners and municipal workers in France in 2011. Actual dermal exposure to glyphosate was assessed with cotton undergarments and gloves, and a cotton coverall changed between mixing and spraying to assess the contribution of each phase and body area to overall contamination. A field monitor observed the whole workshift and filled in a standardized observation grid. RESULTS: The median actual contamination was 5,256 µg for the body, and 4,620 µg for hands. Spraying was more exposing than mixing/loading for all body parts except hands, which contributed to nearly 90% of body exposure during mixing/loading, and 30% during spraying, followed by back (14%). In the most exposed quartile, levels were close to some observations in agriculture. CONCLUSION: Our study provides new data on pesticide exposure levels of knapsack sprayer users; it should lead to a reinforced prevention, in order to make exposures as low as possible and lessen the risk of chronic diseases.

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.

Dissolution behavior of ZnO nanoparticles at environmentally relevant low concentrations in surface waters: Equilibrium and kinetics.

Environmentally relevant fate parameters are essential in accurate prediction of nanomaterial's exposure. This study investigates the dissolution kinetics and equilibrium of ZnO nanoparticles (ZnONPs) using environmentally relevant low concentrations (50-200 µg/L) of ZnONPs in river water and lake water samples, and a seawater-influenced river water. We found that ZnONPs at an initial concentration of 50 µg/L completely dissolved independent of water matrices, while at 100 and 200 µg/L the dissolution level of ZnONPs was strongly dependent on the water chemistry. Carbonate alkalinity was found to control the dissolution levels, and can react with dissolved Zn ion to form secondary solid product hydrozincite. An analysis of our kinetic data and comprehensive literature results reveals that the dissolution kinetic coefficients largely increased with decreased initial ZnONP concentrations especially in environmental water matrices. The result highlights the importance to measure and derive representative dissolution parameters of nanomaterials using environmentally relevant concentrations.